35 results about "Biological entity" patented technology

This disclosure provides a novel method and system for characterizing morphodynamic profiles of objects, such as biological entities. This disclosure provides a shape, appearance, and motion (SAM) phenotype Observation Tool (SPOT). SPOT establishes a standardized SAM “phenome,” image descriptors resembling single-cell transcriptomes, to comprehensively quantify a cell's instantaneous state without prior knowledge. SPOT also establishes a standardized workflow for temporal analysis. SPOT is a generalist tool, applicable to any live-cell imaging and advances biomedical discovery through its standardized, unbiased, streamlined workflow to quantify phenotypic heterogeneity and predict phenotype-genotype-function coupling.

The invention belongs to the technical field of biological data processing, and discloses a multi-omics data fusion method and system based on a hypergraph network. According to the method, a hypergraph structure is adopted for modeling omics data, high-order interaction information can be more efficiently mined, and a complex association mode between biological entities can be more comprehensively revealed; by introducing a hypergraph aggregation mechanism, high-order complex relationships in various omics data can be represented more accurately, the limitation that a traditional method can only capture the relationship between every two nodes is overcome, and the understanding ability of the model to the interaction between biological entities is improved; according to the hypergraph fusion method, multi-omics specific hypergraphs are effectively integrated, unified representation of cross-omics data is realized, the representation capability of the model for complex multi-relational data is enhanced, and more comprehensive technical support is provided for multiple downstream tasks.

A device for detecting and identifying a living or non-living entity includes a real entity unit equipped on an element that is to be detected and identified. At least one detection module is equipped on another element configured to detect the real entity unit, which broadcasts, in a unidirectional manner and without dialogue and in loops, a real image or a virtual image or an avatar of the entity to be detected and information or attributes associated with an identity of the entity. The real entity unit includes a storage and / or digital recording means, a power supply internal means, remote reception means, capture means, and broadcasting means. Each detection module is equipped with reception means, temporary storage means, displaying or listening means, and processing means for processing the data.

A bioengineering method which comprises introducing an inert nucleic acid cassette into a biological entity without introducing or modifying characteristics or traits in the biological entity. The method comprises receiving or providing a sample comprising the biological entity having a nucleic acid sequence; selecting an integration site in the nucleic acid sequence for inserting the inert nucleic acid cassette; designing the inert cassette with optimized primer sequences, optimized probe sequences, optimized stop codons and disrupted start codons, and inserting the inert nucleic acid cassette into the biological entity at the integration site; and validating that no characteristics have been added or modified in the biological entity.

The invention discloses a biomolecular interaction prediction method based on multi-modal attention fusion, and belongs to the technical field of artificial intelligence drug discovery. The method comprises the following steps: acquiring multi-modal characteristics of drugs, targets, diseases and genes: sequence structure characteristics, 3D structure characteristics, similarity network characteristics and biological relation network embedding characteristics; constructing a feature fusion prediction model, and performing training; inputting the multi-modal features of the two biological entities into the trained feature fusion prediction model, and outputting the probability of interaction of the two biological entities; the feature fusion prediction model comprises a Transform encoder and an MLP (Markup Language Protocol) network; the multi-modal features are input into a feature fusion prediction model for stacking and then are input into a Transform encoder, the features are processed by using a multi-head self-attention mechanism, and an output result is flattened and subjected to dimension reduction processing to obtain embedded vector representation; and finally, performing element corresponding multiplication on the embedded vectors of the two biological entities, inputting the embedded vectors into an MLP network, and outputting an interaction probability between the two biological entities.

An implantable membrane construct including a maximum pore size (MPS) less than 2 microns and opposing sides, each side of the construct having a surface roughness (Sa) greater than about 0.5 microns is disclosed. When the luminal surface of the implantable membrane construct has a surface roughness (Sa) greater than about 0.5 microns, mesenchymal cells do not form at the interface of the lumen and the first layer (i.e., luminal interface) such that the mesenchymal cells do not impede the flow of oxygen and nutrients to the graft cells (when implanted). When implanted, the outermost layer of the implantable membrane construct enables cellular penetration, vascularization, and anchoring of the construct. The implantable membrane construct includes single layer embodiments and multiple layer embodiments. Encapsulation devices utilizing the implantable membrane constructs to encapsulate biological entities (e.g., cells) into a patient are also provided.

