8346 results about "Biological system" patented technology

Provided herein are methods of determining a location of a biological analyte in a biological sample.

Provided herein are methods for capturing a connected probe and / or a capture handle sequence to a capture domain of a capture probe.

Provided herein are methods of determining a location of a target analyte in a non-permeabilized biological sample and methods of reducing background binding of an analyte on an array.

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 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.

Provided herein are methods, compositions, and kits for the spatial analysis of target nucleic acids, or complements thereof, by their 5′ end.

This disclosure relates to compositions and methods for analyzing a tissue section from a biological sample.

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.

Compositions, kits, and methods for reducing mislocalization of analytes from a biological sample in the context of an array-based spatial analysis platform are disclosed herein. Also, disclosed herein are a first region of capture probes, where the capture probes include: (i) a spatial barcode, (ii) a first capture domain, and (iii) one or more functional domains, and a second region of capture probes, where the capture probes include a second capture domain. The second region of capture probes can capture analytes from portions of the biological sample that exceed the boundaries of the first region of capture probes, thereby reducing analyte mislocalization and improving the accuracy of the array-based spatial analysis platform.

Disclosed herein are methods of amplifying an analyte in a biological sample using a bridging oligonucleotide that hybridizes to a captured analyte. The methods disclosed herein include steps of (a) contacting a biological sample with a substrate having capture probes comprising a capture domain and a spatial barcode; (b) hybridizing the analyte to the capture domain; and (c) contacting the analyte to a bridging oligonucleotide comprising (i) a capture-probe-binding sequence, and (ii) an analyte-binding sequence; (d) extending the bridging oligonucleotide; and (e) determining (i) all or a part of the sequence of the analyte, or a complement thereof, and (ii) the spatial barcode, or a complement thereof, and using the determined sequence of (i) and (ii) to determine the location of the analyte in the biological sample.

The invention discloses an improved YOLOv8-based penicillin bottle body defect detection method, and belongs to the technical field of computer vision, and the method comprises the following steps: obtaining and preprocessing penicillin bottle body defect data to obtain a training set and a test set; establishing a defect target detection model of the improved YOLOv8; training a defect target detection model by adopting the training set, optimizing a loss function, and updating a model weight parameter until the loss function is converged; and testing the defect target detection model by adopting the test set. According to the invention, a space separable pooling attention module SSPA is introduced, so that the perception capability of the network on subtle features such as bottle body scratches can be enhanced; a feature enhancement module MMFF is introduced, convolution kernels of different sizes are adopted to extract defect features of different sizes, such as smudginess, scratches and loading quantity anomaly, under a complex background, and the feature fusion effect is improved by module stacking.

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 present disclosure relates to systems, devices, and methods for automated cell sorting within a cell processing system. In an embodiment, the present disclosure relates to an automated cell sorting system comprising a cartridge having a cell sorting module, where the cell sorting module comprises a flow cell, and an instrument within a bay of a cell processing workcell, where the instrument comprises a magnetic array couplable to the flow cell, each of the magnets within the magnetic array having a width of w and being spaced apart by between about w / 3 to about ¾w, and wherein the flow cell has a height of between about 1 / 12w to about ⅛w.

The invention discloses an abnormal monitoring method and system for immune cell culture. The method comprises the following steps: collecting cell physical state parameters, culture environment parameters and metabolic biochemical indexes in a culture system in real time; performing motility rate threshold judgment and morphological analysis based on the cell physical state parameters to generate a first abnormal signal; performing dynamic trend analysis on the metabolism biochemical indexes to generate a second abnormal signal; performing grade association on the culture environment parameters, the first abnormal signal and the second abnormal signal, and outputting a monitoring abnormal grade; and performing grading response construction according to the monitoring abnormity grade to obtain an abnormity monitoring report. According to the method, the pollution diffusion risk and the functional cell failure misjudgment rate can be reduced.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

A non-invasive liquid integrity monitoring system using dielectric fingerprinting and machine learning to detect and identify contamination in sealed containers is described. The system may employ externally-mounted sensors that measure dielectric properties through electromagnetic interrogation, comparing measurements against baseline signatures to detect deviations indicating contamination, tampering, or degradation. Industry-specific ML models enable identification of specific contaminants with confidence, providing alerts without breaching container integrity.

