Systems and methods for space health exposomics

Abstract

Provided herein are methods and systems configured to analyze exposome characteristics of a space-traveling subject.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 328,164, filed April 6, 2022, which is incorporated by reference in its entirety. [Background technology]

[0002] The immediate and lasting effects of space travel on mammals, including humans, are unknown. With the dawn of the space exploration era, 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 resulting effects of space travel and any interventions that ameliorate the negative effects of space travel. Summary of the Invention

[0003] In addition to the apparent macroscopic physiological and / or anatomical effects endured by long-term space travel, the effects of space travel and / or temporary space travel on the human body are not well understood. In order to sustain long-term space travel and / or frequent exposure to space travel, there is an unmet need for a platform that can measure the effects of space or simulated space travel on human physiological and / or anatomical structures. The disclosure provided herein may, in some cases, describe a method and system for analyzing the exposome signature of a subject affected by space travel and / or simulated space travel. In some cases, the disclosure provided herein describes a method and system for determining the effect of an intervention and / or diet on one or more exposome signatures and / or exposome features of a subject, which may be used to counteract the effects of space travel on one or more exposome signatures and / or exposome features of the subject.

[0004] The present disclosure addresses these needs through systems and methods that can analyze and classify subjects by their exposome biochemical signatures. Exposome biochemical signature analysis can non-invasively analyze over 50,000 biochemical signatures from a single hair shaft, tooth, and nail sample. Using such analysis systems and methods of the present disclosure, subtle changes in a subject's biochemistry induced by exposure to diet, air pollution, space travel, psychological stress, pesticides, industrial chemicals, etc. can be investigated and correlated with positive responses and outcomes of targeted pharmaceuticals.

[0005] In addition to the large amount of data sets generated by exposome biochemical signature analysis, exposome biochemical signature analysis can also provide insight into the temporal variation of the above biochemical signatures over the lifespan of a subject. Such an approach can be utilized to screen individuals who participate in space travel and / or simulated space travel to determine which single exposome biochemical signatures, or combinations of exposome 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 can predict whether a subject's exposome signature and / or features are associated with space travel.

[0006] Aspects of the disclosure disclosed herein provide a computer-implemented exposomics system including: (a) an exposome biochemical signature database (EDB), the EDB including a plurality of exposome features of a plurality of subjects who have and have not participated in space travel; (b) a clinical database (CDB) including clinical phenotype information for a plurality of subjects who have participated in space travel; (c) an intervention outcome database (IODB) including information about intervention outcome information of subjects who received an intervention after, during, and / or prior to participating in space travel; and (d) a computer processor including: (i) an association software module communicatively connected to the EDB, CDB, and IODB programmed to determine an association between at least one exposome feature of the plurality of subjects, the clinical phenotype information, and the intervention outcome information; and (ii) a recommendation software module communicatively connected 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 features, the clinical phenotype information, the intervention outcome information for at least one of the plurality of subjects, and associations between the exposome features, the clinical phenotype information, and the intervention outcome information.

[0007] In some embodiments, the exposome characteristics include at least 10, at least 100, at least 1,000, or at least 10,000 distinct exposome biochemical signatures. In some embodiments, the intervention outcome information includes classification of non-responders, adverse responders, and positive responders for at least one intervention that mitigates the negative effects of space travel. In some embodiments, the intervention outcome includes one or more inclusion or exclusion criteria for the at least one intervention. In some embodiments, the exposome characteristics are obtained by assaying biological samples of a plurality of subjects. In some embodiments, the biological samples include tooth samples, nail samples, hair samples, or any combination thereof. In some embodiments, the assaying 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 chemicals. In some embodiments, 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. In some embodiments, the exposome features include dynamic temporal biochemical reactions of a plurality of subjects. In some embodiments, the exposome biochemical signature includes a fluorescence image of the biological sample. In some embodiments, the exposome biochemical signature includes a spatial map of Raman spectra of the biological sample. In some embodiments, the exposome biochemical signature is associated with space travel and / or simulated space travel. In some embodiments, the exposome features are analyzed using trained statistical classifiers, machine learning classifiers, and / or artificial intelligence classifiers to determine relevance to space travel.In some embodiments, the classifier is selected from the group consisting of 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.

[0008] Aspects of the present disclosure disclosed herein describe a method for selecting subjects for intervention to participate in space travel, the method comprising: (a) providing a trained predictive model, the trained predictive model being trained on one or more of the subjects' clinical metadata, exposome features, and corresponding intervention outcome information; (b) detecting biochemical signatures obtained from biological samples of the subjects seeking intervention, thereby generating a prospective exposome feature; (c) predicting predicted intervention outcome information of the subjects seeking intervention using the trained predictive model, the exposome features and clinical meta of the subjects seeking intervention being inputs to the trained predictive model; and (d) selecting subjects for intervention or excluding subjects from intervention based at least in part on the subjects' predicted intervention outcome information.

[0009] In some embodiments, the biochemical signature is obtained by assaying a 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 assaying 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 chemicals. In some embodiments, 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. 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 a Raman spectrum of the biological sample. In some embodiments, the exposome biochemical signature is associated with space travel and / or simulated space travel. In some embodiments, the trained predictive model is selected from the group consisting of 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 enrolling the subject in the intervention when the subject is selected for the intervention. In some embodiments, the method further comprises evaluating the subject for another intervention when the subject is excluded from the intervention.

[0010] An embodiment of the disclosure is a method for selecting optimal treatments for subjects participating in space travel and / or simulated space travel, comprising the steps of: (a) detecting one or more biochemical signatures obtained from one or more biological samples from one or more subjects not participating in space travel, thereby generating one or more reference exposome features; (b) detecting features of one or more biochemical signatures obtained from one or more biological samples from subjects who participated in space travel, thereby generating one or more pre-treatment exposome features; (c) administering a treatment to the subjects who participated in space travel; and (d) evaluating the one or more biochemical signatures obtained from the one or more biological samples from the one or more subjects who participated in space travel after a period of time has elapsed since receiving the treatment. (e) determining a difference between one or more reference exposome characteristics of one or more subjects not participating in space travel, one or more pre-treatment exposome characteristics of one or more subjects participating in space travel, and one or more post-treatment exposome characteristics of one or more subjects participating in space travel; and (f) selecting one or more optimal treatments based at least in part on the determined difference between the one or more reference exposome characteristics, the one or more pre-treatment exposome characteristics, and the one or more post-treatment exposome characteristics, wherein the one or more optimal treatments are selected based on the determined difference meeting a predetermined criterion. In some embodiments, the optimal treatment comprises a drug, a functional food, or any combination thereof. In some embodiments, the predetermined criterion comprises a difference between the one or more pre-treatment exposome characteristics and the one or more post-treatment exposome characteristics relative to the one or more reference exposome characteristics. In some embodiments, the period of time comprises at least about one hour, at least about one day, at least about one week, at least about one month, at least about one year, or any combination thereof. In some embodiments, the difference comprises at least a 10% change in one or more post-treatment exposome characteristics relative to one or more reference exposome characteristics.In some embodiments, the pre-treatment exposome characteristics, the post-treatment exposome characteristics, or any combination thereof, are obtained by assaying a 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 assaying 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 chemicals. In some embodiments, the one or more chemicals include chemicals of 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 differences between the reference exposome features, the pre-space travel exposome features, the post-space travel exposome features, or any combination thereof are analyzed using a trained statistical classifier, a machine learning classifier, and / or an artificial intelligence classifier. In some embodiments, the trained classifier is selected from the group consisting of 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.

[0011] The present disclosure provides improved systems and methods for accurately determining the effects of space travel and / or simulated space travel based on the analysis of dynamic biological response data from a biological sample obtained non-invasively from a subject. Such improved systems and methods for accurate determination of one or more exposome signatures and / or exposome features may be based on a combination of dynamic immunohistochemical profiling of the biological sample and artificial intelligence data analysis of such dynamic profiles to evaluate how space travel affects the exposome signatures and / or features. The present disclosure addresses these needs, for example, by providing biological sample biomarkers for analyzing one or more exposome signatures and / or features associated with space travel. The biological sample includes a human biological sample associated with additional growth. In non-limiting embodiments, 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 for the diagnosis of young children, and even infants younger than 1 year old. In some embodiments, f includes health metadata as described elsewhere herein. In some embodiments, one or more exposome features and / or analysis of exposome features affected by space travel and / or simulated space travel may include analyzing one or more physiological parameters including parameters measured during a blood test, e.g., cholesterol, white blood cell count, red blood cell count, hematocrit, presence or absence of bacterial infection, etc.

[0012] In some embodiments, the analyzing step determines the underlying temporal dynamics of the biological process. In some embodiments, the analyzing step includes obtaining a fluorescent image of the stained tooth sample and analyzing the fluorescent intensity of the fluorescent image. In some embodiments, the fluorescent 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 a C-reactive protein immunohistochemical stain. In some embodiments, the method further includes sectioning the tooth sample. In some embodiments, staining the tooth sample includes (1) cutting the tooth sample, (2) demineralizing the tooth sample, (3) sectioning the demineralized sample, (4) staining the demineralized tooth sections with primary and secondary antibodies, (5) measuring spatial antibody fluorescence with a confocal microscope, and / or (6) extracting a temporal profile of the fluorescent intensity.

[0013] In one aspect, the present disclosure provides a method for outputting a response of one or more subjects to participating in a space trip, the method comprising: receiving one or more biological samples of one or more subjects participating in a space trip; determining one or more exposome signatures of the biological samples; calculating an exposome feature of the one or more exposome signatures of the one or more biological samples; and outputting a response of one or more subjects to participating in a space trip by providing the exposome feature of the one or more biological samples or by utilizing the exposome feature of the one or more biological samples as an input to a trained predictive model, providing a binary indicator of a biological response to the space trip. In some embodiments, the trained predictive model is configured to determine a likelihood that the exposome feature is associated with a metabolic signature of one or more subjects participating in the space trip. In some embodiments, the space trip includes traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface. In some embodiments, the trained predictive model is configured to classify whether the samples of the one or more subjects are consistent with a real or simulated space trip. In some embodiments, the one or more biological samples of a subject participating in a space trip are collected before, during, after, or any combination of these periods of space trip. In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0014] In some embodiments, each feature of the exposome features comprises one or more quantitative metrics. In some embodiments, the exposome features comprise a measure of the temporal dynamics of one or more exposome signatures. In some embodiments, the measure of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recurrence quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of the most probable recurrences.

[0015] 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, fluorescent immunohistochemical staining, or any combination thereof. In some embodiments, the exposome features are derived from the analysis of one or more attractors. In some embodiments, the method further comprises analyzing the dynamic relationship between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof, of one or more attractors.

[0016] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0017] In some embodiments, the trained predictive model includes a classification algorithm. In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of one or more subjects associated with participation in space travel. In some embodiments, the classification algorithm is trained using exposome features of one or more biological samples of one or more subjects and corresponding responses, where the one or more subjects have participated in space travel. In some embodiments, the classification algorithm is configured to determine whether the one or more subjects have a metabolic signature associated with space travel.

[0018] In some embodiments, the response comprises a state of the subject's metabolism, the subject's metabolism comprising a healthy state or a state consistent with a subject undergoing space travel.

[0019] In one aspect, the disclosure provides a method for training a predictive model to determine a subject's clinical response to an intervention when the subjects are 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, where the first set of subjects are exposed to space travel and are provided with the intervention, and the second set of subjects are exposed to space travel and do not receive the intervention; determining one or more exposome signatures of the one or more biological samples of the first and second set of subjects; calculating first and second sets of exposome features of the one or more exposome signatures of the first and second set of subjects, where the first and second sets of exposome features include a quantitative metric; and outputting a trained predictive model configured to determine the subject's clinical response to the intervention, where the subjects are participating in space travel, and the trained predictive model is trained with the one or more quantitative metrics of the one or more exposome features of the first and second set of subjects and corresponding clinical responses.

[0020] In some embodiments, the intervention prevents negative side effects of space travel, hi some embodiments, the intervention is provided to a first set of subjects before, during, after, or any combination of space travel to which the subjects are exposed.

[0021] In some embodiments, the method further comprises determining the impact 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 and second sets of exposome features. In some embodiments, the intervention comprises diet, pharmaceutical, therapeutic, or any combination thereof. In some embodiments, the diet comprises iodine supplementation.

[0022] In some embodiments, the first set or the second set of exposome features comprises a measurement of the temporal dynamics of one or more exposome signatures. In some embodiments, the measurement of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0023] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0024] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0025] In some embodiments, the first set or the second set of exposome features are derived from one or more attractors. In some embodiments, the method further comprises analyzing a dynamic relationship between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

[0026] In some embodiments, the first set or the second set of subjects are humans, non-human mammals, or any combination thereof.

[0027] In one aspect, the present disclosure provides a method for generating a predictive model configured to output a health state of one or more subjects as a function of gravity in addition to the gravity of the Earth, the method comprising the steps of receiving one or more biological samples and corresponding health states of a first one or more subjects, the first one or more subjects being exposed to gravity in addition to the gravity of the Earth, determining one or more exposome signatures of the biological samples of the first one or more subjects, calculating exposome features of the one or more exposome signatures of the one or more biological samples, and generating a trained predictive model configured to output a health state as a function of gravity in addition to the gravity of the Earth of a second one or more subjects, the trained predictive model being trained on the exposome features of the one or more exposome signatures of the first one or more subjects and the corresponding health states. In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0028] In some embodiments, the gravity force comprises at least 1 G, at least 2 G, at least 3 G, or at least 4 G.

[0029] In some embodiments, the trained predictive model is configured to determine the likelihood that an exposome feature is associated with a metabolic signature of a first subject or subjects or a second subject or subjects exposed to gravity in addition to the Earth's gravity.

[0030] In some embodiments, a probabilistic threshold determined by a trained predictive model indicates that the metabolism of the first one or more subjects or the second one or more subjects is in a baseline state that is not associated with exposure to gravity in addition to the Earth's gravity.

[0031] In some embodiments, each feature of the exposome features comprises one or more quantitative metrics. In some embodiments, the exposome features comprise a measure of the temporal dynamics of one or more exposome signatures. In some embodiments, the measure of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0032] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0033] In some embodiments, the exposome features are derived from one or more attractors.

[0034] In some embodiments, the method further comprises analyzing dynamic relationships between one or more attractors' signal recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0035] In some embodiments, the first subject or subjects and the second subject or subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0036] In some embodiments, the trained predictive model comprises a classification algorithm.

[0037] In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of the first one or more subjects and the second one or more subjects associated with exposure to gravity in addition to the Earth's gravity.

[0038] In some embodiments, the classification algorithm is trained using exposome features and corresponding health states of one or more biological samples of a first one or more subjects, where the first one or more subjects have been exposed to gravity in addition to Earth's gravity.

[0039] In some embodiments, the classification algorithm is configured to determine whether the first one or more subjects and the second one or more subjects have a metabolic signature associated with exposure to gravity in addition to the Earth's gravity.

[0040] In some embodiments, the health condition comprises the metabolism of the first one or more subjects and the second one or more subjects, and the metabolism of the first one or more subjects and the second one or more subjects comprises a health condition or a condition consistent with a subject exposed to gravity in addition to the Earth's gravity.

[0041] In one aspect, the present disclosure provides a method for using a predictive model to output a health outcome of one or more subjects prior to exposure to gravity, the method comprising: receiving one or more biological samples of one or more subjects; determining one or more exposome signatures of a first one or more subjects' biological samples; calculating exposome features of the one or more exposome signatures of the one or more biological samples; and outputting a health outcome of the one or more subjects prior to exposure to gravity in addition to the Earth's gravity by providing the exposome features of the one or more subjects as input to a trained predictive model.

[0042] In some embodiments, the gravity force comprises at least 1 G, at least 2 G, at least 3 G, or at least 4 G.

[0043] In some embodiments, the trained predictive model is configured to determine the likelihood that an exposome feature is associated with a metabolic signature of one or more subjects exposed to gravity.

[0044] In some embodiments, a probabilistic threshold determined by the trained predictive model indicates that the metabolism of the first one or more subjects or the second one or more subjects is in a baseline state that is not associated with exposure to gravity in addition to the Earth's gravity.

[0045] In some embodiments, each feature of the exposome feature includes one or more quantitative metrics.

[0046] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0047] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0048] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0049] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0050] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0051] In some embodiments, the exposome features are derived from one or more attractors.

[0052] In some embodiments, the method further comprises analyzing dynamic relationships between one or more attractors' signal recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0053] In some embodiments, the one or more subjects include a human, a non-human mammal, a vertebrate, or any combination thereof.

[0054] In some embodiments, the trained predictive model comprises a classification algorithm.

[0055] In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of the subject that are associated with exposure to gravity in addition to the Earth's gravity.

[0056] In some embodiments, the classification algorithm is trained using exposome features and corresponding responses of one or more biological samples from one or more subjects, where the one or more subjects are exposed to gravity in addition to Earth's gravity.

[0057] 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 the Earth's gravity.

[0058] In one aspect, the present disclosure provides a method for administering a treatment to a subject participating in space travel, the method comprising the steps of receiving one or more biological samples from the subject prior to participating in space travel, determining one or more exposome signatures of the one or more biological samples from the subject, calculating exposome features of the one or more exposome signatures of the subject, each feature of the exposome features comprising one or more quantitative metrics, and administering a treatment to the subject participating in space travel at a time point, wherein the treatment and the time to administer the treatment are determined as an output of a trained predictive model when the trained predictive model is provided with the subject's exposome features as input.

[0059] In some embodiments, the trained predictive model is configured to output an efficacy of a treatment.

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

[0061] In some embodiments, the time points include times before, during, after the subject participates in the space trip, or any combination thereof.

[0062] In some embodiments treatment is provided to mitigate the negative side effects of participating in space travel.

[0063] In some embodiments, the efficacy of treatment is determined by the period after the subject has participated in space travel, and the subject's exposome characteristic comprises a baseline exposome characteristic of a subject not participating in space travel.

[0064] In some embodiments, the trained predictive model comprises a classification algorithm.

[0065] In some embodiments, space travel involves traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

[0066] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0067] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0068] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0069] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0070] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0071] In some embodiments, the exposome features are derived from one or more attractors.

[0072] In some embodiments, the method further comprises analyzing dynamic relationships between one or more attractors' signal recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0073] In some embodiments, the subject is a human, a non-human mammal, or any combination thereof.

[0074] In some embodiments, the trained predictive model comprises a classification algorithm.

[0075] In some embodiments, the classification algorithm is trained using exposome features of one or more biological samples from one or more subjects, the treatments administered, the time points at which the treatments were administered, and the corresponding clinical responses, and one or more subjects participated in space travel.

[0076] In some embodiments, the classification algorithm is configured to determine whether one or more subjects have space travel-induced metabolic dysregulation.

[0077] In one aspect, the disclosure provides a method for assessing the response of one or more subjects to an intervention in the presence or absence of gravity, the method comprising: sampling each respective position at a plurality of positions along a baseline of a first set of one or more biological samples of a first set of subjects and a second set of one or more biological samples of a second set of subjects, whereby a plurality of ion samples is obtained, each ion sample of the plurality of ion samples corresponding to a different position at the plurality of positions, each position at the plurality of positions representing a different development period of the first and second set of one or more biological samples, the first set of subjects undergoing space travel and the second set of subjects not undergoing space travel, and the first and second set of subjects undergoing the intervention; The method includes analyzing each ion sample in the sample using a mass spectrometer, thereby obtaining a first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding elemental isotope in a plurality of elemental isotopes collectively determined over time from the plurality of ion samples; deriving a second dataset from the plurality of traces comprising a set of features, each respective feature in the set of features being determined by the variation of a single isotope or combination of isotopes in the plurality of traces; and inputting the set of features into a trained classifier, thereby obtaining probabilities for the first and second sets of subjects from the trained classifier predicting responses of one or more subjects to space travel.

[0078] In some embodiments, the probability indicates that the biological sample of one or more biological samples is dysregulated as a result of space travel.

[0079] In some embodiments, the efficacy of an intervention is measured by the probability of dysregulation.

[0080] In some embodiments, the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

[0081] In some embodiments, the diet includes iodine supplementation.

[0082] In some embodiments, the intervention reduces radiation exposure.

[0083] In some embodiments, the first set or second set of subjects comprises humans, non-human mammals, or any combination thereof.

[0084] In some embodiments, the non-human mammal comprises a mouse.

[0085] In some embodiments, the first set or second set of one or more biological samples comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0086] In some embodiments, the method further comprises treating the first set of one or more biological samples or the second set of one or more biological samples with a solvent or detergent prior to the sampling step.

[0087] In some embodiments, the method further comprises irradiating the first or second set of one or more biological samples with a low power laser prior to the sampling step to remove debris from the first set of one or more biological samples or the second set of one or more biological samples.

[0088] In some embodiments, the sampling step includes irradiating the first or second set of one or more biological samples of the first or second set of subjects with a laser to extract a plurality of particles from the first or second set of one or more biological samples, and ionizing the plurality of particles using an inductively coupled plasma mass spectrometer to obtain a plurality of ion samples.

[0089] In some embodiments, the subject's first set or second set of responses comprises an adverse response, a positive response, or a neutral response.

[0090] In some embodiments, the trained classifier is configured to identify a set of subject features associated with participation in space travel.

[0091] In some embodiments, the trained classifier is trained using a set of features and corresponding responses of one or more biological samples of a first set of subjects.

[0092] In some embodiments, the trained classifier is configured to determine whether the subject suffers from space travel-induced metabolic dysregulation.

[0093] In one aspect, the present disclosure provides a method for outputting a subject's response to an intervention following space travel, the method including the steps of exposing one or more biological samples of a subject to an optical signal comprising a plurality of Raman wavelengths, wherein the subject undergoes space travel; obtaining a plurality of Raman spectra from the one or more biological samples; processing the plurality of Raman spectra to generate a plurality of Raman features; and providing an input of the plurality of Raman features to a trained classifier, thereby outputting a probability of the subject's response to the intervention if the subject undergoes space travel.

[0094] In some embodiments the probability indicates that the subject became dysregulated as a result of space travel.

[0095] In some embodiments, the efficacy of an intervention is measured by the probability of dysregulation.

[0096] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0097] In some embodiments, the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

[0098] In some embodiments, the diet includes iodine supplementation.

[0099] In some embodiments, the intervention reduces radiation exposure.

[0100] In some embodiments, the subject includes a human, a non-human mammal, or any combination thereof.

[0101] In some embodiments, the non-human mammal comprises a mouse.

[0102] In some embodiments, the plurality of Raman spectra comprises wavenumbers from about 200 to about 3700.

[0103] In some embodiments, the obtaining step includes using a Raman spectroscopic microscope.

[0104] In some embodiments, the plurality of Raman features comprises one or more traces derived from a Raman spatial map.

[0105] In some embodiments, one or more traces are reduced using a dimensionality reduction algorithm, where the dimensionality reduction algorithm includes a principal component analysis, an independent component analysis, or any combination thereof.

[0106] In some embodiments, measurements of temporal dynamics are derived from one or more traces, and the temporal dynamics include determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0107] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0108] In some embodiments, the trained classifier comprises a gradient boosting ensemble model.

[0109] In some embodiments, the trained classifier is configured to process one or more features selected from the group consisting of laminarity, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, Lmax, and any combination thereof.

[0110] In some embodiments, the trained classifier is configured to process two or more features selected from the group consisting of laminarity, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, Lmax, and any combination thereof.

[0111] In some embodiments, the trained classifier is trained to identify Raman signatures associated with space travel.