A biocompatible membrane composite including a cell impermeable layer and a mitigation layer is provided. The cell impermeable layer is impervious to vascular ingrowth and prevents cellular contact from the host. Additionally, the mitigation layer includes solid features. In at least one embodiment, mitigation layer has therein bonded solid features. In some embodiments, the cell impermeable layer and the mitigation layer are intimately bonded or otherwise connected to each other to form a composite layer having a tight / open structure. A reinforcing component may optionally be positioned external to or within the biocompatible membrane composite to provide support to and prevent distortion. The biocompatible membrane composite may be used in or to form a device for encapsulating biological entities, including, but not limited to, pancreatic lineage type cells such as pancreatic progenitors.

The invention discloses a biological entity multivariate association prediction system combining linear and nonlinear fusion matrix factorization, which relates to the technical field of biological entity multivariate association prediction and comprises a multivariate biological entity input module, a cross-modal feature extraction module, a dual-channel fusion matrix factorization module, a dynamic feature fusion device and a multivariate association prediction engine. According to the method, the limitation of a traditional biological entity association prediction method is broken through by fusing linear and nonlinear matrix decomposition technologies, and a dynamic feature fusion mechanism realizes optimal combination of cross-modal features through adaptive weight adjustment, so that the problem of insufficient flexibility of a traditional static fusion strategy is overcome; in addition, the system adopts a three-dimensional tensor modeling technology, a unified prediction framework of multiple types of associations such as gene-disease, drug-target, metabolite-pathway and the like is realized, heterogeneity characteristics of biological associations can be effectively maintained, and compared with the prior art, the analysis capability of a complex biological network is remarkably improved.

A computer-implemented method is provided of analysing an image of a tissue specimen, which method comprises steps including detecting in the image each of a primary type of biological entity, detecting in the image one or more secondary types of biological entity, associated with each of the detected primary biological entities, generating an entity graph, in which each graph node is assigned to a primary biological entity, and in which each graph node is associated with predictive features measured from the image in respect of that graph node, including at least one predictive feature that is measured relative to two or more types of biological entity.

A device is provided for facilitating the imaging of biological entities. The device includes a body having upper and lower surfaces. The body defines a channel for accommodating fluid flow therethrough and a well communicating with the channel and the lower surface. A microscopy grid is removably received in the body and has a first side communicating with the channel and a second side directed at well. A retainer extends into the channel and is engageable with the microscopy grid to support the microscopy grid.

The application provides a method for monitoring the effect of a drug on the interaction between a biological body and a fluorescent probe, the method comprising: continuously injecting a first solution comprising a fluorescent probe into a fluorescent chamber and a microfluidic chamber of a microfluidic chip; the fluorescent probe is at least one in kind; at a first time, continuously injecting a solution comprising the fluorescent probe and a drug into the fluorescent chamber and the microfluidic chamber; the drug is at least one in kind; based on the imaging information corresponding to the fluorescent chamber and the microfluidic chamber at each time unit respectively, determining the effect of the drug on the interaction between the biological body and the fluorescent probe; wherein the microfluidic chamber comprises the biological body.

A monitoring device (1) comprises a microwell plate (10) comprising microwells (20) dimensioned to hold a respective biological entity and a channel plate (30) comprising at least one fluid channel (40, 42) in fluid connection with a fluid input port (11; 11A, 11B) and a fluid output port (13; 13A, 13B). An optode composition (25) is present in the microwells (20) and comprising an analyte-sensitive optode compound generating a detectable optical signal in response to an analyte. The channel plate (30) and the microwell plate (10) are slidably arranged together and movable relative to each other between a loading position, in which the at least one fluid channel (40, 42) is in fluid connection with at least a subset (21, 23) of the plurality of microwells (25), and a monitoring position, in which the channel plate (30) closes the at least a subset (21, 23) of the plurality of microwells (20).

