Systems and methods for space health exposure omics

Abstract

Methods and systems configured to analyze exposure omics characteristics of an object undergoing a space travel are provided herein.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 328,164, filed on April 6, 2022, which is incorporated herein by reference in its entirety. Background Art

[0003] The immediate and lasting effects of space travel on mammals, including humans, are unknown. As the age of space exploration dawns, it is necessary to develop a deeper understanding of the possible effects of space travel and how to ameliorate the negative effects imposed on individuals. Thus, there is an unmet need for systems and methods configured to screen individuals participating in space travel in a minimally invasive manner to determine the ultimate effects of space travel and what interventions may ameliorate the negative effects of space travel. Summary of the invention

[0004] Aside from the obvious macroscopic physiological and / or anatomical effects brought about by long-term space travel, the effects of space travel and / or short-term space travel on the human body are less clear. In order to sustain long-term space travel and / or frequent exposure to space travel, the demand for platforms capable of measuring the effects of space or simulated space travel on human physiology and / or anatomy has not been met. In some cases, the disclosure provided herein may describe methods and systems for analyzing the exposure group characteristics of objects affected by space travel and / or simulated space travel. In some cases, the disclosure provided herein describes methods and systems for determining the effects of intervention and / or diet on one or more exposure group characteristics and / or exposure group characteristics of an object, which methods and systems can be used to offset the effects of space travel on one or more exposure group characteristics and / or exposure group characteristics of an object.

[0005] The present disclosure meets these needs through systems and methods that can analyze and classify subjects based on their exposome biochemical profiles. Exposome biochemical profile analysis can analyze more than 50,000 biochemical features from a single hair shaft, tooth and nail sample in a non-invasive manner. Using this analysis system and method of the present disclosure, subtle changes in a subject's biochemistry caused by diet, air pollution, space travel, psychological stress, exposure to pesticides, industrial chemicals, etc. can be studied and correlated with positive responses and outcomes of targeted drugs.

[0006] In addition to the rich datasets generated by the exposure group biochemical signature analysis, the exposure group biochemical signature analysis can also provide insights into the temporal fluctuations of the biochemical signatures over the life span of the subject. This method can be used to screen individuals who participate in space travel and / or simulated space travel to determine which individual exposure group biochemical signatures or combinations of exposure group biochemical signatures are significantly affected by space travel and / or simulated space travel. The identified pathways can then be used to train statistical, machine learning, and / or artificial intelligence predictive models that are able to predict whether the subject's exposure group characteristics and / or traits are associated with space travel.

[0007] Aspects of the disclosure disclosed herein provide a computer-implemented exposome system comprising: (a) an exposome biochemical signature database (EDB), which includes exposome characteristics of multiple subjects who have participated in and have not participated in space travel; (b) a clinical database (CDB), which includes clinical phenotype information of multiple subjects who have participated in space travel; (c) an intervention outcome database (IODB), which includes information about intervention outcome information of subjects who have received intervention after, during and / or before participating in space travel; and (d) a computer processor, which includes: (i) an association software module communicatively coupled to the EDB, CDB and IODB, wherein the association software module is programmed to determine an association between the exposome characteristics, clinical phenotype information and intervention outcome information of at least one of the multiple subjects; and (ii) a recommendation software module communicatively coupled to the EDB, CDB and IODB. The recommendation software module is programmed to provide an intervention recommendation for at least one of the plurality of subjects based at least in part on the exposome characteristics, clinical phenotype information, intervention outcome information, and associations between the exposome characteristics, clinical phenotype information, and intervention outcome information of the at least one of the plurality of subjects.

[0008] In some embodiments, the exposure group characteristics include at least 10, at least 100, at least 1,000 or at least 10,000 different exposure group biochemical features. In some embodiments, the intervention outcome information includes the classification of non-responders, poor responders and positive responders for at least one intervention to improve the negative effects of space travel. In some embodiments, the intervention results include one or more inclusion criteria or exclusion criteria for at least one intervention. In some embodiments, the exposure group characteristics are obtained by measuring biological samples of multiple subjects. In some embodiments, the biological samples include tooth samples, nail samples, hair samples, or any combination thereof. In some embodiments, the determination includes obtaining mass spectrometry measurements, laser induced breakdown spectroscopy measurements, laser ablation inductively coupled plasma mass spectrometry measurements, Raman spectroscopy measurements, breakdown spectroscopy, immunohistochemistry measurements, or any combination thereof. In some embodiments, the mass spectrometry measurements include measurements of one or more chemical substances. In some embodiments, the one or more chemical substances include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof. In some embodiments, the exposure group characteristics include dynamic time biochemical reactions of multiple objects. In some embodiments, the exposure group biochemical characteristics include fluorescent images of biological samples. In some embodiments, the exposure group biochemical characteristics include spatial maps of Raman spectra of biological samples. In some embodiments, the exposure group biochemical characteristics are associated with space travel and / or simulated space travel. In some embodiments, the exposure group characteristics are analyzed using trained statistics, machine learning and / or artificial intelligence classifiers to determine the association with space travel. In some embodiments, the classifier is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

[0009] Aspects of the disclosure disclosed herein describe a method for selecting subjects participating in space travel for intervention, the method comprising: (a) providing a trained prediction model, wherein the trained model is trained on clinical metadata, exposure genomic characteristics, and corresponding intervention outcome information of one or more subjects; (b) detecting biochemical features obtained from biological samples from subjects seeking intervention to produce exposure genomic characteristics; (c) using the trained prediction model to predict predicted intervention outcome information for subjects seeking intervention, wherein the exposure genomic characteristics clinical metadata of the subjects seeking intervention are input to the trained prediction model; and (d) selecting subjects for intervention or excluding subjects from intervention based at least in part on the predicted intervention outcome information of the subjects.

[0010] In some embodiments, the biochemical signature is obtained by measuring the biological sample of the object. In some embodiments, the biological sample includes a tooth sample, a nail sample, a hair sample, or any combination thereof. In some embodiments, the determination includes collecting data from laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof. In some embodiments, the laser ablation inductively coupled plasma mass spectrometry measurements include measurements of one or more chemical substances. In some embodiments, one or more chemical substances include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof. In some embodiments, the biochemical signature includes a fluorescent image of the biological sample. In some embodiments, the biochemical signature includes a spatial map of the Raman spectrum of the biological sample. In some embodiments, the biochemical signature is associated with space travel and / or simulated space travel. In some embodiments, the trained prediction model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof. In some embodiments, the method further comprises when the subject is selected for the intervention, recruiting the subject into the intervention. In some embodiments, the method further comprises when the subject is excluded from the intervention, evaluating the subject for another intervention.

[0011] Aspects of the disclosure describe a method for selecting an optimal treatment for a subject participating in space travel and / or simulated space travel, the method comprising: (a) detecting one or more biochemical features obtained from one or more biological samples from one or more subjects that have not participated in space travel, thereby generating one or more reference exposome profiles; (b) detecting one or more biochemical features obtained from one or more biological samples from subjects that have participated in space travel, thereby generating one or more pre-treatment exposome profiles; (c) administering a treatment to the subject that has participated in space travel; (d) detecting one or more biochemical features obtained from one or more biological samples from one or more subjects that have participated in space travel, thereby generating one or more pre-treatment exposome profiles; (c) administering a treatment to the subject that has participated in space travel; and (d) after a period of time has passed since receiving the treatment, detecting one or more biochemical features obtained from one or more biological samples from one or more subjects that have participated in space travel, thereby generating one or more pre-treatment exposome profiles. (e) determining the difference between one or more reference exposure group characteristics of one or more subjects who have not participated in space travel, one or more pre-treatment exposure group characteristics of one or more subjects who have participated in space travel, and one or more post-treatment exposure group characteristics of one or more subjects who have participated in space travel; and (f) selecting one or more optimal treatments based at least in part on the determined differences between one or more reference exposure group characteristics, one or more pre-treatment exposure group characteristics, and one or more post-treatment exposure group characteristics, wherein the one or more optimal treatments are selected based on the determined differences that meet a predetermined criterion. In some embodiments, the optimal treatment may include a drug, a nutraceutical, or any combination thereof. In some embodiments, the predetermined criterion includes the difference between one or more pre-treatment exposure group characteristics and one or more post-treatment exposure group characteristics and one or more reference exposure group characteristics. In some embodiments, the period of time includes at least about 1 hour, at least about 1 day, at least about 1 week, at least about 1 month, at least about 1 year, or any combination thereof. In some embodiments, the difference includes a change of at least 10% of one or more post-treatment exposure group characteristics to one or more reference exposure group characteristics. In some embodiments, the exposure group characteristics before treatment, the exposure group characteristics after treatment, or any combination thereof are obtained by measuring the biological sample of the subject. In some embodiments, the biological sample includes a tooth sample, a nail sample, a hair sample, or any combination thereof. In some embodiments, the determination includes obtaining laser ablation inductively coupled plasma mass spectrometry data, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof. In some embodiments, the laser ablation inductively coupled plasma mass spectrometry data includes measurements of one or more chemical substances. In some embodiments, one or more chemical substances include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof. In some embodiments, the biochemical feature includes a fluorescent image of the biological sample. In some embodiments, the biochemical feature includes a spatial map of the Raman spectrum of the biological sample.In some embodiments, a trained statistical, machine learning, and / or artificial intelligence classifier is used to analyze the differences between a reference exposure group characteristic, a pre-space travel exposure group characteristic, a post-space travel exposure group characteristic, or any combination thereof. In some embodiments, the trained classifier is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

[0012] The present disclosure provides improved systems and methods for accurately determining the effects of space travel and / or simulating space travel based on analysis of dynamic biological response data from biological samples obtained non-invasively from a subject. This improved system and method for accurately determining one or more exposure group characteristics and / or exposure group characteristics can be based on a combination of dynamic immunohistochemical analysis of biological samples and artificial intelligence data analysis of such dynamic analysis to evaluate how space travel affects exposure group characteristics and / or characteristics. For example, the present disclosure meets these needs by providing biological sample biomarkers for analyzing one or more exposure group characteristics and / or characteristics associated with space travel. The biological sample includes a human biological specimen associated with incremental growth. In a non-limiting embodiment, the biological sample is a hair shaft, a tooth, a toenail, a fingernail, a physiological parameter, or any combination thereof. The non-invasive biomarkers of the present disclosure can be used to diagnose young children, even infants under one year old. In some embodiments, f includes health metadata described elsewhere herein. In some embodiments, analysis of one or more exposure-based signatures and / or exposure-based characteristics affected by space travel and / or simulated space travel may include analyzing one or more physiological parameters, including parameters measured during a blood test, such as cholesterol, white blood cell count, red blood cell count, hematocrit, presence or absence of bacterial infection, and the like.

[0013] In some embodiments, the analysis determines the temporal dynamics of the underlying biological process. In some embodiments, the analysis includes obtaining a fluorescent image of the stained tooth sample and analyzing the fluorescence intensity of the fluorescent image. In some embodiments, the fluorescence intensity varies spatially. In some embodiments, obtaining a fluorescent image of the stained tooth sample includes using an inverted or non-inverted confocal microscope. In some embodiments, staining the tooth sample includes using C-reactive protein immunohistochemical staining. In some embodiments, the method also includes slicing the tooth sample. In some embodiments, staining the tooth sample includes (1) cutting the tooth sample, (2) decalcifying the tooth sample, (3) slicing the decalcified sample, (4) staining the decalcified tooth slice with a primary antibody and a secondary antibody, (5) measuring spatial antibody fluorescence using confocal microscopy, and / or (6) extracting the temporal spectrum of fluorescence intensity.

[0014] In one aspect, the present disclosure provides a method for outputting the response of one or more subjects to participating in space travel, the method comprising: receiving one or more biological samples of one or more subjects participating in space travel; determining one or more exposure group characteristics of one or more biological samples; calculating the exposure group characteristics of one or more exposure group characteristics of one or more biological samples; and outputting the response of one or more subjects to participating in space travel by providing the exposure group characteristics of one or more biological samples, or by using the exposure group characteristics of one or more biological samples as input in a trained prediction model, which provides a binary indicator of biological response to space travel. In some embodiments, the trained prediction model is configured to determine the likelihood that the exposure group characteristics are associated with the metabolic characteristics of one or more subjects participating in space travel. In some embodiments, space travel includes traveling across the Karman line at an altitude of at least 100 kilometers (km) above the surface of the earth. In some embodiments, the trained prediction model is configured to classify whether the samples of one or more subjects are consistent with real or simulated space travel. In some embodiments, one or more biological samples of subjects participating in space travel are collected during a space travel period before space travel, during space travel, after space travel, or any combination thereof. In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0015] In some embodiments, each of the exposome characteristics comprises one or more quantitative metrics. In some embodiments, the exposome characteristics comprise a measurement of the temporal dynamics of one or more exposome characteristics. In some embodiments, the measurement of temporal dynamics comprises: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome characteristics, determining a sudden change in the intensity of one or more exposome characteristics, determining one or more changes in the baseline intensity of one or more exposome characteristics, determining a change in the frequency domain representation of one or more exposome characteristics, determining a change in the power spectrum domain representation of one or more exposome characteristics, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0016] In some embodiments, one or more exposure group characteristics are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemical fluorescence or any combination thereof. In some embodiments, the exposure group characteristics are derived from the analysis of one or more attractors. In some embodiments, the method also includes analyzing the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursive trend, hierarchical, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in the number of recursions, the most likely number of recursions or the dynamic relationship between any combination thereof of one or more attractors.

[0017] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0018] In some embodiments, the trained prediction model includes a classification algorithm. In some embodiments, the classification algorithm is configured to identify one or more exposome features of one or more subjects associated with participation in space travel. In some embodiments, the classification algorithm is trained with one or more biological sample exposome characteristics and corresponding responses of one or more subjects, wherein the one or more subjects participated in space travel. In some embodiments, the classification algorithm is configured to determine whether the one or more subjects have metabolic features associated with space travel.

[0019] In some embodiments, the response comprises a state of the subject's metabolism, wherein the subject's metabolism comprises a healthy state or a state consistent with a subject undergoing space travel.

[0020] In one aspect, the present disclosure provides a method for training a prediction model to determine a clinical response of a subject to an intervention when the subject is participating in space travel, the method comprising: receiving one or more biological samples and clinical responses of a first set of subjects and a second set of subjects, wherein the first set of subjects are exposed to space travel and are provided with the intervention, and wherein the second set of subjects are exposed to space travel and do not receive the intervention; determining one or more exposure genomic features of the one or more biological samples of the first set of subjects and the second set of subjects; calculating a first exposure genomic characteristic set and a second exposure genomic characteristic set of the one or more exposure genomic features of the first set of subjects and the second set of subjects, wherein each feature of the first exposure genomic characteristic set and the second exposure genomic characteristic set includes one or more quantitative metrics; and outputting a trained prediction model configured to determine a clinical response of the subject to the intervention, wherein the subject is participating in space travel, and wherein the trained prediction model is trained on the one or more quantitative metrics of the one or more exposure genomic features of the first set of subjects and the second set of subjects and the corresponding clinical responses.

[0021] In some embodiments, the intervention prevents negative side effects of space travel.In some embodiments, the intervention is provided to the first set of subjects before, during, after, or any combination thereof, of the space travel to which the subjects have been exposed.

[0022] In some embodiments, the method further comprises determining the effect of space travel on the first set of subjects exposed to the intervention at least in part by comparing one or more quantitative metrics of the first expomic characteristic set and the second expomic characteristic set. In some embodiments, the intervention comprises diet, drug treatment, or any combination thereof. In some embodiments, the diet comprises iodine supplementation.

[0023] In some embodiments, the first exposure group or the second exposure group includes a measurement of the temporal dynamics of one or more exposure group features. In some embodiments, the measurement of the temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure group features, determining a sudden change in the intensity of one or more exposure group features, determining one or more changes in the baseline intensity of one or more exposure group features, determining a change in the frequency domain representation of one or more exposure group features, determining a change in the power spectrum domain representation of one or more exposure group features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0024] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0025] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0026] In some embodiments, the first exposure group characteristic set or the second exposure group characteristic set is derived from one or more attractors. In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of the one or more attractors.

[0027] In some embodiments, the first set of subjects or the second set of subjects are humans, non-human mammals, or any combination thereof.

[0028] In one aspect, the present disclosure provides a method for generating a prediction model, the prediction model is configured to output the health state of one or more objects in response to gravity in addition to the earth's gravity, the method comprising: receiving one or more biological samples and corresponding health states of the first one or more objects, wherein the first one or more objects are exposed to gravity in addition to being exposed to the earth's gravity; determining one or more exposure group characteristics of the one or more biological samples of the first one or more objects; calculating the exposure group characteristics of the one or more exposure group characteristics of the one or more biological samples; and generating a trained prediction model, the trained prediction model is configured to output the health state of the second one or more objects in response to gravity in addition to the earth's gravity, wherein the trained prediction model is trained with the exposure group characteristics of the one or more exposure group characteristics of the first one or more objects and the corresponding health state. In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0029] In some embodiments, the gravitational force comprises at least 1G, at least 2G, at least 3G, or at least 4G.

[0030] In some embodiments, the trained predictive model is configured to determine a likelihood that the expomic property is associated with a metabolic signature of the first one or more subjects or the second one or more subjects that were exposed to gravity in addition to exposure to Earth's gravity.

[0031] In some embodiments, the probability threshold determined by the trained prediction model indicates that the metabolism of the first one or more subjects or the second one or more subjects is at a baseline condition that is not associated with exposure to gravity in addition to not being associated with exposure to Earth's gravity.

[0032] In some embodiments, each of the exposome characteristics comprises one or more quantitative metrics. In some embodiments, the exposome characteristics comprise a measurement of the temporal dynamics of one or more exposome characteristics. In some embodiments, the measurement of temporal dynamics comprises: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome characteristics, determining a sudden change in the intensity of one or more exposome characteristics, determining one or more changes in the baseline intensity of one or more exposome characteristics, determining a change in the frequency domain representation of one or more exposome characteristics, determining a change in the power spectrum domain representation of one or more exposome characteristics, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0033] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0034] In some embodiments, the expomic profile is derived from one or more attractors.

[0035] In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors.

[0036] In some embodiments, the first one or more subjects and the second one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0037] In some embodiments, the trained predictive model comprises a classification algorithm.

[0038] In some embodiments, the classification algorithm is configured to identify one or more expomic signatures of the first one or more subjects or the second one or more subjects that are associated with exposure to gravity in addition to exposure to Earth's gravity.

[0039] In some embodiments, the classification algorithm is trained with the expomic profiles and corresponding health states of one or more biological samples of a first one or more subjects, wherein the first one or more subjects are exposed to gravity in addition to exposure to Earth's gravity.

[0040] In some embodiments, the classification algorithm is configured to determine whether the first one or more subjects or the second one or more subjects have a metabolic signature associated with exposure to gravity in addition to exposure to Earth's gravity.

[0041] In some embodiments, the health state comprises a state of metabolism of the first one or more subjects or the second one or more subjects, wherein the metabolism of the first one or more subjects or the second one or more subjects comprises a healthy state or a state consistent with a subject being exposed to gravity in addition to exposure to Earth's gravity.

[0042] In one aspect, the present disclosure provides a method for using a predictive model to output health outcomes of one or more subjects before exposure to gravity, the method comprising: receiving one or more biological samples of the one or more subjects; determining one or more exposure genomic features of the one or more biological samples of the one or more subjects; calculating exposure genomic characteristics of the one or more exposure genomic features of the one or more biological samples; and outputting health outcomes of the one or more subjects before exposure to gravity in addition to before exposure to the Earth's gravity by providing the exposure genomic characteristics of the one or more subjects as input to a trained predictive model.

[0043] In some embodiments, the gravitational force comprises at least 1G, at least 2G, at least 3G, or at least 4G.

[0044] In some embodiments, the trained predictive model is configured to determine a likelihood that an expomic property is associated with a metabolic signature of one or more subjects exposed to gravity.

[0045] In some embodiments, the probability threshold determined by the trained prediction model indicates that the metabolism of one or more subjects is at a baseline condition that is not associated with exposure to gravity in addition to not being associated with exposure to Earth's gravity.

[0046] In some embodiments, each of the expomic profiles includes one or more quantitative metrics.

[0047] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0048] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0049] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0050] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0051] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0052] In some embodiments, the expomic profile is derived from one or more attractors.

[0053] In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors.

[0054] In some embodiments, the one or more subjects include a human, a non-human mammal, a vertebrate, or any combination thereof.

[0055] In some embodiments, the trained predictive model comprises a classification algorithm.

[0056] In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of a subject that are associated with exposure to gravity in addition to exposure to Earth's gravity.

[0057] In some embodiments, a classification algorithm is trained with the expomic profiles and corresponding responses of one or more biological samples of one or more subjects, wherein the one or more subjects are exposed to gravity in addition to exposure to Earth's gravity.

[0058] In some embodiments, the classification algorithm is configured to determine whether one or more subjects have a metabolic signature associated with exposure to gravity in addition to exposure to Earth's gravity.

[0059] In one aspect, the present disclosure provides a method for administering a treatment to a subject participating in space travel, the method comprising: receiving one or more biological samples of the subject prior to participating in space travel; determining one or more exposure genomic features of the one or more biological samples of the subject; calculating exposure genomic characteristics of the one or more exposure genomic characteristics of the subject, wherein each characteristic of the exposure genomic characteristics comprises one or more quantitative metrics; and administering the treatment to the subject participating in space travel at a time point, wherein when the exposure genomic characteristics of the subject are provided as input to a trained prediction model, the treatment and the time point at which the treatment is administered are determined as outputs of the trained prediction model.

[0060] In some embodiments, the trained predictive model is configured to output the effect of a treatment.

[0061] In some embodiments, the trained predictive model is configured to output a measure of whether a treatment is effective.

[0062] In some embodiments, the time point includes a time before the subject participated in the space travel, a time during the subject participated in the space travel, a time after the subject participated in the space travel, or any combination thereof.

[0063] In some embodiments, treatments are provided to alleviate the negative side effects of participating in space travel.

[0064] In some embodiments, the effect of a treatment is determined by a duration of time after the subject has participated in space travel, after which the subject's exposome profile comprises a baseline exposome profile of a subject that has not participated in space travel.

[0065] In some embodiments, the trained predictive model comprises a classification algorithm.

[0066] In some embodiments, space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

[0067] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0068] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0069] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0070] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0071] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0072] In some embodiments, the expomic profile is derived from one or more attractors.

[0073] In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors.

[0074] In some embodiments, the subject is a human, a non-human mammal, or any combination thereof.

[0075] In some embodiments, the trained predictive model comprises a classification algorithm.

[0076] In some embodiments, a classification algorithm is trained using the expomic profiles of one or more biological samples of one or more subjects, the treatments administered, the time points at which the treatments were administered, and the corresponding clinical responses, wherein the one or more subjects participated in space travel.

[0077] In some embodiments, the classification algorithm is configured to determine whether one or more subjects have a metabolic disorder caused by space travel.

[0078] In one aspect, the present disclosure provides a method for evaluating a response of one or more subjects to an intervention in the presence or absence of gravity, the method comprising: sampling each corresponding position of a plurality of positions along a reference line of a first one or more biological sample sets of a first set of subjects and a second one or more biological sample sets of a second set of subjects, thereby obtaining a plurality of ion samples, each ion sample of the plurality of ion samples corresponding to a different position of the plurality of positions, and each position of the plurality of positions representing a different growth period of the first one or more biological sample sets and the second one or more biological sample sets, wherein the first set of subjects undergoes space travel and the second set of subjects does not undergo space travel, and wherein Applying an intervention to a first set of objects and a second set of objects; analyzing each of a plurality of ion samples with a mass spectrometer to obtain a first data set including a plurality of traces, each of the plurality of traces being a concentration over time of a corresponding element isotope of a plurality of element isotopes, the concentration being determined jointly by the plurality of ion samples; deriving a second data set including a feature set from the plurality of traces, each corresponding feature in the feature set being determined by a variation of a single isotope or a combination of isotopes in the plurality of traces; and inputting the feature set into a trained classifier to obtain a probability of predicting a response of one or more objects to space travel from the trained classifiers of the first one or more object sets and the second one or more object sets.

[0079] In some embodiments, the probability indicates that a biological sample in the one or more biological samples is disordered due to space travel.

[0080] In some embodiments, the effectiveness of an intervention is measured by the probability of disorder.

[0081] In some embodiments, the intervention comprises diet, medication, or any combination thereof.

[0082] In some embodiments, the diet includes iodine supplementation.

[0083] In some embodiments, the intervention improves radiation exposure.

[0084] In some embodiments, the first set of subjects or the second set of subjects includes humans, non-human mammals, or any combination thereof.

[0085] In some embodiments, the non-human mammal comprises a mouse.

[0086] In some embodiments, the first one or more biological sample sets or the second one or more biological sample sets include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0087] In some embodiments, the method further comprises treating the first one or more sets of biological samples or the second one or more sets of biological samples with a solvent or a surfactant prior to sampling.

[0088] In some embodiments, the method further comprises irradiating the first one or more biological sample sets or the second one or more biological sample sets with a low power laser prior to sampling to remove debris from the first one or more biological sample sets or the second one or more biological sample sets.

[0089] In some embodiments, sampling includes irradiating a first or more biological sample sets or a second or more biological sample sets of a first object set or a second object set with a laser, thereby extracting multiple particles from the first or more biological sample sets or the second or more biological sample sets, and ionizing the multiple particles using an inductively coupled plasma mass spectrometer to obtain multiple ion samples.

[0090] In some embodiments, the response of the first set of subjects or the second set of subjects includes an adverse response, a positive response, or a neutral response.

[0091] In some embodiments, the trained classifier is configured to recognize a set of characteristics of an object associated with participating in space travel.

[0092] In some embodiments, the trained classifier is trained using a set of characteristics and corresponding responses of one or more biological samples of a first set of subjects.

[0093] In some embodiments, the trained classifier is configured to determine whether a subject comprises a metabolic disorder resulting from space travel.

[0094] In one aspect, the present disclosure provides a method for outputting a subject's response to an intervention after space travel, the method comprising: exposing one or more biological samples of the subject to an optical signal comprising multiple Raman wavelengths, wherein the subject undergoes space travel; acquiring multiple Raman spectra from the one or more biological samples; processing the multiple Raman spectra to generate multiple Raman characteristics; and outputting a probability of the subject's response to the intervention while undergoing space travel by providing an input of the multiple Raman characteristics to a trained classifier.

[0095] In some embodiments, the probability indicates that the subject is deconditioned due to space travel.

[0096] In some embodiments, the effectiveness of an intervention is measured by the probability of disorder.

[0097] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0098] In some embodiments, the intervention comprises diet, medication, or any combination thereof.

[0099] In some embodiments, the diet includes iodine supplementation.

[0100] In some embodiments, the intervention improves radiation exposure.

[0101] In some embodiments, the subject includes a human, a non-human mammal, or any combination thereof.

[0102] In some embodiments, the non-human mammal comprises a mouse.

[0103] In some embodiments, the plurality of Raman spectra includes from about 200 to about 3700 wavenumbers.

[0104] In some embodiments, acquiring comprises using a Raman spectroscopic microscope.

[0105] In some embodiments, the plurality of Raman characteristics includes one or more traces derived from a Raman spatial map.