[0112] In some embodiments, the probability output from the trained classifier indicates that the subject exhibits temporal dynamics associated with space travel.

[0113] In some embodiments, the trained classifier is trained using a plurality of Raman signatures of a population of humans or non-human animals, where the population of humans or non-human animals has experienced space travel.

[0114] In some embodiments, the effectiveness of the intervention is measured by changes in the subject's temporal dynamics relative to the subject's baseline temporal dynamics prior to space travel.

[0115] In one aspect, the 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 set of one or more biological samples of a first set of subjects and a second set of one or more biological samples of a second set of subjects to produce a first set and a second set of stained one or more biological samples, wherein the first set of subjects undergo space travel and the second set of subjects do not undergo space travel, and the first set of subjects and the second set of subjects are administered the intervention; spatially analyzing fluorescence intensity across the first and second set of stained one or more biological samples of the first and second set of subjects; and spatially analyzing fluorescence intensity across the first and second set of stained one or more biological samples of the first and second set of subjects. The method includes calculating two sets of spatial fluorescence intensity features of a first and second set of exposome features for a subject, each feature of the first and second set of exposome features comprising one or more metrics quantifying temporal dynamics in fluorescence intensity traces; and outputting as an output of the trained predictive model a probability of the subject's response to an intervention for reducing the biological effects of space travel when the predictive model is provided with the subject's spatial fluorescence intensity exposome features across the subject's one or more stained biological samples, wherein the trained predictive model is trained using the one or more metrics of the first and second set of exposome features for the subject, the corresponding administered interventions, and the clinical responses to the administered interventions.

[0116] In some embodiments, the trained predictive model is configured to determine whether a subject's biological sample becomes dysregulated via space travel.

[0117] In some embodiments, the trained predictive model is configured to determine the impact of an intervention as a measure of intervention efficacy based at least in part on an analysis of fluorescence intensity spatially across one or more biological samples.

[0118] In some embodiments, the first set or second set of one or more biological samples comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0119] In some embodiments, the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

[0120] In some embodiments, the diet includes iodine supplementation.

[0121] In some embodiments, the intervention reduces radiation exposure.

[0122] In some embodiments, the first set or second set of subjects comprises humans, non-human mammals, or any combination thereof.

[0123] In some embodiments, the non-human mammal comprises a mouse.

[0124] In some embodiments, the trained predictive model comprises a gradient boosting ensemble model.

[0125] In some embodiments, measuring the temporal dynamics of the spatial fluorescence intensity traces includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0126] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0127] In some embodiments, the analyzing step includes obtaining a first set and a second set of fluorescent images of the stained one or more biological samples, and analyzing the spatial fluorescent intensity of the fluorescent images.

[0128] In some embodiments, obtaining a fluorescent image of the stained tooth sample includes using an inverted or non-inverted confocal microscope.

[0129] In some embodiments, staining one or more of the biological samples of the first set and the second set of subjects comprises using a C-reactive stain.

[0130] In some embodiments, the method further comprises sectioning the tooth sample.

[0131] In some embodiments, staining the one or more first and second sets of biological samples comprises decalcifying the one or more first and second sets of biological samples.

[0132] In some embodiments, the trained model is selected from the group consisting of 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.

[0133] In some embodiments, the trained model comprises a gradient boosted decision tree.

[0134] In some embodiments, the trained predictive model comprises a classification algorithm.

[0135] In some embodiments, a classification algorithm is trained to identify spatial fluorescence intensity features associated with space travel.

[0136] In one aspect, the present disclosure provides a method for outputting one or more routes related to space travel, the method comprising: receiving one or more biological samples from a first set of one or more subjects and a second set of one or more subjects, where the first set of one or more subjects are participating in space travel and the second set of one or more subjects are not participating; determining an exposome signature of one or more routes from the one or more biological samples of the first and second set of one or more subjects; calculating an exposome feature of the exposome signature of the one or more routes; and outputting the one or more routes related to space travel by comparing the exposome feature of the one or more routes of the first and second set of subjects.

[0137] In some embodiments, each feature of the exposome feature includes one or more quantitative metrics.

[0138] In some embodiments, space travel involves traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

[0139] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0140] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0141] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0142] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0143] In some embodiments, the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0144] In some embodiments, the exposome features are derived from one or more attractors.

[0145] In some embodiments, the method further includes analyzing dynamic relationships between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof.

[0146] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0147] In one aspect, the present disclosure provides a method for providing a treatment to mitigate the effects of space travel, the method comprising the steps of 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 from the one or more subjects, calculating an exposome feature of the exposome signature of the one or more pathways, and providing a treatment to the one or more subjects, wherein the treatment ameliorates the effect of space travel on the exposome feature of the exposome signature of the one or more pathways for the one or more subjects.

[0148] In some embodiments, the treatment comprises a diet, a medicine, a functional food, a food bar, or any combination thereof.

[0149] In some embodiments, each feature of the exposome feature includes one or more quantitative metrics.

[0150] In some embodiments, space travel involves traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

[0151] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0152] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0153] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0154] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0155] In some embodiments, the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0156] In some embodiments, the exposome features are derived from one or more attractors.

[0157] In some embodiments, the method further includes analyzing dynamic relationships between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof.

[0158] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0159] Aspects of the present 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 set of one or more subjects and a second set of one or more subjects, where the first set of one or more subjects receive the intervention and the second set of one or more subjects do not receive the intervention; (b) determining an exposome signature of one or more pathways from the one or more biological samples of the first set of one or more subjects and the second set of one or more subjects; (c) calculating an exposome feature of the exposome signature of the one or more pathways; and (d) outputting the one or more pathways associated with the intervention by comparing the exposome features of the one or more pathways of the first set of subjects and the second set of subjects. In some embodiments, each feature of the exposome feature 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 one or more subjects are human, non-human mammal, vertebrate, or any combination thereof. In some embodiments, the intervention comprises pharmaceutical, dietary, food bar, functional food, metal ion supplementation, or any combination thereof.

[0160] In some embodiments, the exposome features include a measurement of the temporal dynamics of one or more exposome signatures. 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 exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0161] In some embodiments, the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0162] In some embodiments, the exposome features are derived from one or more attractors. In some embodiments, the method further comprises analyzing dynamic relationships between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, trend at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0163] Aspects of the present disclosure provide a method of determining an exposome characteristic of a subject associated with space travel, the method comprising: (a) receiving one or more biological samples of the subject; (b) determining an exposome signature of one or more pathways from the one or more biological samples; (c) calculating an exposome characteristic of the exposome signature of the one or more pathways; and (d) outputting the exposome characteristic of the subject associated with space travel by comparing the exposome characteristic of the subject with a database of space travel exposome characteristics. In some embodiments, each characteristic of the exposome 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 subject is a human, a non-human mammal, a vertebrate, or any combination thereof.

[0164] In some embodiments, the exposome features include a measurement of the temporal dynamics of one or more exposome signatures. 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 exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recursion time, Shannon entropy at recursion time, and number of most likely recursion. In some embodiments, the exposome features are derived from one or more attractors. In some embodiments, the method further includes analyzing the dynamic relationship between one or more of the signal recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recursion time, Shannon entropy at recursion time, number of most likely recursion, or any combination thereof.

[0165] In some embodiments, the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0166] Aspects of the present disclosure describe a method for determining an exposome feature of a subject associated with space travel using a trained predictive model, the method comprising: (a) receiving one or more biological samples of the subject; (b) determining an exposome signature of one or more pathways from the one or more biological samples; (c) calculating an exposome feature of the exposome signature of the one or more pathways; and (d) outputting the exposome feature of the subject associated with space travel by providing the exposome feature of the subject as input to a trained predictive model, the trained predictive model being trained with the exposome feature of one or more subjects that participated in the space travel. In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof. In some embodiments, the subject is a human, a non-human mammal, a vertebrate, or any combination thereof.

[0167] In some embodiments, each feature of the exposome features comprises one or more quantitative metrics. In some embodiments, the exposome features are derived from one or more attractors. In some embodiments, the exposome features comprise a measure of the temporal dynamics of one or more exposome signatures. In some embodiments, the measure of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion. In some embodiments, the method further includes analyzing a dynamic relationship between one or more of the signal recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, number of most likely recursion, or any combination thereof.

[0168] In some embodiments, the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0169] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto, the computer memory including machine executable code that, upon execution by the one or more computer processors, performs any of the methods described above or elsewhere herein.

[0170] Further aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, in which only exemplary embodiments of the disclosure have been shown and described. As will be seen from the following description, the present disclosure may be in other and different embodiments, each of whose details is capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not restrictive.

[0171] Incorporation by Reference All publications, patents, and patent applications mentioned herein are incorporated 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. To the extent that the publications and patents or patent applications incorporated by reference conflict with disclosures contained herein, the present specification is intended to supersede and / or take precedence over such conflicting material. [Brief description of the drawings]

[0172] The novel features of the present disclosure are set forth with particularity 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 that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings (also referred to herein as "Figures," "FIG.," and "FIGS.").

[0173] [Figure 1] 1 shows an overview of a computer-implemented exposome system as found in some embodiments. [Figure 2A] For example, as seen in some embodiments, a data workflow is shown for training an intervention prediction model using a subject's clinical database data (clinical metadata), exposome biochemical signature, and intervention outcome (FIG. 2A), and using the trained predictive model (FIG. 2B) to predict an intervention outcome for a given subject. [Figure 2B] For example, as seen in some embodiments, a data workflow is shown for training an intervention prediction model using a subject's clinical database data (clinical metadata), exposome biochemical signature, and intervention outcome (FIG. 2A), and using the trained predictive model (FIG. 2B) to predict an intervention outcome for a given subject. [Figure 3A] FIG. 3A shows a data workflow for training a predictive model for optimal selection of a pharmaceutical and / or nutraceutical using a subject's clinical database data (clinical metadata), exposome features, and the percent difference between the subject's post-treatment exposome features and a reference exposome feature. FIG. 3A shows a training workflow for training a predictive model, as in some embodiments, and FIG. 3B shows use of the trained predictive model. [Figure 3B]FIG. 3A shows a data workflow for training a predictive model for optimal selection of a pharmaceutical and / or nutraceutical using a subject's clinical database data (clinical metadata), exposome features, and the percent difference between the subject's post-treatment exposome features and a reference exposome feature. FIG. 3A shows a training workflow for training a predictive model, as in some embodiments, and FIG. 3B shows use of the trained predictive model. [Figure 4] FIG. 1 shows a flow diagram for selecting subjects for intervention based on exposome biochemical signature profiles, as in some embodiments. [Diagram 5] As found in several embodiments herein, a flow diagram is shown for selecting an optimal pharmaceutical or nutraceutical treatment based on a comparison of a subject's exposome characteristic compared to a reference treatment exposome characteristic. [Figure 6A] As seen in several embodiments herein, exposome biochemical profiles for various exposome signatures (e.g., tin, lead, calcium, and magnesium) are shown for subjects not receiving intervention (blue) and subjects receiving intervention (orange, gray, and brown). [Figure 6B] As seen in several embodiments herein, exposome biochemical profiles for various exposome signatures (e.g., tin, lead, calcium, and magnesium) are shown for subjects not receiving intervention (blue) and subjects receiving intervention (orange, gray, and brown). [Figure 6C] As seen in several embodiments herein, exposome biochemical profiles for various exposome signatures (e.g., tin, lead, calcium, and magnesium) are shown for subjects not receiving intervention (blue) and subjects receiving intervention (orange, gray, and brown). [Figure 6D]As seen in several embodiments herein, exposome biochemical profiles for various exposome signatures (e.g., tin, lead, calcium, and magnesium) are shown for subjects not receiving intervention (blue) and subjects receiving intervention (orange, gray, and brown). [Figure 7A] FIG. 7A shows clustering of one or more exposome biochemical profiles of one or more subjects, as seen in some embodiments herein, and how such clustering of data may correlate to one or more subjects that participated in spatial movement (FIG. 7B). [Figure 7B] FIG. 7A shows clustering of one or more exposome biochemical profiles of one or more subjects, as seen in some embodiments herein, and how such clustering of data may correlate to one or more subjects that participated in spatial movement (FIG. 7B). [Figure 8] 1 shows the subtyping of subjects using exposome features derived from hair analysis. Exposome biochemical signature data was extracted via analytical methods disclosed elsewhere herein. Clustering analysis, as seen in some embodiments herein, has been shown to identify distinct subtypes of patients who participated in space travel. [Figure 9] As found in several embodiments herein, we illustrate systems and methods utilized to collect and analyze the geo-temporal dynamics of annotated exposome pathways through a deep data science framework. [Figure 10] As seen in several embodiments herein, a temporal aspect is shown in which 100 data time points are acquired from a single biological sample characterizing the dynamics of physiology at different life stages. [Figure 11] As seen in several embodiments herein, various chemical signatures measured by methods and systems and their respective groupings are shown. [Figure 12]1 shows both temporal and spatial immunohistochemistry (IHC) fluorescence data captured by the methods and systems described herein. [Figure 13] As seen in several embodiments herein, methods are shown to measure dental metallochemical biomarkers and correlate the spatial distribution of metallochemical biomarkers across dental growth lines to determine the effects of space travel and biochemical, physiological, etc. changes. [Figure 14] We present a machine learning, informatics, and deep learning platform configured to generate robust and generalizable predictive models for space travel. [Figure 15] We present methods for phenotyping of various physiological outcomes, pathway identification, metabolic phenotyping, and clinical subtyping through unsupervised pattern recognition of the exposome map. [Figure 16] 1 shows the affected probiotic metabolic pathways and corresponding biochemical pathways measured by the methods and systems as found in several embodiments herein. [Figure 17] 1 shows affected gluten metabolic pathways and corresponding biochemical pathways measured by methods and systems as found in several embodiments herein. [Figure 18] 1 shows pathway importance weights from subjects who have participated in space travel that are utilized by the described methods and systems to recommend pharmaceutical and nutraceutical compounds to ameliorate the adverse effects of space travel. [Figure 19A] 1 illustrates various forms of exposome signature data representation as found in several embodiments herein. [Figure 19B] 1 illustrates various forms of exposome signature data representation as found in several embodiments herein. [Figure 19C] 1 illustrates various forms of exposome signature data representation as found in several embodiments herein. [Figure 20A]As seen in some embodiments, a comparison of calcium (FIGS. 20A-20B) and copper (FIGS. 20C-20D) exposome signatures and their corresponding graphical representations of attractors are shown. [Figure 20B] As seen in some embodiments, a comparison of calcium (FIGS. 20A-20B) and copper (FIGS. 20C-20D) exposome signatures and their corresponding graphical representations of attractors are shown. [Figure 20C] As seen in some embodiments, a comparison of calcium (FIGS. 20A-20B) and copper (FIGS. 20C-20D) exposome signatures and their corresponding graphical representations of attractors are shown. [Figure 20D] As seen in some embodiments, a comparison of calcium (FIGS. 20A-20B) and copper (FIGS. 20C-20D) exposome signatures and their corresponding graphical representations of attractors are shown. [Figure 21A] 1 illustrates a recurrent network as found in some embodiments. [Figure 21B] 1 illustrates a recurrent network as found in some embodiments. [Figure 22] 1 illustrates a flow diagram of a method for outputting one or more quantitative metrics of one or more exposome signatures of a subject, as found in some embodiments. [Figure 23] 1 illustrates a flow diagram of a method for outputting predictions of phenotype data for one or more subjects, as found in some embodiments. [Figure 24] As in some embodiments, quantitative metrics derived from cross-replicate quantitative analysis of signal dyads from control animals housed in a ground facility and mice transferred to the International Space Station are shown. [Diagram 25] As in some embodiments, data derived from analysis of barium, manganese, and zinc signals in human hair collected after space travel are shown. [Figure 26]As in some embodiments, mean BaCA kinetics, Zn:Fe kinetics in mice receiving a control diet or iodine supplementation in their diet are shown. [Figure 27] As seen in some embodiments, mean Ba:CA dynamics, Zn:Fe dynamics in mice fed a food bar diet or standard laboratory chow are shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0174] While 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. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the present disclosure. It is understood that various alternatives to the embodiments of the present disclosure described herein may be employed.

[0175] The present disclosure addresses these needs through systems and methods that can analyze and classify subjects by their exposome biochemical signature profile, as shown in Figure 9. The exposome feature analysis of the present disclosure may include analyzing over 50,000 biochemical signatures (Figure 11) in a non-invasive manner from hair shafts, teeth, fingernails, toenails, physiological parameters, or any combination thereof. Such systems and methods of analysis of the present disclosure can be used to investigate subtle changes in a subject's biochemistry induced, for example, by diet, air pollution, psychological stress, space travel, exposure to pesticides, or industrial chemicals (Figure 10), to name a few factors, and correlate with intervention response outcomes as well as targeted highly effective pharmaceuticals and nutraceuticals.

[0176] In addition to the large amount of data sets generated by exposome biochemical signature analysis, exposome biochemical signature analysis can also provide insight into the temporal variations in biochemical signatures over a subject's lifetime, as shown in FIG. 10. Such an approach can be utilized to screen individuals participating in space travel to determine which single or combinations of exposome features are affected by space travel. The identified pathways may then be used to train statistical, machine learning, and / or artificial intelligence predictive models that can predict whether a subject's exposome features are characteristic of an individual who has participated in space travel.

[0177] Computer-implemented exposome system

[0178] In one aspect, the present disclosure provides a computer-implemented exposome system for collecting, storing, cataloging, comparing, analyzing, or performing any combination thereof, exposome biochemical signatures of one or more subjects. In some embodiments, the exposome biochemical signatures can be used, at least in part, to optimize the selection criteria for subjects participating in an intervention. In some embodiments, the exposome biochemical signatures are used, at least in part, to suggest optimal pharmaceutical or nutritional supplement treatments for subjects in need of treatment. In some embodiments, the intervention may include clinical trials, community trials, or any combination thereof.

[0179] With reference to FIG. 1 , a computer-implemented exposome system (23) may comprise one or more of: (a) an exposome biochemical signature database (EDB) (1), which may further include a plurality of biochemical signature information for a plurality of subjects who have participated in space travel and who have not; (b) a clinical database (CBD) (3), which may further include clinical phenotype information for a plurality of subjects who have participated in space travel and who have not; (c) an intervention requirement database (IODB) (5), which may further include information regarding intervention outcome information for at least one phase of at least one intervention; (d) a treatment database (TDB) (18); and (e) a computer system (11), which may comprise a processing unit (CPU, also referred to herein as “processor” and “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 program or software, e.g., sequences of machine-readable instructions embodied in (i) an association software module located on a storage unit (19) communicatively connected to the EDB1 and the CDB3, i.e., memory, the association software module may be programmed to determine an association between the exposome features and clinical phenotype information for 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 exposome features, the clinical phenotype information, the intervention-outcome information, and the association between the features and the clinical phenotype information for at least one of the plurality of subjects.

[0180] In some embodiments, an exposome biochemical signature database (EDB) (1) may include a plurality of exposome features from a plurality of subjects who have participated in space travel and those who have not. As seen in FIG. 11, it may include biochemical signatures of perfluorinated compounds, parabens, phthalates, lipids, amino acids, metabolites, peptides, metals, their derivatives, or any combination thereof. In some embodiments, the exposome biochemical signature is analyzed or obtained as a function of the subject's life (e.g., as a function of aging or as a function of time), in which case a collection of exposome biochemical signatures, such as those seen in FIGS. 6A-6D, may be analyzed to generate one or more exposome features. In some embodiments, the time period represented by the exposome biochemical signature may include at least one hour, at least one day, at least one week, at least one month, at least one year, or any combination thereof.

[0181] In some embodiments, the number of exposome biochemical signatures can include from about 10 signatures to about 100,000 signatures. In some embodiments, the number of exposome biochemical signatures can include from about 10 signatures to about 100 signatures, from about 10 signatures to about 500 signatures, from about 10 signatures to about 1,000 signatures, from about 10 signatures to about 5,000 signatures, from about 10 signatures to about 7,000 signatures, from about 10 signatures to about 10,000 signatures, from about 10 signatures to about 20,000 signatures, from about 10 signatures to about 50,000 signatures, from about 10 signatures to about 100,000 signatures, 100 signatures to about 500 signatures, about 100 signatures to about 1,000 signatures, about 100 signatures to about 5,000 signatures, about 100 signatures to about 7,000 signatures, about 100 signatures to about 10,000 signatures, about 100 signatures to about 20,000 signatures, about 100 signatures to about 50,000 signatures, about 100 signatures to about 100,000 signatures , about 500 signatures to about 1,000 signatures, about 500 signatures to about 5,000 signatures, about 500 signatures to about 7,000 signatures, about 500 signatures to about 10,000 signatures, about 500 signatures to about 20,000 signatures, about 500 signatures to about 50,000 signatures, about 500 signatures to about 100,000 signatures, about 1000 signatures to about 5,000 signatures, about 1000 signatures to about 7,000 signatures, about 1000 signatures to about 10,000 signatures, 1,000 signatures to about 20,000 signatures, about 1,000 signatures to about 50,000 signatures, about 1,000 signatures to about 100,000 signatures, about 5,000 signatures to about 7,000 signatures, about 5,000 signatures to about 10,000 signatures, about 5,000 signatures to about 20,000 signatures, about 5,000 signatures to about 50,000 signatures, about 5,000 signatures to about 100,000 signatures, about 7,000 signatures to about 10,000 signatures, about 7,000 signatures to about 20,000 signatures, about 7,000 signatures to about 50,000 signatures, about 7,000 signatures to about 100,000 signatures, about 10,000 signatures It may include about 20,000 signatures, about 10,000 signatures to about 50,000 signatures, about 10,000 signatures to about 100,000 signatures, about 20,000 signatures to about 50,000 signatures, about 20,000 signatures to about 100,000 signatures, about 50,000 signatures to about 100,000 signatures. In some embodiments, the number of exposome biochemical signatures may 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 exposome biochemical signatures may 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 may 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.

[0182] In some embodiments, the exposome biochemical signature may be obtained by assaying biological samples from multiple subjects. In some embodiments, the biological samples may include tooth, nail, or hair shaft samples. In some embodiments, the exposome biochemical signature may be obtained using laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectrometry, mass spectrometry, Raman spectrometry, immunohistochemistry, molecular tagging (e.g., with fluorophores), nuclear magnetic resonance, chromatography, or any combination thereof. In some embodiments, the laser ablation inductively coupled plasma mass spectrometry measurement may measure one or more elemental chemicals. In some embodiments, 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, as described elsewhere herein, e.g., in Table 1 and / or Table 2. In some embodiments, the exposome biochemical signature information may include exposome temporal biochemical reactions of multiple subjects. In some embodiments, the biochemical information may include a fluorescence image of the biological sample. In some embodiments, the exposome biochemical signature may include a spatial map of Raman spectra of biological samples of multiple subjects. In some embodiments, the exposome characteristics may be associated with space travel.

[0183] In some embodiments, the plurality of chemicals is selected from the chemicals in Table 1. In some embodiments, the plurality of chemicals includes at least 50%, 60%, 70%, 80% or 90% of the isotopes included in Table 1.

[0184] [Table 1]

[0185] In some embodiments, the plurality of chemicals is selected from the chemicals listed in Table 2, and in some embodiments, the plurality of chemicals comprises at least 50%, 60%, 70%, 80%, or 90% of the isotopes included in Table 2.