The invention provides a biological enlightenment relation triple combined extraction method based on a fine-tuning large language model, and solves the problems of error propagation, difficulty in entity definition and insufficiency in chain dependence modeling in the prior art. The method comprises the following steps: constructing a full-chain bionic field corpus through reverse tracing, generating an initial triple label by using an advanced teacher model, and performing manual verification; and based on the selected base model, reconstructing the extraction task into a text generation task by adopting LoRA fine tuning to realize end-to-end joint extraction. According to the method, a composite entity boundary is accurately analyzed through field fine tuning, and error propagation is avoided; explicitly modeling chained dependence of'biological entity-bionic model-bionic application '; and few-sample field self-adaption is realized. Through accuracy verification, the extraction accuracy and logic consistency of the complex bionic text are remarkably improved, and technical support is provided for constructing a high-quality biological enlightenment knowledge base.

Disclosed is a biological entity for treating brain tumors, particularly gliomas, and a vector comprising the biological entity. The biological entity is a construct comprising at least an anti-tumor transgene, which is composed of at least one GluA knockdown agent, preferably at least two GluA knockdown agents, and preferably further comprises a fusion protein, an anti-PD-1 antibody, an anti-CTLA-4 antibody, and an immune response promoter of IL12. The vector comprises a wild-type HSV-1 virus, and the biological entity replaces the ICP34.5 gene of the wild-type HSV-1 virus. The vector comprising the wild-type HSV-1 virus modified with the biological entity shows little negative effect on neurons, while showing positive effect on human glioblastoma cells, both when neuronal cells and glioblastoma cells are cultured separately and when co-cultured.

The invention relates to the field of additive manufacturing, in particular to electrode ink based on a biological entity and a preparation method and application of the electrode ink. The preparation method of the electrode ink based on the biological entity comprises the following steps: performing degreasing pretreatment on the biological entity to obtain a degreased biological entity; uniformly mixing the degreased biological entity with a binder and a structure reinforcing phase to obtain a mixture; and mixing and grinding the mixture and water to obtain the electrode ink based on the biological entity, the biological entity is plant pollen. The electrode ink provided by the invention has a biological entity with relatively high loading capacity, and can provide sufficient ion transmission gap channels; the electrode ink disclosed by the invention has good printable performance, and can keep a stable structure after pyrolysis and carbonization without cracking, and an electrode precursor frame prepared by printing is controllable in form, good in formability and high in stability.

This invention belongs to the field of biological data processing technology and discloses a multi-omics data fusion method and system based on hypergraph networks. This invention uses a hypergraph structure to model omics data, enabling more efficient mining of high-order interaction information and a more comprehensive revelation of complex relationship patterns between biological entities. By introducing a hypergraph aggregation mechanism, it can more accurately represent the high-order complex relationships within various omics data, overcoming the limitations of traditional methods that can only capture pairwise node relationships, and improving the model's ability to understand the interactions between biological entities. This hypergraph fusion method effectively integrates multi-omics-specific hypergraphs, achieving a unified representation of cross-omics data, enhancing the model's ability to represent complex multi-relationship data, and providing more comprehensive technical support for various downstream tasks.

In a first aspect, the present invention relates to a computer-implemented method for obtaining information about a biological entity based on at least one biological sequence, comprising: (a) providing a repository of fingerprint data strings for a biological sequence database, each fingerprint data string representing a characteristic biological subsequence composed of sequence units, each characteristic biological subsequence having a combination number less than the total number of different sequence units available in the biological sequence database, the combination number of the biological subsequence being defined as the number of different sequence units that appear as consecutive sequence units of the biological subsequence in the biological sequence database; (b) determining one or more fingerprint data strings representative of the biological entity; (c) searching a repository including information associated with the fingerprint data strings for information associated with the one or more representative fingerprint data strings; and (d) processing the information.

The present disclosure provides a metal and biological body detection device, system and method, comprising a detection coil, a comb-shaped capacitor and a signal generator connected in series, the detection coil is placed in overlap with the comb-shaped capacitor; the signal generator is used to provide an electrical signal with a set resonant frequency for the detection coil and the comb-shaped capacitor. The comb-shaped capacitor can output a larger parameter change when detecting a biological body, can amplify the change at both ends of the coil, realize the detection of the biological body, and at the same time, the detection coil is provided to realize the detection of a metal object, the detection coil is placed in overlap with the comb-shaped capacitor, which can cover the same detection area, and the integrated setting realizes the detection of the metal and the biological body.

