162 results about "Cell state" patented technology

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.

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.

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 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.

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.

The invention discloses a bioreactor multi-parameter adaptive control system and method based on machine learning, and belongs to the technical field of bioreactor intelligent control, and the bioreactor multi-parameter adaptive control system comprises a bioreactor hepatocyte culture unit, a data acquisition and control unit and a machine learning intelligent decision-making unit. And the data acquisition and control lower computer controls operation variables such as gas supply, sampling and perfusion in the culture unit execution mechanism, collects culture process data returned by the parameter detection module, constructs a data-driven cell state prediction model by utilizing machine learning, and optimizes model parameters and operation variables based on a prediction result to realize closed-loop control. The method can overcome the defect that there is no precise mechanism model for hepatic cell culture, and multi-parameter collaborative optimization can be realized only depending on process data, so that the state of the hepatic cells is sensed in real time, full-cycle optimization of cell viability and functions is realized, and the activity and density of hepatic cell culture are effectively improved.

The invention relates to the field of distributed storage, and discloses a distributed storage system, method and equipment for wind power plant data and a medium, which are used for realizing flexible adjustment of a storage strategy and meeting the storage requirement of dynamic change of a wind power plant data value. Comprising the following steps: collecting SCADA system data at edge nodes of a wind power plant, inputting an information entropy module and an access heat analysis module in parallel, fusing to generate a data value evaluation result, inputting the evaluation result into a cellular automaton engine, outputting a cellular state change instruction, summarizing collaborative strategy requests of at least two edge nodes to a regional center node, and carrying out collaborative strategy analysis on the regional center node. And a fair resource allocation scheme is output through the Sharpley value calculation module. And each edge node synchronously receives the scheme, the time sequence database controller generates a final storage instruction, and data distributed persistent storage is completed. According to the method, on the basis of the correlation between the long-term data value evaluation result and the actual use value, the entropy value and the popularity weight coefficient are optimized, and self-adaptive optimization of the system is achieved.

The invention provides a method for jointly deducing dynamic cell communication and a cell state transition rate. Relates to the technical field of biological information. The method comprises the following steps: extracting candidate ligands, candidate receptors and characteristic genes from space transcriptome data to be processed; the method comprises the following steps: screening nodes which have an interaction relationship with candidate ligands, candidate receptors and characteristic genes from a pre-constructed prior database, and constructing a multi-layer signal network of which the structure is ligand-receptor-transcription factor-target genes; establishing a gene regulation kinetic model according to a ligand-receptor action relationship, a receptor-transcription factor action relationship and a transcription factor-target gene action relationship in the multilayer signal network; iteratively optimizing parameters to be estimated in the gene regulation and control kinetic model; and obtaining the change rate of the expression quantity of the target gene in the receiving cell based on the potential time after iterative optimization and the parameters to be estimated after iterative optimization. And combined analysis of dynamic cell communication and cell differentiation tracks can be realized.

Provided herein are systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles. The data sets may include multi-omic data. Some embodiments may include the use of machine learning in training a classifier of raw multi-omic data and incorporating system biology knowledge to understand cellular behavior and cell status at the biomolecular level.

The invention relates to the technical field of frozen slicing, in particular to a mammal skin embedding method for a spatiomics technology, which comprises the following steps: acquiring an in-vitro skin sample which comprises an epidermal layer, a corium layer and optional subcutaneous tissue; shearing the fat in the skin sample under the condition a to remove the fat in the skin sample, and freezing and embedding the fat-removed skin sample. The mammal skin embedding method provided by the embodiment of the invention provides a standardized overall scheme for sample analysis of mammal skin and hair follicle tissues, is beneficial to obtaining tissue slices with high quality, complete morphology and good cell state, and realizes space omics sequencing with high sample utilization rate.

The invention belongs to the technical field of bioengineering, and discloses a bacteriophage growth environment monitoring and evaluating method. The method comprises the following steps: firstly, establishing a baseline data set with state labeling by utilizing a historical time sequence of a multi-batch culture process; environment parameters, host cell state parameters and phage replication related signals are synchronously collected in the operation process, and standardized time sequence data are formed through unified preprocessing; extracting an instant value, a short-time variable quantity and a window fluctuation quantity from the standardized data, and generating a deviation degree record; calculating an environment deviation score, a host deviation score and a replication deviation score according to the deviation degree record, and constructing a bacteriophage growth suitability index, a host stress index and a stability factor; performing hierarchical evaluation, trend fitting and significant change point identification on the time sequence of the indexes, and generating a trigger signal when a continuous threshold exceeding condition is met; and in combination with a preset intervention strategy set and the process simulation model, performing simulation verification on the candidate intervention scheme.