[0186] [Table 2]

[0187] In some embodiments, one or more exposome features are calculated from one or more dynamic exposome biochemical signatures. The exposome features derived through data analysis may include descriptive statistics or parameters that are utilized in subsequent statistical, machine learning, or artificial intelligence models. Such exposome features may include standard descriptive metrics such as the mean, median, mode, and range of one or more dynamic exposome signatures, and / or associated measures of error and / or variation such as standard deviation, variance, confidence interval, and / or associated metrics. Deriving exposome features may include application of computational methods to derive linear gradients, nonlinear parameters describing the curvature of one or more dynamic exposome signatures, abrupt changes in intensity of one or more dynamic exposome signatures, changes in baseline intensity of one or more dynamic exposome signatures, changes in frequency domain representations of one or more dynamic exposome signatures, changes in power spectral domain representations of one or more dynamic exposome signatures, recursive quantification analysis parameters, cross-recursive quantification analysis parameters, joint recursive quantification analysis parameters, multidimensional recursive quantification analysis parameters, estimates of Lyapunov spectra or maximum Lyapunov exponents, or any combination thereof.

[0188] In some embodiments, the one or more exposome features include a measure of the temporal dynamics of one or more dynamic exposome signatures. In some embodiments, the measure of the temporal dynamics includes a linear slope, a nonlinear parameter describing the curvature of one or more dynamic exposome signatures, an abrupt change in intensity of one or more dynamic exposome signatures, a change in baseline intensity of one or more dynamic exposome signatures, a change in a frequency domain representation of one or more dynamic exposome signatures, a change in a power spectral domain representation of one or more dynamic exposome signatures, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimate of the maximum Lyapunov exponent of the Lyapunov spectrum, or any combination thereof.

[0189] In some embodiments, recursive quantification analysis, cross-recursive quantification analysis, joint recursive quantification analysis, multidimensional recursive quantification analysis of one or more exposome biochemical signatures may be used to derive descriptive statistics and / or parameters utilized to train the predictive models described elsewhere herein.

[0190] In some examples, the recurrence quantification analysis parameters may include recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recurrences.

[0191] In some examples, one or more exposome features may be derived from one or more attractors (FIGS. 19B, 20B, 20D), whereby one or more attractors are generated from one or more dynamic exposome biochemical signatures (FIGS. 19A, 20A, 20C). In some embodiments, the one or more attractors are analyzed by potential energy analysis to generate a potential energy data space.

[0192] In some embodiments, dynamic relationships (FIG. 19C) may be established, analyzed, and provided as features between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof. In some embodiments, the dynamic relationships are determined by convergent crossover mapping (CCM).

[0193] In some examples, the network is constructed based on similarity of one or more attractor's temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recursions, or any combination thereof (FIGS. 21A-21B). In some embodiments, one or more exposome feature networks of one or more attractor networks are analyzed to determine connectivity, efficiency, feature importance, path importance, associated graph theory based metrics, or any combination thereof.

[0194] In some embodiments, one or more exposome features of one or more dynamic exposome signatures may include phenotypic features. The phenotypic features may include electrocardiogram (ECG), electroencephalography, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome, or any combination of data thereof.

[0195] In some embodiments, the phenotypic exposome features comprise molecular phenotypes. In some instances, the molecular phenotypes are determined by unsupervised analysis including clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0196] In some embodiments, the CBD (3) may include clinical phenotype data of a subject. In some embodiments, the clinical phenotype data includes clinical metadata of a plurality of subjects. In some embodiments, the clinical metadata may include the subject's age, sex, weight, height, blood type, eye vision, current disease, past medical history of familial disease, or any combination thereof. In some embodiments, the subject's clinical metadata and the exposome biochemical signature may be considered independently or in combination to determine whether the subject is a suitable candidate for intervention.

[0197] In some embodiments, the IODB (5) may include intervention outcome information. In some embodiments, the intervention outcome information may comprise eligibility criteria for one or more interventions. In some embodiments, the intervention may include phase 1, 2, 3, or any combination thereof interventions. In some embodiments, the intervention outcome information may include information about one or more subject intervention outcome classifications, including positive responders, negative responders, or non-responders.

[0198] In some embodiments, the TDB (18) may include exposome features related at least in part to pharmaceutical and nutraceutical treatments. In some embodiments, the exposome features of the pharmaceutical and nutraceutical treatments may include one or more reference exposome features of subjects not participating in space travel, one or more pre-treatment exposome features of subjects participating in space travel, and one or more post-treatment exposome features of subjects participating in space travel, obtained by assaying one or more biological samples from one or more subjects.

[0199] In some embodiments, analysis of the difference between one or more pre-treatment exposome features and one or more post-treatment exposome features relative to one or more reference dynamic profile biochemical signatures can be used to determine one or more optimal pharmaceuticals, nutritional supplements, or any combination thereof, for a subject who participated in space travel. In some embodiments, the difference between one or more post-treatment exposome features relative to one or more reference exposome features can provide a basis for recommending one or more pharmaceuticals or nutritional supplements to a subject who participated in space travel, and can be used in combination with one or more subject exposome biochemical signatures to recommend optimal treatments to ameliorate the adverse effects of space travel. In some embodiments, the criteria for optimal pharmaceutical or nutritional supplement treatments can include a complement to a deficiency in a given subject's exposome biochemical signature of one or more subjects.

[0200] In some embodiments, the difference between one or more features of the post-treatment exposome feature and one or more reference exposome features, in some embodiments, the features include an overall mean, a measure of variability, a moving average, or the like, or any combination thereof, as described elsewhere herein.

[0201] In some embodiments, the difference in characteristics comprises a difference of about 10% to about 100%. In some embodiments, the difference in characteristics is between about 10% and about 20%, between about 10% and about 30%, between about 10% and about 40%, between about 10% and about 50%, between about 10% and about 60%, between about 10% and about 70%, between about 10% and about 80%, between about 10% and about 90%, between about 10% and about 100%, between about 20% and about 30%, between about 20% and about 40%, between about 20% and about 50%, between about 20% and about 60%, between about 20% and about 70%, between about 20% and about 80%, between about 20% and about 90%, between about 20% and about 100%, between about 30% and about 40%, between about 30% and about 50%, between about 30% and about 60%, between about 30% and about 70%, between about 30% and about 80%, between about 30% and about 90%, between about 20% and about 100%, between about 30% and about 40%, between about 30% and about 50%, between about 30% and about 60%, between about 30% and about 70%, between about 30% and about 80%, between about 30% and about 90%, between about 3 ... This includes differences of 0% 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 may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 100% difference. In some embodiments, the difference in characteristics may include at least about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% difference. In some embodiments, the difference in characteristics may include up to about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 100% difference.

[0202] The computer system may further include a communication interface (13) (e.g., a network adapter) for communicating with one or more other systems, and peripheral devices (15) such as cache, other memory, data storage, and / or electronic display adapters. The memory (17), storage unit (19), interface (13), and peripheral devices (15) communicate with the 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 operatively coupled to a computer network ("network") using the communication interface (13). The network may be the Internet, an extranet, and / or an intranet that communicates with the Internet. The network is, in some embodiments, a telecommunications and / or data network. The network may include one or more computer servers that may enable distributed computing, such as cloud computing. The network may implement a peer-to-peer network that may enable devices coupled to the computer system (11) to act as clients or servers, possibly with the aid of the computer system (11).

[0203] In some embodiments, the EDB(1), CDB(3), IODB(5), and TDB(18) may be located on a network and accessed remotely by 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 accessed remotely by one or more computer systems (11) within or outside the hospital's network. In some embodiments, the EDB(1), CDB(3), IODB(5), and TDB(18) may be accessed by one or more subjects via a secure network protocol to view recommendations and their personalized data.

[0204] The CPU (21) can execute sequences of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory 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 perform the methods of the present disclosure. Examples of operations performed by the CPU (21) may include fetch, decode, execute, and writeback.

[0205] The CPU (21) may be part of a circuit, such as an integrated circuit. One or more other components of the system (11) may be included in the circuit. In some embodiments, the circuit is an application specific integrated circuit (ASIC).

[0206] The storage unit (19) can store files such as drivers, libraries, saved programs, etc. The storage unit (19) can store user data such as user preferences and user programs, etc. The computer system (11) can optionally include one or more additional data storage units external to the computer system (11), such as located on a remote server that communicates with the computer system (11) via an intranet or the Internet.

[0207] The computer system (11) can communicate with one or more remote computer systems over a network. For example, the computer system (11) can communicate with a remote computer system of a user (e.g., a health care provider, a subject, etc.). Examples of remote computer systems include a personal computer (e.g., a portable PC), a slate or tablet PC (e.g., an Apple® iPad, a Samsung® Galaxy Tab), a phone, a smartphone (e.g., an Apple® iPhone, an Android-enabled device, a Blackberry®), or a personal digital assistant. A user can access the computer system (11) over a network.

[0208] The methods described herein can be implemented by machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system (11), such as the memory (17) or the electronic storage unit (19). The machine executable or machine readable code can be provided in the form of software. In use, the code can be executed by the processor (21). In some embodiments, the code is retrieved from the storage unit (19) and stored in the random access memory (17) for easy access by the processor (21). In some situations, the storage unit (19) can be eliminated and the machine executable instructions are stored in the random access memory (17).

[0209] The code may be pre-compiled and configured for use on a machine having a processor adapted to execute the code, or may be compiled during run-time. The code may be supplied in a programming language that may be selected to allow the code to be executed in a pre-compiled or as-compiled manner.

[0210] Aspects of the systems and methods provided herein, such as the computer system (11), can be embodied in programming. Various aspects of the technology can be considered as a "product" or "article of manufacture," typically in the form of machine (or processor) executable code, and / or associated data carried on or embodied in some 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. A "storage" type medium can include any or all of the tangible memory of a computer, a processor, or its associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage at any time for software programming. All or portions of the software can be communicated from time to time over the Internet or various other telecommunications networks. Such communication can, for example, enable loading of the software from one computer or processor to another, for example, from a management server or host computer to the computer platform of an application server. Thus, other types of media that may carry software elements include optical, electrical, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and over various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., may also be considered media that carry software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0211] Thus, a machine-readable medium such as a computer executable code may take many forms, including but not limited to a tangible storage medium, a carrier wave medium, or a physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer, and may be used to implement the databases, etc., shown in the figures. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire, and optical fibers, including the wires that make up a bus within a computer system. Carrier wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example: a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with a pattern of holes, RAM, ROM, PROM, and EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier wave carrying data or instructions, a cable or link carrying 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.

[0212] The computer system (11) can include or be in communication with an electronic display (7) with a user interface (UT) (9) for providing, for example, fluorescent image data, fluorescent intensity data, a time profile of inflammation, and machine learning classification. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0213] The methods and systems of the present disclosure may be implemented by one or more algorithms, which may be implemented by software upon execution by a central processing unit (21) as described elsewhere herein.

[0214] Predictive Model

[0215] Aspects of the disclosure herein may include a trained predictive model 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 a subject's probability of success for an intervention, pharmaceutical or nutritional supplement treatment for a subject in need of treatment, or any combination thereof. In some embodiments, the trained predictive model may comprise a statistical, machine learning, artificial intelligence classifier, an ensemble of classifiers, or any combination thereof.

[0216] The classifier may comprise one or more statistical, machine learning, or artificial intelligence algorithms. Examples of algorithms utilized may include support vector machines (SVM), naive Bayes classification, random forests, neural networks (such as deep neural networks (DNN), recurrent neural networks (RNN)), deep RNN, long short-term memory (LSTM) recurrent neural networks (RNN), or gated recurrent units (GRU), or other supervised or unsupervised machine learning, statistical deep learning, shallow learning algorithms for classification and regression. The classifier may also include estimation of ensemble models composed of multiple predictive models, and may utilize techniques such as gradient boosting, for example, in constructing gradient boosting decision trees. The classifier may be trained using one or more training datasets corresponding to patient data. In some embodiments, the one or more training datasets may include exposome biochemical signatures, dynamic exposome biochemical signatures, clinical metadata, clinical trial information, exposome features of pharmaceutical and nutraceutical treatments, or any combination thereof.

[0217] In some embodiments, the training data features may include subject dynamic exposome biochemical signature data generated from the biological sample. For each biological sample of a given subject, a plurality of locations of the baseline on the biological sample of the training subject may be sampled to generate measurements therefrom, thereby obtaining a plurality of exposome biochemical signatures. Each exposome biochemical signature in the corresponding plurality of exposome biochemical signatures corresponds to a different location in the corresponding plurality of locations, and each location in the corresponding plurality of locations represents a different growth period of the corresponding biological sample. Each respective location of the biological sample is then analyzed (e.g., using a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS), a fluorescence image sensor, or a Raman spectrometer) to obtain a plurality of traces. Each trace in the corresponding plurality of traces corresponds to an abundance measurement of a corresponding substance, collectively determined over time from the corresponding plurality of dynamic exposome biochemical signatures.

[0218] In some embodiments, the label may include an intervention outcome, such as, for example, a positive response, a negative response (i.e., an adverse response), or a non-responder. The intervention outcome may include a temporal feature associated with the classification of a positive response, a negative response, or a non-responder event for a duration following the treatment provided during the intervention. Such a period may 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.

[0219] The input features for training the classifier may be structured by aggregating data into bins or alternatively by using one-hot coding. The input may also include feature values ​​or vectors derived from the aforementioned inputs, such as cross-correlations calculated between distinct exposome features or other measurements over a fixed time period, and discrete derivatives or finite differences between successive measurements, as described elsewhere herein. Such a time period may 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.

[0220] Training records may be constructed from sequences of observations. Such sequences may include a fixed length for ease of data processing. For example, sequences may be zero padded or selected as an independent subset of a single patient's records.

[0221] A data set can be used to train a classifier model (e.g., by determining model weights and correlations) to generate real-time classifications or predictions. Such a data set may be large enough to generate statistically significant classifications or predictions. For example, a data set may include a database of de-identified data including dynamic profile biological signature data and other clinical metadata measurements from a hospital or other clinical site.

[0222] The dataset may be divided into subsets (e.g., discrete or overlapping), such as a training dataset, a development dataset, and a test dataset. For example, the dataset may be divided into a training dataset that includes 80% of the dataset and a test dataset that includes 20% of the dataset. The training dataset may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. The development dataset may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. The test dataset may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. The training set (e.g., training data set) may be selected by random sampling of a set of data corresponding to one or more subject cohorts to ensure independence of sampling. Alternatively, the training set (e.g., training data set) may be selected by proportional sampling of a set of data corresponding to one or more subject cohorts to ensure independence of sampling.

[0223] To improve the accuracy of model prediction and reduce overfitting of the model, the data set may be augmented to increase the number of samples in the training set. For example, data augmentation may include rearranging the order of observations in the training records. To accommodate a data set with missing observations, methods for imputing missing data may be used, such as forward filling, backfilling, linear interpolation, and multitask Gaussian processes. The data set may be filtered to remove confounding factors. For example, a subset of subjects may be excluded within the database.

[0224] In some embodiments, data science techniques such as dropout or regularization may be used while training the classifier to prevent overfitting. The neural network may comprise multiple sub-networks, each of which is configured to generate classifications or predictions of different types of output information (e.g., which can be combined to form the overall output of the neural network). The classifier may alternatively utilize statistical or related algorithms, including random forests, classification and regression trees, support vector machines, discriminant analysis, regression techniques, and ensembles and gradient augmented variants thereof.

[0225] In some embodiments, the systems and methods of the present disclosure are deployed in a hospital environment for active patients undergoing treatment to ameliorate the negative effects of space travel. When the classifier generates a classification or prediction of optimal medication or nutritional supplement, a notification (e.g., a warning or alarm) may be generated and transmitted to a healthcare provider, such as a doctor, nurse, or other member of the patient's treatment team in the hospital. The notification may be sent via automated phone call, short message service (SMS) or multimedia message service (MMS) message, email, alert in a dashboard, or any combination thereof. The notification may include output information, such as a prediction of the outcome of the intervention, or optimal medication or nutritional supplement.

[0226] To validate 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 ability 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 characteristic curve (ROC) can be used to identify different operating points corresponding to different values ​​of specificity and sensitivity.

[0227] In some embodiments, the performance of the prediction method is evaluated by constructing a table to provide the frequency and overlap of predicted and actual positive cases, predicted and actual negative cases, predicted and actual negative cases, and / or predicted and actual positive cases of individuals having one or more exposome signatures and / or characteristics indicative of space travel. In some cases, the table constructed may be a confusion matrix. In some cases, crosstabulation of the confusion matrix may provide sensitivity, specificity, accuracy, and related performance metrics associated with the systems and methods described elsewhere herein at a given prediction threshold.

[0228] In some embodiments, such as when the dataset is not large enough, cross-validation may be performed to assess the robustness of the classifier model across different training and testing datasets.

[0229] The following definitions can be used to calculate performance metrics such as sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), AUPRC, AUROC, or the like. A "false positive" can refer to a positive result or an outcome that is incorrectly generated (e.g., a subject classified as a positive responder to an intervention experiences adverse or negative effects of participating in the intervention). A "true positive" can refer to a positive result or an outcome that is correctly generated (e.g., a subject is classified as a positive responder to an intervention and they experience a positive response). A "false negative" can refer to a negative result or an outcome that is generated (e.g., a subject is classified as a negative responder and after participating in the intervention, the subject is a non-responder or positive responder). A "true negative" can refer to a negative result or an outcome that is generated (e.g., a subject is classified as a negative responder and after participating in the intervention, the subject reacts adversely to the pharmaceutical treatment of the intervention).

[0230] In some embodiments, the classifier may be trained until some predefined condition for accuracy or performance is met, such as having a minimum desired value corresponding to a predictive accuracy measure. For example, the predictive accuracy measure may correspond to a correct prediction of an intervention outcome, or the recommendation and / or selection of an optimal medicine or dietary supplement. Examples of diagnostic accuracy measures may include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, area under the precision-recall curve (AUPRC). And the area under the curve (AUC) of the receiver operating characteristic (ROC) curve (AUROC) corresponding to whether or not the subject participated in space travel and / or the diagnostic accuracy of detecting the subject's exposome signature and / or features is similar to that of subjects who participated in space travel.

[0231] For example, such a predetermined condition may be that the sensitivity for predicting the outcome of the intervention or optimal pharmaceutical or dietary supplement recommendation and / or selection comprises a value of, e.g., 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%.

[0232] As another example, such a predetermined condition may be that the specificity for predicting the outcome of the intervention or optimal pharmaceutical or dietary supplement recommendation and / or selection comprises a value of, for example, 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%.

[0233] As another example, such a predetermined condition may be that the positive predictive value (PPV) predicting the outcome of the intervention or the optimal pharmaceutical or dietary supplement recommendation and / or selection comprises a value of, for example, 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%.

[0234] As another example, such a predetermined condition may be that the negative predictive value (NPV) of the intervention outcome or optimal pharmaceutical or dietary supplement recommendation and / or selection comprises a value of, for example, 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%.

[0235] As another example, such a predetermined condition may be that the area under the curve (AUC) of a receiver operating characteristic (ROC) curve (AUROC) predicting the outcome of the intervention or optimal pharmaceutical or dietary supplement 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.

[0236] As another example, such predetermined conditions may include values ​​where the area under the precision-recall curve (AUPRC) predicting the outcome of the intervention or optimal pharmaceutical or dietary supplement recommendation and / or selection is 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.

[0237] In some embodiments, the trained classifier may be trained or configured to predict the outcome of an intervention or optimal pharmaceutical or dietary supplement recommendation and / or selection 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%.

[0238] In some embodiments, the trained classifier may be trained or configured to predict an intervention outcome or optimal pharmaceutical or dietary supplement 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%.

[0239] In some embodiments, the trained classifier may be trained or configured to predict an intervention outcome or optimal pharmaceutical or dietary supplement 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%.

[0240] In some embodiments, the trained classifier may be trained or configured to predict an intervention outcome or optimal pharmaceutical or dietary supplement recommendation and / or selection 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%.

[0241] In some embodiments, the trained classifier may be trained or configured to predict an intervention outcome or optimal pharmaceutical or dietary supplement recommendation and / or selection with an area under the curve (AUC) of the 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.

[0242] In some embodiments, the trained classifier may be trained or configured to predict an intervention outcome or optimal pharmaceutical or dietary supplement 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.

[0243] 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.

[0244] 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 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., 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, NY; Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259, 262-265; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York, each of which is incorporated herein by reference. See York, and Furey et al., 2000, Bioinformatics 16, 906-914. When used for classification, SVMs separate a given set of binary labeled data with a hyperplane that is maximally far from the labeled data. When linear separation is not possible, SVMs can work in combination with kernel techniques that automatically achieve a nonlinear mapping to the feature space. The hyperplane found by the SVM in the feature space corresponds to a nonlinear decision boundary in the input space.

[0245] Decision trees are generally described by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is incorporated herein by reference. Tree-based methods divide the feature space into a set of rectangles and then fit a model (such as a constant) to each. In some embodiments, the decision tree is a random forest regression. One particular algorithm that can be used is Classification and Regression Trees (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, Inc., 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, UC Berkeley, September 1999, which is incorporated herein by reference in its entirety.

[0246] Clustering (e.g., unsupervised and supervised clustering model algorithms) are described in Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York (hereinafter "Duda 1973"), pages 211-256, which is incorporated herein by reference in its entirety. As described in section 6.7 of Duda 1973, the clustering problem is described as one of finding natural groupings in a data set. To identify natural groupings, two problems are addressed. First, determine how to measure the similarity (or dissimilarity) between two samples. This metric (similarity measure) is used to ensure that samples in one cluster are more similar to each other than samples in other clusters. Second, a mechanism is determined for partitioning the data into clusters using the similarity measure. Similarity measures are discussed in section 6.7 of Duda 1973, where it is stated that one way to begin a clustering study is to define a distance function and compute a matrix of distances between all pairs of samples in the training set. If distance is a good measure of similarity, then the distance between reference entities in the same cluster will be significantly smaller than the distance between reference entities in different clusters. However, as stated 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 whose value is large when x and x' are "similar" in some way. An example of a non-metric similarity function s(x,x') is provided on page 218 of Duda 1973. Once a method for measuring "similarity" or "dissimilarity" between points in a data set has been chosen, clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function are used to cluster the data, see Duda 1973, p. 217.Criterion functions are discussed in Duda 1973, section 6.8. More recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York has been published. Clustering is described in detail on pages 537-563. Further information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York; Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, New Jersey, each of which is incorporated herein by reference. Certain exemplary clustering techniques that can be used in the present disclosure include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest neighbor, nearest neighbor, average linkage, centroid, or sum of squares algorithms), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. In some embodiments, the clustering comprises unsupervised clustering, where no a priori notion of what clusters should form is imposed when the training set is clustered.

[0247] Regression models, such as the regression model of the multicategory logit model, are described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., 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, gradient boosting models are used, for example, for the classification algorithms described herein; these gradient boosting models are described in Boehmke, Bradley; Greenwell, Brandon (2019). "Gradient Boosting". Hands-On Machine Learning with R. Chapman & Hall. pp. 221-245. ISBN 978-1-138-49568-5, which is incorporated herein by reference in its entirety. In some embodiments, ensemble modeling techniques are used, for example, for the classification algorithms described herein; these ensemble modeling techniques are described in 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, in implementing the classification models herein.

[0248] In some embodiments, the machine learning analysis is performed by a device executing one or more programs (e.g., one or more programs stored in non-persistent memory (i.e., RAM or ROM) (17) or storage unit (19) (i.e., hard disk) of FIG. 1 ) that include instructions for performing the data analysis. In some embodiments, the data analysis is performed by a system that includes at least one processor (e.g., processing core (21)) and memory (e.g., one or more programs stored in non-persistent memory (17) or storage unit (19)) that include instructions for performing the data analysis.

