211 results about "Biological target" patented technology

The present disclosure provides methods and assay systems for use in spatially encoded biological assays, including assays to determine a spatial pattern of abundance, expression, and / or activity of one or more biological targets across multiple sites in a sample. In particular, the biological targets comprise proteins, and the methods and assay systems do not depend on imaging techniques for the spatial information of the targets. The present disclosure provides methods and assay systems capable of high levels of multiplexing where reagents are provided to a biological sample in order to address tag the sites to which reagents are delivered; instrumentation capable of controlled delivery of reagents; and a decoding scheme providing a readout that is digital in nature.

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 may collect a set of known drug compounds from a database, and create augmented datasets with labeled and unlabeled chemical data and biological data. A computer implemented method may train a neural network using the expanded training set to generate a prediction score of bioactivity of candidate drug compounds towards the biological target, wherein the contribution of the second plurality of unlabeled samples in training the neural network is scaled by a parameter. A computer implemented method may output one or more refined neural network models capable of generating candidate drug compounds with predicted activity. A computer implemented method may generate a candidate drug compound by inputting a candidate drug compound with chemical data into the trained neural network model.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention discloses a target detection method suitable for small creatures in an underwater complex scene, and aims to solve the problems that small target features in underwater image detection are fuzzy, small targets are difficult to accurately position and identify by the existing detection algorithm, and efficient detection is difficult to realize on equipment with limited computing resources. According to the method, on the basis of a YOLOv8 network and a backbone network, a P2 feature layer is adopted to enhance the expression ability of a small target, an ADwn lightweight down-sampling module is introduced, efficient feature down-sampling is achieved, a neck network constructs an SOEP small target feature enhancement pyramid module, feature recombination is conducted on P2 through SPDConv, efficient feature integration is conducted through CSP-OmniKernel, and therefore the expression ability of the small target features is improved; and feature fusion is carried out through bidirectional cross-scale connection, so that multi-scale information interaction is enhanced, and the detection capability of small targets is improved. The target detection network is more suitable for small biological target detection in an underwater complex environment, and provides powerful technical support for underwater ecological monitoring, marine organism research and other applications.

A computer implemented method may collect a set of known drug compounds from a database, and create augmented datasets with labeled and unlabeled chemical data and biological data. A computer implemented method may train a neural network using the expanded training set to generate a prediction score of bioactivity of candidate drug compounds towards the biological target, wherein the contribution of the second plurality of unlabeled samples in training the neural network is scaled by a parameter. A computer implemented method may output one or more refined neural network models capable of generating candidate drug compounds with predicted activity. A computer implemented method may generate a candidate drug compound by inputting a candidate drug compound with chemical data into the trained neural network model.

An animal feed additive comprising a polypeptide having alkaline phosphatase activity is described, wherein the selected polypeptides have activity against relevant biological targets, are thermostable, and have high three-dimensional similarity to each other and to human intestinal alkaline phosphatase and close proximity on the phylogenetic tree.

A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures. One or more non-transitory computer readable media are provided that store instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising performing the computer methods described herein for providing chemical structure of hit structures generated by the generative model.

The method comprises the following steps: acquiring compound data, biological target point data and biological activity data between a compound and a biological target point, establishing a compound and target point data set, performing feature extraction on the compound data and the biological target point data respectively, and obtaining a target point data set; performing feature fusion on the obtained compound feature vector and the biological target feature vector, and constructing a deep learning model; a prediction error of a focus loss function measurement model is defined, a deep learning model is trained, the trained deep learning model is evaluated through a Marius correlation coefficient, a compound and biological target data set is predicted, and candidate drugs are screened out; according to the method, the recognition rate of the model to real interaction is improved by defining the focus loss function, the robustness of the model is enhanced, in addition, a traditional Adam optimizer is improved, and the training efficiency and prediction precision of the model are improved through a quadratic regularization term and an adaptive learning rate strategy.

The invention discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and the method specifically comprises the steps: obtaining the brain multi-modal image data of a patient, and extracting a three-dimensional tumor region and a corresponding perfusion parameter; constructing a biological target region hierarchical structure containing a tumor overall region and an anoxic subregion; a relative hypoxia degree parameter is obtained by calculating the cerebral blood flow ratio of the hypoxia subregion to other regions of the tumor; historical treatment case data are deconstructed, and a dose-curative effect associated parameter set is established; training and outputting a dose adjustment strategy model for the hypoxia subregion; and generating a final radiotherapy dose planning scheme according to a dose adjustment value output by the model in combination with a clinical guide basic dose. Through a data-driven dose decision-making mechanism, the limitation of traditional uniform dose irradiation is overcome, so that an objective and reliable evaluation basis is provided for clinical selection and optimization of personalized treatment schemes.

