366 results about "Cell state" patented technology

The invention relates to the technical field of medical treatment, in particular to a child fracture rehabilitation management system based on virtual reality, which comprises a multi-modal sensing and data acquisition module, an intelligent rehabilitation training module, a multi-dimensional evaluation and feedback module and a data intelligent analysis and prediction module, compared with the defects that an existing rehabilitation training technology mainly depends on subjective experience of doctors to formulate a static scheme and lacks multi-source data dynamic feedback, the method comprises the steps that firstly, a comprehensive and accurate patient ability portrait is constructed; intelligent dynamic adjustment of training parameters is realized by utilizing an LSTM gating mechanism, a forgetting gate filters invalid historical data, an input gate fuses real-time action quality evaluation, an output gate generates a personalized difficulty gradient, and a patient ability threshold is continuously memorized in combination with a cell state, so that a training scheme has an adaptive evolution ability, and the training efficiency is improved. The parameter adjustment period of a traditional rehabilitation scheme is compressed from a week level to a real-time level, the accuracy rate of risk prediction of muscle strain and the like is improved, and the utilization rate of effective training time is increased.

The invention discloses a dynamic identification method for unusual cells before young tumors based on multi-omics data, and relates to the technical field of cell unusual identification. A dynamic correlation intensity matrix and a cumulative effect contribution matrix are constructed, a differentiation screening strategy is implemented according to individual response characteristics, and the unusual cells before young tumors are identified. And the abnormal dynamic high-fidelity identification of the young tumor pre-cells is realized. And aiming at individuals of different response types, an instant path, a long-term path or a double-path fusion strategy is respectively adopted, key behavior data is accurately screened, and the input quality is improved. According to the method, redundant interference is effectively eliminated, the simulation capability of the model on key processes such as immunosuppression and DNA damage accumulation is enhanced, the biological rationality and prediction precision of a cell state evolution sequence are remarkably improved, and the problems of model response lag, low calculation efficiency and output distortion caused by data noise in the prior art are solved; and a reliable technical support is provided for early warning and individualized intervention of precancerous lesions.

The invention relates to the technical field of dangerous rock and ground disaster monitoring and early warning, and particularly discloses a dangerous rock and ground disaster monitoring and early warning system and method based on multi-modal data analysis, and the system comprises a data obtaining module which obtains the multi-modal data of a monitoring area; the data fusion processing module is used for carrying out preprocessing, data alignment, abnormal value processing and standardized analysis on the acquired data; and the multi-modal data analysis module is used for carrying out dimension reduction processing on the data set by applying principal component analysis, then inputting the data set into an input gate of the LSTM, dynamically regulating and controlling the write-in proportion of new information to the cell state through a gating mechanism, and optimizing the capturing capability of the model on time sequence characteristics and the like. Classification of dangerous rock state change information can be obtained, the change process of dangerous rock disaster pregnancy can be evaluated, the missing report rate of dangerous rock ground disaster is reduced, and the early warning success rate is improved.

The invention provides a state detection and health diagnosis method and system of an SOFC system, and relates to the technical field of solid oxide battery state monitoring, and the method comprises the steps: obtaining the operation parameters of an SOFC, and optimizing the data consistency and anti-noise capability based on the fusion algorithm of dynamic Bayesian reasoning and the conditional probability distribution of the operation state of the SOFC; frequency domain features are extracted through fast Fourier transform, dimension reduction is carried out in combination with improved principal component analysis, key variables are reserved, and feature expression is optimized for SOFC high-temperature vibration features; predicting SOH (state of health) and RUL (residual life) of the SOFC based on a random forest, a generative adversarial network and an online updating mechanism; and outputting a diagnosis result and generating a dynamic maintenance scheduling plan based on the RUL prediction, and optimizing the system life by minimizing the downtime and the cost. Through integration of multi-source data fusion, frequency domain feature extraction, AI diagnosis and data enhancement and predictive maintenance, limitations in precision, efficiency and service life optimization in the prior art are overcome.

