33 results about "Biological attributes" patented technology

Systems, methods, software and computer-usable media for annotating biomolecule-related data are disclosed. In certain exemplified embodiments, the biomolecules can be nucleic acids and the data can be sequence-related data. In various embodiments, systems can include one or more public or private biological attributes (e.g., annotation information databases, data storage devices and systems, etc.) sources, one or more genomic features data sources (e.g., genomic variant tools, genomic variant databases, genomic variant data storage devices and systems, etc.), a computing device (e.g., workstation, server, personal computer, mobile device, etc.) hosting an annotations module and / or a reporting module, and a client terminal.

A method of generating an anatomical map includes acquiring geometry information and biological information for an anatomical region. The geometry and biological information are associated with each other, for example by associating measured biological attributes with the anatomical locations at which they were measured. A graphical representation of the anatomical region, including a map of at least two biological attributes, can then be superimposed upon a geometric model of the anatomical region. The map can be a blended map and / or can utilize glyphs to represent the displayed biological attributes. The data used to generate the maps can also be stored in a data file.

The invention discloses a real-time microbial particle counter comprising a light path, an air channel intersected with the light path, and a signal processing system connected with the light path, wherein the light path comprises a lighting light path for irradiating tested particles and a collecting light path arranged along the advancing direction of the lighting light path; the collecting light path also comprises a relay system for separating the light path and separately detecting separated light paths; the air channel is used for sampling the tested particles; the signal processing system is used for analyzing and processing a signal, and comprises a fluorescent preamplifier and a scattered light preamplifier. The concentration of microbial particles in air is monitored in real time by taking laser-induced fluorescence detection as the principle, the particle sizes and the biological attributes of the tested particles are judged by detecting the intensities of scattered light and fluorescence emitted by exciting light, and the particles in a sampling airflow are counted, so that the automatic detection on planktonic bacteria microorganisms is achieved, and the real-time microbial particle counter is simple and convenient to operate, strong in detection instantaneity, high in sensitivity and high in accuracy.

A method for monitoring an animal, the method including an electronic processing device (121) for wirelessly receiving at least one signal indicative of first and second biological attributes of the animal, wherein the at least one first biological attribute is at least partially sensed using at least one first sensor (111) worn on a neck region of the animal; and, the at least one second biological attribute is at least partially sensed using at least one second sensor (112) worn on a non-neck region of the animal; and, generating at least one indicator at least partially indicative of the first and second biological attributes.

The invention belongs to the field of bioinformatics technology, and particularly relates to a key-protein recognition method based on fusion of biological and topological features. The method includes: assigning a score, which indicates an importance degree of each protein vertex, to the protein vertex, wherein the scores of all the vertices constitute a vector of n columns; giving initial valuesof the scores, constituting the attribute values of the protein vertices according to biological information and values of topological characteristics, and constituting an attribute matrix; and finally, carrying out descending arrangement according to the scores, and outputting k proteins corresponding to the scores, namely final results. Combination of the topological characteristics of a protein interaction network and the protein biological-attributes facilitates identification of key proteins, and improves efficiency of key-protein recognition.

The invention discloses a biological aerosol real-time detection method and device based on laser induced fluorescence. The primary parts of the device comprise a light path, a gas circuit intersectedwith the light path, and a signal processing system connected to the light path. The light path comprises a laser emitting light path, a scattered light receiving light path and a fluorescence receiving light path; the gas circuit is used for sampling to-be-detected particles; the signal processing system is used for analyzing and processing signals and comprises a scattered light processing system and a fluorescence processing system. Based on the principle of laser induced fluorescence, grain sizes and concentration of microbial particles and non-biological particles in air can be monitoredin real time. The grain size and the biological attribute of the to-be-detected particle are judged through the scattered light and fluorescence signals generated by the single particle under irradiation of laser, and the particles in a sampled air flow are counted, so that the concentration of microorganisms in air is detected automatically. The biological aerosol real-time detection method anddevice are simple to operate, high in real-time monitoring, high in sensitivity and high in accuracy.