[0106] In some embodiments, one or more traces are reduced using a dimensionality reduction algorithm, wherein the dimensionality reduction algorithm comprises principal component analysis, independent component analysis, or any combination thereof.

[0107] In some embodiments, the measurement of temporal dynamics is derived from one or more traces, wherein the temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure-based features, determining a sudden change in the intensity of one or more exposure-based features, determining one or more changes in the baseline intensity of one or more exposure-based features, determining a change in the frequency domain representation of one or more exposure-based features, determining a change in the power spectrum domain representation of one or more exposure-based features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0108] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0109] In some embodiments, the trained classifier comprises a gradient boosting ensemble model.

[0110] In some embodiments, the trained classifier is configured to process one or more characteristics selected from hierarchicality, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, determinism, Lmax, and any combination thereof.

[0111] In some embodiments, the trained classifier is configured to process two or more characteristics selected from hierarchicality, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, and any combination thereof.

[0112] In some embodiments, the trained classifier is trained to recognize Raman signatures associated with space travel.

[0113] In some embodiments, the output probability of the trained classifier indicates that the object is exhibiting temporal dynamics associated with space travel.

[0114] In some embodiments, the trained classifier is trained with a plurality of Raman properties of a population of humans or non-human animals that have experienced space travel.

[0115] In some embodiments, the effect of the intervention is measured by a change in the subject's temporal dynamics toward the subject's baseline temporal dynamics prior to space travel.

[0116] In one aspect, the present disclosure provides a method for assessing a subject's response to an intervention to reduce a biological effect of space travel, the method comprising: staining a first one or more biological sample sets of a first set of subjects and a second one or more biological sample sets of a second set of subjects to produce the stained first one or more biological sample sets and the second one or more biological sample sets, wherein the first set of subjects undergoes space travel and the second set of subjects does not undergo space travel, and wherein the intervention is administered to the first set of subjects and the second set of subjects; analyzing fluorescence intensity spatially across the stained first one or more biological sample sets and the second one or more biological sample sets; and calculating a set of characteristics of spatial fluorescence intensity across the stained first one or more biological sample sets and the second one or more biological sample sets of the first set of subjects and the second set of subjects, wherein each of the characteristics of the exposure group characteristics of the first set of subjects and the second set of subjects includes one or more metrics that quantify the temporal dynamics in the fluorescence intensity trace; and when the exposure group characteristics of the spatial fluorescence intensity of the subject's stained one or more biological samples across the subjects are provided to a trained prediction model, outputting a probability of the subject's response to the intervention to reduce the biological effect of space travel as an output of the prediction model, wherein the trained prediction model is trained with one or more metrics of the exposure group characteristics of the first set of subjects and the second set of subjects, the corresponding administered intervention, and the clinical response to the administered intervention.

[0117] In some embodiments, the trained predictive model is configured to determine whether a biological sample from a subject is deregulated by space travel.

[0118] In some embodiments, the trained predictive model is configured to determine the effect of an intervention based at least in part on an analysis of fluorescence intensity spatially across one or more biological samples as a measure of intervention effectiveness.

[0119] In some embodiments, the first one or more biological sample sets or the second one or more biological sample sets include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0120] In some embodiments, the intervention comprises diet, medication, or any combination thereof.

[0121] In some embodiments, the diet includes iodine supplementation.

[0122] In some embodiments, the intervention improves radiation exposure.

[0123] In some embodiments, the first set of subjects or the second set of subjects includes humans, non-human mammals, or any combination thereof.

[0124] In some embodiments, the non-human mammal comprises a mouse.

[0125] In some embodiments, the trained prediction model comprises a gradient boosting ensemble model.

[0126] In some embodiments, measurement of the temporal dynamics of a spatial fluorescence intensity trace comprises determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure-based features, determining a sudden change in the intensity of one or more exposure-based features, determining one or more changes in the baseline intensity of one or more exposure-based features, determining a change in a frequency domain representation of one or more exposure-based features, determining a change in a power spectrum domain representation of one or more exposure-based features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0127] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0128] In some embodiments, the analyzing includes obtaining fluorescence images of the first stained one or more biological sample sets and the second stained one or more biological sample sets, and analyzing spatial fluorescence intensity of the fluorescence images.

[0129] In some embodiments, obtaining a fluorescent image of the stained dental sample comprises using an inverted or non-inverted confocal microscope.

[0130] In some embodiments, staining the first one or more biological sample sets of the first set and the second set includes using C-reactive staining.

[0131] In some embodiments, the method further comprises sectioning the tooth sample.

[0132] In some embodiments, staining the first one or more biological sample sets and the second one or more biological sample sets includes decalcifying the first one or more biological sample sets and the second one or more biological sample sets.

[0133] In some embodiments, the trained model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

[0134] In some embodiments, the trained model comprises a gradient boosted decision tree.

[0135] In some embodiments, the trained predictive model comprises a classification algorithm.

[0136] In some embodiments, a classification algorithm is trained to recognize characteristics in spatial fluorescence intensity associated with space travel.

[0137] In one aspect, the present disclosure provides a method for outputting one or more pathways associated with space travel, the method comprising: receiving one or more biological samples of a first one or more subject sets and a second one or more subject sets, wherein the first one or more subject sets have participated in space travel and the second one or more subject sets have not participated in space travel; determining exposure genomic features of one or more pathways from the one or more biological samples of the first one or more subject sets and the second one or more subject sets; calculating exposure genomic characteristics of the exposure genomic features of the one or more pathways; and outputting one or more pathways associated with space travel by comparing the exposure genomic characteristics of the one or more pathways of the first subject set and the second subject set.

[0138] In some embodiments, each of the expomic profiles includes one or more quantitative metrics.

[0139] In some embodiments, space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

[0140] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0141] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0142] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0143] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0144] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0145] In some embodiments, the expomic profile is derived from one or more attractors.

[0146] In some embodiments, the method further comprises analyzing the dynamic relationship between one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions, or any combination thereof.

[0147] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0148] In one aspect, the present disclosure provides a method for providing treatment to ameliorate the effects of space travel, the method comprising: receiving one or more biological samples from one or more subjects participating in space travel; determining an exposome signature of one or more pathways from the one or more biological samples of the one or more subjects; calculating an exposome characteristic of the exposome signature of the one or more pathways; and providing treatment to the one or more subjects, wherein the treatment ameliorate the effects of space travel on the exposome characteristic of the exposome signature of the one or more pathways of the one or more subjects.

[0149] In some embodiments, the treatment comprises a diet, a medication, a nutritional supplement, a food bar, or any combination thereof.

[0150] In some embodiments, each of the expomic profiles includes one or more quantitative metrics.

[0151] In some embodiments, space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

[0152] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0153] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0154] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0155] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0156] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0157] In some embodiments, the expomic profile is derived from one or more attractors.

[0158] In some embodiments, the method further comprises analyzing the dynamic relationship between one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions, or any combination thereof.

[0159] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0160] Aspects of the disclosure describe a method for outputting one or more pathways associated with an intervention, the method comprising: (a) receiving one or more biological samples of a first one or more object sets and a second one or more object sets, wherein the first one or more object sets have received an intervention and the second one or more object sets have not received an intervention; (b) determining an exposure group feature of one or more pathways from one or more biological samples of the first one or more object sets and the second one or more object sets; (c) calculating an exposure group characteristic of the exposure group characteristic of one or more pathways; and (d) outputting one or more pathways associated with the intervention by comparing the exposure group characteristics of one or more pathways of the first object set and the second object set. In some embodiments, each characteristic in the exposure group characteristic includes one or more quantitative metrics. In some embodiments, the biological sample includes hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof. In some embodiments, one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof. In some embodiments, the intervention includes a drug, a diet, a food bar, a nutrient, a metal ion supplement, or any combination thereof.

[0161] In some embodiments, the exposure genomic characteristics include a measurement of the temporal dynamics of one or more exposure genomic features. In some embodiments, the measurement of the temporal dynamics includes: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0162] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0163] In some embodiments, the exposure group characteristics are derived from one or more attractors. In some embodiments, the method further comprises analyzing the dynamic relationship between one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in recursion number, the number of most likely recursions, or any combination thereof.

[0164] Aspects of the disclosure provide a method for determining an exposure group characteristic of an object associated with space travel, the method comprising: (a) receiving one or more biological samples of the object; (b) determining an exposure group characteristic of one or more pathways from the one or more biological samples; (c) calculating an exposure group characteristic of the exposure group characteristic of the one or more pathways; and (d) outputting an exposure group characteristic of an object associated with space travel by comparing the exposure group characteristic of the object with a space travel exposure group characteristic database. In some embodiments, each characteristic in the exposure group characteristic comprises one or more quantitative metrics. In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof. In some embodiments, the object is a human, a non-human mammal, a vertebrate, or any combination thereof.

[0165] In some embodiments, the exposure genomic characteristics include a measurement of the temporal dynamics of one or more exposure genomic features. In some embodiments, the measurement of the temporal dynamics includes: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, the measurement of temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursive rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursive trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursive time, Shannon entropy in recursive times, or the number of most likely recursions. In some embodiments, the exposure group characteristics are derived from one or more attractors. In some embodiments, the method also includes analyzing one or more signal recursive rates, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursive trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursive time, Shannon entropy in recursive times, the number of most likely recursions, or any combination thereof.

[0166] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0167] Aspects of the disclosure describe a method for determining an exposure group characteristic of an object associated with space travel using a trained prediction model, the method comprising: (a) receiving one or more biological samples of the object; (b) determining an exposure group characteristic of one or more pathways from the one or more biological samples; (c) calculating an exposure group characteristic of the exposure group characteristic of the one or more pathways; and (d) outputting an exposure group characteristic of an object associated with space travel by providing the exposure group characteristic of the object as an input to a trained prediction model, wherein the trained prediction model is trained on the exposure group characteristic of one or more objects that have participated in space travel. In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof. In some embodiments, the object is a human, a non-human mammal, a vertebrate, or any combination thereof.

[0168] In some embodiments, each characteristic in the exposure group characteristic comprises one or more quantitative metrics. In some embodiments, the exposure group characteristic is derived from one or more attractors. In some embodiments, the exposure group characteristic comprises a measurement of the temporal dynamics of one or more exposure group characteristics. In some embodiments, the measurement of temporal dynamics comprises: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposure group characteristics, determining a sudden change in the intensity of one or more exposure group characteristics, determining one or more changes in the baseline intensity of one or more exposure group characteristics, determining a change in the frequency domain representation of one or more exposure group characteristics, determining a change in the power spectrum domain representation of one or more exposure group characteristics, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating the Lyapunov spectrum, determining the maximum Lyapunov exponent, or any combination thereof. In some embodiments, the measurement of temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions. In some embodiments, the method also includes analyzing the dynamic relationship between one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof.

[0169] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0170] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled to the one or more computer processors. The computer memory includes machine executable code that implements any of the methods above or elsewhere herein when executed by the one or more computer processors.

[0171] Additional aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the disclosure. Therefore, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

[0172] Incorporation by reference

[0173] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. In the event that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such conflicting material. BRIEF DESCRIPTION OF THE DRAWINGS

[0174] The novel features of the disclosure are particularly set forth in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description of illustrative embodiments in which the principles of the disclosure are utilized and the accompanying drawings (also referred to herein as "Figures"), in which:

[0175] Figure 1 An overview of a computer-implemented expomics system, as seen in some embodiments, is shown.

[0176] Figure 2A-2B Clinical database data (clinical metadata), exposome biochemical characteristics, and intervention outcomes for subjects are shown ( Figure 2A ) to train the intervention prediction model and use the trained prediction model ( Figure 2B ) to, for example, predict the outcome of an intervention for a given subject, as seen in some embodiments.

[0177] Figure 3A-3B The data workflow for training a prediction model for optimal selection of drugs and / or nutraceuticals using the subject's clinical database data (clinical metadata), the expomic profile, and the percent difference between the subject's post-treatment expomic profile and the reference expomic profile is shown. Figure 3A shows the training workflow for training a predictive model, and Figure 3B The use of a trained prediction model is shown, as seen in some embodiments.

[0178] Figure 4 A flow chart is shown for selecting subjects for intervention based on their exposome biochemical profile, as seen in some embodiments.

[0179] Figure 5 Shown is a flow chart for selecting an optimal drug or nutraceutical treatment based on a comparison of a subject's expomic profile to a reference treatment's expomic profile, as seen in some embodiments herein.

[0180] Figure 6A-6D Shown are the exposome biochemical profiles of various exposome features (e.g., tin, lead, calcium, and magnesium) for subjects who have not received the intervention (blue) and subjects who have received the intervention (orange, gray, and cyan), as seen in some embodiments herein.

[0181] Figure 7A-7B Clustering of one or more exposome biochemical profiles of one or more subjects is shown ( Fig. 7A ), and how this data cluster can be associated with one or more subjects who have participated in space travel ( Figure 7B ), as seen in some embodiments herein.

[0182] Figure 8 Subtyping of subjects using exposome profiles derived from hair analysis is shown. Exposome biochemical signature data was extracted by analysis methods disclosed elsewhere herein. Cluster analysis was shown to identify discrete subtypes of patients who have participated in space travel, as seen in some embodiments herein.

[0183] Fig. 9 Systems and methods are shown for collecting and analyzing the geo-temporal dynamics of annotated exposome pathways through a deep data science framework, as seen in some embodiments herein.

[0184] Fig.10 Shown are temporal aspects of 100 data time points obtained from a single biological sample, characterizing physiological dynamics at different life stages, as seen in some embodiments herein.

[0185] Fig.11Shown are various chemical features measured by the method and system and their corresponding groupings, as seen in some embodiments herein.

[0186] Fig.12 Shown are temporal and spatial immunohistochemistry (IHC) fluorescence data captured by the methods and systems described herein.

[0187] Fig.13 Methods are shown for measuring metal chemical biomarkers of teeth and correlating the spatial distribution of metal chemical biomarkers across tooth growth lines to determine the effects of space travel and biochemical physiological changes, etc., as seen in some embodiments herein.

[0188] Fig.14 A machine learning, informatics, and deep learning platform configured to generate a robust and generalizable prediction model for space travel is shown.

[0189] Fig.15 Methods for phenotyping, pathway identification, metabolic phenotyping, and clinical subtyping of various physiological outcomes via unsupervised pattern recognition of exposome graphs are shown.

[0190] Fig.16 Shown are the affected probiotic metabolism and corresponding biochemical pathways measured by the method and system, as seen in some embodiments herein.

[0191] Fig.17 Shown are the affected gluten metabolism and corresponding biochemical pathways measured by the method and system, as seen in some embodiments herein.

[0192] Fig.18 Pathway importance weights from subjects who participated in space travel are shown, which pathway importance weights are utilized by the described methods and systems to recommend pharmaceutical and nutraceutical compounds to ameliorate negative effects of space travel.

[0193] Figure 19A-Figure 19C Various forms of exposome signature data representations are shown, as seen in some embodiments herein.

[0194] Figures 20A-20D Shows calcium ( Figure 20A-20B ) and copper ( Figure 20C-Figure 20D ) Comparison of exposure-based profiles and their corresponding attractor graphical representations, as seen in some embodiments.

[0195] Figure 21A-21B A recursive network is shown, as may be seen in some embodiments.

[0196] Fig. 22A flow chart illustrating a method of outputting one or more quantitative metrics of one or more expomic signatures of a subject, as seen in some embodiments.

[0197] Fig.23 A flow chart is shown of a method for outputting a prediction of phenotypic data for one or more subjects, as seen in some embodiments.

[0198] Fig.24 Shown are quantitative metrics derived from cross-recursive quantification analysis of signal pairs from control animals housed in a ground facility and mice that traveled to the International Space Station, as seen in some embodiments.

[0199] Fig.25 Shown are data derived from analysis of barium, manganese, and zinc signals from human hair collected after space travel, as seen in some embodiments.

[0200] Fig.26 Shown are average Ba:CA dynamics, Zn:Fe dynamics in mice receiving a control diet or supplemental iodine in their diet, as seen in some embodiments.

[0201] Fig. 27 Shown are average Ba:CA dynamics, Zn:Fe dynamics in mice receiving a food bar diet or receiving a standard lab chow diet, as seen in some embodiments. DETAILED DESCRIPTION

[0202] Although various embodiments have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Without departing from the disclosure, many variations, changes and substitutions may be envisioned by those skilled in the art. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed.

[0203] The present disclosure meets these needs through systems and methods that can analyze and classify subjects based on their exposome biochemical profiles, such as Fig. 9 The exposure profile analysis of the present disclosure may include non-invasive analysis of more than 50,000 biochemical features ( Fig.11 Using the analytical systems and methods disclosed herein, it is possible to study the effects of, for example, diet, air pollution, psychological stress, space travel, exposure to pesticides or industrial chemicals ( Fig.10 ) and correlates them with intervention responses and targeted, highly effective drugs and nutritional supplements.

[0204] In addition to the rich datasets generated by exposome biochemical profiling, exposome biochemical profiling can also provide insights into the temporal fluctuations of the biochemical signatures over the lifetime of a subject, e.g. Fig.10 This approach can be used to screen individuals who have participated in space travel to determine which individual exposome traits or combinations of exposome traits are affected by space travel. The identified pathways can then be used to train statistical, machine learning, and / or artificial intelligence predictive models that are able to predict whether a subject's exposome trait is characteristic of individuals who have participated in space travel.

[0205] Computer-implemented exposome system

[0206] In one aspect, the present disclosure provides a computer-implemented exposure group system for aggregating, storing, cataloging, comparing, analyzing, or any combination thereof, the exposure group biochemical characteristics of one or more objects. In some embodiments, the exposure group biochemical characteristics can be used at least in part to optimize the selection criteria of the objects participating in the intervention. In some embodiments, the exposure group biochemical characteristics are used at least in part to recommend the best drug or nutritional treatment for the object in need. In some embodiments, the intervention can include a clinical trial, a community trial, or any combination thereof.

[0207] Go to Figure 1, the computer-implemented exposure group system 23 may include one or more of the following: (a) an exposure group biochemical signature database (EDB) 1, the EDB may also include biochemical signature information of multiple subjects who have participated in and have not participated in space travel; (b) a clinical database (CBD) 3, the CBD may also include clinical phenotype information of multiple subjects who have participated in and have not participated in space travel; (c) an intervention requirement database (IODB) 5, the IODB may also include information about intervention result information of at least one stage of at least one intervention; (d) a treatment database (TDB) 18; and (e) a computer system 11, which may include a processing unit (CPU, also referred to herein as a "processor" and a "computer processor") 21, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The processor 21 may execute a sequence of machine-readable instructions embodied in a program or software, such as: (i) an association software module located on a storage unit 19 (i.e., a memory), the association software module being communicatively coupled to the EDB 1 and the CDB 3, the association software module being programmed to determine an association between the exposure group characteristics and the clinical phenotype information of at least one of the plurality of subjects; and (ii) a recommendation software module located on the memory 19. The software may be loaded from the memory 19 into a random access memory (RAM) 17 or a read-only memory (ROM) 17, and may provide an intervention recommendation for at least one of the plurality of subjects based at least in part on the exposure group characteristics, the clinical phenotype information, the intervention outcome information, and the association between the exposure group characteristics and the clinical phenotype information of at least one of the plurality of subjects.

[0208] In some embodiments, the Exposomic Biochemical Signature Database (EDB) 1 may include exposure group characteristics from multiple subjects who have participated in and have not participated in space travel. The exposure group biochemical signature may include biochemical signatures of perfluorinated compounds, parabens, phthalates, lipids, amino acids, metabolites, peptides, metals, derivatives thereof, or any combination thereof, such as in Fig.11 In some embodiments, the exposome biochemical signature is analyzed or obtained as a function of a subject's lifetime (e.g., as a function of age or as a function of time), in which case, as in Figure 6A-6D As seen in, the collection of exposure group biochemical features can be analyzed to generate one or more exposure group characteristics. In some embodiments, the period of time represented by the exposure group biochemical features can include at least 1 hour, at least 1 day, at least 1 week, at least 1 month, at least 1 year or any combination thereof.

[0209] In some embodiments, the number of exposome biochemical signatures may include about 10 signatures to about 100,000 signatures. In some embodiments, the number of exposome biochemical signatures may include about 10 signatures to about 100 signatures, about 10 signatures to about 500 signatures, about 10 signatures to about 1,000 signatures, about 10 signatures to about 5,000 signatures, about 10 signatures to about 7,000 signatures, about 10 signatures to about 10,000 signatures, about 10 signatures to about 20,000 signatures, about 10 signatures to about 50,000 signatures, about 10 signatures to about 100,000 signatures, about 100 signatures to about 500 signatures, about 100 signatures to about 1,000 signatures, about 100 signatures to about 5,000 signatures. features, about 100 features to about 7,000 features, about 100 features to about 10,000 features, about 100 features to about 20,000 features, about 100 features to about 50,000 features, about 100 features to about 100,000 features, about 500 features to about 1,000 features, about 500 features to about 5,000 features, about 500 features to about 7,000 features, about 500 features to about 10,000 features, about 500 features to about 20,000 features, about 500 features to about 50,000 features, about 500 features to about 100,000 features, features, about 1,000 features to about 5,000 features, about 1,000 features to about 7,000 features, about 1,000 features to about 10,000 features, about 1,000 features to about 20,000 features, about 1,000 features to about 50,000 features, about 1,000 features to about 100,000 features, about 5,000 features to about 7,000 features, about 5,000 features to about 10,000 features, about 5,000 features to about 20,000 features, about 5,000 features to about 50,000 features, about 5,000 features to about 100,000 features. In some embodiments, the present invention relates to a method for generating a plurality of genetically modified organisms having at least one genetically modified organism (GMO) ...In some embodiments, the number of exposure group biochemical signatures can include about 10 signatures, about 100 signatures, about 500 signatures, about 1,000 signatures, about 5,000 signatures, about 7,000 signatures, about 10,000 signatures, about 20,000 signatures, about 50,000 signatures, or about 100,000 signatures. In some embodiments, the number of exposure group biochemical signatures can include at least about 10 signatures, about 100 signatures, about 500 signatures, about 1,000 signatures, about 5,000 signatures, about 7,000 signatures, about 10,000 signatures, about 20,000 signatures, or about 50,000 signatures. In some embodiments, the number of exposome biochemical signatures can include up to about 100 signatures, about 500 signatures, about 1,000 signatures, about 5,000 signatures, about 7,000 signatures, about 10,000 signatures, about 20,000 signatures, about 50,000 signatures, or about 100,000 signatures.

[0210] In some embodiments, the exposure group biochemical characteristics can be obtained by measuring the biological samples of multiple objects. In some embodiments, the biological sample can include teeth, nails or hair stem samples. In some embodiments, the exposure group biochemical characteristics can be obtained using laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, mass spectrometry, Raman spectroscopy, immunohistochemistry, molecular labeling (for example, with fluorophores), nuclear magnetic resonance, chromatography or any combination thereof. In some embodiments, laser ablation inductively coupled plasma mass spectrometry can measure one or more element chemicals. In some embodiments, one or more element chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc or any combination of chemicals described elsewhere herein (for example, Table 1 and / or Table 2). In some embodiments, the exposure group biochemical characteristics information can include the exposure group time biochemical reactions of multiple objects. In some embodiments, biochemical information can include the fluorescent image of biological samples. In some embodiments, the exposure group biochemical characteristics can include the spatial map of the Raman spectrum of the biological samples of multiple objects. In some embodiments, the expomic profile may be associated with space travel.

[0211] In some embodiments, the plurality of chemical substances is selected from the chemical substances listed in Table 1. In some embodiments, the plurality of chemical substances includes at least 50%, 60%, 70%, 80% or 90% of the isotopes included in Table 1.

[0212] Table 1. List of chemical substances

[0213] Chemical substances Element Name Li-7(Li) lithium Mg-24(Mg) magnesium Mg-25(Mg25) magnesium Al-27(Al) aluminum

[0214] Chemical substances Element Name P-31(P) phosphorus S-34(S) sulfur Ca-44(Ca) calcium Calcium-43 (Ca43) calcium Cr-52(Cr) chromium Mn-55(Mn) manganese Fe-56(Fe) iron Co-59(Co) cobalt Ni-60(Ni) nickel Cu-63(Cu) copper Zn-66(Zn) Zinc As-75(As) arsenic Sr-88(Sr) strontium Cd-111(Cd) cadmium Sn-118(Sn) tin I-127(I) iodine Ba-138 (Ba) barium Hg-201(Hg) mercury Pb-208(Pb) lead Bi-209(Bi) bismuth Mo-95(Mo) molybdenum

[0215] In some embodiments, the plurality of chemical substances is selected from the chemical substances listed in Table 2. In some embodiments, the plurality of chemical substances includes at least 50%, 60%, 70%, 80% or 90% of the isotopes included in Table 2.

[0216] Table 2. List of chemical substances

[0217]

[0218] In some embodiments, one or more exposure group characteristics are calculated from one or more dynamic exposure group biochemical features. The exposure group characteristics obtained by data analysis may include descriptive statistics or parameters used in subsequent statistics, machine learning or artificial intelligence models. Such exposure group characteristics may include standard descriptive metrics (such as mean, median, mode and range) and / or associated errors and / or variations (such as standard deviation, variance, confidence interval and / or related metrics of one or more dynamic exposure group characteristics). The exposure group characteristics may include: the application of a calculation method for deriving a linear slope, a nonlinear parameter describing the curvature of one or more dynamic exposure group characteristics, a sudden change in the intensity of one or more dynamic exposure group characteristics, a change in the baseline intensity of one or more dynamic exposure group characteristics, a change in the frequency domain representation of one or more dynamic exposure group characteristics, a change in the power spectrum domain representation of one or more dynamic exposure group characteristics, a recursive quantitative analysis parameter, a cross recursive quantitative analysis parameter, a joint recursive quantitative analysis parameter, a multidimensional recursive quantitative analysis parameter, an estimated Lyapunov spectrum or a maximum Lyapunov index, or any combination thereof.

[0219] In some embodiments, one or more expomic characteristics include a measure of the temporal dynamics of one or more dynamic expomic features. In some embodiments, the measure of temporal dynamics includes: a linear slope, a nonlinear parameter describing the curvature of one or more dynamic expomic features, a sudden change in the intensity of one or more dynamic expomic features, a change in the baseline intensity of one or more dynamic expomic features, a change in the frequency domain representation of one or more dynamic expomic features, a change in the power spectrum domain representation of one or more dynamic expomic features, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimated Lyapunov spectrum, a maximum Lyapunov exponent, or any combination thereof.

[0220] In some embodiments, recursive quantitative analysis, cross-recursive quantitative analysis, joint recursive quantitative analysis, multidimensional recursive quantitative analysis of one or more exposure group biochemical characteristics can be used to derive descriptive statistics and / or parameters for training predictive models, as described elsewhere herein.

[0221] In some cases, the recursive quantitative analysis parameters may include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, and the number of most likely recursions.

[0222] In some cases, one or more exposome features can be derived from one or more attractors ( Fig.19B , Fig. 20B , Fig.20D ), whereby one or more attractors are derived from one or more dynamic exposome biochemical features ( Fig.19A , Fig. 20A , Fig. 20C In some embodiments, one or more attractors are analyzed by potential energy analysis to generate a potential energy data space.