A computer-implemented method of predicting biological entities that meet user-defined biological requirements using a knowledge base, the method comprising: providing an inference knowledge base comprising a corpus of textual data; receiving a user query defining biological requirements for which a biological entity is to be predicted; obtaining a query statement text based on the query, the query statement text describing the biological requirements and including a mention of the biological entity, where the biological entity itself is masked for prediction; selecting a candidate biological entity for the masked biological entity and retrieving a plurality of evidence statements from the knowledge base, each evidence statement comprising a mention of the candidate biological entity, where the evidence statements are retrieved based on calculating a similarity of the query statement to statements within the knowledge base; each training query statement and the plurality of retrieved evidence statements are input into an inference model, where mentions of the candidate biological entity are masked in the query statements and the evidence statements, which is trained to predict a probability that the candidate biological entity is a masked biological entity based on the retrieved evidence statements.

The present invention provides a system for receiving biological sequence information and activating the synthesis of a biological entity. The system has a receiving unit for receiving a signal encoding biological sequence information transmitted from a transmitting unit. The transmitting unit can be present at a remote location from the receiving unit. The system also has an assembly unit connected to the receiving unit, and the assembly unit assembles the biological entity according to the biological sequence information. Thus, according to the present invention biological sequence information can be digitally transmitted to a remote location and the information converted into a biological entity, for example a protein useful as a vaccine, immediately upon being received by the receiving unit and without further human intervention after preparing the system for receipt of the information. The invention is useful, for example, for rapidly responding to viral and other biological threats that are specific to a particular locale.

The invention discloses a constructive formalized verification system of a ribosome circulation mechanism, which belongs to the field of molecular biology calculation verification, deduces and translates a full-cycle biological entity from uniformity through symmetric breaking and the like, and encodes the stages of translation starting, extension proofreading, termination, circulation and the like through a state radiation sequence. And realizing complete-cycle unified verification by combining odd complement operators and the like, and mapping into an electronic circuit. Corresponding between a biological mechanism and a number theory is established through the Langlands theory, and accuracy is guaranteed; gTP consumption is optimized through constructive energy accounting, and energy efficiency is achieved. The system can be applied to the fields of pharmaceutical synthesis, mRNA vaccine design and the like, provides a high-fidelity optimization scheme, and is matched with a hardware accelerator to improve verification efficiency. The system solves the problems that the prior art is lack of a formalized framework and depends on empirical parameters, has constructive completeness, hardware realizability and commercial expandability, can shorten supervision and approval time, and provides reliable theoretical and practical support for related fields.

A computer-implemented method of electronically mining medical and scientific datasets to determine a ranking indicating a level of evidence for an association between two entities is disclosed. The method comprises receiving a representation of an entity pair, performing first data mining on one or more unstructured datasets to generate one or more first scores each representing an extent of association between the entities of the entity pair, and performing second data mining on one or more structured datasets to generate one or more second scores each representing an extent of association between the entities of the entity pair. The method also comprises using a classifier to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores, and providing the predicted ranking to a user as an indication of the strength of evidence for an association between the entities of the entity pair.

A method and an electronic device for extracting biological event relationships by integrating structured representations and entity relationship reasoning belong to the field of natural language processing. To address issues such as the insufficient utilization of the hierarchical structure information of events in the task of extracting biological event relationships, the key points are to structurally represent the biological events layer by layer according to the three-level units of the biological events to obtain biological event feature representations; infer the relationship paths between the biological entities of the biological event pairs in the biological event relationship dataset; splice the relationship paths with the biological event data in the biological event relationship dataset to obtain a first spliced feature; extract the semantic features of the first spliced feature; splice the biological event feature representation and the semantic features to obtain a second spliced feature; and use a neural network classifier to map the second spliced feature to a relationship space to judge biological event relationships. The effect is that it can accurately extract biological event relationships.