The invention is a remote sensed image artificial immunity non-monitoring classifying method, characterized in: (1) opening to-be-classified remote sensed image and inputting algorithm parameters; 2) selecting various initial antibody populations and memory antibodies non-monitoring classified and storing them into various antibody arrays and memory antibody arrays; 3) making artificial immunity system training on image antigens until the training of the whole image is finished; 4) judging whether to meet training stop condition: if not, starting the next iteration from the step 3); otherwise ending the artificial immunity non-monitoring classification and outputting classification result. And the method inherits biological attributes of the bio-immunity system, having self-organizing, self-learning, self- recognizing and self-memorizing abilities and high intelligence, and high executing efficiency in practice, applying to non-monitoring classification of multi-spectrum, and high-spectrum remote sensed images and able to effectively improve the accuracy of remote sensed image non-monitoring classification.

In order to solve the problem of a prior art biological attribute measuring device, i.e., measurement data is apt to vary because of unstable contact due to difficulty in controlling a thickness of a saliva layer and pain caused during the measurement, is provided a biological attribute measuring device including a contact device having a portion, at least part of which is a curved surface, for abutting against a measuring region to apply light to the measuring region, a light detecting means for detecting at least one selected from the group consisting of transmitted light, reflected light, scattered light and transmitted / reflected light from the measuring region and a measuring means for measuring a specific component in the measuring region based on the detection result.

A method for monitoring an animal, the method including an electronic processing device (121) for wirelessly receiving at least one signal indicative of first and second biological attributes of the animal, wherein the at least one first biological attribute is at least partially sensed using at least one first sensor (111) worn on a neck region of the animal; and, the at least one second biological attribute is at least partially sensed using at least one second sensor (112) worn on a non-neck region of the animal; and, generating at least one indicator at least partially indicative of the first and second biological attributes.

The invention discloses a system for capturing Asian carps. The system comprises a tugboat, an acousto-optic generator arranged at the tail of the tugboat, and a floating fishing bed arranged at the rear of the tugboat, wherein sound and light production of the acousto-optic generator is controlled through an acousto-optic controller; the floating fishing bed is connected with the tugboat through a traction mooring rope; a sound barrier device is arranged at the front part of the floating fishing bed and is used for reflecting sound and light generated by the acousto-optic generator; during the proceeding process of the tugboat, the acousto-optic generator arranged at the tail of the tugboat is adopted to simulate sound and light rays of nature having a stimulation effect on different fish varieties of Asian Cyprinidae so as to stimulate the Asian carps in water, so that the Asian carps jump out of the water surface, naturally fall on the floating fishing bed on the condition that the floating fishing bed moves in a cooperative manner, and then are captured. According to the invention, the biological attributes of the fish varieties of Asian Cyprinidae are utilized to manually simulate sound and light illumination so as to induce the fish varieties of Asian Cyprinidae to jump out of the water surface in groups and be captured in batches, so that the ecological treatment effect of restraining biological invasion is realized.

The invention discloses a key protein identification method use principle in a network based on dynamic weighting interaction. A key protein identification method comprises the steps of calculating aprotein activity time point and a protein activity probability, constructing a dynamic PPI network, then calculating a protein interaction weight according to the protein activity probability, and constructing a dynamic weighted PPI network; based on the established dynamic weighted PPI network, according to the topological characteristic and biological attribute of the protein network, calculating an edge clustering coefficient, a gene body similarity and a Pearson correlation coefficient between the interaction protein pairs; afterwards, obtaining an importance score, and performing arrangement according to the score value from largest to lowest, and outputting k proteins which correspond with the scores as a final result. The method according to the invention improves key protein identification efficiency and expands application range and practicability of the technique in a bioinformation field.

The invention provides an object recognition method. The object recognition method comprises the steps of obtaining a biological feature image of a to-be-recognized object; acquiring biological information in the biological characteristic image; wherein the biological information comprises biological characteristics and biological attributes; based on the biological attribute, determining a corresponding target biological characteristic sub-library from a biological characteristic library containing a plurality of biological characteristic sub-libraries; and according to the biological characteristics and a target biological characteristic sub-library, identifying the to-be-identified object to obtain an identification result. According to the method, the human face feature library is split in combination with the biological attributes, and a large library is changed into a small library, so that the feature library comparison performance is improved, and the method has the characteristics of low comparison time consumption and high recognition accuracy.

