23 results about "Quantitative methodology" patented technology

This disclosure provides methods and apparatus for generating training data, training machine learning models, and analyzing biological samples. Generating training data includes: acquiring a first image of a biological sample; generating a first set of annotations for the first image based on a second image of the biological sample stained by a quantitative method that converts antibody / antigen complexes into dots; and outputting the first image and a benchmark truth including the first set of annotations as data for training a first machine learning model (“ML”) to analyze the biological sample. Furthermore, the generated (output) training data can then be used to train the ML model, and the trained ML model can be used to analyze the biological sample.

The invention provides an automatic migration rate quantification method for a mesenchymal stem cell scratch experiment. In order to solve the problems of fuzzy cell scratch boundary, low contrast, irregular form and difficulty in automatic migration rate quantification, the method comprises the following steps: firstly, constructing a mesenchymal stem cell (MSCs) scratch microscopic image data set, and carrying out gray scale and contrast enhancement on an original RGB image; and then, automatic segmentation of the scratch and the cell region is realized by utilizing a high-low frequency feature decoupling network, and quantitative analysis of the end-to-end mobility is realized by combining time sequence mask alignment, initial scratch region identification and scratch area measurement at each time point. The method supports batch processing of images at multiple time points, provides a high-precision, automatic and visual mobility analysis means, and can be widely applied to mesenchymal stem cell migration behavior research and regenerative medicine experiments.

The invention discloses a quantitative method of a high-concentration target compound and belongs to the technical field of sample detection. The quantitative method comprises the following steps: (S.1) obtaining a corresponding relation between the concentration of a target analyte and the ion signal response intensity for fitting to obtain a quantitative model; (S.2) fitting signal response intensity data obtained by the target compound with gradient concentration according to primary reaction kinetics to obtain a quantitative model; and (S.3) through detection, substituting the sum of ion signal intensities of all products obtained by ionization of a target compound into the quantitative model to obtain a concentration parameter so as to realize quantification. The quantitative method provides a chemical kinetics modeling method for fitting a nonlinear quantitative curve in a wide analyte concentration range for various analytical instruments. The method is not only suitable for a target compound which only generates monomer product ions, but also suitable for a target compound which simultaneously generates monomer product ions and polymer product ions, so that the quantitative capability of an analytical instrument on a target analyte with unknown concentration is greatly improved.

The application discloses a kind of based on multi-feature fusion and model learning's electromagnetic detection defect qualitative and quantitative method, builds multi-modal electromagnetic detection system, acquires the multi-frequency eddy current of measured component / multi-channel magnetic flux leakage signal and lift-off data, after adaptive denoising, interference suppression and normalization preprocessing, extract four categories 44-dimensional multidimensional features, obtain optimal feature subset by three-level screening strategy;Adopt channel attention and cross attention mechanism to realize multi-feature adaptive fusion, build "shared backbone+double branch" dual-task joint learning model, complete transfer pre-training, fine-tuning and lightweight compression;Finally output defect qualitative and quantitative results, support dual-technology result fusion and risk rating.The application realizes the full-depth coverage detection of special equipment metal components from surface to middle-deep defects, qualitative classification accuracy is greater than or equal to 98%, depth quantitative average relative error is less than or equal to 5%, single-frame inference time is less than or equal to 10ms, which can be directly deployed on handheld devices to realize real-time detection on site.

The invention discloses a metagenome activity quantification method based on an exogenous artificially synthesized endogenous reference gene and application of the metagenome activity quantification method, and relates to the technical field of biology. According to the invention, an endogenous reference gene as shown in SEQ ID NO.1 is firstly designed, and the endogenous reference gene has no homology with an existing organism and can realize homology interference. On the basis, a metagenome activity quantitative method is further designed, and the core thought of the metagenome activity quantitative method comprises the following steps: providing an artificially synthesized vector containing an endogenous reference gene, setting the artificially synthesized vector as a standard system with gradient concentration, and adding the standard system into a sample to be detected; before addition, a to-be-detected sample is pretreated through light-sensitive DNA combined with dye so as to shield dead bacteria DNA interference, then nucleic acid extraction, library construction and sequencing are carried out, and metagenome activity quantification is obtained through bioinformatics analysis and calculation. According to the method, homology interference is eliminated from the source, absolute quantification of microbial activity is realized, the stability and standardization degree of an internal standard are improved, and the method is suitable for quantitative analysis of metagenomes in various scenes.

