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Non-contact measurement based on the point cloud acquisition technology is able to alleviate the stress responses among beef cattle while collecting core body dimension data, but the current 3D data collection for beef cattle is usually time-consuming and easily influenced by the environment, which is in fact inapplicable to the actual breeding environment. In order to overcome the difficulty in obtaining the complete beef cattle point clouds, a non-contact phenotype data acquisition equipment was developed with a 3D reconstruction function, which can provide a large amount of standardized 3D quantitative phenotype data for beef cattle breeding and fattening process. The system is made up of a Kinect DK depth camera, an infrared grating trigger, and an Radio Frequency Identification (RFID) trigger, which enables the multi-angle instantaneous acquisition of beef cattle point clouds when the beef cattle pass through the walkway. The point cloud processing algorithm was developed based on the C++ platform and Point Cloud Library (PCL), and 3D reconstruction of beef cattle point clouds was achieved through spatial and outlier point filtering, Random Sample Consensus (RANSAC) shape fitting, point cloud thinning, and perceptual box filtering based on the dimensionality reduction density clustering to effectively filter out the interference, such as noises from the railings close to the beef cattle, without destroying the integrity of the point clouds. In the present work, a total of 124 sets of point clouds were successfully collected from 20 beef cattles on the actual farm using this system, and the target extraction experiments were completed. Notably, the beef cattle passed through the walkway in a natural state without any intervention during the whole data collection process. The experimental results showed that the acquisition success rate of this device was 91.89%. The coordinate system of the collected point cloud was consistent with the real situation and the body dimension reconstruction error was 0.6%. This device can realize the automatic acquisition and 3D reconstruction of beef cattle point cloud data from multiple angles without human intervention, and can automatically extract the target beef cattle point clouds from a complex environment. The point cloud data collected by this system help to restore the body size and shape of beef cattle, thereby provide solid support for the measurement of core parameters such as body height, body width, body oblique length, chest circumference, abdominal circumference, and body weight.

Focusing on the low level of management and informatization and intelligence of the beef cattle industry in China, a big data platform for commercial beef cattle breeding, referring to the experience of international advanced beef cattle breeding countries, was proposed in this research. The functions of the platform includes integrating germplasm resources of beef cattle, automatic collecting of key beef cattle breeding traits, full-service support for the beef cattle breeding process, formation of big data analysis and decision-making system for beef cattle germplasm resources, and joint breeding innovation model. Aiming at the backward storage and sharing methods of beef cattle breeding data and incomplete information records in China, an information resource integration platform and an information database for beef cattle germplasm were established. Due to the vagueness and subjectivity of the breeding performance evaluation standard, a scientific online evaluation technology of beef cattle breeding traits and a non-contact automatic acquisition and intelligent calculation method were proposed. Considering the lack of scientific and systematic breeding planning and guidance for farmers in China, a full-service support system for beef cattle breeding and nanny-style breeding guidance during beef cattle breeding was developed. And an interactive progressive decision-making method for beef cattle breeding to solve the lack of data accumulation of beef cattle germplasm was proposed. The main body of breeding and farming enterprises was not closely integrated, according to that, the innovative breeding model of regional integration was explored. The idea of commercial beef cattle breeding big data software platform and the technological and model innovation content were also introduced. The technology innovations included the deep mining of germplasm resources data and improved breed management pedigree, the automatic acquisition and evaluation technology of non-contact breeding traits, the fusion of multi-source heterogeneous information to provide intelligent decision support. The future goal is to form a sustainable information solution for China's beef cattle breeding industry and improve the overall level of China's beef cattle breeding industry.

Background Currently, there is a shortage of general practitioners (GPs) in primary care. Job transfer training is one of the main ways to train GPs and had important contributions to the construction of general practitioners team. Objective To analyze the job transfer operation of GPs in Xinjiang Uygur Autonomous Region from 2012 to 2020. Methods During December 2021 to March 2022, this study reviewed China Health Statistical Yearbook from 2013 to 2021 to obtain the data about GPs in Xinjiang from 2012 to 2020, reviewed trainees' lists of GPs job transfer training program from 2010 to 2022 organized by the Health Commission of Xinjiang Uygur Autonomous Region to obtain the real number of GPs transferred to training, and obtained the data about GPs distribution from the Science and Technology Education Department of the Health Commission of Xinjiang Uygur Autonomous Region. Results There were 5 980 GPs in Xinjiang in 2020, and 1 765 (29.52%) of them were qualified GPs through the job transfer training. The gender and age distribution of job transfer training GPs in each prefectures of Xinjiang were statistically different (P

