A new feature encoding scheme for HIV-1 protease cleavage site prediction

HIV-1 protease has been the subject of intense research for deciphering HIV-1 virus replication process for decades. Knowledge of the substrate specificity of HIV-1 protease will enlighten the way of development of HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, various feature encoding techniques and machine learning algorithms have been used frequently. In this paper, a new feature amino acid encoding scheme is proposed to predict HIV-1 protease cleavage sites. In the proposed method, we combined orthonormal encoding and Taylor’s venn-diagram. We used linear support vector machines as the classifier in the tests. We also analyzed our technique by comparing some feature encoding techniques. The tests are carried out on PR-1625 and PR-3261 datasets. Experimental results show that our amino acid encoding technique leads to better classification performance than other encoding techniques on a standalone classifier.     

P1 and P1′ para-fluoro phenyl groups show enhanced binding and favorable predicted pharmacological properties: Structure-based virtual screening of extended lopinavir analogs against multi-drug resistant HIV-1 protease

Crystal structure of multidrug-resistant (MDR) clinical isolate 769, human immunodeficiency virus type-1 (HIV-1) protease in complex with lopinavir (LPV) (PDB ID: 1RV7) showed altered binding orientation of LPV in the expanded active site cavity, causing loss of contacts and decrease in potency. In the current study, with a goal to restore the lost contacts, three libraries of LPV analogs containing extended P1 and/or P1′ phenyl groups were designed and docked into the expanded active site cavity of the MDR769 HIV-1 protease. The compounds were then ranked based on three criteria: binding affinity, overall binding profile and predicted pharmacological properties. Among the twelve proposed extensions in different combinations, compound 14 (consists of para-fluoro phenyl group as both P1 and P1′ moieties) was identified as a lead with improved binding profile, binding affinity against the MDR protease and favorable predicted pharmacological properties comparable to those of LPV. The binding affinity of 14 against wild type (NL4-3) HIV-1 protease was comparable to that of LPV and was better than LPV against an ensemble of MDR HIV-1 protease variants. Thus, 14 shows enhanced binding affinity by restoring lost contacts in the expanded active site cavity of MDR769 HIV-1 protease variants suggesting that it may have higher potency compared to that of LPV and hence should be further synthesized and evaluated against NL4-3 as well as MDR variants of HIV-1.     

A multi-drug resistant HIV-1 protease is resistant to the dimerization inhibitory activity of TLF-PafF

Human immunodeficiency virus type-1 (HIV-1) protease, a homodimeric aspartyl protease, is a critical drug target in designing anti-retroviral drugs to treat HIV/AIDS. Multidrug-resistant (MDR) clinical isolate-769 HIV-1 protease (PDB ID: 3PJ6) has been shown to exhibit expanded active site cavity with wide-open conformation of flaps (Gly48–Gly52) due to the accumulation of multiple mutations. In this study, an HIV-1 protease dimerization inhibitor (PDI)–TLF-PafF, was evaluated against MDR769 HIV-1 protease using X-ray crystallography. It was hypothesized that co-crystallization of MDR769 HIV-1 protease in complex with TLF-PafF would yield either a monomeric or a disrupted dimeric structure. However, crystal structure of MDR769 I10V HIV-1 protease co-crystallized with TLF-PafF revealed an undisrupted dimeric protease structure (PDB ID: 4NKK) that is comparable to the crystal structure of its corresponding apo-protease (PDB ID: 3PJ6). In order to understand the binding profile of TLF-PafF as a PDI, docking analysis was performed using monomeric protease (prepared from the dimeric crystal structure, PDB ID: 4NKK) as docking receptor. Docking analysis revealed that TLF-PafF binds at the N and C termini (dimerization domain) in a clamp shape for the monomeric wild type receptor but not the MDR769 monomeric receptor. TLF-PafF preferentially showed higher binding affinity to the expanded active site cavity of MDR769 HIV-1 protease than to the termini. Irrespective of binding location, the binding affinity of TLF-PafF against wild type receptor (−6.7 kcal/mol) was found to be higher compared to its corresponding binding affinity against MDR receptor (−4.6 kcal/mol) suggesting that the MDR769 HIV-1 protease could be resistant to the PDI-activity of TLF-PafF, thus supporting the dimeric crystal structure (PDB ID: 4NKK).     

