基于多特征算法选择的太赫兹时域光谱用于水稻种子掺假研究

该文提出了一种基于太赫兹时域光谱的水稻种子模式识别方法。实验以10种不同品牌混合掺假的水稻种子为样本,基于采集的样本太赫兹时域光谱数据,通过建立Relief、随机森林（RF）、支持向量机递归特征消除（SVM-RFE）和最大相关最小冗余（mRMR）模型分别对样本光谱波长进行特征选择,最后设计分类器对4种特征选择方法处理后的样本进行分类识别。结果表明,基于布谷鸟算法（CS）优化的极限学习机模型对经RF特征选择算法提取后的样本光谱数据具有最佳识别效果,其准确率可达100%,实验对于法庭科学领域内种子的掺假鉴定具有一定的借鉴意义。     

基于色谱–光谱联用和斜投影法分析丹皮酚含量

将色谱–光谱仪联用(HPLC–UV)法与斜投影法结合,建立了丹皮酚含量快速分析方法。通过色谱–光谱联用采集丹皮酚结晶母液的紫外多波长光谱三维数据,构建不含丹皮酚的背景数据库以及丹皮酚光谱数据库,基于斜投影法步骤测定了批量样本中丹皮酚含量。结果表明:该方法计算结果和高效液相色谱分析结果接近,相对误差小于5.0%。测定结果的相对标准偏差为0.4%~1.0%(n=5),回收率为98.8%~101.3%。该方法快速、准确,操作简单,可为丹皮酚结晶产品及各种制剂质量控制提供可行方案。     

Identification strategies for flame retardants employing time‐of‐flight mass spectrometric detectors along with spectral and spectra‐less databases

In the past, the preferred strategy for the identification of unknown compounds was to search in an appropriate mass spectral database for spectra obtained using either electron ionisation (GC‐MS analyses) or collision‐induced dissociation (LC‐MS/MS analyses). Recently, an increase has been seen in the use of accurate mass instruments and spectra‐less databases, based on monoisotopic accurate mass alone. In this article, we describe a systematic workflow for the screening and identification of new flame retardants. This approach utilises LC‐quadrupole‐time‐of‐flight MS and spectra‐less databases based only on monoisotopic accurate mass for the identification of ‘unknowns’. An in‐house database was built, and the input parameters used in the data analysis process were optimised for flame retardant chemicals, so that it can be easily transferred to other laboratories. The procedure was successfully applied to dust, foam and textiles from car interiors and indoor consumer products. The developed method was demonstrated for the main new flame retardant present in Antiblaze V6 and for the three unreported reaction by‐products/impurities present in the same technical mixture. Copyright © 2015 John Wiley & Sons, Ltd.     

The application of high-resolution mass spectrometry-based data-mining tools in tandem to metabolite profiling of a triple drug combination in humans

Patients are usually exposed to multiple drugs, and metabolite profiling of each drug in complex biological matrices is a big challenge. This study presented a new application of an improved high resolution mass spectrometry (HRMS)-based data-mining tools in tandem to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was metronidazole-pantoprazole-clarithromycin (MET-PAN-CLAR), which is widely used in clinic to treat ulcers caused by Helicobacter pylori. First, mass defect filter (MDF), as a targeted data processing tool, was able to recover all relevant metabolites of MET-PAN-CLAR in human plasma and urine from the full-scan MS dataset when appropriate MDF templates for each drug were defined. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, worked effectively except for several trace metabolites, which were buried in the remaining background signals. Third, an integrated strategy, i.e., untargeted BS followed by improved MDF, was effective for metabolite identification of MET-PAN-CLAR. Most metabolites except for trace ones were found in the first step of BS-processed datasets, and the results led to the setup of appropriate metabolite MDF template for the subsequent MDF data processing. Trace metabolites were further recovered by MDF, which used both common MDF templates and the novel metabolite-based MDF templates. As a result, a total of 44 metabolites or related components were found for MET-PAN-CLAR in human plasma and urine using the integrated strategy. New metabolic pathways such as N-glucuronidation of PAN and dehydrogenation of CLAR were found. This study demonstrated that the combination of accurate mass-based multiple data-mining techniques in tandem, i.e., untargeted background subtraction followed by targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drugs in vivo.     

