5095 results about "Biological system" patented technology

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention discloses an acid-resistant plate dark crack defect detection method and system, and relates to the technical field of acid-resistant plate defect detection.The detection method comprises the steps that multiple sets of multi-mode detection data in an acid-resistant plate detection scene are obtained, and the multiple sets of multi-mode detection data comprise polarized light appearance image data and micro-strain vibration data; and respectively extracting an appearance discriminant value, an ultrasonic discriminant value, a stress discriminant value and a micro-strain discriminant value from the modal data based on the improved twinborn attention network. According to the acid-resistant plate dark crack defect detection method and system, four-mode data of polarized light appearance, multi-frequency ultrasonic, flexible stress and micro-strain vibration are synchronously obtained through a multi-mode intelligent acquisition module: the polarized light appearance data captures surface shallow cracks, and the multi-frequency ultrasonic data penetrates through a plate body to identify deep cracks; the problem of missing detection of a traditional single mode is complementarily solved; in the analysis link, through a multi-modal attention and reinforcement learning fusion weight model, the weight of each discriminant value can be dynamically adjusted according to the environmental change, and the misjudgment of the fixed weight is avoided.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention provides a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on an online Raman spectrum, belongs to the technical field of biological fermentation process control, and aims to solve the problems of unstable process and low efficiency caused by fermentation control lag and incapability of sensing the real metabolic state of cells in the prior art. The method comprises the following steps: acquiring the concentrations of a target product BDO and key byproducts such as acetic acid and ethanol in the fermentation liquor in real time through an online Raman spectrum; according to the method, a metabolic stress index is originally proposed and constructed, the index is obtained by performing weighted operation on the instantaneous generation rate of the by-product and the target product, and the index is used for quantitatively characterizing the intrinsic metabolic stress level of the cells in real time. The control strategy of maintaining the metabolic stress index in the preset optimal stable interval is taken as a core control strategy, the conversion from passive response to active prediction in the fermentation process is realized, and the yield, the stability and the batch repeatability of Bi-BDO production are remarkably improved.

The invention discloses a traditional Chinese medicine internal medicine deficiency syndrome conditioning effect evaluation method and system based on a wearable device, and relates to the technical field of traditional Chinese medicine clinical informationization, and the method comprises the following steps: S1, building an electrode skin complex impedance time-frequency fingerprint, executing full-band sweep frequency perturbation, collecting complex impedance data in a continuous monitoring period, and carrying out the detection of the complex impedance time-frequency fingerprint; extracting resonance peak group and sideband coupling path information, and generating a self-excitation risk spectrum as a response reconstruction reference; and S2, performing anti-fact playback based on the self-excitation risk spectrum, replaying the conductance response in a non-resonance interval, comparing a stripping environment drift signal with contact pressure noise through the risk spectrum, and extracting purified anchor point data as an interface feature calibration reference. Abnormal signal recognition and positioning are achieved through complex impedance time-frequency fingerprints, self-excitation risk spectrums, anti-fact playback and micro-capacitance imaging, interference field regulation and control and self-adaptive impedance control are combined, the accuracy and stability of traditional Chinese medicine deficiency syndrome conditioning evaluation are improved, and the method has the high anti-interference capacity and the intelligent characteristic.

The invention relates to the technical field of model analysis, in particular to an imaging flow cytometry cell detection method based on an improved model. The method comprises the following steps: introducing a cell sample to be detected into an imaging flow cytometry system integrated with a micro-fluidic chip for continuous image acquisition to generate an initial cell image sequence; an automatic digital focusing algorithm is applied to the initial cell image sequence, and a cell image frame set with the optimal focal plane is screened out; inputting the cell image frame set into a preset PA-YOLO improved model for multi-dimensional extraction and fusion, and generating a multi-scale cell characteristic spectrum; carrying out refined feature learning and cell target positioning and classification on the multi-scale cell feature spectrum, and outputting a cell detection result; and carrying out validity verification on the cell detection result, and carrying out comparative analysis in combination with an imaging flow cytometry system to generate a cell detection report. According to the method, the imaging quality and the detection accuracy of cell images with different depths can be remarkably improved.

