Considering Genetic Heterogeneity in the Association Analysis Finds Genes Associated With Nicotine Dependence

While substantial progress has been made in finding genetic variants associated with nicotine dependence (ND), a large proportion of the genetic variants remain undiscovered. The current research focuses have shifted toward uncovering rare variants, gene-gene/gene-environment interactions, and structural variations predisposing to ND, the impact of genetic heterogeneity in ND has been nevertheless paid less attention. The study of genetic heterogeneity in ND not only could enhance the power of detecting genetic variants with heterogeneous effects in the population but also improve our understanding of genetic etiology of ND. As an initial step to understand genetic heterogeneity in ND, we applied a newly developed heterogeneity weighted U (HWU) method to 26 ND-related genes, investigating heterogeneous effects of these 26 genes in ND. We found no strong evidence of genetic heterogeneity in genes such as CHRNA5. However, results from our analysis suggest heterogeneous effects of CHRNA6 and CHRNB3 on nicotine dependence in males and females. Following the gene-based analysis, we further conduct a joint association analysis of two gene clusters, CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6. While both CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6 clusters are significantly associated with ND, there is a much stronger association of CHRNB3-CHRNA6 with ND when considering heterogeneous effects in gender (p-value = 2.11E-07)

Research on Intelligent Decision of Pulmonary Tuberculosis Disease Based on Data Mining

International audienceAiming at the problem that the low diagnostic efficiency and low accuracy of the single data mining method for Diagnosis of pulmonary tuberculosis, In this study, the electronic records of 1203 cases of tuberculosis patients in Changping District City, Beijing City of Beijng and Beijing Institute of tuberculosis control and tuberculosis control were build, Tuberculosis disease diagnosis model is built by application of rough set and decision tree method, On the basis of this, the diagnosis system of pulmonary tuberculosis was constructed. In this study, the combining method of rough set and decision tree was approached to attribute reduction, the model reduced redundant 57 attributes and remained 22 attributes, and articled 7 the decision rules. The model accuracy is 89.46%. Compared with the non reduction method, the decision rule was reduced by 128%, and the accuracy of the model remained unchanged. The research results showed that the algorithm can reduce the time and space complexity of the algorithm while ensuring the accuracy of the model, so as to improve the efficiency of the mining, and provide some references for clinical diagnosis

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Abstract：Semantic not consistency, and knowledge base is difficult to reuse and sharing are the key problems affecting the system development and application. This paper studies how to express the soil fertility level information using of the ontology and generate OWL (Ontology Web Language) document, and how to make use of SWRL (Semantic Web Rule Language) to express inference rules. On this basis, this paper integrates SWRL rules editor and JESS (java expert shell system) rules engine, establishes the reasoning framework based on JESS reasoning engine, and realizes the productivity grade evaluation based on ontology and SWRL

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceSemantic not consistency, and knowledge base is difficult to reuse and sharing are the key problems affecting the system development and application. This paper studies how to express the soil fertility level information using of the ontology and generate OWL (Ontology Web Language) document, and how to make use of SWRL (Semantic Web Rule Language) to express inference rules. On this basis, this paper integrates SWRL rules editor and JESS (java expert shell system) rules engine, establishes the reasoning framework based on JESS reasoning engine, and realizes the productivity grade evaluation based on ontology and SWRL

Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny

Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other elements can significantly affect automated yield estimation and picking in small target apple orchards in natural settings. This study uses the MinneApple public dataset, which is processed to construct a dataset of 829 images with complex weather, including 232 images of fog scenarios and 236 images of rain scenarios, and proposes a lightweight detection algorithm based on the upgraded YOLOv7-tiny. In this study, a backbone network was constructed by adding skip connections to shallow features, using P2BiFPN for multi-scale feature fusion and feature reuse at the neck, and incorporating a lightweight ULSAM attention mechanism to reduce the loss of small target features, focusing on the correct target and discard redundant features, thereby improving detection accuracy. The experimental results demonstrate that the model has an mAP of 80.4% and a loss rate of 0.0316. The mAP is 5.5% higher than the original model, and the model size is reduced by 15.81%, reducing the requirement for equipment; In terms of counts, the MAE and RMSE are 2.737 and 4.220, respectively, which are 5.69% and 8.97% lower than the original model. Because of its improved performance and stronger robustness, this experimental model offers fresh perspectives on hardware deployment and orchard yield estimation.Science, Irving K. Barber Faculty of (Okanagan)Non UBCBiology, Department of (Okanagan)ReviewedFacultyResearche

Research on the Construction and Implementation of Soil Fertility Knowledge Based on Ontology

International audienceSoil fertility is the comprehensive reflection of related factors and the related factors. Soil fertility evaluation knowledge is stored by relational database as usually, and it is difficult to show the correlation and constraints among attributes .In this paper, Nongan county farmland productivity data is as the research object, Using rough set approach to do attribute reduction, using ontology method to establish the soil fertility level knowledge base, using multi Agent technology to implement the prototype system, and complete the reuse and sharing of knowledge, lay the foundation for semantic level reasoning

Research on Construction and SWRL Reasoning of Ontology of Maize Diseases

International audienceIn this paper, according to the characteristics of maize disease knowledge, OWL DL language was used to build maize diseases ontology, and the reasoning rule of maize diseases was defined by using the expressive ability of SWRL rule language.The author introduced several realizable reasoning functions,and achieved the diagnostic reasoning of maize disease knowledge by Jess inference engine.The results indicated that constructing the maize diseases ontology,and introducing SWRL rule into maize disease ontology provided an effective way for the construction of high-intelligent, shareable and reused maize disease knowledge database and diagnostic rule database

Multi-Plant Disease Identification Based on Lightweight ResNet18 Model

Deep-learning-based methods for plant disease recognition pose challenges due to their high number of network parameters, extensive computational requirements, and overall complexity. To address this issue, we propose an improved residual-network-based multi-plant disease recognition method that combines the characteristics of plant diseases. Our approach introduces a lightweight technique called maximum grouping convolution to the ResNet18 model. We made three enhancements to adapt this method to the characteristics of plant diseases and ultimately reduced the convolution kernel requirements, resulting in the final model, Model_Lite. The experimental dataset comprises 20 types of plant diseases, including 13 selected from the publicly available Plant Village dataset and seven self-constructed images of apple leaves with complex backgrounds containing disease symptoms. The experimental results demonstrated that our improved network model, Model_Lite, contains only about 1/344th of the parameters and requires 1/35th of the computational effort compared to the original ResNet18 model, with a marginal decrease in the average accuracy of only 0.34%. Comparing Model_Lite with MobileNet, ShuffleNet, SqueezeNet, and GhostNet, our proposed Model_Lite model achieved a superior average recognition accuracy while maintaining a much smaller number of parameters and computational requirements than the above models. Thus, the Model_Lite model holds significant potential for widespread application in plant disease recognition and can serve as a valuable reference for future research on lightweight network model design