Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

A novel zirconocene/ultradispersed diamond black powder supported catalytic system for ethylene polymerization

A novel carrier of ultradispersed diamond black powder (UDDBP) was used to support metallocene catalyst. Al₂O₃ was also used as carrier in order to compare with UDDBP. Supported catalysts for ethylene polymerization were synthesized by two different reaction methods. One way was direct immobilization of the metallocene on the support, the other was adsorption of MAO onto the support followed by addition of the metallocene. Four supported catalysts Cp₂ZrCl₂/UDDBP, Cp₂ZrCl₂/Al₂O₃, Cp₂ZrCl₂/MAO/UDDBP and Cp₂ZrCl₂/Al₂O₃/MAO were obtained. The content of the zirconium in the supported catalyst was determined by UV spectroscopy. The activity of the ethylene polymerization catalyzed by supported catalyst was investigated. The influence of Al/Zr molar ratio and polymerization temperature on the activity was discussed. The polymerization rate was also observed.     

Statistical discrimination of steroid profiles in doping control with support vector machines

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society.     

Evaluation of SARs for the prediction of skin irritation/corrosion potential–structural inclusion rules in the BfR decision support system†

The German Federal Institute for Risk Assessment (BfR) has developed a Decision Support System (DSS) to assess certain hazardous properties of pure chemicals, including skin and eye irritation/corrosion. The BfR–DSS is a rule-based system that could be used for the regulatory classification of chemicals in the European Union. The system is based on the combined use of two predictive approaches: exclusion rules based on physicochemical cut-off values to identify chemicals that do not exhibit a certain hazard (e.g., skin irritation/corrosion), and inclusion rules based on structural alerts to identify chemicals that do show a particular toxic potential. The aim of the present study was to evaluate the structural inclusion rules implemented in the BfR–DSS for the prediction of skin irritation and corrosion. The following assessments were performed: (a) a confirmation of the structural rules by rederiving them from the original training set (1358 substances), and (b) an external validation by using a test set of 200 chemicals not used in the derivation of the rules. It was found as a result that the test data set did not match the training set relative to the inclusion of structural alerts associated with skin irritation/corrosion, albeit some skin irritants were in the test set.     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

V2O5-Ag2O/η-Al2O3催化剂上甲苯氧化制苯甲醛的研究

制备了一系列负载型V—Ag—Al—O催化剂并用于甲苯的气相选择氧化制苯甲醛反应，考察了催化剂组成对反应性能的影响．研究发现，V205负载量为12．5％、Ag2O负载量为7．5％并经773K活化的4号催化剂，在空速467．6h-1，温度663K时反应，得到了甲苯转化率49．58％、苯甲醛选择性29．22％的较好结果．用XRD、XPS、SEM等测试手段，对催化剂进行了结构表征．