The invention provides a multi-source fusion spectrum cross-domain high-precision prediction method and system based on transfer learning. The method comprises the following steps: S1, acquiring spectrograms collected by a spectral imaging device under multiple wavelengths; s2, extracting various types of features from the spectrogram; s3, training a fusion model to obtain a trained fusion model; and S4, inputting a to-be-predicted spectrum into the trained fusion model, and outputting a result. According to the invention, 10 <-4 > nm-level wavelength prediction can be achieved in a common industrial camera system, and the level of a scientific research-level grating spectrometer (0.001-0.002 nm) can be reached / exceeded; on a narrow-band, middle-band and wide-band multi-scene cross-domain data set, the mean absolute error (MAE) can be reduced from the nanoscale to the magnitude of 10 <-4 > nm, and excellent robustness is kept under the conditions of noise, feature deficiency and few samples.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses a spectrum aliasing decoupling and concentration inversion method under the cross influence of multi-source environmental factors, belongs to the field of industrial process control and environment monitoring, and constructs an environment-spectrum collaborative fusion decoupling model for concentration prediction. The method specifically comprises the following steps: respectively collecting absorption spectrum signals of specified mixed gas at different temperatures, pressures and known concentrations, meanwhile, collecting environmental parameter data, constructing a multi-source data set, and carrying out denoising, dimension reduction and preprocessing on the multi-source data set; constructing a self-supervised feature extraction network for adaptive modulation of environmental parameters to realize deep fusion of spectrum and environmental information; the feature expression capability and generalization performance of the self-supervised feature extraction network are improved by using a self-supervised learning mechanism; and constructing a BPBO-GRNN self-adaptive concentration inversion optimization model for realizing inversion of mixed gas concentration and self-adaptive optimization of model parameters. According to the invention, high-precision concentration inversion and stable detection of the aliasing gas can be realized.

The invention discloses a machine learning assisted polyethylene reaction performance prediction model training method, prediction method and device. The method comprises the following steps: acquiring a training set; screening feature items used for model training; obtaining a gradient boosting regression model for catalytic activity, a gradient boosting regression model for molecular weight and a gradient boosting regression model for molecular weight distribution; extracting feature items for model training from the data of the training set so as to obtain feature vectors; and respectively inputting the feature vectors into each model so as to train each model, thereby respectively obtaining hyper-parameters of the trained gradient-boosted regression model for catalytic activity, hyper-parameters of the trained gradient-boosted regression model for molecular weight and hyper-parameters of the trained gradient-boosted regression model for molecular weight distribution. According to the method, a model relationship between input characteristics and polymerization results (including catalytic activity, molecular weight, molecular weight distribution and the like) is established through training set learning.

The invention provides a method and system for predicting the utilization rate of feed amino acid in multiple stages of laying hens, and relates to the field of bioinformatics, and the method comprises the following steps: obtaining chemical component measured values of feed raw material samples in different growth stages of the laying hens and in-vivo measured values of standard ileum amino acid digestibility; performing predictive factor screening according to the chemical component measured value and the standard ileum amino acid digestibility in-vivo measured value to obtain a predictive factor combination; constructing a prediction model according to the prediction factor combination to obtain a candidate prediction equation; optimizing according to the candidate prediction equation, and screening to obtain an optimized equation; performing model verification according to the optimization equation to obtain a target prediction model; and performing digestibility prediction according to the target prediction model to obtain a predicted standard ileum amino acid digestibility value. According to the method, the standard ileum amino acid digestibility is accurately predicted based on in-vitro detection data, and the limitation of a traditional in-vivo determination method is effectively overcome.

