64 results about "Disease occurrence" patented technology

The invention relates to the field of agricultural management, and particularly discloses a smart agriculture integrated management method and system based on the Internet of Things, and the method comprises the steps: carrying out the coding of multi-source time sequence environment data, such as weather and soil, through a recurrent neural network, and extracting an environment vector representing the potential risk of disease occurrence; meanwhile, a current visual feature map of the crop is extracted from the unmanned aerial vehicle remote sensing image by using a convolutional neural network. Furthermore, topological correlation analysis and saliency re-calibration are carried out on local semantics in the visual feature map through a graph neural network and an attention mechanism by taking an environment vector as guidance, so that a deeply coupled crop vision-environment state saliency fusion vector is generated. The fusion vector not only contains apparent diseases of crops, but also contains environmental causes causing the diseases. And finally, joint prediction of disease probability and severity level is carried out based on the fusion vector, so that closed-loop management from data acquisition to accurate and graded early warning is realized.

This invention discloses an image recognition-based method for assessing grape powdery mildew. By introducing standardized sample preparation and uniform inoculation methods, this invention reduces the interference of randomness and spatial heterogeneity of natural field infection on the assessment results, making the disease occurrence process more controllable among different materials, thereby improving the stability and comparability of phenotypic data. Simultaneously, image recognition and machine learning technologies are used to automatically identify and calculate the area of ​​powdery mildew-infected regions, avoiding the reliance on the experience of assessors in traditional manual visual grading. This enables a continuous quantitative description of the degree of powdery mildew infection, improving the consistency and accuracy of the assessment results. This invention constructs a large-scale image analysis workflow that can be used for batch processing, significantly improving the efficiency of disease assessment, reducing the workload of manual investigation, and making this method applicable to applications requiring the processing of large numbers of samples, such as germplasm resource evaluation and breeding material screening.

The application discloses a solanaceae family seed automatic disinfection working method based on dynamic adjustment of humidity and heat, relates to the technical field of seed automatic disinfection, and is used for solving the problem that precise regulation and control based on physiological state is difficult to realize, and the probability of disease occurrence is increased. The seed coat state and conductivity of solanaceae family seeds after disinfection are detected, the proportion of a highlighted area is calculated, and the ethanol concentration is collected. The humidity response characteristics are analyzed through the proportion of the highlighted area and the ethanol concentration. The number of cracked seeds is counted, and the temperature response characteristics are analyzed in combination with the conductivity. Whether a ventilation processing mechanism is started is comprehensively judged. After ventilation, a regulation and control time is set. The respiration intensity and the tray water film thickness are monitored in real time. The humidity and heat states are classified according to the respiration change trend and the water film thickness. Cooling or drying channels are selected according to the classification results. The dynamic monitoring and regulation and control of the humidity and heat states after disinfection are realized. The ventilation channels are automatically selected through multi-dimensional sensing. The disinfection environment is optimized. The seed quality and storage stability are effectively improved.

According to the invention, atrial fibrillation and hypertension are combined, and a novel HFpEF double-strike animal model is developed. Specifically, the animal model disclosed by the invention can stably develop to a mild structure and functional disease stage within three weeks of double strike, reaches an end stage within two subsequent weeks and gradually dies, and the animal model is highly overlapped with the pathophysiological phenotype of the existing human HFpEF. The animal model stably and rapidly develops in a staged manner, is easy to implement, and provides a valuable tool for molecular mechanism research and drug development of disease occurrence and development.

This invention provides an early warning system for critical care nursing in internal medicine based on multi-parameter monitoring, belonging to the field of early warning system technology. It includes: a vital sign monitoring module for real-time collection of the patient's vital sign factors, taking the average value of the amplitude of each real-time vital sign factor as a benchmark reference value; a similarity early warning analysis module for establishing a mathematical model of the patient's self-monitoring, calculating the early warning similarity index corresponding to each real-time vital sign factor; calculating a reliability index by comparing the total short-term change value with the overall average change value of each real-time vital sign factor; and an early warning decision module for establishing two mechanisms to determine early warnings. This invention extracts various parameters from the patient's past multiple disease occurrence points and calculates the average value, and collects the median value of the disease occurrence point with the largest interval as a reference benchmark value, forming a unique benchmark reference value; thus, the abnormality judgment is entirely based on individual physical differences and historical response patterns, solving the problem of misjudgment due to uniform standards caused by physical differences.

