55 results about "Early identifications" patented technology

Methods, compositions and kits for determining the developmental potential of one or more embryos are provided. These methods, compositions and kits find use in identifying embryos in vitro that are most useful in treating infertility in humans.

The present invention relates to a method and a kit for the identification of animals having greater potential for desirable characteristics of meat quality, rib eye area (REA), weaning weight and 18-month weight by means of the analysis of specific markers.The invention also refers to a method and a kit for the early identification of fat deposition in bovines.

The invention discloses a single-station landslide deformation monitoring and early warning method based on a GNSS environment model. The method comprises the following steps of deploying terminal equipment and performing data acquisition, determining an initial position of a monitoring point, calculating environment modeling source data, performing weekday environment modeling, solving a correlation coefficient and performing early warning application. According to the monitoring and early warning method of the invention, a low-cost consumption-level GNSS positioning terminal is adopted to perform data acquisition and environmental modeling; the landslide acceleration state is subjected to early identification and effective early warning through the change trend of the correlation coefficient of the environment model, so that the monitoring cost is greatly reduced, and a new thought and a new method are provided for GNSS landslide monitoring and early warning.

The invention discloses a depression patient identification system and an identification method thereof. The method comprises the following steps: collecting and arranging data, and constructing an original data set; performing data preprocessing on the original data set to obtain an input data set; dividing the input data set into a training data set and a test data set according to a certain ratio; establishing a deep learning model for identifying depression patients and healthy people, and training the established deep learning model by utilizing the training data set; and performing performance evaluation on the trained deep learning model by using the test set, and continuously optimizing the model in a verification evaluation process to obtain an optimal model. According to the invention, early identification of depression mental disorder can be realized efficiently and conveniently, and depression treatment accuracy is improved.

The invention discloses a method for constructing an evaluation model for the biological age of a Chinese population based on clinical markers. The method comprises the following steps: (1) selectingclinical routine markers representing different pathophysiological systems or functions; (2) constructing a biological age evaluation model by utilizing the selected markers based on a KDM algorithm;(3) evaluating the obtained biological age evaluation model, and if the effect of the model is poor, returning to the steps (1) and (2) and carrying out the next round of model construction operation,or if the effect meets requirements, outputting the constructed biological age evaluation model. The method can be applied to early identification and prevention of aging of people in China and intervention effect evaluation of senile diseases, and has practicability and huge application potential.

The present invention provides methods for detecting donor cell-free DNA in the circulation of an organ transplant recipient for the early identification of transplant rejection.

Elasticity variation within biological tissue is often correlated with its pathology. Variation of elasticity can be assessed by specific ultrasound acquisition sequences during which pressure is applied. The tissue motion and deformation during those sequences is correlated to the tissue stiffness. The present invention describes a hybrid method that combines a real-time monitoring mode during acquisition and a non-real-time fine analysis after acquisition. This method allows early identification and fair assessment of pathology from elastographic analysis to obtain the best possible result for elastography assessment.

A biomarker for early diagnosis of diabetes and its application created by the present invention aim to explore the dynamic changes of proinsulin during sugar load and its significance in early identification of pancreatic islet beta cell function decline, that is, to provide a simpler, Effectively indicate the clinical indicators of early reduction of β-cell function, and provide early basis for preventing the occurrence of diabetes.

The invention discloses an early identification method of a glacier lake outburst, comprising the following steps: a. determining the basic glacier data of the outburst glacial lake and surrounding unbursted glacial lakes; b. determining the prerequisites for a glacier lake outburst. The ratio of the vertical height H from the center of mass of the avalanche to the ice lake and the straight-line distance L from the center of mass of the potential avalanche to the ice lake is greater than or equal to 0.125; 1 Greater than the critical water level H of glacial lake outburst 2 ; c. Calculation of glacier comprehensive discriminant factor S for glacial lake outburst; d. When S > 0.53, the risk is high; when 0.38 < S ≤ 0.53, the risk is medium; when S ≤ 0.38, the risk is low. The present invention conducts internal mechanism research on the occurrence degree of glacial lake outbursts through terrain factors, establishes a calculation model for glacial lake outbursts, comprehensively considers the terrain influencing factors of glacial lake outbursts, and accurately divides glacial lake outburst risk areas in a quantitative manner. Strong.