Accurate identification of agricultural production environment information and agricultural production characteristics, comprehensive classification of meteorological, soil and crop multi-source data, are the bases for improving the efficiency of agricultural resource utilization and optimizing the structure of agricultural cultivation. Based on the meteorological data of nearly 20 years and the statistics of com yield, this study first constructed a database of spatial and temporal distribution characteristics of climate resources and com production in North China Plain, and there were significant spatiotemporal changes in rainfall, activity accumulated temperature, sunshine hours, solar radiation and corn yield. By using the method of fine crop planting regionalization, the summer com planting areas in the North China plain were divided into 5 categories: the extremely unsuitable area, the unsuitable area, the less suitable area, the suitable area, and the most suitable area, the proportions of each type of area in the total area is about 10%, 11%, 25%, 30%, 24%, respectively, further through using the Environmental Category attribution analysis method, each large class was divided into 5 subcategories, the probability was greater than 75% the relatively stable region accounts for about 63% of the total area, the fluctuation area of less than 75% is about the stable spatial and temporal distribution of 37%; the extremely unsuitable area, the unsuitable area and the less suitable area, these three kinds of spatial and temporal distributions were relatively stable, the belonging degree was 100%, accounting for 87.67%, 70.41% and 84.28%, respectively, the fluctuation zone mainly occurs between the extremely suitable zone and the suitable zone, and between the suitable zone and the relatively suitable zone. The fine zoning of summer com in North China Plain has important guiding significance for improving the utilization efficiency of local resources and optimizing the layout of com industry.

Internet of things for Aquaculture is an integrated modern system based on computing and communications technology like smart sensor technology, reliable telecommunication, intelligent information processing, which can collect data and images, transmit and process data intelligently, forecast future trend and early-warning for decision support. First of all, it is a key issue to establish scientific and rational index system and evaluation method for internet of things for aquaculture to guarantee its effectiveness. With the rapid development of information technology in China, the internet of things for aquaculture has been promoted and applied in Jiangsu, Shandong, Hunan, Hubei, Zhejiang and Guangdong. However, the internet of things for aquaculture in China is at an early stage and there are some problems, which cause negative impact on the promotion and application of the system, for examples, redundant functions, high cost, unstable performances, and so on. Secondly, in order to assess internet of things for aquaculture system, index system to assess internet of things for aquaculture was built in this paper by indicator optimization model to solve randomicity, redundancy, cross-connection and overlap caused subjective selection. Three steps composed of the selection process: 1) first round selection, three categories indices of function indicator, performance indicator and effectiveness indicator, targeted to 40 indicators were selected; 2) second round selection, 40 indicators representing perception layer, transmission layer and application layer, were optimized to 26 by method proposed by Dale and Beyeler, in which the standard conformity degree of each indicator was checked one by one and indicators need to meet at least 5 standards, otherwise they will be eliminated; 3) indicator screening model, by which 26 indicators were reduced to 14, with only 35% of total indicators representing 88.45% of total information, capturing the requirements of completeness and simplicity. Thirdly, fuzzy comprehensive evaluation approach, which comprise of three levels fuzzy comprehensive evaluation, was established to assess the application of internet of things for aquaculture. Most of the 14 indicators got in the first phase are qualitative factors and difficult to be quantified. That is why fuzzy comprehensive evaluation approach was used in this paper. Meanwhile, it is better to adopt multi-level factors as weights are not easy to be assigned reasonably when factors are too many, which leads unreasonable and wrong results. Multi-level fuzzy comprehensive method works well to solve this problem and can be applied to assess function characteristics, performance and effectiveness of the system of internet of things for aquaculture. Multi-level fuzzy comprehensive was built by steps of establishing comment set, membership and got quantitative score A, which can fall into five levels of excellence (A⩾82.5), good (82.5>A⩾67.5), satisfactory (67.5>A⩾52.5), barely adequate (52.5>A⩾35) and fail (A <35). Finally, case studies were carried out in Yixing of crab cultivation, Jiangsu province and Zhanjiang of white shrimp cultivation, Guangdong province, in which the index system and multi-level fuzzy comprehensive method were tested based on the internet of things for aquaculture system developed independently by China Agricultural University (CAU). Results showed that the score in Yixing was 87.371 indicating application of internet of things for aquaculture system in Yixing is excellent, meanwhile, the score in Zhanjiang is 74.921, which is good. Both of the two results are consistent with the actual situation of two bases, showing that index system and multi-level fuzzy comprehensive method proposed in this paper are feasible and reasonable for evaluation of application of internet of things for aquaculture and can guide its construction and improvement. [ABSTRACT FROM AUTHOR]

Objective To detect the serum levels of soluble T cell immunoglobulin mucin molecule 3 (sTim-3), IFN-γ and IL-4 in patients with pulmonary tuberculosis. Methods Peripheral blood was collected from 48 patients with pulmonary tuberculosis and 20 healthy controls. The serum level of sTim-3 was detected by ELISA. The levels of IFN-γ and IL-4 were determined by cytokine bead array (CBA). The correlations between sTim-3, IFN-γ and IL-4 levels in sera of patients with pulmonary tuberculosis were analyzed by Pearson correlation test. Results Compared with the control group, the serum levels of sTim-3 and IL-4 increased significantly, while the level of IFN-γ decreased significantly. Pearson analysis showed that there was a negative correlation between sTim-3 and IFN-γ as well as between IFN-γ and IL-4 in patients with pulmonary tuberculosis, while sTim-3 and IL-4 were positively correlated. Conclusion The serum levels of sTim-3 and IL-4 increase, but IFN-γ decreases in the patients with pulmonary tuberculosis.