The invention discloses a key marker gene-based intelligent cell type annotation method, which comprises the following steps of: constructing a static knowledge base by using known marker genes in a reference database, and endowing the marker genes with cell specific weights by using a TF-IDF method, so that the annotation accuracy and interpretability are improved. Meanwhile, under the condition that static matching is insufficient, the literature is understood through a large language model, mark information is extracted, dynamic completion of the knowledge base is achieved, the defect that updating of a traditional knowledge base is lagged is overcome, and good adaptability and expansibility are achieved. Besides, static and dynamic matching scores are fused in the annotation process, so that more robust cell type identification is realized, annotation requirements of multi-tissue, multi-species and novel cell states are adapted, high-precision and extensible cell type annotation can be realized in a scene with insufficient reference knowledge or a fuzzy sample, and the annotation efficiency is improved. And the method has good universality and practicability.

The invention discloses a biological monitoring equipment fault-tolerant control method based on a network security policy, and relates to the technical field of medical equipment management, and the method comprises the steps: S1, obtaining a historical operation log monitored by biological monitoring equipment, extracting a service life, combining with a time sequence data sequence and fault history, fusing long-term dependence capture, and forming an initial data set; s2, processing the initial data set through a long short-term memory network, capturing a time sequence dependency relationship between performance degradation and fault history by applying a forgetting gate mechanism and input gate control, obtaining a quantization vector of a change trend by utilizing output gate adjustment and cell state updating, and fusing hidden state output; according to the biological monitoring equipment fault-tolerant control method based on the network security policy, the reliability and the fault-tolerant capability of wetland zone micro algae monitoring equipment are remarkably improved, the service life of the equipment is prolonged, and the maintenance cost is reduced.

The embodiment of the invention relates to the technical field of bioinformatics, and provides a bulk data cell analysis method, device and equipment, and the method comprises the following steps: constructing an initial reference matrix according to a single cell data set and a cell type annotation template, each element in the initial reference matrix represents the gene expression quantity of each cell state under each characteristic gene; performing deconvolution on the bulk data to be analyzed according to the initial reference matrix to obtain a first deconvolution result; updating the initial reference matrix according to the first deconvolution result to obtain a first reference matrix; and according to the first reference matrix, performing deconvolution on the bulk data to be analyzed to obtain a second proportion and a second gene expression quantity of each cell type in the bulk data to be analyzed. According to the embodiment of the invention, the accuracy of cell analysis in bulk data can be improved.

The invention discloses a thinking cellular system construction method for simulating spatio-temporal evolution of industrial chain nodes, and relates to the technical field of brain network construction. Comprising the steps of obtaining a thinking cell vector space; obtaining a thinking cell state; obtaining an adjacent topological relation; establishing a logic evolution dynamic potential function; constructing a thinking cellular system according to the thinking cellular vector space, the thinking cellular state, the adjacency topological relation and the logic evolution dynamics potential function; and identifying the observed state and the hidden state of the industrial chain key node based on the thinking cellular system to obtain an identification result. According to the method, dynamic simulation is performed on spatio-temporal evolution of the industrial chain key node, the evolution path of the industrial chain key node state is explicitly expressed, the evolution rule is adjusted in real time to cope with environmental disturbance, adaptive identification and risk prediction of the industrial chain key node state are realized, and the external risk resistance of the industrial chain is improved.

A cell culture system includes: a plurality of cell culture channels each of which includes a fluidic device in which cells are cultured, a pump which makes a liquid flow into the fluidic device, a detection unit which detects a state of the cells that are cultured, and a controller which controls the pump and the detection unit; a measurement unit that measures culture environment of the cells which are cultured in the plurality of cell culture channels; and an information processing apparatus that adjusts the culture environment such that an absolute value of a difference between evaluation values for the states of the cells that are cultured in the plurality of cell culture channels is a threshold value or less, on the basis of the state of the cells detected by the detection unit and the culture environment of the cells measured by the measurement unit.