The present invention relates to a method of inducing mesenchymal-epithelial transition (MET) in a basal-like (or mesenchymal-like) cancer cell by contacting the cancer cell with an inducer of mesenchymal-epithelial transition for a time and under conditions sufficient to induce MET in the cell. Additionally, there is also provided a method of inhibiting epithelial-mesenchymal transition (EMT) of a cancer in a subject, the method comprising administering an inhibitor or regulator of lipid metabolism for a sufficient time and under conditions to inhibit epithelial mesenchymal transition (EMT) of the cancer in the subject.

To provide a more automatable, consistent and comprehensive method for providing a diagnosis of the state of cells present in a patient sample.SOLUTION: The detection of pathological abnormalities in a tissue sample and / or a plurality of cells is a highly specialized and time-consuming task, usually performed by a group of selected clinicians and technicians. Described herein are methods for more automatable, consistent and comprehensive cell sample analysis to provide rapid and reliable detailed classification, for example, but not limited to cancer diagnosis, particularly diagnosis of the state of the cells present in the sample.SELECTED DRAWING: Figure 1A

An analytical method of and system for monitoring cell status whereby excitation energy is focused to an excitation cytometry volume in a cell sample to fluoresce first and second fluorophores of a cell. Fluorescence signals from the first and second fluorescing cell fluorophores are directed to a detection subsystem. A first peak in fluorescence intensity for the first fluorophore is detected as is a second peak in fluorescence intensity for the second fluorophore. The fluorescence signals are processed to identify when the first and second peaks occur substantially simultaneously indicating the presence of a cell at the excitation cytometry volume instead of background fluorescence and in response, the cellular status of the cell is measured using the intensity of the levels of the first and second peaks.

The invention relates to a spatial analysis single cell state modeling method and system based on domain self-adaption and layered fine tuning, and the method comprises the steps: obtaining multiple immunofluorescence images and single cell segmentation masks, and constructing a no-label data set; constructing a mask auto-encoder composed of a ViT encoder and a linear decoder, adding a classification token in front of the image, carrying out field adaptive training on the mask auto-encoder based on the unlabeled data set, learning the classification token, and obtaining a field adaptive weight of the ViT encoder; obtaining a labeled data set; constructing a state embedding generation model, wherein the state embedding generation model comprises a shared ViT backbone network and a two-stage classifier; a classification token is added in front of an image feature sequence in the labeled data set, hierarchical training is carried out on the state embedding generation model, and the classification token is learned; and inputting the cell image blocks into the trained state embedding generation model, outputting a classification result and cell state embedding, and carrying out interpretability analysis. Compared with the prior art, the method has the advantages that accurate cell classification can be realized, and cell state representation with biological interpretability can be generated.

The application discloses a kind of young tumor pre-cell abnormal dynamic identification method based on multi-omics data, it is related to cell abnormality identification technical field, the application is by constructing dynamic correlation strength matrix and cumulative effect contribution matrix, according to individual response characteristics Implementation of differentiating screening strategy, realize the high fidelity identification of young tumor pre-cell abnormal dynamics.For different response type individuals, respectively using instant path, long-term path or double-path fusion strategy, accurately screening key behavior data, improve input quality.The method effectively eliminates redundant interference, enhances the model's ability to simulate key processes such as immune suppression and DNA damage accumulation, significantly improves the biological rationality and prediction accuracy of cell state evolution sequence, solves the model response lag, low computational efficiency and output distortion problems caused by data noise in the prior art, and provides reliable technical support for early warning and individualized intervention of pre-tumor lesions.

The application discloses application of long-chain non-coding RNA LncZFHX2 in preparation of a drug for treating osteoarthritis. LncZFHX2 is specifically combined with KLF4 protein through a specific structure and a base sequence of LncZFHX2, and promotes transcriptional regulation of the KLF4 protein on RIF1 protein. LncZFHX2 indirectly regulates RIF1 protein to improve a cell state. Overexpression of LncZFHX2 inhibits osteoarthritis in vitro. In the results of the examples of the application, it is verified that overexpression of LncZFHX2 inhibits development of osteoarthritis, and a new intervention approach and a candidate drug are provided for clinical prevention and treatment of osteoarthritis. LncZFHX2 has a short sequence, does not need to be translated into a protein to play a role, has the characteristics of quick effect, high efficiency and low cost, and has high cost performance in synthesis and application.

