Conserved water mediated H-bonding dynamics of Ser117 and Thr119 residues in human transthyretin–thyroxin complexation: Inhibitor modeling study through docking and molecular dynamics simulation

Transthyretin (TTR) is a protein whose aggregation and deposition causes amyloid diseases in human beings. Amyloid fibril formation is prevented by binding of thyroxin (T4) or its analogs to TTR. The MD simulation study of several solvated X-ray structures of apo and holo TTR has indicated the role of a conserved water molecule and its interaction with T4 binding residues Ser117 and Thr119. Geometrical and electronic consequences of those interactions have been exploited to design a series of thyroxin analogs (Mod1–4) by modifying 5′ or 3′ or both the iodine atoms of thyroxin. Binding energy of the designed ligands has been calculated by docking the molecules in tetrameric structure of the protein. Theoretically investigated pharmacological parameters along with the binding energy data indicate the potentiality of 3′,5′-diacetyl-3,5-dichloro-l-thyronine (Mod4) to act as a better inhibitor for TTR-related amyloid diseases.     

Fault detection effectiveness of mutation and data flow testing

We report results from an experiment to compare the fault detection effectiveness of mutation, its variants and the all-uses data flow criteria. Adequate test sets were generated randomly, as opposed to by human testers as in some previous studies. We view our results in the light of those from earlier studies comparing mutation with path-oriented testing strategies. We identify and discuss factors that one might consider while evaluating an adequacy criterion for use in practice. Results from our experiments strengthen a hypothesis that an adequacy criterion based on one of the two variants of mutation has superior fault detection effectiveness than that of the all-uses criterion.     

A reference based analysis framework for understanding anomaly detection techniques for symbolic sequences

Anomaly detection for symbolic sequence data is a highly important area of research and is relevant in many application domains. While several techniques have been proposed within different domains, understanding of their relative strengths and weaknesses is limited. The key factor for this is that the nature of sequence data varies significantly across domains, and hence while a technique might perform well in its original domain, its performance is not guaranteed in a different domain. In this paper, we aim at establishing this understanding for a wide variety of anomaly detection techniques for symbolic sequences. We present a comparative evaluation of a large number of anomaly detection techniques on a variety of publicly available as well as artificially generated data sets. Many of these are existing techniques while some are slight variants and/or adaptations of traditional anomaly detection techniques to sequence data. The analysis presented in this paper allows relative comparison of the different anomaly detection techniques and highlights their strengths and weaknesses. We extend the reference based analysis (RBA) framework, which was originally proposed to analyze multivariate categorical data, to analyze symbolic sequence data sets. We visualize the symbolic sequences using the characteristics provided by the RBA framework and use the visualization to understand various aspects of the sequence data. We then use the characterization done by RBA to understand the performance of the different techniques. Using the RBA framework, we propose two anomaly detection techniques for symbolic sequences, which show consistently superior performance over the existing techniques across the different data sets.     

Proteomics in human cancer research

Proteomics is now widely employed in the study of cancer. Many laboratories are applying the rapidly emerging technologies to elucidate the underlying mechanisms associated with cancer development, progression, and severity in addition to developing drugs and identifying patients who will benefit most from molecular targeted compounds. Various proteomic approaches are now available for protein separation and identification, and for characterization of the function and structure of candidate proteins. In spite of significant challenges that still exist, proteomics has rapidly expanded to include the discovery of novel biomarkers for early detection, diagnosis and prognostication (clinical application), and for the identification of novel drug targets (pharmaceutical application). To achieve these goals, several innovative technologies including 2-D-difference gel electrophoresis, SELDI, multidimensional protein identification technology, isotope-coded affinity tag, solid-state and suspension protein array technologies, X-ray crystallography, NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulation have evolved, and are being used in different combinations. This review provides an overview of the field of proteomics and discusses the key proteomic technologies available to researchers. It also describes some of the important challenges and highlights the current pharmaceutical and clinical applications of proteomics in human cancer research.     

