33 results about "Cell number" patented technology

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.

In a bioprinting system and method, a bioprinter may include one or more reservoirs, a printhead, and a print surface on which the bioprinting is dispensed. An optical system may collect imaging data of a plurality of cells within a cross-linkable material during and / or after passage of the plurality of cells through the bioprinting system. A computing system may generate, via a machine learning algorithm executing on the image data, a plurality of segmented regions in the image data, wherein each segmented region in the plurality of segmented region defines a boundary around a different set of cell aggregates in the plurality of cells, identify a first segmented region in the plurality of segmented regions that defines a boundary around at least a visible portion of a first cell aggregate, wherein the first cell aggregate includes an occluded portion, determine, based on an estimated shape of the first cell aggregate, an extrapolated boundary around at least some of the visible portion of the first cell aggregate and at least some of the occluded portion of the first cell aggregate, compute a quantity of cells within the first cell aggregate based on an area surrounded by the extrapolated boundary, and determine one or more parameters associated with the plurality of cells, the material flow, or the bioprinting system based on the quantity of cells.

A method, apparatus, and system for calculating cell concentration are disclosed. The method includes: determining an effective measurement region of a cell container, the cell container including a first encapsulation layer, a second encapsulation layer, and a solution cavity disposed between the first and second encapsulation layers; a first positioning mark is disposed on one side of the first encapsulation layer facing the solution cavity, and a second positioning mark is disposed on one side of the second encapsulation layer facing the solution cavity; dividing the effective measurement region into multiple sub-regions; focusing and acquiring an image of each sub-region; detecting the number of cells in each image and summing the number of cells in multiple images to obtain the total number of cells in the effective measurement region; focusing on the first and second positioning marks to obtain a first original cavity thickness; multiplying the first original cavity thickness by a preset adjustment coefficient to obtain an adjusted cavity thickness; calculating the volume of the effective measurement region based on the adjusted cavity thickness; and calculating the cell concentration based on the total number of cells and the volume of the effective measurement region.

This invention discloses a single-cell spatial annotation method that integrates image and sequencing data. It obtains candidate cue point locations, cell nucleus instance segmentation masks, and cell nucleus type probability vectors by processing full-field H&E stained tissue images. It also obtains a preprocessed spatial transcriptome raw sequencing matrix and corresponding cell type labels by processing the raw spatial transcriptome sequencing matrix. Each capture site and its corresponding integrated data structure are constructed. Based on rigid constraints on the number of cells allocated to each type within each capture site and main type quota constraints, the main type annotation is written. For cells in the intercapture zone, one or more neighboring capture sites with existing annotation results are found. The main type and cell subtype of the intercapture zone cells are inferred through morphological feature similarity calculation, thus obtaining the annotation result. This solves the problem that existing technologies cannot determine the exact type of each cell in ST-sequencing tissue.

1. Name of the product in this design: Cell Measurement Plate. 2. Purpose of this design: This design is used to hold cell fluid for calculating cell count. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 1. 5. Other situations requiring explanation: The entire product of this design is made of transparent material.

The embodiment of the invention provides a communication method and device, relates to the field of communication, and is used for reducing the situation that CSI-RS measurement resources of different network devices conflict and improving the accuracy of a CSI-RS measurement result. In the method, a first network device can determine a resource of a reference signal of a first cell according to a first cell number uniquely identifying the first cell of the first network device, and send the reference signal to a terminal device in the first cell according to the resource of the reference signal of the first cell. The first cell belongs to one of the plurality of cells, and the resource of the reference signal of each cell in the plurality of cells can be determined according to the respective cell number of each cell, and based on the unique cell number corresponding to each cell in the plurality of cells, the resource of the reference signal of each cell in the plurality of cells can be determined. According to the embodiment of the invention, the resources of the reference signal corresponding to each cell in the plurality of cells are different, and the condition that the reference signal measurement resources of different network devices and different cells of the same network device conflict is reduced.

The invention discloses an influence parameter optimization method and device for a cascaded SOFC system, the method is applied to the cascaded SOFC system, and the method comprises the steps: obtaining influence parameters of the cascaded SOFC system, the influence parameters at least comprising a battery number ratio and a shunt ratio of a first-stage electric pile and a second-stage electric pile; the influence parameters are sequentially changed, a pre-established target system model is utilized, the system electric efficiency and the average net power under the condition that the influence parameters are changed are calculated, the target system model is established according to the net alternating current power of the system, and the net alternating current power of the system is determined at least according to the total power generation power of the first-stage electric pile and the second-stage electric pile; and screening a target influence parameter from the changed influence parameters according to the calculated system electrical efficiency and the calculated on-chip net power. According to the method, the influence parameters of the cascaded SOFC system can be optimized, so that the requirements of different application scenes can be met, the electric efficiency of the cascaded SOFC system is effectively improved, and the operation cost is reduced.