[0249] Intervention stratification prediction model

[0250] In some embodiments, the predictive model (26) may comprise one or more classifiers that may be trained to predict the probability of an intervention outcome for one or more subjects based on their exposome features, as seen in FIG. 2A. In some embodiments, inputs for training the classifiers may include subject clinical metadata (20), subject exposome biochemical signature data (22), and the subject's corresponding intervention outcome (24). In some embodiments, the intervention outcome for a given subject may include non-responders, adverse responders, or positive responders. In some embodiments, the predictive model may first be trained on a dataset of one or more subjects that have received treatment from the intervention. In some embodiments, the training dataset used to train the classifiers may be generated, for example, from the subject's clinical metadata and the corresponding subject's exposome biochemical signature profile, or features derived from the exposome biochemical signature profile, for example, via RQA as described elsewhere herein, and the intervention outcome.

[0251] In some embodiments, the clinical metadata features may include subject demographic information derived from electronic medical records (EMR), physiological measurements, and intervention outcomes. Additionally, the training features may include clinical features, such as, for example, specific 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 collectively serve as a signature of CBD (3) and EDB (1), which may indicate the subject's health state or condition.

[0252] In some embodiments, the trained predictive model (32) may comprise a trained classifier described elsewhere herein configured to provide a prediction of an intervention outcome for a subject interested in participating in the intervention, as seen in FIG. 2B. In some embodiments, one or more subject clinical metadata (28) and corresponding exposome features (30) may be provided as input to the trained predictive model (32). The trained predictive model (32) may then output a probability of a predicted subject test outcome (34). In some embodiments, the output probability of a predicted subject test outcome may include a classification of a positive responder, a negative or adverse responder, or a non-responder. In some embodiments, the subject clinical metadata may include clinical metadata, such as the subject's age, sex, weight, height, blood type, eye vision, current illness, past medical history of familial illness, or any combination thereof. Various machine learning techniques may be cascaded such that the output of the machine learning techniques may also be used as input features to subsequent layers or subsections of the classifier.

[0253] Predictive models for optimal drug or dietary supplement selection

[0254] In some embodiments, the predictive model (42) may comprise one or more classifiers, described elsewhere herein, that may be trained to generate a trained predictive model (48) configured to predict an optimal pharmaceutical or dietary supplement to be administered for one or more subjects participating in space travel based on features derived from their respective one or more exposome features, as seen in FIG. 3A, described elsewhere herein. In some embodiments, inputs for training the statistical classifier, machine learning classifier, and / or artificial intelligence classifier may include (a) the subject's space travel classification (36), (b) the subject's pre-treatment features derived from the one or more exposome features (38), (c) the pharmaceutical or dietary supplement treatment to be administered (40), (d) a percent difference between the subject's post-treatment features in the one or more exposome features compared to one or more reference features derived from the exposome features of one or more features derived from the biochemical signature of a subject not participating in space travel.

[0255] In some embodiments, the trained predictive model (48) may comprise a trained classifier described elsewhere herein configured to provide a prediction (50) of a percent difference between one or more exposome features after a subject's treatment and one or more reference exposome features, as seen in FIG. 3B. In some embodiments, the trained predictive model may take as inputs (a) the subject's clinical data (44), (b) the pharmaceutical or nutraceutical treatment under consideration (46), and (c) one or more exposome features before the subject's treatment (49). In some embodiments, one or more pharmaceutical or nutraceutical treatments may be taken into account.

[0256] Exposome cluster analysis

[0257] In some embodiments, one or more exposome signature profiles of one or more subjects may be analyzed by clustering methods to categorize or group subjects based on whether or not the subjects participated in space travel, as seen in Figures 7A-7B. One or more subjects are represented by exposome data clusters (97), (100) of one or more exposome features (104). One or more exposome features of a subject may be compared to a cohort mean (102) for analysis or classification. Alternatively, the exposome features may be used to subtype subjects before, during, or after a clinical intervention to understand which subjects may respond positively, negatively, or have no response to a given intervention.

[0258] method

[0259] Aspects of the disclosure herein may include methods of intervention optimization and recommendations of optimal medications and / or dietary supplements for a subject to ameliorate the negative effects of space travel. In some embodiments, the methods described herein may be performed on the systems of the disclosure described elsewhere herein.

[0260] Optimizing interventions

[0261] In some embodiments, the disclosed method may include a method of optimizing an outcome of an intervention for a subject (60), as seen in FIG. 4. In some embodiments, the intervention may include a clinical trial in phase I, phase II, phase III, or any combination thereof. In some embodiments, the method includes (a) providing a trained predictive model (61), the trained predictive model being trained with one or more subject clinical metadata, exposome features, and corresponding intervention outcome information; (b) detecting features derived from a biochemical signature obtained from a biological sample from the subject seeking the intervention, thereby generating a retrospective exposome biochemical signature (62); (c) predicting predicted intervention outcome information for the subject seeking the intervention using the trained predictive model, the retrospective exposome biochemical signature profile and clinical meta of the subject seeking the intervention being inputs to the trained predictive model (64); and (d) selecting the subject for the intervention or excluding the subject from the intervention (66) based at least in part on the subject's predicted intervention outcome information. Alternatively, the intervention may include community testing, which may or may not be conducted in a clinical setting.

[0262] In some cases, one or more exposome features of a subject may be used to determine the efficacy or effectiveness of a given intervention. For example, as seen in Figures 6A-6D, exposome analysis of a subject's biological sample to generate an exposome feature may provide insight into the efficacy of a lead-based poisoning intervention. Figure 6A shows an exposome biochemical signature (81) with the x-axis being days and the y-axis being exposome signature intensity. Figure 6A represents an exposome signature for tin with the start point (77) and end point (79) of the intervention shown. Figures 6B-6D show contrasting exposome biochemical signatures for lead, calcium, and magnesium exposome signatures, respectively, for one or more subjects (85), (89), and (93) that did not receive the intervention compared to subjects (83), (87), and (91) that did receive the intervention. In this particular example, the exposome biochemical signature of lead in FIG. 6C may be observed to decrease, indicating that the intervention may prove effective. However, other exposome signatures, such as magnesium, may also be observed to increase, potentially resulting in undesirable effects. In some embodiments, such exposome features of biochemical signatures are utilized to monitor the effectiveness of the intervention or to recommend adjunctive interventions to compensate for indications of undesirable increases or decreases in one or more exposome features that are not the target of the intervention. In some embodiments, such approaches of intervention efficacy or efficacy analysis are used to repurpose the intervention for an unintended use or to assist with symptoms from space travel for which the intervention was not originally intended.

[0263] In some embodiments, the exposome features obtained from subjects undergoing an intervention are further analyzed using multiple analysis modules. In some embodiments, the first analysis module (module 1) focuses on the effect of clinical intervention on element signal intensity, where the time course of the intervention is established relative to the timing of exposome biochemical signature signal intensity. The time-varying signal intensity for the exposome biochemical signature can be attached to the timing of the clinical intervention, allowing the exposome biochemical signature signal intensity to be depicted as occurring before, at the same time as, or after the intervention. The exposome biochemical signature signal intensity during these periods can be summarized at the subject level via summary statistics, such as the mean or median exposome biochemical signature signal intensity detected during the period. The effect of the intervention can then be evaluated across subjects participating in the study through the application of a traditional general linear model, where the exposome biochemical signature signal intensity before and after the intervention is compared across all subjects to identify statistically significant differences in exposome biochemical signature signal intensity that correspond to the effect of the intervention. In this context, statistical significance is assessed by standard probabilistic hypothesis tests.

[0264] In some embodiments, the second analysis module (Module 2) may include a focus on the simultaneous effects of interventions on multiple exposome features, i.e., biochemical signature pathways. As in Module 1, this module may be applied where a time course of intervention is established for the timing of exposome biochemical signature signal strength, thereby allowing aggregation of pre-intervention, co-intervention, and post-intervention strength with descriptive statistics. Aggregate measurements obtained at the individual level are then pooled across clinical trial participants and used in building multivariate models. These may take the form of unsupervised analyses, such as principal component analysis, factor analysis, or related methods, whereby dimensionality reduction techniques are applied to derive metrics (principal components; factor scores) that summarize the exposome biochemical signature signal strength for multiple exposome biochemical signature pathways, which can then be used in subsequent general linear models, as in Module 1, to test hypotheses related to clinical interventions. 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 machine regression can be used to directly link the effect of an intervention to changes in exposome biochemical signature signal strength across multiple exposome biochemical signatures.

[0265] In some embodiments, the third analytical module (Module 3) is used in situations where the exact timing of the intervention is unknown or in embodiments where the effect of the intervention is expected to have a time lag effect, for example, when changes in exposome biochemical signature signal intensity do not appear for some time after treatment, or when the timing of changes induced by the treatment varies between individuals. In these cases, modeling strategies derived from econometrics may be used, particularly implementations of distributed lag models and related nonlinear methods. These methods may be extended, as in Module 2, to include simultaneous assessment of intervention effects on multiple exposome biochemical signatures, for example, through the implementation of lag-weighted quantile sum regression. Alternative analytical strategies may include the use of change-point detection methods via moving average methods, self-excited threshold autoregressive (SETAR) models, autoregressive moving average models (ARMA), Bayesian change-point detection, and related methodologies, particularly for longitudinal modeling and change-point detection.

[0266] In some embodiments, the fourth analysis module (module 4) may focus on the analysis of signal dynamics derived from the analysis of longitudinal biochemical signature profile signals, unlike conventional models. Signal dynamics in this context refers to parameters derived from the analysis of biochemical signature profile signal strength, and may include estimation of parameters describing underlying processes such as periodicity, entropy, and stationarity. One approach to achieve this may include the application of recursive quantitative analysis (RQA) to the individual biochemical signature profile signals measured in each target study participant. For each longitudinal measurement of a given biochemical signature profile pathway, application of RQA may result in multiple quantitative measures or features, including recursion rate, determinism, average length of diagonal, maximum length of diagonal, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum length of perpendicular, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion. These features may be extracted from multiple biochemical signature pathways and from the analysis of the interactions between pathways via cross-recursion quantitative analysis (CRQA) and used in subsequent analysis, where features from each subject are pooled and used to test for the effect of the whole study related to the intervention. This approach may be particularly applicable to case-control study designs where some subjects receive placebo treatment, and the interest is in distinguishing the difference in elemental signal dynamics between the intervention and placebo experimental conditions. In such situations, parameters derived from the RQA / CRQA may be tested in a conventional analytical framework, for example via a general linear model, to assess differences in signal parameters between treatment conditions. This approach may also be applicable in cases similar to those described in Modules 1 and 2, where the time course of a given treatment is known and there is interest in distinguishing pre-treatment, co-treatment, and post-treatment conditions.In this context, RQA / CRQA may be applied to either subsets of the total elemental traces corresponding to pre-treatment, co-treatment, and post-treatment conditions; alternatively, a variant of RQA / CRQA utilizing window binning techniques may be utilized to derive longitudinal measures of RQA / CRQA parameters, which may then be analyzed with the methods described in Modules 1, 2, or 3. In any combination of these conditions, features derived from dynamic signal analysis via RQA and related methods may also be used in supervised and unsupervised dimensionality reduction techniques, as described in Module 2, for subtyping of subjects based on biochemical signature profiles. These approaches may be used toward the goal of identifying patient / participant subtypes prior to intervention, or post-treatment, in order to link the effects of clinical interventions to relevant metabolic pathways. As seen in Figure 8, hair samples from subjects with autism spectrum disorder (ASD) were analyzed by this method. The resulting projections show the derivation of three ASD subtypes from the analysis of biochemical signature profiles, in this case by applying k-means clustering to the RQA of elemental profiles. The specification of the type of subject can then be used in subsequent clinical analysis and decision-making.

[0267] Pharmaceutical and nutritional recommendations

[0268] In some embodiments, the disclosed method may include a method of optimal pharmaceutical and / or nutritional supplement recommendations (68) for a subject participating in space travel, as seen in Figure 5. In some embodiments, the method includes (a) detecting features derived from one or more biochemical signatures obtained from one or more biological samples from one or more subjects not participating in space travel, thereby generating one or more reference exposome features (70); (b) detecting features derived from one or more biochemical signatures obtained from one or more biological samples from one or more subjects participating in space travel, thereby generating one or more features of a pre-treatment exposome feature (71); (c) administering a treatment to one or more subjects participating in space travel (72); and (d) detecting one or more exposome features obtained from one or more biological samples from one or more subjects participating in space travel after a period of time has elapsed since receiving the treatment. The method may include steps of: (a) determining (73) a difference between one or more features of a reference exposome feature of one or more subjects not participating in space travel, one or more pre-treatment exposome features of one or more subjects who participated in space travel, and one or more post-treatment exposome features of one or more subjects who participated in space travel; and (b) selecting one or more optimal treatments based at least in part on the determined difference between the one or more features of the reference exposome feature, the one or more pre-treatment exposome features, and the one or more post-treatment exposome features, wherein the one or more optimal treatments are selected based on the determined difference meeting a predetermined criterion.

[0269] In some embodiments, determining the difference between one or more reference exposome features of one or more subjects not participating in space travel, one or more pre-treatment exposome features of one or more subjects who participated in space travel, and one or more post-treatment exposome features of one or more subjects who participated in space travel may be implemented using a predictive model described elsewhere herein.

[0270] In some embodiments, the method of optimal pharmaceutical and / or nutraceutical recommendations for a subject is used to analyze the impact and / or significance of exposome biochemical signature pathways affected by an intervention (e.g., pharmaceutical and / or nutraceutical), as seen in Figures 16-18. In some cases, the exposome biochemical signature pathway signatures of the intervention and control groups may be compared. In some embodiments, the intervention includes the use of probiotics (Figure 16), a gluten-free diet (Figure 17), cannabidiol, zinc (Figure 18), or any combination thereof. In some cases, the intervention may be delivered as infant formula (Figure 18). The results of the exposome biochemical signature pathway signatures analysis may include recommended interventions whose exposome biochemical signature pathways are complementary or otherwise affected in a similar manner by space travel.

[0271] Determining health outcomes

[0272] In some embodiments, the present disclosure describes a method (2301) for outputting one or more quantitative metrics of one or more dynamic exposome features of a subject, as seen in Figure 22. The method may include the operations of (a) receiving a biological sample from a subject (2300), (b) determining one or more dynamic exposome signatures from the subject's biological sample (2302), (c) calculating a first one or more features of the one or more dynamic exposome signatures, each feature of the one or more features comprising one or more quantitative metrics (2304), and (d) outputting (2306) the one or more quantitative metrics of the one or more features of the subject.

[0273] In some embodiments, the method may further include outputting a health outcome for the subject based at least in part on the association of the normalized score of the one or more features of the subject to the normalized score of the second set of one or more features of the subject. In some cases, the one or more quantitative metrics of the one or more features of the second set of subjects may be stored in a database, the database being a local server or a cloud-based server. In some cases, the health outcome may include clinical subtypes, non-clinical subgroupings related to physiology, body mass index, behavioral index, socioeconomic index, body mass index, intelligence quotient, socioeconomic status, or any combination thereof. In some embodiments, the one or more features may include a measurement of the temporal dynamics of one or more dynamic exposome signatures.

[0274] In some examples, measurements of temporal dynamics may include linear slopes, nonlinear parameters describing the curvature of one or more dynamic exposome signatures, abrupt changes in intensity of one or more dynamic exposome signatures, changes in baseline intensity of one or more dynamic exposome signatures, changes in frequency domain representations of one or more dynamic exposome signatures, changes in power spectral domain representations of one or more dynamic exposome signatures, recursive quantification analysis parameters, cross-recursive quantification analysis parameters, joint recursive quantification analysis parameters, multidimensional recursive quantification analysis parameters, estimates of the Lyapunov spectrum, maximum Lyapunov exponents, or any combination thereof.

[0275] In some examples, one or more features of the one or more dynamic signatures may include phenotypic features, and the phenotypic features include electrocardiogram (ECG), electroencephalography, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome, or any combination of data thereof. In some examples, the one or more dynamic exposome signatures may be measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof. In some examples, the biological sample may include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof. In some examples, the phenotypic features may include molecular phenotypes. In some examples, the molecular phenotypes may be determined by supervised or unsupervised analysis, where the supervised or unsupervised analysis may include clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0276] In some embodiments, the recursion quantification analysis parameters may include recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recursion time, Shannon entropy at recursion time, and number of most likely recursions. In some embodiments, the method further includes analyzing one or more attractors by potential energy analysis, thereby generating a potential energy data space. In some cases, the one or more dynamic exposome signatures of the subject may include backward, forward, or any combination thereof dynamic exposome data. In some examples, the method may further include analyzing the dynamic relationship between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof, of one or more attractors. In some embodiments, the dynamic relationship is determined by mutually convergent mapping (CCM). The method of claim 1, further comprising reducing one or more dynamic exposome signatures to one or more reduced dynamic exposome signatures. In some examples, the method may further include constructing the network based on similarity of one or more attractor's temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recursions, or any combination thereof. In some examples, the method may further include analyzing one or more characteristics of the network to determine network connectivity, efficiency, feature importance, path importance, related graph theory based metrics, or any combination thereof.

[0277] Prediction of phenotypic data

[0278] In some embodiments, the disclosure describes a method (2401) for outputting predictions of phenotypic data of one or more subjects, as seen in Figure 23. The method includes steps of (a) receiving one or more biological samples and phenotypic data from a first set of subjects (2400), (b) determining a first set of exposome signatures from the one or more biological samples of the first set of subjects (2402), (c) calculating a first set of features of the first set of exposome signatures (2404), (d) training a predictive model using the first set of features and the phenotypic data of the first set of subjects (2406), and (e) calculating a first set of features of the first set of exposome signatures (2407). The method may include the operations of (2408) receiving one or more biological samples from a second set of subjects distinct from the first set of subjects; (f) determining a second set of exposome signatures from the one or more biological samples of the second set of subjects (2410); (g) calculating a second set of features of the second set of exposome signatures (2412); and (h) outputting a prediction of the second set of phenotypic data for the subjects determined by inputting the second set of features into the trained predictive model (2414).

[0279] In some embodiments, the first and second sets of features comprise one or more quantitative metrics. In some cases, the one or more quantitative metrics may include a measure of the temporal dynamics of the first and second sets of exposome signatures. In some examples, the measure of the temporal dynamics may include a linear slope, a nonlinear parameter describing the curvature of the first and second sets of exposome signatures, an abrupt change in the intensity of the first and second sets of exposome signatures, a change in the baseline intensity of the first and second sets of exposome signatures, a change in the frequency domain representation of the first and second sets of exposome signatures, a change in the power spectral domain representation of the first and second sets of exposome signatures, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional recursive quantification analysis parameter, an estimate of the Lyapunov spectrum or the maximum Lyapunov exponent, or any combination thereof.

[0280] In some examples, the first and second sets of features may 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 and second sets of exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof. In some cases, the one or more biological samples of the first and second subjects may include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0281] In some embodiments, the phenotypic characteristic comprises a molecular phenotype. In some examples, the molecular phenotype can be determined by unsupervised analysis, where the unsupervised analysis comprises clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof. In some examples, the recursion quantification analysis parameters may comprise recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion, or any combination thereof.

[0282] In some examples, the method may further include analyzing one or more attractors by potential energy analysis, thereby generating a potential energy data space. In some embodiments, the first and second sets of exposome signatures include backward looking, forward looking, or any combination thereof, exposome data.

[0283] In some embodiments, the method further analyzes dynamic relationships between one or more attractors' recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recursions, or any combination thereof. In some embodiments, the dynamic relationships are determined by convergent intersection mapping (CCM).

[0284] In some examples, the method may further include constructing the network based on similarity of one or more attractor's temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions, or any combination thereof. In some embodiments, the method further includes analyzing one or more characteristics of the network to determine network connectivity, efficiency, feature importance, path importance, associated graph theory based metrics feature importance, path importance, or any combination thereof.

[0285] Although the above steps represent each of a method or set of operations, one of ordinary skill in the art will recognize many variations based on the teachings described herein. Steps may be completed in different orders. Steps may be added or omitted. Some of the steps may include sub-steps. Many of the steps may be repeated as frequently as is useful.

[0286] One or more of the steps of each of the methods or sets of operations may be performed using one or more of the circuitry described herein, e.g., a processor or logic circuitry, such as programmable array logic for a field programmable gate array. The circuitry may be programmed to provide one or more of the steps of each of the methods or sets of operations described elsewhere herein, and the program may comprise program instructions stored on a computer readable memory, or programmed steps of a logic circuitry, e.g., programmable array logic or a field programmable gate array.

[0287] Applications to spatial health exposomes

[0288] In some aspects, the present disclosure describes a method for outputting one or more subject responses to participating in real or simulated space travel. Various exposures may affect a subject during real or simulated space travel. In some embodiments, real or simulated space travel may expose a subject to increased levels of radiation, increased levels of high energy radiation (e.g., gamma radiation, micro-radiation, X-rays, and UV radiation), high G-forces, extended low G-forces, altered atmospheric pressure, altered atmospheric composition, or any combination thereof. In some cases, real or simulated space travel may result in effects such as motion sickness, changes in bone density, bone deterioration, muscle deterioration, fluid redistribution, changes in cranial pressure, sensory confusion, psychological disorders, or any combination thereof. Various exposures and / or various effects of real or simulated space travel may generate an exposome signature in the subject's body or part thereof. In some embodiments, an exposome signature associated with space travel may be detectable or may be detected by the methods of the present disclosure. In some embodiments, the method includes receiving one or more biological samples of one or more subjects participating in space travel, hi some embodiments, the method includes determining one or more exposome signatures of the one or more biological samples.

[0289] FIG. 24 shows quantitative metrics derived from cross-replicated quantitative analysis of signal dyads from control animals housed in ground facilities and mice traveled to the International Space Station, as seen in some embodiments. Several significant differences in elemental dynamics between mice that experienced space travel and those that did not are shown, 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 may constitute detectable signatures of space travel. In some cases, detectable signatures may serve as biomarkers of spaceflight or as targets for ameliorative interventions.

[0290] FIG. 25 shows data derived from an analysis of barium, manganese, and zinc signals of human hair collected after spaceflight, as in some embodiments. The time-varying (expressed in days) magnitude of these signals relative to the timing of spaceflight can be observed in the signals, with the day of spaceflight identified as day 0. Notable changes in the dynamics of barium, manganese, and zinc were identified after spaceflight. This pattern of change provides a signature of spaceflight that can be used as a biomarker of stress or target in remediation efforts. Although a specific embodiment of the exposome signature was considered in this analysis, various other exposome signatures may also provide a signature for spaceflight.

[0291] In some embodiments, the method includes outputting a response of one or more subjects to participating in space travel by providing an exposome signature of one or more biological samples as an input to a trained predictive model. In some embodiments, the trained predictive model is configured to determine a likelihood that the exposome signature is associated with a metabolic signature of one or more subjects participating in space travel. In some embodiments, the metabolic signature may be indicative of a current symptom, an underlying cause of a current symptom, a prediction of a symptom, a prediction of the onset of a disease, or any combination thereof.

[0292] In some embodiments, the trained predictive model is configured to determine whether the subject's metabolism is in a baseline state not associated with space travel. In some embodiments, the base condition may be based at least in part on measurements made on the subject when the subject is on Earth. In some embodiments, the base condition may be based at least in part on measurements made on the subject after the subject has been in space for a predetermined duration (e.g., one week, one month, etc.).

