LRD : Latent Relation Discovery for vector space expansion and information retrieval

In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

Recombinant protein production in yeast:methods and protocols

In the last few years, significant advances have been made in understanding how a yeast cell responds to the stress of producing a recombinant protein, and how this information can be used to engineer improved host strains. The molecular biology of the expression vector, through the choice of promoter, tag and codon optimization of the target gene, is also a key determinant of a high-yielding protein production experiment. Recombinant Protein Production in Yeast: Methods and Protocols examines the process of preparation of expression vectors, transformation to generate high-yielding clones, optimization of experimental conditions to maximize yields, scale-up to bioreactor formats and disruption of yeast cells to enable the isolation of the recombinant protein prior to purification. Written in the highly successful Methods in Molecular Biology™ series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.

The relationship between high-sensitivity CRP and polyclonal Free Light Chains as markers of inflammation in chronic disease

Introduction: Serum concentrations of polyclonal free light chains (FLC) represent the activity of the adaptive immune system. This study assessed the relationship between polyclonal FLC and the established marker of innate immunity, C-reactive protein (CRP), in chronic and acute disease. Methods: We utilized four cross-sectional chronic disease patient cohorts: chronic kidney disease (CKD), diabetes, vasculitis and kidney transplantation; and a longitudinal intensive care case series to assess the kinetics of production in acute disease. Results: There was a weak association between polyclonal FLC and high-sensitivity CRP (hs-CRP) in the study cohorts. A longitudinal assessment in acute disease showed a gradual increase in FLC concentrations over time, often when CRP levels were falling, demonstrating clear differences in the response kinetics of CRP and FLC in this setting. Conclusion: Polyclonal FLC and hs-CRP provide independent information as to inflammatory status. Prospective studies are now required to assess the utility of hs-CRP and polyclonal FLC in combination for risk stratification in disease populations. © 2013 John Wiley & Sons Ltd.

Modulation of interleukin-6 and matrix metalloproteinase 2 expression in human fibroblast-like synoviocytes by functional ionotropic glutamate receptors

Objective. Patients with rheumatoid arthritis (RA) have increased concentrations of the amino acid glutamate in synovial fluid. This study was undertaken to determine whether glutamate receptors are expressed in the synovial joint, and to determine whether activation of glutamate receptors on human synoviocytes contributes to RA disease pathology. Methods. Glutamate receptor expression was examined in tissue samples from rat knee joints and in human fibroblast-like synoviocytes (FLS). FLS from 5 RA patients and 1 normal control were used to determine whether a range of glutamate receptor antagonists influenced expression of the proinflammatory cytokine interleukin-6 (IL-6), enzymes involved in matrix degradation and cytokine processing (matrix metalloproteinase 2 [MMP-2] and MMP-9), and the inhibitors of these enzymes (tissue inhibitor of metalloproteinases 1 [TIMP-1] and TIMP-2). IL-6 concentrations were determined by enzyme-linked immunosorbent assay, MMP activity was measured by gelatin zymography, and TIMP activity was determined by reverse zymography. Fluorescence imaging of intracellular calcium concentrations in live RA FLS stimulated with specific antagonists was used to reveal functional activation of glutamate receptors that modulated IL-6 or MMP-2. Results. Ionotropic and metabotropic glutamate receptor subunit mRNA were expressed in the patella, fat pad, and meniscus of the rat knee and in human articular cartilage. Inhibition of N-methyl-D-aspartate (NMDA) receptors in RA FLS increased proMMP-2 release, whereas non-NMDA ionotropic glutamate receptor antagonists reduced IL-6 production by these cells. Stimulation with glutamate, NMDA, or kainate (KA) increased intracellular calcium concentrations in RA FLS, demonstrating functional activation of specific ionotropic glutamate receptors. Conclusion. Our findings indicate that activation of NMDA and KA glutamate receptors on human synoviocytes may contribute to joint destruction by increasing IL-6 expression. © 2007, American College of Rheumatology.