The application discloses an application of a prostate hyperplasia related marker in preparation of a product for detecting prostate hyperplasia, and the prostate hyperplasia related marker is at least one selected from MINP1, ACTBL and APOC4 proteins. In the technical scheme, MINP1, ACTBL and APOC4 are combined as a detection marker for prostate hyperplasia (BPH), so that the complementarity of the three in different stages of diseases can be fully exerted, and the comprehensive performance of the detection product is obviously improved. MINP1 and ACTBL participate in disease progression by regulating prostate cell proliferation and apoptosis, and APOC4 can present significant expression changes in blood cells of early BPH patients, so that BPH disease screening can be accurately performed. When the three are applied in cooperation, early screening of BPH can be realized, and the activity and severity of the lesion can be evaluated, so that a detection system covering the whole process of disease occurrence and development is formed, the limitation of a single marker in the diagnosis dimension is effectively made up, and the comprehensiveness and accuracy of BPH detection are greatly improved.

The invention discloses a specific PCR (Polymerase Chain Reaction) detection primer IKF5 / IKR4 of Karst colletotrichum gloeosporioides and application of the specific PCR detection primer IKF5 / IKR4, the specific detection primers IKF5: 5 '-GTAGGCCGTCCCCTGAAAAG-3' and IKR4: 5 '-CCAAGCAGCTTGAGGGTT-3' on the basis of ITS (International Transcription Sequence) sequences, and the sensitivity of the specific detection primers IKF5: 5 '-GTAGGCCGTCCCTGAAAAG-3' and IKR4: 5 '-CCAAGCAGCTTGAGGGTT-3' is 100 pg. Mu L <-1 >. Research results show that the pair of primers can efficiently and specifically detect the Karst colletotrichum in the mango leaves, and a reliable technical tool is provided for early diagnosis of mango anthracnose, analysis of a disease occurrence rule and construction of a precise prevention and control strategy.

Provided, according to one embodiment of the present disclosure, are an artificial intelligence-based disease prediction method performed by a computing device, a program, and a device. The method may comprise: obtaining a biosignal of a subject to be predicted; generating physiological information of the subject on the basis of the obtained biosignal using a pre-trained first neural network model; and generating prediction information regarding disease occurrence in the subject on the basis of the obtained biosignal and the generated physiological information using a pre-trained second neural network model.

The invention relates to the field of material analytical chemistry and biochemistry, and provides a continuous batch pretreatment device and method for proteomics, glycoproteomics and phosphorylated proteomics samples. According to the continuous batch pretreatment device for the proteomics, glycoproteomics and phosphorylated proteomics samples, under the assistance of the filler, the whole process of protein denaturation, aggregation capture, enzymolysis and complete glycopeptide and phosphorylated peptide enrichment in biological samples can be achieved. Compared with a free solution method conventionally used in proteomics, the sample pretreatment method based on the device has the advantages that the sample treatment time can be effectively shortened, and continuous analysis of mass proteome, glycoprotein group and phosphorylated proteome of trace protein samples can be realized; the method is expected to be widely applied to the aspects of disease occurrence mechanisms, drug action mechanisms, clinical detection, biomarker discovery and the like.

