Sensitivity,specificity, and predictive values of reagent test strip estimations of blood urea nitrogen

Azostix-reagent-tests(R) strips (Ames, Miles, Inc., Diagnostic Division, Elkhart, IN) were used to measure blood urea nitrogen values in blood samples from 125 dogs and cats at the North Carolina State University, College of Veterinary Medicine. Results of the tests were compared with standard serum urea nitrogen results. Sensitivity, specificity, and negative predictive values were high (86.4, 90.3, and 96.5%, respectively). Positive predictive value was low, 65.5% of the dogs and cats with elevated blood urea nitrogen values were correctly classified as abnormal The test performs well when the prevalence of abnormal values is near 50%.     

综合设计性实验在食品微生物实验中的应用探讨

随着人们对食品营养与安全问题重视程度的提高,高校对食品微生物实验教学质量提出了更高要求,而综合设计性实验为这种高要求提供了实现平台。探讨了综合设计性实验在食品微生物实验教学中应用的必要性,分析了综合设计性实验实施过程中体现的优势和出现的问题,并提出相应的解决方案,为综合设计性实验的广泛开展提供一定借鉴。     

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

微生物在环境保护中的应用及前景

从环境保护的角度出发,重点论述了微生物在这一领域的应用现状与发展前景,指出微生物将会在21世纪的环境保护领域中发挥重要作用。     

基于无人机多光谱遥感的冬小麦叶绿素含量反演及监测

旨在实现冬小麦各生育期叶绿素含量的准确估测,探究其时空变化规律。利用无人机获取冬小麦越冬期、返青期、拔节期、孕穗期和灌浆期的高分辨率多光谱图像,同时采集地面SPAD数据。选取三类光谱参数建立反演模型,优选出各生育期的最佳预测模型,并定量监测试验区冬小麦叶绿素含量时间变化和空间分布。结果表明：原始波段模型和波段倒数对数模型分别为越冬期及其他生育期叶绿素含量预测的最佳模型,拟合精度R²>0.59;时空分布上,灌浆期前试验区冬小麦叶绿素含量呈南北高、中部低特点,灌浆期则呈北高南低的趋势,叶绿素含量从越冬期到拔节期逐步增加,拔节期到孕穗期开始降低,孕穗期到灌浆期则大幅度降低。本研究建立的倒数对数预测模型,精度较高,且适用于返青到灌浆的4个生育期,对于试验区冬小麦叶绿素含量有较好的时空监测效果。     

基于模型预测控制的农机主从跟随作业控制方法

针对实际环境中由于农业机械（简称农机）作业过程的作业量以及土壤条件的变化等不确定性因素的影响，导致协同作业跟随农机的行驶工况不稳定、跟随协同作业响应慢、控制困难等问题，在综合考虑不确定性以及响应性能的基础上，提出了一种农机跟随分层控制架构，搭建农机田间作业下的纵向跟随动力学模型，并以间距保持、速度跟随、燃油经济性、加速度跟随性能为目标，进行基于模型预测控制（MPC）算法的上层控制器推导，基于前馈以及PI反馈的控制器作为下层控制，以上层控制器获得的控制加速度为目标，进行力矩（电流）跟踪，在保证抗不确定性以及干扰噪声的同时，提高跟随农机的响应能力。通过Matlab/Simulink仿真和田间试验验证，结果表明，该控制方法可以有效解决农机作业的跟随控制问题，与滑模变结构控制器相比，能够实现稳定跟随行驶，且速度误差和加速度误差更小，速度误差控制在-0.29132~0.18001m/s，加速度误差控制在-0.05678~0.05628m/s2，稳定跟随距离误差为±0.45m，具有良好的跟随效果。     

PREDICTIVE INTELLIGENT CONTROL OF THE COLD STORAGE

The predictive intelligent control scheme is proposed, which is based on approximation theory and numerical method of Chcbyshev orthogonal polynomial. It has the practical value to the cold storge using widely electromagnetic valve and can be also applied similar systems in orther area.     

食源性致病菌单细胞观测与预测的研究进展

微生物在单细胞水平下的生长普遍具有随机性和变异性,同时低菌量污染食品所造成的危害风险亦不可忽视,因此食源性致病菌单细胞的生长观测及预测研究逐渐成为食品预测微生物学及食源性致病菌风险评估的研究热点。在分析现有相关文献的基础上,将目前主要的食源性致病菌单细胞生长观测研究分为间接推断和直接观测两类方法;同时对食源性致病菌单细胞生长预测的模型参数与建模过程进行概述,比较了传统预测微生物的决定模型与微生物单细胞的随机模型二者之间的区别,强调了单细胞生长模型需在随机建模的基础上开展,并通过个体建模方法进一步联系其与所在食品环境之间的关系。最后,分析了食源性致病菌单细胞生长观测与预测的现状,并展望了其未来与食品安全风险评估相结合的发展趋势。     

微生物在环境保护中的应用及前景

从环境保护的角度出发,重点论述了微生物在这一领域的应用现状与发展前景,指出微生物将会在21世纪的环境保护领域中发挥重要作用。