医学留学生化学实验全英文教学资源建设与实践*

针对目前化学实验全英文在线教学资源匹配度较低、相对匮乏等问题,以医学留学生为中心,建设了有声课件、模块化实景实验操作视频以及测试题库3种不同类型的化学实验全英文教学资源,通过资源的线下使用和基于雨课堂的在线预习,实现从“集中、定时、定点”的传统教学模式向“模块化、碎片化、信息化”的混合教学模式转变,可以丰富教学资源和教学手段,改进教学秩序,提高医学留学生化学知识和技能学习的积极性和主动性,使“教”“学”良性循环,可切实提高化学实验国际化教学质量。     

美国《科学教师培养标准(2020年版)》述评*

美国《科学教师培养标准(2020年版)》针对《新一代科学教育标准》的推行而修订,立足于科学教育最新研究成果,尤其突出学科内容知识的重要性,补充对科学本质和科学文化的要求,强化对工程和技术、交叉概念、学科观念的理解,突出学习进阶规律在科学教师培养中的应用。此外,在学科教学法、学习环境、安全、对学生学习的影响等维度做出相应的改动。该标准启示我国:借鉴国际改革趋势出台中国特色的科学教师培养标准,但科学教师培养也要本土化;学科内容知识对科学教师专业发展非常重要;科学教师的培养应立足于学习进阶的规律;职前科学教师培养需要注重学习环境建设。     

基于语义的软件服务故障自动诊断模型

模仿学习是机器人仿生机制研究的主要内容之一,即通过观察、理解、学习、模仿示教行为实现机器人的仿生特性。基于高斯过程分别表达采集离散示教信号所构成的示教轨迹和含有未知参数策略的模仿轨迹,构建模仿学习方法框架,将概率模型匹配引入到模仿学习中,以KL散度为代价函数比较两种轨迹的概率分布,运用梯度下降法寻求使KL散度最小的最优模仿控制策略,将策略应用于模仿机器人以完成与示教相同的模仿任务。以关节型机器人的机械臂摆动行为模仿为学习任务进行仿真,结果表明基于概率轨迹匹配的模仿学习方法能够实现机械臂摆动行为模仿,学习过程较传统方法简易且学习效果较好。     

纯电动汽车磷酸铁锂电池组的建模及优化

鉴于传统神经网络和支持向量机机理复杂、计算量大的缺陷,很难实时跟踪磷酸铁锂电池组复杂快速的内部反应,影响电池荷电状态的估算精度,提出应用一种简单、有效的极限学习机对一额定容量为100Ah、额定电压为72V的纯电动汽车磷酸铁锂电池组建模,并分别与BP神经网络、RBF神经网络、支持向量机进行对比。随后,以学习时间和泛化性能为优化目标,应用粒子群方法寻找最佳隐层节点个数。结果表明,基于极限学习机的磷酸铁锂电池组模型的学习时间、泛化性能优于BP神经网络、RBF神经网络、支持向量机;隐层节点优化后,模型的学习时间和泛化性能达到最优。     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

Taba: A Tool to Analyze the Binding Affinity

Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba . © 2019 Wiley Periodicals, Inc.     

案例教学与翻转学习结合提升物理化学教学质量

结合我校应用型人才培养方案和培养目标,通过改革物理化学课堂教学提高学生知识掌握能力和应用能力。在传统课堂的基础上,将案例教学结合在翻转学习的教学过程中,充分提高学生的学习积极性与主动性。以稀溶液的依数性为教学案例,阐述翻转学习与案例教学的实施过程。学生深度参与课堂、融入课堂并互动,师生之间在教学中达到共鸣,实现教学相长,从而提高物理化学教学质量。     

Cheminformatics in MS-based environmental exposomics: Current achievements and future directions

Compound annotation using MS/MS data is the major bottleneck in interpretation of mass spectrometry data during non-targeted screening and suspect screening exposomics studies. Apart from compound identification using available databases or mass spectral libraries, the true challenge comes when completely new compounds have to be identified. Along with recent advances in MS instrumentation that set grounds to a new revolutionary age in environmental exposomics, a multitude of cheminformatics annotation approaches has been developed. Herein, we review the basic principles of the cutting-edge cheminformatics MS-based approaches employed in eco-exposome annotation.We give a solid background discussing the eco-exposome concept in relation to the advances in MS instrumentation, and define the three crucial cheminformatics tasks used in the eco-exposome annotation: molecular formula assignment, compound prioritization and compound annotation. The basic principles of compound annotation are discussed, which are based on three approaches of utilizing structural information inherent to MS data. These involve direct, indirect and joint annotation approaches. We assess their performance through the ability to annotate eco-exposome constituents. We discuss future perspectives and give directions to new annotation strategies and performance evaluation protocols aiming to solve current issues hampering the incorporation of cheminformatics annotation approaches in regular eco-exposome annotation workflows.