A computer-implemented method of training a machine learning model to identify biological entities for drug discovery is disclosed. The method comprises providing a training data set comprising a plurality of entity-linked text sequences, each text sequence including a mention of a biological entity, where the biological entity is linked to a corresponding biological entity identifier from a set of possible biological entity identifiers; masking the mention of the biological entity within each text sequence; encoding each masked text sequence into an input representation for a machine learning model; and training a machine learning model to predict the unique entity identifier of the masked biological entity based on the input representation. The described method is able to utilise the full breadth of the rich contextual information available in the biomedical text corpus to predict new biological targets for drug discovery and avoids the restrictions intrinsic to relationship prediction using knowledge graphs. The ability to identify more promising, biologically relevant targets in an automated manner, significantly reduces the requirement of human input and reduces the failure rate in targets that are progressed in the drug delivery pipeline.

A computer-implemented method of training a machine learning model to identify biological entities for drug discovery is disclosed. The method comprises providing a training data set comprising a plurality of entity-linked text sequences, each text sequence including a mention of a biological entity, where the biological entity is linked to a corresponding biological entity identifier from a set of possible biological entity identifiers; masking the mention of the biological entity within each text sequence; encoding each masked text sequence into an input representation for a machine learning model; and training a machine learning model to predict the unique entity identifier of the masked biological entity based on the input representation. The described method is able to utilise the full breadth of the rich contextual information available in the biomedical text corpus to predict new biological targets for drug discovery and avoids the restrictions intrinsic to relationship prediction using knowledge graphs. The ability to identify more promising, biologically relevant targets in an automated manner, significantly reduces the requirement of human input and reduces the failure rate in targets that are progressed in the drug delivery pipeline.

A method and apparatus for predicting drug-target interactions based on a representation alignment space. The method initializes the protein representation dimension, drug representation dimension, common embedding representation dimension, the number of multilayer perceptron layers, and batch dimension parameters. Multiple single-source network data are obtained from multiple pharmaceutical databases and compared to construct a biomedical heterogeneous network. Based on a contrastive learning strategy, protein target and drug representations are integrated into a common embedding representation space, and a model is trained using DTI associations. The protein target and its corresponding drug representations are input into the trained model for DTI prediction, obtaining the most relevant drugs. By constructing a biomedical heterogeneous network, the present invention facilitates the prediction of DTI associations associated with unknown drugs. Furthermore, by establishing an embedding representation alignment framework, the method utilizes high-quality representation methods to characterize biomedical networks and biological entities, while also addressing the issue of sparse training data using contrastive loss.

This disclosure provides a cell annotation method, apparatus, electronic device, and storage medium. The method includes: constructing biological knowledge relation data, wherein the biological knowledge relation data represents the biological relationships between different biological entities; acquiring gene expression data of a target sample, determining a target gene based on the gene expression data, and identifying the biological entity corresponding to the target gene in the biological knowledge relation data as a starting node; performing relation reasoning in the biological knowledge relation data based on the starting node to determine a target node, and performing cell annotation based on the biological entity represented by the target node. The embodiments of this application can improve the flexibility of cell annotation.

The present invention describes an integrated acoustic spectroscopy system and method for the analysis, characterization, and classification of organic, inorganic, and biological matter, assisted by supervised artificial intelligence, through the analysis of samples obtained from specific entities. The system integrates a computing unit (2) with a graphical interface (1), a function generator (3), a signal processing device (4), and an acoustic coupling clamp (5) with coaxial transducers. As an application example, glass samples (from inorganic entities), culture media (from organic entities), and cell culture lines (from biological entities) were successfully characterized and classified.The method involves capturing a digitized acoustic signature from a sample, generating a multidimensional data hierarchy that ranges from time-domain signals to high-density spectrograms obtained by Continuous Wavelet Transform (CWT) and post-processing vectors for classification. Using a 2% rescaling, standardization, and dimensionality reduction (UMAP) chain, an acoustic signature is extracted and fed into a Support Vector Machine (SVM) model optimized by Bayesian inference. The invention is notable for a management and synchronization module that allows for incremental retraining of the model through manual labeling in the dynamic repository. The system enables the differentiation of healthy and pathological cell phenotypes with a Matthews Correlation Coefficient (MCC) greater than 0.94 in less than a minute, optimizing the analysis, characterization and classification of matter without dependence on reagents or complex infrastructures, with outstanding application in cancer screening.