The invention discloses a specific PCR (Polymerase Chain Reaction) detection primer TKF10 / TKR10 of Karst colletotrichum gloeosporioides and an application of the specific PCR detection primer TKF10 / Wherein the sensitivity of primers TKF10: 5 '-GCGGGGCTAACTCTGAAT-3' and TKR10: 5 '-CGCCAGAGATGTTTGCCTG-3' based on a TUB2 (beta-tubulin) gene is 1 ng * [mu] L <-1 >, and the sensitivity of the primers TKF10: 5 '-GCGGGGCTAACTCTGAAT-3' and TKR10: 5 '-CGCCAGAGATGTTTGCCTG-3' based on the TUB2 (beta-tubulin) gene is 1 ng * [ Research results show that the pair of primers can efficiently and specifically detect the Karst colletotrichum in the mango leaves, and a reliable technical tool is provided for early diagnosis of mango anthracnose, analysis of a disease occurrence rule and construction of a precise prevention and control strategy.

This invention discloses a method for assessing shrimp health status based on multi-source information fusion, which relates to the field of data processing technology. The method includes the following steps: acquiring and preprocessing shrimp images at different growth stages; generating training images using artificial labels; extracting features such as shrimp outline length, area, and shell contrast; establishing a target recognition and feature extraction model based on the training images; identifying shrimp in the monitored aquaculture environment and extracting their behavioral characteristic parameters after training; calculating the behavioral assessment influence coefficient and growth assessment influence coefficient; and obtaining water source characteristic parameters to calculate the disease incidence influence coefficient; finally, generating a comprehensive health status assessment index, comparing it with a health judgment threshold, and outputting the health status judgment result. This achieves effective assessment of shrimp health status, real-time monitoring, and early warning, helping farmers take timely measures to prevent disease occurrence and improve shrimp survival rate and economic benefits.

The invention relates to the technical field of agricultural meteorological disaster monitoring and early warning, and discloses a potato late blight ensemble forecasting method and system based on weather forecast. According to the method, firstly, the error characteristics of historical weather forecast in a farmland microclimate station are analyzed to obtain localized error statistical characteristic information, on this basis, a random disturbance method of error variance following is adopted to perform scientific disturbance on real-time forecast to obtain multi-member ensemble forecast, the multi-member ensemble forecast is input into a preset potato late blight model, and a potato late blight model is obtained. According to the method, multiple groups of disease occurrence and development forecast results are obtained, probability forecast and deterministic products containing multiple percentile forecast are generated through release, and according to a preset optimal decision rule, an optimal set percentile forecast is selected from a series of deterministic products and is popularized and applied. According to the method, the problems that a traditional early warning method is insufficient in advance, single in determinacy prediction error conduction and lack of risk quantification capability are solved, and the accuracy and perspectiveness of disease prediction are remarkably improved.

This invention discloses a method for regulating early maturity of citrus fruits based on environmental data processing under plastic film mulching, specifically relating to the fields of agricultural data processing and technology extension services. It addresses the problem that existing early maturity regulation models, due to their singular optimization objective, neglect the microecological changes caused by plastic film mulching, leading to increased pest and disease risks. By acquiring soil temperature and humidity data after plastic film mulching, and based on a pre-established correlation model, the method determines the pathogen inhibition function imbalance index and carbon and nitrogen cycle disturbance intensity, characterizing soil microecological health, and assesses the risk level of pest and disease occurrence. Combined with sensitivity analysis results of the original agricultural operation plan, key agricultural operation parameters are adjusted in a targeted manner. The generated comprehensive regulation plan is serialized into a distributable and executable agricultural technology extension service package and promoted for implementation. This achieves end-to-end data processing from environmental data to microecological risk assessment to customized agronomic plan generation and services, improving the accuracy of agricultural technology extension.

The application belongs to the technical field of biomedical diagnosis, and provides application of neutrophil extracellular traps in preparation of a lymphangitic inflammation diagnosis kit, wherein the application first discloses high expression of neutrophil extracellular traps in lymphangitic inflammation lesion tissues, and confirms that the expression level is closely related to disease occurrence and development, so that the neutrophil extracellular traps are used as an early diagnosis index to fill the technical blank of lack of early non-invasive screening means for lymphangitic inflammation in the prior art, precise diagnosis and disease monitoring of subclinical stage occult lymphangitic inflammation can be realized, misdiagnosis and missed diagnosis can be effectively avoided, reliable technical support and theoretical basis are provided for early prevention and timely intervention of serious complications such as secondary lymphedema, and thus the incidence of complications is significantly reduced and the prognosis of patients is improved.

