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.

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.

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.

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 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 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 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 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.