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 provides a fish disease identification method based on deep learning. Firstly, a new multi-branch adaptive reparametric module CKDB is provided, a plurality of parallel branches are used in a training stage to capture abundant fish disease features, and the complex branches are combined into an efficient convolutional layer through adaptive reparameterization in a reasoning stage, so that the feature expression ability of the model is effectively improved. Secondly, providing a novel multi-scale adaptive feature pyramid network N-MAFPN: the network adopts an adaptive feature fusion strategy, and through effective fusion and multi-branch feature extraction of features of different scales, the detection precision of the model on fish diseases is improved; finally, a multi-scale attention enhancement module SM-Detect is introduced to the head of the network; and disease area features concerned by the model are dynamically adjusted by using a self-adaptive attention mechanism on feature maps of different scales, so that the fish disease detection capability of the model is improved. The effectiveness of the proposed algorithm structure is proved through an ablation experiment, the problem of insufficient identifiability of fishes caused by diversity of intensive aquaculture environments and rapidity of movement is solved, and the method is a key progress for promoting intelligent health monitoring of fish diseases.

The invention discloses a multi-wave detection and imaging system for fish schools, which relates to the field of detection and imaging of fish schools and comprises a metamaterial acoustic emission module, a self-adaptive signal receiving module, a three-dimensional point cloud generation module, an image enhancement and generation module and an intelligent decision control module. According to the multi-wave detection and imaging system for the fish school, clustering parameters can be dynamically adjusted based on the real-time fish school density, the neighborhood radius is automatically expanded in a high-density area to avoid segmentation errors, and the core point judgment threshold is reduced in a low-density area to reduce missing detection; an acoustic attenuation model is constructed in combination with water body environment parameters, emission parameters are calibrated in real time, images can be enhanced, space and time dimension features are fused, the image resolution and definition are improved, accurate recognition of multiple fish species is achieved, the fish species can be better distinguished, meanwhile, a generative network is adopted to directly convert acoustic data into visual images, and the recognition accuracy is improved. Therefore, the generated image can reflect the actual distribution condition of the fish school more truly and accurately.

The invention relates to the technical field of centrifugal machine PID control, in particular to a centrifugal machine control method and system for soybean protein production. Obtaining time sequence data of the rotating speed and the torque of the centrifugal machine, analyzing data differences and fluctuation trends, calculating abnormal indexes of state parameters at all moments, accurately identifying abnormal data points and abnormal moments, and positioning rotating speed sudden change key nodes; determining an abnormal time period based on the time sequence characteristic of the abnormal moment, analyzing the relation between the rotating speed and the torque, the similarity of abnormal indexes and the rotating speed fluctuation characteristic in the time period, quantifying the dynamic relation between the two, and formulating an adjustment index; aiming at the problem of control lag caused by fixed parameters of the traditional PID, the PID parameters are dynamically adjusted by combining the abnormal index difference of the rotating speed and the torque at the current moment and the adjustment index determined in the early stage, so that the control strategy is adaptively optimized along with the change of the working condition, and finally, the stable, efficient and accurate control on the rotating speed of the centrifugal machine is realized.

The invention discloses a gas concentration identification method and system based on an AI model, and relates to the technical field of data processing. The method comprises the following steps: acquiring an environment infrared image sequence and a gas concentration time sequence signal; dynamically adjusting the length of the sliding window, and intercepting a window image sequence and a window concentration sequence; dTCWT decomposition is adopted to extract multi-direction sub-band energy features of the infrared image, the multi-direction sub-band energy features are compressed into image feature vectors through 1D-CNN, concentration time sequence feature vectors are extracted through an LSTM network, and joint representation is generated through fusion; outputting a concentration predicted value and danger level probability distribution through a mixed deep learning model; a probability thermodynamic diagram is generated by combining Monte Carlo diffusion simulation, and a leakage source coordinate is accurately positioned by fusing a concentration gradient; triggering a grading response instruction according to the highest risk probability, and generating risk map real-time visualization; the problems that a traditional system is delayed in response, inaccurate in positioning and insufficient in utilization of multi-source data are solved, closed-loop management and control of leakage monitoring, early warning, positioning and disposal are achieved, and the positioning precision is improved.

The invention relates to the technical field of medical image analysis, in particular to a CAR-T cell culture monitoring system based on image recognition, which comprises a cell region division module, a cell morphology analysis module, a cell behavior dynamic analysis module and a cell population collaborative monitoring module. According to the method, through cell image gradient intensity analysis, refined cell edge detection and dynamic adjustment of a segmentation threshold value, high-precision extraction of cell boundaries is ensured, noise interference is avoided, cell shapes, textures and geometric features are refined, the identification degree of morphological features is improved, and tiny changes of cell morphologies are accurately captured; dynamic tracking and trend evaluation are carried out on cell behaviors based on adjacent frame images, tiny dynamic changes such as cell division, aggregation and migration are accurately reflected, excessive smoothness in the dynamic process is avoided, cell population behavior monitoring is combined with interaction and corresponding speed calculation, the population synergistic effect change trend is comprehensively evaluated, and the cell population behavior monitoring effect is improved. And finer cell culture environment optimization and clinical research support are provided.