The invention provides a disease and pest identification, prevention and early warning system and method, and relates to the technical field of agriculture, and the method comprises the steps: S1, collecting multi-dimensional data of a crop growth environment, the multi-dimensional data including but not limited to soil humidity, temperature, illumination intensity, air humidity and gas concentration, and S2, processing the acquired image data by using a deep learning algorithm, extracting pest and disease damage characteristics of the crops, and carrying out preliminary identification on the pest and disease damage characteristics. According to the disease and pest identification, prevention and early warning system and method, multi-mode sensor data and a deep learning algorithm are combined, and high-precision disease and pest identification and risk assessment can be achieved. By collecting multi-dimensional data and high-definition image data of a crop growth environment, the system can comprehensively monitor growth conditions of crops and signs of diseases and insect pests in real time.

The invention relates to a method for monitoring the richness and morbidity of litchi pocking spot disease infection sources, and provides a simple and accurate litchi pocking spot disease prediction method for monitoring and early warning of litchi pocking spot disease latent infection sources in litchi producing areas of different areas. According to the method, the separation frequency of pathogenic bacteria of the asymptomatic fruit pocking spot disease and the severity of the field pocking spot disease are collected and detected regularly (one week) in the fruiting period, and the method can accurately monitor the richness of latent infection sources of the pocking spot disease. On the basis, the occurrence condition of the litchi pocking spot disease in the region in the current year can be predicted by combining weather forecast (rainfall days) of the local litchi fruit bearing period.

The application discloses a banana fusarium wilt secreted protein FoXYG1 and application thereof, and belongs to the technical field of molecular plant pathology. The secreted protein FoXYG1 can induce plant defense response, including causing plant cell hypersensitive necrosis, inducing callose accumulation and up-regulated expression of a plant immune-related pathway marker gene, activating SA, JA, ET and HR pathway-mediated defense pathways to induce plant immune response, and can significantly improve the resistance of plants to fusarium oxysporum and / or fusarium oxysporum. A new way is provided for improving plant disease resistance and reducing disease occurrence, and thus has a wide application prospect in the green development of modern agriculture.

The invention discloses a leech breeding device which comprises a water inlet device, a barrel body, an anti-escape device, a vortex cleaning device, a drainage device, a sensor device, an isolation inhabitation device, a feeding frame and a temperature control device, the anti-escape device is arranged at the top end of the barrel body, and the drainage device is arranged at the bottom end of the barrel body; the sensor device and the temperature control device are connected with the barrel body; a detachable isolation inhabitation net is arranged between the barrel body and the drainage device, and a vortex cleaning device is arranged between the isolation inhabitation device and the drainage device; the feeding frame is arranged to be movable; the temperature controller is set to be capable of automatically monitoring, regulating and controlling the temperature in the barrel body; and the water inlet device is used for conveying water into the barrel body. According to the leech breeding device and the leech breeding method, the leech breeding density can be increased to 4-6 jin per square meter, the breeding survival rate can reach 80% or above, the disease incidence rate can reach 10% or below, the accumulation fermentation effect of pollutants can be reduced, and pollution caused by breeding can be effectively reduced.

An information processing apparatus according to an embodiment includes a hardware processor connected to a memory. The hardware processor estimates a first disease incidence probability of a disease based on first information about expression levels of one or more types of biomarkers of a specimen. The hardware processor estimates a second disease incidence probability of a disease type based on the first information and the first disease incidence probability.