一种基于2DLPP和2DLDA的人脸识别方法研究

局部保持投影（locality preserving projection,LPP）和线性鉴别分析（linear discrimin antanalysis,LDA）是两种有效的一维特征提取方法,广泛应用于人脸识别领域。但采用一维特征提取方法时会存在列向量化时样本的结构信息被破坏和样本在提取特征时必须对协方差矩阵进行特征分解,对于高维小样本的问题很容易出现协方差矩阵奇异的问题。文中提出将二维局部保持投影（2DLPP）和二维线性鉴别分析（2DLDA）这两种方法在特征层进行融合并应用在人脸识别。基于人脸库AR上的实验表明,该方法比传统的IJPP和LDA识别性能更高,因此可作为一种新的人脸识别方法。     

An automatic optical and SAR image registration method with iterative level set segmentation and SIFT

Although optical image registration methods have been successfully developed over the past decades, the registration of optical and synthetic aperture radar (SAR) images is still a challenging problem in remote sensing. Feature-based methods are considered to be more effective for multi-source image registration. However, almost all of these methods rely on the feature extraction algorithms. In this article, a simultaneous segmentation and feature-based registration method based on an iterative level set and scale-invariant feature transform (ILS-SIFT) is proposed. The core idea consists of three aspects: (1) an iterative procedure that combines image segmentation and matching is proposed to avoid registration failure caused by poor feature extraction; (2) a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features; and (3) an improved SIFT algorithm is employed to determine whether the registration was successful. Experimental results have shown the effectiveness and universality of the proposed method.     

Flap opening mechanism of HIV-1 protease

The active site of aspartic proteases, such as HIV-1 protease (PR), is covered by one or more flaps, which restrict access to the active site. For HIV-1 PR, X-ray diffraction studies suggested that in the free enzyme the two flaps are packed onto each other loosely in a semi-open conformation, while molecular dynamics (MD) studies observed that the flaps can also separate into open conformations. In this study, the mechanism of flap opening and the structure and dynamics of HIV-1 PR with semi-open and open flap conformations were investigated using molecular dynamics simulations. The flaps showed complex dynamic behavior as two distinct mechanisms of flap opening and various stable flap conformations (semi-open, open and curled) were observed during the simulations. A network of weakly polar interactions between the flaps were proposed to be responsible for stabilizing the semi-open flap conformation. It is hypothesized that such interactions could be responsible for making flap opening a highly sensitive gating mechanism which control access to the active site.     

Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem

An important issue involved in kernel methods is the pre-image problem. However, it is an ill-posed problem, as the solution is usually nonexistent or not unique. In contrast to direct methods aimed at minimizing the distance in feature space, indirect methods aimed at constructing approximate equivalent models have shown outstanding performance. In this paper, an indirect method for solving the pre-image problem is proposed. In the proposed algorithm, an inverse mapping process is constructed based on a novel framework that preserves local linearity. In this framework, a local nonlinear transformation is implicitly conducted by neighborhood subspace scaling transformation to preserve the local linearity between feature space and input space. By extending the inverse mapping process to test samples, we can obtain pre-images in input space. The proposed method is non-iterative, and can be used for any kernel functions. Experimental results based on image denoising using kernel principal component analysis （PCA） show that the proposed method outperforms the state-of-the-art methods for solving the pre-image problem.     

Consistency measures for feature selection

The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process. For this reason, many methods of automatic feature selection have been developed. Some of these methods are based on the search of the features that allows the data set to be considered consistent. In a search problem we usually evaluate the search states, in the case of feature selection we measure the possible feature sets. This paper reviews the state of the art of consistency based feature selection methods, identifying the measures used for feature sets. An in-deep study of these measures is conducted, including the definition of a new measure necessary for completeness. After that, we perform an empirical evaluation of the measures comparing them with the highly reputed wrapper approach. Consistency measures achieve similar results to those of the wrapper approach with much better efficiency.     