The invention relates to the technical field of material testing, and particularly discloses an intelligent quality detection method and system for a micro-mineral bio-organic fertilizer, local thermal excitation is applied to a fertilizer sample through a micro-area thermal pulse excitation device, a gas release kinetic curve and a spectrum change track are synchronously collected, and a gas spectrum coupling data cube is constructed; performing differential transformation and modal decomposition on the time sequence feature set, extracting transient response feature vectors and generating an activity response distribution diagram; establishing component-activity correlation analysis, and decoupling biological metabolic activity and matrix background interference through feature separation and a comparative learning strategy to obtain a dynamic metabolic fingerprint; constructing a multi-dimensional feature space by using the dynamic metabolic fingerprints and the apparent characteristic parameters, and performing information aggregation and feature reconstruction by using a graph convolutional network to generate a comprehensive quality index; and finally, realizing quality grade judgment based on a quality characteristic pyramid structure, and establishing a dynamic early warning mechanism by analyzing time sequence evolution characteristics of the activity response distribution diagram.

The invention relates to the technical field of image analysis, and discloses an end-to-end visual tactile perception method and system based on a morphology-force field analysis model, a terminal and a storage medium, and the method comprises the steps: employing a morphology reconstruction module to achieve the analysis of a micron-order contact surface, employing a force field analysis model to achieve the precise analysis of a force field in each direction, and taking the two results as a data set, inputting and mapping the image into contact morphology, normal and shear force distribution through an end-to-end network, integrating the high efficiency of data driving and the reliability of physical consistency, and finally introducing partial differential equation residual error sum into a loss function to carry out optimization. And the morphology and the force field meet the set physical consistency on the whole. According to the method, micron-sized morphology analysis and multi-axial force synchronous estimation are realized, the real-time robust performance under a high-frequency dynamic task is realized, the cost of tactile perception is reduced, and meanwhile, the accuracy of a tactile perception result is improved.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention relates to the technical field of Brassica rapa polysaccharide detection, and discloses a quantitative detection method of Brassica rapa polysaccharide based on near infrared spectroscopy. The method comprises the following steps: acquiring near infrared spectrum data of a brassica rapa sample, and dividing detection stages by combining spectral characteristics to obtain a plurality of detection stage identifiers; performing differential mapping on the spectrum load spectrum according to the identifier to obtain an optical parameter set of each stage; constructing a staged near infrared spectrum quantitative model by using the set; performing importance weighting on the original spectral data according to the identifier to generate optimized spectral data; and extracting polysaccharide related characteristics from the optimized spectral data through a staged model, and outputting a predicted value of the content of the Brassica rapa polysaccharide. According to the method, through stage division, differential mapping and data weighting, the spectral data utilization rate and the model suitability are improved, a targeted technical path is provided for quantitative detection of Brassica rapa polysaccharide, and the method can be used for quality control of Brassica rapa related products.

The invention discloses a single-molecule conductance signal semantic segmentation method based on multi-domain feature fusion, and relates to the field of single-molecule electric transport data analysis. The core of the method is a deep learning framework, and the deep learning framework comprises a time domain-frequency domain double-flow encoder, an attention mechanism module, a projection layer, a hierarchical clustering module and a segmentation head. The method specifically comprises the following steps of: performing data enhancement on time domain and frequency domain information of an input sample, generating views which are related to semantics and have different forms, and inputting the views into corresponding encoders; aligning two-modal coding features by adopting an attention mechanism; inputting the projected original time domain features into a hierarchical clustering module to generate a high-quality pseudo-tag, and taking the high-quality pseudo-tag as a supervision signal training segmentation head; and performing joint optimization by taking the weighted sum of the comparison loss and the segmentation loss as a total training target. The evaluation indexes comprise the accuracy rate, the F1 score and the MIoU. The method is applied to single-molecule electrical transport signal analysis, and provides effective data method support for single-molecule electronics basic research and application research.

