108 results about "Biological target" patented technology

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 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.

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 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 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 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 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 application relates to image analysis technology, disclose a kind of water rare biological detection method and system based on underwater image analysis, comprising: monitoring target water area in preset time period in light signal data stream and spectrum data stream, identify the light signal data stream light emitting event, obtain light emitting event timestamp sequence, based on light emitting event timestamp sequence obtain light emitting image sequence in light signal data stream and spectrum data sequence in spectrum data stream, to light emitting image sequence and spectrum data sequence is carried out denoising correction and space-time synchronization processing, obtain after alignment image block sequence and after alignment spectrum curve, according to after alignment spectrum curve constructs spectrum attention weight, to after alignment image block sequence is carried out multi-scale morphological feature extraction, obtain multi-scale morphological feature map, to multi-scale morphological feature map is carried out rare biological target detection, obtain target detection result.The present application can improve the accuracy of water rare biological detection.

The invention discloses an accurate pest expelling device for forest breeding, which comprises a monitoring station and a moving device, the upper end of the monitoring station is fixedly connected with a camera, the monitoring station is internally provided with an information assembly, a spraying assembly and an evaluation assembly, the moving device is provided with a shell, a feeding bin and an arc-shaped rail, and the arc-shaped rail is slidably connected with the evaluation assembly; through alternate use of two kinds of reverse pheromones built in the information assembly, pest expelling and luring effects can be achieved in different time periods, pests are caught into a collecting barrel in cooperation with the spraying assembly, then the situation of pest reproduction is effectively avoided and reduced, the caught pests can be collected through the evaluation assembly, meanwhile, a moving device is linked, and the pest trapping effect is improved. The general variety and number of pests are recognized through the recognition head, meanwhile, the distribution conditions of the pests in different areas can be obtained in cooperation with the camera, so that hostile organisms are automatically discharged, biological targeted releasing and repelling are conducted, and the pest repelling precision is improved.

The invention relates to the technical field of aquaculture, in particular to an intelligent dynamic disinfecting and killing method and system for aquaculture water. The method comprises the following steps: acquiring a multi-source sensor information set arranged in a shrimp pond water body, and analyzing a dynamic coupling relationship between a cultured shrimp group and a water quality state based on the multi-source sensor information set to obtain a water body dynamic load evaluation parameter set; based on the dynamic load evaluation parameter set of the water body, analyzing the safety and disinfection suitability of the unshelled shrimps and the collaborative pollution relationship between algae and pathogenic bacteria to obtain a differentiated biological targeted disinfection parameter set; and based on the differentiated biological targeted killing parameter set, analyzing the composite stress intensity of algae and pathogenic bacteria on the shelled shrimps, executing a dynamically optimized electric pulse killing instruction, and generating and outputting a killing process log. The disinfection measures are accurately matched with the water body load state, excessive or insufficient disinfection is avoided, the health management level of the culture water body is improved, and the sustainable development of shrimp culture is promoted.

The application discloses a kind of asynchronous fusion method and system of deep-sea biological space-time trajectory analysis, the method is extracted from the single video data stream collected by underwater equipment biological target using visual detection module concurrently, and using video text recognition module extracts the space-time metadata superimposed in video picture;Then using asynchronous data fusion module based on a confidence-aware asynchronous extended Kalman filtering algorithm, the visual feature information and space-time metadata are fused, and the unified space-time trajectory point with geographic registration is generated.The application solves the technical problems that biological visual information and accurate space-time information are difficult to automatically integrate from single video source in the prior art, and through innovative asynchronous data fusion algorithm and diversified visualization output, the robustness, accuracy and application breadth of the automatic ecological monitoring and analysis system are significantly improved.

The present invention features a number of methods for identifying one or more compounds that bind to a biological target. The methods include synthesizing a library of compounds, wherein the compounds contain a functional moiety having one or more diversity positions. The functional moiety of the compounds is operatively linked to an initiator oligonucleotide that identifies the structure of the functional moiety.

The invention provides a farmland laser operation planning method and device based on artificial intelligence and electronic equipment, and relates to the technical field of laser intelligent equipment. According to the specific implementation scheme, the method comprises the steps of obtaining visual related data of a target area and agricultural machine poses; based on the visual related data and the agricultural machine pose, performing biological target detection to obtain a biological target detection result; working path planning is conducted based on the biological target detection result, laser control parameters in the laser working process are determined, and the laser control parameters are used for controlling a laser device to output laser.