Elevated serum free light chains predict cardiovascular events in type 2 diabetes

OBJECTIVE: Elevated polyclonal serum immunoglobulin free light chains (FLCs; combined FLCκ+FLCλ [cFLC]) are associated with adverse clinical outcomes and increased mortality; we investigated cFLC and cardiovascular disease (CVD) events in type 2 diabetes. RESEARCH DESIGN AND METHODS: In a cohort study of 352 south Asian patients with type 2 diabetes, serum cFLC, high-sensitivity C-reactive protein (hsCRP), and standard biochemistry were measured. CVD events over 2 years were recorded and assessed usingmultiple logistic regression. RESULTS: cFLC levels were elevated significantly in 29 of 352 (8%) patients with CVD events during 2 years of follow-up (50.7 vs. 42.8mg/L; P = 0.004). Inmultivariate analysis, elevated cFLC (>57.2 mg/L) was associated with CVD outcomes (odds ratio 3.3 [95% CI 1.3-8.2]; P = 0.012) and remained significant after adjusting for age, albumin-to-creatinine ratio, diabetes duration, or treatment. CONCLUSIONS: cFLC elevation is a novel marker for CVD outcomes in type 2 diabetes that warrants further investigation. © 2014 by the American Diabetes Association.

Resource-efficient supply chains:a research framework, literature review and research agenda

Purpose: To define a research agenda for creating Resource-Efficient Supply Chains (RESC) by identifying and analysing their key characteristics as well as future research opportunities. Design/methodology/approach: We follow a systematic review method to analyse the literature and to understand RESC taking a substantive theory approach. Our approach is grounded in a specific domain, the agri-food sector, because it is an intensive user of an extensive range of resources. Findings: The review shows that literature has looked at the use of resources primarily from the environmental impact perspective. It shows a lack of understanding of the specific RESC characteristics, and concludes more research is needed on multi-disciplinary methods for resource use and impact analyses as well as assessment methods for resource sensitivity and responsiveness. There is a need to explore whether or not, and how, logistics/supply chain decisions will affect the overall configuration of future food supply chains in an era of resource scarcity and depletion and what the trade-offs will be. Research limitations/implications: The paper proposes an agenda for future research in the area of resource–efficient supply chain. The framework proposed along with the key characteristics identified for RESC can be applied to other sectors. Practical implications: Our research should facilitate further understanding of the implications and trade-offs of supply chain decisions taken on the use of resources by supply chain managers. Originality/value: The paper explores the interaction between supply chains and natural resources and also defines the key characteristics of RESC.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Investigating the Relations of Attacks, Methods and Objects in Regard to Information Security in Network TCP/IP Environment

Possibilities for investigations of 43 varieties of file formats (objects), joined in 10 groups; 89 information attacks, joined in 33 groups and 73 methods of compression, joined in 10 groups are described in the paper. Experimental, expert, possible and real relations between attacks’ groups, method’ groups and objects’ groups are determined by means of matrix transformations and the respective maximum and potential sets are defined. At the end assessments and conclusions for future investigation are proposed.

Advance of the Access Methods

The goal of this paper is to outline the advance of the access methods in the last ten years as well as to make review of all available in the accessible bibliography methods.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

Limit Cycles of Perturbations of a Class of Quadratic Hamiltonian Vector Fields

We prove that in quadratic perturbations of generic Hamiltonian vector fields with two saddle points and one center there can appear at most two limit cycles. This bound is exact.

Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.

An Iterative Method for the Matrix Principal n-th Root

In this paper we give an iterative method to compute the principal n-th root and the principal inverse n-th root of a given matrix. As we shall show this method is locally convergent. This method is analyzed and its numerical stability is investigated.

Bit error rate estimation methods for QPSK CO-OFDM transmission

Coherent optical orthogonal frequency division multiplexing (CO-OFDM) is an attractive transmission technique to virtually eliminate intersymbol interference caused by chromatic dispersion and polarization-mode dispersion. Design, development, and operation of CO-OFDM systems require simple, efficient, and reliable methods of their performance evaluation. In this paper, we demonstrate an accurate bit error rate estimation method for QPSK CO-OFDM transmission based on the probability density function of the received QPSK symbols. By comparing with other known approaches, including data-aided and nondata-aided error vector magnitude, we show that the proposed method offers the most accurate estimate of the system performance for both single channel and wavelength division multiplexing QPSK CO-OFDM transmission systems. © 2014 IEEE.

Distance Matrix Approach to Content Image Retrieval

As the volume of image data and the need of using it in various applications is growing significantly in the last days it brings a necessity of retrieval efficiency and effectiveness. Unfortunately, existing indexing methods are not applicable to a wide range of problem-oriented fields due to their operating time limitations and strong dependency on the traditional descriptors extracted from the image. To meet higher requirements, a novel distance-based indexing method for region-based image retrieval has been proposed and investigated. The method creates premises for considering embedded partitions of images to carry out the search with different refinement or roughening level and so to seek the image meaningful content.