[0293] In some embodiments, each feature of the exposome feature comprises one or more quantitative metrics, various examples of which are disclosed elsewhere in this application.

[0294] In some embodiments, one or more biological samples of a subject participating in space travel are collected before, during, after, or any combination of the duration of the space travel. In some embodiments, one or more biological samples may be analyzed to determine the suitability of a given subject for space travel when collected before space travel. In some embodiments, one or more biological samples may be analyzed to determine a therapeutic intervention for a given subject when collected during space travel. In some embodiments, one or more biological samples may be analyzed to determine a recovery intervention for a given subject when collected after space travel.

[0295] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0296] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0297] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0298] In some embodiments, the exposome features are derived from one or more attractors. In some embodiments, the method further comprises analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

[0299] In some embodiments, the trained predictive model includes a classification algorithm. In some embodiments, the classification algorithm is configured to identify samples associated with participating in space travel. In some embodiments, the classification algorithm is trained using exposome features and corresponding responses of one or more biological samples of one or more subjects. In some embodiments, the one or more subjects have participated in space travel. In some embodiments, the classification algorithm is configured to determine whether the one or more subjects have a metabolic signature associated with space travel.

[0300] In some embodiments, the response comprises a metabolic state of the subject, hi some embodiments, the metabolic state of the subject comprises a state consistent with a healthy state or a subject undergoing space travel.

[0301] Figure 26 shows the average Ba:CA dynamics, Zn:Fe dynamics in mice that consume a control diet or mice that consume iodine supplementation to their diet, as seen in some embodiments. Iodine supplementation significantly reduces 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 signatures, and the results show how the effects of ameliorative treatments can be related to the action of ameliorative effects related to molecular mechanisms and / or effectors.

[0302] Figure 37 shows the average Ba:CA dynamics, Zn:Fe dynamics in mice fed food bar diet or standard laboratory chow diet, as seen in some embodiments. It can be seen that dietary manipulation can lead to significant changes in the levels of barium:calcium (P=0.004) and zinc:iron (P=0.02) dynamics. The results show the effect of ameliorative treatment, e.g., nutritional intervention, and the association of the action of ameliorative effect with molecular mechanisms and / or effectors.

[0303] The studies shown in Figures 26 and 27 show that a statistically significant exposome signature can be induced in organisms undergoing space travel. In various embodiments disclosed herein, the exposome signature can serve as a basis for training a machine learning algorithm to detect symptoms associated with space travel, resistance to space travel, potential therapeutic interventions for treating conditions associated with space travel, for example, in various embodiments disclosed herein, the exposome signature can serve as a basis for predicting a subject's resistance to space travel using a machine learning algorithm, or potential therapeutic interventions for treating conditions associated with space travel, for example.

[0304] In some aspects, the disclosure describes a method for training a predictive model to determine a subject's clinical response to an intervention when the subject is participating in space travel. In some embodiments, a first set of subjects is exposed to space travel and the intervention is provided. In some aspects, the method includes receiving one or more biological samples and a clinical response of a second set of subjects. In some embodiments, the second set of subjects is exposed to space travel and does not receive the intervention. In some embodiments, the method includes determining one or more exposome signatures of one or more biological samples of the first and second set of subjects. In some embodiments, the method includes calculating a first and second set of exposome features of the one or more exposome signatures of the first and second set of subjects. In some embodiments, each feature of the first and second set of exposome features includes one or more quantitative metrics. In some embodiments, the method includes outputting a trained predictive model configured to determine a subject's clinical response to the intervention. In some embodiments, the subjects are participating in space travel. In some embodiments, the trained predictive model is trained on one or more exposome features, one or more quantitative metrics, and corresponding clinical responses of a first set and a second set of subjects. 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 are exposed. In some embodiments, the intervention comprises a diet, a pharmaceutical treatment, or any combination thereof. In some embodiments, the diet comprises iodine supplementation.

[0305] In some embodiments, the method further includes determining an effect of space travel on a first set of subjects exposed to the intervention, at least in part, by comparing one or more quantitative metrics of the first and second sets of exposome features.

[0306] In some embodiments, the first set or the second set of exposome features comprises a measurement of the temporal dynamics of one or more exposome signatures. In some embodiments, the measurement of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursions. Various statistical analyses of the exposome signature may be used.

[0307] 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, fluorescent immunohistochemical staining, or any combination thereof. In some embodiments, one or more exposome signatures can be measured from gene sequencing, proteomics, or both.

[0308] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0309] In some embodiments, the first set or the second set of exposome features are derived from one or more attractors. In some embodiments, the method further comprises analyzing a dynamic relationship between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

[0310] In some embodiments, the first set or the second set of subjects are humans, non-human mammals, or any combination thereof.

[0311] In some aspects, the disclosure describes a method for generating a predictive model configured to output a health state of one or more subjects as a function of gravity in addition to the gravity of the Earth. In some embodiments, the method includes receiving one or more biological samples of a first one or more subjects and corresponding health states. In some embodiments, the first one or more subjects have been exposed to gravity in addition to the gravity of the Earth. In some embodiments, the method includes determining one or more exposome signatures of one or more biological samples of the first one or more subjects. In some embodiments, the method includes calculating exposome features of the one or more exposome signatures of the one or more biological samples. In some embodiments, the method includes generating a trained predictive model configured to output a health state of a second one or more subjects as a function of gravity in addition to the gravity of the Earth. In some embodiments, the trained predictive model is trained using the exposome features of the first one or more subjects of the one or more exposome signatures and corresponding health states.

[0312] In some embodiments, the gravity comprises at least 1 G, at least 2 G, at least 3 G, or at least 4 G. In some embodiments, the trained predictive model is configured to determine a likelihood that the exposome feature is associated with a metabolic signature of the first or second one or more subjects exposed to gravity in addition to the gravity of the Earth. In some embodiments, the probability threshold determined by the trained predictive model indicates that the metabolism of the first or second one or more subjects is in a baseline state that is not associated with exposure to gravity in addition to the gravity of the Earth.

[0313] In some embodiments, the exposome features include a measurement of the temporal dynamics of one or more exposome signatures. 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 exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0314] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0315] In some embodiments, the exposome features are derived from one or more attractors. In some embodiments, the method further comprises analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

[0316] In some embodiments, the first one or more subjects and the second one or more subjects include human subjects, non-human mammalian subjects, vertebrate subjects, or any combination thereof.

[0317] In some embodiments, the trained predictive model includes a classification algorithm. In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of the first one or more subjects and the second one or more subjects associated with exposure to gravity in addition to the gravity of the Earth. In some embodiments, the classification algorithm is trained using the exposome features of one or more biological samples of the first one or more subjects and the corresponding health status. In some embodiments, the first one or more subjects were exposed to gravity in addition to the gravity of the Earth. In some embodiments, the classification algorithm is configured to determine whether the first or second one or more subjects have a metabolic signature associated with exposure to gravity in addition to the gravity of the Earth.

[0318] In some embodiments, the health state comprises a state of the metabolism of the first or second one or more subjects. In some embodiments, the metabolism of the first or second one or more subjects comprises a healthy state or a state consistent with a subject exposed to gravity in addition to the gravity of the Earth.

[0319] In some aspects, the present disclosure describes a method for outputting a health outcome of one or more subjects prior to exposure to gravity using a predictive model. In some embodiments, the method includes receiving one or more biological samples of one or more subjects. In some embodiments, the method includes determining one or more exposome signatures of the one or more biological samples of the one or more subjects. In some embodiments, the method includes calculating an exposome feature of the one or more exposome signatures of the one or more biological samples. In some embodiments, the method includes outputting a health outcome of one or more subjects prior to exposure to gravity in addition to the gravity of the Earth by providing the exposome features of the one or more subjects as input to a trained predictive model.

[0320] In some embodiments, the gravity force comprises at least 1 G, at least 2 G, at least 3 G, or at least 4 G. In some embodiments, the gravity force comprises at least 5, 6, 7, 8, 9, or 10 G. In some embodiments, the gravity force comprises up to 6, 7, 8, 9, or 10 G.

[0321] In some embodiments, the trained predictive model is configured to determine the likelihood that an exposome feature is associated with a metabolic signature of one or more subjects exposed to gravity.

[0322] In some embodiments, a probabilistic threshold determined by the trained predictive model indicates that the metabolism of the first one or more subjects or the second one or more subjects is in a baseline state that is not associated with exposure to gravity in addition to the Earth's gravity.

[0323] In some embodiments, each feature of the exposome feature includes one or more quantitative metrics.

[0324] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0325] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0326] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and 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, fluorescent immunohistochemical staining, or any combination thereof.

[0329] In some embodiments, the exposome features are derived from one or more attractors.

[0330] In some embodiments, the method further comprises analyzing dynamic relationships between one or more attractors' signal recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0331] In some embodiments, the one or more subjects include a human, a non-human mammal, a vertebrate, or any combination thereof.

[0332] In some embodiments, the trained predictive model comprises a classification algorithm.

[0333] In some embodiments, the classification algorithm is configured to identify one or more exposome signatures of the subject that are associated with exposure to gravity in addition to the Earth's gravity.

[0334] In some embodiments, the classification algorithm is trained using exposome features and corresponding responses of one or more biological samples of one or more subjects. In some embodiments, the one or more subjects are exposed to gravity in addition to the gravity of the Earth.

[0335] 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 the Earth's gravity.

[0336] In some aspects, the disclosure describes a method for administering a treatment to a subject participating in space travel. In some embodiments, the method includes receiving one or more biological samples of the subject prior to participating in space travel. In some embodiments, the method includes determining one or more exposome signatures of the one or more biological samples of the subject. In some embodiments, the method includes calculating an exposome feature of the one or more exposome signatures of the subject. In some embodiments, each feature of the exposome feature includes one or more quantitative metrics. In some embodiments, the method includes administering a treatment to a subject participating in space travel at a time, the treatment and the time to administer the treatment are determined as an output of a trained predictive model when the trained predictive model is provided with the subject's exposome feature as an input. In some embodiments, the trained predictive model is configured to output an efficacy of the treatment.

[0337] In some embodiments, the trained predictive model is configured to output a measurement of whether a treatment is effective.

[0338] In some embodiments, the time points include times before, during, after the subject participates in the space trip, or any combination thereof.

[0339] In some embodiments, treatment is provided to mitigate 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.

[0340] In some embodiments, the efficacy of treatment is determined by the period after the subject has participated in space travel, and the subject's exposome characteristic comprises a baseline exposome characteristic of a subject not participating in space travel.

[0341] In some embodiments, the trained predictive model comprises a classification algorithm.

[0342] In some embodiments, space travel involves traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

[0343] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0344] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0345] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0346] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0347] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0348] In some embodiments, the exposome features are derived from one or more attractors.

[0349] In some embodiments, the method further comprises analyzing dynamic relationships between one or more attractors' signal recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0350] In some embodiments, the subject is a human, a non-human mammal, or any combination thereof.

[0351] In some embodiments, the trained predictive model comprises a classification algorithm.

[0352] In some embodiments, the classification algorithm is trained using exposome features of one or more biological samples from one or more subjects, the treatments administered, the time points at which the treatments were administered, and the corresponding clinical responses, and one or more subjects participated in space travel.

[0353] In some embodiments, the classification algorithm is configured to determine whether one or more subjects have space travel-induced metabolic dysregulation. In some embodiments, the metabolic dysregulation may include altered expression of genes associated with metabolism, mutations in genes associated with metabolism, or both.

[0354] In some aspects, the 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 a first set of one or more biological samples of a first set of subjects and a second set of one or more biological samples of a second set of subjects at each of a plurality of locations along a baseline. In some embodiments, the method includes obtaining a plurality of ion samples. In some embodiments, each ion sample in the plurality of ion samples corresponds to a different location in the plurality of locations. In some embodiments, each location in the plurality of locations represents a different growth period of the first and second sets of one or more biological samples. 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, the first and second sets of subjects are administered an intervention.

[0355] In some embodiments, the method includes analyzing each ion sample in the plurality of ion samples with a mass spectrometer. In some embodiments, the method includes acquiring 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 elemental isotope in a plurality of elemental isotopes over time collectively determined from the plurality of ion samples.

[0356] In some embodiments, the method includes deriving a second dataset from the plurality of traces, the second dataset including a set of features, hi some embodiments, each respective feature of the set of features is determined by a change in a single isotope or a combination of isotopes in the plurality of traces.

[0357] In some embodiments, the method includes inputting the set of features into a trained classifier, thereby obtaining from the trained classifier a probability of the first and second sets of one or more subjects that predicts a response of the one or more subjects to space travel.

[0358] In some embodiments, the probability indicates that one or more of the biological samples are dysregulated as a result of space travel.

[0359] In some embodiments, the efficacy of an intervention is measured by the probability of dysregulation.

[0360] In some embodiments, the intervention comprises a diet, a pharmaceutical treatment, or any combination thereof.

[0361] In some embodiments, the dietary regimen includes iodine supplementation.

[0362] In some embodiments, the intervention improves radiation exposure.

[0363] In some embodiments, the first set or second set of subjects comprises humans, non-human mammals, or any combination thereof.

[0364] In some embodiments, the non-human mammal comprises a mouse.

[0365] In some embodiments, the first set or second set of one or more biological samples comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0366] In some embodiments, the method includes treating the first set or the second set of one or more biological samples with a solvent or detergent prior to the sampling step.

[0367] In some embodiments, the method includes irradiating the first or second set of one or more biological samples with a low power laser to remove debris from the first or second set of one or more biological samples prior to the sampling step.

[0368] In some embodiments, the sampling step includes irradiating the first or second set of one or more biological samples of the first or second set of subjects with a laser, thereby extracting a plurality of particles from the first or second set of one or more biological samples. In some embodiments, the sampling step includes ionizing the plurality of particles using an inductively coupled plasma mass spectrometer, thereby obtaining a plurality of ion samples.

[0369] In some embodiments, the subject's first set or second set of responses comprises an adverse response, a positive response, or a neutral response.

[0370] In some embodiments, the trained classifier is configured to identify whether the sample was collected from a subject that participated in space travel.

[0371] In some embodiments, the trained classifier is trained using a set of features and corresponding responses of one or more biological samples of a first set of subjects.

[0372] In some embodiments, the trained classifier is configured to determine whether the subject suffers from space travel-induced metabolic dysregulation.

[0373] In some aspects, the disclosure describes a method for outputting a subject's response to an intervention following space travel. In some embodiments, the method includes exposing one or more biological samples of the subject to an optical signal comprising a plurality of Raman wavelengths. In some embodiments, the subject undergoes space travel. In some embodiments, the method includes obtaining a plurality of Raman spectra from the one or more biological samples. In some embodiments, the method includes processing the plurality of Raman spectra to generate a plurality of Raman features. In some embodiments, the method includes outputting a probability of the subject's response to the intervention upon undergoing space travel as a result of providing an input of the plurality of Raman features to a trained classifier.

[0374] In some embodiments the probability indicates that the subject is dysregulated as a result of space travel.

[0375] In some embodiments, the efficacy of an intervention is measured by the probability of dysregulation.

[0376] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0377] In some embodiments, the intervention comprises a dietary therapy, a pharmaceutical treatment, or any combination thereof.

[0378] In some embodiments, the dietary regimen includes iodine supplementation.

[0379] In some embodiments, the intervention ameliorates radiation exposure.

[0380] In some embodiments, the subject includes a human, a non-human mammal, or any combination thereof.

[0381] In some embodiments, the non-human mammal comprises a mouse.

[0382] In some embodiments, the plurality of Raman spectra comprises from about 200 to about 3700 wavenumbers.

[0383] In some embodiments, the obtaining comprises using a Raman spectroscopic microscope.

[0384] In some embodiments, the plurality of Raman features comprises one or more traces derived from a Raman spatial map.

[0385] In some embodiments, one or more traces are reduced using a dimensionality reduction algorithm, hi some embodiments, the dimensionality reduction algorithm comprises principal component analysis, independent component analysis, or any combination thereof.

[0386] In some embodiments, measurements of temporal dynamics are derived from one or more traces, where the temporal dynamics include: determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0387] In some embodiments, measuring the time dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recursion rate, determinant, average diagonal length, maximum diagonal length, branching, Shannon entropy of diagonal length, recursion tendency, laminar flow, trapping time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in recursion time, or number of most likely recursions.

[0388] In some embodiments, the trained classifier comprises a gradient augmented ensemble model.

[0389] In some embodiments, the trained classifier is configured to process one or more features selected from the group consisting of laminar flow, entropy, trapping time (TT), mean diagonal length (MDL), recursion time (RT), Vmax, determinism, Lmax, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency to recursion, maximum vertical line length, Shannon entropy in vertical line length, mean recursion time, Shannon entropy in recursion time, number of most likely recursions, or any combination thereof.

[0390] In some embodiments, the trained classifier is configured to process two or more features selected from the group consisting of laminar flow, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, Lmax, maximum diagonal length, divergence, Shannon entropy on diagonal length, tendency to recur, maximum vertical line length, Shannon entropy on vertical line length, mean recurrence time, Shannon entropy on recurrence time, number of most likely recursions, or any combination thereof.

[0391] In some embodiments, the trained classifier is configured to process 1, 2, 3, 4, 5, 6, 7, or 8 features selected from the group consisting of laminar flow, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, Lmax, maximum diagonal length, divergence, Shannon entropy on diagonal length, tendency to recurrence, maximum vertical line length, Shannon entropy on vertical line length, mean recurrence time, Shannon entropy on recurrence time, number of most likely recursions, or any combination thereof.

[0392] In some embodiments, the trained classifier is trained to identify Raman signatures associated with space travel.

[0393] In some embodiments, the probability output from the trained classifier indicates that the object exhibits time dynamics associated with space travel.

[0394] In some embodiments, the trained classifier is trained using a population of Raman features of humans or non-human animals, where the population of humans or non-human animals have undergone space travel.

[0395] In some embodiments, the efficacy of the intervention is measured by a change in the subject's time dynamics towards the subject's baseline time dynamics prior to space travel.

[0396] In some aspects, the disclosure discloses 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 set of one or more biological samples of a first set of subjects and a second set of one or more biological samples of a second set of subjects to generate a first and second set of stained one or more biological samples. 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, the first and second sets of subjects are administered the intervention. In some embodiments, the method includes analyzing the fluorescence intensity spatially across the stained first and second sets of one or more biological samples. In some embodiments, the method includes calculating a set of spatial fluorescence intensity features of the first and second sets of subjects across the stained first and second sets of one or more biological samples. In some embodiments, each feature of the exposome feature of the first and second sets of subjects includes one or more metrics that quantify the temporal dynamics in the fluorescence intensity trace.

[0397] In some embodiments, the method includes outputting as an output of the trained predictive model a probability of a subject's response to an intervention to reduce a biological effect of space travel when a subject's exposome signature of spatial fluorescence intensity across one or more stained biological samples of the subject is provided to the predictive model. In some embodiments, the trained predictive model is trained using the first and second sets of subject's exposome signatures, corresponding administered interventions, and clinical responses to the administered interventions.

[0398] In some embodiments, the trained predictive model is configured to determine whether a subject's biological sample is dysregulated via space travel.

[0399] In some embodiments, the trained predictive model is configured to determine an effect of an intervention as a measure of intervention efficacy based at least in part on an analysis of fluorescence intensity spatially across one or more biological samples.

[0400] In some embodiments, the first or second set of one or more biological samples comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0401] In some embodiments, the intervention comprises a dietary regimen, a pharmaceutical treatment, or any combination thereof.

[0402] In some embodiments, the dietary regimen includes iodine supplementation.

[0403] In some embodiments, the intervention ameliorates radiation exposure.

[0404] In some embodiments, the first or second set of subjects comprises humans, non-human mammals, or any combination thereof.

[0405] In some embodiments, the non-human mammal comprises a mouse.

[0406] In some embodiments, the trained predictive model comprises a gradient augmented ensemble model.

[0407] In some embodiments, measuring the temporal dynamics of the spatial fluorescence intensity trace includes: determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0408] In some embodiments, measuring the time dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recursion rate, determinant, average diagonal length, maximum diagonal length, branching, Shannon entropy of diagonal length, recursion tendency, laminar flow, trapping time, maximum vertical line length, Shannon entropy in vertical line length, average recursion time, Shannon entropy in recursion time, or number of most likely recursions.

[0409] In some embodiments, the analyzing step comprises obtaining first and second sets of fluorescent images of stained one or more biological samples and analyzing the spatial fluorescent intensity of the fluorescent images.

[0410] In some embodiments, obtaining a fluorescent image of the stained tooth sample comprises using an inverted or non-inverted confocal microscope.

[0411] In some embodiments, staining the first set of one or more biological samples of the first set of subjects and the second set of subjects comprises using a C-reactive stain. In some embodiments, staining the first and second sets of one or more biological samples comprises decalcifying the first and second sets of one or more biological samples.

[0412] Figure 24 shows quantitative metrics derived from cross-replicate quantitative analysis of signal dyads from control animals housed in a ground facility and mice traveled to an international space station, as described in some embodiments herein. Statistically significant differences in ion dynamics were observed between the control and flight cohorts of mice, with Ba:Ca dynamics being the most evident.

[0413] In some embodiments, the trained model is selected from the group consisting of 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.

[0414] In some embodiments, the trained model comprises a gradient augmented decision tree.

[0415] In some embodiments, the trained predictive model comprises a classification algorithm.

[0416] In some embodiments, a classification algorithm is trained to identify spatial fluorescence intensity features associated with space travel.

[0417] In some aspects, the present disclosure describes a method for outputting one or more routes associated with space travel. In some embodiments, the method includes receiving one or more biological samples of a first set of one or more subjects and a second set of one or more subjects. In some embodiments, one or more subjects of the first set are participating in space travel and one or more subjects of the second set are not participating.

[0418] In some embodiments, the method comprises determining an exposome signature of one or more pathways from one or more biological samples of a first and second set of one or more subjects.

[0419] In some embodiments, the method includes calculating an exposome feature of an exposome signature of one or more pathways.

[0420] In some embodiments, the method includes outputting one or more routes associated with space travel by comparing the first and second subject exposome characteristics of the one or more routes.

[0421] In some embodiments, each feature of the exposome feature includes one or more quantitative metrics.

[0422] In some embodiments, space travel involves traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

[0423] In some embodiments, the one or more biological samples include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0424] In some embodiments, the exposome characteristics include measurements of the time dynamics of one or more exposome signatures.

[0425] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0426] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0427] 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, fluorescent immunohistochemical staining, or any combination thereof.

[0428] In some embodiments, the exposome features are derived from one or more attractors.

[0429] In some embodiments, the method further comprises analyzing dynamic relationships between one or more attractors' signal recurrence rates, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0430] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0431] In some aspects, the disclosure describes a method for providing a treatment to ameliorate the effects of space travel. In some embodiments, the method includes receiving one or more biological samples of one or more subjects participating in the space travel. In some embodiments, the method includes determining an exposome signature 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 an exposome feature of the exposome signature of the one or more pathways. In some embodiments, the method includes providing a treatment to the one or more subjects. In some embodiments, the treatment ameliorates the effects of space travel on the one or more subjects' exposome feature of the exposome signature of the one or more pathways.

[0432] In some embodiments, the treatment comprises a diet, a medicine, a dietary supplement, a food bar, or any combination thereof.

[0433] In some embodiments, each feature of the exposome feature includes one or more quantitative metrics.

[0434] In some embodiments, space travel involves traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

[0435] In some embodiments, the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0436] In some embodiments, the exposome characteristics include measurements of the temporal dynamics of one or more exposome signatures.