A high-speed laser speckle contrast imaging system is for characterizing pressure wave or pulse wave propagation or vascular conducted response in at least one vessel of a biological target. The apparatus includes: a laser source generating a laser radiation; a high-speed camera configured for capturing at least 1000 frames per second (fps), preferably at least 5000 fps, more preferably 6000 fps of the target; an optical sub-system configured for 1) guiding the laser radiation from the laser source to the target 2) and for collecting and guiding a back-scattered light from the target to the camera; and a processing unit configured for receiving and processing raw image data from the camera for calculating at least one feature related to the pressure wave propagation or vascular conducted response.

The invention discloses a radiotherapy planning method and system based on biological optimization, and the method comprises the steps: carrying out the fusion of a positron emission computed tomography (PET) image and a computed tomography (CT) image, carrying out the sketching of a biological target region according to the threshold value of a developer standard uptake value (SUV), and meanwhile, carrying out the sketching of an endangered organ; setting physical constraints for the biological target region and the organ at risk, and performing primary optimization to obtain a primary dose result; setting biological constraints for the biological target region and the organ at risk based on the primary dose result; and constructing an objective function containing a biological constraint penalty term and a physical constraint penalty term, and obtaining a final radiotherapy plan through iterative optimization. According to the method, a biological target region is used for replacing a traditional target region, a tumor region is depicted finely, a plan optimization model combining biological constraint and physical constraint is used, the dose coverage of the target region can be ensured, meanwhile, the dose of a crisis organ is reduced, and finally a plan meeting clinical requirements is generated.

The invention relates to the field of drug discovery and delivery collaboration, in particular to an autonomous evolutionary drug discovery and delivery collaboration method and system based on a large language model. The method comprises the following steps: aiming at a given biological target three-dimensional structure, generating a candidate molecular library which is complementary with a target pocket and gives consideration to druggability through an SE (3) isovariant hybrid generation model; according to a two-stage funnel type high-throughput virtual screening process, screening out the molecule with the highest comprehensive potential from the candidate molecule library; generating a customized delivery scheme through a three-stage process; in a digital twinborn model for simulating a real in-vivo tumor microenvironment, a targeted delivery process of a drug and carrier complex is simulated, and efficiency is evaluated; atomic-scale performance confirmation is carried out on a medicine, carrier and target point ternary system through molecular dynamics simulation. The invention is suitable for drug research and development.

The invention relates to an efficient enrichment method for low-concentration bacteriophages in river and lake water bodies, which remarkably improves the recovery rate and the concentration multiple of the bacteriophages through a multi-stage synergistic technology in combination with physical interception, chemical precipitation and biological targeted adsorption. The method specifically comprises the following steps: pretreatment: removing large particles and suspended solids through gradient filtration and low-speed centrifugation; ultrafiltration concentration: adopting a hollow fiber ultrafiltration membrane with the molecular weight cut-off of 30-100 kDa, and realizing 10-50 times concentration under the pressure of 0.1-0.3 MPa; chemical precipitation strengthening: optimizing synergistic precipitation conditions of PEG and NaCl, and reducing phage inactivation in combination with low-temperature standing; specific adsorption of host bacteria: combining the host bacteria in the logarithmic phase with bacteriophage according to the volume ratio of 1: 100, and carrying out pH regulation and control elution to obtain a high-purity enriched solution. The method has the advantages that the total concentration multiple reaches 500-1000 times, and the recovery rate is gt; the activity retention rate is gt; and the method is suitable for a complex water body environment. Through a multi-technology combination strategy and targeted adsorption design, an efficient tool is provided for environmental monitoring, phage therapy and microbiological research.

