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The follicle-stimulating hormone (FSH), which is synthesized and secreted by the anterior pituitary gland, plays an important role in regulating reproductive processes. In this study, using the TargetScan program, we predicted that microRNAs (miRNAs) regulate FSH secretion. Dual-luciferase reporter assays were performed and identified miR-7a-5p. MiR-7a-5p has been reported to regulate diverse cellular functions. However, it is unclear whether miR-7a-5p binds to mRNAs and regulates reproductive functions. Therefore, we constructed a suspension of rat anterior pituitary cells and cultured them under adaptive conditions, transfected miR-7a-5p mimics or inhibitor into the cell suspension and detected expression of the FSHb gene. The results demonstrated that miR-7a-5p downregulated FSHb expression levels, while treatment with miR-7a-5p inhibitors upregulated FSHb expression levels relative to those of negative control groups, as shown by quantitative PCR analysis. The results were confirmed with a subsequent experiment showing that FSH secretion was reduced after treatment with mimics and increased in the inhibitor groups, as shown by enzyme-linked immunosorbent assay. Our results indicated that miR-7a-5p downregulates FSHb expression levels, resulting in decreased FSH synthesis and secretion, which demonstrates the important role of miRNAs in the regulation of FSH and animal reproduction.

This paper introduces concepts and algorithms for feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms by a categorizing framework based on search strategies, evaluation criteria, and data mining tasks and provides guidelines in selecting feature selection algorithms. Search strategies include complete ones, sequential ones and random ones. Evaluation criteria includes filter, wrapper and hybrid. Data mining tasks include classification and clustering. Then, a feature selecting platform is proposed as an intermediate step based on the data and requirement of the task. According to the platform and categorizing framework, some appropriate algorithms are compared. At last, an experiment based on data oilsk81, oilsk83, oilsk85 wells of Jianghan oil fields in China was operated by using one of the appropriate algorithms. This algorithm utilizes fusion of soft computing methods to distinguish the key features of reservoir oil-bearing formation and establishes a model with fusion of soft computing methods to forecast these key features. The following part is the process: Firstly, use genetic algorithm (GA) and fuzzy c-means algorithm (GA-FCM) to reduce well log features of oil-bearing formation and to obtain the key features that can describe oil-bearing formation of reservoir. Secondly, fuse genetic algorithm with BP neural network (GA-BP) to construct the fusion model that forecasts these key features. GA-BP searches the inputs and optimal number nodes of hidden layer of BP neural network through GA to choose the optimal structure of BP neural network forecasting model. Then test effectiveness of the forecasting model with recognition accuracy of testing samples. Finally, the optimal model for forecasting key features can be obtained.

Firstly, the paper establishes a mathematical model for single depot and heterogeneous fleet vehicle routing problem (SHVRP) according to the actual situation of Zhengzhou Coal Electricity Material Supply and Marketing Limited Company in China, then based on the model, uses improved genetic algorithm(IGA) to optimize the vehicle routing problem (VRP) of Zhengzhou Coal Electricity Material Supply and Marketing Limited Company, finally by comparing the performance of IGA with classical heuristics algorithm (CHA) and sweeping algorithm(SA) in transportation cost, the number of used vehicle and computing time, the results show that CHA obtains the best objective function value, SA takes the second place, and CHA is the poorest; however, from the number of used vehicles, the optimum solution of CHA uses the least vehicles, followed by SA and IGA; but CHA is most efficient on computing time, the time needed for calculation is only two fifth of that of SA, two twenty five of that of IGA.

The paper proposed a method of data driven gray relational analysis for recognizing oil-bearing characteristics in reservoir. The method follows the objective process from data to information and from information to recognition. Firstly, reduce attributes based on training data and obtain the key attributes for recognizing oil-bearing characteristics (oil layer, inferior oil layer, dry layer and water layer) by fusion of genetic algorithm and fuzzy c-means. Secondly, take the center of clusters (different oil-bearing formation characteristics) of training data as the reference sequence of recognizing oil-bearing characteristics in reservoir. Thirdly, obtain the weight of each key attribute through relief algorithm. At last, the testing data was estimated by data driven gray relational analysis. The paper takes oilsk81 well data in Jianghan oilfield of China as training data and takes oilsk83 well data as testing data, the estimated results are the same as the real oil-bearing characteristics of each layer in oilsk83 well.