This invention provides a method and system for detecting the cellular state in biological samples. [Solution] A method for determining cell type or cell state is provided. The method includes: providing, or already providing, a sample containing DNA or RNA, and creating, or providing, or already providing, a methylation profile of the DNA or RNA in the sample.

The invention relates to the technical field of pharmacy and biology, in particular to a high-content drug screening method and system based on ControlNet-PPO. The method comprises the following specific steps: according to a current cell state, adopting a PPO algorithm reinforcement learning strategy to generate a combined action instruction for controlling release of various drugs; applying drug disturbance to cells based on the combined action instruction, and obtaining a disturbed cell image; inputting the cell image as a condition into a pre-trained ControlNet, and generating an expected cell state image under the action instruction; calculating a composite reward value based on a state difference between the expected cell state image and a normal cell reference image; and updating the reinforcement learning strategy by using the composite reward value, and iteratively optimizing the drug combination action until the cell state is converged to be close to the normal state, thereby obtaining an optimal drug combination path. By adopting the method, the synergistic effect of multiple drugs can be effectively captured and utilized, and the method has the characteristic of high optimization capacity.

This application relates to the field of electrochemical energy storage battery cluster technology, specifically to a method and apparatus for estimating the state of charge of individual battery cells, an energy storage battery cluster system, a computer-readable storage medium, and a computer program product. The method includes: acquiring global features and multiple individual battery cell voltages; linearly projecting the global features and multiple individual cell voltages onto a pre-defined dimension representation space to obtain global feature projections corresponding to the global features and individual cell voltage projections corresponding to the multiple individual cell voltages; determining the input representation based on the individual cell voltage projections, global feature projections, and bidirectional position encoding; performing temporal self-attention processing and individual cell distribution self-attention processing on the input representation to obtain individual cell relationship representations; and determining the individual cell state of charge regression value, individual cell health state regression value, individual cell equalizable charge regression value, and individual cell anomaly binary classification probability based on the individual cell relationship representation and a multi-task collaborative prediction head. This method effectively solves the problem of redundant consumption of computational and storage resources.

The invention discloses an evaluation method, system and device for cell specific genetic variation disturbance and a storage medium. An integrated single cell data set is used for training a variational auto-encoder, and a high-dimensional gene expression profile is mapped into a cell state vector in a low-dimensional hidden space through an encoder of the variational auto-encoder; freezing parameters of the encoder, training a conditional disturbance module by using training data with a mononucleotide variation tag, and generating conditional variation embedding; performing anti-fact prediction for target cell and target mononucleotide variation, and generating a reference prediction expression profile and a disturbance prediction expression profile through a conditional disturbance module and a decoder of a variational auto-encoder based on the original genotype and the overturned genotype of the target cell; and calculating the difference between the two to obtain the disturbance effect score of the single nucleotide variation in the target cell, and quantitatively describing and predicting the specific disturbance mode of the SNV in different cell states.

The present invention optimizes movement control methods for a base station device and a terminal device that support a network power reduction technique, and improves the efficiency of reducing network power consumption. A terminal device is provided with a control unit that performs control corresponding to each of a first cell state, which is a first mode, and a second cell state, which is different from the first mode. Furthermore, the terminal device is provided with a reception unit that receives, from the base station device, a first condition for determining that the first time has been exceeded, and a second condition for determining that a second time has elapsed after a prescribed duration has elapsed from the first time. Furthermore, the terminal device is provided with a processing unit that evaluates an event condition for conditional switching corresponding to the state of the first cell before a first condition is satisfied, evaluates an event condition for conditional switching corresponding to the state of the second cell when the first condition is satisfied, and evaluates an event condition for conditional switching corresponding to the state of the second cell when the second condition is satisfied. And starting a condition switching process.

The invention provides a BMS sleep equalization method and device based on fuzzy control, and the method comprises the steps: monitoring the voltage and electric quantity of a battery cell with the highest voltage in a charging state of a power battery, and obtaining a data table; for each battery cell, based on the cut-off voltage value at the moment of reaching the cut-off voltage and the data table, obtaining the accumulated electric quantity in the charging process; calculating to-be-balanced electric quantity based on the accumulated electric quantity of each battery cell; when the BMS sends a sleep instruction, calculating to obtain initial equalization time required for equalizing the to-be-equalized electric quantity of each battery cell; obtaining an adjustment coefficient based on the cell state information and a fuzzy control algorithm; adjusting each initial equalization time based on the adjustment coefficient to obtain sleep equalization time; and when the BMS is in dormancy, performing dormancy equalization based on the dormancy equalization time. According to the scheme, the problem that the curve cannot be recorded again to update the to-be-balanced electric quantity under the working condition of being not fully charged for a long time is solved; and a dormancy equalization strategy is optimized by using fuzzy control, so that the consistency of the battery cells is kept in variable working conditions.