[0437] In some embodiments, measuring the temporal dynamics includes determining a linear slope, determining a number of nonlinear parameters describing the curvature of one or more exposome signatures, determining an abrupt change in intensity of one or more exposome signatures, determining one or more changes in baseline intensity of one or more exposome signatures, determining a change in a frequency domain representation of one or more exposome signatures, determining a change in a power spectral domain representation of one or more exposome signatures, 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.

[0438] In some embodiments, measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

[0439] In some embodiments, the exposome signature is measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0440] In some embodiments, the exposome features are derived from one or more attractors.

[0441] In some embodiments, the method includes analyzing dynamic relationships between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof.

[0442] In some embodiments, the one or more subjects are humans, non-human mammals, vertebrates, or any combination thereof.

[0443] Working Example

[0444] Example 1: Obtaining an exposome biochemical signature from a biological sample

[0445] Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) was used to obtain temporal exposome biochemical signatures from biological samples. In this particular example, hair shaft samples were used to obtain chemical exposome data. A hair shaft was taken from a subject and the sample was pretreated by washing with one or more solvents and / or surfactants followed by drying. In particular, the hair shaft sample was washed in TRITON X-100 and ultra-pure metal-free (e.g., MILLI-Q water) and dried overnight in an oven (e.g., at 60°C). Pretreatment further includes preparing the hair shaft for measurement by placing the hair shaft on a glass slide (e.g., a microscope glass slide) with an adhesive film (e.g., double-sided tape). The hair shaft is positioned so that the hair shaft is substantially straight. The glass slide with the hair shaft is then placed into the LA-ICP-MS system for analysis.

[0446] The analysis begins with the LA-ICP-MS system completing a pre-ablation step that ablates the hair shaft sample to remove surface debris and / or impurities from the hair shaft. Pre-ablation is performed at low laser energy, approximately 10% of the laser energy, which releases particles only above the surface of the hair shaft sample but not from below the surface. Pre-ablation is performed with a laser wavelength of 193 nm and 0.6 J / cm 2 (For example, the laser energy is 0.6 J / cm 2 , 0.5J / cm 2 , 0.4J / cm 2 , 0.3J / cm 2 , 0.2J / cm 2 , 0.1J / cm 2 The method is carried out using a laser energy of less than 1000 .mu.m.

[0447] After pre-ablation, the LA-ICP-MS system irradiates the hair shaft sample with laser energy less than 1.8 J / cm2 with a laser spot size of 10 μm to 30 μm to obtain chemical samples from each location along the reference line of the hair shaft sample. Each location along the hair shaft approximates 2.2 hours or 130 minutes of the subject's life. The laser irradiation spatially irradiates the entire length of the hair shaft, generating particles at any location of the hair shaft, which are then ionized with inductively coupled plasma. The mass spectrometer then analyzes the resulting chemical samples, providing a respective chemical data readout of which chemicals are present in what amounts at a given spatial location. This process is repeated, with chemical data being collected sequentially at multiple locations along the hair shaft, from the root of the hair shaft to the tip of the hair shaft, which is furthest from the root. The position data, indicating the time, and the respective isotopes present at each location of the hair shaft, are correlated for further analysis.

[0448] The output data is further analyzed and processed by spike removal, which removes extreme peaks in the exposome biochemical signature and smooths the data. Outliers are identified by calculating the mean absolute difference between each adjacent measurement along the hair shaft. Values ​​that show a mean absolute difference from the preceding point that exceeds three standard deviations of the mean are flagged as outliers. These outliers are then replaced with a moving median filter, which calculates the moving median of the original exposome biochemical signature in bins of 10 adjacent data points.

[0449] The processed data can then be used in a variety of bioinformatic analysis methods to identify the effects of clinical interventions on elemental signal intensities and signal dynamics.

[0450] Example 2: Dynamic molecular profiling in dental samples to determine disease risk

[0451] Using the disclosed methods and systems, molecular profiles in dental samples were generated and then analyzed to determine disease risk in subjects. In general, the temporal dynamics of biological responses (e.g., inflammation) were found to be imprinted in the sample (e.g., dental sample) and can be analyzed to determine disease risk in subjects. Dynamic molecular profiles were created for C-reactive protein (CRP), a marker of inflammation. Using dental biomarkers, dynamic time series profiles of CRP and inflammation were generated during a period including fetal (prenatal) development and early childhood in two sets of children, the first set with autism spectrum disorder (37 cases) and the second set without autism spectrum disorder (77 controls). The time series CRP profiles were analyzed to reveal novel features of the dynamics of the CRP signal, which accurately distinguished autism cases from controls. For example, the inflammation profile present before 1 year of age was highly differential between cases and controls. In comparison, clinical diagnosis of autism is usually determined around 3-4 years of age.

[0452] A raw tooth sample was obtained from each pediatric subject. The tooth sample was dissected, demineralized, and an immunohistochemical stain (e.g., dentin) was applied to the tooth sample. The immunohistochemical stain effectively mapped C-reactive protein (a molecular marker of inflammation) along the growth ring of the tooth sample to develop a temporal profile of inflammation across the prenatal and postnatal periods. The machine learning algorithms of the present disclosure were used to analyze the temporal profile and train highly accurate classifiers to determine disease risk (e.g., autism).

[0453] Figure 12 shows an example of a subject's daily C-reactive protein profile over time, with the y-axis showing CRP intensity and the x-axis showing developmental age. The child subject's developmental age included a period ranging from the second trimester (e.g., starting 140 days before birth, when the subject was in the prenatal stage) to approximately 6 months of age. As shown in Figure 12, the inflammation (as indicated by CRP intensity) profile of an autistic child with high prenatal CRP intensity.

[0454] Example 3: Detection of space travel signatures in animal model hair samples

[0455] Between-subject experimental studies were conducted on mice housed in ground facilities (N=10) and mice 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 for multiple elements, which were then compared between experimental conditions. Representative data of signal dynamics of multiple elements are seen in FIG. 24. This approach revealed several significant differences in elemental dynamics between mice that had and 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. Dysregulation of these patterns constitutes a detectable signature of space travel, which may serve as a biomarker of spaceflight or as a target for ameliorative interventions.

[0456] Example 4: Detection of space travel signatures in human hair samples

[0457] The elemental dynamics of human hair samples from individuals who traveled to space via the commercial spaceflight initiative (Blue Origin) were analyzed by the methods and systems described elsewhere herein. The dynamics of longitudinal signal intensity of several fundamental pathways were quantified. In particular, elemental pathways identified from animal model studies as dysregulated by space travel, including barium, manganese, and zinc, were analyzed. In FIG. 25, the magnitude of the time variation (expressed in days) of these signals relative to the timing of spaceflight can be observed, with the day of spaceflight being day 0. Conclusively, and consistent with the patterns observed in the animal model study of Example 3, we identified significant changes in the dynamics of barium, manganese, and zinc after spaceflight. This pattern of change provides a spaceflight signature that can be used as a biomarker of stress or target in remedial efforts.

[0458] Example 5: Detection of ameliorative treatments in animal model hair samples

[0459] To determine the effects of spaceflight ameliorative treatments, a between-subject experimental study was conducted with mice housed in a ground facility (N=20). In this particular study, one group (N=10) of animals received iodine supplements (4 ppm in the water supply) and two groups (N=10) of animals did not receive iodine supplements. From each group, we collected hair (beard) samples for analysis by the methods and systems described elsewhere herein. In FIG. 26, it can be seen that iodine supplementation significantly reduced the levels of barium:calcium (P=0.01) and zinc:iron (P=0.0001) dynamics. Importantly, given previous findings showing that space travel reduces the levels of these signatures (see Example 1), these findings highlight how these methods can be used to demonstrate the effects of ameliorative treatments and link the action of ameliorative effects to molecular mechanisms and / or effectors.

[0460] Example 6: Detecting dietary effects on space travel signatures

[0461] A between-subject experimental study with mice housed in a ground facility (N=20) was conducted to determine the dietary effects on the space travel signature. In this study, one group of mice (N=10) was fed standard rodent chow and a second group (N=10) was fed rodent food bars. From each group, we collected hair (whisker) samples for analysis by the methods and systems described elsewhere herein. From FIG. 27, it can be seen that dietary manipulation led to significant changes in the levels of barium:calcium (P=0.004) and zinc:iron (P=0.02) dynamics. Importantly, given previous findings showing that space travel reduces the levels of these signatures (see Example 1), these findings highlight how these methods can be used to demonstrate the effects of ameliorative treatments, particularly nutritional interventions; the action of the ameliorative effects can be linked to molecular mechanisms and / or effectors.

[0462] Embodiment

[0463] Embodiment 1. A computer-implemented exposomics system comprising: (a) an exposome biochemical signature database (EDB), the EDB including a plurality of exposome features of a plurality of subjects who have participated in space travel and who have not; (b) an intervention outcome database (IODB), the IODB including information regarding intervention outcome information for at least one phase of at least one intervention; and (c) a computer processor including: (i) an association software module communicatively connected to the EDB and the IODB programmed to determine an association between the exposome features, clinical phenotype information, and intervention outcome information of at least one of the plurality of subjects; and (ii) a recommendation software module communicatively connected to the EDB and the IODB programmed to provide an intervention recommendation for at least one of the plurality of subjects based at least in part on the exposome features, clinical phenotype information, intervention outcome information information of at least one of the plurality of subjects, and the association between the exposome features, clinical phenotype information, and intervention outcome information.

[0464] Embodiment 2. The system of embodiment 1, further comprising a clinical database (CDB) comprising clinical phenotype information for a plurality of subjects.

[0465] 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 distinct exposome biochemical signatures.

[0466] Embodiment 4. The system of embodiment 1, wherein the intervention outcome information includes classification of non-responders, adverse responders, and positive responders for at least one intervention.

[0467] Embodiment 5. The system of embodiment 1, wherein the intervention outcome comprises one or more inclusion or exclusion criteria for at least one intervention.

[0468] Embodiment 6. The system of embodiment 1, wherein the exposome biochemical signature is obtained by assaying biological samples from multiple subjects.

[0469] Embodiment 7. The system of embodiment 6, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, a physiological parameter, or any combination thereof.

[0470] Embodiment 8. The system of embodiment 6, wherein assaying comprises 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.

[0471] Embodiment 9. The system of embodiment 8, wherein the mass spectrometry measurements include measurements of one or more chemicals.

[0472] Embodiment 10. The system of 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.

[0473] Embodiment 11. The system of embodiment 1, wherein the exposome signature comprises dynamic temporal biochemical response signatures of multiple subjects.

[0474] Embodiment 12. The system of embodiment 1, wherein the corresponding plurality of exposome features for corresponding subjects in the plurality of subjects comprises one or more fluorescence images of one or more biological samples of the corresponding subjects.

[0475] Embodiment 13. The system of embodiment 1, wherein the corresponding plurality of exposome signatures for corresponding subjects in the plurality of subjects comprises one or more spatial maps of one or more Raman spectra of the corresponding subjects' biological samples.

[0476] Embodiment 14. The system of embodiment 1, wherein the exposome biochemical signature is associated with space travel.

[0477] Embodiment 15. The system of embodiment 14, wherein at least one program further includes instructions for analyzing a corresponding plurality of exposome features using the trained model to determine relevance to space travel.

[0478] Embodiment 16. The system of embodiment 15, wherein the exposome features are analyzed using a classifier trained to determine relevance to space travel.

[0479] Embodiment 17. The system of embodiment 16, wherein the trained classifier is selected from the group consisting of 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.

[0480] Embodiment 18. A method for selecting subjects for intervention comprising: (a) providing a trained predictive model, the trained predictive model being trained on one or more of the subjects' clinical metadata, exposome features, and corresponding intervention outcome information; (b) detecting biochemical signatures obtained from biological samples of the subjects seeking intervention, thereby generating a prospective exposome feature; (c) predicting predicted intervention outcome information of the subjects seeking intervention using the trained predictive model, the exposome features and clinical meta of the subjects seeking intervention being inputs to the trained predictive model; and (d) selecting subjects for intervention or excluding subjects from intervention based at least in part on the subjects' predicted intervention outcome information.

[0481] Embodiment 19. The method of embodiment 18, wherein the biochemical signature is obtained by assaying a biological sample of the subject.

[0482] Embodiment 20 The method of embodiment 19, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

[0483] Embodiment 21. The method of embodiment 19, wherein assaying 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.

[0484] Embodiment 22. The method of embodiment 21, wherein the laser ablation inductively coupled plasma mass spectrometry measurements include measurements of one or more elemental chemicals.

[0485] Embodiment 23. The method of 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.

[0486] Embodiment 24. The method of embodiment 18, wherein the biochemical signature comprises a fluorescent image of the biological sample.

[0487] Embodiment 25. The method of embodiment 18, wherein the biochemical signature comprises a spatial map of the Raman spectrum of the biological sample.

[0488] Embodiment 26. The method of embodiment 18, wherein the biochemical signature is associated with space travel.

[0489] Embodiment 27. The method of embodiment 18, wherein the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

[0490] Embodiment 28. The method of embodiment 18, wherein the trained predictive model is selected from the group consisting of 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.

[0491] Embodiment 29 The method of embodiment 18, further comprising the step of enrolling the subject in the intervention when the subject is selected for the intervention.

[0492] Embodiment 30 The method of embodiment 18, further comprising the step of evaluating the subject for an alternative intervention when the subject is excluded from the intervention.

[0493] Embodiment 31. A method for selecting an optimal treatment for the negative effects of space travel, comprising the steps of: (a) detecting one or more biochemical signatures obtained from one or more biological samples from one or more subjects not participating in space travel, thereby generating one or more reference exposome characteristics; (b) detecting one or more biochemical signature characteristics obtained from one or more biological samples from one or more subjects who participated in space travel, thereby generating one or more pre-treatment exposome characteristics; (c) administering a treatment to one or more subjects who participated in space travel; and (d) detecting one or more biochemical signatures obtained from one or more biological samples from one or more subjects who participated in space travel after a period of time has elapsed since receiving the treatment. (e) determining differences between one or more reference exposome characteristics of one or more subjects not participating in space travel, one or more pre-treatment exposome characteristics of one or more subjects who participated in space travel, and one or more post-treatment exposome characteristics of one or more subjects who 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 exposome characteristics, the one or more pre-treatment exposome characteristics, and the one or more post-treatment exposome characteristics, wherein the one or more optimal treatments are selected based on the determined differences meeting predetermined criteria.

[0494] Embodiment 32. The method of embodiment 31, wherein the treatment of choice comprises a drug, a functional food, or any combination thereof.

[0495] Embodiment 33. The method of embodiment 31, wherein the predetermined criteria comprises a difference between one or more pre-treatment exposome features and one or more post-treatment exposome features relative to one or more reference exposome features.

[0496] Embodiment 34. The method of embodiment 33, 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.

[0497] Embodiment 35. The method of embodiment 33, wherein the difference comprises at least a 10% change in one or more post-treatment exposome characteristics relative to one or more reference exposome characteristics.

[0498] Embodiment 36 The method of embodiment 31, wherein the pre-treatment exposome characteristic, the post-treatment exposome characteristic, or any combination thereof, is obtained by assaying a biological sample of the subject.

[0499] Embodiment 37. The method of embodiment 36, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, a physiological parameter, or any combination thereof.

[0500] Embodiment 38. The method of embodiment 36, wherein assaying comprises obtaining laser ablation inductively coupled plasma mass spectrometry measurements, laser induced breakdown spectroscopy measurements, Raman spectroscopy measurements, immunohistochemistry measurements, or any combination thereof.

[0501] Embodiment 39. The method of embodiment 38, wherein the laser ablation inductively coupled plasma mass spectrometry measurements include measurements of one or more elemental chemicals.

[0502] 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.

[0503] Embodiment 41 The method of embodiment 31, wherein the biochemical signature comprises a fluorescent image of the biological sample.

[0504] Embodiment 42. The method of embodiment 31, wherein the biochemical signature comprises a spatial map of the Raman spectrum of the biological sample.

[0505] Embodiment 43 The method of embodiment 38, wherein the immunohistochemistry measurements comprise measurements of C-reactive staining.

[0506] Embodiment 44 The method of embodiment 31, wherein differences between the reference exposome features, the pre-treatment exposome features, the post-treatment exposome features, or any combination thereof are analyzed using a trained classifier.

[0507] Embodiment 45. The method of embodiment 44, wherein the trained classifier is selected from the group consisting of 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.

[0508] Embodiment 46. A method for assessing the effect of an intervention in a plurality of subjects, comprising: For each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a baseline on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, each chemical sample in the plurality of chemical samples corresponding to a different location in the plurality of locations, and each location in the plurality of locations representing a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations including one or more locations representing a growth period before the intervention, one or more locations representing a growth period during the intervention, and one or more locations representing a growth period after the intervention; analyzing for each respective subject in the plurality of subjects using a mass spectrometer, thereby obtaining a corresponding first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in the plurality of chemicals collectively determined over time from the plurality of chemical samples; and generating corresponding isotopic datasets, the isotopic datasets including a set of pre-intervention features corresponding to a growth period of the biological sample before the intervention, the set of pre-intervention features including, for each respective subject in the plurality of subjects, one or more features derived from concentrations of each chemical measured from one or more locations representative of the period of growth before the intervention, a set of intervention features corresponding to a growth period of the biological sample between the intervention, the set of inter-intervention features including, for each respective subject in the plurality of subjects, one or more features derived from concentrations of each chemical measured from one or more locations representative of the period of growth after the intervention, and a set of post-intervention features corresponding to a growth period of the biological sample after the intervention, the set of post-intervention features including, for each respective subject in the plurality of subjects, one or more features derived from concentrations of each chemical measured from one or more locations representative of the period of growth after the intervention; and evaluating, for each chemical in the one or more chemicals, a change in chemical dynamics in response to the intervention using the corresponding isotopic dataset.

[0509] Embodiment 47. The method of embodiment 46, wherein the evaluating step comprises, for each of the one or more chemicals, performing a probabilistic hypothesis test using (i) the set of pre-intervention features, and (ii) one or both of the set of intervention features and the set of post-intervention features.

[0510] Embodiment 48. A method of assessing an impact of an intervention in a plurality of subjects, comprising: for each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a baseline on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, wherein each chemical sample in the plurality of chemical samples corresponds to a different location within the plurality of locations, and wherein each location within the plurality of locations represents a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations including one or more locations representing a growth period before the intervention, one or more locations representing a growth period during the intervention, and one or more locations representing a growth period after the intervention; analyzing for each respective subject in the plurality of subjects the corresponding plurality of chemical samples for the respective subject using a mass spectrometer, thereby obtaining a corresponding first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in the plurality of chemicals collectively determined over time from the plurality of chemical samples; generating a respective aggregated isotopic dataset for the set of substances, each aggregated isotopic dataset including: a set of pre-intervention features corresponding to growth of the biological sample before the intervention, the set of pre-intervention features including, for each respective subject in the plurality of subjects, a value for one or more dimensionality reduction components formed from features derived from respective concentrations of two or more chemicals measured from one or more locations representative of the growth period before the intervention; a set of intervention features corresponding to growth of the biological sample during the intervention, the set of intervention features including, for each respective subject in the plurality of subjects, a value for one or more dimensionality reduction components formed from features derived from respective concentrations of the two or more chemicals measured from one or more locations representative of the growth period during the intervention; and a set of post-intervention features corresponding to growth of the biological sample after the intervention, the set of post-intervention features including, for each respective subject in the plurality of subjects, a value for one or more dimensionality reduction components formed from features derived from respective concentrations of the set of two or more chemicals measured from one or more locations representative of the growth period during the intervention.and using the aggregate isotopic dataset to assess changes in chemical dynamics in response to an intervention.

[0511] Embodiment 49. The method of embodiment 48, wherein the evaluating step comprises performing a probabilistic hypothesis test using (i) the set of pre-intervention features, and (ii) one or both of the set of intervention features and the set of post-intervention features.

[0512] Embodiment 50. The method of embodiment 48 or 49, wherein for each of the set of pre-intervention features, the set of intervention features, and the set of post-intervention features, a value for the reduced dimensionality reduction component is each determined from features derived from features of each of two or more chemicals measured from each single subject in the plurality of subjects.

[0513] Embodiment 51. The method of embodiment 48 or 49, wherein for each of the set of pre-intervention features, the set of intervention features, and the set of post-intervention features, a value for the reduced dimensionality reduction component is each determined from an aggregate derived from features of each of two or more chemicals measured from each of the multiple subjects in the multiple subjects.

[0514] Embodiment 52. The method of any one of embodiments 45 to 51, wherein the one or more features derived from the concentrations of each chemical measured from one or more locations representing a growth period are a concentration, a normalized concentration thereof, or an associated descriptive statistic or derived parameter thereof.

[0515] Embodiment 53. The method of any one of embodiments 46 to 51, wherein the one or more features derived from the concentrations of each chemical measured from one or more locations representing the growth period are selected from recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions, and these measures are derived by application of recurrence quantification analysis parameters, cross recurrence quantification analysis parameters, joint recurrence quantification analysis parameters, and / or multidimensional recurrence quantification analysis.

[0516] Embodiment 54. A method of assessing an impact of an intervention in a plurality of subjects, comprising: for each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a baseline on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for each subject, wherein each chemical sample in the plurality of chemical samples corresponds to a different location within the plurality of locations, and wherein each location within the plurality of locations represents a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations including one or more locations representing a growth period before the intervention, one or more locations representing a growth period during the intervention, and one or more locations representing a growth period after the intervention; and for each respective subject in the plurality of subjects, analyzing the corresponding plurality of chemical samples for the respective subject using a mass spectrometer, thereby obtaining a corresponding plurality of chemical samples for the respective subject. obtaining a corresponding first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in the plurality of chemicals collectively determined over time from the plurality of chemical samples; applying, for each respective subject in the plurality of subjects, and for each of one or more chemicals in the plurality of chemicals, a distributed lag model or similar non-linear distribution model as a function of time relative to the intervention to the concentration of the respective chemical measured from the plurality of locations or features derived therefrom, to generate a corresponding contribution dataset representing a contribution of the intervention to the concentration of the respective chemical in the respective subject as a function of time; and assessing, for each of the one or more chemicals, a change in chemical dynamics in response to the intervention using the corresponding contribution dataset from each respective subject in the plurality of subjects.

[0517] Embodiment 55. A method of assessing an impact of an intervention in a plurality of subjects comprising: for each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a baseline on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, wherein each chemical sample in the plurality of chemical samples corresponds to a different location within the plurality of locations, and wherein each location within the plurality of locations represents a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations including one or more locations representing a growth period before the intervention, one or more locations representing a growth period during the intervention, and one or more locations representing a growth period after the intervention; and for each respective subject in the plurality of subjects, analyzing the corresponding plurality of chemical samples for the respective subject using a mass spectrometer, thereby obtaining a corresponding first dataset comprising a plurality of traces, wherein each trace of the plurality of traces represents a plurality of chemical samples. generating, for each respective subject in the plurality of subjects, and for the set of two or more chemicals in the plurality of chemicals, a corresponding aggregated isotopic dataset including a set of features formed from features derived from each of the concentrations of the two or more chemicals measured over different growth periods for each subject, the set of features including a value for one or more dimensionality reduction components; applying, for each respective subject in the plurality of subjects, a distributed lag model or similar non-linear distribution model as a function of time to the intervention to generate a corresponding contribution dataset representing a contribution of the intervention to the concentrations of the set of two or more chemicals in the respective subject as a function of time; and assessing changes in chemical dynamics in response to the intervention using the corresponding contribution data for each respective subject in the plurality of subjects.

