Quantitative proteomic approaches for biomarker discovery

The rapid advances in proteomic technologies have made possible systematic analysis of hundreds to thousands of proteins in clinical samples with the promise of uncovering novel protein biomarkers for various disease conditions. We will discuss in this review article current MS and protein chip-based quantitative proteomic approaches and their application in biomarker discovery. The emphasis will be placed on new quantification strategies employing stable isotopic labeling coupled with MS/MS, and antibody-based protein chips and nanodevices. The strength and weakness of each technology are briefly highlighted.     

Proteomic approaches to the analysis of atopic dermatitis and new insights from interactomics

In this review, we summarized the recent findings regarding atopic dermatitis (AD) skin disease based on proteomic studies. AD is a chronic relapsing inflammatory skin disease typically characterized by a distribution of eczematous skin lesions with lichenification, pruritic excoriations and dry skin with wide varieties of pathophysiological aspects. We summarized the alterations of the protein expressions in the primary cultured AD cells from the patients'-biopsy samples that were mostly analyzed by 2-D PAGE and MALDI-TOF. Further, we also conducted protein-protein interaction mapping according to the obtained candidate proteins. As a result, we found that several hub proteins, i.e. heat shock 70-kDa protein 2, heat shock 70-kDa protein 9, tumor rejection antigen-1 (gp96), spermatogenesis-associated factor, protein kinase C inhibitor 1, vimentin, tenascin, semaphorin 4f (SEMA4F), complement component C1r deficiency (C1R) and apolipoprotein A (LPA), respectively, could receive important consideration in future studies. Since the mechanism of AD disease has been shown to be complex, our results may provide new clues to aid understanding of AD.     

Optimization framework for DFG-based automated process discovery approaches

The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

     

Phosphoproteomics for the discovery of kinases as cancer biomarkers and drug targets

Early detection and targeted therapy represent a novel regimen of cancer management. The understanding of receptor tyrosine kinases in tumorigenesis at the molecular level has led to the first generation of kinase inhibitors for anticancer therapy that targets a specific kinase or pathway. While the therapeutic advantage is obvious, targeted therapy often relapses and results in drug resistance for advanced cancers. To achieve feasible early detection and better efficacy of therapeutics targeting multiple pathways, significantly more biomarkers and drug targets are in demand, especially for individualized therapy. Recent advances in phosphoprotein enrichment and MS technologies for quantitative phosphoproteome analysis provide great opportunities in the identification and validation of kinases as drug targets. The MS-based phosphoproteomic technologies would be useful tools as well for the identification of phosphosignatures unique to a specific type or subtype of cancer and drug responsive biomarkers. This review summarizes the major kinases acting as cancer biomarkers and drug targets, the advances of MS-based phosphoproteomic technologies, and some potential values and challenges of this emerging phosphoproteomics-based biomarker and drug target discovery field. Strategies for global, targeted, and quantitative phosphoproteomics are discussed, and some recent interesting applications are also evaluated.     

A new tracker for air-to-air missile targets

An extended Kalman filter is developed to aid the tracking of an air-to-air missile from a maneuvering target aircraft. The filter exploits knowledge of the dominant aerodynamically induced lift and drag forces of a nonthrusting missile employing proportional navigation guidance, and it also accounts for the dynamic lag and bandwidth effects of the missile seeker, guidance, and control systems. Incorporating the refined missile acceleration model enhances the filter's tracking estimate precision and provides meaningful threat predictive capabilities. Identifiability of parameters within the acceleration model is established, an adaptive filter is developed, and its performance capabilities portrayed through realistic Monte Carlo simulations.     

Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM

This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs.     