The invention discloses a small-sample named entity recognition method based on data enhancement and active learning. The purpose is to improve the F1 value of the early recognition of unlabeled data by the active learning method, and is effective for various query strategies. The technical solution is to first construct an active named entity recognition system combined with data enhancement; prepare the data set required for training the named entity recognition model. The active learning module, the data labeling module, and the data enhancement module train the named entity recognition model in the named entity recognition module in a multi-round cycle and label and enhance the data. The trained named entity recognition module performs named entity recognition on the text in the test data pool T, and obtains the predicted label sequence. The present invention realizes the effect of rapidly improving named entity recognition when there are few labeled data participating in training in the early stage, so that compared with the original named entity recognition method under active learning, F1 values ​​of various query strategies are all improved.

The invention provides an early identification method for excellent maize varieties. The method comprises the following steps of: S1, obtaining a classification standard and classification indexes according to variety data of excellent maize varieties; S2, obtaining diffusion models of the excellent maize varieties according to the classification standard and the classification indexes; S3, obtaining key classification indexes of the excellent maize varieties on the basis of the diffusion models of the excellent maize varieties; and S4, identifying the excellent maize varieties on the basis ofthe key classification indexes so as to obtain identification results, and classifying the identification results on the basis of the classification standard so as to obtain a classification result.According to the early identification method for excellent maize varieties, the diffusion models and key classification indexes of the excellent maize varieties are explored from different spatial scales, so that the problem that the time and spatial differences in the diffusion processes of different traditional maize varieties cannot be sufficiently considered as the traditional maize varietiesare different in the aspect of suitability.

The invention provides a rice DPS1 gene and encoded protein and application thereof. The sequence of the rice DPS1 gene is shown in the SEQ ID No.2, and the sequence of the encoded protein is shown in the SEQ ID No.1. It is found that the top spikelet of the mutant of the rice DPS1 gene is dry up and necrotic before flowering, and the middle spikelet closely adjacent to the top degenerated spikelet is sterile, which results in a significant decrease in the seed setting rate and the grain number per spike. The wild DPS1 gene is transformed into the rice DPS1 mutant, the degraded phenotype of the mutant spike top can be recovered, the result suggests that the DPS1 gene directly controls degeneration of the rice spike top by regulating programmed cell death, and the rice DPS1 gene can be applied to the directional design of rice spike types and used as an important genetic resource, is helpful to develop molecular markers in molecular breeding practice for conducting early identification and elimination on spike top degradation, and has important guiding significance for improving the rice yield.

The invention discloses a method for early recognition of small watershed geological disaster hidden danger points. The method comprises the following steps: (1) constructing a small watershed geological disaster three-dimensional high-precision digital scene through a composite high-definition space recognition technology and ground fine survey; (2) carrying out small watershed geological disaster space and time distribution rule identification research by means of an artificial intelligence method; (3) combining indoor and outdoor tests and a numerical simulation method to construct a geological disaster starting identification model and a critical discrimination index system; and (4) establishing interpretation identification marks of various potential hidden dangers, and forming an early-stage identification technical method for geological hidden dangers. Through a composite high-definition space recognition technology, an artificial intelligence method, indoor and outdoor tests, disaster starting numerical simulation and the like, geological disaster hidden danger point early recognition research is carried out for a small watershed, the result can provide a theoretical basis for a geological disaster forecasting system, and important parameters are provided for management project planning, design and the like.

The present application discloses a method and system for automatically extracting suspected land disaster hidden danger areas based on InSAR deformation information. The time-series SAR satellite image is used to extract the stable point target in the monitoring area, and the surface deformation information of the stable point target is obtained, and the digital elevation model data is used to obtain the slope information of the stable point target. According to the slope information, set the deformation center threshold and deformation noise threshold, identify outlier targets in the deformation results, set them as noise points and eliminate them. In the deformation result after removing the noise points, the interest point target is extracted according to the slope threshold and the deformation center threshold. Construct a Delaunay triangulation network for the point of interest target. The edge length constraint is imposed on the triangular network to obtain locally adaptive spatial neighborhood. The point target clusters in the spatial neighborhood automatically generate the vector outline of the suspected ground disaster hidden danger area. This scheme realizes the early identification of large-scale potential land disaster hazards, avoids the interference of noise information, and improves the identification efficiency of land disaster hazards in related areas.