一种3D眼镜虚拟试戴系统的实现

针对人脸图像试戴3D眼镜过程中存在的镜腿遮挡人脸问题,文中提出一种基于人脸图像的3D眼镜虚拟试戴技术。利用构建的人脸形状的三维模型,使其在虚拟试戴中对镜腿起到消隐作用,解决镜腿的遮挡问题。文中对输入的人脸图像进行关键点检测,结合Graham扫描法求得人脸形状的凸多边形,利用平移扫描构建人脸形状的三维模型。此外,文中根据定位人脸图像上的关键点以及姿态估计后对三维眼镜模型的变换,将眼镜模型佩戴到人脸图像上。实验结果表明,该方法对于多视角的人脸图像实现了虚拟试戴效果,解决了多种视角下人脸图像试戴过程中镜腿的遮挡问题,虚拟试戴中镜腿遮挡平均准确率为94.5%,遮挡精度较高。     

The matched queueing network PH/M/c→oPH/PH/1

We consider a matched queueing network PH/M/coPH/PH/1, where the output of the first subsystem PH/M/c is regarded as one of the inputs of the second subsystem and matched with another input of PH-renewal process, and they are served by a single server with PH-service-distribution. The stationary state probabilities, the distribution of the virtual waiting times in the two subsystems, and the distribution of the total virtual waiting time are derived.The work was supported by the Max-Planck-Gesellschaft, Germany, and the Chinese Academy ofSciences.     

In vitro and in silico study of Aloe vera leaf extract against human breast cancer

Abstract

Aloe vera leaf contains some bioactive compounds that have a strong binding affinity toward estrogen receptor as compared to standard drug tamoxifen. In this study, we have found that the IC₅₀ of Aloe vera leaf extract against breast cancer cell line (MCF-7) is 23?µg/mL which is much lower than the IC₅₀ (332?µg/mL) of Aloe vera leaf extract against non-cancerous cell line (NIH-3T3). We have also calculated the total concentration of phenolic acid (385.662?µg/mL), flavonoids (160.402?µg/mL) and alkaloids (276.754?µg/mL) in Aloe vera leaf extract. The free radical scavenging activity of Aloe vera leaf extract is 67% to 89% (at 50 to 300?µg/ml). Our virtual molecular docking study suggests that bioactive compounds like Aloe-emodin (?8.8?Kcal/mol), 7-hydroxy-2,5 dimethylchromone (?7.5?Kcal/mol), Beta-sitosterol (?7.3?Kcal/mol) etc. have a greater binding affinity toward estrogen alpha receptor as compared to standard drug Tamoxifen (?6.4?Kcal/mol).

     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

SARS-CoV protease inhibitors design using virtual screening method from natural products libraries

Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.     

Validation of an empirical RNA-ligand scoring function for fast flexible docking using RiboDock®

We report the design and validation of a fast empirical function for scoring RNA-ligand interactions, and describe its implementation within RiboDock, a virtual screening system for automated flexible docking. Building on well-known protein-ligand scoring function foundations, features were added to describe the interactions of common RNA-binding functional groups that were not handled adequately by conventional terms, to disfavour non-complementary polar contacts, and to control non-specific charged interactions. The results of validation experiments against known structures of RNA-ligand complexes compare favourably with previously reported methods. Binding modes were well predicted in most cases and good discrimination was achieved between native and non-native ligands for each binding site, and between native and non-native binding sites for each ligand. Further evidence of the ability of the method to identify true RNA binders is provided by compound selection ('enrichment factor') experiments based around a series of HIV-1 TAR RNA-binding ligands. Significant enrichment in true binders was achieved amongst high scoring docking hits, even when selection was from a library of structurally related, positively charged molecules. Coupled with a semi-automated cavity detection algorithm for identification of putative ligand binding sites, also described here, the method is suitable for the screening of very large databases of molecules against RNA and RNA-protein interfaces, such as those presented by the bacterial ribosome.     

Numerical Explanation of Slow Transients in an AlGaN/GaN HEMT

观察了AlGaN/GaN HEMT器件在短期应力后不同栅偏置下的一组漏极电流瞬态,发现瞬态的时间常数随栅偏压变化很小,据此判断这组瞬态由电子陷阱的释放引起.为了验证这个判断,采用数值仿真手段计算了上述瞬态.分别考虑了在器件中不同空间位置的电子陷阱,分析了应力和瞬态中相应的陷阱行为,对比和解释了仿真曲线与测量结果的异同.基于上述讨论,提出测量的瞬态可能是表面深陷阱和GaN层体陷阱的综合作用的结果.