The invention relates to the technical field of sonar imaging, and discloses a marine organism distribution detection system and method based on sonar imaging, and the method comprises the steps: setting a plurality of depth layers, and collecting an acoustic echo signal of each depth layer; marine organism block sparse observation is carried out based on the three-dimensional acoustic echo signals, an observation matrix is obtained, and an effective biological signal block set is calculated; calculating space coordinates and reflection intensity of each biological target according to the effective biological signal block set, obtaining biological positioning data of each depth layer, and obtaining a three-dimensional acoustic image of each depth layer through three-dimensional imaging; the biological density index, the Shannon-Vaniner diversity index and the vertical migration speed of the scattering coefficient in each ecological layer are calculated, the biological detection result of each depth layer is output, data collection is carried out on the multiple depth layers, the detection reliability in the low-signal-to-noise-ratio marine environment is improved, and the detection efficiency is improved. And marine organism populations and distribution characteristics of different depth layers can be accurately identified.

The invention discloses a sewage treatment system based on artificial intelligence and biological targeting dynamic regulation and control, and relates to the technical field of sewage treatment. By collecting inflow water quality data of sewage in real time, taking the inflow water quality data, a historical water quality fluctuation period, an environment temperature and meteorological information as input data, and combining with a pre-trained water quality prediction model, a water quality prediction model is established; obtaining water quality prediction data; matching a functional microorganism combination based on the water quality prediction data, and calculating a microorganism release amount; and the reactor execution unit releases the corresponding microorganisms through a release device based on the microorganism release amount. The problems that a traditional biological treatment system is lagging in response, low in degradation efficiency, high in operation cost and the like are solved, and the artificial intelligence and biological targeting dynamic regulation sewage treatment system is developed.

The invention provides a specific binding polypeptide of a targeted cell membrane receptor Fc epsilon RI alpha and application of the specific binding polypeptide. The amino acid sequence of the polypeptide is shown in any one of (I) SEQ ID NO: 1-6; (II) an amino acid sequence with at least 90% identity as shown in any one of SEQ ID NO: 1-6, and is specifically combined with a cell membrane receptor Fc epsilon RI alpha. Experimental results show that the Fc [epsilon] RI [alpha] specific binding polypeptide can inhibit IgE-mediated mast cell dependent anaphylaxis to a certain extent, which prompts that the Fc [epsilon] RI [alpha] specific binding polypeptide has a certain inhibition effect on IgE-mediated I-type anaphylaxis, and lays a foundation for further research and development of novel biological targeting drugs for treating allergic diseases.

The present disclosure provides compounds comprising at least one GalNAc moiety. The compounds may further comprise or be a therapeutic moiety and / or compound. The compounds thus provide a means of controlled delivery of therapeutic moieties to biological targets by incorporation of the GalNAc moiety. The present disclosure also provides synthetic precursors to the compounds. The present disclosure further provides pharmaceutical compositions and kits comprising the compounds, methods of inhibiting expression of a target nucleic acid, and methods of treating or preventing a disease or disorder by administering to a subject in need thereof the compounds or pharmaceutical compositions.

The invention relates to the technical field of smart home, and particularly discloses an intelligent alarm method and system based on an infrared thermal imaging technology, and the method comprises the steps: collecting an infrared thermal image sequence of a home environment based on an infrared detector; performing threshold segmentation on each frame of image in the infrared thermal image sequence based on a preset temperature threshold to extract a high-temperature region image sequence; forming a cross-frame high-temperature region tracking trajectory between continuous frames of the high-temperature region image sequence by using a target tracking algorithm; obtaining time sequence change parameter information of each high-temperature region according to the high-temperature region tracking trajectory and the high-temperature region image sequence; distinguishing the target type of each high-temperature area according to the time sequence change parameter information; if the high-temperature area of the biological target type exists, generating an alarm signal; the method has the capability of accurately distinguishing the biological target and the sunlight target, the reliability and accuracy of detection are effectively improved, and the false alarm rate can be effectively reduced.

The invention relates to the technical field of data processing, in particular to an active cliff data set construction method and device, electronic equipment and a storage medium, and aims to improve the accuracy and consistency of active cliff recognition. The method comprises the following steps: for each molecule which is in a pre-constructed molecular data set, can be combined with the same biological target spot and is subjected to biological activity measurement under the same experimental condition, obtaining the similarity between molecular fingerprints of every two molecules in each molecule, and analyzing the similarity of every two molecules on substructures to obtain the bioactivity of the molecules. Selecting a matching molecule pair meeting a preset similarity evaluation condition; for each biological target spot, selecting a similarity as a molecular activity cliff threshold value corresponding to the biological target spot according to distribution conditions of the corresponding matched molecule pairs under different similarities, and selecting a target molecule pair from the molecules according to the molecular activity cliff threshold value; and constructing an activity cliff data set based on the biological activity parameter difference corresponding to each selected target molecule pair.