A method and associated memory system for randomizing memory storage data. The method and system receive at a data inverter user data and meta data sequence having an inversion seed bit, determine a value for the inversion seed bit from a look up table specifying inversion seeds for different pages of data to be stored in a memory, depending on the value of the inversion seed bit, bit-flip the user data and meta data sequence except for the inversion seed bit; and regardless of bit-flipping, exclusive OR (XOR) the user data and meta data sequence with a random sequence to produce an XORed sequence for storage in the memory as randomized data.

The invention discloses a cell morphology dynamic monitoring method and system fused with time sequence image analysis, and the method comprises the steps: firstly obtaining a time sequence image sequence of a cell at a plurality of continuous time points, and extracting an initial feature vector set containing the morphology, size, texture and the like of the cell; inputting the time sequence image into a time sequence image multi-focus fusion model to obtain a fusion feature vector, and forming a refined feature vector through dimension transformation and feature screening; and inputting an improved long-short-term memory network to learn a cell state evolution mode, determining the state of the cell at each time point, generating a change track and predicting a future state. The system comprises an image acquisition and initial feature extraction unit, a time sequence image multi-focus fusion unit, a dimension transformation and feature screening unit, an improved long-short-term memory network unit, a cell state determination unit and a cell state prediction unit, all the units are connected in sequence, image features at different time points are associated through a fusion model, the network is optimized in combination with cell parameters, and the cell state is predicted. The accurate monitoring on the dynamic change of the cell morphology is realized.

The invention provides a battery management system, a control method and a vehicle with the battery management system. The slave control module is electrically connected with the battery pack, the slave control module is used for periodically acquiring a plurality of battery cell state parameters of the battery pack, and the battery cell state parameters at least comprise battery cell real-time temperature data, battery cell real-time current and battery cell real-time voltage; the display module is used for displaying the charge state of the battery pack; and the master control module is electrically connected with the slave control module and the display module, and the master control module is used for controlling the display module to display a target charge state based on the battery cell state parameters. Through close cooperation of the slave control module, the master control module and the display module, accurate control of the state of charge under the large-current charging and discharging condition is realized, and the problem of inaccuracy of state of charge estimation under the traditional large-current charging and discharging condition in the prior art is solved.

The invention provides an SOFC state monitoring and health diagnosis method and system based on artificial intelligence, and relates to the technical field of solid oxide battery state monitoring, and the method comprises the steps: collecting the operation parameters of an SOFC in real time; an algorithm based on extended Kalman filtering and information entropy optimization data is adopted, and weighting processing is carried out on the collected operation parameters; on the basis of an improved hybrid neural network model and a reinforcement learning algorithm, the state of the battery is monitored in real time, the expected durability period is predicted, abnormity is recognized, and model parameters are dynamically adjusted through sliding window online learning and a self-correction mechanism; and a feedback control signal is generated by combining a monitoring result, health prediction and an abnormal early warning signal, and the running parameters of the SOFC are dynamically adjusted. The method has the advantages of high precision, strong robustness and adaptivity, and is suitable for intelligent management of the SOFC in energy storage and efficient power generation.

Systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles are provided herein. The data set may include multiple omics data. Some embodiments may include using machine learning to train classifiers of raw multi-omics data and incorporate systematic biological knowledge to understand cellular behavior and cellular status at biomolecular levels.

The invention discloses a power supply cell state diagnosis method and device, a storage medium and a program product, and belongs to the technical field of battery safety management. The method comprises the following steps: acquiring internal stress, temperature, terminal voltage and charge and discharge current values of a battery unit; based on the parameters, constructing a thermal runaway symptom index through a statistical correlation algorithm; inputting the historical state parameters into a long-short term memory (LSTM) network model, and predicting a future terminal voltage value; calculating the residual error between the predicted terminal voltage and the actual terminal voltage, and carrying out anomaly detection on the residual error; and performing comprehensive state diagnosis by combining the thermal runaway symptom index and the anomaly detection result. Internal stress parameters are introduced, LSTM prediction and OC-SVM detection are combined, early degradation symptoms can be captured from mechanical and electrochemical double angles, and the accuracy and advance of thermal runaway early warning are improved.

