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/.     

Dendrite‐Free Sodium Metal Anodes Enabled by a Sodium Benzenedithiolate‐Rich Protection Layer

Sodium metal is an ideal anode material for metal rechargeable batteries, owing to its high theoretical capacity (1166 mAh g^?1), low cost, and earth‐abundance. However, the dendritic growth upon Na plating, stemming from unstable solid electrolyte interphase (SEI) film, is a major and most notable problem. Here, a sodium benzenedithiolate (PhS₂Na₂)‐rich protection layer is synthesized in situ on sodium by a facile method that effectively prevents dendrite growth in the carbonate electrolyte, leading to stabilized sodium metal electrodeposition for 400 cycles (800 h) of repeated plating/stripping at a current density of 1 mA cm^?2. The organic salt, PhS₂Na₂, is found to be a critical component in the protection layer. This finding opens up a new and promising avenue, based on organic sodium slats, to stabilize sodium metals with a protection layer.     

自由体积与亲水性对环氧涂层防护性能的影响

报道了涂层聚合物自由体积和亲水性对涂层腐蚀防护性能影响的初步研究结果.实验表明,相对于自由体积,树脂的亲水性是决定树脂吸水率的更主要因素.低亲水性涂层腐蚀防护性能的提高主要源自到达涂层/金属界面的水量的显著降低.但由于采用活性酯固化的环氧树脂涂层中水通道的尺寸相对较大,水合离子扩散较容易,因此该类涂层覆盖下的金属基体被润湿部分的腐蚀趋势反而较一般涂层为大.     

A sequence-based computational model for the prediction of the solvent accessible surface area for α-helix and β-barrel transmembrane residues

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and β-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and β-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.     

In silico prediction and screening of γ‐secretase inhibitors by molecular descriptors and machine learning methods

γ‐Secretase inhibitors have been explored for the prevention and treatment of Alzheimer's disease (AD). Methods for prediction and screening of γ‐secretase inhibitors are highly desired for facilitating the design of novel therapeutic agents against AD, especially when incomplete knowledge about the mechanism and three‐dimensional structure of γ‐secretase. We explored two machine learning methods, support vector machine (SVM) and random forest (RF), to develop models for predicting γ‐secretase inhibitors of diverse structures. Quantitative analysis of the receiver operating characteristic (ROC) curve was performed to further examine and optimize the models. Especially, the Youden index (YI) was initially introduced into the ROC curve of RF so as to obtain an optimal threshold of probability for prediction. The developed models were validated by an external testing set with the prediction accuracies of SVM and RF 96.48 and 98.83% for γ‐secretase inhibitors and 98.18 and 99.27% for noninhibitors, respectively. The different feature selection methods were used to extract the physicochemical features most relevant to γ‐secretase inhibition. To the best of our knowledge, the RF model developed in this work is the first model with a broad applicability domain, based on which the virtual screening of γ‐secretase inhibitors against the ZINC database was performed, resulting in 368 potential hit candidates. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

军校大学化学创新实验课:VCI技术在装备储存中的应用

将经过实际应用验证的科研成果--气相缓蚀技术设计成军队院校开设的大学化学课程实验。该实验涉及武器装备存储中的问题与需求和气相缓蚀技术的工作原理、优势以及配方筛选基本原则,并介绍了该实验的具体课堂实施环节。通过本实验的开设,期望达到预定的各层次教学目标,提高军校人才培养质量。     

Synthesis and characterization of α-N,N-dihydroxyethylaminopropyl-ω-butylpolydimethylsiloxanes

A series of α-N,N-dihydroxyethylaminopropyl-ω-butylpolydimethylsiloxanes with different molecular weight were synthesized. The preparation included five steps which were hydroxyl protection, alkylation, anionic ring-opening polymerization, hydrosilylation and deprotection. The structure of reaction products was characterized by FT-IR, GC, 1H NMR. 2007 Qing Si Zhang. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.     

Measurements Of Biogenic Hydrocarbons And Their Atmospheric Degradation In Forests

Abstract

An analytical procedure for the sampling and quantitative determination of biogenic hydrocarbons in the atmosphere is presented. The method is based on the adsorptive preconcentration of the hydrocarbons using Tenax TA/Carbopack B filled sampling tubes followed by thermodesorption of the trapped compounds and gas chromatographic analysis. To avoid losses of the biogenic alkenes as a consequence of their reaction with ozone on the adsorbents during the sampling step, an ozone scrubber is used in front of the adsorption tubes. Diurnal variations of the monoterpene- and ozone-concentrations are determined at different heights in and above a forest stand (coniferous and deciduous trees) in the Vosges (Col du Donon, 760 m a.s.l.) in order to elucidate the importance of terpene-oxidation reactions on the formation of peroxidic products in forest air.     

Deprotection‐free preparation of propargyl ether‐containing phosphinated benzoxazine and the structure–property relationship of the resulting thermosets

Generally, protection and deprotection procedures of amino groups are required in preparing propargyl ether‐containing benzoxazines. In this study, we report a facile, deprotection‐free preparation of a propargyl ether‐containing phosphinated benzoxazine (2) from the nucleophilic substitution of a phenolic OH‐containing phosphinated benzoxazine (1) and propargyl bromide in the catalysis of potassium carbonate. The structure of (2) was characterized and confirmed by a high‐resolution mass spectrum, ¹H, ¹³C, ¹H‐¹H, ¹H‐¹³C nuclear magnetic resonance (NMR) spectra, and X‐ray single crystal diffractogram. infrared (IR) and differential scanning calorimetry were used to monitor the ring‐opening of benzoxazine and crosslinking of propargyl ether. The microstructure and the structure–property relationship of the resulting homopolymers and copolymers are discussed. The T_g of homopolymer of (2) is 208 °C by dynamic mechanical analysis, the coefficient of thermal expansion is 43 ppm/°C, and T_d 5% (N₂) is 393 °C, respectively, which are higher than those of the homopolymer of (1) . Similar trends were observed in the copolymerization system. The results demonstrate the beneficial effect of crosslinking afforded by the propargyl ether group is higher than that by the phenolic OH group. © 2011 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2012