[0518] Embodiment 56. The method according to any one of embodiments 46 to 55, wherein the intervention is the ingestion of a functional food composition.

[0519] Embodiment 57. The method according to embodiment 56, further comprising modifying the composition of the functional food composition to adjust the effect of ingestion of the functional food composition in response to the step of evaluating the change in chemical dynamics.

[0520] Embodiment 58. The method of embodiment 56, further comprising supplementing the intake of the dietary supplement composition with the intake of one or more dietary supplements in response to the step of evaluating the change in chemical dynamics.

[0521] Embodiment 59. The method of any one of embodiments 46 to 58, further comprising evaluating changes in the metabolism of one or more additional metabolites in response to the intervention.

[0522] Embodiment 60. The method of embodiment 59, 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.

[0523] Embodiment 61. A method for outputting one or more quantitative metrics of one or more exposome signatures of a subject, comprising: (a) receiving a biological sample from the subject; (b) determining one or more exposome signatures from the subject's biological sample; (c) calculating one or more features of the one or more exposome signatures, each feature of the one or more features comprising one or more quantitative metrics; and (d) outputting the one or more quantitative metrics of the one or more features of the subject.

[0524] Embodiment 62. The method of embodiment 61, further comprising outputting a health outcome for the subject based at least in part on an association between the normalized score of the one or more features of the subject and the normalized score of the one or more quantitative metrics of the one or more features of the second set of subjects.

[0525] Embodiment 63. The method of embodiment 62, wherein one or more quantitative metrics of one or more features of the second set of subjects are stored in a database, the database being a local server or a cloud-based server.

[0526] Embodiment 64. The method of embodiment 62, wherein the health outcomes include space travel classification, clinical subtype, non-clinical subgroups related to physiology, anthropometric indices, behavioral indices, socioeconomic indices, body mass index, intelligence quotient, socioeconomic status, or any combination thereof.

[0527] Embodiment 65. The method of embodiment 61, wherein the one or more features include measuring the temporal dynamics of one or more exposome signatures.

[0528] Embodiment 66. The method according to claim 6, wherein the measurement of temporal dynamics is a linear gradient, a nonlinear parameter describing the curvature of one or more exposome signatures, an abrupt change in intensity of one or more exposome signatures, a change in baseline intensity of one or more exposome signatures, a change in frequency domain representation of one or more exposome signatures, a change in power spectral domain representation of one or more exposome signatures, a recursive quantification analysis parameter, a cross-recursive quantification analysis parameter, a joint recursive quantification analysis parameter, a multidimensional ... 66. The method of embodiment 65, comprising: a quantification analysis parameter, an estimate of the Lyapunov spectrum, a maximum Lyapunov exponent, laminarity, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recurrences, or any combination thereof.

[0529] Embodiment 67. The method of embodiment 61, wherein the subject comprises a plurality of subjects.

[0530] Embodiment 68. The method of embodiment 61, wherein one or more features of the one or more signatures include phenotypic features, including electrocardiogram (ECG), electroencephalography, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genomic, epigenomic, transcriptomic, proteomic, metabolomic, or any combination of such data.

[0531] Embodiment 69. The method of embodiment 61, wherein one or more features are derived from one or more attractors.

[0532] Embodiment 70. The method of embodiment 61, wherein one or more exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0533] Embodiment 71 The method of embodiment 61, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

[0534] Embodiment 72 The method of embodiment 68, wherein the phenotypic characteristic comprises a molecular phenotype.

[0535] Embodiment 73. The method of embodiment 72, wherein the molecular phenotype is determined by unsupervised analysis including clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0536] Embodiment 74. The method of embodiment 66, wherein the recursion quantification analysis parameters include recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion.

[0537] Embodiment 75. The method of embodiment 69, further comprising a step of analyzing one or more attractors by potential energy analysis, thereby generating a potential energy data space.

[0538] Embodiment 76. The method of embodiment 61, wherein the subject's one or more exposome signatures include retrospective exposome features.

[0539] Embodiment 77. The method of embodiment 69, further comprising analyzing the dynamic relationship between one or more attractors' signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, tendency at recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, or any combination thereof.

[0540] Embodiment 78. The method of embodiment 77, wherein the dynamic relationship is determined by convergent crossover mapping (CCM).

[0541] Embodiment 79. The method of embodiment 61, further comprising reducing one or more exposome signatures to one or more reduced exposome signatures.

[0542] Embodiment 80. The method of embodiment 69, further comprising constructing a network of one or more attractors based on similarity of one or more attractor temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof.

[0543] Embodiment 81. The method of embodiment 80, further comprising analyzing one or more characteristics of the network of one or more attractors to determine network connectivity, efficiency, feature importance, path importance, related graph theory based metrics, or any combination thereof.

[0544] A method for outputting a prediction of phenotypic data of one or more subjects of embodiment 82.1 or more, comprising: (a) receiving one or more biological samples and phenotypic data from a first set of subjects; (b) determining a first set of exposome signatures from one or more biological samples of the first set of subjects; (c) calculating a first set of features of the first set of exposome signatures; (d) training a predictive model using the first set of features 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 set of exposome signatures from one or more biological samples of the second set of subjects; (g) calculating a second set of features of the second set of exposome signatures; and (h) outputting a prediction of the second set of phenotypic data of the subjects determined by inputting the second set of features into the trained predictive model.

[0545] Embodiment 83. The method of embodiment 82, wherein the first set of features and the second set of features include one or more quantitative metrics.

[0546] Embodiment 84. The method of embodiment 82, wherein the one or more quantitative metrics include a measurement of the temporal dynamics of one or more exposome signatures.

[0547] Embodiment 85. The method of embodiment 83, wherein the measurements of temporal dynamics include linear gradients, nonlinear parameters describing the curvature of the first and second set of exposome signatures, abrupt changes in intensity of the first and second set of exposome signatures, changes in baseline intensity of the first and second set of exposome signatures, changes in frequency domain representations of the first and second set of exposome signatures, changes in power spectral domain representations of the first and second set of exposome signatures, recursive quantification analysis parameters, cross-recursive quantification analysis parameters, joint recursive quantification analysis parameters, multidimensional recursive quantification analysis parameters, estimates of the Lyapunov spectrum or maximum Lyapunov exponent, or any combination thereof.

[0548] Embodiment 86. The method of embodiment 82, wherein the first set of features of the first set of exposome signature comprises a first set of phenotypic features and the second set of features of the second set of exposome signature comprises a second set of phenotypic features.

[0549] Embodiment 87. The method of embodiment 86, wherein the first set of phenotypic features or the second set of phenotypic features comprises electrocardiogram (ECG), electroencephalography, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genome, epigenome, transcriptome, proteome, metabolome, or any combination of data thereof.

[0550] Embodiment 88. The method of embodiment 82, wherein the first set of features and the second set of features are represented as or derived from one or more attractors.

[0551] Embodiment 89. The method of embodiment 88, wherein the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof.

[0552] Embodiment 90. The method of embodiment 82, wherein the first set of exposome signatures and the second set of exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

[0553] Embodiment 91. The method of embodiment 82, 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.

[0554] Embodiment 92. The method of embodiment 86, wherein the first set of phenotypic characteristics or the second set of phenotypic characteristics comprises a molecular phenotype.

[0555] Embodiment 93. The method of embodiment 92, wherein the molecular phenotype is determined by unsupervised analysis including clustering, dimensionality reduction, factor analysis, stacked autoencoding, or any combination thereof.

[0556] Embodiment 94. The method of embodiment 85, wherein the recursion quantification analysis parameters include recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion, or any combination thereof.

[0557] Embodiment 95. The method of embodiment 88, further comprising a step of analyzing one or more attractors by potential energy analysis, thereby generating a potential energy data space.

[0558] Embodiment 96. The method of embodiment 82, wherein the first set of exposome signatures and the second set of exposome signatures comprise dynamic exposome data, retrospective, forward, or any combination thereof.

[0559] Embodiment 97. The method of embodiment 88, further comprising analyzing the dynamic relationship between one or more attractors' recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions, or any combination.

[0560] Embodiment 98. The method of embodiment 97, wherein the dynamic relationship is determined by convergent crossover mapping (CCM).

[0561] Embodiment 99. The method of embodiment 88, further comprising constructing a network of one or more attractors based on similarity of one or more attractor temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recurrences, or any combination thereof.

[0562] Embodiment 100. The method of embodiment 99, further comprising analyzing one or more features of the network of one or more attractors to determine network connectivity, efficiency, feature importance, path importance, related graph theory based metrics feature importance, path importance, or any combination thereof.

Claims

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

2. the memory further storing a clinical database (CDB) comprising clinical phenotype information for the plurality of subjects; the association software module is programmed to determine associations between the corresponding plurality of exposome features for at least one subject, the clinical phenotype information, and the intervention outcome information; the recommendation software module is communicatively programmed to provide the intervention recommendation to the at least one subject based at least in part on the corresponding plurality of exposome features of the at least one subject, the clinical phenotype information of the at least one subject, the intervention outcome information for the at least one subject, and an association between the corresponding plurality of exposome features, the clinical phenotype information, and the intervention outcome information for the at least one subject; 10. The computer-implemented exposomics system of claim 1.

3. 2. The computer-implemented exposomics system of claim 1, wherein the EDB comprises a distinct corresponding plurality of exposome features for each of at least 100, at least 1,000, or at least 10,000 subjects.

4. 2. The computer-implemented exposomics 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 of the plurality of subjects includes classification of non-responders, adverse responders, and positive responders.

5. The computer-implemented exposomics 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 of the plurality of subjects includes one or more inclusion or exclusion criteria.

6. 10. The computer-implemented exposomics system of claim 1, wherein the corresponding plurality of exposome signatures for subjects in the plurality of subjects are obtained by assaying biological samples of the subjects.

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

8. 7. The computer-implemented exposomics system of claim 6, wherein the assaying 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. 10. The computer-implemented exposomics system of claim 8, wherein said assaying comprises obtaining one or more mass spectrometry measurements, said one or more mass spectrometry measurements comprising measurements of one or more chemicals.

10. 10. The computer-implemented exposomics 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. 2. The computer-implemented exposomics system of claim 1, wherein the corresponding plurality of exposome features for corresponding objects in the plurality of subjects comprises one or more features of one or more dynamic temporal biochemical responses of the corresponding objects.

12. 2. The computer-implemented exposomics system of claim 1, wherein the corresponding plurality of exposome features for corresponding subjects in the plurality of subjects comprises one or more fluorescence images of one or more biological samples of the corresponding subjects.

13. 2. The computer-implemented exposomics system of claim 1, wherein the corresponding plurality of exposome features for corresponding subjects in the plurality of subjects comprise one or more spatial maps of one or more Raman spectra of biological samples of the corresponding subjects.

14. 2. The computer-implemented exposomics system of claim 1, wherein each corresponding plurality of exposome features for each corresponding subject in the plurality of subjects is associated with non-participation in space travel or participation in space travel in the corresponding subject.

15. 15. The computer-implemented exposomics system of claim 14, wherein the at least one program further comprises instructions for analyzing the corresponding plurality of exposome features using a trained model to determine relevance to space travel.

16. 16. The computer-implemented exposomics system of claim 15, wherein the trained model is selected from the group consisting of 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. 1. A method for selecting a subject for intervention, comprising: (a) providing a trained predictive model, the trained predictive model being trained on one or more of clinical metadata, exposome features, and corresponding intervention outcome information of a training cohort; (b) detecting a biochemical signature using a biological sample obtained from said subject, thereby providing a plurality of prospective exposome characteristics; (c) inputting the plurality of prospective exposome features and clinical metadata of the subject into the trained predictive model, thereby obtaining predicted intervention outcome information for the subject; and (d) selecting said subject for said intervention or excluding said subject from said intervention based at least in part on said predicted intervention outcome information for said subject; A method comprising:

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

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

20. 20. The method of claim 18, wherein the assaying 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. 20. The method of claim 18, wherein the assaying comprises collecting data from laser ablation inductively coupled plasma mass spectrometry measurements, the laser ablation inductively coupled plasma mass spectrometry measurements comprising measurements of one or more elemental chemicals.

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

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

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

25. 20. The method of claim 17, wherein the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

26. 18. The method of claim 17, wherein the trained predictive model is selected from the group consisting of 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. 20. The method of claim 17, further comprising the step of enrolling the subject in a trial in which the intervention is provided when the subject is selected for the intervention.

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

29. 1. A method for selecting an optimal treatment for a subject participating in space travel, comprising: (a) detecting one or more biochemical signatures obtained from one or more biological samples from one or more subjects not participating in space travel, thereby generating one or more reference exposome characteristics; (b) detecting one or more biochemical signature features obtained from one or more biological samples from one or more subjects participating in the space travel, thereby generating one or more pre-treatment exposome features; (c) administering a procedure to one or more subjects participating in said space travel; and (d) detecting one or more biochemical signature features from one or more biological samples from one or more subjects participating in said space travel after a period of time following treatment, thereby generating one or more post-treatment exposome features; (e) determining a difference between the one or more reference exposome characteristics of one or more subjects not participating in the space travel, the one or more pre-treatment exposome characteristics of one or more subjects who participated in the space travel, and the one or more post-treatment exposome characteristics of one or more subjects who participated in the space travel; (f) selecting one or more optimal treatments based at least in part on the determined differences between the one or more reference exposome characteristics, the one or more pre-treatment exposome characteristics, and the one or more post-treatment exposome characteristics, wherein the one or more optimal treatments are selected based on the determined differences meeting predetermined criteria; A method comprising:

30. 30. The method of claim 29, wherein the optimal treatment in the one or more optimal treatments comprises a medicine, a functional food, a meal, a food bar, or any combination thereof.

31. 30. The method of claim 29, wherein the predetermined criteria comprises a difference between the one or more pre-treatment exposome characteristics and the one or more post-treatment exposome characteristics relative to the one or more reference exposome characteristics.

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

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

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

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

37. 35. The method of claim 34, wherein the assaying comprises obtaining laser ablation inductively coupled plasma mass spectrometry measurement data, the laser ablation inductively coupled plasma mass spectrometry data comprising one or more elemental chemicals.

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

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

40. 30. 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. 30. The method of claim 29, wherein differences between the one or more reference exposome features of one or more subjects not participating in space, the one or more pre-treatment exposome features of one or more subjects that participated in the space travel, and the one or more post-treatment exposome features of one or more subjects that participated in the space travel are analyzed using a trained model.

42. 42. The method of claim 41 , wherein the trained model is selected from the group consisting of 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. 1. A method for assessing the impact of an intervention in a plurality of subjects, comprising: For each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a reference line on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, each chemical sample within the plurality of chemical samples corresponding to a different location within the plurality of locations, and each location within the plurality of locations representing a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations comprising: one or more locations representative of a period of growth prior to said intervention; one or more locations representing a period of growth between said interventions; and one or more locations representing a period of growth following said intervention and for each respective subject in the plurality of subjects, analyzing the corresponding plurality of chemical samples for each respective subject with a mass spectrometer, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in a plurality of chemicals collectively determined over time from the plurality of chemical samples; generating a corresponding isotope data set for each of one or more chemicals in the plurality of chemicals, the isotope data set comprising: a set of pre-intervention features corresponding to the growth period of the pre-intervention biological sample, the set of pre-intervention features comprising, for each respective subject in the plurality of subjects, one or more features derived from concentrations of respective chemicals measured from one or more locations representative of the growth period of the pre-intervention; a set of intervention features corresponding to the period of development of the biological sample during the intervention, the set of intervention features including, for each respective subject in the plurality of subjects, one or more features derived from respective chemical concentrations measured from the one or more locations representative of the period of development of the intervention; and a set of post-intervention features corresponding to a period of growth of the biological sample after the intervention, the set of post-intervention features including, for each respective subject in the plurality of subjects, one or more features derived from concentrations of respective chemicals measured from one or more locations representative of a period of growth after the intervention; and for each chemical in said one or more chemicals, using said corresponding isotopic dataset to assess a change in chemical dynamics in response to said intervention; A method comprising:

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

45. 1. A method for assessing the impact of an intervention in a plurality of subjects, comprising: For each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a reference line on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, each chemical sample within the plurality of chemical samples corresponding to a different location within the plurality of locations, and each location within the plurality of locations representing a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations comprising: one or more locations representative of a period of growth prior to said intervention; one or more locations representing a period of growth between said interventions; and one or more locations representing a period of growth following said intervention and for each respective subject in the plurality of subjects, analyzing the corresponding plurality of chemical samples for each respective subject with a mass spectrometer, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in a plurality of chemicals collectively determined over time from the plurality of chemical samples; generating a respective aggregated isotopic data set for a set of two or more chemicals in the plurality of chemicals, the respective aggregated isotopic data set comprising: a set of pre-intervention features corresponding to growth of the biological sample pre-intervention, the set of pre-intervention features including, for each respective subject in the plurality of subjects, values ​​for one or more dimensionality reduction components formed from features derived from respective concentrations of the two or more chemicals measured from the one or more locations representative of a period of growth pre-intervention; a set of intervention features corresponding to growth of the biological sample during the intervention, the set of intervention features including, for each respective subject in the plurality of subjects, values ​​for one or more dimensionality reduction components formed from features derived from respective concentrations of the two or more chemicals measured from the one or more locations representative of a growth period during the intervention; and a set of post-intervention features corresponding to growth of the biological sample after the intervention, the set of post-intervention features including, for each respective subject in the plurality of subjects, values ​​for one or more dimensionality reduction components formed from features derived from respective concentrations of the set of two or more chemicals measured from the one or more locations representative of a period of growth after the intervention; and using the aggregated isotopic dataset to assess changes in chemical dynamics in response to the intervention; A method comprising:

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

47. 47. The method of claim 45 or 46, wherein for each of the pre-intervention feature set, the intervention feature set, and the post-intervention feature set, a value for the dimensionality reduction component is determined from features derived from features of each of the two or more chemicals measured from each single subject in the plurality of subjects.

48. 48. The method of claim 46 or 47, wherein for each of the pre-intervention feature set, the intervention feature set, and the post-intervention feature set, a value for the dimensionality reduction component is determined from a compilation of features derived from features of each of the sets of two or more chemicals measured from each of a plurality of subjects in the plurality of subjects.

49. 49. The method of any one of claims 45 to 48, wherein the one or more features derived from the concentrations of each chemical measured from the one or more locations representing a growth period are a concentration, a normalized concentration thereof, or an associated descriptive statistic or derived parameter thereof.

50. the one or more features derived from the respective chemical concentrations measured from the one or more locations representative of a growth period are selected from: recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of the most probable recurrences; A method according to any one of claims 44 to 49, wherein these measures are derived by application of recursive quantification analysis parameters, cross-recursive quantification analysis parameters, joint recursive quantification analysis parameters and / or multidimensional recursive quantification analysis.

51. 1. A method for assessing the effect of an intervention in a plurality of subjects, comprising: For each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a reference line on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, each chemical sample within the plurality of chemical samples corresponding to a different location within the plurality of locations, and each location within the plurality of locations representing a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations comprising: one or more locations representative of a period of growth prior to said intervention; one or more locations representing a period of growth between said interventions; and one or more locations representing a period of growth following said intervention and for each respective subject in the plurality of subjects, analyzing the corresponding plurality of chemical samples for each respective subject with a mass spectrometer, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in a plurality of chemicals collectively determined over time from the plurality of chemical samples; applying a distributed lag model, or a similar non-linear distribution model, for each respective subject in the plurality of subjects, and for each of one or more chemicals in the plurality of chemicals as a function of time to an intervention to a concentration of the respective chemical measured from the plurality of locations, or features derived therefrom, to generate a corresponding contribution data set representing a contribution of the intervention to the concentration of the respective chemical in each respective subject as a function of time; for each of said one or more chemicals, assessing a change in chemical dynamics in response to said intervention using said corresponding contribution data set from each respective subject in said plurality of subjects; A method comprising:

52. 1. A method for assessing the effect of an intervention in a plurality of subjects, comprising: For each respective subject in the plurality of subjects, sampling each respective location within a plurality of locations along a reference line on a corresponding biological sample associated with the chemical dynamics of the respective subject, thereby obtaining a corresponding plurality of chemical samples for the respective subject, each chemical sample within the plurality of chemical samples corresponding to a different location within the plurality of locations, and each location within the plurality of locations representing a different growth period of the corresponding biological sample associated with the chemical dynamics, the plurality of locations comprising: one or more locations representative of a period of growth prior to said intervention; one or more locations representing a period of growth between said interventions; and one or more locations representing a period of growth following said intervention and for each respective subject in the plurality of subjects, analyzing the corresponding plurality of chemical samples for each respective subject with a mass spectrometer, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace of the plurality of traces being a concentration of a corresponding chemical in a plurality of chemicals collectively determined over time from the plurality of chemical samples; generating, for each respective subject in the plurality of subjects and for a set of two or more chemicals in the plurality of chemicals, a corresponding aggregated isotopic data set including a set of features formed from features derived from concentrations of each of the two or more chemicals measured over different growth periods for each subject, the set of features including values ​​for one or more dimensionality reduction components; for each respective subject in the plurality of subjects, applying a distributed lag model, or a similar non-linear distribution model, to the corresponding aggregated isotopic dataset as a function of time relative to the intervention to generate a corresponding contribution dataset representing the contribution of the intervention to concentrations of the set of two or more chemicals in the respective subject as a function of time; assessing a change in chemodynamics in response to the intervention using the corresponding contribution data for each respective subject in the plurality of subjects; A method comprising:

53. The method according to any one of claims 45 to 52, wherein the intervention is the ingestion of a functional food composition.

54. 54. The method of claim 53, further comprising modifying the composition of the functional food composition to adjust the impact of the intake of the functional food composition in response to the step of evaluating the change in chemical dynamics.

55. 54. The method of claim 53, further comprising the step of supplementing the intake of said nutritional supplement composition with the intake of one or more nutritional supplements in response to the step of evaluating the change in chemical dynamics.

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

57. 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. 1. A method for outputting one or more quantitative metrics of one or more exposome signatures 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 features of the one or more exposome signatures, each feature of the one or more features comprising one or more quantitative metrics; (d) outputting the one or more quantitative metrics of the one or more features of the object; A method comprising:

59. 60. The method of claim 58, further comprising: outputting a health outcome for the first subject based at least in part on an association between the normalized scores of the one or more features of the subject and the normalized scores of a second set of one or more features of a second subject, wherein the first subject and the second subject are different.

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

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

62. 59. The method of claim 58, wherein the one or more features comprise a measure of the temporal dynamics of the one or more exposome signatures.

63. 63. The method of claim 62, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

64. 59. The method of claim 58, wherein the one or more features of the one or more exposome signatures comprise phenotypic features comprising data from electrocardiogram (ECG), electroencephalography, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genomic, epigenomic, transcriptomic, proteomic, metabolomic, or any combination thereof.

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

66. 59. 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, fluorescent immunohistochemical staining, or any combination thereof.

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

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

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

70. 63. The method of claim 62, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

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

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

73. 59. The method of claim 58, wherein the one or more quantitative metrics comprise a measure of the temporal dynamics of the one or more exposome signatures.

74. 66. The method of claim 65, further comprising analyzing the one or more attractors by potential energy analysis, thereby generating a potential energy data space.

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

76. 66. The method of claim 65, further comprising analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

77. 77. The method of claim 76, wherein the dynamic relationships are determined by cross-convergent mapping (CCM).

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

79. 66. The method of claim 65, further comprising constructing a network of the one or more attractors based on similarity of the temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof.

80. 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, feature importance, path importance, related graph theory based metrics, or any combination thereof.