[0223] In some embodiments, a dynamic relationship established between one or more of signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in number of recursions, number of most likely recursions, or any combination thereof may be analyzed ( Fig.19C ), and provide the dynamic relationship as a characteristic. In some embodiments, the dynamic relationship is determined by a cross convergence mapping (CCM).

[0224] In some cases, a network can be constructed based on similarity of one or more attractors' temporal exposure omics data signal recurrence rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recurrence trend, hierarchicality, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recurrence time, Shannon entropy in number of recurrences, number of most likely recurrences, or any combination thereof ( Figure 21A-21B In some embodiments, one or more exposome properties of a network of one or more attractors are analyzed to determine network connectivity, efficiency, property importance, pathway importance, correlation graph theory-based metrics, or any combination thereof.

[0225] In some embodiments, one or more exposome characteristics of one or more dynamic exposome signatures may include phenotypic characteristics. Phenotypic characteristics may include: electrocardiogram (ECG), electroencephalogram, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome, or any combination thereof.

[0226] In some embodiments, the phenotypic exposure group characteristics include molecular phenotypes. In some cases, the molecular phenotype is determined by unsupervised analysis, wherein the unsupervised analysis includes clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0227] In some embodiments, CBD 3 may include clinical phenotype data of the subject. In some embodiments, clinical phenotype data includes clinical metadata of multiple subjects. In some embodiments, clinical metadata may include age, sex, weight, height, blood type, eye vision, current disease, family medical history, or any combination thereof of the subject. In some embodiments, clinical metadata and exposure group biochemical characteristics of the subject may be considered independently or in combination to determine whether the subject is a suitable candidate for intervention.

[0228] In some embodiments, the IODB 5 may include intervention outcome information. In some embodiments, the intervention outcome information may include one or more eligibility criteria for an intervention. In some embodiments, an intervention may include a stage 1, 2, 3, or any combination thereof intervention. In some embodiments, the intervention outcome information may include information on the classification of the intervention outcome for one or more subjects, including: positive responder, negative responder, or non-responder.

[0229] In some embodiments, TDB 18 may include at least in part an exposome profile associated with a drug and nutraceutical treatment. In some embodiments, the exposome profile of a drug and nutraceutical may include one or more reference exposome profiles of a subject who has not participated in space travel, one or more pre-treatment exposome profiles of a subject who has participated in space travel, and one or more post-treatment exposome profiles of a subject who has participated in space travel, obtained by measuring one or more biological samples of one or more subjects.

[0230] In some embodiments, analysis of the differences between one or more pre-treatment exposure group characteristics and one or more post-treatment exposure group characteristics and one or more reference dynamic spectrum biochemical characteristics can be used to determine one or more optimal drugs, nutrients, or any combination thereof for subjects who have participated in space travel. In some embodiments, the difference between one or more post-treatment exposure group characteristics and one or more reference exposure group characteristics can provide a basis for recommending one or more drugs or nutrients to subjects who have participated in space travel, and can be used in conjunction with the exposure group biochemical characteristics of one or more subjects to recommend optimal treatment to improve the negative effects of space travel. In some embodiments, the criteria for optimal drug or nutritional treatment can include supplementing deficiencies in the exposure group biochemical characteristics of one or more subjects for a given subject.

[0231] In some embodiments, one or more characteristics of the post-treatment exposure group characteristics are different from one or more reference exposure group characteristics. In some embodiments, the characteristics include an overall mean, a measure of variability, a moving average, the like, or any combination thereof, as described elsewhere herein.

[0232] In some embodiments, the difference in a property comprises a difference of about 10% to about 100%. In some embodiments, the difference in a property comprises a difference of about 10% to about 20%, about 10% to about 30%, about 10% to about 40%, about 10% to about 50%, about 10% to about 60%, about 10% to about 70%, about 10% to about 80%, about 10% to about 90%, about 10% to about 100%, about 20% to about 30%, about 20% to about 40%, about 20% to about 50%, about 20% to about 60%, about 20% to about 70%, about 20% to about 80%, about 20% to about 90%, about 20% to about 100%, about 20% to about 30%, about 20% to about 40%, about 20% to about 50%, about 20% to about 60%, about 30% to about 70%, about 30% to about 80%, about The present invention also provides a difference of about 30% to about 90%, about 30% to about 100%, about 40% to about 50%, about 40% to about 60%, about 40% to about 70%, about 40% to about 80%, about 40% to about 90%, about 40% to about 100%, about 50% to about 60%, about 50% to about 70%, about 50% to about 80%, about 50% to about 90%, about 50% to about 100%, about 60% to about 70%, about 60% to about 80%, about 60% to about 90%, about 60% to about 100%, about 70% to about 80%, about 70% to about 90%, about 70% to about 100%, about 80% to about 90%, about 80% to about 100%, or about 90% to about 100%. In some embodiments, the difference in characteristics can include a difference of about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 100%. In some embodiments, the difference in characteristics can include a difference of at least about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 90%. In some embodiments, the difference in characteristics can include a difference of at most about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 100%.

[0233] The computer system may also include a communication interface 13 (e.g., a network adapter) and a peripheral device 15 (such as a cache, other memory, data storage, and / or an electronic display adapter) for communicating with one or more other systems. The memory 17, storage unit 19, interface 13, and peripheral device 15 communicate with CPU 21 via a communication bus (solid line) such as a motherboard. The storage unit 19 may be a data storage unit (or data repository) for storing data. The computer system 11 may be operably coupled to a computer network ("network") by means of the communication interface 13. The network may be the Internet, an extranet, and / or an intranet communicating with the Internet. In some embodiments, the network is a telecommunications and / or data network. The network may include one or more computer servers that may implement distributed computing, such as cloud computing. In some cases, with the aid of the computer system 11, the network may implement a peer-to-peer network, which may enable a device coupled to the computer system 11 to act as a client or server.

[0234] In some embodiments, the EDB 1, CDB 3, IODB 5, and TDB 18 may be located on a network and remotely accessed by the computer system 11. In some embodiments, the EDB 1, CDB 3, IODB 5, and TDB 18 may reside on a secure encrypted network server that protects personal health information. In some embodiments, the EDB 1, CDB 3, IODB 5, and TDB 18 may be remotely accessed by one or more computer systems 11 within or outside the hospital network. In some embodiments, the EDB 1, CDB 3, IODB 5, and TDB 18 may be accessed by one or more subjects through a secure network protocol to view the recommendations and their personalized data.

[0235] The CPU 21 may execute a sequence of machine-readable instructions that may be embodied in a program or software. The instructions may be stored in a storage location such as the random access memory 17. The instructions may be directed to the CPU 21, which may then program or otherwise configure the CPU 21 to implement the methods of the present disclosure. Examples of operations performed by the CPU 21 may include reading, decoding, executing, and writing back.

[0236] CPU 21 may be part of a circuit such as an integrated circuit. One or more other components of system 11 may be included in the circuit. In some embodiments, the circuit is an application specific integrated circuit (ASIC).

[0237] Storage unit 19 may store files, such as drivers, libraries, and saved programs. Storage unit 19 may store user data, such as user preferences and user programs. In some cases, computer system 11 may include one or more additional data storage units external to computer system 11, such as located on a remote server that communicates with computer system 11 via an intranet or the Internet.

[0238] Computer system 11 may communicate with one or more remote computer systems via a network. For example, computer system 11 may communicate with a remote computer system of a user (e.g., a healthcare provider, a subject, etc.). Examples of remote computer systems include a personal computer (e.g., a portable PC), a notebook computer, or a tablet PC (e.g., iPad, Galaxy Tab), phones, smartphones (e.g. iPhone, Android-supported devices, ) or a personal digital assistant. Users can access the computer system 11 through the network.

[0239] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of computer system 11 (e.g., such as stored in memory 17 or electronic storage unit 19, for example). Machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by processor 21. In some embodiments, the code is retrieved from storage unit 19 and stored in random access memory 17 for ready access by processor 21. In some cases, storage unit 19 may be excluded and machine executable instructions are stored in random access memory 17.

[0240] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be supplied in a programming language, which may be selected to enable the code to be executed in a precompiled or compiled manner.

[0241] Aspects of the systems and methods provided herein (such as computer system 11) can be embodied in programming. Various aspects of the technology can be considered as "products" or "articles" that typically exist in the form of machine (or processor) executable code and / or associated data carried on or embodied in a type of machine-readable medium. The machine executable code can be stored in an electronic storage unit (such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk). "Storage" type media can include any or all tangible memories of computers, processors, etc., or their associated modules (such as various semiconductor memories, tape drives, disk drives, etc.), which can provide non-transient storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communication can load software from one computer or processor to another computer or processor, such as from a management server or host computer to an application server's computer platform. Therefore, another type of medium that can carry software elements includes optical waves, radio waves, and electromagnetic waves such as those used through wired and optical landline networks and through various air links across physical interfaces between local devices. The physical elements that carry such waves (such as wired or wireless links, optical links, etc.) can also be considered as the medium that carries the software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0242] Thus, machine-readable media (such as computer executable code) can take a variety of forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any computer, etc., which can be used to implement the database shown in the accompanying drawings, etc. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wires; and optical fibers, which include the lines that make up the bus within a computer system. Carrier transmission media can take the form of electrical or electromagnetic signals or sound or light waves (such as those waves generated during radio frequency (RF) and infrared (IR) data communications). Thus, common forms of computer-readable media include, for example: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punched card tapes, any other physical storage media with a pattern of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier wave that transmits data or instructions, a cable or link that transmits such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0243] The computer system 11 may include or communicate with an electronic display 7, which includes a user interface (UI) 9 for providing, for example, fluorescence image data, fluorescence intensity data, temporal profiles of inflammation, and machine learning classifications. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0244] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 21, as described elsewhere herein.

[0245] Prediction Model

[0246] Aspects of the disclosure herein may include trained predictive models implemented on a computer-implemented exposome system 23. In some embodiments, the trained predictive model may be configured to provide a retrospective or prospective prediction of the probability of success of an intervention, drug, or nutraceutical treatment for a subject in need thereof, or any combination thereof. In some embodiments, the trained predictive model may include a statistical, machine learning, artificial intelligence classifier, an ensemble of classifiers, or any combination thereof.

[0247] The classifier may include one or more statistics, machine learning or artificial intelligence algorithms. The example of the algorithm used may include support vector machine (SVM), naive Bayes classification, random forest, neural network (such as deep neural network (DNN), recursive neural network (RNN), deep RNN, long short-term memory (LSTM) recursive neural network (RNN) or gated recursive unit (GRU)), or other supervised learning algorithms or unsupervised machine learning, statistics, deep learning algorithms, shallow learning algorithms for classification and regression. The classifier may also be related to the estimation of the integrated model consisting of multiple prediction models, and for example, in the construction of a gradient boosting decision tree, a technique such as gradient boosting is utilized. The classifier may be trained using one or more trained data sets corresponding to patient data. In some embodiments, one or more trained data sets may include the exposome characteristics of the exposure group biochemical characteristics, dynamic exposure group biochemical characteristics, clinical metadata, clinical trial information, drugs and nutraceuticals treatment or any combination thereof.

[0248] In some embodiments, the training data characteristics may include dynamic exposure group biochemical feature data of an object generated from a biological sample. For each biological sample of a given object, multiple positions of a reference line on the biological sample of the training object can be sampled to generate measurements therefrom, thereby obtaining multiple exposure group biochemical features. Each exposure group biochemical feature in the corresponding multiple exposure group biochemical features corresponds to a different position in the corresponding multiple positions, and each position in the corresponding multiple positions represents a different growth period of the corresponding biological sample. Next, each corresponding position of the biological sample is analyzed (for example, using a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS), a fluorescent image sensor, or a Raman spectrometer) to obtain multiple traces. Each trace in the corresponding multiple traces corresponds to an abundance measurement of a corresponding substance, which is jointly determined over time by the corresponding multiple dynamic exposure group biochemical features.

[0249] In some embodiments, mark can include intervention results, for example, such as positive reaction, negative reaction (that is, adverse reaction) or non-responder.Intervention results can include the time characteristics associated with the classification of the positive reaction, negative reaction or non-responder event of the duration after the application of the treatment provided during the intervention.This period of time can be, for example, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 10 days, about 2 weeks, about 3 weeks, about 4 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 6 months, about 8 months, about 10 months, about 1 year or more than about 1 year.

[0250] The input characteristics for training a classifier can be constructed by aggregating data in a receiver or alternatively using a unique hot encoding. Input can also include characteristic values ​​or vectors drawn from the aforementioned input, such as the cross-correlation calculated between independent exposure group characteristics or other measured values ​​within a fixed period of time, and discrete derivatives or finite differences between the continuous measured values ​​described elsewhere herein. This period of time can be, for example, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 10 days, about 2 weeks, about 3 weeks, about 4 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 6 months, about 8 months, about 10 months, about 1 year or more than about 1 year.

[0251] The training records can be constructed from sequences of observations. To facilitate data processing, such sequences can include fixed lengths. For example, the sequences can be zero-padded or selected as independent subsets of a single patient record.

[0252] In order to train a classifier model (e.g., by determining the weights and relevance of the model) to generate real-time classifications or predictions, a data set can be used to train the model. Such a data set can be large enough to generate statistically significant classifications or predictions. For example, the data set can include: a database of de-identified data that includes dynamic spectrum biometric data and other clinical metadata measurements from a hospital or other clinical environment.

[0253] The data set can be divided into subsets (e.g., discrete or overlapping), such as a training data set, a development data set, and a test data set. For example, the data set can be divided into a training data set including 80% of the data set and a test data set including 20% ​​of the data set. The training data set can include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80% or about 90% of the data set. The development data set can include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80% or about 90% of the data set. The test data set can include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80% or about 90% of the data set. The training set (e.g., training data set) can be selected by randomly sampling the data set corresponding to one or more object queues to ensure the independence of sampling. Alternatively, a training set (eg, training data set) may be selected by proportionally sampling data sets corresponding to one or more subject cohorts to ensure independence of the sampling.

[0254] In order to improve the accuracy of model predictions and reduce overfitting of the model, the data set can be expanded to increase the number of samples in the training set. For example, data expansion can include rearranging the order of observations in the training records. In order to adapt to data sets with missing observations, methods for filling missing data can be used, such as forward filling, backward filling, linear interpolation, and multi-task Gaussian processes. Data sets can be filtered to remove confounding factors. For example, within a database, a subset of objects can be excluded.

[0255] In some embodiments, data science techniques (such as random dropout or regularization) can be used during training of the classifier to prevent overfitting. The neural network can include multiple subnetworks, each of which is configured to generate classifications or predictions of different types of output information (e.g., different types of output information can be combined to form the overall output of the neural network). The classifier can alternatively utilize statistical or related algorithms, including random forests, classification and regression trees, support vector machines, discriminant analysis, regression techniques, and ensembles and gradient boosting variants thereof.

[0256] In some embodiments, the systems and methods of the present disclosure are deployed in a hospital setting for patients actively receiving treatment to improve the negative effects of space travel. When the classifier generates a classification or prediction of the best medication or nutritional supplement, a notification (e.g., an alert or alarm) can be generated and transmitted to a medical service provider (such as a doctor, nurse, or other member of the patient's treatment team in the hospital). The notification can be transmitted via an automated phone call, a short message service (SMS) or multimedia message service (MMS) message, an email, an alert within a dashboard, or any combination thereof. The notification can include output information, such as a prediction of the outcome of an intervention or the best medication or nutritional supplement.

[0257] To verify the performance of the classifier model, different performance metrics can be generated. For example, the area under the receiver operating curve (AUROC) can be used to determine the predictive power of the classifier. For example, the classifier can use an adjustable classification threshold so that the specificity and sensitivity are adjustable, and the receiver operating curve (ROC) can be used to identify different operating points corresponding to different specificity and sensitivity values.

[0258] In some embodiments, the performance of the prediction method is evaluated by constructing a table to provide the frequency and overlap of predicted cases and actual positive cases, predicted positive cases and actual negative cases, predicted negative cases and actual negative cases, and / or predicted negative cases and actual positive cases for individuals with one or more exposomes features and / or characteristics indicative of space travel. In some cases, the constructed table can be a confusion matrix. In some cases, cross-tabulation of the confusion matrix can provide sensitivity, specificity, accuracy, and related performance metrics associated with the systems and methods described elsewhere herein at a given prediction threshold.

[0259] In some embodiments, such as when the dataset is not large enough, cross-validation can be performed to evaluate the robustness of the classifier model across different training and testing datasets.

[0260] In order to calculate performance metrics (such as sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), AUPRC, AUROC or similar metrics), the following definitions can be used. "False positive" can refer to the result in which a positive result or outcome is mistakenly generated (for example, the subject is classified as a positive responder to the intervention, but they experience the adverse or negative effects of participating in the intervention). "True positive" can refer to the result in which a positive result or outcome is correctly generated (for example, the subject is classified as a positive responder to the intervention, and they experience a positive reaction). "False negative" can refer to the result in which a negative result or outcome is generated (for example, the subject is classified as a negative responder, and the subject after participating in the intervention is a non-responder or a positive responder). "True negative" can refer to the result in which a negative result or outcome is generated (for example, the subject is classified as a negative responder, and after participating in the intervention, the subject has an adverse reaction to the drug treatment of the intervention).

[0261] In some embodiments, the classifier can be trained until certain predetermined conditions for accuracy or performance are met, such as having a minimum expected value corresponding to a predictive accuracy measure. For example, a predictive accuracy measure can correspond to a correct prediction of an intervention outcome or an optimal drug or nutrient recommendation and / or selection. Examples of diagnostic accuracy measures can include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, area under the precision-recall curve (AUPRC), and area under the curve (AUC) of a receiver operating characteristic (ROC) curve (AUROC) corresponding to the diagnostic accuracy of detecting whether a subject has participated in space travel and / or whether the subject's exposure group characteristics and / or characteristics are similar to the exposure group characteristics and / or characteristics of subjects who have participated in space travel.

[0262] For example, such a predetermined condition can be that the sensitivity for predicting an intervention outcome or optimal drug or nutritional recommendation and / or selection includes, for example, a value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98% or at least about 99%.

[0263] For another example, such a predetermined condition may be that the specificity of predicting an intervention outcome or optimal drug or nutritional recommendation and / or selection includes, for example, a value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98% or at least about 99%.

[0264] For another example, such a predetermined condition may be that the positive predictive value (PPV) for predicting an intervention outcome or optimal drug or nutritional recommendation and / or selection includes, for example, a value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98% or at least about 99%.

[0265] For another example, such a predetermined condition may be that the negative predictive value (NPV) of the intervention outcome or optimal drug or nutritional recommendation and / or selection includes, for example, a value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98% or at least about 99%.

[0266] For another example, such a predetermined condition may be that the area under the curve (AUC) of the receiver operating characteristic (ROC) curve (AUROC) predicting the intervention outcome or the optimal drug or nutritional product recommendation and / or selection comprises a value of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98 or at least about 0.99.

[0267] For another example, such a predetermined condition may be that the area under the precision-recall curve (AUPRC) for predicting the intervention outcome or the optimal drug or nutritional product recommendation and / or selection comprises a value of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98 or at least about 0.99.

[0268] In some embodiments, the trained classifier can be trained or configured to predict intervention outcomes or optimal medication or nutritional recommendations and / or selections with a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0269] In some embodiments, the trained classifier can be trained or configured to predict an intervention outcome or optimal medication or nutritional recommendation and / or selection with a specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0270] In some embodiments, the trained classifier can be trained or configured to predict an intervention outcome or optimal medication or nutritional recommendation and / or selection with a positive predictive value (PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0271] In some embodiments, the trained classifier can be trained or configured to predict intervention outcomes or optimal medication or nutritional recommendations and / or selections with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0272] In some embodiments, the trained classifier can be trained or configured to predict intervention outcomes or optimal drug or nutritional recommendations and / or selections with an area under the curve (AUC) of a receiver operating characteristic (ROC) curve (AUROC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0273] In some embodiments, the trained classifier can be trained or configured to predict intervention outcomes or optimal medication or nutritional recommendation and / or selection with an area under the precision-recall curve (AUPRC) of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0274] In some embodiments, the classifier is a neural network or a convolutional neural network. See Vincent et al., 2010, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion", J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, "Exploring strategies for training deep neural networks", J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology, each of which is incorporated herein by reference.

[0275] SVMs are described in Cristianini and Shawe-Taylor, 2000, "An Introduction to Support Vector Machines", Cambridge University Press, Cambridge; Boser et al., 1992, "A training algorithm for optimal margin classifiers", in Proceedings of the Fifth Annual ACM Symposium on Computational Learning Theory, ACM Press, Pittsburgh, Pennsylvania, pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York; Duda, Pattern Classification, 2nd ed., 2001, John Wiley & Sons, Inc., pp. 259, 262-265; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al., 2000, Bioinformatics: 16, 906-914, each of which is incorporated herein by reference in its entirety. When used for classification, SVM separates a given binary labeled data set with the hyperplane that is farthest from the labeled data. For situations where linear separation is not possible, SVM can be used in conjunction with the "kernels" technique, which automatically implements a nonlinear mapping to feature space. The hyperplane found by the SVM in feature space corresponds to a nonlinear decision boundary in the input space.

[0276] Decision tree is generally described by Duda, 2001, Pattern Classification, John Wiley & Sons, New York, pp. 395-396 (which is incorporated herein by reference). The tree-based method divides the feature space into a set of rectangles, and then fits the model (as a constant) in each rectangle. In some embodiments, the decision tree is a random forest regression. A specific algorithm that can be used is classification and regression tree (CART). Other specific decision tree algorithms include but are not limited to ID3, C4.5, MART and random forest. CART, ID3 and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, New York, pp. 396-408 and pp. 411-412 (which are incorporated herein by reference). CART, MART and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9 (which is incorporated herein by reference in its entirety). Random forests are described in Breiman, 1999, "Random Forests—Random Features", Technical Report 567, Statistics Department, University of California, Berkeley, September 1999 (which is incorporated herein by reference).

[0277] Clustering (e.g., unsupervised clustering model algorithms and supervised clustering model algorithms) is described in Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, New York, (hereinafter referred to as "Duda 1973"), pages 211-256 (which are incorporated herein by reference in their entirety). As described in Section 6.7 of Duda 1973, the clustering problem is described as the problem of finding natural groupings in a data set. In order to identify natural groupings, two problems are solved. First, a method for measuring the similarity (or dissimilarity) between two samples is determined. The metric (similarity measure) is used to ensure that the samples in one cluster are more similar to each other than the samples in other clusters. Secondly, a mechanism for dividing data into clusters using similarity measures is determined. Similarity measures are discussed in Section 6.7 of Duda 1973, where it is pointed out that a method for starting a clustering study is to define a distance function and calculate the distance matrix between all pairs of samples in the training set. If the distance is a good similarity measure, the distance between the reference entities in the same cluster will be significantly smaller than the distance between the reference entities in different clusters. However, as described on page 215 of Duda 1973, clustering does not require the use of a distance metric. For example, a non-metric similarity function s(x, x') can be used to compare two vectors x and x'. Conventionally, s(x, x') is a symmetric function that is larger when x and x' are "similar" to some extent. An example of a non-metric similarity function s(x, x') is provided on page 218 of Duda 1973. Once a method for measuring the "similarity" or "dissimilarity" between points in a data set is selected, clustering requires a standard function that measures the clustering quality of any partition of the data. The data is clustered using the partition of the data set that maximizes the standard function. See page 217 of Duda 1973. Standard functions are discussed in Section 6.8 of Duda 1973. Recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, New York, has been published. Clustering is described in detail on pages 537-563.More information about clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, NY; Everitt, 1993, Cluster analysis (3rd ed.), Wiley, New York, NY; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, NJ, each of which is incorporated herein by reference. Specific exemplary clustering techniques that can be used in the present disclosure include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest neighbor, farthest neighbor, average link, centroid, or sum of squares algorithms), k-means clustering, fuzzy k-means clustering, and Jarvis Patrick clustering. In some embodiments, clustering includes unsupervised clustering, in which no preconceived notions of what clusters should be formed when clustering a training set are imposed.

[0278] Regression models, such as multi-class logit models, are described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, New York, Chapter 8 (which is incorporated herein by reference in its entirety). In some embodiments, the classifier utilizes the regression model disclosed in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York (which is incorporated herein by reference in its entirety). In some embodiments, for example, a gradient boosting model is used for the classification algorithm described herein; these gradient boosting models are described in the following literature: Boehmke, Bradley; Greenwell, Brandon (2019). "Gradient Boosting". Hands-On Machine Learning with R. Chapman & Hall. Pages 221-245. ISBN 978-1-138-49568-5, which is incorporated herein by reference in its entirety. In some embodiments, for example, integrated modeling techniques are used for the classification algorithms described herein; these integrated modeling techniques described in the implementation of the classification models herein are described in the following document: Zhou Zhihua (2012). Ensemble Methods: Foundations and Algorithms. Chapman and Hall / CRC. ISBN 978-1-439-83003-1, which is incorporated herein by reference in its entirety.

[0279] In some embodiments, the machine learning analysis is performed by executing one or more programs (e.g., stored in Figure 1 In some embodiments, the data analysis is performed by a system including at least one processor (e.g., processing core 21) and a memory (e.g., one or more programs stored in non-persistent memory 17 or storage unit 19), the memory including instructions for performing data analysis.

[0280] Intervention Stratification Prediction Model

[0281] In some embodiments, the prediction model 26 may include one or more classifiers that can be trained to predict the probability of an intervention outcome for one or more subjects based on the exposome characteristics of the one or more subjects, such as in Figure 2AIn some embodiments, the input for training the classifier may include clinical metadata 20 of the object, exposure group student chemical signature data 22 of the object, and the corresponding intervention outcome 24 of the object. In some embodiments, the intervention outcome of a given object may include non-responders, poor responders, or positive responders. In some embodiments, the predictive model may initially be trained on a dataset of one or more objects that have undergone treatment from the intervention. In some embodiments, the training dataset for training the classifier may be generated from, for example, the clinical metadata of the object and the exposure group student chemical signature spectrum of the corresponding object or characteristics derived from the exposure group student chemical signature spectrum (e.g., by RQA, described elsewhere herein) and the intervention outcome.

[0282] In some embodiments, clinical metadata features may include demographic information of a subject derived from an electronic medical record (EMR), physiological measurements, and intervention outcomes. Additionally, training features may include clinical features, such as, for example, certain ranges or categories of dynamic exposome biochemical signature data. For example, a set of features collected from a given patient at a given time point may be used together as features of CBD 3 and EDB 1, which may indicate a subject's health state or condition.

[0283] In some embodiments, the trained prediction model 32 may include a trained classifier as described elsewhere herein, configured to provide predictions of intervention outcomes for subjects interested in participating in the intervention, such as in Figure 2B In some embodiments, the clinical metadata 28 and corresponding exposure group characteristics 30 of one or more objects can be fed as input into the trained prediction model 32. The trained prediction model 32 can then output the probability of the predicted test result 34 of the object. In some embodiments, the output probability of the predicted test result of the object can include the classification of positive responders, negative or adverse responders, or non-responders. In some embodiments, the clinical metadata of the object can include clinical metadata, such as the age, sex, weight, height, blood type, eye vision, current disease, family history, or any combination thereof of the object. Various machine learning techniques can be cascaded so that the output of the machine learning technique can also be used as an input feature of a subsequent layer or sub-part of the classifier.