The application discloses a large-scale mass spectrometry queue-oriented joint alignment and quantification method, relates to the cross field of bioinformatics, artificial intelligence and high-performance computing, and outputs a quantitative feature matrix through an end-to-end deep learning network; wherein, in a unified back propagation process, the end-to-end deep learning network synchronously optimizes the retention time alignment, the cross-batch intensity drift correction and the quantitative regression based on a joint loss function, the application regards the retention time alignment, the cross-batch intensity drift correction and the quantitative regression as a whole joint operation instead of linearly performing the operations one by one, and overcomes the defects of the step-by-step strategy in the prior art.

The present application relates to the technical field of elemental analysis detection, and particularly relates to an accurate non-calibration quantitative method based on automatic recognition and matching of different substrates, comprising: detection; acquiring actual fluorescence intensity and an actual spectrum diagram, and determining the type of main dark matter; determining the corresponding diagram of the current detection sample and selecting a corresponding non-calibration quantitative model; acquiring a simulation diagram; calculating the similarity of the simulation diagram and the actual spectrum diagram, if the similarity is greater than a similarity threshold value, outputting each content in the sample, if the similarity is less than or equal to the similarity threshold value, outputting new each element content, and re-performing full spectrum matching of the simulation diagram and the actual spectrum diagram until the matching result converges. By constructing a diagram library, the change trend of the actual measurement diagram and the simulation diagram is compared, so that the condition that the same matter is determined as different matters due to the pure comparison of the similarity is avoided, the error caused by the wrong model is avoided, and the accuracy of the non-calibration quantification is increased.

The application relates to the technical field of SCGD-OES online detection, and specifically provides a multi-element online quantitative method, system, equipment and medium of a high-salt matrix solution, which comprises the following steps: synchronously collecting multi-modal data; constructing a state vector according to the multi-modal data; calculating a stability index through robust principal component analysis and Mahalanobis distance; combining with working condition parameters to distinguish the working condition and the stability state; and adaptively selecting a quantitative analysis mode; under the working condition constraint, inputting spectral line characteristics into a segmented weighted least square regression model which is fused with monotonicity constraint, multi-spectral line consistency and self-absorption / quenching penalty term, compensating for the matrix effect and calculating the concentration; and finally, fusing the stability index, spectral line consistency deviation and model residual error to calculate the comprehensive confidence, and outputting the concentration and the confidence. The application improves the accuracy, robustness and result reliability of multi-element online analysis under high-salt, dynamic and nonlinear complex working conditions.

The invention discloses a stage-by-stage dynamic flood control water level regulation and control method suitable for a hydropower station, and the method comprises the steps: obtaining watershed hydro meteorology, engineering operation and topographic geology multi-source basic data, and carrying out the Spark distributed cleaning, wavelet denoising and GAN missing value complementation preprocessing, thereby forming a standardized data set; an optimal flood season staging scheme is screened through coupling of qualitative analysis and a quantitative method; then, the safe pre-discharge amount and the effective pre-discharge time serve as sensitive factors, a flood control water level dynamic control model is built in combination with fuzzy mathematics and Copula joint distribution, and flood control water level process lines and regulation and control domain values of all flood season stages are generated; the safety of a regulation and control threshold value is verified through finite element simulation and a two-dimensional hydrodynamic model, a regulation and control scheme meeting flood control safety and efficient utilization of water resources is output, dynamic and accurate regulation and control of the flood control water level of the hydropower station are achieved, and the problems that in traditional static flood control water level dispatching, the contradiction between flood control and benefit making is prominent, the staging precision is low, and safety verification is insufficient are solved.

This invention provides a method for quantitative analysis of intracellular lipid content based on the Bodipy 493 / 503 fluorescent probe, relating to the field of lipid metabolism detection technology. The method includes: (1) determining the absorption spectrum and excitation / emission spectrum of Bodipy 493 / 503 in isopropanol, and determining the maximum absorption wavelength and maximum excitation / emission wavelength of the fluorescent probe in isopropanol based on the spectral results; (2) staining the target cells with the Bodipy 493 / 503 probe after treatment with the compound, observing the changes in fluorescence signal in the field of view before and after isopropanol extraction, and taking pictures; (3) measuring the absorbance and fluorescence intensity of the isopropanol extract, analyzing the experimental results, and comparing them with the results of triglyceride content detection. This quantitative method is simple, fast, sensitive, effective, and has good experimental controllability and repeatability. The analytical results obtained are consistent with those obtained using other lipid content analysis methods, and it can be of great application in the field of research on the lipid metabolism interference effects of chemicals or environmental pollutants.

The present invention is a quantifying method by which a sample containing an analyte is quantified for the analyte, the method including steps of: (1) measuring the same sample a plurality of times by using a measurement apparatus to obtain a plurality of quantitative values of the analyte in the sample; and (2) calculating an amount of change in the analyte derived from the measurement apparatus from the plurality of the quantitative values, and correcting at least one of the plurality of the quantitative values with the amount of change to obtain a true quantitative value of the analyte. This provides a method for more accurately quantifying an analyte in a measurement system in which an amount of the analyte increases or decreases in a measurement apparatus during measurement of the sample.