The application relates to a single-cell Hi-C clustering method and system based on contact number weight smoothing and feature fusion, which comprises the following steps: (1) data preprocessing; (2) contact number weight-based smoothing; using a weight matrix to quantify the influence of different neighbor fragments on a target chromosome fragment; (3) restart random walk smoothing; using a restart random walk algorithm to perform smoothing processing on the chromosome contact matrix after the contact number weight-based smoothing processing; (4) contact binarization; (5) principal component extraction; using KPCA to perform dimension reduction and generate cell embedding; (6) feature fusion; (7) spectral clustering; using a spectral clustering algorithm to realize cell clustering. The single-cell Hi-C clustering method provided by the application is obviously superior to existing clustering methods, greatly improves the clustering effect when a large-scale single-cell Hi-C data set is processed, and can identify cell types with a small number of cells in the data set.

Disclosed herein are methods to assess off-target sites resulting from CRISPR Cas nuclease systems wherein the methods require fewer cells than conventional strategies and can provide robust signal reads over noise. Also provided are methods of identifying Cas nuclease off-target cut-sites from the resulting reads using a computational pipeline including an artificial neural network model trained on known on-target and off-target cut-sites for the Cas nuclease.

The phagocytic cell AI identification training, detection and proportion detection analysis method comprises the following steps: preprocessing a sample to obtain a microscopic sample; shooting a microscopic examination sample to obtain a microscopic examination sample image; identifying and labeling phagocytic cells in the microscopic examination sample image to obtain a phagocytic cell labeling picture; and AI training is carried out by using the labeled picture to obtain an AI identification feature data set A, and the AI identification feature data set A comprises phagocytic cell AI identification feature data. Identifying the microscopic examination sample image by using an AI identification algorithm, identifying phagocytic cells in hydrothorax and ascites in a selected area S1 of the image, obtaining the number NUM1 of the phagocytic cells, and obtaining the number NUM2 of phagocytic cells; in a microscopic examination sample, the content of phagocytic cells in unit volume is equal to NUM1 / V1. The ratio of phagocytic cells is equal to NUM1 / NUM2.

The present application relates to the technical field of lithium ion battery safety monitoring, and discloses a method and device for early warning and positioning thermal runaway of lithium ion battery pack; first, real-time voltage, temperature, current and number data of each cell are collected to construct a cell state observation tensor with a space-time label; then, a cell thermal-electric state extended Kalman estimation algorithm is run based on the tensor to generate a cell health state partial order lattice; then, a thermal runaway risk propagation phase space grid is constructed, and finally, local attractor region identification and early warning decision based on manifold analysis are executed under grid constraints to output high-risk source cell number and early warning level. The present application solves the problems of existing technology, such as lagging early warning caused by dependence on late physical signs, lack of dynamic evaluation of cell group state consistency, and inability to accurately locate faulty cells, and improves the timeliness and positioning accuracy of thermal runaway early warning.

Provided is an operation method of a network controller supporting communication by using a unique cell number and cluster number. The operation method of the network controller includes identifying identification information corresponding to each of at least one cell, identifying identification information corresponding to each of at least one cluster including the at least one cell, identifying identification information corresponding to at least one cluster and at least one cell corresponding to a first area covered by a beam transmitted from a first satellite, identifying identification information corresponding to at least one cluster and at least one cell corresponding to a second area not covered by the beam due to movement of the first satellite, and identifying information on a second satellite covering the second area.

Downlink Synchronization Method of User Equipment to a 5G Cellular Network and Associated User Equipment. The downlink synchronization method (100) of user equipment to a 5G cellular network comprises: - a receiving step (105); - a first frequency-time synchronization step (110), comprising first estimates of a frequency gap (DF1) and a time gap (DT1) between the transmitted and received signals (S) and a determination of a first component (N_PSS) of the cell number (N_cell) by autocorrelation and cross-correlation; and - a second frequency-time synchronization step (120), comprising second estimates of the frequency gap (DF2) and the time gap (DT2) by autocorrelation and cross-correlation, taking into account the first component (N_PSS). The second estimates are more accurate than the first estimates. Figure for the abstract: Figure 3