基于粒子群和人工免疫的混合入侵检测系统研究

目前,漏报率和误报率高一直是入侵检测系统（IDS）的主要问题,而IDS主要有误用型和异常型两种检测技术。根据这两种检测技术各自的优点以及它们的互补性,本文给出一种基于人工免疫的异常检测技术和基于粒子群优化（PSO）的误用检测技术相结合的IDS模型;同时,该系统还结合特征选择技术降低数据维度,提高系统检测性能。实验表明,该
系统具有较高的检测率和较低的误报率,可以自动更新规则库,并且记忆未知类型的攻击,是一种有效的检测方法。     

Top-down mass spectrometry of cardiac myofilament proteins in health and disease

Myofilaments are composed of thin and thick filaments that coordinate with each other to regulate muscle contraction and relaxation. PTMs together with genetic variations and alternative splicing of the myofilament proteins play essential roles in regulating cardiac contractility in health and disease. Therefore, a comprehensive characterization of the myofilament proteins in physiological and pathological conditions is essential for better understanding the molecular basis of cardiac function and dysfunction. Due to the vast complexity and dynamic nature of proteins, it is challenging to obtain a holistic view of myofilament protein modifications. In recent years, top-down MS has emerged as a powerful approach to study isoform composition and PTMs of proteins owing to its advantage of complete sequence coverage and its ability to identify PTMs and sequence variants without a priori knowledge. In this review, we will discuss the application of top-down MS to the study of cardiac myofilaments and highlight the insights it provides into the understanding of molecular mechanisms in contractile dysfunction of heart failure. Particularly, recent results of cardiac troponin and tropomyosin modifications will be elaborated. The limitations and perspectives on the use of top-down MS for myofilament protein characterization will also be briefly discussed.     

Urine proteomics for profiling of human disease using high accuracy mass spectrometry

Knowledge of the biologically relevant components of human tissues has enabled the invention of numerous clinically useful diagnostic tests, as well as non-invasive ways of monitoring disease and its response to treatment. Recent use of advanced MS-based proteomics revealed that the composition of human urine is more complex than anticipated. Here, we extend the current characterization of the human urinary proteome by extensively fractionating urine using ultra-centrifugation, gel electrophoresis, ion exchange and reverse-phase chromatography, effectively reducing mixture complexity while minimizing loss of material. By using high-accuracy mass measurements of the linear ion trap-Orbitrap mass spectrometer and LC-MS/MS of peptides generated from such extensively fractionated specimens, we identified 2362 proteins in routinely collected individual urine specimens, including more than 1000 proteins not described in previous studies. Many of these are biomedically significant molecules, including glomerularly filtered cytokines and shed cell surface molecules, as well as renally and urogenitally produced transporters and structural proteins. Annotation of the identified proteome reveals distinct patterns of enrichment, consistent with previously described specific physiologic mechanisms, including 336 proteins that appear to be expressed by a variety of distal organs and glomerularly filtered from serum. Comparison of the proteomes identified from 12 individual specimens revealed a subset of generally invariant proteins, as well as individually variable ones, suggesting that our approach may be used to study individual differences in age, physiologic state and clinical condition. Consistent with this, annotation of the identified proteome by using machine learning and text mining exposed possible associations with 27 common and more than 500 rare human diseases, establishing a widely useful resource for the study of human pathophysiology and biomarker discovery.     

A survey of current trends in computational predictions of protein-protein interactions

Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the stateof-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.     

MAIDS-多检测技术的IDS模型

目前,入侵检测系统的漏报率和误报率高一直是困扰IDS用户的主要问题,而入侵检测系统主要有误用型和异常型两种检测技术。针对这一问题,根据这两种检测技术各自的优点,以及它们的互补性,将两种检测技术结合起来的方案越来越多地应用于IDS中。论文提出了基于统计的异常检测技术和基于模式匹配的误用检测技术及其它检测技术相结合的IDS模型-MAIDS,以期达到减少入侵检测系统的漏报率和误报率的目的,从而提高系统的安全性。     

Bayesian evidence and model selection

In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Bayes, importance sampling, thermodynamic integration, and nested sampling and its recent variants. Analogies to statistical physics, from which many of these techniques originate, are discussed in order to provide readers with deeper insights that may lead to new techniques. The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.     