[0284] Predictive model for optimal drug or nutraceutical selection

[0285] In some embodiments, the prediction model 42 may include one or more classifiers described elsewhere herein, which may be trained to produce a trained prediction model 48 configured to predict the best medication or nutritional supplement to be administered to one or more subjects participating in space travel based on characteristics derived from the corresponding one or more expomic characteristics of the one or more subjects, such as in Figure 3A In some embodiments, the inputs for training a statistical, machine learning, and / or artificial intelligence classifier may include: (a) a subject's space travel classification 36; (b) a subject's pre-treatment characteristics 38 derived from one or more exposome characteristics; (c) an administered medication or nutraceutical treatment 40; and (d) a percent difference between a subject's post-treatment characteristics in one or more exposome characteristics and one or more reference characteristics of an exposome characteristic derived from one or more characteristics derived from a biochemical signature of a subject who has never participated in space travel.

[0286] In some embodiments, the trained prediction model 48 may include a trained classifier as described elsewhere herein, configured to provide a prediction 50 of the percent difference between one or more exposure genomic characteristics of a subject after treatment and one or more reference exposure genomic characteristics, such as in Figure 3B In some embodiments, the trained predictive model may take as input: (a) clinical data 44 for the subject; (b) the drug or nutraceutical treatment under consideration 46; and (c) one or more exposome characteristics of the subject prior to treatment 49. In some embodiments, one or more drug or nutraceutical treatments may be considered.

[0287] Exposure group cluster analysis

[0288] In some embodiments, one or more exposure group profiles of one or more subjects can be analyzed by clustering methods to classify or group subjects based on whether the one or more subjects have participated in space travel, such as in Figure 7A-7B One or more subjects are represented by an exposure group data cluster 97, 100 of one or more exposure group characteristics 104. One or more exposure group characteristics of the subject can be compared with the average value 102 of the cohort for analysis or classification. Alternatively, the exposure group characteristics can be used to subtype the subjects before, during or after the clinical intervention to understand which subjects may have a positive response, a negative response or no response to a given intervention.

[0289] method

[0290] Aspects of the present disclosure may include methods for intervention optimization and recommending optimal medications and / or nutrition for a subject to ameliorate the negative effects of space travel. In some embodiments, the methods described herein may be performed on a system of the present disclosure as described elsewhere herein.

[0291] Intervention Optimization

[0292] In some embodiments, the methods of the present disclosure may include a method 60 of optimizing an intervention outcome in a subject, such as in Figure 4 . In some embodiments, the intervention may include a clinical trial study of Phase I, Phase II, Phase III, or any combination thereof. In some embodiments, the method includes the following steps: (a) providing a trained prediction model, wherein the trained model is trained on clinical metadata, exposure group characteristics, and corresponding intervention outcome information of one or more subjects 61; (b) detecting characteristics derived from biochemical features obtained from biological samples from subjects seeking intervention, thereby generating retrospective exposure group biochemical features 62; (c) using the trained prediction model to predict predicted intervention outcome information of subjects seeking intervention, wherein the retrospective exposure group biochemical feature spectrum and clinical metadata of the subjects seeking intervention are inputs to the trained prediction model 64; (d) selecting subjects for intervention or excluding subjects from intervention based at least in part on the predicted intervention outcome information of the subjects 66. Alternatively, the intervention may include a community trial, which may or may not be performed in a clinical setting.

[0293] In some cases, one or more exposome characteristics of a subject can be used to determine the effectiveness or efficacy of a given intervention. Figure 6A-6D As seen in , expomic analysis of subjects’ biological samples to generate expomic signatures can provide insight into the effectiveness of lead-based poisoning interventions. Fig. 6A The exposome biochemical signature 81 with the number of days on the x-axis and the intensity of the exposome signature on the y-axis is shown. Fig. 6A Representative exposome profile for tin, with the start 77 and end 79 points of the intervention indicated. Figure 6B-6D The comparative exposome biochemical profiles for lead, calcium, and magnesium are shown for subjects who did not receive interventions 85, 89, and 93, compared to subjects who received interventions 83, 87, and 91, respectively. For this particular example, it can be observed that Figure 6CThe biochemical characteristics of the exposure group of lead in the medium are reduced, indicating that the intervention can be proved to be effective. However, it can also be observed that increases in other exposure group characteristics (such as magnesium) may lead to unwanted effects. In some embodiments, this exposure group characteristic of the biochemical characteristic is used to observe the effectiveness of the intervention, or to recommend an auxiliary intervention to supplement the indication of unwanted increase or decrease of one or more exposure group characteristics of non-intervention targets. In some embodiments, this method of intervention effect or effectiveness analysis is used to reuse the intervention for unexpected applications, or to help alleviate symptoms from space travel that the intervention was not originally intended to target.

[0294] In some embodiments, multiple analysis modules are used to further analyze the exposure group characteristics obtained from the object receiving the intervention. In some embodiments, the first analysis module (module 1) focuses on the impact of clinical intervention on element signal intensity, wherein the time course of intervention is established relative to the time of the exposure group biochemical characteristic signal intensity. The time-varying signal intensity of the exposure group biochemical characteristic can be traced back to the time of clinical intervention, thereby allowing the exposure group biochemical characteristic signal intensity to be described as occurring before, during or after the intervention. The exposure group biochemical characteristic signal intensity during these time periods can be aggregated at the level of the object by summarizing statistical data (such as the mean or median of the exposure group biochemical characteristic signal intensity detected during the time period). Then, by applying a traditional general linear model, the impact of the intervention can be evaluated across the objects participating in the study, wherein the exposure group biochemical characteristic signal intensity before and after the intervention is compared across all objects to identify the statistically significant differences in the exposure group biochemical characteristic signal intensity corresponding to the impact of the intervention. In this case, statistical significance is evaluated by standard probability hypothesis testing.

[0295] In some embodiments, the second analysis module (module 2) can include a focus on the simultaneous impact of intervention on multiple exposure group characteristics (i.e., biochemical signature pathways). As in module 1, this module can be applied when the time course of intervention is established relative to the time of the exposure group biochemical signature signal intensity, thereby allowing the intensity before, during and after the intervention to be aggregated with descriptive statistics. Then, the aggregated measurements obtained at the individual level across the participants of the clinical trial are merged and used for the construction of a multivariate model. These can take the form of unsupervised analysis, such as principal component analysis, factor analysis or related methods, whereby dimensionality reduction techniques are applied to derive metrics (principal components; factor scores), which summarize the exposure group biochemical signature signal intensity of multiple exposure group biochemical signature pathways, which can then be used in subsequent general linear models to test hypotheses related to clinical interventions, as in module 1. Alternatively, supervised dimensionality reduction techniques (such as partial least squares, partial least squares discriminant analysis, linear discriminant analysis, weighted quantile sum regression, or Bayesian kernel regression) can be used to directly link the effects of an intervention to changes in the signal strength of an exposure biochemical signature across multiple exposure biochemical signatures.

[0296] In some embodiments, the third analysis module (module 3) is used in situations where the exact time of the intervention is unknown, or in embodiments where the effect of the expected intervention has a time lag effect; for example, if the change in the signal intensity of the biochemical characteristic of the exposure group is not manifested for a period of time after treatment, or if the time of the treatment-induced change varies between individuals. In these cases, modeling strategies derived from econometrics can be used; in particular, implementations of distributed lag models and related nonlinear methods. These methods can be extended, as in module 2, to include simultaneous assessments of the effects of interventions in multiple exposure group biochemical characteristics, for example by implementing lagged weighted quantiles and regressions. Alternative analysis strategies can include the use of change point detection methods through moving average methods, self-excited threshold autoregression (SETAR) models, autoregressive moving average models (ARMA), Bayesian change point detection, and related methods (particularly methods related to longitudinal modeling and change point detection).

[0297] In some embodiments, different from existing models, the fourth analysis module (module 4) can focus on the analysis of the signal dynamics drawn from the analysis of the longitudinal biochemical signature signal. In this case, signal dynamics refers to the parameter drawn from the analysis of the biochemical signature signal intensity, which can include the estimation of the parameter describing the potential process (such as periodicity, entropy and stationarity). A method for realizing this point can include applying recursive quantitative analysis (RQA) to the individual biochemical signature signal measured in each object trial participant. For each longitudinal measurement of a given biochemical signature pathway, the application of RQA can produce a variety of quantitative measurements or characteristics, including recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursive trend, hierarchical, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in the number of recursions and the most likely number of any combination of recursions. These features can be extracted from multiple biochemical signature pathways and from the analysis of interactions between pathways via Cross-Recursive Quantification Analysis (CRQA) for subsequent analysis, where features derived from each subject are combined and used to examine study-wide effects associated with the intervention. This approach can be particularly applicable to case-control study designs where some subjects receive placebo treatment, and the goal is to distinguish differences in the dynamics of element signals between intervention and placebo experimental conditions. In this case, the parameters derived from RQA / CRQA can be examined in a traditional analytical framework, such as via a general linear model, to assess differences in signal parameters between treatment conditions. This approach can also be applicable to situations similar to those described in Modules 1 and 2, where the time course of a given treatment is known and the goal is to distinguish conditions before, during, and after treatment. In this case, RQA / CRQA can be applied to subsets of the full element traces (corresponding to pre-treatment, concurrent with treatment, and post-treatment conditions); or, to a variant of RQA / CRQA that utilizes window binning techniques, which can be used to derive longitudinal measures of RQA / CRQ parameters, which can then be analyzed using the methods described in Module 1, Module 2, or Module 3. In any combination of these conditions, features derived from dynamic signal analysis via RQA and related methods can also be used with supervised and unsupervised dimensionality reduction techniques, as described in Module 2, for subtyping subjects based on biochemical profiles. These methods can be used prior to intervention to achieve the goal of identifying patient / participant subtypes, or can be used after treatment to link the effects of a clinical intervention to associated metabolic pathways. As in Figure 8As can be seen in Figure 2, hair samples provided by subjects with autism spectrum disorder (ASD) were analyzed by this approach. The resulting projections illustrate the three ASD subtypes derived from the analysis of the biochemical profiles—in this case, by applying k-means clustering to the RQA of the elemental profiles. Thereafter, the description of the subject type can be used for subsequent clinical analysis and decision making.

[0298] Drug and nutritional supplement recommendations

[0299] In some embodiments, the methods of the disclosure may include a method 68 of recommending optimal medication and / or nutritional supplements for a subject participating in space travel, such as in Figure 5 In some embodiments, the method may include the following steps: (a) detecting properties derived from one or more biochemical signatures obtained from one or more biological samples from one or more subjects that have not participated in space travel, thereby generating one or more reference exposure profiles 70; (b) detecting properties derived from one or more biochemical signatures obtained from one or more biological samples from one or more subjects that have participated in space travel, thereby generating one or more properties of the pre-treatment exposure profile 71; (c) administering a treatment to one or more subjects that have participated in space travel 72; (d) detecting one or more exposure profiles obtained from one or more biological samples from one or more subjects that have participated in space travel after a period of time has passed since receiving the treatment. (e) determining differences between one or more features in a reference exposure genomic profile of one or more subjects that have not participated in space travel, features in one or more pre-treatment exposure genomic profiles of one or more subjects that have participated in space travel, and features in one or more post-treatment exposure genomic profiles of one or more subjects that have participated in space travel 74; and (f) selecting one or more optimal treatments based at least in part on the determined differences between features in one or more reference exposure genomic profiles, one or more pre-treatment exposure genomic profiles, and one or more post-treatment exposure genomic profiles, wherein the one or more optimal treatments are selected based on the determined differences that meet predetermined criteria 75.

[0300] In some embodiments, differences between one or more reference exposure genomic characteristics of one or more subjects who have not participated in space travel, one or more pre-treatment exposure genomic characteristics of one or more subjects who have participated in space travel, and one or more post-treatment exposure genomic characteristics of one or more subjects who have participated in space travel can be determined by a predictive model described elsewhere herein.

[0301] In some embodiments, the method for recommending optimal medication and / or nutritional supplements for a subject is used to analyze the impact and / or importance of the biochemical signature pathways of the exposure group affected by the intervention (e.g., medication and / or nutritional supplement), such as in Figure 16-Figure 18 In some cases, the characteristics of the biochemical signature pathways of the exposed group can be compared between the intervention group and the control group. In some embodiments, the intervention includes the use of probiotics ( Fig.16 ), gluten-free diet( Fig.17 ), cannabidiol, zinc ( Fig.18 ) or any combination thereof. In some cases, the intervention may be delivered as an infant formula ( Fig.18 ). The results of the analysis of the characteristics of the exposome biochemical signature pathways can include recommended interventions where the exposome biochemical signature pathways are complementary to space travel or are otherwise affected by space travel in a similar manner.

[0302] Determining health outcomes

[0303] In some embodiments, the disclosure describes a method 2301 for outputting one or more quantitative metrics of one or more dynamic expomic characteristics of a subject, such as in Fig. 22 The method may include the following operations: (a) receiving a biological sample from a subject 2300; (b) determining one or more dynamic exposome signatures from the biological sample of the subject 2302; (c) calculating first one or more characteristics of the one or more dynamic exposome signatures, wherein each of the one or more characteristics includes one or more quantitative metrics 2304; and (d) outputting one or more quantitative metrics of the one or more characteristics of the subject 2306.

[0304] In some embodiments, the method may also include outputting a health outcome for the object based at least in part on an association of standardized scores for one or more characteristics of the object with standardized scores for one or more quantitative measures of one or more characteristics of a second set of objects. In some cases, one or more quantitative measures of one or more characteristics of the second set of objects may be stored in a database, wherein the database is a local server or a cloud-based server. In some cases, the health outcomes may include clinical subtypes, non-clinical subgroups associated with physiology, anthropometric indicators, behavioral indicators, socioeconomic indicators, body mass index, IQ, socioeconomic status, or any combination thereof. In some embodiments, one or more characteristics may include a measurement of the temporal dynamics of one or more dynamic exposure group characteristics.

[0305] In some cases, measures of temporal dynamics may include: a linear slope, a nonlinear parameter describing the curvature of one or more dynamic exposure omics features, a sudden change in the intensity of one or more dynamic exposure omics features, a change in the baseline intensity of one or more dynamic exposure omics features, a change in the frequency domain representation of one or more dynamic exposure omics features, a change in the power spectrum domain representation of one or more dynamic exposure omics features, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimated Lyapunov spectrum, a maximum Lyapunov exponent, or any combination thereof.

[0306] In some cases, one or more characteristics of one or more dynamic features may include phenotypic characteristics, wherein the phenotypic characteristics include: electrocardiogram (ECG), electroencephalogram, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome or any combination thereof. In some embodiments, one or more characteristics are derived from one or more attractors. In some cases, one or more dynamic exposure group characteristics can be measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemical fluorescence or any combination thereof. In some cases, biological samples may include hair, teeth, toenails, fingernails, physiological parameters or any combination thereof. In some cases, phenotypic characteristics may include molecular phenotypes. In some cases, molecular phenotypes may be determined by supervised or unsupervised analysis, wherein supervised or unsupervised analysis may include clustering, dimensionality reduction, factor analysis, stacked autoencoding or any combination thereof.

[0307] In some embodiments, the recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, hierarchical property, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in recursion times and the number of most likely recursions. In some embodiments, the method also includes analyzing one or more attractors by potential energy analysis, thereby generating potential energy data space. In some cases, one or more dynamic exposure group characteristics of an object may include retrospective, prospective or any combination thereof dynamic exposure group data. In some cases, the method may also include analyzing the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, hierarchical property, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in recursion times, the number of most likely recursions or the dynamic relationship between any combination thereof of one or more attractors. In some embodiments, the dynamic relationship is determined by cross convergence mapping (CCM). The method according to claim 1 further comprises reducing one or more dynamic exposure group characteristics to one or more reduced exposure group dynamic characteristics. In some cases, the method may also include constructing a network based on the similarity of the temporal exposure group data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, hierarchical, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors. In some cases, the method may also include analyzing one or more characteristics of the network to determine network connectivity, efficiency, characteristic importance, pathway importance, correlation graph theory-based metrics, or any combination thereof.

[0308] Predicted phenotypic data

[0309] In some embodiments, the disclosure describes a method 2401 for outputting a prediction of phenotypic data for one or more subjects, as in Fig.23The method may include the following operations: (a) receiving one or more biological samples and phenotypic data from a first subject set 2400; (b) determining a first exposure group feature set from one or more biological samples of the first subject set 2402; (c) calculating a first feature set of the first exposure group feature set 2404; (d) training a prediction model 2406 with the first feature set and phenotypic data of the first subject set; (e) receiving one or more biological samples from a second subject set different from the first subject set 2408; (f) determining a second exposure group feature set from one or more biological samples of the second subject set 2410; (g) calculating a second feature set from the second exposure group feature set 2412; and (h) outputting a prediction 2414 of the phenotypic data of the second subject set determined by inputting the second feature set into the trained prediction model.

[0310] In some embodiments, the first feature set and the second feature set include one or more quantitative metrics. In some cases, the one or more quantitative metrics may include a measurement of the temporal dynamics of the first exposure group feature set and the second exposure group feature set. In some cases, the measurement of temporal dynamics may include: a linear slope, a nonlinear parameter describing the curvature of the first exposure group feature set and the second exposure group feature set, a sudden change in the intensity of the first exposure group feature set and the second exposure group feature set, a change in the baseline intensity of the first exposure group feature set and the second exposure group feature set, a change in the frequency domain representation of the first exposure group feature set and the second exposure group feature set, a change in the power spectrum domain representation of the first exposure group feature set and the second exposure group feature set, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimated Lyapunov spectrum or a maximum Lyapunov exponent, or any combination thereof.

[0311] In some cases, the first characteristic set and the second characteristic set can be represented as or derived from one or more attractors. In some embodiments, the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof. In some embodiments, the first exposure group feature set and the second exposure group feature set are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, Raman spectroscopy, immunohistochemical fluorescence, or any combination thereof. In some cases, one or more biological samples of the first object and the second object may include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0312] In some embodiments, the phenotypic characteristics include molecular phenotypes. In some cases, the molecular phenotype can be determined by unsupervised analysis, wherein the unsupervised analysis includes clustering, dimensionality reduction, factor analysis, stacked autoencoding or any combination thereof. In some cases, the recursive quantitative analysis parameters can include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, hierarchical, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in the number of recursions and the number of most likely recursions or any combination thereof.

[0313] In some cases, the method may further include analyzing the one or more attractors by potential analysis to generate a potential data space. In some embodiments, the first expomic feature set and the second expomic feature set include retrospective, prospective, or any combination thereof expomic data.

[0314] In some embodiments, the method also analyzes the dynamic relationship between the recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, hierarchicality, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors. In some embodiments, the dynamic relationship is determined by cross convergence mapping (CCM).

[0315] In some cases, the method may also include constructing a network based on the similarity of the temporal exposure group data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, hierarchical, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in recursion times, and the number of most likely recursions or any combination thereof of one or more attractors. In some embodiments, the method also includes analyzing one or more characteristics of the network to determine network connectivity, efficiency, feature importance, pathway importance, metric feature importance based on correlation graph theory, pathway importance, or any combination thereof.

[0316] Although the above steps illustrate each method or set of operations, those of ordinary skill in the art will recognize many variations based on the teachings described herein. These steps can be completed in a different order. Steps can be added or omitted. Some steps can include sub-steps. Many steps can be repeated as often as possible.

[0317] One or more steps of each method or set of operations may be performed using circuits described herein (e.g., one or more processors or logic circuits, such as programmable array logic for a field programmable gate array). The circuits may be programmed to provide one or more steps of each method or set of operations described elsewhere herein, and the program may include, for example, program instructions stored on a computer-readable memory or programmed steps of logic circuits (such as programmable array logic or a field programmable gate array).

[0318] Application of space health exposomes

[0319] In some aspects, the present disclosure describes a method for outputting the reaction of one or more objects to participating in real or simulated space travel. During real or simulated space travel, various exposures may affect the object. In some embodiments, real or simulated space travel may expose the object to increased radiation levels, increased high-energy radiation levels (e.g., gamma radiation, micro-radiation, X-radiation and ultraviolet radiation), large gravity, long-term low gravity, changed atmospheric pressure, changed atmospheric composition or any combination thereof. In some cases, real or simulated space travel may lead to effects such as motion sickness, bone density changes, bone deterioration, muscle deterioration, fluid redistribution, intracranial pressure changes, sensory disorders, psychological disorders or any combination thereof. Various exposures and / or various effects of real or simulated space travel may produce exposure group characteristics in the body or part thereof of the object. In some embodiments, the exposure group characteristics associated with space travel may be detectable or detected by the method of the present disclosure. In some embodiments, the method includes receiving one or more biological samples of one or more objects participating in space travel. In some embodiments, the method includes determining one or more exposure group characteristics of one or more biological samples. In some embodiments, the method includes calculating an expomic property of one or more expomic features of one or more biological samples.

[0320] Fig.24 Quantitative metrics derived from cross-recursive quantification analysis of signal pairs from control animals raised in ground facilities and mice traveling to the International Space Station are shown, as seen in some embodiments. Several significant differences in elemental dynamics were shown between mice that had experienced space travel and mice that had not experienced space travel, including perturbations in barium:calcium dynamics (P=0.02), manganese:iodine dynamics (P=0.04), and zinc:iron (P=0.002) dynamics. These patterns of dysregulation can constitute detectable features of space travel. In some cases, detectable features can be used as biomarkers for space flight or as targets for improved intervention.

[0321] Fig.25Data derived from analysis of barium, manganese, and zinc signals from human hair collected after space travel are shown, as seen in some embodiments. The time-varying (expressed in days) amplitude of these signals relative to the space flight time can be observed in the signals, where the space flight date is identified as day 0. Significant changes in the dynamics of barium, manganese, and zinc were identified after space flight. This pattern of change provides a signature of space flight that can be used as a biomarker of stress or a target for improvement efforts. Although specific embodiments of exposure group characteristics are considered in this analysis, various other exposure group characteristics can also provide characteristics for space travel.

[0322] In some embodiments, the method includes outputting a response of one or more subjects to participating in space travel by providing the exposome characteristics of the one or more biological samples as input to a trained prediction model. In some embodiments, the trained prediction model is configured to determine a likelihood that the exposome characteristics are associated with a metabolic signature of the one or more subjects participating in space travel. In some embodiments, the metabolic signature may indicate a current symptom, an underlying cause of a current symptom, a prediction of a symptom, a prediction of disease progression, or any combination thereof.

[0323] In some embodiments, the trained prediction model is configured to determine whether the subject's metabolism is in a baseline condition not associated with space travel. In some embodiments, the baseline condition can be based at least in part on measurements taken of the subject while the subject is on Earth. In some embodiments, the baseline condition can be based at least in part on measurements taken of the subject after the subject has been in space for a predetermined duration (e.g., 1 week, 1 month, etc.).

[0324] In some embodiments, each of the exposome profiles includes one or more quantitative metrics. Various examples of quantitative metrics are disclosed elsewhere in this application.

[0325] In some embodiments, one or more biological samples of subjects participating in space travel are collected during the space travel period before the space travel, during the space travel, after the space travel, or any combination thereof. In some embodiments, when collected before the space travel, the one or more biological samples can be analyzed to determine whether the given subject is suitable for space travel. In some embodiments, when collected during the space travel, the one or more biological samples can be analyzed to determine therapeutic interventions for the given subject. In some embodiments, when collected after the space travel, the one or more biological samples can be analyzed to determine rehabilitation interventions for the given subject.

[0326] In some embodiments, the exposure genomic characteristics include a measurement of the temporal dynamics of one or more exposure genomic features. In some embodiments, the measurement of the temporal dynamics includes: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0327] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0328] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0329] In some embodiments, the exposome characteristics are derived from one or more attractors. In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of the one or more attractors.

[0330] In some embodiments, the trained prediction model includes a classification algorithm. In some embodiments, the classification algorithm is configured to identify samples associated with participation in space travel. In some embodiments, the classification algorithm is trained with one or more biological sample exposome characteristics and corresponding responses of one or more subjects. In some embodiments, one or more subjects participated in space travel. In some embodiments, the classification algorithm is configured to determine whether one or more subjects have metabolic characteristics associated with space travel.

[0331] In some embodiments, the response comprises a state of the subject's metabolism. In some embodiments, the subject's metabolism comprises a healthy state or a state consistent with a subject undergoing space travel.

[0332] Fig.26 Shown are the average Ba:CA dynamics, Zn:Fe dynamics in mice receiving a control diet or receiving iodine supplementation in their diet, as seen in some embodiments. Iodine supplementation significantly reduced the levels of barium:calcium (P=0.01) and zinc:iron (P=0.0001) dynamics. In some cases, space travel reduces the levels of these features, and the results show how the effects of improved treatments can be linked to the effects of improved effects associated with molecular mechanisms and / or effectors.

[0333] Figure 37 shows the average Ba:CA dynamics, Zn:Fe dynamics in mice receiving a food bar diet or receiving a standard laboratory food diet, as seen in some embodiments. It can be seen that dietary manipulation can produce significant changes in the levels of barium:calcium (P=0.004) and zinc:iron (P=0.02) dynamics. The results suggest that the effects of improved treatments (e.g., nutritional interventions) and the connection between the effects of improved effects and molecular mechanisms and / or effectors.

[0334] Depend on Fig.26 and Fig. 27 The studies presented indicate that statistically significant exposome signatures can be induced in organisms that undergo space travel. In various embodiments disclosed herein, the exposome signatures can be used as a basis for training a machine learning algorithm to detect, for example, signs associated with space travel, tolerance to space travel, the possibility of therapeutic interventions for treating conditions associated with space travel. In various embodiments disclosed herein, the exposome signatures can be used as a basis for using a machine learning algorithm to predict a subject's tolerance to space travel or the possibility of therapeutic interventions for treating conditions associated with space travel.

[0335] In some aspects, the present disclosure describes a method for training a prediction model to determine a clinical response of a subject to an intervention when the subject is participating in space travel. In some embodiments, the method includes receiving one or more biological samples and clinical responses of a first subject set. In some embodiments, the first subject set is exposed to space travel and is provided with an intervention. In some embodiments, the method includes receiving one or more biological samples and clinical responses of a second subject set. In some embodiments, the second subject set is exposed to space travel and does not receive an intervention. In some embodiments, the method includes determining one or more exposure group characteristics of one or more biological samples of the first subject set and the second subject set. In some embodiments, the method includes calculating a first exposure group characteristic set and a second exposure group characteristic set of one or more exposure group characteristics of the first subject set and the second subject set. In some embodiments, each characteristic in the first exposure group characteristic set and the second exposure group characteristic set includes one or more quantitative metrics. In some embodiments, the method includes outputting a trained prediction model configured to determine a clinical response of the subject to the intervention. In some embodiments, the subject is participating in space travel. In some embodiments, the trained prediction model is trained on one or more quantitative metrics and corresponding clinical responses of one or more exposure group characteristics of the first subject set and the second subject set. In some embodiments, the intervention prevents negative side effects of space travel. In some embodiments, the intervention is provided to the first set of subjects at a time point before, during, after, or any combination thereof, to which the subjects have been exposed to space travel. In some embodiments, the intervention comprises diet, medication, or any combination thereof. In some embodiments, the diet comprises iodine supplementation.