Circular dichroism sensor based on cadmium sulfide quantum dots for chiral identification and detection of penicillamine

A new chemical sensor based on the measuring of circular dichroism signal (CD) was fabricated from cysteamine capped cadmium sulfide quantum dots (Cys-CdS QDs). The chiral-thiol molecules, d-penicillamine (DPA) and l-penicillamine (LPA), were used to evaluate potentials of this sensor. Basically, DPA and LPA provide very low CD signals. However, the CD signals of DPA and LPA can be enhanced in the presence of Cys-CdS QDs. The CD spectra of DPA and LPA exhibited a mirror image profile. Parameters affecting the determination of DPA and LPA were thoroughly investigated in details. Under the optimized condition, the CD signals of DPA and LPA displayed a linear relationship with the concentrations of both enantiomers, ranging from 1 to 35 μM. Detection limits of this sensor were 0.49 and 0.74 μM for DPA and LPA, respectively. To demonstrate a potential application of this sensor, the proposed sensor was used to determine DPA and LPA in real urine samples. It was confirmed that the proposed detection technique was reliable and could be utilized in a broad range of applications.     

Diversity‐Oriented Approach for Chemical Biology

Synthetic molecules that modulate and probe biological events are critical tools in chemical biology. Utilizing combinatorial and diversity‐oriented synthetic strategies, access to large numbers of small molecules is becoming more and more feasible, and research groups in this field can take advantage of the power of chemical diversity. Since the majority of early studies were focused on the discovery of compounds that perturb protein functions, diversity‐based approaches are often considered as therapeutic lead discovery tactics. However, the diversity‐oriented approach can also be applied to advance distinct aims, such as target protein identification, or the development of imaging probes and sensors. This review provides a personal perspective of the chemical‐diversity‐based approach and how this principle can be adapted to various chemical biology studies.     

Unsupervised LoS/NLoS identification in mmWave communication using two-stage machine learning framework

Identification of line-of-sight (LoS)/ non-LoS (NLoS) condition in millimeter wave (mmWave) communication is important for localization and unobstructed transmission between a base station (BS) and a user. A sudden obstruction in a link between a BS and a user can result in poorly received signal strength or termination of communication. Channel features obtained by the estimation of channel state information (CSI) of a user at the BS can be used for identifying LoS/NLoS condition. With the assumption of labeled CSI, existing machine learning (ML) methods have achieved satisfactory performance for LoS/NLoS identification. However, in a real communication environment, labeled CSI is not available. In this paper, we propose a two-stage unsupervised ML based LoS/NLoS identification framework to address the lack of labeled data. We conduct experiments for the outdoor scenario by generating data from the NYUSIM simulator. We compare the performance of our method with the supervised deep neural network (SDNN) in terms of accuracy and receiver characteristic curves. The proposed framework can achieve an accuracy of 87.4% and it outperforms SDNN. Further, we compare the performance of our method with other state-of-the-art LoS/NLoS identification schemes in terms of accuracy, recall, precision, and F1-score.     

初中化学“酸和碱的性质”创意教学*——探析食品发热包

以食品发热包为主题情境,通过探析发热包成分、解密发热原理、计算原料配比、配制发热包等环节梳理酸和碱的性质、用途。从元素观、转化观的视角认识物质转化规律,体现物质研究的一般思路,凸显科学探究的基本过程。通过设计方案测定发热包中碳酸钠的含量,总结混合物成分鉴定及定量测定的方法;归纳气体定量测定的影响因素。呈现一堂学科观念、学科方法教学具体课例,凸显物质研究、定性检验、定量测定的基本方法。     

“宏观辨识与微观探析”视域下的初中化学教学*——中和反应

在“宏观辨识与微观探析”视域下进行中和反应教学,首先以常规实验为载体,从宏观视角认识中和反应的外在表现,然后以模型、动画为载体,从微观视角了解中和反应的微观过程,再以数字化实验为载体,从宏观与微观相结合的视角理解中和反应的内在本质,最后联系生产生活实际,体验中和反应的应用价值。这样设计,学生能深刻理解中和反应的本质,增进化学学科理解,培养化学学科核心素养。