Characterization of a novel antibody immobilization combining protein G with parylene-H for surface plasmon resonance immunosensors

Here we present a method for selectively and efficiently immobilizing antibodies to enhance the detection performance of surface plasmon resonance immune-sensors (SPRIs) for diagnostic applications. To improve the performance of antibody arrays, protein G was used as antibody-selective linkage layer with aldehyde functionalized poly-(para-xylylene) film. To estimate the efficiency of antibody immobilization, immunoglobulin G (IgG) was measured using the anti-IgG immobilized SPRIs. To demonstrate the proof-of-concept validation, the signal detected from the IgG using parylene-H film was compared with that of a combination of parylene-H and protein G in SPRIs. The results showed that the detection of IgG on the immobilized anti-IgG layer using the combination of parylene-H and protein G has a larger change of signal than that of using parylene-H layer. These results also imply that the anti-IgG was densely and efficiently immobilized on the modified surface with the linkage layer in a combination with parylene-H and protein G. Therefore, we believe that this combinatorial approach could selectively immobilize the antibodies, and also be applied for detection and diagnosis of immune diseases in the field of many SPRIs applications.     

Proteins and protein fragments in nephrotic syndrome: Clusters, specificity and mechanisms

Proteinuria is the hallmark of renal diseases and the characterization of the urinary protein composition may become an important source of information for diagnosis and research. So far, protein analysis in urine has been utilized for a generic individuation of site-specific defects (glomerular vs. tubular) but there is a need for an extension of proteomics to specific urinary biomarkers in selected clinical conditions. The identification of fragments of proteins in plasma and urine may increase the spectrum of urinary biomarkers. The unique speculative application so far proposed for protein fragments is nephrotic syndrome, and specifically focal segmental glomerulosclerosis, in which case they reflect intrinsic proteolysis occurring in plasma and represent surrogate biomarkers of the disease activity. Albumin is probably the most studied protein. Several of the albumin fragments present a peculiar distribution of the fingerprint peptide pattern containing both the N-terminal region and the C-terminal domain with a complete lack of any MS signals for the internal sequence region. Their characterization utilizing new strategies based on 2-D nondenaturing electrophoresis is now in progress. Studies on a direct characterization of proteases in plasma and urine will also define the participation of proteases to the genesis of renal diseases.     

Proteomic analysis of human reproductive fluids

Fertilization, fetal development, and delivery depend upon a coordinated series of events in the oocyte, the embryo, and the supporting tissues and fluids. Proteomic techniques which are capable of identifying and characterizing multiple proteins simultaneously have added new dimensions to the field of human reproduction. Application of these high throughput methodologies in pregnancy-related research has begun to provide a novel perspective on the biochemical pathways involved in pregnancy and its related disorders. Most of the existing research on human reproduction and gestation has focused on follicular fluid (FF) and amniotic fluid (AF). Proteome analysis of FF has yielded significant information relevant to oocyte maturation and quality. Studies performed on the protein content of AF cells and supernatant contributed to the comprehension of the underlying pathophysiology, clinical diagnosis of pregnancy-related disorders and identification of relevant disease biomarkers. Although proteome technologies in reproduction research are not as yet widely applied, characterization of the proteome of reproductive fluids can be expected to significantly improve maternal healthcare in the future.     

Protein microarray platforms for clinical proteomics

Proteomics for clinical applications is presently in a state of transition. It has become clear that the classical approaches based on 2-DE and/or MS need to be complemented by different kinds of technologies. The well-known problems include sample complexity, sensitivity, quantitation, reproducibility, and analysis time. We suggest that the new technologies for clinical proteomics can be supported by antibody-centric protein microarray platforms. These platforms presently include antibody microarrays and lysate, or reverse capture/reverse phase protein microarrays. Other forms of these arrays are in less mature developmental stages, including ORF and self assembling protein microarrays. Bioinformatic support for interpreting these arrays is becoming more available as the whole field of systems biology begins to mature. The present set of applications for these platforms is profoundly focused on certain common cancers, immunology, and cystic fibrosis. However, we predict that many more disease entities will become studied as knowledge of the power and availability of these platforms becomes more widely established. We anticipate that these platforms will eventually evolve to accommodate label-free detection technologies, human genome-scale numbers of analytes, and increases in analytic and bioinformatic speeds.     

Trace- and failure-based semantics for responsiveness

We study open systems modeled as Petri nets with an interface for asynchronous (i.e., buffered) communication with other open systems. As a minimal requirement for successful communication, we investigate responsiveness, which guarantees that an open system and its environment always have the possibility to communicate. We investigate responsiveness with and without final states and also their respective bounded variants, where the number of pending messages never exceeds a previously known bound. Responsiveness accordance describes when one open system can be safely replaced by another open system. We present a trace-based characterization for each accordance variant. As none of the relations turns out to be compositional (i.e., it is no precongruence), we characterize the coarsest compositional relation (i.e., the coarsest precongruence) that is contained in each relation, using a variation of should testing. For the two unbounded variants, the precongruences are not decidable, but for the two bounded variants we show decidability.     