[0336] In some embodiments, the method further includes determining an effect of space travel on the first set of subjects exposed to the intervention at least in part by comparing one or more quantitative metrics of the first expomic characteristic set and the second expomic characteristic set.

[0337] In some embodiments, the first exposure group or the second exposure group includes a measurement of the temporal dynamics of one or more exposure group features. In some embodiments, the measurement of the temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure group features, determining a sudden change in the intensity of one or more exposure group features, determining one or more changes in the baseline intensity of one or more exposure group features, determining a change in the frequency domain representation of one or more exposure group features, determining a change in the power spectrum domain representation of one or more exposure group features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, the measurement of temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursive rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursive trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursive time, Shannon entropy in recursive number, or the number of most likely recursions. Statistical analysis of various exposure group characteristics can be used.

[0338] In some embodiments, one or more exposure group characteristics are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemical fluorescence, or any combination thereof. In some embodiments, one or more exposure group characteristics can be measured by gene sequencing, proteomics, or both.

[0339] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0340] In some embodiments, the first exposure group characteristic set or the second exposure group characteristic set is derived from one or more attractors. In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of the one or more attractors.

[0341] In some embodiments, the first set of subjects or the second set of subjects are humans, non-human mammals, or any combination thereof.

[0342] In some aspects, the present disclosure describes a method for generating a prediction model that is configured to output the health status of one or more objects in response to gravity in addition to responding to the earth's gravity. In some embodiments, the method includes receiving one or more biological samples of the first one or more objects and the corresponding health status. In some embodiments, in addition to being exposed to the earth's gravity, the first one or more objects are also exposed to gravity. In some embodiments, the method includes determining one or more exposure group characteristics of one or more biological samples of the first one or more objects. In some embodiments, the method includes calculating the exposure group characteristics of one or more exposure group characteristics of one or more biological samples. In some embodiments, the method includes generating a trained prediction model, which is configured to output the health status of the second one or more objects in response to gravity in addition to responding to the earth's gravity. In some embodiments, the trained prediction model is trained with the exposure group characteristics of one or more exposure group characteristics of the first one or more objects and the corresponding health status.

[0343] In some embodiments, gravity comprises at least 1 G, at least 2 G, at least 3 G, or at least 4 G. In some embodiments, the trained prediction model is configured to determine a likelihood that the exposome characteristic is associated with a metabolic signature of a first one or more subjects or a second one or more subjects that are exposed to gravity in addition to exposure to Earth's gravity. In some embodiments, a probability threshold determined by the trained prediction model indicates that the metabolism of the first one or more subjects or the second one or more subjects is at a baseline condition that is not associated with exposure to gravity in addition to being associated with exposure to Earth's gravity.

[0344] In some embodiments, the exposure genomic characteristics include a measurement of the temporal dynamics of one or more exposure genomic features. In some embodiments, the measurement of the temporal dynamics includes: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof. In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0345] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0346] In some embodiments, the exposome characteristics are derived from one or more attractors. In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical length, Shannon entropy in vertical length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of the one or more attractors.

[0347] In some embodiments, the first one or more subjects and the second one or more subjects include a human subject, a non-human mammalian subject, a vertebrate subject, or any combination thereof.

[0348] In some embodiments, the trained prediction model includes a classification algorithm. In some embodiments, the classification algorithm is configured to identify one or more exposure group characteristics of the first one or more objects or the second one or more objects that are associated with exposure to gravity in addition to being associated with exposure to the earth's gravity. In some embodiments, the classification algorithm is trained with the exposure group characteristics of one or more biological samples of the first one or more objects and the corresponding health status. In some embodiments, in addition to being exposed to the earth's gravity, the first one or more objects are also exposed to gravity. In some embodiments, the classification algorithm is configured to determine whether the first one or more objects or the second one or more objects have metabolic characteristics associated with exposure to gravity in addition to being associated with exposure to the earth's gravity.

[0349] In some embodiments, the health state comprises a state of metabolism of the first one or more subjects or the second one or more subjects. In some embodiments, the metabolism of the first one or more subjects or the second one or more subjects comprises a health state or a state consistent with a subject exposed to gravity in addition to exposure to Earth's gravity.

[0350] In some aspects, the present disclosure describes a method for using a predictive model to output health outcomes for one or more subjects prior to exposure to gravity. In some embodiments, the method includes receiving one or more biological samples of the one or more subjects. In some embodiments, the method includes determining one or more exposure genomic features of the one or more biological samples of the one or more subjects. In some embodiments, the method includes calculating the exposure genomic characteristics of the one or more exposure genomic features of the one or more biological samples. In some embodiments, the method includes outputting health outcomes for one or more subjects prior to exposure to gravity in addition to prior to exposure to Earth's gravity by providing the exposure genomic characteristics of the one or more subjects as input to a trained predictive model.

[0351] In some embodiments, the gravitational force comprises at least 1 G, at least 2 G, at least 3 G, or at least 4 G. In some embodiments, the gravitational force comprises at least 5, 6, 7, 8, 9, or 10 G. In some embodiments, the gravitational force comprises at most 5, 6, 7, 8, 9, or 10 G.

[0352] In some embodiments, the trained predictive model is configured to determine a likelihood that an expomic property is associated with a metabolic signature of one or more subjects exposed to gravity.

[0353] In some embodiments, the probability threshold determined by the trained prediction model indicates that the metabolism of one or more subjects is at a baseline condition that is not associated with exposure to gravity in addition to not being associated with exposure to Earth's gravity.

[0354] In some embodiments, each of the expomic profiles includes one or more quantitative metrics.

[0355] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0356] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0357] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0358] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0359] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0360] In some embodiments, the expomic profile is derived from one or more attractors.

[0361] In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors.

[0362] In some embodiments, the one or more subjects include a human, a non-human mammal, a vertebrate, or any combination thereof.

[0363] In some embodiments, the trained predictive model comprises a classification algorithm.

[0364] In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of a subject that are associated with exposure to gravity in addition to exposure to Earth's gravity.

[0365] In some embodiments, the classification algorithm is trained with the exposome characteristics and corresponding responses of one or more biological samples of one or more subjects.In some embodiments, in addition to being exposed to Earth's gravity, one or more subjects are also exposed to gravity.

[0366] In some embodiments, the classification algorithm is configured to determine whether one or more subjects have a metabolic signature associated with exposure to gravity in addition to exposure to Earth's gravity.

[0367] In some aspects, the present disclosure describes a method for administering treatment to an object participating in space travel. In some embodiments, the method includes receiving one or more biological samples of an object before participating in space travel. In some embodiments, the method includes determining one or more exposure group characteristics of one or more biological samples of the object. In some embodiments, the method includes calculating the exposure group characteristics of one or more exposure group characteristics of the object. In some embodiments, each characteristic in the exposure group characteristics includes one or more quantitative metrics. In some embodiments, the method includes administering treatment to an object participating in space travel at a time point, wherein when the exposure group characteristics of the object are provided as input to a trained prediction model, the treatment and the time point of administering the treatment are determined as the output of the trained prediction model. In some embodiments, the trained prediction model is configured to output the effect of the treatment.

[0368] In some embodiments, the trained predictive model is configured to output a measure of whether a treatment is effective.

[0369] In some embodiments, the time point includes a time before the subject participated in the space travel, a time during the subject participated in the space travel, a time after the subject participated in the space travel, or any combination thereof.

[0370] In some embodiments, treatment is provided to mitigate the negative side effects of participating in space travel. In some embodiments, treatment may be provided prophylactically. In some embodiments, treatment may be provided after space travel.

[0371] In some embodiments, the effect of a treatment is determined by a duration of time after the subject has participated in space travel, after which the subject's exposome profile comprises a baseline exposome profile of a subject that has not participated in space travel.

[0372] In some embodiments, the trained predictive model comprises a classification algorithm.

[0373] In some embodiments, space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

[0374] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0375] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0376] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0377] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0378] In some embodiments, one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0379] In some embodiments, the expomic profile is derived from one or more attractors.

[0380] In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof of one or more attractors.

[0381] In some embodiments, the subject is a human, a non-human mammal, or any combination thereof.

[0382] In some embodiments, the trained predictive model comprises a classification algorithm.

[0383] In some embodiments, a classification algorithm is trained using the expomic profiles of one or more biological samples of one or more subjects, the treatments administered, the time points at which the treatments were administered, and the corresponding clinical responses, wherein the one or more subjects participated in space travel.

[0384] In some embodiments, the classification algorithm is configured to determine whether one or more subjects have a metabolic disorder caused by space travel. In some embodiments, the metabolic disorder can include changes in gene expression associated with metabolism, mutations in genes associated with metabolism, or both.

[0385] In some aspects, the present disclosure describes a method for evaluating the response of one or more subjects to an intervention in the presence or absence of gravity. In some embodiments, the method includes sampling each corresponding position of a plurality of positions along a reference line of a first one or more biological sample sets of a first set of subjects and a second one or more biological sample sets of a second set of subjects. In some embodiments, the method includes obtaining a plurality of ion samples. In some embodiments, each ion sample of the plurality of ion samples corresponds to a different position of the plurality of positions. In some embodiments, each position of the plurality of positions represents a different growth period of the first one or more biological sample sets and the second one or more biological sample sets. In some embodiments, the first set of subjects undergoes space travel, and the second set of subjects does not undergo space travel. In some embodiments, an intervention is administered to the first set of subjects and the second set of subjects.

[0386] In some embodiments, the method includes analyzing each ion sample in a plurality of ion samples with a mass spectrometer. In some embodiments, the method includes obtaining a first data set including a plurality of traces. In some embodiments, each trace in the plurality of traces is a concentration of a corresponding element isotope in a plurality of element isotopes over time, the concentration being determined collectively by the plurality of ion samples.

[0387] In some embodiments, the method includes deriving a second data set including a set of characteristics from the plurality of traces. In some embodiments, each corresponding characteristic in the set of characteristics is determined by a variation of a single isotope or a combination of isotopes in the plurality of traces.

[0388] In some embodiments, the method includes inputting the set of features into a trained classifier to obtain a probability for predicting the one or more subjects' response to space travel from the trained classifiers for the first one or more sets of subjects and the second one or more sets of subjects.

[0389] In some embodiments, the probability indicates that a biological sample in the one or more biological samples is disordered due to space travel.

[0390] In some embodiments, the effectiveness of an intervention is measured by the probability of disorder.

[0391] In some embodiments, the intervention comprises diet, medication, or any combination thereof.

[0392] In some embodiments, the diet includes iodine supplementation.

[0393] In some embodiments, the intervention improves radiation exposure.

[0394] In some embodiments, the first set of subjects or the second set of subjects includes humans, non-human mammals, or any combination thereof.

[0395] In some embodiments, the non-human mammal comprises a mouse.

[0396] In some embodiments, the first one or more biological sample sets or the second one or more biological sample sets include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0397] In some embodiments, the method includes treating the first one or more sets of biological samples or the second one or more sets of biological samples with a solvent or a surfactant prior to sampling.

[0398] In some embodiments, the method includes irradiating the first one or more biological sample sets or the second one or more biological sample sets with a low power laser prior to sampling to remove debris from the first one or more biological sample sets or the second one or more biological sample sets.

[0399] In some embodiments, sampling includes irradiating a first or more biological sample set or a second or more biological sample set of the first subject set or the second subject set with a laser, thereby extracting a plurality of particles from the first or more biological sample set or the second or more biological sample set. In some embodiments, sampling includes ionizing a plurality of particles using an inductively coupled plasma mass spectrometer, thereby obtaining a plurality of ion samples.

[0400] In some embodiments, the response of the first set of subjects or the second set of subjects includes an adverse response, a positive response, or a neutral response.

[0401] In some embodiments, the trained classifier is configured to identify whether a sample was collected from a subject that participated in space travel.

[0402] In some embodiments, the trained classifier is trained using a set of characteristics and corresponding responses of one or more biological samples of a first set of subjects.

[0403] In some embodiments, the trained classifier is configured to determine whether a subject comprises a metabolic disorder resulting from space travel.

[0404] In some aspects, the present disclosure describes a method for outputting a subject's response to an intervention after space travel. In some embodiments, the method includes exposing one or more biological samples of the subject to a light signal including multiple Raman wavelengths. In some embodiments, the subject undergoes space travel. In some embodiments, the method includes acquiring multiple Raman spectra from the one or more biological samples. In some embodiments, the method includes processing the multiple Raman spectra to generate multiple Raman characteristics. In some embodiments, the method includes outputting a probability of the subject's response to the intervention while undergoing space travel by providing an input of multiple Raman characteristics to a trained classifier.

[0405] In some embodiments, the probability indicates that the subject is deconditioned due to space travel.

[0406] In some embodiments, the effectiveness of an intervention is measured by the probability of disorder.

[0407] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0408] In some embodiments, the intervention comprises diet, medication, or any combination thereof.

[0409] In some embodiments, the diet includes iodine supplementation.

[0410] In some embodiments, the intervention improves radiation exposure.

[0411] In some embodiments, the subject includes a human, a non-human mammal, or any combination thereof.

[0412] In some embodiments, the non-human mammal comprises a mouse.

[0413] In some embodiments, the plurality of Raman spectra includes from about 200 to about 3700 wavenumbers.

[0414] In some embodiments, acquiring comprises using a Raman spectroscopic microscope.

[0415] In some embodiments, the plurality of Raman characteristics includes one or more traces derived from a Raman spatial map.

[0416] In some embodiments, a dimensionality reduction algorithm is used to reduce one or more traces. In some embodiments, the dimensionality reduction algorithm includes principal component analysis, independent component analysis, or any combination thereof.

[0417] In some embodiments, the measurement of temporal dynamics is derived from one or more traces, wherein the temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure-based features, determining a sudden change in the intensity of one or more exposure-based features, determining one or more changes in the baseline intensity of one or more exposure-based features, determining a change in the frequency domain representation of one or more exposure-based features, determining a change in the power spectrum domain representation of one or more exposure-based features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0418] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0419] In some embodiments, the trained classifier comprises a gradient boosting ensemble model.

[0420] In some embodiments, the trained classifier is configured to process one or more characteristics selected from stratification, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, maximum vertical line length, Shannon entropy in vertical line length, mean recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof.

[0421] In some embodiments, the trained classifier is configured to process two or more characteristics selected from stratification, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, maximum vertical line length, Shannon entropy in vertical line length, mean recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof.

[0422] In some embodiments, the trained classifier is configured to process 1, 2, 3, 4, 5, 6, 7 or 8 characteristics selected from stratification, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, maximum vertical line length, Shannon entropy in vertical line length, mean recursion time, Shannon entropy in number of recursions, number of most likely recursions or any combination thereof.

[0423] In some embodiments, the trained classifier is trained to recognize Raman signatures associated with space travel.

[0424] In some embodiments, the output probability of the trained classifier indicates that the object is exhibiting temporal dynamics associated with space travel.

[0425] In some embodiments, the trained classifier is trained with a plurality of Raman properties of a population of humans or non-human animals that have experienced space travel.

[0426] In some embodiments, the effect of the intervention is measured by a change in the subject's temporal dynamics toward the subject's baseline temporal dynamics prior to space travel.

[0427] In some aspects, the present disclosure describes a method for assessing a subject's response to an intervention to reduce the biological effects of space travel. In some embodiments, the method includes staining a first one or more biological sample sets of a first object set and a second one or more biological sample sets of a second object set to produce a stained first one or more biological sample sets and a second one or more biological sample sets. In some embodiments, the first object set undergoes space travel, and the second object set does not undergo space travel. In some embodiments, an intervention is administered to the first object set and the second object set. In some embodiments, the method includes analyzing the fluorescence intensity of the first one or more biological sample sets and the second one or more biological sample sets that are spatially stained. In some embodiments, the method includes calculating a characteristic set of spatial fluorescence intensity of the first one or more biological sample sets and the second one or more biological sample sets that are stained across the first object set and the second object set. In some embodiments, each characteristic of the exposure group characteristics of the first object set and the second object set includes one or more metrics that quantify the temporal dynamics in the fluorescence intensity trace.

[0428] In some embodiments, the method includes outputting, as an output of the trained prediction model, a probability of a subject's response to an intervention to reduce a biological effect of space travel when the subject's exposome characteristics of spatial fluorescence intensity of one or more biological samples stained across subjects are provided to the trained prediction model. In some embodiments, the trained prediction model is trained with one or more metrics of the exposome characteristics of the first set of subjects and the second set of subjects, the corresponding administered interventions, and the clinical responses to the administered interventions.

[0429] In some embodiments, the trained predictive model is configured to determine whether a biological sample from a subject has been disordered by space travel.

[0430] In some embodiments, the trained predictive model is configured to determine the effect of an intervention based at least in part on an analysis of fluorescence intensity spatially across one or more biological samples as a measure of intervention effectiveness.

[0431] In some embodiments, the first one or more biological sample sets or the second one or more biological sample sets include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0432] In some embodiments, the intervention comprises diet, medication, or any combination thereof.

[0433] In some embodiments, the diet includes iodine supplementation.

[0434] In some embodiments, the intervention improves radiation exposure.

[0435] In some embodiments, the first set of subjects or the second set of subjects includes humans, non-human mammals, or any combination thereof.

[0436] In some embodiments, the non-human mammal comprises a mouse.

[0437] In some embodiments, the trained prediction model comprises a gradient boosting ensemble model.

[0438] In some embodiments, measurement of the temporal dynamics of a spatial fluorescence intensity trace comprises determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure-based features, determining a sudden change in the intensity of one or more exposure-based features, determining one or more changes in the baseline intensity of one or more exposure-based features, determining a change in a frequency domain representation of one or more exposure-based features, determining a change in a power spectrum domain representation of one or more exposure-based features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0439] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0440] In some embodiments, the analyzing includes obtaining fluorescence images of the first stained one or more biological sample sets and the second stained one or more biological sample sets, and analyzing spatial fluorescence intensity of the fluorescence images.

[0441] In some embodiments, obtaining a fluorescent image of the stained dental sample comprises using an inverted or non-inverted confocal microscope.

[0442] In some embodiments, staining the first one or more biological sample sets of the first object set and the second object set comprises using C-reactive staining. In some embodiments, the method further comprises sectioning the dental sample. In some embodiments, staining the first one or more biological sample sets and the second one or more biological sample sets comprises decalcifying the first one or more biological sample sets and the second one or more biological sample sets.

[0443] Fig.24Quantitative metrics derived from cross-recursive quantification analysis of signal pairs from control animals raised in ground facilities and mice traveling to the International Space Station are shown, as described in some embodiments herein. Statistically significant differences in ion dynamics were observed between the ground control cohort and the flight cohort of mice, with Ba:Ca dynamics being the most pronounced.

[0444] In some embodiments, the trained model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

[0445] In some embodiments, the trained model comprises a gradient boosted decision tree.

[0446] In some embodiments, the trained predictive model comprises a classification algorithm.

[0447] In some embodiments, a classification algorithm is trained to recognize characteristics in spatial fluorescence intensity associated with space travel.

[0448] In some aspects, the present disclosure describes a method for outputting one or more pathways associated with space travel. In some embodiments, the method includes receiving one or more biological samples of a first one or more sets of subjects and a second one or more sets of subjects. In some embodiments, the first one or more sets of subjects have participated in space travel, and the second one or more sets of subjects have not participated in space travel.

[0449] In some embodiments, the method includes determining an expomic signature of one or more pathways from one or more biological samples of the first one or more sets of subjects and the second one or more sets of subjects.

[0450] In some embodiments, the method comprises calculating an expomic property of an expomic signature of one or more pathways.

[0451] In some embodiments, the method includes outputting one or more pathways associated with space travel by comparing the expomic characteristics of the one or more pathways of the first set of subjects and the second set of subjects.

[0452] In some embodiments, each of the expomic profiles includes one or more quantitative metrics.

[0453] In some embodiments, space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

[0454] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0455] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0456] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0457] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0458] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0459] In some embodiments, the expomic profile is derived from one or more attractors.

[0460] In some embodiments, the method includes analyzing the dynamic relationship between one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof.

[0461] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0462] In some aspects, the present disclosure describes a method for providing treatment to improve the effects of space travel. In some embodiments, the method includes receiving one or more biological samples of one or more subjects participating in space travel. In some embodiments, the method includes determining the exposure genomic characteristics of one or more pathways from the one or more biological samples of the one or more subjects. In some embodiments, the method includes calculating the exposure genomic characteristics of the exposure genomic characteristics of the one or more pathways. In some embodiments, the method includes providing treatment to one or more subjects. In some embodiments, the treatment improves the effects of space travel on the exposure genomic characteristics of the exposure genomic characteristics of the one or more pathways of the one or more subjects.

[0463] In some embodiments, the treatment comprises a diet, a medication, a nutritional supplement, a food bar, or any combination thereof.

[0464] In some embodiments, each of the expomic profiles includes one or more quantitative metrics.

[0465] In some embodiments, space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

[0466] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0467] In some embodiments, the expomic characterization includes measurements of the temporal dynamics of one or more expomic features.

[0468] In some embodiments, the measurement of temporal dynamics includes: determining a linear slope, determining multiple nonlinear parameters describing the curvature of one or more exposure genomic features, determining a sudden change in the intensity of one or more exposure genomic features, determining one or more changes in the baseline intensity of one or more exposure genomic features, determining a change in the frequency domain representation of one or more exposure genomic features, determining a change in the power spectrum domain representation of one or more exposure genomic features, determining one or more recursive quantification analysis parameters, determining one or more cross-recursive quantification analysis parameters, determining one or more joint recursive quantification analysis parameters, determining one or more multidimensional recursive quantification analysis parameters, estimating a Lyapunov spectrum, determining a maximum Lyapunov exponent, or any combination thereof.

[0469] In some embodiments, measurement of temporal dynamics includes determining one or more recursive quantification analysis parameters, wherein the one or more recursive quantification analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

[0470] In some embodiments, the expomic signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0471] In some embodiments, the expomic profile is derived from one or more attractors.

[0472] In some embodiments, the method further comprises analyzing the dynamic relationship between one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions, or any combination thereof.

[0473] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0474] Example

[0475] Example 1: Obtaining biochemical characteristics of the exposure group from biological samples

[0476] Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is used to obtain time exposure group biochemical characteristics from biological samples. For this specific embodiment, hair shaft samples were used and chemical exposure group data were obtained. The hair shaft was collected from the subject and pre-treated by washing the sample with one or more solvents and / or surfactants and then drying. In particular, the hair shaft sample was washed in TRITON X-100 and ultrapure metal-free water (e.g., MILLI-Q water) and dried in an oven (e.g., at 60°C) overnight. Pretreatment also includes preparing the hair shaft for measurement by placing the hair shaft on a slide (e.g., a microscope slide) with an adhesive film (e.g., double-sided tape). The hair shaft is positioned so that the hair shaft is substantially straight. The slide with the hair shaft is then placed in the LA-ICP-MS system for analysis.

[0477] The analysis begins with the LA-ICP-MS system completing a pre-ablation step in which the hair shaft sample is ablated to remove surface debris and / or impurities from the hair shaft. The pre-ablation is performed with a low laser energy (approximately 10% of the laser energy) that releases only particles on the surface of the hair shaft sample and not particles from below the surface. A laser wavelength of 193 nm and a concentration of less than 0.6 J / cm 2 Laser energy (for example, laser energy of 0.6 J / cm 2 , 0.5J / cm 2 , 0.4J / cm 2 , 0.3J / cm 2 , 0.2J / cm 2 or 0.1J / cm 2 ) to perform pre-ablation.

[0478] After pre-ablation, the LA-ICP-MS system used less than 1.8 J / cm 2 A hair shaft sample is irradiated with laser energy (wherein the laser spot size is 10 μm to 30 μm) to obtain chemical samples from corresponding positions along a reference line of the hair shaft sample. Each position along the hair shaft is approximately equivalent to 2.2 hours or 130 minutes of time in the life of the subject. The spatially irradiated laser irradiates the entire length of the hair shaft, produces particles at specific positions of the hair shaft, and then ionizes the particles using an inductively coupled plasma. The mass spectrometer then analyzes the obtained chemical sample, providing a corresponding chemical data readout of which chemicals are present in what number at a given spatial position. This process is repeated and chemical substance data is collected sequentially at multiple positions along the hair shaft from the root of the hair shaft to the tip of the hair shaft farthest from the root. The position data indicating time and the corresponding isotope present at each position of the hair shaft are correlated for further analysis.

[0479] The output data was further analyzed and processed by spike removal, which removed extreme peaks in the exposome biochemical signature and smoothed the data. Outliers were identified by calculating the mean absolute difference between each adjacent measurement along the hair shaft. Values ​​indicating a mean absolute difference from the previous point exceeding three standard deviations from the mean were marked as outliers. These outliers were then replaced with a moving median filter, which calculated a running median of the raw exposome biochemical signature in a bin of 10 adjacent data points.

[0480] The processed data can then be used in various bioinformatic analysis methods to identify the effects of clinical interventions on elemental signal intensity and signal dynamics.

[0481] Example 2: Dynamic molecular profiling in dental samples for determining disease risk

[0482] Using the method and system disclosed herein, the molecular spectrum in the tooth sample is generated and then analyzed to determine the disease risk of the object. In general, the temporal dynamics of biological reactions (e.g., inflammation) are found to be imprinted in samples (e.g., tooth samples) and can be analyzed to determine the disease risk of the object. A dynamic molecular spectrum of C-reactive protein (CRP), a marker of inflammation, was generated. Using dental biomarkers, a dynamic time series spectrum of CRP and inflammation was generated during the time period of fetal (prenatal) development and early childhood of two groups of children-the first group of children suffered from autism spectrum disorder (37 cases), and the second group of children did not suffer from autism spectrum disorder (77 controls). The time series CRP spectrum was analyzed to reveal the dynamic new characteristics of the CRP signal, so as to accurately distinguish autism cases and controls. For example, the inflammatory spectrum that appears before the age of 1 is very different between cases and controls. In contrast, the clinical diagnosis of autism is usually determined at around 3 to 4 years old.

[0483] From each child subject, a sample of deciduous teeth was obtained. The tooth samples were cut, decalcified, and immunohistochemically stained (e.g., dentin). Immunohistochemical staining effectively mapped C-reactive protein (a molecular marker of inflammation) along the growth rings of the tooth samples to form a temporal profile of inflammation in the prenatal and postnatal periods. The temporal profiles were analyzed using a machine learning algorithm disclosed herein to train a highly accurate classifier to determine disease risk (e.g., autism).

[0484] Fig.12 An example of a daily C-reactive protein profile of a subject over time is shown, wherein the y-axis indicates CRP intensity and the x-axis indicates developmental age. The developmental age of a child subject includes the period from mid-pregnancy (e.g., beginning 140 days before birth, when the subject is in the prenatal stage) to about 6 months of age. Fig.12 Figure 2. Inflammation profile (as indicated by CRP intensity) in children with autism who had high prenatal CRP intensity.

[0485] Example 3: Detection of space travel signatures in hair samples from animal models

[0486] A between-subjects experimental study was conducted on mice housed in a ground facility (N=10) and mice that traveled to the International Space Station (ISS; N=10). Hair (whisker) samples were collected for analysis as described by the methods and systems described elsewhere herein. Hair samples were analyzed to quantify signal dynamics of multiple elements, which were then compared between experimental conditions. Fig.24Representative data for signal dynamics of multiple elements are shown. This approach revealed several significant differences in elemental dynamics between mice that had experienced space travel and mice that had not, including perturbations in barium:calcium dynamics (P=0.02), manganese:iodine dynamics (P=0.04), and zinc:iron (P=0.002) dynamics. These dysregulated patterns constitute detectable signatures of space travel that could be used as biomarkers for space flight or as targets for ameliorative interventions.