The present invention relates to the field of therapy and drug delivery. More specifically, disclosed herein are therapeutic methods and pharmaceutical compositions for treating disorders of blood tissue barrier shielded organs having a large number of populations of post-mitotic neurons, such as organs derived from neural tubes, including the central nervous system and the eye. The disclosed methods and compositions involve topically applying into such organs a combination of an effector component that targets a biological target in a cell, and a saponin component that promotes effective ingestion of the effector component into the cell and / or enhances effective path steering of the effector component within the cell in which the biological target is present. For example, the effector component may be an oligonucleotide therapeutic agent targeting gene products associated with CNS and / or disorders of the eye. Due to the cellular uptake stimulating effect and / or endosomal escape enhancing effect of the saponin component, the neuropharmaceutical and ophthalmic compositions presented herein for topical administration into the CNS and / or eye, respectively, may be formulated with lower concentrations of effector components and / or in smaller volumes, this brings safety benefits to the neurons and the comfort of the patient.

This invention relates to the field of bio-intelligent manufacturing technology, and particularly to an intelligent manufacturing equipment and method for a multi-biological targeted nutritional protection compound preparation. The equipment includes a manufacturing shell, the interior of which is equipped with an AI molecular design and virtual screening unit, an intelligent targeted delivery system preparation unit, a multi-scale online quality monitoring module, a four-biological effect simulation and verification unit, and a central control and digital twin server. In this invention, when vibrations generated during the repeated reciprocating motion of the microfluidic sampling needle and the use of other components cause a misalignment between the microfluidic sampling needle and the support frame, the pointer slides on the surface of the scale plate. The deviation distance is magnified and displayed using a magnifying glass. Then, a drive source is activated, causing the threaded rod to rotate, which in turn moves the pull plate left and right. This pushes the moving frame, the moving plate, and the magnetic block to slide on the electromagnet, thereby adjusting the position of the support frame and the microfluidic sampling needle, and returning the pointer to its initial position.

The present disclosure relates to a chromatography system comprising a buffer valve arrangement configured to allow independent control of a first buffer feed and a second buffer feed; a pump arrangement configured to supply the first buffer feed, the second buffer feed, and a feed comprising a biological target compound and one or more impurities; a selector valve arrangement comprising a first chromatography device selector valve; a first chromatography device comprising a first chromatography material comprising a support material functionalized with a ligand wherein the ligand comprises an anion exchange group or an affinity group having a binding affinity for a biological target compound; a second chromatography device comprising a second chromatography material, the second chromatography material comprising a regulatory chromatography material; wherein the selector valve arrangement is configured to be able to separate a biological target compound from one or more impurities by allowing a feed comprising the biological target compound and the one or more impurities to continuously pass through the first chromatography device and the second chromatography device, and wherein the first chromatography device and the second chromatography device are configured to be connected in series. The present disclosure also provides uses of the chromatography system and a method for separating enveloped or membranous bio-particles from one or more impurities.

A system and method for protein detection are provided, configured to non-invasively identify proteins exhibiting specific structural conformations within a biological target. The system comprises a light source operable to irradiate the target at a predetermined pulse cycle, a sound detection device configured to capture acoustic signals generated via the photoacoustic effect, and an information processing unit that analyzes the detected acoustic signals to determine the presence or accumulation of a target protein. This technique facilitates early-stage detection of disease-associated proteins, such as amyloid-β and misfolded α-synuclein fibrils, without requiring complex imaging modalities or invasive biopsy procedures.

In various examples, the subject matter of this disclosure relates to developing and using machine learning models to predict biological activity profiles based on multimodal datasets. An example method of predicting biological activity profiles includes: providing a multimodal dataset comprising a plurality of distinct dataset modalities (including at least one chemical structure dataset) to a foundation model, training a foundation model by applying contrastive learning to the multimodal dataset, creating a learned representation of each dataset modality, training a predictive model based on the learned representation of the chemical structure dataset modality and a classifier function that assigns modulation status for the biological target, and introducing an agent to the predictive model to predict modulation of the biological target.

The present application relates to a kind of multi-target tracking method based on aerial biological motion situation feature auxiliary, belong to radar tracking technical field, this method is constructed by biological three-dimensional space distribution situation matrix according to the three-dimensional space distribution of biological in radar scanning period;Then the shift of the inner product maximum of cyclic convolution calculation matrix is calculated using the three-dimensional situation matrix of two successive scanning periods of radar, biological motion velocity estimation is obtained according to the shift;Finally, the estimated motion situation information and track head position information are combined to predict target state, and the correlation gate is established to assist the correlation tracking of biological target using the predicted position information.In the embodiment, compared with traditional tracking algorithm using simulation experimental data, the effectiveness of the proposed method is verified.