The invention discloses a quantitative method of SCCP, and relates to the field of quantitative detection of short-chain chlorinated paraffin, and the method comprises a homologous substance quantitative method and a blank deduction method; the method comprises the following steps: drawing a standard curve by using configured chlorine content and total response, and checking the standard curve to obtain response factors of different family substances according to theoretical chlorine content of different family substances; and obtaining the concentration of the homologous substances by using the response of different homologous substances in the sample and using a response factor method, and the like. According to the invention, SCCP homologous substance quantification is realized, homologous substances which exist in a laboratory blank but do not exist in a sample are identified on the basis, the blank level is reduced, and the quantification accuracy of SCCP is improved.

The invention provides a quantification method and a chromatograph device, which can reduce the energy required for quantification of a substance to be measured contained in an unknown sample. A quantification method for quantifying a predetermined substance to be measured using a chromatograph device (100) comprises: a reference substance measurement step for measuring, for a reference substance, an index for detecting the measured retention time of the reference substance and the mass of the reference substance; a measurement target substance measurement step for measuring, with respect to the measurement target substance, an index for detecting the measured retention time of the measurement target substance and the mass of the measurement target substance; a measurement target substance identification step for identifying a measurement target substance on the basis of the measurement results; and a quantification step in which the substance to be measured is quantified.

This invention relates to the field of quantitative source tracing, and discloses a quantitative method for surface water source tracing that integrates water quality concentration and chemical composition characteristics, providing a more accurate technical means for surface water source tracing. The method includes screening chemical characteristic indicators, constructing a pollution source chemical characteristic matrix after water sample analysis, calculating the degree of variation of each indicator and generating a dynamic weight vector, inputting this weight vector and the recipient water sample vector into a non-negative constraint optimization model to obtain a preliminary contribution ratio. Through multiple iterations with introduced random perturbations, the method performs statistical analysis and outputs quantitative source tracing results. The results are also used for compliance assessment to identify suspected pollution sources. Based on the source tracing results, a key monitoring list is generated, monitoring plans are optimized, and the source tracing results are updated. This invention integrates multi-dimensional characteristics, considers uncertainties, and forms a closed-loop management system, enabling precise source tracing of surface water pollution sources and providing a basis for environmental management.

This application discloses a joint alignment and quantification method for large-scale mass spectrometry queues, involving the interdisciplinary fields of bioinformatics, artificial intelligence, and high-performance computing. The method outputs a quantitative feature matrix through an end-to-end deep learning network. In the unified backpropagation process of the end-to-end deep learning network, retention time alignment, cross-batch intensity drift correction, and quantitative regression are simultaneously optimized based on a joint loss function. This application regards retention time alignment, cross-batch intensity drift correction, and quantitative regression as a whole joint operation, rather than sequential operations, thus overcoming the shortcomings of the step-by-step strategy in traditional techniques.

An object of the present invention is to provide a method for more accurately quantifying a test substance in a measurement system in which the amount of the test substance increases or decreases in a measurement device during measurement of a sample.SOLUTION: A method for quantifying a test substance in a sample containing the test substance, the method comprising (1) measuring the same sample a plurality of times using a measurement device to obtain a plurality of quantitative values of the test substance in the sample, and (2) calculating, from the plurality of quantitative values, an increase or decrease amount of the test substance caused by the measurement device, and correcting at least one of the plurality of quantitative values with the increase or decrease amount to obtain a true quantitative value of the test substance.SELECTED DRAWING: Figure 1

The invention relates to the technical field of code generation, in particular to a code generation method and system based on AI programming and structured modeling, and aims at obtaining real-time interaction log data generated in a multi-agent collaborative environment. Deep multi-dimensional feature extraction is carried out on real-time interaction log data, the real-time interaction log data is mapped to a preset game income model, a multi-dimensional game income matrix of the current debate round is constructed, and the strategy income value of each asymmetric agent in the current code state is accurately represented. Through mathematical description of the current code state, fine-grained monitoring of the code generation process is achieved, fuzziness of code quality evaluation is effectively reduced, and accuracy and reliability of evaluation are improved. According to the method, a quantitative methodology framework is provided for the code generation process, dynamic tracking and rational pre-judgment of multi-agent interaction are ensured through the tensor expression of the game theory, high-quality codes can be generated finally, and the code development efficiency is remarkably improved.

