590 results about "Cell state" patented technology

The invention discloses an immune cell state analysis method and system, and relates to the technical field of image processing. The method comprises the following steps: acquiring a motion image sequence of immune cells by using microscopic imaging equipment; performing edge detection on the motion image sequence, and marking contour pixel data labels of immune cells; performing trajectory tracking on the immune cells under a time sequence, and outputting motion trajectory pixel data; inputting the motion trail pixel data into a dynamic change rate recognition model, obtaining a first-order change rate, a second-order change rate and a change rate mean value, and generating a dynamic change feature embedding vector; inputting into an immune cell state prediction large model for state prediction to obtain an active state prediction index, and performing active labeling on the immune cells according to the active state prediction index. The technical problems of inaccurate analysis and incomplete prediction of the immune cell activity state in the prior art are solved, and the technical effect of improving the accuracy and comprehensiveness of immune cell activity state prediction is achieved.

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 virus infection positioning detection method and system based on diffractive optics. The method comprises the following steps: acquiring diffraction fingerprints corresponding to all to-be-detected cells in an imaging area by utilizing a diffraction imaging principle; respectively extracting texture features of each diffraction fingerprint in the imaging area by using a gray level co-occurrence matrix method; the extracted texture features are compared with standard texture features pre-stored in a relation model of cell morphological change and diffraction fingerprints, so that the'cell state 'of each cell to be detected in the imaging area can be obtained, and the virus infection state of each cell in the imaging area can be positioned and identified. The efficient light diffraction spectrum system is used for virus infection detection, and the requirement for professionals and expensive and complex professional field detection equipment is eliminated. The continuous and high-throughput evaluation on the cell condition after virus infection is met, and a foundation is laid for popularization and application of a virus infection instant detection platform.

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 an intelligent cell storage state monitoring method and system. The method comprises the steps of data acquisition, data preprocessing, cell storage control and cell storage state monitoring. The invention belongs to the field of cell monitoring, and particularly relates to an intelligent cell storage state monitoring method and system.According to the scheme, a differential factor and a nonlinear factor are introduced to enhance an activation function, and then cell state prediction is conducted based on an LSTM model; the sensitivity to abnormal states of cells is improved; by constructing a feedback-response system, different control methods are adopted in a targeted manner for abnormal states of different degrees; the stable and efficient monitoring of the cell storage state is realized; abnormal judgment is performed on the storage state through a dynamically adjusted convergence time judgment mechanism, and a flexible convergence rate adjustment mechanism is designed by introducing an adjustment item and an error item nonlinear index; a rigorous anomaly judgment mechanism is designed to reduce the risk of false alarm; and the accuracy of monitoring the cell storage state is further realized.

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 current disclosure is centered on the hypothesis that a better understanding of the neuroendocrine transdifferentiation (NEtD) process will provide therapeutic targets for inhibiting this transition and thus improving standard of care therapies. Accordingly, the current disclosure provides for a method for treating cancer in a subject, the method comprising administering an inhibitor of a gene, wherein the gene comprises one or more of tenascin C (TNC), advillin (AVIL), S100A7, PPARG, LOX lysyl oxidase, KLF5, APOBEC2, FOSL1, FOXM1, hedgehog, RELA, p65, IKK complex, JAK, STAT, TFAP4, geminin, OCA-1, OCA-2, Nf-kB, Nf-kB family and associated regulators, angiogenesis regulators such as BMX kinase, TEK kinase, periostin (POSTN), and VEGF family, stress genes, such as the JUN / FOS family, a gene from the S100 family, and cell state regulators such as ASCL1, POU2F3, NEUROD1, ASCL2, and YAP1.