An inverse docking approach for identifying new potential anti-cancer targets

Inverse docking is a relatively new technique that has been used to identify potential receptor targets of small molecules. Our docking software package MDock is well suited for such an application as it is both computationally efficient, yet simultaneously shows adequate results in binding affinity predictions and enrichment tests. As a validation study, we present the first stage results of an inverse-docking study which seeks to identify potential direct targets of PRIMA-1. PRIMA-1 is well known for its ability to restore mutant p53's tumor suppressor function, leading to apoptosis in several types of cancer cells. For this reason, we believe that potential direct targets of PRIMA-1 identified in silico should be experimentally screened for their ability to inhibit cancer cell growth. The highest-ranked human protein of our PRIMA-1 docking results is oxidosqualene cyclase (OSC), which is part of the cholesterol synthetic pathway. The results of two followup experiments which treat OSC as a possible anti-cancer target are promising. We show that both PRIMA-1 and Ro 48-8071, a known potent OSC inhibitor, significantly reduce the viability of BT-474 and T47-D breast cancer cells relative to normal mammary cells. In addition, like PRIMA-1, we find that Ro 48-8071 results in increased binding of p53 to DNA in BT-474 cells (which express mutant p53). For the first time, Ro 48-8071 is shown as a potent agent in killing human breast cancer cells. The potential of OSC as a new target for developing anticancer therapies is worth further investigation.     

A new efficient algorithm for optimal assignment of smart weapons to targets

In the modern battlefields smart weapons inherently rely on the sensors. The benefit of assigning a given weapon to a target often depends on the pre-assigned sensor. In this paper we present an efficient algorithm to optimally assign sensors and weapons to targets. This algorithm is derived from the well-known auction algorithm, and it is named as Swt-opt. We prove that Swt-opt converges to an optimal solution.     

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-A11 binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes.     

基于OC-SVM的新情感词识别

当前对新词发现、情感词极性标注与情感词库构建的研究比较多,却少有一个专门针对新情感词识别的方法.提出一种基于OC-SVM的新情感词识别方法,通过种子词扩展方法获得词语集,并用旧词典、词频和停用词等对扩展的词进行过滤,获取新词,对新词获取的实验评估显示在适当的F值下,正确率可以达到45.5％.由于情感词和非情感词训练集的不平衡性,采用词频、相邻词及其词性等作为特征用OC-SVM(one-class support vector machine)对新词进行分类,获得新情感词,构建一个有效的新情感词识别系统.实验结果在召回率为26.6％的情况下,正确率可以达到45.7％,证明了算法的有效性.     

一种新型自适应RBF神经网络滑模制导律

针对导弹拦截问题,提出一种自适应RBF神经网络滑模制导律．首先根据准平行接近原理和变结构控制理论设计滑模面,然后将滑模面作为RBF神经网络的输入变量,输出量即为导弹的加速度．为了使得导弹系统能够到达滑模面,采用自适应算法实时在线调整RBF神经网络的连接权值．该导引律将目标机动视为干扰量,在拦截过程中不需要测量目标加速度,因此该导引律对目标机动具有较强的鲁棒性．在执行上,只用到了视线角速率,因而实现简单．仿真结果表明,所提出的导引律和比例导引相比在脱靶量、拦截时间等方面有了很大的提高．     

Safe semi supervised multi-target regression (MTR-SAFER) for new targets learning

Multimedia Tools and Applications - Multi-target regression (MTR) is a challenging research problem which aims to predict more than one continuous variable as output in a pattern. In recent time, a...     

A new efficient and direct solution for pose estimation usingquadrangular targets: algorithm and evaluation

Pose estimation is an important operation for many vision tasks. In this paper, the authors propose an algorithm for pose estimation based on the volume measurement of tetrahedra composed of feature-point triplets extracted from an arbitrary quadrangular target and the lens center of the vision system. The inputs to this algorithm are the six distances joining all feature pairs and the image coordinates of the quadrangular target. The outputs of this algorithm are the effective focal length of the vision system, the interior orientation parameters of the target, the exterior orientation parameters of the camera with respect to an arbitrary coordinate system if the target coordinates are known in this frame, and the final pose of the camera. The authors have also developed a shape restoration technique which is applied prior to pose recovery in order to reduce the effects of inaccuracies caused by image projection. An evaluation of the method has shown that this pose estimation technique is accurate and robust. Because it is based on a unique and closed form solution, its speed makes it a potential candidate for solving a variety of landmark-based tracking problems     