The present disclosure provides a technique for quantitatively detecting denatured LDL or stimulative AGEs in a quick and easy manner. The present disclosure also provides a device or kit for detecting or quantizing aberrant forms of biomarker molecules by forming a conjugate with the biomarker molecules, the device or kit including a membrane that carries out development of a specimen due to a capillary phenomenon, wherein: the membrane includes a detection part including a detection binding agent that specifically binds to the biomarker molecules or to competing molecules thereof, and a control part including a control binding agent that specifically binds to bound molecules and has a function for forming a conjugate with aberrant forms of the biomarker molecules or with competing molecules thereof; and the device or kit includes a specimen contact part and fluorescent nanoparticles as part of the membrane or as separate elements.

This invention provides a method, apparatus, and electronic device for quantitative depression analysis based on multimodal feature adaptation. The method includes: acquiring data to be identified from at least two modalities; reducing the dimensionality of the data to be identified based on the correlation between the data to be identified and low-dimensional features, and extracting features from the dimensionality-reduced data to obtain the emotional features of the data to be identified; wherein the dimensionality of the low-dimensional features is lower than the feature dimension of the data to be identified; and performing quantitative analysis of depressive mood based on the emotional features of the data to be identified from at least two modalities. The method, apparatus, electronic device, and storage medium provided by this invention avoid the memory pressure caused by directly extracting features from high-dimensional data, and can directly perform quantitative analysis of depressive mood based on complete, unsegmented data to be identified, thereby ensuring the reliability and accuracy of quantitative analysis of depressive mood.

This invention relates to the field of quantitative prediction technology and discloses a method and system for quantifying insect pheromone content based on spectral analysis. The method includes: collecting infrared or near-infrared spectral data of insect pheromones in a sample; calculating the optimal scaling factor that minimizes the residual water vapor peak, and correcting the second spectral data based on the optimal scaling factor; performing SNV transformation to obtain a preprocessed spectrum; performing iterative screening to determine the optimal wavelength subset based on the iterative screening results; constructing a diagonal weight matrix; using the optimal wavelength subset and the diagonal weight matrix to perform hierarchical weighted partial least squares regression modeling, with minimizing the weighted sum of squared residuals as the objective function of the model, and solving for the regression coefficients; independently calculating the infrared or near-infrared spectral data of the sample to be tested and the regression coefficients to obtain the predicted value of insect pheromone content in the sample. This invention solves the problems of weak anti-interference ability and poor model robustness in existing quantitative methods.

The purpose of the present invention is to provide a method for more accurately quantifying a test substance in a measurement system in which the amount of the test substance is increased or decreased in a measurement device when measuring a sample. A quantification method for quantifying a substance to be detected in a sample containing the substance to be detected, the method being characterized by comprising: (1) a step for measuring the same sample a plurality of times using a measurement device to obtain a plurality of quantitative values for the substance to be detected in the sample; and (2) a step for calculating, from the plurality of quantitative values, an increase / decrease amount of the test substance by the measurement device, and correcting at least one of the plurality of quantitative values by means of the increase / decrease amount to obtain a real quantitative value of the test substance.

This invention discloses a quantitative analysis method for target components in mixtures based on optimal transport cross-domain distribution alignment. This method addresses the significant shortcomings of current mainstream fluorescence spectroscopy-based classification and quantification methods in continuous concentration quantification and generalization of unknown adulterants. Specifically, the types of adulterants appearing during the testing phase must have been covered during the training phase; otherwise, the predictive performance stability will significantly decrease. The method of this invention employs a technical path of "spectral acquisition and preprocessing—distribution alignment—regression prediction," constructing a two-stage analysis workflow of "optimal transport + regression." This achieves robust quantification of the continuous content of target components in a mixture system without relying on prior enumeration of adulterants or using target domain concentration labels. This invention is applicable to other spectral analysis applications involving the recovery of continuous physical quantities, such as food safety testing, food chemical analysis, and material composition analysis.

The invention belongs to the technical field of environmental monitoring, relates to a method for collecting and quantifying new pollutants in water, and relates to an intelligent passive sampling and non-targeted analysis method for monitoring new pollutants in water. According to the method, the diffusion coefficient and ionization efficiency of a compound are predicted by constructing a machine learning model, and the quantitative problem caused by key parameter deficiency in the DGT and NTA technology coupling process is solved, so that the advantages of DGT in the aspects of obtaining representative water samples and time-weighted concentration are fully played, and the efficient screening capability of NTA is combined, and the quantitative analysis of the compound is realized. And high-flux quantitative analysis of new pollutants is realized. The model constructed by the method covers richer chemical structures and experimental conditions, and is wide in application range. By combining the predicted key parameters, reliable high-flux concentration estimation can be carried out on the new pollutants identified in the water body without a standard substance, and powerful technical support is provided for new pollutant monitoring and risk assessment.