Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10^–6), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.     

Teaching small animal reproduction via virtual patients

Virtual patients have become an interesting alternative in medical education. Due to increasing demands regarding theoretical and clinical teaching and to improve an interdisciplinary approach, a new blended learning concept including virtual patients was developed and implemented in the veterinary curriculum of the Freie Universität Berlin. In the presented project, three virtual patients from the field of canine reproduction were developed. They focus on pregnancy diagnosis with suspected luteal insufficiency, pyometra and benign prostatic hyperplasia, respectively. The results of an evaluation by veterinary students of the 7th semester showed a high acceptance of virtual patients in a blended learning reproduction module in the interdisciplinary lectures. Students especially preferred videos, such as video lectures, hands-on videos and animations as well as a glossary for background information, to successfully and autonomously work on a virtual case. The content covered by the new modules that were developed in the context of this project is part of a spiral curriculum; they will be revised and enhanced during the clinical year.     

1类最大度为6的平面图的全着色

证明了:如果图G是不含3-面上有5-顶点和(4,6,6)-面的最大度为6的平面图,则它满足全着色猜想,即对图G有Δ(G)+1≤χT(G)≤Δ(G)+2.     

基于图论的带模糊约束最小费用与最小时间问题

为了寻求工程实施中费用与时间的最小化问题,在构造多因素隶属度PERT图和建立隶属函数模型的基础上,给出了最短路径的算法,并运用模糊约束量来解决带最小化时间因素的最小费用流问题。同时,给出了相应的数学模型及算法。     

西部大开发与西部农业高校的发展机遇

本分析了西部大开发的实施为西部地区农业高校提供难得的发展机遇。提出西部农业高校必须主动适应中大开发的战略需要，明确办学思想，调整学科专业结构：加大教学改革力度，培养创新性人才，重视继续教育；加科研成果转化和高新技术产业化。     

屏幕绘图在肉制品感官质量评价上的应用

本文所建立的模式可用于灌肠类产品的质量评价。应用模糊数学和计算机屏幕绘图技术处理数据,绘制不同表情的脸谱之形象化雷达折线分析图,表现评价的结果。     

完全图的准强边着色图的计数

使用准强边着色矩阵讨论了完全图的准强边着色图的计数。     

大学生党史学习教育实效性提升的着力点

强化大学生的党史学习教育,对于增强大学生对中国共产党的认同,增强做中国人的志气、骨气、底气,树立正确党史观、激发学习热情,积极融入强国建设、应对大变局具有特殊意义。提升大学生党史学习教育的实效性,要充分发挥国家在增强党史读物的通俗性和普及性,建强党史教育基地,整合线上线下宣传平台,加强党史学习教育的持续性和系统性,加强党史研究和净化网络空间等方面的根本性作用。要全面建强高校这一主阵地,需在增强教师的内生能力、优化党史学习教育课程结构和搭建多元立体的校园平台的同时,不断增强大学生党史文化自觉与自信及辨识能力。     

基于Sentinel-2多时相影像的果树种植区遥感提取

为提取果树的空间分布信息,以果树生长期内不同月份的Sentinel-2多光谱遥感影像为数据源,以大沙河流域果树为研究对象,通过分析不同月份的光谱信息得出最佳监测时期,并在此基础上,选择不同时期的5种植被指数[归一化植被指数(NDVI)、比值植被指数(RVI)、增强型植被指数(EVI)、结构密集型色素指数(SIPI)和归一化水指数(NDWI)],结合机器学习技术构建决策树提取模型。结果发现,3、4、7、8月份的影像适于果树面积提取。通过Feature_importances_属性筛选出贡献度高的不同时期的植被指数作为输入特征,结合超参数学习曲线和网格搜索技术确定决策树模型的Max_depth和Min_samples_leaf参数分别为5和10时模型的效果最佳。参数调整后绘制决策树模型,模型在训练集和测试集上的精度分别达到了0.919 4和0.875 1。提取结果表明,研究区内的果树主要种植在大沙河两岸,东部与西北部的果树种植地块较为零碎,总的果树种植面积为6 838 hm²。在验证样本的基础上,通过混淆矩阵计算提取结果的精度,结果显示,Kappa系数为0.87,果树种植区提取的用户精度和制图精度分别为92.91%和90.77%。结果说明,本文所提出的方法适用于大区域果树的遥感提取,可为基于中高分辨率遥感影像的果树种植区监测提供有效的技术手段。     

云南省高职院校学生英语学习动机研究

英语作为全球使用范围最广泛的语言之一,其重要作用已渗透到各行各业,高职院校学生掌握好英语技能既是时代发展的要求,也是提升就业本领的要求。学习动机是直接推动学生学习的动力,激发学生英语学习动机是学生积极主动学习英语的关键。本文从主观因素学习目的、态度、期值以及客观因素授课质量、教材、成绩等方面调查了影响云南省高职学生英语学习动机的问题,并从教师和学校两个维度提出相应的对策,以期激发学生英语学习动机。