Some new approaches to building and implementation of wall-functions for modeling of near-wall turbulent flows

To study and develop wall-functions for modeling of near-wall turbulent flows, a linear model equation is introduced. This equation simulates major mathematical peculiarities of the low-Reynolds-number model including a near wall sub-layer and transition region. Dirichlet and Newman boundary-value problems are considered. The standard and analytical wall-functions are investigated on different properties including the mesh sensitivity of a solution. A Robin-type interpretation of wall-functions as boundary conditions is suggested. It is shown that solution of a problem is mesh independent and more accurate in this case. General type analytical and numerical wall-functions are developed on the basis of a boundary condition transfer. An effective numerical method of decomposition is suggested. The method can be used in application to either high-Reynolds-number models with the numerical wall-functions or low-Reynolds-number models directly. Although a model equation is considered, the formulas, methods and conclusions are valid and can be directly used for the Reynolds Averaged Navier-Stokes (RANS) equations.     

一种新的雷达动目标检测方法

基于线性调频(LFM)脉冲压缩雷达原理及雷达动目标显示(MTI)的数学模型,通过与传统二脉冲对消和三脉冲对消的方法相对比,采用四脉冲对消和参差脉冲重复频率处理盲速的方法进行动目标检测。仿真实验证明,在动目标检测中使用线性调频脉冲压缩的四脉冲对消和参差脉冲重复频率方法,能够更好地提取杂波中的运动目标。     

Genetic-based approaches in ranking function discovery and optimization in information retrieval — A framework

An Information Retrieval (IR) system consists of document collection, queries issued by users, and the matching/ranking functions used to rank documents in the predicted order of relevance for a given query. A variety of ranking functions have been used in the literature. But studies show that these functions do not perform consistently well across different contexts. In this paper we propose a two-stage integrated framework for discovering and optimizing ranking functions used in IR. The first stage, discovery process, is accomplished by intelligently leveraging the structural and statistical information available in HTML documents by using Genetic Programming techniques to yield novel ranking functions. In the second stage, the optimization process, document retrieval scores of various well-known ranking functions are combined using Genetic Algorithms. The overall discovery and optimization framework is tested on the well-known TREC collection of web documents for both the ad-hoc retrieval task and the routing task. Utilizing our framework we observe a significant increase in retrieval performance compared to some of the well-known stand alone ranking functions.     

A new strategy combining empirical and analytical approaches for grasping unknown 3D objects

This paper proposes a novel strategy for grasping 3D unknown objects in accordance with their corresponding task. We define the handle or the natural grasping component of an object as the part chosen by humans to pick up this object. When humans reach out to grasp an object, it is generally in the aim of accomplishing a task. Thus, the chosen grasp is quite related to the object task. Our approach learns to identify object handles by imitating humans. In this paper, a new sufficient condition for computing force-closure grasps on the obtained handle is also proposed. Several experiments were conducted to test the ability of the algorithm to generalize to new objects. They also show the adaptability of our strategy to the hand kinematics.     

一种新型的防盗报警电路设计

目前大多教防盗报警系统都利用电磁波或红外线辐射来检测人体移动,这两种报警系统都因有发射和接收电路而结构复杂,价格昂贵.针对这种状况,本文介绍了一种新型防盗报警电路设计方法.该设计利用人体作为感应电容的一个极板,借助高精度电压比较器MAX912检测感应电容的变化.同时该电路还具有双门限检测报警功能,采用震动传感器感受震动信号,利用单片机设置告警距离和持续时间双门限.测试结果表明该电路结构简单,性能稳定,已成功用于某防盗报警系统中.