The invention provides a biological target magnetic separation method based on a carbon-based sheet-shaped magnetic label, and relates to the technical field of microbiological detection. The method comprises the following steps: respectively obtaining a biological target solution to be separated and a carbon-based sheet-shaped magnetic aggregate, wherein the carbon-based sheet-shaped magnetic aggregate is a two-dimensional flexible magnetic aggregate containing a carbon-based two-dimensional sheet-shaped material and magnetic nanoparticles; the surface of the carbon-based sheet-shaped magnetic aggregate is modified with biological targeting molecules, the carbon-based sheet-shaped magnetic label is obtained, and the biological targeting molecules correspond to biological targets in the biological target homogeneous solution; capturing a biological target in the biological target solution to be separated by using a carbon-substrate-shaped magnetic label to obtain a captured solution; and separating to obtain the biological target in the captured solution. According to the separation method, the conversion rate of the biological target to the magnetic information can be effectively improved, and the problems of low sensitivity and high difficulty in biological target detection can be effectively solved.

The present application relates to computer vision and underwater detection technology, provide a kind of underwater small biological target detection method and system based on deep learning, the WLDA-YOLOv8 deep learning model constructed includes main network, neck network and detection head, main and neck embed light multi-scale module, detection head introduces dynamic detection head and adaptive sample selection strategy;Training stage uses improved WIoUv3s loss to carry out boundary box regression, the model is trained using underwater biological data set, finally realizes the accurate detection of small target in underwater image, enhances the capture and fusion ability to small target subtle features.

The present invention relates to the field of smart home technology, and specifically discloses an intelligent alarm method and system based on infrared thermal imaging technology, wherein the method includes the following steps: collecting an infrared thermal image sequence of a home environment based on an infrared detector; performing threshold segmentation on each frame image in the infrared thermal image sequence based on a preset temperature threshold to extract a high-temperature area image sequence; using a target tracking algorithm between consecutive frames of the high-temperature area image sequence to form a high-temperature area tracking trajectory across frames; obtaining time-series change parameter information of each high-temperature area based on the high-temperature area tracking trajectory and the high-temperature area image sequence; distinguishing the target type of each high-temperature area based on the time-series change parameter information; generating an alarm signal if there is a high-temperature area of ​​biological target type; the method has the ability to accurately distinguish between biological targets and sunlight targets, effectively improves the reliability and accuracy of detection, and can effectively reduce the false alarm rate.

The invention discloses a density detection and intrusion early warning method for a shallow sea biological model, and the method comprises the following steps: 1, density detection: a system firstly analyzes an input image or video frame through a target detection module based on an improved YOLO algorithm, recognizes a shallow sea biological target in the image or video frame, determines the type of the shallow sea biological target, and carries out the early warning of the intrusion of the shallow sea biological target; the shallow sea biological density detection is realized by combining an image processing method; step 2, acquisition of a shallow sea organism population coefficient: according to the detected shallow sea organism category, acquiring a corresponding population coefficient which reflects contribution of different types of shallow sea organisms to intrusion intensity; the method has the beneficial effects that the shallow sea organism data set is self-made, the content of the data set is enriched through an image enhancement technology and a data enhancement means, the diversity of samples is increased, and the recognition capability and generalization performance of the model on different types of shallow sea organisms are improved, so that the model can adapt to a complex underwater environment and a variable target form.

The present invention discloses a method for preparing water-soluble quantum dots. The preparation method uses a surfactant solution to coat oil-soluble quantum dots, retaining the fluorescence of the oil-soluble quantum dots, and uses water-soluble polymer ligands (polyvinyl alcohol and polyvinyl pyrrolidone) to complete functional modification, thereby solving the problems of easy quenching of quantum dot fluorescence, inability of nanoparticles to stably agglomerate, and prone to nonspecific adsorption, resulting in low detection sensitivity. When the water-soluble quantum dots prepared by the preparation method of the present invention are used for biomarker detection, they can be directly dissolved in water, and the fluorescent group is stable, can stably agglomerate and specifically adsorb biological targets; therefore, the immunochromatographic test strips prepared using the water-soluble quantum dots can simply and stably adsorb drug antibodies, thereby improving detection sensitivity, with the detection sensitivity for morphine reaching 0.1 ng / mL.