The invention aims to provide a key protein identification method based on Markov random walk, and belongs to the technical field of biological information. The key protein identification method basedon the Markov random walk comprises the following steps that: using a Markov random walk thought to endow each vertex with a score which expresses the importance degree of the vertex, giving the initial value of the score, and according to a certain probability, enabling the score to realize random walk, and carrying our revision in transfer, wherein the scores of all vertexes form an n-column vector; and finally, according to the descending order of the scores, outputting k pieces of protein corresponding to the scores as a final result. By use of the method, biological attributes and topological characteristics are combined to improve accuracy for identifying key protein, meanwhile, a prediction result is more accurate, and prediction efficiency is improved.

SimRank is used for carrying out protein similarity calculation based on a key protein identification method in an uncertain protein interaction network. A SimRank calculation problem in an uncertainnetwork is converted into SimRank calculation in a deterministic network; and then, considering the topological characteristics of the protein interaction network and the biological characteristics ofthe protein, and calculating an edge clustering coefficient, gene ontology similarity, a Pearson's correlation coefficient and a subcellular localization score to obtain an importance score. And finally, ranking the scores from large to small, and outputting the first k proteins corresponding to the scores as final results. On the basis of an uncertain interaction network, the accuracy of key protein recognition is improved by fusing biological attributes and topological characteristics, meanwhile, the prediction result is more accurate, the prediction efficiency is improved, the applicationrange of the technology in the field of biological information is widened, and the practicability of the technology in the field of biological information is improved.

The invention discloses a method for recognizing biological attributes, which comprises the following steps of: acquiring target images of organisms to be recognized, extracting morphological featuresof organisms to be recognized in the target images, and obtaining similarity between the target images and each sample picture by using a preset recognition model; the identification model being usedto identify the morphological features of the organisms to be identified and the similarity degree is calculated to determine the attributes of the organisms to be identified according to each similarity degree, so as to avoid the identification bias of the organisms to be identified due to the different knowledge reserves and experiences of the quarantine personnel, and thus the accuracy of theidentification of the biological attributes is improved. The invention also discloses a device and a device for recognizing biological attributes, which have the above beneficial effects.

The invention discloses a control method and device for an air conditioner, a storage medium and a processor. The control method comprises the steps that a first control instruction used for indicating the air conditioner to operate is obtained, and the first control instruction carries biological attribute information of a user; the biological attribute information of the user is sent to a serveraccording to the first control instruction; running parameters sent by the server according to the biological attribute information of the user are received, and the biological attribute informationof the user is matched with the running parameters; and the air conditioner is controlled to operate according to the running parameters. The control method and device for the air conditioner, the storage medium and the processor solve the technical problem that the control of the air conditioner is more complicated.

The invention discloses a tongue image matching method, an electronic device, a computer device and a storage medium. The method comprises the steps of obtaining a to-be-detected face image, extracting a to-be-detected tongue image from the to-be-detected face image; and matching a reference tongue image closest to the to-be-detected tongue image from a database, and generating a tongue image output result of the to-be-detected tongue image based on the biological attribute description corresponding to the reference tongue image. According to the tongue image matching method, the electronic device, the computer device and the storage medium, the to-be-detected tongue image can be extracted from the to-be-detected face image, the reference tongue image closest to the to-be-detected tongue image is matched, the tongue image output result of the to-be-detected tongue image is generated based on the biological attribute description corresponding to the reference tongue image, and the tongue image output result can be visually and conveniently given, so that the convenience and accuracy that a user knows his / her physical condition through the tongue image are effectively improved.

The invention provides a method for constructing necessary protein recognition between multi-dimensional biological attribute information and PPI topological characteristics by using a deep neural network, and in order to solve the problem that gene expression data is not comprehensive, absent data is supplemented so as to improve robustness. And the convergence speed of the deep neural network is reduced by respectively constructing a PPI network topology structure, a Pearson's correlation coefficient and a homologous correlation coefficient. And finally, searching an optimal incidence relation of three features of the degree of the node, the Pearson's correlation coefficient and the homologous correlation coefficient through the deep neural network, thereby improving the recognition precision of the essential protein.