This application provides a method, apparatus, device, and storage medium for locating a 5G base station. The method includes: acquiring N raw sampling data; each raw sampling data includes the identifier of the sampled 4G cell and the Physical Cell Identifier (PCI) of the corresponding 5G neighbor cell; N is an integer, and N≥1; determining the cell number of the 5G neighbor cell corresponding to the 4G cell based on the identifier of the 4G cell, the PCI of the corresponding 5G neighbor cell, and a pre-configured operating parameter table; the operating parameter table includes the mapping relationship between the identifier of the 4G cell, the PCI of the corresponding 5G neighbor cell, and the cell number of the corresponding 5G neighbor cell; determining N sampling data based on the N raw sampling data and the cell number of the corresponding 5G neighbor cell in each raw sampling data; and determining the target location of the 5G base station corresponding to the 5G neighbor cell based on M sampling data from the N sampling data; M is an integer, and 1≤M≤N.

The invention discloses a sorting equipment package type intelligent arrangement method and system based on an intelligent algorithm, and relates to the technical field of express sorting, and the intelligent arrangement steps are as follows: S1, calculating an optimal package type, integrating multi-dimensional data to construct a two-dimensional traffic prediction model, and generating a package type traffic prediction result; establishing a digital twin model of the sorting equipment, and adaptively analyzing physical attributes and running states of the equipment; establishing a packet type priority system based on a service rule and equipment capability, and screening an optimal packet type set; and S2, distribution of the number of the bag type grids: sequentially completing grid distribution of a necessary bag type and a replaceable bag type according to the principles of necessary building priority, dynamic adaptation and closed-loop verification. The problem of traditional manual pain point arrangement is solved, and standardized, automatic and efficient operation is achieved; through dual-dimensional traffic prediction and equipment digital twin adaptation and in combination with four-level priority screening, packet type prediction is enabled to be precisely fit with a service and highly matched with equipment capability, and occupation of invalid resources is avoided.

A data processing device (14) is provided with: an acquisition unit (34) that acquires first cell count data (42) and second cell count data (24); a calculation unit (36) that calculates a rate ratio, which is the ratio between a first rate of change of the first cell count data (42) during a first predetermined period and a second rate of change of the second cell count data (24) during a second predetermined period corresponding to the first predetermined period; and a predicted data generation unit (38) that generates predicted data (48) indicating the current and subsequent change in the number of cells under the second culture condition on the basis of first partial data (42a), which is a portion after the first prescribed period in the first cell number data (42), and the rate ratio.

The present disclosure provides methods, device, assemblies, and systems for dispensing and visualizing single cells. For example, provided herein are systems and methods for dispensing a dispense volume into a plurality of wells of a multi-well device, where, on average, a pre-determined number of cells (e.g., 1-20) are present in the dispense volume, and determining, via a cellular label, the number of cells present in each of the plurality of wells. Such dispensing and cell detection may be repeated a number of times with respect to wells identified as having less than the pre-determined number of cells in order increase the number wells in the multi-well device containing the desired number (e.g., a single cell).

This invention discloses a deep learning-based method and related equipment for locating and counting cells in *Scenedesmus* (a type of algae). The method first acquires microscopic images of *Scenedesmus* populations and labels the cell locations to form a set of labeled cell points. Then, a *Scenedesmus* cell counting network is constructed, including a backbone network, density branches, and regression branches, to extract features from the images and model the spatial distribution of cells. Enhanced images are generated by preprocessing the microscopic images, and the network is trained using the labeled information as supervision. After model training, new microscopic images are input into the network to obtain initial cell location and counting results. Furthermore, by incorporating the biological prior constraint that the number of cells in a *Scenedesmus* population is even, the initial counting results are post-processed and corrected, finally outputting the cell location and counting results. This invention enables automatic location and accurate counting of cells in *Scenedesmus* populations, exhibiting high recognition accuracy and application value.

This invention relates to an equal-phase feeding method for a discretized array arrangement in the terahertz band, comprising: determining the coordinates of the cell number and the port number based on the known optimized array arrangement and T / R output port arrangement; determining the correspondence between the cell number and the port number according to the principle of the shortest distance between the cell and the port; determining the distance between all channel cells and ports on the projection plane based on the correspondence between the cell number and the port number; calculating the actual path difference between each channel based on the distance between all channel cells and ports on the projection plane; using the current standard rectangular waveguide in the terahertz band as the feeding transmission segment, determining the physical length of all transmission segments and calculating the average physical length, and taking the channel k closest to the average length as the reference channel. This invention solves the problem of transmission path and feeding phase mismatch between uniformly arranged transmit channels and discretely arranged array cells, and is applicable to different array arrangement and transmit channel arrangement methods.