A method of detection of a status of cells within a cell culture is described. The method is based on comparing an IR spectrum of a test sample obtained from the cell culture with an IR spectrum of a control sample or samples or with an IR spectrum of a sample or samples obtained at an earlier time point in the cell culture and correlating differences between the spectra with the status of the cells. The status may be classified into healthy or unhealthy. The test sample may or may not contain cells. The test sample may contain cell fragments, cell components or cell media, or may be cell supernatant. The comparison may be performed using a predictive model based on pattern recognition algorithms, such as support vector machines SVM, linear discriminant analysis LDA, principal component discriminant function analysis PC-DFA, neural networks, or random forests). The analysis results may be used to monitor and / or control the cell culturing process.

The present invention relates to a method, preferably a computer-implemented method, and to an apparatus for generating at least one intermediate cellular state of a cellular state evolution from an initial cellular state to a corresponding advanced cellular state. The present invention also relates to a computer program for generating at least one intermediate cellular state and to a non-transitory computer readable data medium storing the computer program. Moreover, the present invention relates to a method and to an apparatus for training a model for generating at least one intermediate cellular state of a cellular state evolution from an initial cellular state to a corresponding advanced cellular state. The present invention also relates to a computer program for training a model for generating at least one intermediate cellular state and to a non-transitory computer readable data medium storing the computer program. Furthermore, the present invention relates to a method for diagnosing a disease and forecasting patients' future clinical outcome making use of the method for generating at least one intermediate cellular state. Moreover, the present invention relates to an in-vitro method for transforming the identity of a cell.

The invention discloses a multi-modal large model power grid industry PDF file review method based on reinforcement learning optimization, and aims to solve the problems that power grid industry PDF table review is low in efficiency, insufficient in accuracy and dependent on manpower. The method is implemented in two stages: in the first stage, large model table structure understanding pre-training is completed through multi-modal input construction, cell state modeling, inference chain execution and a reinforcement learning reward mechanism, and the complex table structure recognition capability is improved; and in the second stage, multi-modal embedded representation of the to-be-compared table is constructed, an explicit reward mechanism and GRPO unsupervised strategy optimization are combined, semantic comparison and difference recognition of table fields are realized, and a multi-dimensional review result containing a difference list, an interpretation text, a comparison track and a visual report is output. The power grid industry PDF table review efficiency and precision are remarkably improved, auditing traceability is supported, and the method is suitable for professional table review scenes of electric power engineering budget, equipment parameters and the like.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

A method for determining a state of a cell, the cell being placed in a sample in contact with a culture medium, includes illuminating the sample with a light source and acquiring an image of the sample with an image sensor, the image sensor lying in a detection plane. From the acquired image, a position of the cell in a plane parallel to the detection plane is located and a refractive index or a relative refractive index of the cell is estimated, the relative refractive index corresponding to a refractive index of the cell relative to the refractive index of the culture medium. From the estimation, an index of interest of the cell is determined. From the index, a state of the cell among predetermined states is classified, the predetermined states including at least one apoptosis state and one living state.

The invention discloses a sequence data modeling method and system. The method comprises the following steps: S1, converting a multi-source heterogeneous original feature set at the current moment into embedding vectors with unified dimensions through a learnable mapping function, and integrating the embedding vectors into a comprehensive feature embedding vector Et; s2, calculating a space-time difference between a current event and a preorder event, processing the space-time difference into structured features, and inputting the structured features into the learnable network to generate a space-time correlation score vector Rst; and S3, inputting the Rst as a regulating signal into the gating recurrent neural network to regulate gating, and updating the cell state to obtain a sequence state vector st. According to the method, the sensitivity of state characterization to space-time dynamic is enhanced, and the interpretability of a space-time correlation mode is improved.