The invention relates to the technical field of bioinformatics, in particular to a data classification method and system for single cell sequencing. The method comprises the following steps: acquiring an original gene expression matrix; performing gene data preprocessing on the original gene expression matrix to obtain a preprocessed gene expression matrix; constructing a cell topological graph according to the preprocessed gene expression matrix to obtain the cell topological graph; performing cell division rate calculation according to the cell topological graph and the preprocessed gene expression matrix, and performing RNA rate calculation to obtain RNA rate data; adding node rates to the cell topological graph according to the cell division rate data and the RNA rate data to obtain an enhanced cell topological graph. According to the method, the dynamic graph is constructed and the change rule of the dynamic graph is analyzed, so that the dynamic change of the cell state is better captured, the sensitivity to parameters is reduced, a complex cell population structure is processed, and a quantitative cell heterogeneity evaluation index is provided.

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.

The invention discloses a monitoring and lifting integrated device for a high-throughput biological culture system. The monitoring and lifting integrated device comprises a central cantilever, a rotating platform mechanism and a lifting mechanism, wherein the central cantilever is coaxially connected with the rotating platform mechanism in a rotating manner; the lifting mechanism is arranged at one end of the central cantilever and used for lifting the pore plate to move in the Z-axis direction; the monitoring mechanism is arranged at the other end of the central cantilever and is used for illuminating and observing the cell state of the pore plate. Compared with an existing cell or organoid culture and analysis mainstream scheme, the system has the advantages that the integration degree is higher, the observation and analysis speed is higher, strict environmental conditions can be kept in the culture and analysis process and the like, the brand-new requirements of a high-flux biological culture system under the industrial and automatic background can be met, and the application prospect is wide. Technical and theoretical possibilities are provided for successful cell or organoid culture and observation of an intelligent automatic production line.

The invention relates to the field of cell biology, and particularly discloses a method for preparing mesoderm progenitor cells from human pluripotent stem cells and an induced differentiation culture medium of the mesoderm progenitor cells. The method comprises the following steps: inoculating human pluripotent stem cells in a single cell state into a culture dish covered with matrigel; and inducing the human pluripotent stem cells to be differentiated into the mesoderm progenitor cells within 1-3 days by using the mesoderm progenitor cell induced differentiation culture medium. Wherein the mesoderm progenitor cell induced differentiation culture medium is composed of a basic culture medium and an additive, and the additive contains a B-27 additive, non-essential amino acid, glutamine, ascorbic acid, polyvinyl alcohol, a GSK-3 inhibitor and a CDK8 inhibitor. According to the method, the human pluripotent stem cells are subjected to induced differentiation by using the mesoderm progenitor cell induced differentiation culture medium with specific composition, the method does not depend on cloning of the pluripotent stem cells, a large number of high-uniformity mesoderm progenitor cells can be obtained through induced differentiation within 1-3 days, the induction efficiency is high, and the stability is high.

The invention discloses a super-resolution image mitochondrial subtype-based cell classification and analysis device and method, and the method comprises the steps: 1, calculating the characteristics of each mitochondrial according to all mitochondrial segmentation images in cells, carrying out the dimension reduction processing, carrying out the clustering of the mitochondrial characteristics after the dimension reduction, and obtaining the type of each mitochondrial; and step 2, calculating the proportion of each type of mitochondria in the cells according to the cells and the types of the mitochondria obtained in the step 1, determining the characteristics of the cells, performing dimension reduction processing, and clustering the characteristics of the cells after dimension reduction to obtain the types of the cells. According to the method, the form and position parameters of the single mitochondria can be efficiently represented, and favorable conditions are provided for quantifying the cell state.

The disclosure relates to a computer-implemented method for configuring at least one operation parameter in a network cell (140) served by a network node (110) of a communication network (100), the method comprising: (S210) obtaining at least one cell feature for each one of a plurality of network cells (140), the cell feature representing a cell state of respective network cell (140), (S220) clustering a number of network cells (140) into a number of cell clusters (160), wherein the clustering of network cells (140) is performed based on the obtained at least one cell feature of respective network cell (140), (S230) determining one network cell (140) of respective cell cluster (160) to be a representor cell (170), and for at least one cell cluster (160): (S240) determining at least one action (a) to be performed for configuring at least one operation parameter for a number of network cells (140) of respective cell cluster (160).