The invention provides a biological microenvironment image analysis and classification method, which relates to the technical field of cell image analysis and comprises the following steps: processing an input biological microenvironment image, segmenting cell components and extracting multi-parameter initial features of the cell components; a first machine learning model is adopted to deduce a refined functional sub-state exceeding a traditional cell type based on the internal characteristics and local microenvironment information of the cells; in combination with biomolecule interaction knowledge, calculating and generating a spatially resolved biomolecule interaction potential field map so as to quantify intercellular communication potential; forming a comprehensive state descriptor set; and inputting the descriptor set into a second machine learning classification model to generate a precise classification result of the biological microenvironment. According to the method, functional, quantitative and interpretable analysis and classification of the biological microenvironment are realized, and the evaluation depth, objectivity and accuracy are remarkably improved.

The invention relates to the technical field of smartphone surface defect detection, and discloses a smartphone defect detection method and system, and the method comprises the steps: firstly collecting defect detection image data through multi-dimensional optical imaging, and dividing the defect detection image data into a plurality of detection sub-regions; extracting surface defect features based on a texture enhancement technology, and extracting defect difference indexes in combination with pixel distribution and spatial correlation; then, the regularity of defect modes is analyzed and evaluated through multi-scale decomposition, the defect fluctuation amplitude is dynamically analyzed and evaluated through a sequential sequence, and the stability of defect difference indexes is judged by integrating the regularity and the fluctuation amplitude; and finally, comparing the stable defect difference index with reference data, and determining a detection result. According to the method, through multi-dimensional imaging, multi-scale analysis and dynamic evaluation, the recognition precision and reliability of complex surface defects are improved, the method is suitable for surface detection of smart phones of different materials and structures, and the high-quality detection requirement in large-scale production is met.

Systems for analyzing biological components are provided. The systems may include a fluidic device and an energy source in communication with the fluidic device. The energy source may supply energy to the fluidic device to form a polymer matrix on or adjacent to a biological component within the fluidic device. Methods of using the systems to analyze biological components are also provided.

Systems and methods described herein include embodiments for generating a geometrical structure of a binding complex formed between a plurality of macromolecules, comprising: processing an input representation comprising a plurality of representations of the plurality of macromolecules to generate a geometry prior; sampling an initial geometrical structure of the binding complex based on the geometry prior; and processing, using a neural network, the initial geometrical structure to generate the geometrical structure of the binding complex formed by the plurality of macromolecules.

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 micro-flow field measurement method of a mechanical biological net port flowmeter, and belongs to the technical field of micro-flow field measurement, and the micro-flow field measurement method comprises the following steps: firstly, constructing a net port cavity digital model, and realizing micro-flow field high-resolution characterization by using fluorescent particle tracing liquid and a laser confocal system; and recording a fluid movement track and processing original data by applying a track optimization function under the condition that the microfluidic driving system generates a constant pressure gradient. And solving fluid pressure distribution by adopting an orthogonal grid system and a Navier-Stokes equation, and processing speed field and pressure distribution data by utilizing a pre-trained micro-flow field analysis neural network model. Finally, the flow and pressure relation curve is obtained by calculating the flow flux of the cross section of the network port, a micro-flow field measurement correction coefficient table is generated, and the technical problems that in the prior art, the accurate measurement difficulty of the biological network port micro-flow field is large, and multi-scale flow characteristic representation is insufficient are solved.

The invention discloses a dynamic blood glucose monitoring and intelligent pump speed adjusting system and method, and relates to the technical field of biomedical engineering.The dynamic blood glucose monitoring and intelligent pump speed adjusting method comprises the steps that precise monitoring is conducted through a multi-mode sensing module, an enzyme electrode sensor adopts an anti-interference coating, a Raman spectrum dynamic correction algorithm is combined, and motion artifacts and temperature drift are restrained; the metabolic feature fusion calibration module is integrated with a time convolution network, time sequence modeling is carried out on diet, exercise and historical blood glucose data, an individualized metabolic parameter database is constructed, and self-adaptive signal drift compensation is achieved; the self-adaptive pump control module innovatively designs a double-layer fuzzy PID controller, a base layer generates an infusion rate according to a real-time blood glucose state and insulin activity, an optimization layer introduces an LSTM metabolism prediction model, and the slope of an infusion curve is dynamically adjusted in combination with future behavior pre-judgment; in the edge-cloud collaborative architecture, a lightweight TCN model is locally deployed to guarantee real-time control, and a cloud end optimizes a global digital twinborn model through federal learning and generates a multi-dimensional metabolic risk early warning report.