The invention relates to the technical field of lithium ion battery safety monitoring, and discloses a method and a device for early warning and positioning thermal runaway of a lithium ion battery pack in advance. The method comprises the following steps: firstly, collecting real-time voltage, temperature, current and serial number data of each battery cell, and constructing a battery cell state observation tensor with a time-space label; running a battery cell thermoelectric state extended Kalman estimation algorithm based on the tensor to generate a battery cell health state partial sequence lattice; and then constructing a thermal runaway risk propagation phase space grid, finally executing manifold analysis-based local attractor region identification and early warning decision under the constraint of the grid, and outputting a high-risk source cell number and an early warning level. According to the invention, the problems of early warning lag, lack of cell group state consistency dynamic evaluation and incapability of accurately positioning fault cells caused by dependence on later physical symptoms in the prior art are solved, and the thermal runaway early warning timeliness and positioning accuracy are improved.

The invention belongs to the technical field of battery distribution management, and provides an intelligent energy distribution system and method for a multi-battery module, and the method comprises the steps: constructing a battery cell network topological graph according to the internal battery cells of a battery pack and the connection relation between the battery cells; according to the cell network topological graph, real-time voltage, current and temperature signals of part of cells in each battery module are obtained, and part of sampling observation data is formed; on the basis of a graph signal processing method, mapping part of sampling observation data into graph signals, estimating the state of an unsampled battery cell by adopting a graph Laplacian regularization method, detecting abnormal nodes in a graph on the basis of a state estimation value of the battery cell, and correspondingly identifying an abnormal battery cell; constructing an energy distribution model according to the state estimation value data of each battery cell, which is estimated by graph signal processing; risks caused by reduction of sampling wire harnesses and reduction of monitoring precision under a CTP architecture are effectively relieved, the cell state estimation precision and the energy distribution stability are improved, and the overall safe life of the battery pack is prolonged.

The invention discloses a nucleic acid transfection system and method based on automatic control, and relates to the technical field of genetic engineering.The method comprises the steps that after target cells are intelligently cultured to be in a suitable state, an automatic system selects a transfection reagent and prepares a compound according to cell types; the cells and the compound are mixed through low shear force and then incubated; multi-modal monitoring equipment is used for tracking nucleic acid distribution and cell states in real time, and incubation conditions are dynamically adjusted; analyzing the monitoring data based on a machine learning algorithm and optimizing transfection parameters; after transfection, culture and multi-dimensional analysis are automatically executed. The system correspondingly comprises a cell culture module, a reagent preparation module, a mixed incubation module, a real-time monitoring module, a dynamic optimization module and a subsequent analysis module. Through closed-loop automatic control and intelligent optimization, the transfection efficiency and stability are remarkably improved, the cytotoxicity is reduced, manual intervention is reduced, and a standardized solution is provided for recombinant gene expression.

The invention discloses a self-adaptive human hand trajectory prediction method and system, computer equipment and a storage medium. The method comprises the following steps: acquiring N-step historical trajectory data of a human hand; the current historical track data is input into the EGU, the unit state and the hidden state are updated through an input gate, a forgetting gate and an output gate, the hidden state of the EGU at the last time step is output of the EGU, and the output state is transmitted to a KAN network; the KAN network receives output from the EGU, performs multi-layer processing on input data by using an activation function, gradually extracts features in the data, generates high-dimensional feature representation, and obtains preliminary prediction; and in each time step in the overall prediction process, the corrected Kalman filter is used to carry out online adaptive adjustment on parameters of the prediction model, and a final prediction result is obtained and output. By tracking the motion trail of the wrist, the operation intention of human beings can be effectively understood and predicted, so that the man-machine cooperation efficiency in the medical dispensing process is improved.