The invention provides an object management method. The object management method comprises the steps of obtaining biological attributes of a management object in a management area; determining an environment attribute of an environment where the management object is located; determining a corresponding similarity threshold value according to the biological attribute and the environmental attributeof the management object and a predetermined similarity threshold value set; and based on the similarity between the biological characteristics of the management object and the biological characteristics in the personnel library and a similarity threshold, identifying the management object. According to the invention, a method of setting different thresholds according to age groups in different scenes is adopted; in this way, instability caused by retrieval alarm under a fixed threshold value due to distribution difference of biological characteristics of different scenes and biological characteristics of different age groups is overcome, the defects of a current dynamic deploying and controlling system can be obviously overcome, the problems of false alarm and missing alarm are reduced,the correct alarm number is increased, and the stable retrieval characteristic of the dynamic deploying and controlling system is kept.

A method and apparatus for non-invasively measuring a biological attribute, such as the concentration of an analyte, particularly a blood analyte in tissue such as glucose. The method utilizes spectrographic techniques in conjunction with an improved subject-tailored calibration model. In a calibration phase, calibration model data is modified to reduce or eliminate subject-specific attributes, resulting in a calibration data set modeling within-subject physiological variation, sample location, insertion variations, and instrument variation. In a prediction phase, the prediction process is tailored for each target subject separately using a minimal number of spectral measurements from each subject.

The invention provides an intelligent prompting method and an intelligent prompting system of an ordering machine. The ordering machine is provided with an image acquisition device. A sensor is arranged on a seat and used for acquiring biological attribute information of customers, the normal food intake of the customer is speculated according to the biological attribute information of the customer, ordered total food materials are obtained according to the quantity of ordered food of the customer and the food material quantity of each dish, the ratio of the total food quantity of all customers to the total food material quantity is calculated, and whether the food ordering quantity is enough or not is judged. If the ordering quantity is insufficient, matched food materials or adding foodmaterials are recommended according to the ordered food materials. If the food ordering quantity exceeds the food intake of the customer, the customer is reminded of possible waste, and how to removerepeated dishes is recommended. According to the method, a proper amount of food can be deduced according to customer information. Obvious insufficient food or waste due to excessive food ordering ofcustomers are avoided. The targeted food adding or removing recommendation can be carried out according to the insufficient food and the excessive food ordering. The requirements of customers are effectively met, and food is also saved.

The invention provides a protein complex identification method based on a core-accessory structure. The protein complex identification method based on the core-attachment structure employs the idea of core-attachment, and comprises the steps: constructing a weighted dynamic PPI network, and then finding seed nodes in the PPI network by calculating related attributes of nodes; forming a core of a protein complex through seed nodes and neighbor nodes, then searching a proper attachment for each core, and finally deleting some protein complexes with relatively large overlapping degree to obtain a finally predicted protein complex. The method not only considers the topological characteristics of the protein interaction network, but also considers the biological attributes of the protein, thereby overcoming the negative influence caused by high data noise. By fusing biological attributes and topological characteristics, the accuracy of identifying the protein complex is improved, meanwhile, the prediction result is more accurate, and the prediction efficiency is improved. And the application range and practicability of the technology in the field of biological information are expanded.

The invention provides a data prediction method and device, computing equipment and a storage medium, and the method comprises the steps: obtaining a to-be-tested data set; the data set to be tested comprises at least one type of organic substance and at least one biological attribute; determining a target association relationship between different types of organic substances and different biological attributes; and determining at least one biological attribute of each organic substance based on the determined target association relationship. According to the embodiment of the invention, the target association relationship between different types of organic substances and different biological attributes is automatically determined according to the obtained organic substances and biological attributes in the to-be-tested data set, manual processing is not needed, the time for exploring unknown biological attributes of the organic substances is shortened, and the exploring efficiency is improved.