81. 1. A method for outputting a prediction 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 set of exposome signatures from one or more biological samples of the first set of subjects; (c) calculating a first set of features of the exposome signature; (d) training a predictive model using the first set of features and the phenotypic data for 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 set of exposome signatures from the one or more biological samples of a second set of subjects; (g) calculating a second set of features of the exposome signature; (h) outputting a prediction of the second set of phenotypic data of the subject, determined by inputting the second set of features into a trained predictive model; A method comprising:

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

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

84. 84. The method of claim 83, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining one or more nonlinear parameters describing a curvature of the first and second set of exposome signatures, determining one or more abrupt changes in intensity of the first and second set of exposome signatures, determining one or more changes in baseline intensity of the first and second set of exposome signatures, determining one or more changes in a frequency domain representation of the first and second set of exposome signatures, determining one or more changes in a power spectral domain representation of the first and second set of exposome signatures, 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 or a maximum Lyapunov exponent, or any combination thereof.

85. 82. The method of claim 81 , wherein the first set of features of the first set of exposome signatures comprises a first set of phenotypic features and the second set of features of the second set of exposome signatures comprises a second set of phenotypic features.

86. 86. The method of claim 85, wherein the first set of phenotypic features or the second set of phenotypic features comprises electrocardiogram (ECG), electroencephalography, magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), genomic, epigenomic, transcriptomic, proteomic, metabolomic, or any combination thereof data.

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

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

89. 82. The method of claim 81, wherein the first set of exposome signatures and the second set of exposome signatures are measured by mass spectrometry, laser ablation inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, Raman spectroscopy, fluorescent immunohistochemical staining, or any combination thereof.

90. 82. 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. 86. The method of claim 85, wherein each of the first set of phenotypic characteristics and the second set of phenotypic characteristics comprises a plurality of molecular phenotypes.

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

93. 84. The method of claim 83, wherein the measuring of the temporal dynamics comprises determining one or more recurrence quantification analysis parameters, the one or more recurrence quantification analysis parameters comprising one or more of recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof.

94. 88. The method of claim 87, further comprising analyzing the one or more attractors by potential energy analysis, thereby generating a potential energy data space.

95. 82. The method of claim 81, wherein the first set of exposome signatures and the second set of exposome signatures comprise dynamic exposome data, retrospective, forward looking, or any combination thereof.

96. 88. The method of claim 87, further comprising analyzing dynamic relationships between the one or more attractors' recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions, or any combination.

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

98. 88. The method of claim 87, further comprising constructing a network of the one or more attractors based on similarity of the temporal exposome data signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrence, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recurrences, or any combination thereof, of the one or more attractors.

99. 99. The method of claim 98, further comprising analyzing one or more features of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, path importance, related graph theory based metrics feature importance, path importance, or any combination thereof.

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

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

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

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

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

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

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

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

108. 1. A method for outputting a response of one or more subjects to participating in actual or simulated space travel, comprising: (a) receiving one or more biological samples from one or more subjects participating in space travel; (b) determining one or more exposome signatures of the one or more biological samples; (c) calculating exposome features of the one or more exposome signatures of the one or more biological samples; (d) outputting a response of the one or more subjects to participating in a space trip as an output of a trained predictive model provided with the exposome features of the one or more biological samples as input; A method comprising:

109. 109. The method of claim 108, wherein the trained predictive model is configured to determine the likelihood that the exposome features are associated with a subject participating in an actual or simulated space trip.

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

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

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

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

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

115. 109. The method of claim 108, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

116. The method of claim 115, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

117. 117. The method of claim 116, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion.

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, fluorescent immunohistochemical staining, or any combination thereof.

119. 109. The method of claim 108, wherein the exposome features are derived from one or more attractors.

120. 120. The method of claim 119, further comprising analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

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

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

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

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

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

126. 109. The method of claim 108, wherein the response comprises a metabolic state of the subject, and wherein the metabolic state of the subject comprises a healthy state or a state consistent with a subject undergoing space travel.

127. 1. A method for training a predictive model to determine a subject's clinical response to an intervention when the subject is participating in space travel, 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 are exposed to space travel and are provided with an intervention, and the second set of subjects are exposed to space travel and do 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 set and a second set of exposome features of one or more exposome signatures for the first set of subjects and the second set of subjects, each feature in the first and second sets of exposome features comprising one or more quantitative metrics; (a) outputting a trained predictive model configured to determine a subject's clinical response to an intervention, the subject participating in space travel, the trained predictive model being trained on one or more quantitative metrics of one or more exposome features and corresponding clinical responses of a first set and a second set of the subject; A method comprising:

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

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

130. 128. The method of claim 127, further comprising determining an impact 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 and second sets of exposome features.

131. 128. The method of claim 127, wherein the intervention comprises a diet, a pharmaceutical, a therapeutic, a food bar, or any combination thereof.

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

133. 128. The method of claim 127, wherein the first set or second set of exposome features comprises a measurement of the temporal dynamics of the one or more exposome signatures.

134. The method of claim 133, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

135. 134. The method of claim 133, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and 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, fluorescent immunohistochemical staining, or any combination thereof.

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

138. 128. The method of claim 127, wherein the first set or second set of exposome features are derived from one or more attractors.

139. 139. The method of claim 138, further comprising analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

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

141. 1. A method for generating a predictive model configured to output a health status of one or more subjects as a function of gravity in addition to the gravity of the Earth, comprising: (a) receiving one or more biological samples and corresponding health states of a first one or more subjects, the first one or more subjects being exposed to gravity in addition to the Earth's gravity; (b) determining one or more exposome signatures of one or more biological samples of the first one or more subjects; (c) calculating exposome features of the one or more exposome signatures of the one or more biological samples; (d) generating a trained predictive model configured to output a health state of a second one or more subjects in response to gravity in addition to the gravity of the Earth, the trained predictive model being trained using exposome features of the one or more exposome signatures and corresponding health states of the first one or more subjects; A method comprising:

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

143. 142. The method of claim 141, wherein the trained predictive model is configured to determine the likelihood that the exposome features are 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 the Earth's gravity.

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

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

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

147. 142. The method of claim 141, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

148. The method of claim 147, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

149. 148. The method of claim 147, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion.

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, fluorescent immunohistochemical staining, or any combination thereof.

151. 142. The method of claim 141, wherein the exposome features are derived from one or more attractors.

152. 152. The method of claim 151, further comprising analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

153. 142. 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. 142. The method of claim 141, wherein the trained predictive model comprises a classification algorithm.

155. 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 subjects and the second one or more subjects associated with exposure to the gravity in addition to the Earth's gravity.

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

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

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

159. 1. A method for using a predictive model to output a health outcome for one or more subjects prior to exposure to gravity, comprising: (a) receiving one or more biological samples from one or more subjects; (b) determining one or more exposome signatures of one or more biological samples of said one or more subjects; (c) calculating exposome features of the one or more exposome signatures of the one or more biological samples; (d) providing the exposome features of the one or more subjects as input to a trained predictive model, thereby outputting a health outcome of the one or more subjects prior to exposure to gravity in addition to the Earth's gravity.

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

161. 160. The method of claim 159, wherein the trained predictive model is configured to determine the likelihood that the exposome feature is associated with the subject's exposure to gravity.

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

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

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

165. 160. The method of claim 159, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

166. The method of claim 165, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

167. 166. The method of claim 165, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and 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, fluorescent immunohistochemical staining, or any combination thereof.

169. 160. The method of claim 159, wherein the exposome features are derived from one or more attractors.

170. 170. The method of claim 169, further comprising analyzing dynamic relationships between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

171. 160. 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. 160. The method of claim 159, wherein the trained predictive model comprises a classification algorithm.

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

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

175. 173. 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 the Earth's gravity.

176. 1. A method of administering a procedure to a subject participating in space travel, comprising: (a) receiving one or more biological samples from a subject prior to participating in space travel; (b) determining one or more exposome signatures of the one or more biological samples of the subject; (c) calculating exposome features of the one or more exposome signatures of the subject, each feature of the exposome features comprising one or more quantitative metrics; (d) administering a treatment to the subject participating in the space travel at a time point, the treatment and the time point to administer the treatment being determined as an output of a trained predictive model when the trained predictive model is provided with the subject's exposome features as input; A method comprising:

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

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

179. 177. The method of claim 176, wherein the time points include times before, during, after the subject participates in a space trip, or any combination thereof.

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

181. 178. The method of claim 177, wherein the efficacy of the treatment is determined by a period of time after the subject has participated in space travel, after which the subject's exposome characteristic comprises a baseline exposome characteristic of a subject not participating in space travel.

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

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

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

185. 177. The method of claim 176, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

186. The method of claim 185, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

187. 186. The method of claim 185, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, and number of most likely recursion.

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, fluorescent immunohistochemical staining, or any combination thereof.

189. 177. The method of claim 176, wherein the exposome features are derived from one or more attractors.

190. 190. The method of claim 189, further comprising analyzing the dynamic relationship between the signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, or any combination thereof, of the one or more attractors.

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

192. 177. 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 exposome features of one or more biological samples of one or more subjects, treatments administered, time points at which the treatments were administered, and corresponding clinical responses, and wherein the one or more subjects have participated in space travel.

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

195. 1. A method for assessing the response of one or more subjects to an intervention in the presence or absence of gravity, comprising: (a) sampling each respective position at a plurality of locations along a baseline of a first set of one or more biological samples of a first set of subjects and a second set of one or more biological samples of a second set of subjects, whereby a plurality of ion samples is obtained, each ion sample of the plurality of ion samples corresponding to a different position at the plurality of locations, each position at the plurality of locations representing a different development period of the first and second sets of one or more biological samples, wherein the first set of subjects undergo space travel and the second set of subjects do not undergo space travel, and wherein the first and second sets of subjects undergo an intervention; (b) analyzing each ion sample in the plurality of ion samples with a mass spectrometer, thereby obtaining a first data set comprising a plurality of traces, each trace in the plurality of traces being a concentration of a corresponding elemental isotope in a plurality of elemental isotopes determined collectively over time from the plurality of ion samples; (c) deriving a second data set from the plurality of traces comprising a set of features, each respective feature in the set of features being determined by variation of a single isotope or combination of isotopes in the plurality of traces; (d) inputting the set of features into a trained classifier, thereby obtaining probabilities for the first and second sets of the one or more subjects from the trained classifier predicting a response of the one or more subjects to space travel; A method comprising:

196. 200. The method of claim 195, wherein the probability indicates that one biological sample of the one or more biological samples has become dysregulated as a result of the space travel.

197. 200. The method of claim 196, wherein the effectiveness of the intervention is measured by the probability of dysregulation.

198. 196. The method of claim 195, wherein the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

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

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

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

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

203. 200. The method of claim 195, wherein the first set or second set of one or more biological samples comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

204. 200. The method of claim 195, comprising treating the first set or second set of one or more biological samples with a solvent or detergent prior to the sampling step.

205. 200. The method of claim 195, comprising irradiating the first or second set of one or more biological samples with a low power laser prior to the sampling step to remove debris from the first or second set of one or more biological samples.

206. 200. The method of claim 195, wherein the sampling step comprises irradiating a first or second set of one or more biological samples of the first or second set of subjects with a laser to extract a plurality of particles from the first or second set of one or more biological samples, and ionizing the plurality of particles using an inductively coupled plasma mass spectrometer to obtain the plurality of ion samples.

207. 196. The method of claim 195, wherein the subject's first set or second set of responses comprises an adverse response, a positive response, or a neutral response.

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

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

210. 200. The method of claim 195, wherein the trained classifier is configured to determine whether a subject is suffering from space travel induced metabolic dysregulation.

211. 1. A method for outputting a subject's response to a post-space travel intervention, comprising: (a) exposing one or more biological samples from a subject to an optical signal comprising a plurality of Raman wavelengths, the subject undergoing 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 signatures; (d) providing an input of the plurality of Raman features to a trained classifier, and outputting a probability of the subject's response to the intervention if undergoing space travel; A method comprising:

212. 212. The method of claim 211, wherein the probability indicates that the subject has become dysregulated as a result of the space travel.

213. The method of claim 211, wherein the effectiveness of the intervention is measured by the probability of dysregulation.

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

215. 212. The method of claim 211, wherein the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

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

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

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

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

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

221. 212. The method of claim 211, wherein the acquiring step comprises using a Raman spectroscopic microscope.

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

223. 223. The method of claim 222, wherein the one or more traces are reduced using a dimensionality reduction algorithm, the dimensionality reduction algorithm comprising a principal component analysis, an independent component analysis, or any combination thereof.

224. The method of claim 223, wherein measurements of temporal dynamics are derived from the one or more traces, the temporal dynamics comprising determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

225. 225. The method of claim 224, wherein measuring the temporal dynamics includes determining the one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

226. 212. The method of claim 211, wherein the trained classifier comprises a gradient boosting ensemble model.

227. 212. The method of claim 211, wherein the trained classifier is configured to process one or more features selected from the group consisting of laminarity, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, Lmax, and any combination thereof.

228. 212. The method of claim 211, wherein the trained classifier is configured to process two or more features selected from the group consisting of laminarity, entropy, trapping time (TT), mean diagonal length (MDL), recurrence time (RT), Vmax, determinism, Lmax, and any combination thereof.

229. 212. The method of claim 211, wherein the trained classifier is trained to identify Raman signatures associated with space travel.

230. 212. The method of claim 211, wherein the probability output from the trained classifier indicates that the subject exhibits temporal dynamics associated with space travel.

231. 212. The method of claim 211, wherein the trained classifier is trained using a plurality of Raman signatures of a population of humans or non-human animals, the population of humans or non-human animals having experienced space travel.

232. 212. The method of claim 211, wherein the effectiveness of the intervention is measured by a change in the subject's temporal dynamics relative to the subject's baseline temporal dynamics prior to space travel.

233. 1. A method for assessing a subject's response to an intervention for reducing the biological effects of space travel, comprising: (a) staining a first set of one or more biological samples of a first set of subjects and a second set of one or more biological samples of a second set of subjects to produce a first set and a second set of stained one or more biological samples, wherein the first set of subjects undergo space travel and the second set of subjects do not undergo space travel, and wherein the first set of subjects and the second set of subjects are administered an intervention; (b) spatially analyzing fluorescence intensity across the first and second sets of the stained one or more biological samples; (c) calculating a set of spatial fluorescence intensity features of the first and second set of objects across the first and second sets of stained one or more biological samples, each feature of the exposome features of the first and second set of objects comprising one or more metrics quantifying temporal dynamics in fluorescence intensity traces; (d) outputting as an output of the trained predictive model a probability of a subject's response to an intervention for reducing the biological effects of space travel when the predictive model is provided with the subject's spatial fluorescence intensity exposome signature across the subject's one or more stained biological samples, wherein the trained predictive model is trained using one or more metrics of the subject's first and second set of exposome signatures, corresponding administered interventions, and clinical responses to the administered interventions; A method comprising:

234. 234. The method of claim 233, wherein the trained predictive model is configured to determine whether the subject's biological sample has become dysregulated via space travel.

235. 234. The method of claim 233, wherein the trained predictive model is configured to determine the impact of the intervention as a measure of intervention effectiveness based at least in part on an analysis of fluorescence intensity spatially across one or more biological samples.

236. 234. The method of claim 233, wherein the first set of one or more biological samples or the second set of one or more biological samples comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

237. 234. The method of claim 233, wherein the intervention comprises dietary, pharmaceutical, therapeutic, or any combination thereof.

238. 238. The method of claim 237, wherein the diet includes iodine supplementation.

239. The method of claim 233, wherein the intervention improves radiation exposure.

240. 234. 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. 241. The method of claim 240, wherein the non-human mammal comprises a mouse.

242. 234. The method of claim 233, wherein the trained predictive model comprises a gradient boosting ensemble model.

243. The method of claim 233, wherein measuring the temporal dynamics of the spatial fluorescence intensity trace comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

244. 234. The method of claim 233, wherein measuring the temporal dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and number of most likely recursions.

245. 234. The method of claim 233, wherein the analyzing step comprises obtaining a first set and a second set of fluorescent images of the stained one or more biological samples, and analyzing the spatial fluorescent intensity of the fluorescent images.

246. 234. The method of claim 233, wherein obtaining a fluorescent image of the stained tooth sample comprises using an inverted or non-inverted confocal microscope.

247. 247. The method of any one of claims 233-246, wherein staining the one or more biological samples of the first and second sets of subjects comprises using a C-reactive stain.

248. 247. The method of any one of claims 233 to 246, further comprising the step of sectioning the tooth sample.

249. 234. The method of claim 233, wherein the step of staining the first and second sets of one or more biological samples comprises decalcifying the first and second sets of one or more biological samples.

250. 234. The method of claim 233, wherein the trained model is selected from the group consisting of 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. 234. The method of claim 233, wherein the trained model comprises a gradient boosted decision tree.

252. 234. The method of claim 233, wherein the trained predictive model comprises a classification algorithm.

253. 253. The method of claim 252, wherein the classification algorithm is trained to identify spatial fluorescence intensity features associated with space travel.

254. 1. A method for outputting one or more routes associated with space travel, comprising: (a) receiving one or more biological samples from a first set of one or more subjects and a second set of one or more subjects, wherein the first set of one or more subjects are participating in space travel and the second set of one or more subjects are not participating; (b) determining an exposome signature of one or more pathways from the one or more biological samples of the first set and the second set of the one or more subjects; (c) calculating exposome features of an exposome signature of the one or more pathways; (d) outputting one or more routes associated with the space travel by comparing exposome characteristics of one or more routes of the first and second sets of objects; A method comprising:

255. 255. The method of claim 254, wherein each feature of the exposome feature comprises one or more quantitative metrics.

256. 255. The method of claim 254, wherein the space travel includes traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the surface of the Earth.

257. 255. The method of claim 254, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

258. 255. The method of claim 254, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

259. The method of claim 258, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

260. The method of claim 258, wherein the measuring of the temporal dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, or number of most likely recursions.

261. 255. 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, fluorescent immunohistochemical staining, or any combination thereof.

262. 255. The method of claim 254, wherein the exposome features are derived from one or more attractors.

263. The method of claim 260, further comprising analyzing the dynamic relationship between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, 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. 1. A method for providing a treatment to ameliorate the effects of space travel, comprising: (a) receiving one or more biological samples from one or more subjects participating in space travel; (b) determining an exposome signature of one or more pathways from the one or more biological samples of the one or more subjects; (c) calculating exposome features of an exposome signature of the one or more pathways; (d) providing a treatment to the one or more subjects, the treatment ameliorating an effect of space travel on an exposome feature of the one or more subjects of an exposome signature of the one or more pathways; A method comprising:

266. 266. The method of claim 265, wherein the treatment comprises a diet, a pharmaceutical, a functional food, a food bar, or any combination thereof.

267. 266. The method of claim 265, wherein each feature of the exposome feature comprises one or more quantitative metrics.

268. 266. The method of claim 265, wherein the space travel includes traveling beyond the Kármán line at an altitude of at least 100 kilometers (km) above the Earth's surface.

269. 266. The method of claim 265, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

270. 266. The method of claim 265, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

271. The method of claim 270, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

272. 271. The method of claim 270, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recursion rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recursion time, Shannon entropy in recursion time, or number of most likely recursion.

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, fluorescent immunohistochemical staining, or any combination thereof.

274. 266. The method of claim 265, wherein the exposome features are derived from one or more attractors.

275. 273. The method of claim 272, further comprising analyzing the dynamic relationship between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy at diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy at perpendicular length, average recurrence time, Shannon entropy at recurrence time, number of most likely recurrences, 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. 1. A method for outputting one or more pathways associated with an intervention, comprising: (a) receiving one or more biological samples from a first set of one or more subjects and a second set of one or more subjects, wherein the first set of one or more subjects has received an intervention and the second set of one or more subjects has not received the intervention; (b) determining an exposome signature of one or more pathways from the one or more biological samples of the first set and the second set of the one or more subjects; (c) calculating exposome features of an exposome signature of the one or more pathways; (d) outputting one or more pathways associated with the intervention by comparing exposome signatures of one or more pathways of the first and second sets of the subject.

278. 278. The method of claim 277, wherein each feature of the exposome feature comprises one or more quantitative metrics.

279. 278. The method of claim 277, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

280. 278. The method of claim 277, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

281. The method of claim 280, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

282. The method of claim 280, wherein the measuring of the temporal dynamics comprises determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters comprising recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, or 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, fluorescent immunohistochemical staining, or any combination thereof.

284. 278. The method of claim 277, wherein the exposome features are derived from one or more attractors.

285. The method of claim 282, further comprising analyzing the dynamic relationship between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, 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. 278. The method of claim 277, wherein the intervention comprises pharmaceutical, dietary, food bar, functional food, metal ion supplementation, or any combination thereof.

288. 1. A method for determining an exposome signature of a subject associated with space travel, comprising: (a) receiving one or more biological samples from a subject; (b) determining an exposome signature of one or more pathways from the one or more biological samples; (c) calculating exposome features of an exposome signature of the one or more pathways; (d) outputting an exposome signature of an object related to space travel by comparing the exposome signature of the object with a database of space travel exposome signatures; A method comprising:

289. 289. The method of claim 288, wherein each feature of the exposome feature 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. 289. The method of claim 288, wherein the exposome characteristics include a measurement of the temporal dynamics of the one or more exposome signatures.

292. The method of claim 291, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

293. 292. The method of claim 291, wherein the measuring of the temporal dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursion, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and 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, fluorescent immunohistochemical staining, or any combination thereof.

295. 289. The method of claim 288, wherein the exposome features are derived from one or more attractors.

296. 300. The method of claim 293, further comprising analyzing the dynamic relationship between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, 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. 1. A method for determining a subject's exposome signature associated with space travel using a trained predictive model, comprising: (a) receiving one or more biological samples from a subject; (b) determining an exposome signature of one or more pathways from the one or more biological samples; (c) calculating exposome features of an exposome signature of the one or more pathways; (d) outputting exposome features of the objects associated with space travel by providing the exposome features of the objects as input to a trained predictive model, the trained predictive model being trained with exposome features of one or more objects that have participated in space travel; A method comprising:

299. 300. The method of claim 298, wherein each feature of the exposome feature comprises one or more quantitative metrics.

300. 300. The method of claim 298, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

301. 300. The method of claim 298, wherein the exposome characteristics comprise a measurement of the temporal dynamics of the one or more exposome signatures.

302. The method of claim 301, wherein the measuring of the temporal dynamics comprises determining a linear slope, determining a plurality of nonlinear parameters describing the curvature of the one or more exposome signatures, determining an abrupt change in intensity of the one or more exposome signatures, determining one or more changes in baseline intensity of the one or more exposome signatures, determining a change in a frequency domain representation of the one or more exposome signatures, determining a change in a power spectral domain representation of the one or more exposome signatures, 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.

303. 302. The method of claim 301, wherein the measuring of the temporal dynamics includes determining one or more recursion quantification analysis parameters, the one or more recursion quantification analysis parameters including recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, tendency in recursions, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, and 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, fluorescent immunohistochemical staining, or any combination thereof.

305. 300. The method of claim 298, wherein the exposome features are derived from one or more attractors.

306. The method of claim 303, further comprising analyzing dynamic relationships between one or more of signal recurrence rate, determinism, average diagonal length, maximum diagonal length, divergence, Shannon entropy in diagonal length, trend in recurrences, laminarity, trapping time, maximum perpendicular length, Shannon entropy in perpendicular length, average recurrence time, Shannon entropy in recurrence time, number of most likely recurrences, 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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