The invention provides an optimization method and system of a distributed battery management system. The method comprises the following steps: acquiring battery cell parameters, scene parameters and historical charging and discharging data of a battery cell, and evaluating a battery aging stage; calling a matched battery cell state calculation algorithm according to the scene parameters and the battery aging stage; calculating battery cell state information of the battery cell according to the battery cell parameters, the scene parameters and a battery cell state calculation algorithm; according to the cell state information, the scene parameters and the battery aging stage, dynamically adjusting an equalization threshold and an equalization rate, and executing an equalization strategy to obtain equalization state information; periodically uploading the operation data to a cloud server, receiving an algorithm update package, and updating an adaptive algorithm library; and calculating the overall state information of the battery pack according to the cell state information and the equilibrium state information. The calculation precision of the battery cell state can be effectively improved, the cycle life of the battery is prolonged, and the use safety of the battery is improved.

The invention belongs to the technical field of biological intelligent detection, and particularly relates to a circulating tumor cell automatic detection device based on a micro-fluidic nano chip and AI analysis and application. The device comprises a micro-fluidic chip module, an optical acquisition module, an image recognition module, a feedback control module and a liquid path control module, and can realize efficient enrichment, image recognition and dynamic fluidic regulation of circulating tumor cells in peripheral blood. By constructing a change trend model of an image frame sequence, cell state changes are recognized in real time, flow velocity and channel control instructions are output, and collaborative optimization of recognition precision and the sample utilization rate is achieved. The device is suitable for tumor early screening, curative effect monitoring and recurrence early warning scenes.

One or more embodiments include predicting, using one or more perturbation response models configured to take as input one or more basal states associated with one or more biological systems and a first plurality of perturbations, a first plurality of responses of the one or more biological systems to the first plurality of perturbations, predicting, using one or more cell state models configured to take as input the first plurality of responses, a first plurality of cell states associated with the first plurality of responses, wherein the one or more cell state models have been trained using a second plurality of training samples comprising observations of a plurality of biological systems paired with a second plurality of cell states, and selecting one or more additional perturbations included in the first plurality of perturbations for experimental evaluation based on the first plurality of cell states.

The invention discloses a multichannel tissue-derived neutrophil detection chip and an application system, the chip is a multichannel detection chip integrating migration-related markers and functional markers, rapid and synchronous identification of neutrophil tissue sources is realized, the migration-related markers are CD62L and CXCR4, the functional markers are CD44 and MPO, and the migration-related markers are CD62L and CXCR4. According to the method, migration related markers and functional state markers are subjected to integrated and collaborative analysis, tissue sources are locked from the cell state and function two dimensions, four-channel parallel detection is performed, and the tissue sources of the neutrophil are accurately judged through expression profile modes of the four components instead of a single marker, so that the neutrophil detection accuracy is improved. According to the method, the neutrophil tissue source is rapidly, synchronously and accurately identified, the technical bottleneck of neutrophil tissue source detection is broken through, high efficiency, accuracy and universality are achieved, and an efficient and accurate tool is provided for immune mechanism research and clinical diagnosis.

The present application describes methods and systems for cell selection and reselection. One aspect describes the apparatus including a non-transitory memory including executable instructions for cell selection or cell reselection, which when executed by a processor, performs the instructions of determining one or more new public land mobile networks (PLMNs) have been selected. The processor is also configured to execute the instructions of scanning radio frequencies for one or more cells in one or more of the selected PLMNs. The processor is further configured to execute the instructions of determining the one or more cells to be an acceptable cell, a suitable cell, not an acceptable cell, or not a suitable cell. The processor is even further configured to execute the instructions of determining a state of the apparatus to be a multi-camped normally state, a reduced power any cell selection state, a reduced power camped on any cell, a camped normally state, an any cell selection state, or a camped on any cell state.