Development and validation of a high‐throughput online solid‐phase extraction–liquid chromatography–tandem mass spectrometry method for the detection of gonyautoxins 1&4 and gonyautoxins 2&3 in human urine

Paralytic shellfish toxins (PSTs), including gonyautoxins and saxitoxins, are produced by multiple species of microalgae and dinoflagellates, and are bioaccumulated by shellfish and other animals. Human exposure to PSTs typically occurs through ingestion of recreationally harvested contaminated shellfish and results in nonspecific symptomology. Confirmation of exposure to PSTs has often relied on the measurement of saxitoxin, the most toxic congener; however, gonyautoxins (GTXs), the sulfated carbamate derivatives of saxitoxin, may be present in shellfish at higher concentrations. To improve identification of PST exposures, our group has developed an online solid phase extraction hydrophilic interaction liquid chromatography method to identify GTX1–4 in human urine with tandem mass spectrometry. The reportable range varied for each analyte, with all falling within 0.899 and 250 ng/mL in urine with precision <15% and >85% accuracy as determined for all quality control samples. This new online method quantitates GTX1–4 following exposures to PSTs, supporting the work of public health authorities.     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints

Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.     

Online resource for theoretical study of hydration of biopolymers

An online resource has been developed for the theoretical study of hydration of biopolymers by the RISM (Reference Interaction Site Model) method, deriving from the integral equation theory of liquids. The online resource is based upon original software developed by the authors and includes all steps in studying a biopolymer with a given spatial structure and force field. It prepares the input data and carries out the RISM calculation yielding the atom-atom correlation functions of the biopolymer with water as solvent. From these functions the algorithm finds atomic partial contributions to the hydration free energy using various free energy expressions from integral equation theory. The calculated results are automatically recorded in a database, and become available on the website as tables of partial thermodynamic quantities. In addition, the website displays an interactive 3D model of a given molecule, the atoms of which can be painted in different colors in accordance with their partial contributions to the thermodynamic quantity chosen by the user. The user can interactively choose atoms on this molecule and their correlation functions will be displayed. The aim of our work was to develop and present a publicly-accessible resource on the basis of original software which could be used for scientific and educational purposes.     

基于改进极根学习机的回转窑煅烧带温度预测方法

针对传统算法预测回转窑煅烧带温度存在精度低、速度慢的问题,提出了基于改进极限学习机(ELM)的回转窑煅烧带温度预测方法。对ELM输入权值矩阵定义了变换系数,采用黄金分割法在给定区间内搜寻变换系数的最佳值,改进了ELM网络参数的确定方式,弥补了随机确定输入权值并且不作调整的缺陷,在保证ELM训练速度的前提下提高预测精度、减小模型随机性。实验结果表明,改进的ELM预测精度高、训练速度快、模型性能优,可满足工况恶劣的回转窑的生产需要。     

基于MetropolisQ学习的蜂窝网络在线信道分配

针对现有的蜂窝网络的在线动态分配模型具有的信道需求量大、呼叫动态变化时阻塞率高和收敛速度慢的缺点,设计了一种基于MetropoisQ学习的蜂窝网络的在线信道分配方法。首先,在考虑同信道限制、邻居信道限制和同小区限制的基础上,设计了在线信道分配的数学模型,然后在Q-Learning算法基础上的设计了一种基于资格迹的Q(λ)算法实现信道的在线分配,为了进一步提高收敛速度,采用Metropois规则对算法中动作的选择方式进行改进,实现探索和利用的平衡。为了验证文中方法,采用Matlab工具上进行实验,仿真实验结果表明文中方法能实现蜂窝通信网络的在线信道分配,且与其它方法比较,具有较少的信道需求量、较低的阻塞率和收敛速度,较其它方法具有较大优越性。     

分光光度法测定亚甲基蓝溶液浓度线上实验设计

基于朗伯-比尔定律与互补色原理,利用QQ截图读取色块RGB三原色组成的功能,获得不同亚甲基蓝浓度色阶对应的红色和绿色信号,设计了亚甲基蓝测定的简易紫外-可见分光光度法实验方案。该方案包括标准色阶制作、颜色信号读取、标准曲线绘制,以及待测样品测试等。通过学生实验,获得了满意的可靠性。该实验方案尤其适用于线上教学或缺少分光光度计的线下教学。     

结合PBL课例的无机化学线下对分课堂教学设计与实践*

以原电池一节教学设计为例,探索了将PBL课例融入对分课堂教学模式。教学实践表明,新教学模式激发了学生的学习兴趣,提高了学生学习的积极性和主动性,提升了课程教学效果,达到了课程教学目标。对PBL课例与对分课堂有机融合的全新课堂教学模式的建设与发展进行了展望。     

基于深度学习理论的有机合成课程教学改革

有机合成既是有机化学学习中的一个独立构成部分,也是安排于有机化学课程之中的重要学习主题。在目前的有机合成课程教学中,教师的自觉性引导不足,学生容易陷入浅层学习状态,导致教学效果不佳。结合有机化学学科特点,从有机合成课程教学现状出发,围绕深度学习教学设计的四要素——深度学习目标、挑战性学习主题、深度学习活动、持续性评价,以典型的教学片段为例阐述深度学习理论在教学实践中的应用,为相关教学工作提供参考。