Extending the reflexion method for consolidating software variants into product lines

Software variants emerge from ad-hoc copying in-the-large with adaptations to a specific context. As the number of variants increases, maintaining such software variants becomes more and more difficult and expensive. In contrast to such ad-hoc reuse, software product lines offer organized ways of reuse, taking advantage of similarities of different products. To re-gain control, software variants may be consolidated as organized software product lines. In this paper, we describe a method and supporting tools to compare software variants at the architectural level extending the reflexion method to software variants. Murphy’s reflexion method allows one to reconstruct the module view, a static architectural view describing the static components, their interfaces and dependencies and their grouping as layers and subsystems. The method consists of the specification of the module view and the mapping of implementation components onto the module view. An automatic analysis determines differences between the module view and its implementation. We extend the reflexion method from single systems to software variants. Because software variants share a very large amount of code, we can expect components of one variant to re-occur in the other variant either identically or at least similarly. We use similarity metrics to determine this type of correspondence among implementation components between two variants. Because the similarity metrics are expensive to compute, we use clone detection techniques to reduce the number of candidate pairs of implementation components for which the similarity is computed. The correspondence is then used to transfer as much as possible of the mapping for the analyzed variants to the next variant to be analyzed. Furthermore, we describe how to unify the individual product architectures into a software product line architecture.     

Robust CoHOG feature extraction in human-centered image/video management system

Many human-centered image and video management systems depend on robust human detection. To extract robust features for human detection, this paper investigates the following shortcomings of co-occurrence histograms of oriented gradients (CoHOGs) which significantly limit its advantages: 1) The magnitudes of the gradients are discarded, and only the orientations are used; 2) the gradients are not smoothed, and thus, aliasing effect exists; and 3) the dimensionality of the CoHOG feature vector is very large (e.g., 200,000). To deal with these problems, in this paper, we propose a framework that performs the following: 1) utilizes a novel gradient decomposition and combination strategy to make full use of the information of gradients; (2) adopts a two-stage gradient smoothing scheme to perform efficient gradient interpolation; and (3) employs incremental principal component analysis to reduce the large dimensionality of the CoHOG features. Experimental results on the two different human databases demonstrate the effectiveness of the proposed method.     

Recycled paper visual indexing for quality control

In this paper, we describe the development of a system for evaluating an specific quality characteristic of recycled paper sheets using techniques of image analysis and pattern recognition. We call Bumpiness the phenomenon of interest, which is new in the literature on paper quality. This phenomenon is characterized by the appearance of macroscopic undulations on the paper sheet surface that may emerge shortly or some time after its production. We explore the detection and measurement of this defect by means of computer vision and statistical pattern recognition techniques that may allow early detection at the production site. Our goal is to give an scalar continuous measure of Bumpiness. We propose features computed from Gabor filter banks (GFB) and discrete wavelet transforms (DWT) for the characterization of paper sheet surface Bumpiness in recycled paper images. The starting point is to state the problem as a classification of the paper sheet images into two classes: low and high Bumpiness. In this setting we obtain, with both proposed texture modelling approaches (GFB and DWT), classification accuracies comparable to the agreement between human observers. The best performance is obtained using DWT features. Finally, we propose as the scalar index of Bumpines the fisher discriminant analysis (FDA) function defined on the space of the best features for the classification task. We perform an innovative validation process of this Bumpiness index, based on the ordering of random pairs of images, obtaining a very high agreement with the human observers.     

Microfluidic whole-blood immunoassays

Immunoassay is one of the most widely used biomedical diagnostic methods due to its sensitivity and specificity. Microfluidic lab-on-a-chip technology has the advantages of portability, integration, and automation. The combination of these two technologies leads to a pathway for point-of-care diagnostics using the unprocessed samples such as the whole blood. This article reviews the recent advancement and the major development in the microfluidic-based whole-blood immunoassays. After a survey of the recent studies on microfluidic whole-blood immunoassays, an in-depth review about the detection methods that can be miniaturized and integrated in the immunoassay chips is provided. Point-of-care diagnostics applications require developing a fully integrated, disposable, low-cost, and handheld microfluidic device for the whole-blood immunoassay. In this regard, some comments and suggestions for future research are given.