A communication method and apparatus. The method comprises: when a user equipment is in a first state, determining that a first condition is met, wherein the first state is a camped-normally state or a camped-on-any-cell state or an any-cell-selection state; and the user equipment entering a second state, in which the user equipment can detect a first synchronization signal and an alert signal, wherein the first synchronization signal is used by the user equipment to synchronize with a network device in the second state, and the alert signal is used for prompting the occurrence of service missing or service arrival. The embodiments of the present application provide a user equipment with a second state which can support an alert signal, such that the alert signal can be applied, thereby increasing the probability of the user equipment being successfully paged. Moreover, switching between the second state and a first state can also be achieved, such that the user equipment can enter different states under different conditions, so as to adapt to operating conditions of the user equipment.

Systems or techniques that facilitate a dual quantum recurrent neural network with an attention mechanism for time series prediction are provided. In various embodiments, a system can receive a time series. In various cases, the system can further generate a prediction of the time series via a dual quantum recurrent neural network (QRNN), the dual QRNN comprising: a primary QRNN; and a controller QRNN that determines, via an attention mechanism, relevant past cell states of the primary QRNN, and wherein the primary QRNN generates the prediction of the time series based on the relevant past cell states.

This useful solution, in the field of biotechnology and regenerative medicine, relates to a process for enhancing the efficiency of induced pluripotent stem cell (iPSC) colony formation from human peripheral blood mononuclear cells (PBMCs). The solution aims to overcome the low reprogramming efficiency, unstable colony formation, and limited iPSC cloning rates commonly encountered in PBMC iPSC generation processes. The technical essence of the solution lies in the synchronized optimization of the reprogramming process, including pre-activating PBMCs with an appropriate cytokine combination at predetermined times before reprogramming, using a non-integrated Sendai virus system under optimized transformation conditions, switching culture media at defined intervals to promote cell state transitions, and applying a selection, enrichment, and stabilization process for iPSC cloning in the initial passages after colony formation.The process also incorporates cell line quality control steps through assessment of residual Sendai virus RNA removal and mycoplasma contamination testing during line maintenance. Experimental results show that the proposed process increases the number of iPSC colonies formed, improves the rate of establishing stable iPSC lines, and enhances cell homogeneity and pluripotency maintenance. The solution can be applied in basic research, pathogenesis modeling, drug screening, regenerative medicine, and studies using induced pluripotent stem cells derived from human peripheral blood cells.

The invention particularly relates to a cluster error code correction parameter learning method and system, and relates to the technical field of distributed cluster communication and error code correction. A dynamic neighborhood topology construction module; a local transfer rule engine module; an asynchronous random updating engine module; and an entropy monitoring and stability detection module. According to the method, cellular neighbors are defined according to physical and logic two dimensions, and cluster dynamic changes such as node online and offline, link faults and replica migration are accurately adapted; and physical parameter microsecond acquisition is realized through direct reading of a hardware register, communication and calculation overhead is greatly reduced through discrete coding and lightweight custom messages, and the core requirement of lightweight interaction of the cluster is met.

The invention relates to the field of biomedical detection, and particularly discloses a kit for detecting chromosome alloploid necrotic cells and application thereof, the kit comprises a metabolism tracer, a lactic acid specific binding probe connected with a detectable marker, a membrane-impermeable nucleic acid dye and a DNA metal mutual embedding agent; the use method comprises the following steps: co-incubating a to-be-detected sample and the metabolic tracer agent to enable cells to generate labeled lactic acid; sequentially contacting with a lactic acid probe, a membrane-impermeable nucleic acid dye and a DNA metal mutual embedding agent; and finally analyzing the cell state by detecting each signal. According to the method, the metabolic activity, the cell dead and living state and the DNA ploidy are synchronously detected on the single cell level, the special cell subpopulation with high glycolytic activity, necrosis characteristics and chromosome heteroploidy can be accurately recognized, and the technical problem that multi-dimensional cell information is difficult to integrate and analyze is solved; the kit has important application value in cancer diagnosis, prognosis evaluation, curative effect monitoring and drug screening.