[0487] Example 4: Detection of Space Travel Signatures in Human Hair Samples

[0488] Elemental dynamics in human hair samples from individuals traveling to space via a commercial spaceflight program (Blue Origin) were analyzed by methods and systems described elsewhere herein. The dynamics of longitudinal signal intensity for several elemental pathways were quantified. In particular, elemental pathways identified in animal model studies as being dysregulated by space travel were analyzed, including barium, manganese, and zinc. Fig.25 In the Figure 1, the time-varying (in days) amplitude of these signals relative to the time of space flight can be observed, where the date of space flight is identified as day 0. Crucially and consistent with the patterns observed in the animal model studies of Example 3, we identified significant changes in the dynamics of barium, manganese, and zinc after space flight. This pattern of change provides a signature of space flight that can be used as a biomarker of stress or a target for ameliorative efforts.

[0489] Example 5: Detection of Improved Treatment in Hair Samples of Animal Models

[0490] A between-subjects experimental study was conducted on mice (N=20) housed in a ground facility to determine the effects of spaceflight-modified therapy. In this particular study, one group (N=10) of animals was provided with iodine supplementation (4 ppm added to the water supply), and a second group (N=10) of animals was not provided with iodine supplementation. From each group, we collected hair (whiskers) samples for analysis using the methods and systems described elsewhere herein. Fig.26 As can be seen in the Figure 1, iodine supplementation significantly reduced the levels of barium:calcium (P=0.01) and zinc:iron (P=0.0001) dynamics. Crucially, given previous findings showing that space travel reduces the levels of these features (see Example 1), these findings highlight how these methods can be used to demonstrate improved treatment effects and link the effects of improved outcomes to molecular mechanisms and / or effectors.

[0491] Example 6: Examination of the Effects of Diet on Characteristics of Space Travel

[0492] A between-subjects experimental study was conducted on mice (N=20) housed in a ground facility to determine the effects of diet on characteristics of space travel. In this study, one group of mice (N=10) was housed on a standard rodent chow diet, and a second group of mice (N=10) was housed on rodent food bars. From each group, we collected hair (whiskers) samples for analysis using the methods and systems described elsewhere herein. Fig. 27 As can be seen, dietary manipulation produced significant changes in the levels of barium:calcium (P=0.004) and zinc:iron (P=0.02) dynamics. Crucially, given previous findings showing that space travel reduces the levels of these traits (see Example 1), these findings highlight how these methods can be used to demonstrate improved treatment effects, particularly nutritional interventions; and can link the effects of improved outcomes to molecular mechanisms and / or effectors.

[0493] Implementation

[0494] Embodiment 1. A computer-implemented exposome system, the system comprising: (a) an exposome biochemical signature database (EDB), the EDB comprising exposome characteristics of multiple subjects who have participated in and have not participated in space travel; and (b) an intervention outcome database (IODB), the IODB comprising information about intervention outcome information for at least one stage of at least one intervention; (c) a computer processor, the computer processor comprising: (i) an association software module communicatively coupled to the EDB and the IODB, wherein the association software module is programmed to determine an association between the exposome characteristics, clinical phenotype information, and the intervention outcome information for at least one of the multiple subjects; and (ii) a recommendation software module communicatively coupled to the EDB and the IODB, the recommendation software module is programmed to provide an intervention recommendation for at least one of the multiple subjects based at least in part on the exposome characteristics, the clinical phenotype information, the intervention outcome information, and the association between the exposome characteristics, the clinical phenotype information, and the intervention outcome information for the at least one of the multiple subjects.

[0495] Embodiment 2. The system according to embodiment 1 further includes a clinical database (CDB), wherein the CDB includes clinical phenotype information of the multiple subjects.

[0496] Embodiment 3. The system of embodiment 1, wherein the exposome biochemical signature comprises at least 100, at least 1,000, or at least 10,000 different exposome biochemical signatures.

[0497] Embodiment 4. A system according to embodiment 1, wherein the intervention outcome information includes classification of non-responders, poor responders and positive responders to at least one of the interventions.

[0498] Embodiment 5. A system according to embodiment 1, wherein the intervention outcome comprises one or more inclusion criteria or exclusion criteria for at least one of the interventions.

[0499] Embodiment 6. A system according to embodiment 1, wherein the exposome biochemical signature is obtained by measuring biological samples of the multiple subjects.

[0500] Embodiment 7. A system according to embodiment 6, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, a physiological parameter or any combination thereof.

[0501] Embodiment 8. A system according to embodiment 6, wherein the determination includes obtaining mass spectrometry measurements, laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof.

[0502] Embodiment 9. A system according to embodiment 8, wherein the mass spectrometry measurements include measurements of one or more chemical substances.

[0503] Embodiment 10. A system according to embodiment 9, wherein the one or more chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

[0504] Embodiment 11. The system of embodiment 1, wherein the expomic profile comprises a profile of dynamic temporal biochemical responses of the plurality of subjects.

[0505] Embodiment 12. A system according to embodiment 1, wherein the corresponding multiple exposome characteristics of a corresponding object among the multiple objects include one or more fluorescent images of one or more biological samples of the corresponding object.

[0506] Embodiment 13. A system according to embodiment 1, wherein the corresponding multiple exposome characteristics of a corresponding object among the multiple objects include one or more spatial maps of one or more Raman spectra of a biological sample of the corresponding object.

[0507] Embodiment 14. The system of embodiment 1, wherein the exposome biochemical signature is associated with space travel.

[0508] Embodiment 15. A system according to embodiment 14, wherein the at least one program further comprises instructions for using the trained model to analyze the corresponding plurality of exposure-based characteristics to determine an association with space travel.

[0509] Embodiment 16. A system according to embodiment 15, wherein the exposome profile is analyzed using a trained classifier to determine the association with the space travel.

[0510] Embodiment 17. A system according to embodiment 16, wherein the trained classifier is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm and any combination thereof.

[0511] Embodiment 18. A method for selecting a subject for an intervention, the method comprising: (a) providing a trained prediction model, wherein the trained prediction model is trained on clinical metadata, exposure group characteristics and corresponding intervention outcome information of one or more subjects; (b) detecting biochemical features obtained from biological samples from subjects seeking the intervention, thereby generating prospective exposure group characteristics; (c) using the trained prediction model to predict predicted intervention outcome information of the subject seeking the intervention, wherein the exposure group characteristics and clinical metadata of the subject seeking the intervention are inputs to the trained prediction model; and (d) selecting the subject for the intervention or excluding the subject from the intervention based at least in part on the predicted intervention outcome information of the subject.

[0512] Embodiment 19. A method according to embodiment 18, wherein the biochemical characteristics are obtained by measuring a biological sample of the subject.

[0513] Embodiment 20. A method according to embodiment 19, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample or any combination thereof.

[0514] Embodiment 21. A method according to embodiment 19, wherein the determining includes collecting data from laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof.

[0515] Embodiment 22. A method according to Embodiment 21, wherein the laser ablation inductively coupled plasma mass spectrometry measurements include measurements of one or more elemental chemicals.

[0516] Embodiment 23. A method according to Embodiment 22, wherein the one or more elemental chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

[0517] Embodiment 24. A method according to embodiment 18, wherein the biochemical characteristics include a fluorescent image of the biological sample.

[0518] Embodiment 25. A method according to embodiment 18, wherein the biochemical characteristics include a spatial map of the Raman spectrum of the biological sample.

[0519] Embodiment 26. A method according to embodiment 18, wherein the biochemical signature is associated with space travel.

[0520] Embodiment 27. The method of embodiment 18, wherein the intervention comprises diet, drug therapy, or any combination thereof.

[0521] Embodiment 28. A method according to embodiment 18, wherein the trained prediction model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm and any combination thereof.

[0522] Embodiment 29. The method according to embodiment 18 also includes recruiting the subject into the intervention when the subject is selected for the intervention.

[0523] Embodiment 30. The method of embodiment 18 further comprises evaluating the subject for another intervention when the subject is excluded from the intervention.

[0524] Embodiment 31. A method for selecting an optimal treatment to ameliorate the negative effects of space travel, comprising: (a) detecting one or more biochemical features obtained from one or more biological samples from one or more subjects that have not participated in space travel, thereby generating one or more reference exposure group characteristics; (b) detecting characteristics of one or more biochemical features obtained from one or more biological samples from one or more subjects that have participated in space travel, thereby generating one or more pre-treatment exposure group characteristics; (c) administering a treatment to the one or more subjects that have participated in space travel; (d) after a period of time has passed after receiving the treatment, detecting one or more biochemical features obtained from one or more biological samples from the one or more subjects that have participated in space travel. (e) determining differences between the one or more reference exposure genomic characteristics of the one or more subjects that have not participated in space travel, the one or more pre-treatment exposure genomic characteristics of the one or more subjects that have participated in space travel, and the one or more post-treatment exposure genomic characteristics of the one or more subjects that have participated in space travel; and (f) selecting one or more optimal treatments based at least in part on the determined differences between the one or more reference exposure genomic characteristics, the one or more pre-treatment exposure genomic characteristics, and the one or more post-treatment exposure genomic characteristics, wherein the one or more optimal treatments are selected based on the determined differences that meet predetermined criteria.

[0525] Embodiment 32. The method of embodiment 31, wherein the optimal treatment may include medication, nutritional supplements, or any combination thereof.

[0526] Embodiment 33. A method according to Embodiment 31, wherein the predetermined criteria include differences between the one or more pre-treatment exposure genomic characteristics and the one or more post-treatment exposure genomic characteristics and the one or more reference exposure genomic characteristics.

[0527] Embodiment 34. A method according to embodiment 33, wherein the period of time includes at least about 1 hour, at least about 1 day, at least about 1 week, at least about 1 month, at least about 1 year, or any combination thereof.

[0528] Embodiment 35. The method of Embodiment 33, wherein the difference comprises at least a 10% change in the one or more post-treatment exposome profiles toward the one or more reference exposome profiles.

[0529] Embodiment 36. The method of Embodiment 31, wherein the pre-treatment exposure profile, the post-treatment exposure profile, or any combination thereof is obtained by measuring a biological sample of the subject.

[0530] Embodiment 37. A method according to embodiment 36, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, a physiological parameter or any combination thereof.

[0531] Embodiment 38. A method according to embodiment 36, wherein the determining includes obtaining laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof.

[0532] Embodiment 39. A method according to Embodiment 38, wherein the laser ablation inductively coupled plasma mass spectrometry includes measurement values ​​of one or more elemental chemicals.

[0533] Embodiment 40. The method of Embodiment 39, wherein the one or more elemental chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

[0534] Embodiment 41. A method according to embodiment 31, wherein the biochemical characteristics include a fluorescent image of the biological sample.

[0535] Embodiment 42. A method according to embodiment 31, wherein the biochemical characteristics include a spatial map of the Raman spectrum of the biological sample.

[0536] Embodiment 43. A method according to embodiment 38, wherein the immunohistochemical measurements include measurements of c-reactive staining.

[0537] Embodiment 44. The method of Embodiment 31, wherein a trained classifier is used to analyze the differences between the reference exposure profile, the pre-treatment exposure profile, the post-treatment exposure profile, or any combination thereof.

[0538] Embodiment 45. A method according to embodiment 44, wherein the trained classifier is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm and any combination thereof.

[0539] Embodiment 46. A method for evaluating the impact of an intervention in multiple subjects, the method comprising: for each corresponding subject in the multiple subjects, sampling each corresponding position in a plurality of positions along a reference line on a corresponding biological sample associated with the chemical dynamics of the corresponding subject, thereby obtaining a corresponding plurality of chemical samples of the corresponding subject, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth period of the corresponding biological sample associated with the chemical dynamics, wherein the plurality of positions include: one or more positions representing a growth period before the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each corresponding subject in the multiple subjects, analyzing the corresponding plurality of chemical samples of the corresponding subject with a mass spectrometer, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding chemical substance in a plurality of chemical substances over time, the concentration being determined jointly by the plurality of chemical samples; for one or more of the plurality of chemical substances for each of the plurality of chemical substances, generating a corresponding isotope dataset, the corresponding isotope dataset comprising: a pre-intervention characteristic set corresponding to a time period of biological sample growth before the intervention, the pre-intervention characteristic set comprising, for each corresponding object among the plurality of objects, one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more locations representing the growth period before the intervention; an intervention characteristic set corresponding to a time period of biological sample growth during the intervention, the intervention characteristic set comprising, for each corresponding object among the plurality of objects, one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more locations representing the growth period during the intervention; and a post-intervention characteristic set corresponding to a time period of biological sample growth after the intervention, the post-intervention characteristic set comprising, for each corresponding object among the plurality of objects, one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more locations representing the growth period after the intervention; and for each of the one or more chemical substances, using the corresponding isotope dataset to assess changes in chemical dynamics in response to the intervention.

[0540] Embodiment 47. A method according to embodiment 46, wherein the evaluation includes performing a probabilistic hypothesis test on each of the one or more chemical substances using (i) the pre-intervention characteristic set and (ii) one or both of the intervention characteristic set and the post-intervention characteristic set.

[0541] Embodiment 48. A method for evaluating the impact of an intervention in multiple subjects, the method comprising: for each corresponding subject in the multiple subjects, sampling each corresponding position of a plurality of positions along a reference line on a corresponding biological sample associated with the chemical dynamics of the corresponding subject, thereby obtaining a corresponding plurality of chemical samples of the corresponding subject, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth phase of the corresponding biological sample associated with the chemical dynamics, wherein the plurality of positions include: one or more positions representing a growth phase before the intervention, one or more positions representing a growth phase during the intervention, and one or more positions representing a growth phase after the intervention; for each corresponding subject in the multiple subjects, analyzing the corresponding plurality of chemical samples of the corresponding subject with a mass spectrometer, thereby obtaining a corresponding first data set including a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding chemical substance in a plurality of chemical substances over time, the concentration being determined jointly by the plurality of chemical samples; generating a corresponding aggregated isotope data set for two or more chemical substance sets in the plurality of chemical substances, the corresponding aggregated isotope data set including: a pre-intervention feature set corresponding to biological sample growth prior to the intervention, the pre-intervention feature set comprising values ​​of one or more reduced dimensionality components, for each respective one of the plurality of subjects, the values ​​of the one or more reduced dimensionality components being formed by features derived from concentrations of each of the two or more chemical substances measured from the one or more locations representing a growth period prior to the intervention; an intervention feature set corresponding to biological sample growth during the intervention, the intervention feature set comprising values ​​of one or more reduced dimensionality components, for each respective one of the plurality of subjects, the values ​​of the one or more reduced dimensionality components being formed by features derived from concentrations of each of the two or more chemical substances measured from the one or more locations representing a growth period during the intervention; and a post-intervention feature set corresponding to biological sample growth after the intervention, the post-intervention feature set comprising values ​​of one or more reduced dimensionality components, for each respective one of the plurality of subjects, the values ​​of the one or more reduced dimensionality components being formed by features derived from concentrations of each of the two or more chemical substances measured from the one or more locations representing a growth period after the intervention; and using the aggregated isotope dataset to assess changes in chemical dynamics in response to the intervention.

[0542] Embodiment 49. A method according to embodiment 48, wherein the evaluation includes performing a probability hypothesis test using (i) the pre-intervention feature set and (ii) one or both of the intervention feature set and the post-intervention feature set.

[0543] Embodiment 50. A method according to embodiment 48 or 49, wherein for each of the pre-intervention characteristic set, the intervention characteristic set and the post-intervention characteristic set, the value of the reduced dimension component is each determined by the characteristic of each of the two or more chemical substances measured from a single corresponding object among the multiple objects.

[0544] Embodiment 51. A method according to embodiment 48 or 49, wherein for each of the pre-intervention characteristic set, the intervention characteristic set and the post-intervention characteristic set, the value of the reduced dimension component is each determined by an aggregation of the characteristics of each of the two or more chemical substances measured from multiple corresponding objects among the multiple objects.

[0545] Embodiment 52. A method according to any one of embodiments 46-51, wherein the one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more positions representing the growth period are the concentration, its standardized concentration, or related descriptive statistics or parameters derived therefrom.

[0546] Embodiment 53. A method according to any one of embodiments 46-51, wherein the one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more positions representing the growth period are selected from recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions and the number of most likely recursions; these measurements are obtained by applying recursive quantification analysis parameters, cross-recursive quantification analysis parameters, joint recursive quantification analysis parameters and / or multidimensional recursive quantification analysis.

[0547] Embodiment 54. A method for evaluating the impact of an intervention in multiple subjects, the method comprising: for each corresponding subject in the multiple subjects, sampling each corresponding position in a plurality of positions along a reference line on a corresponding biological sample associated with the chemical dynamics of the corresponding subject, thereby obtaining a corresponding plurality of chemical samples of the corresponding subject, each chemical sample in the plurality of chemical samples corresponds to a different position in the plurality of positions, and each position in the plurality of positions represents a different growth period of the corresponding biological sample associated with the chemical dynamics, wherein the plurality of positions include: one or more positions representing a growth period before the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each corresponding subject in the multiple subjects, analyzing the corresponding plurality of chemical samples of the corresponding subject with a mass spectrometer, thereby obtaining obtaining a corresponding first data set including a plurality of traces, each of the plurality of traces being the concentration of a corresponding chemical of the plurality of chemicals over time, the concentration being determined jointly by the plurality of chemical samples; for each corresponding object of the plurality of objects and for each of one or more chemical substances of the plurality of chemicals, applying a distributed lag model or a similar nonlinear distribution model to the concentration of the corresponding chemical substance measured from the plurality of locations, or a characteristic derived from the concentration, as a function of time relative to the intervention to generate a corresponding contribution data set, the corresponding contribution data set representing the contribution of the intervention to the concentration of the corresponding chemical substance in the corresponding object as a function of time; and for each of the one or more chemical substances, using the corresponding contribution data set from each corresponding object of the plurality of objects to assess changes in chemical dynamics in response to the intervention.

[0548] Embodiment 55. A method for evaluating the impact of an intervention in multiple subjects, the method comprising: for each corresponding subject in the multiple subjects, sampling each corresponding position of a plurality of positions along a reference line on a corresponding biological sample associated with the chemical dynamics of the corresponding subject, thereby obtaining a corresponding plurality of chemical samples of the corresponding subject, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth period of the corresponding biological sample associated with the chemical dynamics, wherein the plurality of positions include: one or more positions representing a growth period before the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each corresponding subject in the multiple subjects, analyzing the corresponding plurality of chemical samples of the corresponding subject with a mass spectrometer, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace in the plurality of traces being a corresponding chemical of a plurality of chemical substances The invention relates to a method for preparing an isotopic data set comprising: determining a concentration of a chemical substance over time, the concentration being determined collectively by the plurality of chemical samples; generating, for each respective one of the plurality of subjects and for two or more sets of chemicals among the plurality of chemicals, a corresponding aggregated isotopic data set comprising a set of characteristics, the set of characteristics comprising values ​​of one or more reduced-dimensionality components, the values ​​of the one or more reduced-dimensionality components being formed by characteristics derived from the concentration of each of the two or more chemicals measured across different growth periods of the respective subjects; for each respective one of the plurality of subjects, applying a distributed lag model or a similar nonlinear distribution model to the corresponding aggregated isotopic data set as a function of time relative to the intervention to generate a corresponding contribution data set representing a contribution of the intervention to the concentration of the two or more sets of chemicals in the respective subject as a function of time; and using the corresponding contribution data for each respective one of the plurality of subjects to assess changes in chemical dynamics in response to the intervention.

[0549] Embodiment 56. The method of any one of embodiments 46-55, wherein the intervention is the ingestion of a nutritional composition.

[0550] Embodiment 57. The method of embodiment 56 further comprises, in response to said evaluating changes in chemical dynamics, altering ingredients of said nutritional composition to modulate the effect of said ingestion of said nutritional composition.

[0551] Embodiment 58. The method of embodiment 56 further comprises supplementing the intake of the nutritional composition with the intake of one or more dietary supplements in response to the changes in the assessed chemical dynamics.

[0552] Embodiment 59. The method of any one of Embodiments 46-58, further comprising assessing changes in the metabolism of one or more additional metabolites in response to the intervention.

[0553] Embodiment 60. The method of embodiment 59, wherein the one or more additional metabolites are selected from perfluorinated compounds, parabens, phthalates, lipids, amino acids, amino acid derivatives, and peptides.

[0554] Embodiment 61. A method for outputting one or more quantitative measurements of one or more exposure genomic features of a subject, comprising: (a) receiving a biological sample from the subject; (b) determining one or more exposure genomic features from the biological sample of the subject; (c) calculating one or more characteristics of the one or more exposure genomic features, wherein each of the one or more characteristics comprises one or more quantitative measurements; and (d) outputting the one or more quantitative measurements of the one or more characteristics of the subject.

[0555] Embodiment 62. The method according to embodiment 61 also includes outputting a health outcome of the object based at least in part on the association of standardized scores of one or more characteristics of the object with standardized scores of one or more quantitative metrics of one or more characteristics of a second set of objects.

[0556] Embodiment 63. A method according to embodiment 62, wherein one or more quantitative measurements of one or more characteristics of the second set of objects are stored in a database, wherein the database is a local server or a cloud-based server.

[0557] Embodiment 64. A method according to embodiment 62, wherein the health outcomes include space travel classification, clinical subtypes, physiologically related non-clinical subgroups, anthropometric indicators, behavioral indicators, socioeconomic indicators, body mass index, intelligence quotient, socioeconomic status, or any combination thereof.

[0558] Embodiment 65. The method of Embodiment 61, wherein the one or more characteristics comprise measurements of the temporal dynamics of the one or more exposome features.

[0559] Embodiment 66. A method according to embodiment 65, wherein the measurement of the temporal dynamics includes: a linear slope, a nonlinear parameter describing the curvature of the one or more exposure genomic features, a sudden change in the intensity of the one or more exposure genomic features, a change in the baseline intensity of the one or more exposure genomic features, a change in the frequency domain representation of the one or more exposure genomic features, a change in the power spectrum domain representation of the one or more exposure genomic features, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimated Lyapunov spectrum, a maximum Lyapunov exponent, stratification, entropy, capture time (TT), a mean diagonal length (MDL), a recursion time (RT), Vmax, certainty, a maximum diagonal length, a divergence, a Shannon entropy in a diagonal length, a recursion trend, a maximum vertical line length, a Shannon entropy in a vertical line length, a mean recursion time, a Shannon entropy in a number of recursions, a number of most likely recursions, or any combination thereof.

[0560] Embodiment 67. A method according to embodiment 61, wherein the object includes multiple objects.

[0561] Embodiment 68. A method according to embodiment 61, wherein the one or more characteristics of the one or more features include phenotypic characteristics, wherein the phenotypic characteristics include: electrocardiogram (ECG), electroencephalogram, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome or any combination thereof.

[0562] Embodiment 69. A method according to embodiment 61, wherein the one or more characteristics are derived from one or more attractors.

[0563] Embodiment 70. A method according to embodiment 61, wherein the one or more exposome features are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0564] Embodiment 71. A method according to embodiment 61, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters or any combination thereof.

[0565] Embodiment 72. A method according to embodiment 68, wherein the phenotypic characteristic comprises a molecular phenotype.

[0566] Embodiment 73. A method according to embodiment 72, wherein the molecular phenotype is determined by unsupervised analysis, wherein the unsupervised analysis comprises clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0567] Embodiment 74. A method according to embodiment 66, wherein the recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions and the most likely number of recursions.

[0568] Embodiment 75. The method according to embodiment 69 also includes analyzing the one or more attractors through potential energy analysis to generate a potential energy data space.

[0569] Embodiment 76. The method of Embodiment 61, wherein the one or more exposome characteristics of the subject comprise retrospective exposome data.

[0570] Embodiment 77. The method according to embodiment 69 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

[0571] Embodiment 78. A method according to embodiment 77, wherein the dynamic relationship is determined by cross convergence mapping (CCM).

[0572] Embodiment 79. The method of Embodiment 61, further comprising reducing the one or more expomic signatures to reduced one or more expomic signatures.

[0573] Embodiment 80. The method according to embodiment 69 also includes constructing a network of the one or more attractors based on the similarity of the time exposure omics data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in the number of recursions, the number of most likely recursions, or any combination thereof of the one or more attractors.

[0574] Embodiment 81. The method according to embodiment 80 also includes analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, characteristic importance, pathway importance, correlation graph theory-based metrics, or any combination thereof.

[0575] Embodiment 82. A method for outputting predictions of phenotypic data for one or more subjects, comprising: (a) receiving one or more biological samples and phenotypic data from a first set of subjects; (b) determining a first exposure group feature set from one or more biological samples of the first set of subjects; (c) calculating a first characteristic set of the first exposure group feature set; (d) training a prediction model using the first characteristic set and the phenotypic data of the first set of subjects; (e) receiving one or more biological samples from a second set of subjects different from the first set of subjects; (f) determining a second exposure group feature set from the one or more biological samples of the second set of subjects; (g) calculating a second characteristic set from the second exposure group feature set; and (h) outputting the predictions of the phenotypic data of the second set of subjects determined by inputting the second characteristic set into the trained prediction model.

[0576] Embodiment 83. A method according to embodiment 82, wherein the first set of characteristics and the second set of characteristics include one or more quantitative metrics.

[0577] Embodiment 84. A method according to Embodiment 82, wherein the one or more quantitative metrics include measurements of the temporal dynamics of the one or more exposome features.

[0578] Embodiment 85. A method according to embodiment 83, wherein the measurement of the temporal dynamics includes: a linear slope, a nonlinear parameter describing the curvature of the first exposure group feature set and the second exposure group feature set, a sudden change in the intensity of the first exposure group feature set and the second exposure group feature set, a change in the baseline intensity of the first exposure group feature set and the second exposure group feature set, a change in the frequency domain representation of the first exposure group feature set and the second exposure group feature set, a change in the power spectrum domain representation of the first exposure group feature set and the second exposure group feature set, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimated Lyapunov spectrum or a maximum Lyapunov exponent, or any combination thereof.

[0579] Embodiment 86. The method of Embodiment 82, wherein the first property set of the first exposure genomic feature set comprises a first phenotypic property set, and wherein the second property set of the second exposure genomic feature set comprises a second phenotypic property set.

[0580] Embodiment 87. A method according to embodiment 86, wherein the first phenotypic characteristic set or the second phenotypic characteristic set comprises: electrocardiogram (ECG), electroencephalogram, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genomics, epigenome, transcriptome, proteome, metabolome or any combination thereof.

[0581] Embodiment 88. A method according to embodiment 82, wherein the first set of characteristics and the second set of characteristics are represented as or derived from one or more attractors.

[0582] Embodiment 89. A method according to embodiment 88, wherein the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof.

[0583] Embodiment 90. A method according to Embodiment 82, wherein the first exposure-based feature set and the second exposure-based feature set are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

[0584] Embodiment 91. A method according to embodiment 82, wherein the one or more biological samples of the first object and the second object include hair, teeth, toenails, fingernails, physiological parameters or any combination thereof.