The invention provides a traditional Chinese medicine disease prevention risk prediction method, device and system and a storage medium, and relates to the technical field of biomedical engineering. The traditional Chinese medicine disease-free risk prediction method comprises the following steps: performing enhanced sampling molecular dynamics simulation on a target protein related to a disease to obtain a simulation track; performing clustering analysis on the basis of the simulated trajectory to divide metastable states representing different conformations; calculating a conformation entropy change rate based on the probability distribution and the transition time of the metastable state; and outputting a prediction result according to the conformation entropy change rate. According to the method, a key dynamic conformation change of protein is simulated and captured by enhancing sampling molecular dynamics, and a conformation entropy change rate is introduced as a quantitative index, so that a critical point of disease occurrence is accurately identified on a molecular level. The timeliness and accuracy of'disease prevention 'risk prediction can be remarkably improved, an early warning window is greatly advanced to 18-24 months, and a reliable basis is provided for implementing early and accurate intervention measures.

The invention provides a large yellow croaker full-period factory-like efficient cultivation method. The method comprises six aspects of cultivation density and grading feeding, water quality regulation and control, cultivation management and control, feeding management, disease prevention and control and daily management. According to the method, key links such as graded feeding, water quality regulation and control, feeding management and disease prevention and control are integrated, a replicable and generalizable factory efficient culture mode is formed, technical transformation of large yellow croakers from traditional sea area net cage culture to land-based factory culture is achieved, and the awkward situation that the current culture space is seriously insufficient can be effectively relieved. Through precise control of environmental factors and disease prevention and control management, the culture survival rate can be remarkably improved, the disease occurrence rate is remarkably reduced, and the overall culture benefit is superior to that of conventional offshore net cage culture.

The invention discloses a livestock and poultry disease root cause diagnosis and intervention recommendation method, relates to the technical field of livestock and poultry disease diagnosis, and solves the problem that root cause diagnosis is difficult to realize in the prior art. According to the embodiment of the invention, the disease occurrence of the individual livestock and poultry is regarded as the result of the multi-agent dynamic game, and the root cause back-stepping is carried out by utilizing the inverse reinforcement learning, so that the root cause diagnosis result and the intervention recommendation suggestion are obtained, decision support for direct action is provided, and the management intelligence degree is greatly improved. Meanwhile, the game reasoning model comprehensively considers the effects of environment, group behaviors and biological pathogens, avoids the one-sidedness of single factor diagnosis, and is more scientific and reliable in conclusion.

The invention provides application of bacillus licheniformis and a compound microbial agent in prevention and control of peanut fruit rot, and belongs to the technical field of microbial agents. The strain and the compound microbial agent provided by the invention have a remarkable and stable prevention and control effect on peanut fruit rot, can effectively reduce the occurrence of diseases, reduce the loss of pods, and remarkably improve the yield and quality of peanuts; the microbial agent can still play a significant prevention and control effect when applied at the initial stage of peanut fruit rot, breaks through the limitation that prevention focuses on treatment in the prior art, and provides a brand-new green prevention and control scheme for safe production of peanuts. The application is beneficial to reduction of chemical pesticide application, protection of farmland ecological environment and promotion of soil health and agricultural sustainable development, meanwhile, the method is easy and convenient to operate and wide in applicability, especially has application value on continuous cropping land parcels, and has remarkable economic, ecological and social benefits.

A method for predicting disease incidence, progression, aggressiveness, and prognosis of brain tumors including gliomas and astrocytomas using palladin as a marker. Palladin expression is also disclosed as a therapeutic target for the treatment of brain tumors, in addition to a method of treating brain tumors by regulating and / or modifying and / or deleting palladin expression. In embodiments, palladin is targeted by therapeutic agents that are systemically or locally administered. Targeted glioma tumors include astrocytomas, oligodendrogliomas, and glioblastomas. The disclosure provides that palladin expression can be linked to adult astrocytoma progression and is associated with a worsening prognosis.

The present disclosure relates to a device and method for predicting the risk of disease occurrence by utilizing single nucleotide polymorphisms and the presence or absence of monogenic variants in a subject. According to the device and method according to an aspect, prediction of the risk of disease occurrence is enabled based on a more accurate genetic risk by integrating together monogenic variants, which are based on the subject's genetic information and have a clear causal relationship but appear rarely, and the polygenic risk score, which is based on commonly occurring single nucleotide polymorphisms that, in comparison, do not have high individual association.