The invention relates to an artificial intelligence technology, and discloses a product anti-risk capability identification method comprising the following steps: obtaining a to-be-protected product, a to-be-protected product coordinate, and a biological attribute of the to-be-protected product; calculating an initial anti-risk score of the to-be-protected product according to the historical business record and a preset risk calculation strategy; obtaining landform features in the field sampling set by using a geographic feature extraction network to obtain a landform feature vector set; performing biological-environment matching on the landform feature vector set, the quantized biological attribute vector and the quantized coordinate vector by using an adaptability recognition model to obtain an environment adaptation level; and performing weight calculation on the initial anti-risk score by using an environment score corresponding to the environment adaptation level to obtain an anti-risk capability value of the to-be-protected product. The invention further provides a product anti-risk capability identification device, electronic equipment and a storage medium. According to the invention, the accuracy of risk control of the product can be improved.

The invention provides an identification method for existence of fungus in ganoderma lucidum tissue and an assessment method for necessity of fungus for formation of ganoderma lucidum. The identification method comprises the steps of a, measuring the dissolving temperature of a ganoderma lucidum tissue block; b, preparing a ganoderma lucidum tissue dissolving solution; c, filtering the ganoderma lucidum tissue dissolving solution with a disposable needle filter, so as to enrich the fungus that cannot pass through a filter membrane on the filter membrane, and mycoplasma is retained in the filter liquor by passing through the filter membrane; d, taking out the filter membrane in step c and cutting up the filter membrane, then extracting DNA by using a CTAB method; e, selecting respective positive control fungus of five fungi, and processing by the same method as steps b to d; f, identification of the existence of the five fungi: identifying the mycoplasma by taking the filter liquor as an amplification template, and identifying the other four fungi by taking the DNA extracted from the thallus on the filter membrane as an amplification template. By applying the identification method provided by the invention, the existence of the five fungi can be measured, the fungus constitution information in the ganoderma lucidum can be accurately acquired, and a foundation is laid for quicklyand more accurately verifying the biological attributes.

The invention discloses an identity recognition method based on patient biological information. The method includes the following steps that 1, the similarity weight W(Ri, Rj) of to-be-recognized patient data Ri and other any piece of data Rj in a patient identity database is acquired through social attribute feature matching; 2, the similarity S(Ri, Rj) of the to-be-recognized patient data Ri and other any piece of data Rj in the patient identity database is acquired through biological attribute feature matching; the similarity weight W(Ri, Rj) and the similarity S(Ri, Rj) are subjected to weighting calculation, and the comprehensive similarity S(Ri, Rj) of Ri and Rj is obtained; S(Ri, Rj) is compared with a set threshold value lambda, if S(Ri, Rj) is larger than lambda, it is thought that the two pieces of data Ri and Rj belong to the same person, and therefore the patient identity is recognized. The efficiency of patient prime index recognition can be improved, and the success rate of patient prime index recognition can be increased.

The invention provides a data prediction method and device, computing equipment and a storage medium, and the method comprises the steps: obtaining a to-be-tested data set; the data set to be tested comprises at least one type of organic substance and at least one biological attribute; based on the to-be-tested data set, determining first vector information corresponding to each type of organic substances and second vector information corresponding to each biological attribute; and on the basis of the first vector information and the second vector information, matching at least one corresponding biological attribute for each type of organic substance from the at least one biological attribute. According to the embodiment of the invention, the first vector information corresponding to the organic substances and the second vector information corresponding to the biological attributes can be automatically determined, the biological attributes of each organic substance are determined based on the first vector information and the second vector information, manual processing is not needed, the whole process is automatically completed, and the efficiency is improved. The time for exploring unknown biological attributes of organic substances is shortened, and the exploring efficiency is improved.

A method for signing up a person for biometric verification purposes is provided, the method including: acquiring an image of a biological attribute of the person, the biological attribute including a set of characteristic elements, defining, within the acquired image, at least one area that includes at least part of the biological attribute, and storing, in a biometric database, at least one piece of information indicating that the number of characteristic elements included in said defined area is less than a respective predetermined integer. A related biometric verification method is also provided.