50 results about "Cell number" patented technology

Methods, systems, and devices for enumerating the number of cells present in each event for cell-secretion based sorting applications are described herein. The cell secretion applications use carrier to encapsulate cells and collect the biomolecules they secrete. The carriers are then sorted using flow based particle sorting. The total number of cells present in a carrier is very important for several applications. A convolutional neural network is used to count the number of cells present from a brightfield image, and outputs the information to be used as part of the sort logic.

The invention discloses a preparation method and application of a photo-thermal biosensor for tumor cell quantification and vitality dual-index liquid biopsy. Phosphatidylserine (PS) is exposed by enriching and cracking tumor cells, is identified and combined by an aptamer Apt2, and is driven to be separated from Apt2-Cu2-xTe NSs; after magnetic separation, separated supernatant is subjected to photo-thermal detection; the photo-thermal signal intensity is positively correlated with the number of cells, so that quantitative detection is realized; during cell apoptosis, PS is turned out of the membrane, Apt2-Cu2-xTe NSs is combined with the surface of the membrane, separated supernatant is subjected to photo-thermal detection after magnetic separation, and the photo-thermal signal enhancement amplitude is in negative correlation with cell activity. The platform has the advantages of POCT characteristics, low cost and portability, and a new tool is provided for precise tumor diagnosis and treatment.

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.

The invention provides a battery cell installation detection method, system and device and a storage medium, and relates to the technical field of battery manufacturing, and the method comprises the steps: obtaining a detection signal triggered by a to-be-detected battery cell module at a detection station, and recognizing the key features of the to-be-detected battery cell module; based on the key features, obtaining the cell number, the cell polarity arrangement sequence and the cell placement angle of the to-be-detected cell module, and judging the installation result of the to-be-detected cell module according to the cell number, the cell polarity arrangement sequence and the cell placement angle; if the installation is qualified, conveying the current battery cell module to the next process; if the installation is not qualified, conveying the current cell module to a manual detection station, and generating an abnormal installation picture; and conveying the battery cell module processed at the manual detection station to the detection station for re-detection. In this way, the battery cell modules which are recognized to be abnormal are conveyed to the manual detection station, the mounting abnormity picture is displayed for troubleshooting, meanwhile, the next group of battery cell modules continues to be detected at the detection station, and automatic and efficient mounting detection of batched battery cells is achieved.

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 invention provides a single cell ATAC sequencing library construction method and a kit, the construction method comprises the following steps: (1) subpackaging a cell sample into a porous plate, adding a mixed solution containing a first transposon and a second transposon into each pore, and co-incubating to obtain a cell containing fragmented DNA; (2) carrying out splitting decomposition on the cells containing the fragmented DNA, and retaining the fragmented DNA; and (3) amplifying the fragmented DNA to obtain the sequencing library. The construction method is high in transposition efficiency, not limited by a microfluidic or microwell plate platform, appropriate in label combination information amount, low in requirements for the cell number, the cell type and the tissue state, suitable for a conventional double-end sequencing platform, high in sequencing data quality and high in application value.

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 present invention relates to the field of biomedicine and particularly provides a method for identifying a target. The method specifically comprises determining the number of target-adjusting systems of a plurality of cells to be tested to determine the influence of adjusting a candidate target on cell function; and before and / or during the determination, adjusting the expression or activity of the candidate target in said plurality of cells by means of the plurality of target-adjusting systems, wherein each of the target-adjusting systems targets a different candidate target site, and during the determination, the number of said cells is at least 5 times the number of the target-adjusting systems. Further provided is a system for identifying a target.

The application relates to the technical field of biochemistry, and provides a cell counting method and a cell counting device of a micro-fluidic chip. The cell counting method of the micro-fluidic chip comprises the following steps: controlling a cell solution to fill a plurality of flow channels with height differences; collecting a target image corresponding to a set region in each flow channel, obtaining a cell clumping rate corresponding to each flow channel based on the target image, and determining a flow channel with the lowest cell clumping rate as a calculation flow channel; wherein the cell clumping rate is the ratio of the number of cell groups in the set region to the total number of cells; obtaining a calculation cell number in a target region of the calculation flow channel; obtaining a preset volume corresponding to the target region based on the area of the target region and the height of the calculation flow channel, and determining the ratio of the calculation cell number to the preset volume as the concentration of the cell solution. The cell counting method of the micro-fluidic chip provided by the application provides the cell solution with a flow channel with a suitable height, so that the cell solution is uniformly distributed in the flow channel as much as possible, and the cell counting accuracy is higher.