The invention discloses a vegetable disease incubation period detection method and system based on bimodal time sequence collaborative fusion, and the method comprises the steps: obtaining a leaf image through building an acquisition environment, constructing a training sample data set, and constructing a downy mildew incubation period spectral feature adaptive enhancement (AW-FPF) module; the method comprises the following steps: decomposing a hyperspectral signal into low-frequency and high-frequency components through spectrum time sequence adaptive wavelet decoupling, obtaining an enhanced feature tensor through spectrum multi-scale pathological feature frequency-time dual-path aggregation fusion frequency domain and time domain paths, and obtaining a first feature sequence through weighted screening by using a multi-head attention mechanism; a downy mildew incubation period prediction (HyChl-TFNet) model containing a hyperspectral branch, a chlorophyll fluorescence parameter branch, a feature fusion branch and a classifier is constructed, bimodal features are processed and fused to output a day number prediction result, accurate recognition of the downy mildew incubation period is achieved, the detection precision can be controlled to the day number level, and the detection accuracy is improved. And an accurate time basis is provided for early prevention and control of diseases.

The invention provides an implantable nerve probe integrated with a biological protection layer, a preparation method and a system. The nerve probe sequentially comprises a bottom biological protection layer, a bottom flexible substrate layer, a conductive layer, a top flexible substrate layer and a top biological protection layer from bottom to top, the bottom biological protection layer and the top biological protection layer form a biological protection layer located on the outermost periphery of the nerve probe and are used for avoiding erosion of a damp and hot cerebrospinal fluid environment in brain tissue. The integrated biological protection layer located on the outermost periphery of the flexible nerve probe is adopted, corrosion of cerebrospinal fluid to the nerve probe can be inhibited in in-vivo application, the long-term biocompatibility of the flexible nerve probe is effectively improved, the in-vivo long-term service life of the flexible nerve probe is effectively prolonged, and the nerve probe has the advantages of being high in integration level, high in stability and the like; and the requirement of in-vivo long-term effective service of the device can be met.

The invention provides a method for rapidly detecting the processing degree of rhizoma pinellinae praeparata, and the method comprises the steps: calculating a wrinkle intensity feature from a preprocessed image based on a gray-level co-occurrence matrix, and determining the type of the processing degree of rhizoma pinellinae praeparata by analyzing the wrinkle intensity change of texture distribution in an evolution process from disorder to order; the method comprises the following steps: acquiring surface texture reconstruction data of a plurality of rhizoma pinellinae praeparata samples at each time node under different processing conditions, and establishing a corresponding relationship between image features and processing degree categories; according to the rhizoma pinellinae praeparata processing degree evaluation result, the temperature condition is adjusted, rhizoma pinellinae praeparata image data are collected again, the adjusted wrinkle density feature is extracted, and the rhizoma pinellinae praeparata dynamic change trend is obtained; and by comparing the adjusted rhizoma pinellinae praeparata dynamic change trend with the initial rhizoma pinellinae praeparata dynamic change trend, the processing quality control consistency is judged, and rapid detection of the rhizoma pinellinae praeparata processing degree is completed.

The present invention provides assays and assay systems for use in spatially encoded biological assays. The invention provides an assay system comprising an assay capable of high levels of multiplexing where reagents are provided to a biological sample in defined spatial patterns; instrumentation capable of controlled delivery of reagents according to the spatial patterns; and a decoding scheme providing a readout that is digital in nature.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

Provided herein are methods for capturing an analyte from a first region of interest of a biological sample on a substrate, where the biological sample comprises the first region of interest and a second region, and where the method includes contacting the second region with a sealant in order to create a hydrophobic seal thereby preventing an interaction between an analyte from the second region with a capture domain of a capture probe.

The invention discloses a method for detecting the concentration of cyclosporine in blood through fluorescence and electrochemical double signals, and belongs to the technical field of biochemical analysis and medical detection. Aiming at the technical problems of insufficient sensitivity and high false positive rate of traditional single-signal detection of blood concentration of cyclosporine, the method comprises the following steps: activating carboxyl microspheres by a coupling agent, fixing double-stranded DNA (deoxyribonucleic acid) formed by a cyclosporine aptamer and an initiator chain, inducing the initiator to release by using a cyclosporine sample, realizing strand displacement through an A2-PEI composite system, and detecting the blood concentration of cyclosporine by using an A < 2 >-PEI composite system. DNA modified cadmium telluride quantum dots are respectively adopted to detect fluorescence signals, eATRP electrochemical signal amplification is carried out to detect electrochemical signals, concentration values obtained by the two signals are verified, and an average value is taken as a result. The method can accurately capture low-concentration cyclosporine signals in blood, eliminates interference of a single method, is mainly used for accurately monitoring the blood concentration of cyclosporine, and provides a reliable detection means for medication safety and curative effect control of cyclosporine in clinical scenes such as organ transplantation.