The application discloses a sound source positioning and identification method and device based on multi-modal fusion and a storage medium, relates to the technical field of sound source positioning, and discloses a sound source positioning and identification method based on multi-modal fusion, which comprises the following steps: detecting a biological target with lip movement characteristics and / or vocal cord vibration characteristics in a scene through a millimeter wave radar, and acquiring the spatial coordinates of the biological target; acquiring the sound source direction angle of a sound source through a microphone array, and generating a corresponding sound source direction vector based on the sound source direction angle; judging whether the biological target is an effective sound production target based on the spatial relationship between the spatial coordinates and the sound source direction vector; and if the biological target is the effective sound production target, determining the identity information of the effective sound production target. The application effectively fuses multi-modal data such as radars and audio, significantly improves the accuracy of sound source positioning and identification, and realizes the technical effects of stable and reliable speaker positioning and identity recognition in a complex environment.

A method for establishing and predicting a chemical potential biological target point model, the method for establishing comprising: obtaining a protein interaction network and transcriptome data containing differential genes under the influence of a chemical; diffusing the differential scores of the differential genes to adjacent gene nodes along the protein-protein interaction network to obtain diffused differential gene nodes; calculating the average shortest distance between each gene node and the diffused differential gene nodes; sorting the gene nodes in the protein interaction network based on the average shortest distance to obtain a target protein prediction list; adjusting the diffusion parameters until the target protein prediction list meets a first prediction accuracy condition; and selecting a cutoff value of the target protein prediction list that meets the first prediction accuracy condition so that the target protein prediction list before the cutoff value meets a second prediction accuracy condition. The prediction model can quickly and accurately predict potential biological target points.

A computer-implemented method may collect a set of known drug compounds from a database and create an enhanced dataset using labeled and unlabeled chemical and biological data. The computer-implemented method may train a neural network using the extended training set to generate a predicted score of the biological activity of the candidate drug compound to the biological target, where the contribution of a second plurality of unlabeled samples in training the neural network is scaled by a parameter. The computer-implemented method may output one or more fine tuning neural network models capable of generating candidate drug compounds with predicted activity. A computer-implemented method may generate a candidate drug compound by inputting the candidate drug compound having chemical data into a trained neural network model.

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 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, in particular, microfluidic device based instrumentation; and a decoding scheme providing a readout that is digital in nature.

The present disclosure relates to a method for isolating a biological target material from a liquid sample in a multiwell plate using magnetic particles, wherein high efficiency and low elution volumes are achieved by specific movements of the multiwell plate and a magnetic separation plate in relation to each other. Also disclosed is a pre-analytical system suitable for carrying out the method.

The embodiment of the invention provides an infrared biological detection radar module, a detection method and device thereof, equipment and a medium. The infrared biological detection radar module comprises a housing, which is provided with an infrared perspective window which is installed in an inclined manner; the static sensing assembly comprises an infrared emission array and a receiving array, a collimated infrared light beam emitted by the infrared emission array is deflected by the polyhedral rotary reflection assembly and then penetrates out of the infrared anti-reflection window to form a scanning light beam, and an echo signal is reflected by the same reflection surface and then returns to the receiving array; the polyhedral rotary reflection assembly enables emergent light beams to generate layered jumping in the vertical direction in the horizontal rotation process, and a multi-layer stacked three-dimensional scanning field is formed; the driving motor is used for driving the polyhedral rotary reflection assembly to rotate; and the zero position sensor is used for providing a rotating reference datum position. Based on this, the embodiment of the invention can realize three-dimensional space detection under the condition of compact volume, does not need to configure a conductive slip ring, and can accurately identify a tiny biological target.

The invention discloses an asynchronous fusion method and system for deep-sea organism space-time trajectory analysis, and the method comprises the steps: concurrently extracting a biological target from a single video data stream collected by underwater equipment through employing a visual detection module, and extracting space-time metadata superposed in a video image through employing a video text recognition module; and then fusing the visual feature information and the space-time metadata by adopting an asynchronous data fusion module based on an asynchronous extended Kalman filtering algorithm of confidence perception to generate a unified space-time trajectory point with geographic registration. The technical problem that in the prior art, it is difficult to automatically integrate biological visual information and precise spatio-temporal information from a single video source is solved, and the robustness, accuracy and application range of an automatic ecological monitoring and analysis system are remarkably improved through an innovative asynchronous data fusion algorithm and diversified visual output.

Disclosed are biosensor chip systems, kits, and methods usable for identifying, quantifying, and / or monitoring at least one target nucleic acid sequence of at least one microbial target in at least one sample. The systems employ nucleic acid-based affinity moieties coupled to electrodes to generate an electrical signal indicative of target presence and / or amount. In some embodiments, multiplex configurations enable simultaneous detection of multiple microbial targets in a single assay for rapid screening and longitudinal monitoring across clinical, environmental, and food-related matrices.