The present invention relates to the field of therapy and drug delivery. More specifically, therapeutic methods and pharmaceutical compositions for treating disorders of blood-tissue barrier-protected organs that harbor substantial populations of postmittal neurons, such as organs derived from the neural tube, including the central nervous system and the eye. The disclosed methods and compositions involve topical administration of an effector component that targets intracellular biological targets to such organs, in combination with a saponin component that enhances the effective uptake of the effector component into cells and / or enhances the effective delivery of the effector component within cells where the biological target is present. For example, the effector component may be an oligonucleotide therapeutic that targets gene products associated with CNS and / or ocular disorders. Due to the cellular uptake stimulating and / or endosomal escape enhancing effects of the saponin component, the neuropharmaceuticals and ophthalmic compositions presented herein for topical administration to the CNS and / or eye, respectively, can be formulated with lower concentrations of the effector component and / or lower volumes, which provides safety benefits to neurons and patient comfort.

The mechanisms driving the development of extracapillary lesions in focal segmental glomerulosclerosis (FSGS) and crescentic glomerulonephritis (CGN) remain poorly understood. A key question is how parietal epithelial cells (PECs) invade glomerular capillaries, thereby promoting injury and kidney failure. Here the inventors show that expression of the tetraspanin CD9 increases markedly in PECs in mouse models of CGN and FSGS, and in kidneys from individuals diagnosed with these diseases. Cd9 gene targeting in PECs prevents glomerular damage in CGN and FSGS mouse models. Mechanistically, CD9 deficiency prevents the oriented migration of PECs into the glomerular tuft and their acquisition of CD44 and β1 integrin expression. These findings highlight a critical role for de novo expression of CD9 as a common pathogenic switch driving the PEC phenotype in CGN and FSGS, while offering a potential therapeutic avenue to treat these conditions. Accordingly, CD9 represents a reliable biomarker and as well as a biotargets in glomerulonephritides.

The invention provides an ecological risk evaluation method and system based on new pollutants, and the method comprises the steps: obtaining mass spectrum data of sample data, determining a normal mass spectrum fingerprint corresponding to a geographic position and time, marking an ion peak as an unknown peak when the ion peak in the mass spectrum data deviates from a preset range of the normal mass spectrum fingerprint, and determining that the ion peak is an unknown peak; the method comprises the following steps: acquiring a fragmented spectrum of an unknown peak based on secondary mass spectrometry, inferring a possible molecular structure of the unknown peak through a graph neural network model in combination with a preset chemical database, and constructing a quantitative structure-activity relationship of possible molecular structure analogues of the unknown peak based on a preset structural similarity network. The method comprises the following steps: acquiring predicted toxicity and signal channels influenced by the predicted toxicity, performing ecological risk assessment in a preset range of a petrochemical plant based on the predicted toxicity and the signal channels influenced by the predicted toxicity, and acquiring potential ecological risks of new pollutants. And a basic basis is provided for environment management and decision making.

The present study of the regulatory T phenotype of Sézary cells led to the discovery of the expression of GARP (LRRC32) by Sézary cells. GARP has also been shown to be overexpressed in samples from patients with acute lymphoblastic leukemia. GARP therefore appears as a diagnostic marker, for monitoring T-cell malignancies, and as a therapeutic target. Accordingly, the present invention relates to methods for the diagnosis and treatment of T-cell malignancies.

The present disclosure is directed to methods and / or uses of oligonucleotide conjugates for assays and flow cytometry detections and related systems and / or kits. Certain methods are directed to a method for detecting one or more biological targets of a sample in a detection assay, comprising: providing a molecular probe, comprising a binding moiety and an oligonucleotide sequence, to a sample comprising one or more biological targets; binding the one or more biological targets with the binding moiety; providing a detectable component to the sample, wherein the detectable component comprises a signal generating moiety conjugated to an oligonucleotide sequence complementary to the oligonucleotide sequence of the molecular probe; hydridizing the oligonucleotide sequence of the target-bound molecular probe to the detectable component; and detecting a signal generated from the hydridized detectable component. Various other embodiments, applications etc. are disclosed herein.