The invention discloses a detection method for killing efficiency of NK cells and a kit. The detection method comprises the following steps: S1, preparing a target cell suspension and an NK cell suspension; s2, setting experiment groups including a standard curve group, an experiment group and a blank group; the cells are inoculated to two 96-well plates respectively, each 96-well plate is provided with three multiple holes, and inoculation is carried out according to experimental groups; s3, respectively adding a CCK-8 reagent into the experiment holes of a 96-well plate, and measuring the absorbance of each hole at the position of 450 nm through a microplate reader; s4, placing another 96-well plate in the incubator for continuous co-culture for a certain period of time, respectively adding a CCK-8 reagent into each experiment hole, and measuring the absorbance of each hole at the position of 450 nm through the microplate reader; and S5, carrying out regression analysis on the OD value and the cell number of the standard curve group, and if R2 is greater than or equal to 0.9, calculating the killing rate according to a formula. According to the detection method and the kit for the killing efficiency of the NK cells, the killing activity of the NK cells on K562 and A549 is detected by applying CCK-8, the detection method for detecting the killing tumor cells of the NK cells through a CCK-8 method is established, operation is simpler and more convenient, consumed time is short, repeatability is good, damage to the cells is small, and the result is accurate.

The invention relates to a cell frame selection method, a cell frame selection device, computer equipment and a computer readable storage medium. Based on a preset aspect ratio, center point coordinates of cell frames in a cell view block diagram and center point coordinates of a user preset position in the cell view block diagram; determining a weighted Chebyshev distance between the central point of the preset position of the user and the central point of each cell frame; sorting the cell frames from small to large based on the weighted Chebyshev distance between the central point of the preset position of the user and the central point of each cell frame; accumulating the cell numbers of the cell frames from front to back according to the sorting result, and taking the cell frames participating in cell number accumulation when the cell number accumulation result is closest to the cell number preset by the user as target cell frames; and outputting the minimum bounding rectangle of the target cell frame based on the vertex coordinates of the target cell frame, thereby effectively improving the cell frame selection efficiency.

The present invention relates to an automatic culture device using a single-use closed system flow path for culturing a cell or a tissue, and realizes reduction in manufacturing cost and high integration property of a device. A cell culture system including an automatic culture device and an information processing device. The automatic culture device includes a plurality of types of closed system flow paths possible to be installed and removed, and a plurality of culture devices. The information processing device includes: an input device configured to receive, as an input, at least one piece of data selected from a group consisting of data of an identifier of a patient, data related to a transplantation method, data related to a type of cell, data related to the required number of cells, and data related to a treatment plan; an arithmetic device configured to select, based on the input data, from options of a cell culture method, the culture devices, and the closed system flow paths, the cell culture method, the culture device, and the closed system flow path to be used; and an output device configured to output a number of the closed system flow path to be used and a number of the culture device to be used.

This application relates to the field of culture medium prediction technology and discloses an artificial intelligence-based method for predicting serum-free culture medium components. The method includes: constructing a phenotypic condition vector based on cell type identifiers and cell phenotypic specification parameters; constructing a phenotypic-recipe pairing dataset using historical culture batch data; training a conditional variational autoencoder using the phenotypic condition vector as conditional constraints; sampling latent variables from a standard normal distribution and decoding them through a decoder to obtain a set of candidate culture medium recipes; and using a forward validator to screen the candidate recipes with the smallest Euclidean distance to obtain a recommended culture medium recipe. This application solves the problem that existing artificial intelligence-based serum-free culture medium component prediction methods use cell number as the optimization objective and cannot reverse-engineer culture medium recipes based on clinical phenotypic quality specifications.

In a bioprinting system and method, a bioprinter may include one or more reservoirs, a printhead, and a print surface on which the bioprinting is dispensed. An optical system may collect imaging data of a plurality of cells within a cross-linkable material during and / or after passage of the plurality of cells through the bioprinting system. A computing system may generate, via a machine learning algorithm executing on the image data, a plurality of segmented regions in the image data, wherein each segmented region in the plurality of segmented region defines a boundary around a different set of cell aggregates in the plurality of cells, identify a first segmented region in the plurality of segmented regions that defines a boundary around at least a visible portion of a first cell aggregate, wherein the first cell aggregate includes an occluded portion, determine, based on an estimated shape of the first cell aggregate, an extrapolated boundary around at least some of the visible portion of the first cell aggregate and at least some of the occluded portion of the first cell aggregate, compute a quantity of cells within the first cell aggregate based on an area surrounded by the extrapolated boundary, and determine one or more parameters associated with the plurality of cells, the material flow, or the bioprinting system based on the quantity of cells.

Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping / occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.

The present application relates to a method for improving the accuracy of in vivo cell tracking using iron chelators, comprising the following steps: S1, preparing drug materials; S2, preparing animals and cells: preparing bone marrow cells, S3, removing free iron chelators in vivo 89 Zr and 68 Ga, S4, radiolabeling mesenchymal stem cells and bone marrow cells; S5, performing Micro-PET imaging: S6, performing Micro-PET imaging data processing and analysis: S7, in vitro biodistribution; S8, performing pharmacokinetic studies; S9, extracting hAlu genomic DNA and performing qPCR detection; S10, establishing a standard curve of cell number and cycle threshold; S11, performing absorbed dose estimation; S12, performing in vitro cell experiments; S13, final statistical analysis. The present application provides a method for improving the accuracy of in vivo cell tracking using iron chelators, which realizes the effect of removing free radioactive metal ions in vivo using iron chelators, and ensures the accuracy of in vivo tracking labeled cells.

The purpose of the present disclosure is to accurately calculate the number of cells. A cell culture device 1 includes a sensor 26 for measuring the lactic acid concentration in a medium. The sensor measures the concentration of lactic acid in a medium in a culture vessel 21 during the cultivation of cells as a first physical quantity. The sensor also measures the output value of the lactic acid concentration in the medium in a supply bag 23, i.e., the output value of the lactic acid concentration in the medium supplied to the culture vessel 21, as an error output value. Further, the cell culture device 1 includes a calculator 14 that calculates the number of cells in the culture vessel 21 on the basis of the value of the corrected physical quantity that is calculated by subtracting the error output value from the value of the first physical quantity.

The application discloses a data structure optimization method, a cardiac electrophysiology simulation method, a device, equipment and a medium, the optimization method comprises the following steps: constructing a three-dimensional cardiac tissue model and saving the coordinate positions of each cell; dividing and unfolding the model according to the thread bundle size of a GPU to obtain cell blocks including dense blocks, sparse blocks and empty blocks; removing all empty blocks, and performing descending order sorting on the remaining cell blocks according to the number of effective cells; performing fusion and removal processing on the cell blocks after descending order sorting to obtain each cell block after fusion; one-dimensional unfolding is performed on each cell block after fusion, and a position index table is constructed according to the coordinate positions of each cell in the one-dimensional cell sequence. The application avoids the problem of empty thread scheduling caused by sparse data, avoids the waste of thread resources, significantly improves the GPU parallel efficiency and the calculation throughput; meanwhile, the cells that are similar in space are stored in the similar memory areas, the memory locality is improved, and the GPU data access is accelerated.

This invention discloses a data processing method, apparatus, base station, and computer-readable storage medium. The method includes: determining the operating frequency band range of at least one cell corresponding to the target RRU based on a first bandwidth of the target RRU; setting the operating frequency band according to the operating frequency band range of each cell; the number of at least one cell is the number of cells after adjusting the number of cells for the target RRU; the at least one cell includes at least one first cell corresponding to the target RRU before the cell number adjustment; the first bandwidth is the operating bandwidth of the first cell before the cell number adjustment; the operating frequency band range is the first bandwidth or a portion of the first bandwidth; allocating storage space for each cell in memory space, or sending first information to the forward extension / switching unit to trigger the forward extension / switching unit to allocate storage space for each cell in memory space; the address of the storage space of each cell is related to the operating frequency band range of the cell.

The application provides a self-encoder training method, a cell quantity detection method and related equipment. The self-encoder training method comprises the following steps: extracting a first feature vector in a sample medical image and inputting the first feature vector into the self-encoder to obtain a first latent feature; obtaining a first prediction value of the cell quantity based on the first latent feature; inputting the first latent feature into the self-encoder to obtain a reconstructed image; and optimizing the self-encoder based on the reconstructed image. The cell quantity detection method comprises the following steps: extracting a second feature vector of a medical image to be detected; inputting the second feature vector into the self-encoder to obtain a second latent feature; and obtaining the cell quantity based on the second latent feature. The image detection result can be quickly obtained through the application.