Detection of cows with insemination problems using selected classification models

In the present study, the detection of cows with artificial insemination (AI) difficulties using selected statistical and machine learning methods is presented. Cows were divided into two classes: those that conceived after one or two services (“GOOD”) and those that required more than two services per conception (“POOR”). The best performance was exhibited by one of the artificial neural networks (ANN) and the multivariate adaptive regression spline (MARS) method (AIC, BIC, RMS and accuracy); whereas logistic regression (LR) and classification functions (CF) were of somewhat lower quality. The detection of cows with AI difficulties, performed on the basis of the test set comprising new instances, showed that the ANN and MARS were more precise in comparison with the statistical methods. Sensitivity and specificity were over 85% for the perceptron with two hidden layers (MLP2) and MARS and approximately 80% or lower for LR and CF. From among variables determining the AI category, the average calving interval and cow body condition index were the most important. Other significant variables were lactation number, pregnancy length, sex of calf from previous calving and cow age. The prognoses obtained using ANN and MARS can be used for the appropriate preparation of cows for AI.     

Effect of Different Mulberry Leaf Extracts on Spleen Lymphocyte Proliferation in Mice

This experiment was aimed to compare the effects of different mulberry leaf extract on spleen lymphocyte proliferation in mice. Mulberry leaf polysaccharides (MLP-1, MLP-2, MLP-3, MLP-4 and MLP-5) were prepared with mulberry leaf aqueous extract (MLAE) by different concentration of alcohol deposit, polysaccharides concentration of all extracts were determined by phenol sulfuric acid method. Five different MLPs and MLAE were as the experimental drug, when polysaccharide concentration was 15.625, 31.25, 62.5, 125 and 250 μg/mL, the changes of spleen B and T lymphocytes proliferation in mice which were stimulated by MLPs and MLAE in single or synergistical stimulation of drugs with LPS and PHA were determined by MTT method. The results showed that when polysaccharide concentration was in the range of 15.625 to 250 μg/mL, MLPs and MLAE in single or synergistical stimulation with LPS and PHA could stimulate the spleen B and T lymphocytes proliferation, MLP-1 at low concentration could stimulate significantly spleen B lymphocyte proliferation remarkably (P<0.05), and MLP-3 and MLP-5 at low concentrations showed strong stimulation ability of T lymphocyte proliferation. When MLAE was in synergistical stimulation with LPS at most concentrations points, the spleen B lymphocytes proliferation in mice were significantly higher than those of the cell control and LPS control groups, which revealed that MLAE could significantly improve the humoral immune function (P<0.05).     

昆明市土地利用变化趋势的多模型对比分析

[目的]对比分析逻辑回归(LogReg)、多层神经网络(MLP)和相似度加权学习(SimWeight)3种土地利用变化趋势模型在多地类变化分析中的模拟效果与土地利用预测精度,为云贵高原地区的国土空间规划、水土保持和生态修复等提供参考。[方法]以2000—2020年昆明市土地利用变化为例,分别采用3种模型对该区域建模,并使用受试者工作特征曲线、ROC曲线下面积和Kappa系数等多种方法评估精度。[结果]对于大多数地类的变化趋势,MLP和SimWeight模型的模拟效果要好于LogReg模型,特别是在未利用地的变化分析中MLP和SimWeight模型的AUC值均大于0.9。在整体土地利用变化预测上,LogReg, MLP和SimWeight3种模型的Kappa值分别为0.9066,0.904 1,0.925 3,整体预测结果表现接近,但SimWeight模型略微优于其他模型。[结论]对于昆明市LUCC建模的模型选择,若为追求更高精度可选择SimWeight模型,若更在意运算速度则优先选择MLP模型,若需要进一步分析驱动因子与土地变化的关系应选择LogReg模型。     

CAG和MLP启动子控制的重组禽腺病毒构建

根据鸡胚致死孤儿病毒(CELOV)的序列设计引物,通过PCR方法扩增了禽腺病毒FAVI-JS株的晚期启动子(MLP),序列与CELOV的MLP同源性为99%。分别构建了MLP和商品化启动子CAG控制的表达增强型绿色荧光蛋白(eGFP)基因的禽腺病毒转移载体。通过载体与禽腺病毒FAVI-JS株在原代鸡胚肾细胞内进行同源重组,分别获得了MLP和CAG控制的表达eGFP的重组禽腺病毒,为探讨不同启动子对禽腺病毒活载体疫苗免疫效果的影响提供了条件。     

Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield

The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).     