Various embodiments provide methods, apparatus, systems, computing entities, and / or the like, providing a temporal disease risk profile describing a likelihood of disease onset over time for an individual in a dynamically interpretable manner. Interpretability of the temporal disease risk profile is enabled by providing additional and contextual information, such as weight distributions of various health indicators, factors, and features. In an embodiment, an example method comprises generating a temporal disease risk profile comprising risk score nodes based at least in part on providing a plurality of record data objects to a risk scoring machine learning model configured to generate a risk score; providing the temporal disease risk profile for display via a first user interface comprising a plurality of interactable node mechanisms each corresponding to a risk score node; and providing a node-specific weight distribution comprising one or more sub-nodal weight values for display via a second user interface.

The invention relates to an early disease detection device based on high-depth cell-free nucleic acid sequencing, and the device can comprise a library preparation part which is used for preparing a library set comprising a whole genome sequencing library, a whole epigenome sequencing library, a transcriptome sequencing library and an epitranscriptome sequencing library based on cell-free nucleic acid extracted from blood; the sequencing part is used for sequencing the library set to generate high-depth cell-free nucleic acid sequencing data; the signal calculation part is used for mapping the high-depth cell-free nucleic acid sequencing data to a genome reference sequence so as to determine the position of the high-depth cell-free nucleic acid sequencing data on a genome, and calculating at least one disease-specific signal value based on the mapped high-depth cell-free nucleic acid sequencing data; a prediction signal value generation section for processing the at least one disease-specific signal value to generate a disease prediction signal value; and a prediction model generation unit for generating a disease prediction model using the marker information relating to the blood and the disease prediction signal value to detect the occurrence or absence of a disease in the input blood and the type of the disease.

The application belongs to the technical field of biomedical diagnosis, and provides application of neutrophil extracellular traps in preparation of a lymphangitic inflammation diagnosis kit, wherein the application first discloses high expression of neutrophil extracellular traps in lymphangitic inflammation lesion tissues, and confirms that the expression level is closely related to disease occurrence and development, so that the neutrophil extracellular traps are used as an early diagnosis index to fill the technical blank of lack of early non-invasive screening means for lymphangitic inflammation in the prior art, precise diagnosis and disease monitoring of subclinical stage occult lymphangitic inflammation can be realized, misdiagnosis and missed diagnosis can be effectively avoided, reliable technical support and theoretical basis are provided for early prevention and timely intervention of serious complications such as secondary lymphedema, and thus the incidence of complications is significantly reduced and the prognosis of patients is improved.

The invention provides a method for predicting the risk of drug-induced depression based on multi-dimensional characterization. According to the method, a disease cluster of depression is established by using a Louvain algorithm, and a relative average shortest distance between the disease cluster and a drug target in a biological network is calculated by using a network proximity algorithm so as to characterize disease occurrence and development characteristics; meanwhile, identifying a structure alarm of depression and calculating Mordred descriptors of drug molecules to obtain molecular structure characteristics; and based on the obtained multi-dimensional features, performing modeling prediction by using random forest (RF), extreme gradient boosting (XGB), a support vector machine (SVM) and naive Bayes (NB). Compared with the traditional method, the invention establishes a brand new method for evaluating the potential depression risk of the drug from the molecular structure of the drug and the pathogenesis of the disease to characterize the drug characteristics.

The application discloses a detection method for unknown pathogens and application thereof, and the method does not need to separate the unknown pathogens, only needs to perform metagenome sequencing on diseased animal lesion tissues, and then the primer and the probe for the unknown pathogens can be obtained through the method, efficient and convenient detection of the unknown pathogens is realized, and further, according to the detection result, the disease occurrence of a breeding farm and epidemiological investigation and prevention and control can be mastered, the spread of the unknown pathogens can be prevented and controlled in a short time, and the harm of the pathogens to the breeding farm is reduced.