苯乳酸抑制链格孢霉作用靶位的分析

以从自然腐败的樱桃上分离的链格孢霉（Alternaria sp.）LD3.0086为指示菌，研究苯乳酸对链格孢霉的主要抑制作用靶位。应用分光光度法测定苯乳酸对链格孢霉的最小抑菌浓度，通过卡尔科弗卢尔荧光增白剂染液（calcofluor white，CFW）染色观察苯乳酸对菌丝顶端生长的破坏作用，利用扫描电子显微镜和透射电子显微镜观察链格孢霉的超微结构变化，通过测定苯乳酸作用前后链格孢霉上清液中N-乙酰葡萄糖胺质量浓度变化研究苯乳酸对菌丝细胞壁的破坏作用，应用荧光双染色法观察苯乳酸对链格孢霉菌丝细胞膜的损伤作用。结果表明，12.5 mmol/L的苯乳酸能有效抑制链格孢霉的生长；与对照组（无菌水处理）相比，苯乳酸处理后链格孢霉顶端生长细胞无明显形变，经12.5 mmol/L苯乳酸处理的链格孢霉上清液中N-乙酰葡萄糖胺质量浓度基本不变；苯乳酸处理24 h，链格孢霉菌丝细胞壁表面无明显损伤，细胞内结构发生明显变化；苯乳酸短时间（4 h）处理链格孢霉，菌丝细胞膜仍较为完整，加入苯乳酸较长时间（8 h）后细胞膜发生破裂。综合分析可知，苯乳酸对链格孢霉的主要作用靶位应不是菌丝体的细胞壁和细胞膜，而是在菌丝体内部，通过破坏菌丝内部细胞器结构或引起细胞内的生化反应，从而抑制链格孢霉的生长和繁殖，发挥抑菌活性。     

牛磺酸对运动能力影响的研究进展

牛磺酸是一种含硫条件性必需氨基酸,是人和动物机体内不可缺少的一种生理活性物质。牛磺酸对于机体的运动能力有着重要的影响,本文在介绍牛磺酸的代谢过程及生理作用的基础上,对目前国内外关于补充牛磺酸与提高人体的运动能力的关系、牛磺酸的安全性及副作用等方面研究情况做一综述。牛磺酸已开发成颇具潜力的抗运动性疲劳的营养补充剂,同时在医药、卫生、保健等领域里也具有广泛商业价值和社会价值。     

基于蛋白质组学技术研究秦川牛肉宰后贮藏过程中肌红蛋白含量及其衍生物转化

本实验采用4D-非标记蛋白质组学技术研究秦川牛肉贮藏过程中（0～8 d）肌红蛋白含量及其衍生物的转化情况，阐释冷却秦川牛肉中肌红蛋白含量及其衍生物转化的分子机制。结果表明：贮藏过程中，肌红蛋白表达量在宰后0～4 d上升、4～8 d下降，利用非标记蛋白质组学技术鉴定出与肌红蛋白及其衍生物相关的差异蛋白14 种，具体包括代谢酶、氧化还原酶、过氧化物酶、伴侣蛋白4 类，这4 类蛋白的表达共同调控贮藏过程中肌红蛋白含量的变化及其3 种衍生物之间的转化，具体表现为贮藏过程中肌红蛋白表达量整体呈下降趋势，氧合肌红蛋白相对含量持续下降，脱氧肌红蛋白、高铁肌红蛋白相对含量逐渐增加，导致肉色发生褐变。本研究结果有利于理解秦川牛肉贮藏过程肉类变色的复杂生化变化机制。     

基于柠檬酸-铕纳米配位聚合物构建荧光探针快速检测汤煲中5’-肌苷酸含量

基于柠檬酸-铕金属有机纳米配体聚合物（citrate/europium lanthanide coordination polymer nanoparticles，Cit/Eu LCP NPs）构建快速检测肉品汤煲中5’-肌苷酸（inosine-5’-monophosphate，5’-IMP）的荧光探针。研究结果表明，5’-IMP对Cit/Eu?LCP?NPs有良好的荧光猝灭作用。在最佳条件下，该荧光探针在5’-IMP?2.5～200?μg/mL的质量浓度范围内呈现出良好的线性关系，检出限为0.17?μg/mL，且具备良好的抗干扰、稳定性和重复性。为了验证方法可行性，将该方法应用于实际鸡汤样品中的5’-IMP检测，测得加标回收率为97.85%～103.95%，可为快速检测肉品汤煲中5’-IMP提供新的思路和方法。     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

一种5 重real-time PCR筛查转基因水稻方法的建立

以转基因水稻中最常用的CaMV35S启动子、NOS终止子、Cry1Ab/Ac基因、HPT基因及SPS水稻内标基因为研究对象，利用5 种不同的荧光信号（FAM、HEX、Taxas Red、Cy5、Cy5.5）进行多重实时聚合酶链式反应（real-time polymerase chain reaction，real-time PCR）检测方法的研究。通过引物组合筛选、反应体系优化、特异性测试、灵敏度测试、适用性测试等一系列实验，建立了5 重real-time PCR方法，灵敏度可达0.032%。此方法具有灵敏度高、结果准确、通量大等优点，可实现水稻中转基因成分的快速、高效检测。     

起·承·转·合 ——解读巍宝山长春洞空间布局的结构章法

长春洞是巍宝山保存最完整的宫 观，虽占地不到两亩①，却在有限的空间中 创造出九楼十院的空间格局。本文以长春洞 空间布局序列为研究对象，从知觉体验的 连续性入手，提出了空间变化对参观者情感 的影响，论述了在建筑空间布局序列中参观 者知觉体验逐层递进的控制机制，探讨了以 “起、承、转、合”为结构，以“知觉体验”为 章法的动态空间布局设计手法，希望为道教 建筑空间与其他超越日常的精神空间的研 究提供启示。     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.