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.

The present invention provides methods for collecting desired cells from a sample using countercurrent centrifugal elutriation. The method of the present invention provides a continuous elutriation workflow with optimized conditions, which improves cell purity and retention during the elutriation process. In particular, the methods of the invention use a continuous counter-current centrifugal elutriation process which does not require harvesting cells between elutriation cycles or removal of cells from the separation chamber. The method of the present invention increases the purity, cell number yield, cell recovery, and cell viability of a desired cell population to improve downstream processing. The method of the invention also reduces the volume of buffers and reagents required for the treatment and accelerates the elutriation process, thereby achieving a cost-effective and more efficient cell manufacturing workflow.

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 application relates to the technical field of culture medium prediction, and discloses a serum-free culture medium component prediction method based on artificial intelligence. The method comprises the following steps: constructing a phenotype condition vector according to a cell type identifier and a cell phenotype specification parameter, constructing a phenotype-formulation pairing data set according to historical culture batch data, training a conditional variational autoencoder with the phenotype condition vector as a conditional constraint, sampling a latent variable from a standard normal distribution, decoding the latent variable through a decoder to obtain a candidate culture medium formulation set, screening a candidate formulation with the minimum Euclidean distance through a forward verifier, and obtaining a recommended culture medium formulation. The application solves the problem that an existing serum-free culture medium component prediction method based on artificial intelligence cannot generate a culture medium formulation in reverse according to a clinical phenotype quality specification because the number of cells is taken as an optimization target.

The invention belongs to the technical field of image recognition, and provides a cell counting method and system based on a multi-modal image. The method comprises the following steps: shooting an RGB optical image and a fluorescence image of a blood cell culture sample to be identified, and converting the RGB optical image into an HSV optical image to form a multi-modal image; performing cell recognition processing on the fluorescence image, marking cell monomers and cell clusters in the fluorescence image, and counting each cell monomer to obtain a first cell number; extracting a multi-modal feature corresponding to the cell cluster based on the multi-modal image, and analyzing and processing the multi-modal feature by using a composite counting model constructed based on a plurality of graph variational auto-encoders to obtain a second cell number; and summing the first cell number and each second cell number to complete cell counting. According to the method, the complementary information of different modal images is fully utilized, and the cell number is obtained by analyzing the multi-modal characteristics of the cell cluster by using the composite counting model, so that the accuracy of cell counting is effectively improved.

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.

The present application relates to the technical field of biology, and particularly relates to a cell counting method based on a micro-fluidic chip. The cell counting method comprises the following steps: introducing dyed cells into a plurality of micro-channels of the micro-fluidic chip; the plurality of micro-channels have different heights; obtaining images of the plurality of micro-channels respectively; determining an optimal micro-channel according to the image results; and counting the cell density in the optimal micro-channel as a cell counting result. The present application selects the optimal micro-channel with the most suitable height to count cells, so that the number of cells in the field of view of the microscope is in the most suitable range, and the most accurate cell counting result is obtained. Furthermore, the optimal micro-channel is selected adaptively, and the concentration of the cell liquid is automatically calculated, so that the practicability of the micro-fluidic chip is significantly improved.

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 data structure optimization method, a heart electrophysiology simulation method, a heart electrophysiology simulation device, heart electrophysiology simulation equipment and a medium. The optimization method comprises the following steps: constructing a three-dimensional heart tissue model and storing coordinate positions of cells; performing block division and expansion on the model according to the size of a thread bundle of the GPU to obtain cell blocks including dense blocks, sparse blocks and empty blocks; all empty blocks are removed, and the remaining cell blocks are sorted in a descending order according to the number of effective cells; carrying out fusion and rejection treatment on the cell blocks subjected to descending sorting to obtain fused cell blocks; and performing one-dimensional expansion on each fused cell block, and constructing a position index table according to the coordinate position of each cell in the one-dimensional cell sequence. According to the method, the problem of empty thread scheduling caused by sparse data is avoided, thread resource waste is avoided, and GPU parallel efficiency and calculation throughput are remarkably improved; meanwhile, cells similar in space are stored in similar memory areas, the locality of the memory is improved, and GPU data access is accelerated.