不同桑叶提取物对小鼠脾脏淋巴细胞增殖能力的影响

试验旨在比较不同桑叶提取物对小鼠脾脏淋巴细胞增殖作用的影响。用不同浓度乙醇醇沉桑叶水提物,制备桑叶粗多糖MLP-1、MLP-2、MLP-3、MLP-4、MLP-5,采用苯酚硫酸法测定各提取物多糖含量。以5种桑叶粗多糖和桑叶水提物（MLAE）为试验药物,采用MTT法比较多糖含量分别在15.625、31.25、62.5、125、250 μg/mL浓度时各提取物单独刺激及协同LPS、PHA共同刺激小鼠脾脏B、T淋巴细增殖的变化。结果显示,多糖浓度在15.625~250 μg/mL范围内,各提取物无论单独还是协同LPS和PHA时均能促进小鼠脾脏B、T淋巴细胞的增殖,其中,MLP-1多糖含量在低浓度时能显著刺激脾脏B淋巴细胞增殖（P<0.05）;MLP-3和MLP-5在低浓度时表现出较强的刺激T淋巴细胞增殖的能力;MLAE协同LPS在大部分浓度点的脾脏B淋巴细胞的增殖能力均显著高于细胞对照组和LPS对照组,可显著提升体液免疫功能（P<0.05）。     

基于时间序列与横截面数据的吉林省水稻产量预测对比分析

【目的】对比不同模型预测效果,分析各模型预测水稻产量的特点、不足及适用条件,为粮食产量预测问题模型选择提供依据。【方法】从时间序列预测和横截面数据预测两种角度,利用ARIMA、LSTM、SVR、MLP这4种模型,通过吉林省水稻产量、病虫害及其他特征历史数据对吉林省水稻产量进行预测,并对不同模型的预测结果进行了对比分析。【结果】基于ARIMA模型和LSTM模型的时间序列预测结果较好,横截面数据预测中,原始数据经主成分分析PCA降维处理后,可提高模型预测性能。【结论】对于水稻产量预测,应根据掌握的影响产量因素的数据以及趋势延续性情况合理选择预测模型,以达到较理想的预测效果。     

基于机器视觉技术的小粒中药材种子净度快速检测

为探究机器视觉技术用于小粒中药材种子净度快速检测的可行性,以黄芩、桔梗、黄芪、紫苏和柴胡5种常见小粒中药材种子为材料,使用扫描仪获取净种子、其他植物种子和所含杂质的图像,采用种子自动化分析系统(PhenoSeed)批量提取种子、其他植物种子及所含杂质的颜色、尺寸及纹理信息,通过相关性分析和主成分分析进行特征变量的筛选,采用多层感知器(MLP)和二元逻辑回归(BLR)建立上述5种中药材种子净度快速检测模型。结果表明,净种子、其他植物种子及所含杂质在物理指标方面存在显著差异,针对不同种子,采用不同指标建立的MLP净度模型的训练集和测试集准确率均在96.0%以上,该模型在不同中药材种子上的稳定性均优于BLR模型;以特征指标建立的模型稳定性优于全部指标的建模效果,运用特征变量建立的MLP模型对不同净度梯度(75.0%~100.0%)的混合样本进行预测,回归曲线的决定系数均达到0.99以上。采用机器视觉技术获取种子、其他植物种子及所含杂质颜色、尺寸和纹理等信息,以特征指标建立MLP模型可用于小粒中药材种子的净度快速检测。     

基于植物电子病历多类型数据融合的作物病害诊断方法

植物电子病历(EMR)以结构化和非结构化的形式记录了大量关于疾病症状、环境特征以及诊断开方的信息，为病害的智能诊断提供了优质知识来源，但是其样本量少、公开数据集缺乏和多种类型数据并存的特点给相关研究带来困难。根据植物EMR多类型数据混合的特点，提出了一种基于BERT-MPL数据融合与注意力机制优化的作物病害诊断模型(BERT-MPL data fusion model based on attention mechanism, BM-Att)。首先采用BERT预训练语言模型抽取电子病历中非结构化部分的文本语义特征；其次通过one-hot编码和多层感知机(MLP)对结构化数据进行编码和向量维度的扩增；最后在特征融合阶段采用注意力机制强调关键特征，利用多层全连接层实现病害诊断。构建了番茄、黄瓜、生菜和西瓜4种作物的15种病害数据集验证模型的效果并进行消融实验，并且对比了CNN、RCNN、AttRNN、FastText、Transformer、BERT和ERNIE等处理文本数据的常见模型，以及BERT-ALEX、BERT-1dCNN、BERT-1dLSTM、BERT-1dAttLSTM、BE...