The invention discloses a distillate oil property prediction method based on deep learning feature extraction and partial least squares regression. The method comprises the following steps: firstly, carrying out classification training on a near infrared spectrum through a convolution-attention double-branch fusion network, and extracting high-dimensional spectral features with local and global information; then, historical samples are retrieved from a database based on prediction categories, a plurality of most similar samples are selected by adopting cosine similarity measurement to construct a correction set, and the spectral features and property labels are subjected to standardization processing; and finally, carrying out partial least squares regression modeling on the correction set, extracting latent variables to maximize covariance between spectral features and physicochemical properties, and inputting feature vectors of an oil sample to be detected into the trained PLS model to obtain a corresponding property prediction result. According to the method, the modeling requirement and the category specificity characteristics under the small sample condition are considered while the prediction precision is guaranteed, and the method is suitable for rapid property detection and intelligent analysis in the refining process.

The invention discloses a lie detection system and method based on AC excitation multi-frequency skin electric signals, and belongs to the technical field of psychological and physiological detection and signal processing. According to the system, a multi-frequency AC excitation signal is applied to human skin, skin complex impedance is measured by adopting a four-electrode method, and the problems of electrode polarization and baseline drift of direct current excitation are solved. A high-dimensional feature vector is formed by extracting the amplitude, phase and differential ratio of multi-frequency impedance and Cole-Cole relaxation model parameters, and an individualized baseline is established. Feature changes are analyzed through a machine learning algorithm, and lie judgment is achieved. The method has the advantages of strong anti-interference capability, abundant information dimensions, high measurement accuracy and good safety.

The invention provides a probe pairing method for a gas ultrasonic flowmeter, which is used for testing and screening a batch of probes with the same structure and comprises the following steps of: comparing the consistency of static capacitance of the probes, primarily screening the probes, and obtaining a primarily screened probe group; comparing the consistency of the probe admittance sweep frequency curve, and determining an alternative paired probe; and comparing the consistency of output signal waveforms when the alternative pairing probes work, and judging whether the alternative pairing probes can be successfully paired or not.

The invention relates to the technical field of spectrum detection, in particular to a Raman spectrum-based rice oil detection method, which comprises the following steps: extracting main and auxiliary peaks to construct a peak group structure, performing sliding comparison to output a conformity region, identifying jump to remove an abnormal signal, calibrating a peak position to generate a correction structure, and performing homing numbering to complete a detection scheme. According to the method, the feature matching rule is constructed by combining the peak position spacing, the intensity ratio and the wavenumber index, the recognition precision of the multi-peak structure in the spectrum is enhanced, non-continuous response signals are recognized and eliminated through derivative change trend and proportion deviation constraint, and the accuracy of stable response extraction is improved. Dynamic calibration and position adjustment of peak positions are realized by utilizing an offset trend sequence, the consistency of a peak group structure in an overall direction and the continuity of a local structure are ensured, a numbering system and structure attribution division is completed after homing sorting, and the integrity of feature expression in a complex sample and the orderliness and reliability of spectral data processing are improved.

The invention relates to the technical field of spectrum detection, and discloses a quality control method for traditional Chinese medicine capsules, which comprises the following steps: continuously scanning by using a micro light spot probe during capsule movement to obtain a spatial spectrum response sequence; performing adjacent micro-area differential operation on the sequence, and filtering a shell background and retaining a particle scattering signal by utilizing a microstructure difference between a capsule shell continuous film and a powder content discrete accumulation; according to the method, a traditional path of a shell standard model is established, through a spatial frequency domain decoupling mechanism, reference errors caused by batch drifting of shell physical attributes are avoided, and the detection sensitivity of trace component fluctuation and foreign matter mixing is improved.