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 belongs to the technical field of cell biology, and particularly relates to a method for detecting cell stability, a cell sample is detected in three aspects of cell performance, cell mutation and cell stability, and when the cell proliferation effect is good, the cell does not migrate, and the cell activity is gt; the cell of which the cell concentration is 80%, the cell early-stage and late-stage apoptosis ratio is less than or equal to 10%, the cell autophagy ratio is less than 5%, no proto-oncogene is remarkably expressed, the cell aging ratio is less than or equal to 20% and the cell multiplication time is less than 48 hours is a stable cell; if one of the indexes does not reach the standard, it is determined that cell passage is unstable, and the cell culture process is stopped. The method provided by the invention can comprehensively judge the life cycle of the cell, so that the cell is applied to the optimal economic / efficient service life, the cell state can be effectively and accurately judged, and the method has good reliability.

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.

A method performed by at least one processor of a network device in communication with a plurality of base stations, the method including: receiving historical data collected by one or more base stations from the plurality of base stations, the historical data indicating one or more of a power consumption, handover data, and quality of service (QOS); generating, from the historical data, training data comprising a plurality of cell states and a corresponding random action for each cell state; and training one or more neural network estimators based on the training data, where the one or more neural network estimators comprise one or more of a power consumption estimator, a QoS estimator, and a handover prediction estimator, and where each base station from the plurality of base stations is associated with a respective cell.

The passage is required to be carried out at a proper time according to the actual cell state. This problem is solved by a passage timing determination method comprising: an imaging step for imaging cells at a plurality of prescribed locations in a culture vessel at a first imaging timing, and acquiring cell images at each of the plurality of prescribed locations at the first imaging timing; a primary information acquisition step for acquiring primary information of cells captured in the cell image at each location at a first imaging time; a statistical index value acquisition step for calculating a statistical index value of the primary information of the cells; and a determination step in which the imaging time corresponding to the time at which the statistical index value reaches the predetermined threshold value is determined as the time at which the passage timing is reached.

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 provides a method for evaluating cell population heterogeneity based on an energy index, and belongs to the technical field of biological recognition, and the method comprises the following steps: S1, preprocessing single cell transcriptome data; s2, carrying out feature selection on the preprocessed data, and defining a cell state; s3, constructing a gene local network based on a Hupfield neural network; s4, calculating gene local network energy and cell energy, and carrying out normalization processing on the cell energy; s5, introducing a Gaussian mixture model, identifying the potential state of the cells by analyzing the energy distribution difference of the cells, and clustering the cells in the same differentiation potential state; and step S6, evaluating the heterogeneity of the cell population. By adopting the method for evaluating the heterogeneity of the cell population based on the energy index, the complexity and the difference in the cell population can be disclosed more accurately.

The invention discloses a construction method and application of a cell senescence model, and belongs to the technical field of biology, firstly, cells are cultured in an embryonic-stage animal serum complete medium to a logarithmic phase for passage, then the cells are inoculated with an embryonic-stage animal serum complete medium, a newborn-stage animal serum complete medium or an adult-stage animal serum complete medium respectively, and the cell senescence model is obtained. The method comprises the following steps of: culturing and observing the cell state, and determining cell senescence by SA-beta-gal staining and detecting the transcriptional level of senescence-related genes when the cells grow slowly and become large and flat in comparison with an animal serum group in an embryonic period, so as to prove that the cell senescence model is successfully constructed. The cell senescence model is simple and convenient to operate, and the senescence induction process is simple, stable and rapid; the cell senescence model induced by newborn animal serum and adult animal serum simulates the senescence process of organisms of different age groups in vitro, and breaks through the limitation that cell senescence phenotypes are generated by oxidative stress treatment.

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.