[0585] Embodiment 92. A method according to Embodiment 86, wherein the first set of phenotypic characteristics or the second set of phenotypic characteristics comprises a molecular phenotype.

[0586] Embodiment 93. A method according to embodiment 92, wherein the molecular phenotype is determined by unsupervised analysis, wherein the unsupervised analysis comprises clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0587] Embodiment 94. A method according to embodiment 85, wherein the recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions and the most likely number of recursions or any combination thereof.

[0588] Embodiment 95. The method according to embodiment 88 also includes analyzing the one or more attractors through potential energy analysis to generate a potential energy data space.

[0589] Embodiment 96. The method of Embodiment 82, wherein the first expomic feature set and the second expomic feature set comprise dynamic expomic data that is retrospective, prospective, or any combination thereof.

[0590] Embodiment 97. The method according to embodiment 88 also includes analyzing the dynamic relationship between the recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions and the most likely number of recursions or any combination of the one or more attractors.

[0591] Embodiment 98. A method according to embodiment 97, wherein the dynamic relationship is determined by cross convergence mapping (CCM).

[0592] Embodiment 99. The method according to embodiment 88 also includes constructing a network of the one or more attractors based on the similarity of the time exposure omics data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in the number of recursions and the most likely number of recursions or any combination thereof of the one or more attractors.

[0593] Embodiment 100. The method according to embodiment 99 also includes analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, metric feature importance based on correlation graph theory, pathway importance or any combination thereof.

Claims

1. A computer-implemented exposome system, the system comprising: one or more processors; and a memory addressable by the one or more processors, the memory storing: (a) an exposome biochemical signature database (EDB), the EDB including, in electronic form, a corresponding plurality of exposome characteristics for each of a plurality of subjects who have participated in or have not participated in space travel; and (b) an intervention outcome database (IODB) comprising, in electronic form, information regarding intervention outcome information for at least one phase of at least one intervention for at least one subject of the plurality of subjects; The memory further stores at least one program for execution by the one or more processors, the at least one program comprising: (i) a correlation software module communicatively coupled to the EDB and the IODB, wherein the correlation software module is programmed to determine a correlation between the corresponding plurality of expomic characteristics of the at least one subject and the intervention outcome information of at least one stage; and (ii) a recommendation software module communicatively coupled to the EDB and the IODB, the recommendation software module being programmed to provide an intervention recommendation for the at least one subject based at least in part on the corresponding plurality of expomic characteristics of the at least one subject, the intervention outcome information of the at least one subject, and the association between the corresponding plurality of expomic characteristics, clinical phenotype information, and the intervention outcome information of the at least one subject.

2. The computer-implemented exposome system of claim 1 , wherein the memory further stores a clinical database (CDB) comprising clinical phenotype information of the plurality of subjects, and wherein The association software module is programmed to determine an association between the corresponding plurality of expomic characteristics, the clinical phenotype information, and the intervention outcome information of at least one subject; and The recommendation software module is communicatively programmed to provide the intervention recommendation for the at least one subject based at least in part on the corresponding plurality of exposure genomic characteristics of the at least one subject, the clinical phenotype information of the at least one subject, the intervention outcome information of the at least one subject, and the association between the corresponding plurality of exposure genomic characteristics, the clinical phenotype information, and the intervention outcome information of the at least one subject.

3. The computer-implemented expomic system of claim 1, wherein the EDB comprises a different corresponding plurality of expomic characteristics for each of at least 100, at least 1,000, or at least 10,000 subjects.

4. The computer-implemented exposome system of claim 1 , wherein the information about intervention outcome information for at least one phase of at least one intervention for at least one subject among the plurality of subjects comprises classification as a non-responder, a poor responder, or a positive responder.

5. The computer-implemented expomics system of claim 1, wherein the information regarding intervention outcome information for at least one phase of at least one intervention for at least one subject among the plurality of subjects comprises one or more inclusion criteria or exclusion criteria. 6 . The computer-implemented expomic system of claim 1 , wherein the corresponding plurality of expomic characteristics of a subject in the plurality of subjects are obtained by measuring a biological sample of the subject.

7. The computer-implemented expomics system of claim 6, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

8. The computer-implemented exposome system of claim 6, wherein the determining comprises obtaining mass spectrometry measurements, laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, physiological parameters, or any combination thereof.

9. The computer-implemented expomics system of claim 8, wherein the determining comprises obtaining one or more mass spectrometry measurements, wherein the one or more mass spectrometry measurements comprise measurements of one or more chemical substances.

10. The computer-implemented expomics system of claim 9, wherein the one or more chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

11. The computer-implemented expomic system of claim 1 , wherein the corresponding plurality of expomic characteristics of a corresponding subject of the plurality of subjects comprises one or more characteristics of one or more dynamic temporal biochemical reactions of the corresponding subject.

12. The computer-implemented expomic system of claim 1, wherein the corresponding plurality of expomic characteristics of a corresponding subject among the plurality of subjects comprises one or more fluorescent images of one or more biological samples of the corresponding subject.

13. The computer-implemented expomic system of claim 1, wherein the corresponding plurality of expomic characteristics of a corresponding subject of the plurality of subjects comprises one or more spatial maps of one or more Raman spectra of a biological sample of the corresponding subject.

14. The computer-implemented expomic system of claim 1, wherein each corresponding plurality of expomic characteristics of each corresponding subject of the plurality of subjects is associated with an absence of space travel or a presence of space travel in the corresponding subject.

15. The computer-implemented expomic system of claim 14, wherein the at least one program further comprises instructions for analyzing the corresponding plurality of expomic characteristics using a trained model to determine an association with space travel.

16. The computer-implemented expomics system of claim 15, wherein the trained model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

17. A method for selecting a subject for intervention, the method comprising: (a) providing a trained prediction model, wherein the trained prediction model is trained on one or more of clinical metadata, exposome characteristics, and corresponding intervention outcome information of a training cohort; (b) using biological samples obtained from the subject to detect biochemical signatures, thereby generating a plurality of prospective exposome profiles; (c) inputting the plurality of prospective exposome characteristics and clinical metadata of the subject into the trained prediction model, thereby obtaining predicted intervention outcome information of the subject; as well as (d) selecting the subject for the intervention or excluding the subject from the intervention based at least in part on the predicted intervention outcome information for the subject.

18. The method of claim 17, wherein the biochemical profile is obtained by assaying a biological sample of the subject.

19. The method of claim 18, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

20. The method of claim 18, wherein the determining comprises collecting data from laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof.

21. The method of claim 18, wherein the determining comprises collecting data from laser ablation inductively coupled plasma mass spectrometry measurements, and wherein the laser ablation inductively coupled plasma mass spectrometry measurements comprise measurements of one or more elemental chemical species.

22. The method of claim 21, wherein the one or more elemental chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

23. The method of claim 17, wherein the biochemical signature comprises a spatial map of a Raman spectrum of the biological sample.

24. The method of claim 17, wherein the biochemical signature is associated with space travel.

25. The method of claim 17, wherein the intervention comprises diet, medication, or any combination thereof.

26. The method of claim 17, wherein the trained prediction model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

27. The method of claim 17, further comprising, when the subject is selected for the intervention, enrolling the subject in a study in which the intervention will be provided.

28. The method of claim 17, further comprising evaluating the subject for a second intervention when the subject is excluded from the intervention.

29. A method for selecting an optimal treatment for a subject participating in space travel, the method comprising: (a) detecting one or more biochemical features obtained from one or more biological samples from one or more subjects who have not participated in space travel, thereby generating one or more reference exposome profiles; (b) detecting characteristics of one or more biochemical signatures obtained from one or more biological samples from one or more subjects who have participated in space travel, thereby generating one or more pre-treatment exposome profiles; (c) administering treatment to the one or more subjects who have participated in space travel; (d) detecting characteristics of one or more biochemical signatures obtained from one or more biological samples from the one or more subjects who have participated in the space travel after a period of time has elapsed since receiving the treatment, thereby generating one or more post-treatment exposome characteristics; (e) determining differences between the one or more reference exposome profiles of the one or more subjects that have not participated in space travel, the one or more pre-treatment exposome profiles of the one or more subjects that have participated in space travel, and the one or more post-treatment exposome profiles of the one or more subjects that have participated in space travel; as well as (f) selecting one or more optimal treatments based at least in part on the determined differences between the one or more reference exposure genomic profiles, the one or more pre-treatment exposure genomic profiles, and the one or more post-treatment exposure genomic profiles, wherein the one or more optimal treatments are selected based on the determined differences satisfying a predetermined criterion.

30. The method of claim 29, wherein the optimal treatment of the one or more optimal treatments comprises a medication, a nutritional supplement, a diet, a food bar, or any combination thereof.

31. The method of claim 29, wherein the predetermined criteria comprises differences between the one or more pre-treatment exposome profiles and the one or more post-treatment exposome profiles and the one or more reference exposome profiles.

32. The method of claim 29, wherein the period of time comprises at least about 1 hour, at least about 1 day, at least about 1 week, at least about 1 month, at least about 1 year, or any combination thereof.

33. The method of claim 29, wherein the difference comprises at least a 10% change in the one or more post-treatment exposome profiles toward the one or more reference exposome profiles.

34. The method of claim 29, wherein the one or more pre-treatment exposome profiles, the one or more post-treatment exposome profiles, or any combination thereof are obtained by measuring a biological sample of a corresponding subject.

35. The method of claim 34, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

36. The method of claim 34, wherein the determining comprises obtaining laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof.

37. The method of claim 34, wherein the determining comprises obtaining laser ablation inductively coupled plasma mass spectroscopy data, and wherein the laser ablation inductively coupled plasma mass spectroscopy data comprises measurements of one or more elemental chemical species.

38. The method of claim 37, wherein the one or more elemental chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

39. The method of claim 29, wherein the one or more biochemical characteristics comprise a fluorescent image of the biological sample.

40. The method of claim 29, wherein the one or more biochemical signatures comprise a spatial map of a Raman spectrum of the biological sample.

41. The method of claim 29, wherein a trained model is used to analyze the differences between the one or more reference exposure genomic characteristics of the one or more subjects that have not participated in space travel, the one or more pre-treatment exposure genomic characteristics of the one or more subjects that have participated in space travel, and the one or more post-treatment exposure genomic characteristics of the one or more subjects that have participated in space travel.

42. The method of claim 41, wherein the trained model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

43. A method for evaluating the impact of an intervention in a plurality of subjects, the method comprising: For each corresponding object in the plurality of objects, sampling each corresponding position in a plurality of positions along a reference line on a corresponding biological sample associated with a chemical dynamics of the corresponding object, thereby obtaining a corresponding plurality of chemical samples of the corresponding object, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth phase of the corresponding biological sample associated with a chemical dynamics, wherein the plurality of positions include: represents one or more positions during the growth period prior to said intervention, one or more locations representing a growth phase during the intervention period, and representing one or more locations during the growth phase following said intervention; for each corresponding object in the plurality of objects, analyzing the corresponding plurality of chemical samples of the corresponding object with a mass spectrometer to obtain a corresponding first data set including a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding chemical in a plurality of chemical substances over time, the concentrations being collectively determined by the plurality of chemical samples; For each of one or more chemical substances in the plurality of chemical substances, a corresponding isotope data set is generated, the corresponding isotope data set comprising: a pre-intervention set of characteristics corresponding to a period of growth of the biological sample prior to the intervention, the pre-intervention set of characteristics comprising, for each respective subject in the plurality of subjects, one or more characteristics derived from concentrations of respective chemical species measured from the one or more locations representing the growth period prior to the intervention; an intervention characteristic set corresponding to a time period of growth of the biological sample during the intervention period, the intervention characteristic set comprising, for each respective subject of the plurality of subjects, one or more characteristics derived from concentrations of respective chemical species measured from the one or more locations representing a growth phase during the intervention period; and a post-intervention set of characteristics corresponding to a period of growth of the biological sample following the intervention, the post-intervention set of characteristics comprising, for each respective subject in the plurality of subjects, one or more characteristics derived from concentrations of respective chemical species measured from the one or more locations representing a period of growth following the intervention; and For each of the one or more chemical substances, the corresponding isotopic data set is used to assess changes in chemical dynamics in response to the intervention.

44. The method of claim 43, wherein the evaluating comprises performing a probabilistic hypothesis test on each of the plurality of chemicals using (i) the pre-intervention characteristic set and (ii) one or both of the intervention characteristic set and the post-intervention characteristic set.

45. A method for evaluating the impact of an intervention in a plurality of subjects, the method comprising: For each corresponding object in the plurality of objects, sampling each corresponding position in a plurality of positions along a reference line on a corresponding biological sample associated with a chemical dynamics of the corresponding object, thereby obtaining a corresponding plurality of chemical samples of the corresponding object, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth phase of the corresponding biological sample associated with a chemical dynamics, wherein the plurality of positions include: represents one or more positions during the growth period prior to said intervention, one or more locations representing a growth phase during the intervention period, and representing one or more locations during the growth phase following said intervention; for each corresponding object in the plurality of objects, analyzing the corresponding plurality of chemical samples of the corresponding object with a mass spectrometer to obtain a corresponding first data set including a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding chemical in a plurality of chemical substances over time, the concentrations being collectively determined by the plurality of chemical samples; For two or more chemical substance sets in the plurality of chemical substances, corresponding aggregated isotopic data sets are generated, wherein the corresponding aggregated isotopic data sets include: a pre-intervention feature set corresponding to growth of the biological sample prior to the intervention, the pre-intervention feature set comprising values ​​of one or more reduced-dimensionality components, the values ​​of the one or more reduced-dimensionality components being formed, for each respective subject in the plurality of subjects, from features derived from concentrations of each of the two or more chemical substances measured from the one or more locations representing a growth period prior to the intervention; an intervention characteristic set corresponding to growth of the biological sample during the intervention period, the intervention characteristic set comprising values ​​of one or more reduced-dimensionality components, the values ​​of the one or more reduced-dimensionality components formed, for each respective subject in the plurality of subjects, from characteristics derived from concentrations of each of the two or more chemical substances measured from the one or more locations representing a phase of growth during the intervention period; and a post-intervention feature set corresponding to growth of the biological sample after the intervention, the post-intervention feature set comprising values ​​of one or more reduced-dimensionality components, the values ​​of the one or more reduced-dimensionality components formed, for each respective subject in the plurality of subjects, from features derived from concentrations of each of the two or more sets of chemicals measured from the one or more locations representing a growth period after the intervention; and The aggregated isotopic dataset was used to assess changes in chemical dynamics in response to the intervention.

46. ​​The method of claim 45, wherein the evaluating comprises performing a probabilistic hypothesis test using (i) the pre-intervention feature set and (ii) one or both of the intervention feature set and the post-intervention feature set.

47. A method according to claim 45 or 46, wherein for each of the pre-intervention characteristic set, the intervention characteristic set and the post-intervention characteristic set, the value of the reduced dimension component is determined by the characteristic derived from the characteristic of each of the two or more chemical substances measured from a single corresponding object among the multiple objects.

48. A method according to claim 46 or 47, wherein for each of the pre-intervention characteristic set, the intervention characteristic set and the post-intervention characteristic set, the value of the reduced dimension component is determined by an aggregation of the characteristics of each of the two or more chemical sets measured from multiple corresponding objects in the multiple objects.

49. The method of any one of claims 45-48, wherein the one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more locations representing a growth period are the concentration, its standardized concentration, or related descriptive statistics or parameters derived therefrom.

50. A method according to any one of claims 44-49, wherein the one or more characteristics derived from the concentration of the corresponding chemical substance measured from the one or more positions representing the growth period are selected from recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions and the number of most likely recursions; these measurements are obtained by applying recursive quantification analysis parameters, cross-recursive quantification analysis parameters, joint recursive quantification analysis parameters and / or multidimensional recursive quantification analysis.

51. A method for evaluating the impact of an intervention in a plurality of subjects, the method comprising: For each corresponding object in the plurality of objects, sampling each corresponding position in a plurality of positions along a reference line on a corresponding biological sample associated with a chemical dynamics of the corresponding object, thereby obtaining a corresponding plurality of chemical samples of the corresponding object, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth phase of the corresponding biological sample associated with a chemical dynamics, wherein the plurality of positions include: represents one or more positions during the growth period prior to said intervention, one or more locations representing a growth phase during the intervention period, and representing one or more locations during the growth phase following said intervention; for each corresponding object in the plurality of objects, analyzing the corresponding plurality of chemical samples of the corresponding object with a mass spectrometer to obtain a corresponding first data set including a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding chemical in a plurality of chemical substances over time, the concentrations being collectively determined by the plurality of chemical samples; for each respective one of the plurality of subjects and for each of one or more chemicals in the plurality of chemicals, applying a distributed lag model or similar nonlinear distribution model to the concentrations of the respective chemical measured from the plurality of locations, or a property derived therefrom, as a function of time relative to the intervention to generate a corresponding contribution data set representing a contribution of the intervention to the concentrations of the respective chemical in the respective subject as a function of time; For each of the one or more chemical substances, a change in chemical dynamics in response to the intervention is assessed using the corresponding contributing data set from each respective subject in the plurality of subjects.

52. A method for evaluating the impact of an intervention in a plurality of subjects, the method comprising: For each corresponding object in the plurality of objects, sampling each corresponding position in a plurality of positions along a reference line on a corresponding biological sample associated with a chemical dynamics of the corresponding object, thereby obtaining a corresponding plurality of chemical samples of the corresponding object, each chemical sample in the plurality of chemical samples corresponding to a different position in the plurality of positions, and each position in the plurality of positions representing a different growth phase of the corresponding biological sample associated with a chemical dynamics, wherein the plurality of positions include: represents one or more positions during the growth period prior to said intervention, one or more locations representing a growth phase during the intervention period, and representing one or more locations during the growth phase following said intervention; for each corresponding object in the plurality of objects, analyzing the corresponding plurality of chemical samples of the corresponding object with a mass spectrometer to obtain a corresponding first data set including a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding chemical in a plurality of chemical substances over time, the concentrations being collectively determined by the plurality of chemical samples; generating, for each respective one of the plurality of objects and for two or more sets of chemicals in the plurality of chemicals, a corresponding aggregated isotopic data set comprising a set of characteristics, the set of characteristics comprising values ​​of one or more reduced dimensionality components, the values ​​of the one or more reduced dimensionality components being formed from characteristics derived from concentrations of each of the two or more chemicals measured across different growth periods of the respective objects; for each respective subject in the plurality of subjects, applying a distributed lag model or similar nonlinear distribution model to the respective aggregated isotope data set as a function of time relative to the intervention to generate a respective contribution data set representing a contribution of the intervention to the concentrations of the set of two or more chemical substances in the respective subject as a function of time; The corresponding contribution data for each respective subject in the plurality of subjects is used to assess changes in chemical dynamics in response to the intervention.

53. The method of any one of claims 45-52, wherein the intervention is ingestion of a nutritional composition.

54. The method of claim 53, further comprising, in response to said evaluating a change in chemical dynamics, altering an ingredient of said nutritional composition to modulate an effect on said ingestion of said nutritional composition.

55. The method of claim 53, further comprising supplementing ingestion of the nutraceutical composition with ingestion of one or more dietary supplements in response to evaluating changes in chemical dynamics.

56. The method of any one of claims 44-55, further comprising assessing changes in metabolism of one or more additional metabolites in response to the intervention.

57. The method of claim 56, wherein the one or more additional metabolites are selected from the group consisting of perfluorinated compounds, parabens, phthalates, lipids, amino acids, amino acid derivatives, and peptides.

58. A method for outputting one or more quantitative metrics of one or more expomic features of a first subject, comprising: (a) receiving a biological sample from the subject; (b) determining one or more exposome signatures from the biological sample of the subject; (c) calculating one or more characteristics of the one or more expomic signatures, wherein each of the one or more characteristics comprises one or more quantitative metrics; as well as (d) outputting the one or more quantitative measures of the one or more characteristics of the object.

59. The method of claim 58 further comprises outputting a health outcome for the subject based at least in part on an association of the standardized scores for the one or more characteristics of the subject with standardized scores for a second set of one or more characteristics of a second subject, wherein the subject and the second subject are different.

60. The method of claim 59, wherein the second one or more characteristic sets are stored in a database, wherein the database is hosted on a local server, a cloud-based server, or a virtual machine.

61. The method of claim 59, wherein the health outcome comprises a diagnosis of a clinical subtype, a physiologically relevant non-clinical subgroup, an anthropometric measure, a behavioral measure, a socioeconomic measure, body mass index, intelligence quotient, socioeconomic status, or any combination thereof.

62. The method of claim 58, wherein the one or more characteristics comprise measures of the temporal dynamics of the one or more exposome signatures.

63. The method of claim 62, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

64. The method of claim 58, wherein the one or more properties of the one or more exposome signatures comprise phenotypic properties, wherein the phenotypic properties comprise: Electrocardiogram (ECG), electroencephalogram, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome, or any combination thereof data.

65. The method of claim 58, wherein the one or more characteristics are derived from one or more attractors.

66. The method of claim 58, wherein the one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

67. The method of claim 58, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

68. The method of claim 64, wherein the phenotypic characteristic comprises a molecular phenotype.

69. The method of claim 68, wherein the molecular phenotype is determined by unsupervised analysis, wherein the unsupervised analysis comprises clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

70. A method according to claim 62, wherein the measurement of the temporal dynamics includes determining one or more of the recursive quantization analysis parameters, wherein the one or more of the recursive quantization analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

71. The method of claim 66, wherein the one or more exposome signatures include measurements of one or more elemental chemicals measured by the laser ablation inductively coupled plasma mass spectrometry.

72. The method of claim 71, wherein the one or more elemental chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

73. The method of claim 58, wherein the one or more quantitative metrics comprise measurements of the temporal dynamics of the one or more expomic signatures.

74. The method of claim 65 further comprises analyzing the one or more attractors by potential energy analysis to generate a potential energy data space.

75. The method of claim 58, wherein the one or more expomic profiles of the subject comprise retrospective, prospective, or any combination thereof expomic data.

76. The method according to claim 65 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

77. The method of claim 76, wherein the dynamic relationship is determined by cross convergence mapping (CCM).

78. The method of claim 58, further comprising reducing the one or more expomic signatures to reduced one or more expomic signatures.

79. The method according to claim 65 also includes constructing a network of the one or more attractors based on the similarity of the time-exposure omics data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in the number of recursions, the number of most likely recursions, or any combination thereof of the one or more attractors.

80. The method of claim 79, further comprising analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, characteristic importance, pathway importance, correlation graph theory-based metrics, or any combination thereof.

81. A method for outputting a prediction of phenotypic data for one or more subjects, the method comprising: (a) receiving one or more biological samples and phenotypic data from a first set of subjects; (b) determining a first expomic signature set from one or more biological samples of the first set of subjects; (c) calculating a first feature set of the first exposome feature set; (d) training a prediction model using the first set of characteristics and the phenotypic data of the first set of subjects; (e) receiving one or more biological samples from a second set of subjects different from the first set of subjects; (f) determining a second expomic signature set from the one or more biological samples of the second set of subjects; (g) calculating a second feature set from the second expomic feature set; and (h) outputting the predictions for the phenotypic data of the second set of subjects determined by inputting the second set of features into the trained prediction model.

82. The method of claim 81, wherein the first set of characteristics and the second set of characteristics include one or more quantitative metrics.

83. The method of claim 82, wherein the one or more quantitative metrics comprise measurements of the temporal dynamics of the one or more exposome signatures.

84. The method of claim 83, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine one or more nonlinear parameters describing the curvature of the first exposure group feature set and the second exposure group feature set, determine one or more sudden changes in the intensity of the first exposure group feature set and the second exposure group feature set, determine one or more changes in the baseline intensity of the first exposure group feature set and the second exposure group feature set, determine one or more changes in the frequency domain representation of the first exposure group feature set and the second exposure group feature set, determine one or more changes in the power spectrum domain representation of the first exposure group feature set and the second exposure group feature set, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum or a maximum Lyapunov exponent, or any combination thereof.

85. The method of claim 81, wherein the first property set of the first expomic signature set comprises a first phenotypic property set, and wherein the second property set of the second expomic signature set comprises a second phenotypic property set.

86. The method of claim 85, wherein the first set of phenotypic characteristics or the second set of phenotypic characteristics comprises: Electrocardiogram (ECG), electroencephalogram, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genomics, epigenomics, transcriptome, proteome, metabolome, or any combination thereof data.

87. The method of claim 81, wherein the first set of characteristics and the second set of characteristics are represented as or derived from one or more attractors.

88. The method of claim 87, wherein the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof.

89. The method of claim 81, wherein the first expomic feature set and the second expomic feature set are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

90. The method of claim 81, wherein the one or more biological samples of the first subject and the second subject comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

91. The method of claim 85, wherein the first set of phenotypic characteristics and the second set of phenotypic characteristics each comprise a plurality of molecular phenotypes.

92. The method of claim 91, wherein the molecular phenotype is determined by unsupervised analysis, wherein the unsupervised analysis comprises clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

93. A method according to claim 83, wherein the measurement of the temporal dynamics includes determining one or more recursive quantization analysis parameters, wherein the one or more recursive quantization analysis parameters include one or more recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions, or any combination thereof.

94. The method according to claim 87 also includes analyzing the one or more attractors through potential energy analysis to generate a potential energy data space.

95. The method of claim 81, wherein the first expomic feature set and the second expomic feature set comprise dynamic expomic data that is retrospective, prospective, or any combination thereof.

96. The method according to claim 87 also includes analyzing the dynamic relationship between the recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions and the most likely number of recursions or any combination of the one or more attractors.

97. The method of claim 96, wherein the dynamic relationship is determined by cross convergence mapping (CCM).

98. The method according to claim 87 also includes constructing a network of the one or more attractors based on the similarity of the time exposure omics data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in the number of recursions and the number of most likely recursions or any combination thereof of the one or more attractors.

99. The method of claim 98 further comprises analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, a metric feature importance based on correlation graph theory, pathway importance, or any combination thereof.

100. The method of claim 17, wherein the biochemical signature comprises a fluorescent image of the biological sample.

101. The computer-implemented expomics system of claim 16, wherein the trained model is a regressor or a classifier.

102. The computer-implemented expomics system of claim 16, wherein the trained model comprises one or more regression tasks, one or more classification tasks, or a combination of one or more regression tasks and one or more classification tasks.

103. The method of claim 17, wherein the trained prediction model is a regressor.

104. The method of claim 17, wherein the trained predictive model is a classifier.

105. The method of claim 17, wherein the trained prediction model comprises one or more regression tasks, one or more classification tasks, or a combination of one or more regression tasks and one or more classification tasks.

106. The method of claim 42, wherein the trained model is a regressor or a classifier.

107. The method of claim 42, wherein the trained model comprises one or more regression tasks, one or more classification tasks, or a combination of one or more regression tasks and one or more classification tasks.

108. A method for outputting one or more subjects' reactions to participating in real or simulated space travel, the method comprising: (a) receiving one or more biological samples from one or more subjects participating in space travel; (b) determining one or more exposome profiles of the one or more biological samples; (c) calculating an expomic property of one or more expomic features of the one or more biological samples; and (d) outputting the response of the one or more subjects to participating in space travel as an output of a trained prediction model, and providing the exposome characteristics of the one or more biological samples as input to the trained prediction model.