Relaxed local preserving regression for image feature extraction

The latest linear least regression (LSR) methods improved the performance of image feature extraction effectively by relaxing strict zero-one labels as slack forms. However, these methods have the following three disadvantages: 1) LSR-based methods are sensitive to the noises and may lose effectiveness in feature extraction task; 2) they only focus on the global structures of data, but ignore locality which is important to improve the performance; 3) they suffer from small-class problem, which means the number of projections learned by methods is limited by the number of classes. To address these problems, we propose a novel method called Relaxed Local Preserving Regression (RLPR) for image feature extraction. By incorporating the relaxed label matrix and similarity graph-based regularization term, RLPR can not only explore the latent structure information of data, but also solve the small-class problem. In order to enhance the robustness to noises, we further proposed an extended version of RLPR based on l_{2, 1}-norm, termed as ERLPR. The experimental results on image databases consistently show that the recognition rates of RLPR and ERLPR are superior to the compared methods and can achieve 98% in normal cases. Especially, even on the corrupted databases, the proposed methods can also achieve the classification accuracy of more than 58%.

     

基于PSO面向K近邻分类的特征权重学习算法

特征权重学习是基于特征赋权的K近邻算法需要解决的重要问题之一,传统上提出了许多启发式的学习方法。近年来,随着进化计算技术在模式识别及数据挖掘领域的广泛应用,基于进化计算的权重学习和距离学习方法也得到越来越多的重视。本研究针对基于特征赋权的K近邻算法的权重学习问题,提出了一种基于PSO进行权重学习的算法PSOKNN,通过与传统KNN、GAKNN及ReliefKNN的实验比较分析表明,该方法可有效地搜索出合适的特征权重,获得较好的分类精度并淘汰冗余或无关的特征。     

A method of HIV-1 inactivation compatible with antibody-based depletion of abundant proteins from plasma

We have examined several methods, including heat treatment and treatment with detergents, to inactivate HIV-1 present in plasma to be depleted of abundant proteins utilizing an antibody-based technology. Treatment with Triton X-100 was not compatible with abundant protein depletion with an antibody column and heat treatment alters the composition of the plasma proteome. However, treatment with 1.2% N-octylglucoside for 5?min completely inhibited HIV-1 infectivity. The detergent was easily removed through buffer exchange, and this treatment had no discernable effect on protein depletion.     

Feature selection using tabu search method

Selecting an optimal subset from original large feature set in the design of pattern classifier is an important and difficult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential forward (backward) floating search methods. Based on the results of experiments, tabu search is shown to be a promising tool for feature selection in respect of the quality of obtained feature subset and computation efficiency. The effects of parameters in tabu search are also analyzed by experiments.     

DeepLabv3plus-IRCNet：小目标特征提取的图像语义分割

目的 为了解决经典卷积神经网络无法满足图像中极小目标特征提取的准确性需求问题,本文基于DeepLabv3plus算法,在下采样过程中引入特征图切分模块,提出了DeepLabv3plus-IRCNet（IR为倒置残差（inverted residual,C为特征图切分（feature map cut））图像语义分割方法,支撑图像极小目标的特征提取。方法 采用由普通卷积层和多个使用深度可分离卷积的倒置残差模块串联组成的深度卷积神经网络提取特征,当特征图分辨率降低到输入图像的1/16时,引入特征图切分模块,将各个切分特征图分别放大,通过参数共享的方式提取特征。然后,将每个输出的特征图进行对应位置拼接,与解码阶段放大到相同尺寸的特征图进行融合,提高模型对小目标物体特征的提取能力。结果 本文方法引入特征图切分模块,提高了模型对小目标物体的关注,充分考虑了图像上下文信息,对多个尺度下的各个中间层特征进行融合,提高了图像分割精度。为验证方法的有效性,使用CamVid（Cambridge-driving labeled video database）数据集对提出的方法进行验证,平均交并比（mean intersection over union,mIoU）相对于DeepLabv3plus模型有所提升。验证结果表明了本文方法的有效性。结论 本文方法充分考虑了图像分割中小目标物体的关注度,提出的DeepLabv3plus-IRCNet模型提升了图像分割精度。     

Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction

Cross-project defect prediction (CPDP) refers to predicting defects in a target project using prediction models trained from historical data of other source projects. And CPDP in the scenario where source and target projects have different metric sets is called heterogeneous defect prediction (HDP). Recently, HDP has received much research interest. Existing HDP methods only consider the linear correlation relationship among the features (metrics) of the source and target projects, and such models are insufficient to evaluate nonlinear correlation relationship among the features. So these methods may suffer from the linearly inseparable problem in the linear feature space. Furthermore, existing HDP methods do not take the class imbalance problem into consideration. Unfortunately, the imbalanced nature of software defect datasets increases the learning difficulty for the predictors. In this paper, we propose a new cost-sensitive transfer kernel canonical correlation analysis (CTKCCA) approach for HDP. CTKCCA can not only make the data distributions of source and target projects much more similar in the nonlinear feature space, where the learned features have favorable separability, but also utilize the different misclassification costs for defective and defect-free classes to alleviate the class imbalance problem. We perform the Friedman test with Nemenyi’s post-hoc statistical test and the Cliff’s delta effect size test for the evaluation. Extensive experiments on 28 public projects from five data sources indicate that: (1) CTKCCA significantly performs better than the related CPDP methods; (2) CTKCCA performs better than the related state-of-the-art HDP methods.     

Using class-based feature selection for the classification of hyperspectral data

The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms of classification accuracy, suggesting the effectiveness of the proposed algorithms.     

Music classification via the bag-of-features approach

A central problem in music information retrieval is audio-based music classification. Current music classification systems follow a frame-based analysis model. A whole song is split into frames, where a feature vector is extracted from each local frame. Each song can then be represented by a set of feature vectors. How to utilize the feature set for global song-level classification is an important problem in music classification. Previous studies have used summary features and probability models which are either overly restrictive in modeling power or numerically too difficult to solve. In this paper, we investigate the bag-of-features approach for music classification which can effectively aggregate the local features for song-level feature representation. Moreover, we have extended the standard bag-of-features approach by proposing a multiple codebook model to exploit the randomness in the generation of codebooks. Experimental results for genre classification and artist identification on benchmark data sets show that the proposed classification system is highly competitive against the standard methods.     

Correspondence-free determination of the affine fundamental matrix

Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion. Existing algorithms typically analyze the relative geometries of matched feature points identified in both projected views. Automated feature matching is itself a challenging problem. Results typically have a large number of false matches. Traditional fundamental matrix estimation methods are very sensitive to matching errors, which led naturally to the application of robust statistical estimation techniques to the problem. In this work, an entirely novel approach is proposed to the fundamental matrix estimation problem. Instead of analyzing the geometry of matched feature points, the problem is recast in the frequency domain through the use of integral projection, showing how this is a reasonable model for orthographic cameras. The problem now reduces to one of identifying matching lines in the frequency domain which, most importantly, requires no feature matching or correspondence information. Experimental results on both real and synthetic data are presented that demonstrate the algorithm is a practical technique for fundamental matrix estimation. The behavior of the proposed algorithm is additionally characterized with respect to input noise, feature counts, and other parameters of interest     

一种新的设备指纹特征选择及模型构建方法

近年来,随着移动互联网的快速发展,越来越多的业务从浏览器端转移到了移动端。但是,寄生在移动互联网上的黑色产业链也达到了泛滥的地步。设备指纹技术应运而生,即利用设备的特征属性为每个设备生成独一无二的标识。其间涌现了很多利用机器学习方法进行设备唯一性认证的策略,其中大部分方法注重于模型的建立,很少对特征选择部分展开深入研究,而特征选择直接关系到最终模型的性能。针对该问题,文中提出了一种新的设备指纹特征选择及模型构建方法(Feature Selection Based on Discrimination and Stability and Weight-based Similarity Calculation,FSDS-WSC),即根据不同设备的特征区分度和相同设备的特征稳定性选出最具价值的一些特征,并将这些特征的重要程度作为特征权重应用到模型建立的后续过程中。在真实场景中的6 424台Android设备上,将FSDS-WSC与当今主流的其他特征选择方法进行了对比实验。结果表明,FSDS-WSC相比其他方法有了较大改进,设备唯一性认证的准确率达到了99.53%,证实了FSDS-WSC的优越性...