109. The method of claim 108, wherein the trained predictive model is configured to determine a likelihood that the exposome profile is associated with a subject participating in real or simulated space travel.

110. The method of claim 109, wherein the trained predictive model is configured to determine whether the subject's metabolism is at a baseline condition not associated with space travel.

111. The method of claim 108, wherein each of the exposome profiles comprises one or more quantitative metrics.

112. The method of claim 108, wherein the one or more biological samples of the subject participating in the space travel are collected during the space travel period before the space travel, during the space travel, after the space travel, or any combination thereof.

113. The method of claim 108, wherein the space travel comprises travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

114. The method of claim 108, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

115. The method of claim 108, wherein the expomic characterization comprises a measure of the temporal dynamics of the one or more expomic features.

116. The method of claim 115, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

117. A method according to claim 116, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

118. The method of claim 117, wherein the one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

119. The method of claim 108, wherein the expomic signature is derived from one or more attractors.

120. The method according to claim 119 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

121. The method of claim 108, wherein the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

122. The method of claim 108, wherein the trained predictive model comprises a classification algorithm.

123. The method of claim 122, wherein the classification algorithm is configured to identify the one or more exposome signatures of the one or more subjects associated with participation in space travel.

124. The method of claim 122, wherein the classification algorithm is trained using one or more biological sample exposome characteristics and corresponding responses of the one or more subjects, wherein the one or more subjects participated in space travel.

125. The method of claim 122, wherein the classification algorithm is configured to determine whether the one or more subjects have metabolic characteristics associated with space travel.

126. The method of claim 108, wherein the response comprises a state of the subject's metabolism, wherein the subject's metabolism comprises a healthy state or a state consistent with a subject undergoing space travel.

127. A method for training a predictive model to determine a subject's clinical response to an intervention while the subject is participating in space travel, the method comprising: (a) receiving one or more biological samples and clinical responses of a first set of subjects and a second set of subjects, wherein the first set of subjects were exposed to space travel and provided with an intervention, and wherein the second set of subjects were exposed to space travel and did not receive the intervention; (b) determining one or more exposome signatures of the one or more biological samples of the first set of subjects and the second set of subjects; (c) calculating a first expomic characteristic set and a second expomic characteristic set of one or more expomic features for the first set of subjects and the second set of subjects, wherein each characteristic of the first expomic characteristic set and the second expomic characteristic set comprises one or more quantitative metrics; as well as (a) outputting a trained prediction model configured to determine a subject's clinical response to an intervention, wherein the subject is participating in space travel, and wherein the trained prediction model is trained on one or more quantitative measures of one or more expomic properties and corresponding clinical responses for the first set of subjects and the second set of subjects.

128. The method of claim 127, wherein the intervention prevents negative side effects of space travel.

129. The method of claim 127, wherein the intervention is provided to the first set of subjects before, during, after, or any combination thereof, of the space travel to which the subjects have been exposed.

130. The method of claim 127, further comprising determining an effect of space travel on the first set of subjects exposed to the intervention at least in part by comparing one or more quantitative metrics of the first expomic characteristic set and the second expomic characteristic set.

131. The method of claim 127, wherein the intervention comprises diet, medication, food bars, or any combination thereof.

132. The method of claim 131, wherein the diet comprises iodine supplementation.

133. The method of claim 127, wherein the first expomic characteristic set or the second expomic characteristic set comprises a measure of the temporal dynamics of the one or more expomic features.

134. The method of claim 133, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

135. A method according to claim 133, wherein the measurement of the temporal dynamics includes determining one or more of the recursive quantization analysis parameters, wherein the one or more of the recursive quantization analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

136. The method of claim 127, wherein the one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

137. The method of claim 127, wherein the one or more biological samples comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

138. The method of claim 127, wherein the first expomic feature set or the second expomic feature set is derived from one or more attractors.

139. The method according to claim 138 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

140. The method of claim 127, wherein the first set of subjects or the second set of subjects are humans, non-human mammals, or any combination thereof.

141. A method for generating a predictive model, the predictive model configured to output a health state of one or more objects in response to gravity in addition to the earth's gravity, the method comprising: (a) receiving one or more biological samples and corresponding health states of a first one or more subjects, wherein the first one or more subjects are exposed to gravity in addition to exposure to Earth's gravity; (b) determining one or more exposome profiles of one or more biological samples of said first one or more subjects; (c) calculating an expomic property of one or more expomic features of the one or more biological samples; as well as (d) generating a trained prediction model, wherein the trained prediction model is configured to output a health status of the second one or more objects in response to gravity in addition to the earth's gravity, wherein the trained prediction model is trained with the exposure genomic characteristics and corresponding health status of the one or more exposure genomic features of the first one or more objects.

142. The method of claim 141, wherein the gravitational force comprises at least 1G, at least 2G, at least 3G, or at least 4G.

143. The method of claim 141, wherein the trained predictive model is configured to determine a likelihood that the expomic characteristic is associated with a metabolic signature of the first one or more subjects or the second one or more subjects exposed to gravity in addition to exposure to Earth's gravity.

144. The method of claim 141, wherein the trained prediction model indicates whether the metabolism of the first one or more subjects or the second one or more subjects is at a baseline condition that is not associated with exposure to gravity in addition to not being associated with exposure to Earth's gravity.

145. The method of claim 141, wherein each of the exposome profiles comprises one or more quantitative metrics.

146. The method of claim 141, wherein the one or more biological samples comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

147. The method of claim 141, wherein the expomic characterization comprises a measurement of the temporal dynamics of the one or more expomic features.

148. The method of claim 147, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

149. A method according to claim 147, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

150. The method of claim 141, wherein the one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

151. The method of claim 141, wherein the exposome profile is derived from one or more attractors.

152. The method according to claim 151 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

153. The method of claim 141, wherein the first one or more subjects and the second one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

154. The method of claim 141, wherein the trained predictive model comprises a classification algorithm.

155. The method of claim 154, wherein the classification algorithm is configured to identify the one or more exposome signatures of the first one or more objects or the second one or more objects that are associated with exposure to gravity in addition to exposure to Earth's gravity.

156. The method of claim 154, wherein the classification algorithm is trained using the exposure genomic characteristics and corresponding health states of one or more biological samples of the first one or more subjects, wherein the first one or more subjects are exposed to the gravity in addition to exposure to the Earth's gravity.

157. The method of claim 154, wherein the classification algorithm is configured to determine whether the first one or more subjects or the second one or more subjects have a metabolic signature associated with exposure to gravity in addition to exposure to Earth's gravity.

158. The method of claim 154, wherein the health state comprises a state of metabolism of the first one or more subjects or the second one or more subjects, wherein the metabolism of the first one or more subjects or the second one or more subjects comprises a health state or a state consistent with a subject being exposed to the gravity in addition to being exposed to the Earth's gravity.

159. A method for using a predictive model to output health outcomes for one or more subjects prior to exposure to gravity, the method comprising: (a) receiving one or more biological samples from one or more subjects; (b) determining one or more exposome profiles of one or more biological samples of the one or more subjects; (c) calculating an expomic property of one or more expomic features of the one or more biological samples; as well as (d) outputting health outcomes for the one or more subjects prior to exposure to gravity in addition to prior to exposure to Earth's gravity by providing the expomic characteristics of the one or more subjects as input to a trained predictive model.

160. The method of claim 159, wherein the gravitational force comprises at least 1G, at least 2G, at least 3G, or at least 4G.

161. The method of claim 159, wherein the trained predictive model is configured to determine a likelihood that the exposome signature is associated with the subject's exposure to the gravitational force.

162. The method of claim 161, wherein a probability threshold determined by the trained prediction model indicates that the metabolism of one or more subjects is at a baseline condition that is not associated with exposure to Earth's gravity in addition to not being associated with exposure to said gravity.

163. The method of claim 159, wherein each of the exposome profiles comprises one or more quantitative metrics.

164. The method of claim 159, wherein the one or more biological samples comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

165. The method of claim 159, wherein the expomic characterization comprises a measurement of the temporal dynamics of the one or more expomic features.

166. The method of claim 165, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

167. A method according to claim 165, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

168. The method of claim 159, wherein the one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

169. The method of claim 159, wherein the exposome profile is derived from one or more attractors.

170. The method according to claim 169 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

171. The method of claim 159, wherein the one or more subjects comprise a human, a non-human mammal, a vertebrate, or any combination thereof.

172. The method of claim 159, wherein the trained predictive model comprises a classification algorithm.

173. The method of claim 172, wherein the classification algorithm is configured to identify the one or more exposome signatures of the subject that are associated with exposure to gravity in addition to exposure to Earth's gravity.

174. The method of claim 172, wherein the classification algorithm is trained using the exposome characteristics and corresponding responses of one or more biological samples of one or more subjects, wherein the one or more subjects are exposed to gravity in addition to exposure to Earth's gravity.

175. The method of claim 172, wherein the classification algorithm is configured to determine whether the one or more subjects have a metabolic signature associated with exposure to gravity in addition to exposure to Earth's gravity.

176. A method for administering a therapy to a subject participating in space travel, the method comprising: (a) receiving one or more biological samples from a subject prior to participating in space travel; (b) determining one or more exposome profiles of the one or more biological samples of the subject; (c) calculating an exposome profile of one or more exposome characteristics of the subject, wherein each of the exposome profiles comprises one or more quantitative metrics; as well as (d) administering a treatment to the subject participating in space travel at a point in time, wherein when the exposome characteristics of the subject are provided as input to a trained predictive model, the treatment and the point in time at which the treatment is administered are determined as outputs of the trained predictive model.

177. The method of claim 176, wherein the trained predictive model is configured to output the effect of the treatment.

178. The method of claim 176, wherein the trained predictive model is configured to output a measure of whether the treatment is effective.

179. The method of claim 176, wherein the time point comprises a time before the subject participates in the space travel, a time during the subject participates in the space travel, a time after the subject participates in the space travel, or any combination thereof.

180. The method of claim 176, wherein the treatment is provided to mitigate negative side effects of participating in space travel.

181. The method of claim 177, wherein the effect of the treatment is determined by a duration after the subject has participated in space travel, after which the subject's exposome profile comprises a baseline exposome profile of a subject who has not participated in space travel.

182. The method of claim 176, wherein the trained predictive model comprises a classification algorithm.

183. The method of claim 176, wherein the space travel comprises travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

184. The method of claim 176, wherein the one or more biological samples comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

185. The method of claim 176, wherein the expomic characterization comprises a measurement of the temporal dynamics of the one or more expomic features.

186. The method of claim 185, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

187. A method according to claim 185, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

188. The method of claim 176, wherein the one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

189. The method of claim 176, wherein the exposome signature is derived from one or more attractors.

190. The method according to claim 189 also includes analyzing the dynamic relationship between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof of the one or more attractors.

191. The method of claim 176, wherein the subject is a human, a non-human mammal, or any combination thereof.

192. The method of claim 176, wherein the trained predictive model comprises a classification algorithm.

193. The method of claim 192, wherein the classification algorithm is trained using the exposome characteristics of one or more biological samples of one or more subjects, the treatments administered, the time points at which the treatments were administered, and the corresponding clinical responses, wherein the one or more subjects participated in space travel.

194. The method of claim 192, wherein the classification algorithm is configured to determine whether one or more subjects have a metabolic disorder caused by the space travel.

195. A method for evaluating a response of one or more subjects to an intervention in the presence or absence of gravity, the method comprising: (a) sampling each corresponding position of a plurality of positions along a reference line of a first one or more biological sample sets of a first subject set and a second one or more biological sample sets of a second subject set, thereby obtaining a plurality of ion samples, each ion sample of the plurality of ion samples corresponding to a different position of the plurality of positions, and each position of the plurality of positions representing a different growth period of the first one or more biological sample sets and the second one or more biological sample sets, wherein the first subject set undergoes space travel and the second subject set does not undergo space travel, and wherein an intervention is administered to the first subject set and the second subject set; (b) analyzing each of the plurality of ion samples with a mass spectrometer to obtain a first data set including a plurality of traces, each of the plurality of traces being a concentration of a corresponding element isotope of a plurality of element isotopes over time, the concentrations being collectively determined by the plurality of ion samples; (c) deriving a second data set comprising a set of characteristics from the plurality of traces, each corresponding characteristic in the set of characteristics being determined by the variation of a single isotope or a combination of isotopes in the plurality of traces; as well as (d) inputting the set of features into a trained classifier to obtain a probability of predicting the reaction of the one or more objects to the space travel from the trained classifiers of the first one or more sets of objects and the second one or more sets of objects.

196. The method of claim 195, wherein the probability indicates that a biological sample of the one or more biological samples is disordered due to the space travel.

197. A method according to claim 196, wherein the effect of the intervention is measured by the probability of disorder.

198. The method of claim 195, wherein the intervention comprises diet, medication, or any combination thereof.

199. The method of claim 198, wherein the diet comprises iodine supplementation.

200. The method of claim 195, wherein the intervention improves radiation exposure.

201. The method of claim 195, wherein the first set of subjects or the second set of subjects comprises humans, non-human mammals, or any combination thereof.

202. The method of claim 201, wherein the non-human mammal comprises a mouse.

203. The method of claim 195, wherein the first one or more biological sample sets or the second one or more biological sample sets comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

204. The method of claim 195, comprising treating the first one or more biological sample sets or the second one or more biological sample sets with a solvent or a surfactant prior to the sampling.

205. The method of claim 195, comprising irradiating the first one or more biological sample sets or the second one or more biological sample sets with a low-power laser before the sampling to remove debris from the first one or more biological sample sets or the second one or more biological sample sets.

206. The method of claim 195, wherein the sampling comprises irradiating the first one or more biological sample sets or the second one or more biological sample sets of the first object set or the second object set with a laser, thereby extracting a plurality of particles from the first one or more biological sample sets or the second one or more biological sample sets, and ionizing the plurality of particles using an inductively coupled plasma mass spectrometer, thereby obtaining the plurality of ion samples.

207. The method of claim 195, wherein the response of the first set of subjects or the second set of subjects comprises an adverse response, a positive response, or a neutral response.

208. The method of claim 195, wherein the trained classifier is configured to recognize a set of characteristics of an object associated with participation in space travel.

209. The method of claim 195, wherein the trained classifier is trained using a set of characteristics and corresponding responses of one or more biological samples of the first set of subjects.

210. The method of claim 195, wherein the trained classifier is configured to determine whether a subject comprises a metabolic disorder resulting from the space travel.

211. A method for outputting a subject's response to an intervention following space travel, the method comprising: (a) exposing one or more biological samples of a subject to a light signal comprising a plurality of Raman wavelengths, wherein the subject undergoes space travel; (b) acquiring a plurality of Raman spectra from the one or more biological samples; (c) processing the plurality of Raman spectra to generate a plurality of Raman features; as well as (d) outputting a probability of the subject's response to the intervention while undergoing space travel by providing the plurality of Raman characteristics as input to a trained classifier.

212. The method of claim 211, wherein the probability indicates that the subject is deconditioned as a result of the space travel.

213. A method according to claim 211, wherein the effect of the intervention is measured by the probability of disorder.

214. The method of claim 211, wherein the one or more biological samples comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

215. The method of claim 211, wherein the intervention comprises diet, medication, or any combination thereof.

216. The method of claim 215, wherein the diet comprises iodine supplementation.

217. The method of claim 215, wherein the intervention improves radiation exposure.

218. The method of claim 215, wherein the subject comprises a human, a non-human mammal, or any combination thereof.

219. The method of claim 218, wherein the non-human mammal comprises a mouse.

220. The method of claim 211, wherein the plurality of Raman spectra comprises from about 200 to about 3700 wave numbers.

221. A method according to any one of claims 211, wherein acquiring includes using a Raman spectroscopic microscope.

222. The method of claim 211, wherein the plurality of Raman characteristics comprises one or more traces derived from a Raman spatial map.

223. The method of claim 222, wherein a dimensionality reduction algorithm is used to reduce the one or more traces, wherein the dimensionality reduction algorithm comprises principal component analysis, independent component analysis, or any combination thereof.

224. The method of claim 223, wherein a measure of temporal dynamics is derived from the one or more traces, wherein the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

225. A method according to claim 224, wherein the measurement of the temporal dynamics includes determining one or more of the recursive quantization analysis parameters, wherein the one or more of the recursive quantization analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

226. A method according to claim 211, wherein the trained classifier comprises a gradient boosting ensemble model.

227. A method according to claim 211, wherein the trained classifier is configured to process one or more characteristics selected from hierarchicality, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, determinism, Lmax and any combination thereof.

228. A method according to claim 211, wherein the trained classifier is configured to process two or more characteristics selected from hierarchicality, entropy, capture time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof.

229. The method of claim 211, wherein the trained classifier is trained to recognize Raman signatures associated with space travel.

230. The method of claim 211, wherein the output probability of the trained classifier indicates that the object is exhibiting temporal dynamics associated with space travel.

231. The method of claim 211, wherein the trained classifier is trained using a plurality of Raman properties of a population of humans or non-human animals that have experienced space travel.

232. The method of claim 211, wherein the effect of the intervention is measured by a change in the subject's temporal dynamics toward the subject's baseline temporal dynamics prior to space travel.

233. A method for evaluating a subject's response to an intervention to reduce the biological effects of space travel, the method comprising: (a) staining a first one or more biological sample sets of a first subject set and a second one or more biological sample sets of a second subject set to produce the stained first one or more biological sample sets and the second one or more biological sample sets, wherein the first subject set has undergone space travel and the second subject set has not undergone space travel, and wherein an intervention is administered to the first subject set and the second subject set; (b) analyzing the fluorescence intensity spatially across the stained first set of one or more biological samples and the second set of one or more biological samples; as well as (c) calculating a set of properties of spatial fluorescence intensity across the stained first one or more biological sample sets and second one or more biological sample sets for the first set of objects and the second set of objects, wherein each of the exposome properties of the first set of objects and the second set of objects comprises one or more metrics that quantify temporal dynamics in the fluorescence intensity traces; as well as (d) outputting as an output of the predictive model a probability of the subject's response to an intervention to reduce the biological effect of space travel when the trained predictive model is provided with the subject's expomic characteristics of spatial fluorescence intensity across one or more biological samples stained for the subject, wherein the trained predictive model is trained with one or more metrics of the expomic characteristics of the first set of subjects and the second set of subjects, the corresponding administered interventions, and the clinical responses to the administered interventions.

234. The method of claim 233, wherein the trained predictive model is configured to determine whether the subject's biological sample has become disordered by space travel.

235. A method according to claim 233, wherein the trained predictive model is configured to determine the effect of the intervention based at least in part on analysis of the fluorescence intensity spatially across one or more biological samples as a measure of the intervention effectiveness.

236. The method of claim 233, wherein the first one or more biological sample sets or the second one or more biological sample sets comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

237. The method of claim 233, wherein the intervention comprises diet, medication, or any combination thereof.

238. The method of claim 237, wherein the diet comprises iodine supplementation.

239. The method of claim 233, wherein the intervention improves radiation exposure.

240. The method of claim 233, wherein the first set of subjects or the second set of subjects comprises humans, non-human mammals, or any combination thereof.

241. The method of claim 240, wherein the non-human mammal comprises a mouse.

242. The method of claim 233, wherein the trained prediction model comprises a gradient boosting ensemble model.

243. The method of claim 233, wherein the measuring of the temporal dynamics of the spatial fluorescence intensity trace comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

244. A method according to claim 233, wherein the measurement of the temporal dynamics includes determining one or more of the recursive quantization analysis parameters, wherein the one or more of the recursive quantization analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

245. The method of claim 233, wherein the analysis comprises obtaining fluorescent images of one or more sets of biological samples stained by the first stain and one or more sets of biological samples stained by the second stain, and analyzing the spatial fluorescence intensity of the fluorescent images.

246. The method of claim 233, wherein obtaining a fluorescent image of the stained dental sample comprises using an inverted or non-inverted confocal microscope.

247. The method of any one of claims 233-246, wherein staining the first one or more biological sample sets of the first set of subjects and the second set of subjects comprises using C-reactive staining.

248. The method of any one of claims 233-246, further comprising slicing the tooth sample.

249. The method of claim 233, wherein staining the first one or more biological sample sets and the second one or more biological sample sets comprises decalcifying the first one or more biological sample sets and the second one or more biological sample sets.

250. The method of claim 233, wherein the trained model is selected from a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

251. The method of claim 233, wherein the trained model comprises a gradient boosted decision tree.

252. The method of claim 233, wherein the trained predictive model comprises a classification algorithm.

253. The method of claim 252, wherein the classification algorithm is trained to recognize characteristics in spatial fluorescence intensity associated with the space travel.

254. A method for outputting one or more pathways associated with space travel, the method comprising: (a) receiving one or more biological samples from a first one or more sets of subjects and a second one or more sets of subjects, wherein the first one or more sets of subjects have participated in space travel and the second one or more sets of subjects have not participated in space travel; (b) determining an expomic signature of one or more pathways from the one or more biological samples of the first one or more sets of subjects and the second one or more sets of subjects; (c) calculating an expomic property of the expomic signature of the one or more pathways; as well as (d) outputting the one or more pathways associated with space travel by comparing the expomic characteristics of the one or more pathways of the first subject set and the second subject set.

255. The method of claim 254, wherein each of the exposome characteristics comprises one or more quantitative metrics.

256. A method according to claim 254, wherein the space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the earth.

257. The method of claim 254, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

258. The method of claim 254, wherein the expomic characteristics comprise measurements of the temporal dynamics of the one or more expomic features.

259. The method of claim 258, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

260. A method according to claim 258, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

261. The method of claim 254, wherein the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

262. The method of claim 254, wherein the exposome signature is derived from one or more attractors.

263. The method according to claim 260 also includes analyzing the dynamic relationship between the one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof.

264. The method of claim 254, wherein the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

265. A method for providing treatment to ameliorate the effects of space travel, the method comprising: (a) receiving one or more biological samples from one or more subjects participating in space travel; (b) determining an expomic signature of one or more pathways from the one or more biological samples of the one or more subjects; (c) calculating an expomic property of the expomic signature of the one or more pathways; and (d) providing treatment to the one or more subjects, wherein the treatment ameliorates the effect of the space travel on the expomic properties of the expomic signature of the one or more pathways of the one or more subjects.

266. The method of claim 265, wherein the treatment comprises diet, medication, nutritional supplements, food bars, or any combination thereof.

267. The method of claim 265, wherein each of the exposome characteristics comprises one or more quantitative metrics.

268. A method according to claim 265, wherein the space travel includes travel across the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the earth.

269. The method of claim 265, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

270. The method of claim 265, wherein the expomic properties comprise measurements of the temporal dynamics of the one or more expomic features.

271. The method of claim 270, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

272. A method according to claim 270, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

273. The method of claim 265, wherein the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

274. The method of claim 265, wherein the exposome signature is derived from one or more attractors.

275. The method according to claim 272 also includes analyzing the dynamic relationship between the one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof.

276. The method of claim 265, wherein the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

277. A method for outputting one or more pathways associated with an intervention, the method comprising: (a) receiving one or more biological samples from a first one or more subject sets and a second one or more subject sets, wherein the first one or more subject sets have undergone an intervention and the second one or more subject sets have not undergone an intervention; (b) determining an expomic signature of one or more pathways from the one or more biological samples of the first one or more sets of subjects and the second one or more sets of subjects; (c) calculating an expomic property of the expomic signature of the one or more pathways; as well as (d) outputting the one or more pathways associated with the intervention by comparing the expomic characteristics of the one or more pathways of the first set of subjects and the second set of subjects.

278. The method of claim 277, wherein each of the exposome characteristics comprises one or more quantitative metrics.

279. The method of claim 277, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

280. The method of claim 277, wherein the expomic properties comprise measurements of the temporal dynamics of the one or more expomic features.

281. The method of claim 280, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

282. A method according to claim 280, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

283. The method of claim 277, wherein the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

284. The method of claim 277, wherein the exposome signature is derived from one or more attractors.

285. The method according to claim 282 also includes analyzing the dynamic relationship between the one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof.

286. The method of claim 277, wherein the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

287. The method of claim 277, wherein the intervention comprises a medication, a diet, a food bar, a nutritional supplement, a metal ion supplement, or any combination thereof.

288. A method for determining an expomic profile of a subject associated with space travel, the method comprising: (a) receiving one or more biological samples from a subject; (b) determining an expomic signature of one or more pathways from the one or more biological samples; (c) calculating an expomic property of the expomic signature of the one or more pathways; and (d) outputting the expomic profile of the subject associated with space travel by comparing the expomic profile of the subject with a space travel expomic profile database.

289. The method of claim 288, wherein each of the exposome characteristics comprises one or more quantitative metrics.

290. The method of claim 288, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

291. The method of claim 288, wherein the expomic characteristics comprise measurements of the temporal dynamics of the one or more expomic features.

292. The method of claim 291, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

293. A method according to claim 291, wherein the measurement of the temporal dynamics includes determining one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

294. The method of claim 288, wherein the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

295. The method of claim 288, wherein the exposome signature is derived from one or more attractors.

296. The method according to claim 293 also includes analyzing the dynamic relationship between the one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof.

297. The method of claim 288, wherein the subject is a human, a non-human mammal, a vertebrate, or any combination thereof.

298. A method for determining an exposome profile of a subject associated with space travel using a trained predictive model, the method comprising: (a) receiving one or more biological samples from a subject; (b) determining an expomic signature of one or more pathways from the one or more biological samples; (c) calculating an expomic property of the expomic signature of the one or more pathways; as well as (d) outputting the expomic signature of the subject associated with space travel by providing the expomic signature of the subject as input to a trained prediction model, wherein the trained prediction model is trained on the expomic signatures of one or more subjects who have participated in space travel.

299. The method of claim 298, wherein each of the exposome characteristics comprises one or more quantitative metrics.

300. The method of claim 298, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

301. The method of claim 298, wherein the expomic properties comprise measurements of the temporal dynamics of the one or more expomic features.

302. The method of claim 301, wherein the measuring of the temporal dynamics comprises: Determine a linear slope, determine multiple nonlinear parameters describing the curvature of the one or more exposure genomic features, determine a sudden change in the intensity of the one or more exposure genomic features, determine one or more changes in the baseline intensity of the one or more exposure genomic features, determine a change in the frequency domain representation of the one or more exposure genomic features, determine a change in the power spectrum domain representation of the one or more exposure genomic features, determine one or more recursive quantification analysis parameters, determine one or more cross-recursive quantification analysis parameters, determine one or more joint recursive quantification analysis parameters, determine one or more multidimensional recursive quantification analysis parameters, estimate a Lyapunov spectrum, determine a maximum Lyapunov exponent, or any combination thereof.

303. A method according to claim 301, wherein the measurement of the temporal dynamics includes determining one or more recursive quantization analysis parameters, wherein the one or more recursive quantization analysis parameters include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, or the number of most likely recursions.

304. The method of claim 298, wherein the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, immunohistochemistry fluorescence, or any combination thereof.

305. The method of claim 298, wherein the exposome signature is derived from one or more attractors.

306. The method according to claim 303 also includes analyzing the dynamic relationship between the one or more signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in number of recursions, the most likely number of recursions or any combination thereof.

307. The method of claim 298, wherein the subject is a human, a non-human mammal, a vertebrate, or any combination thereof.

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