Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

LATENT RANK CHANGE DETECTION FOR ANALYSIS OF SPLICE-JUNCTION MICROARRAYS WITH NONLINEAR EFFECTS

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

ProbFAST: Probabilistic Functional Analysis System Tool

Background: The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis. Results: We proposed a web platform named ProbFAST for MDE analysis which uses Bayesian inference to identify key genes that are intuitively prioritized by means of probabilities. A simulated study revealed that our method gives a better performance when compared to other approaches and when applied to public expression data, we demonstrated its flexibility to obtain relevant genes biologically associated with normal and abnormal biological processes. Conclusions: ProbFAST is a free accessible web-based application that enables MDE analysis on a global scale. It offers an efficient methodological approach for MDE analysis of a set of genes that are turned on and off related to functional information during the evolution of a tumor or tissue differentiation. ProbFAST server can be accessed at http://gdm.fmrp.usp.br/probfast.

Biomolecular interaction analysis of IFN gamma-induced signaling events in whole-cell lysates: Prevalence of latent STAT1 in high-molecular weight complexes

The basic framework for the JAK/STAT pathway is well documented. Recruitment of latent cytoplasmic STAT transcription factors to tyrosine phosphorylated docking sites on cytokine receptors and their JAK-mediated phosphorylation instigates their translocation to the nucleus and their ability to bind DNA, The biochemical processes underlying recruitment and activation of this pathway have commonly been studied in reconstituted in vitro systems using previously defined recombinant signaling components. We have dissected the Interferon gamma (IFN gamma) signal transduction pathway in crude extracts from wild-type and STAT1-negative mutant cell Lines by real-time BIAcore analysis, size-exclusion (SE) chromatography and immune-detection. The data indicate that in detergent-free cell extracts: (1) the phospho-tyrosine (Y440P)-containing peptide motif of the IFN gamma-receptor ct-chain interacts directly with STAT1, or STAT1 complexes, and no other protein; (2) nonactivated STAT 1 is present in a higher molecular weight complex(es) and, at least for IFN gamma-primed cells, is available for recruitment to the activated IFN gamma-receptor from only a subset of such complexes; (3) activated STAT1 is released from the receptor as a monomer.

A probabilistic analysis of Clostridium perfringens growth during food service operations

The purpose of this study was threefold: first, the study was designed to illustrate the use of data and information collected in food safety surveys in a quantitative risk assessment. In this case, the focus was on the food service industry; however, similar data from other parts of the food chain could be similarly incorporated. The second objective was to quantitatively describe and better understand the role that the food service industry plays in the safety of food. The third objective was to illustrate the additional decision-making information that is available when uncertainty and variability are incorporated into the modelling of systems. (C) 2002 Elsevier Science B.V. All rights reserved.

Ex vivo analysis of T-cell responses to Epstein-Barr virus-encoded oncogene latent membrane protein 1 reveals highly conserved epitope sequences in virus isolates from diverse geographic regions

Epstein-Barr virus (EBV)-encoded oncogene latent membrane protein (LMP) 1, which is consistently expressed in multiple EBV-associated malignancies, has been proposed as a potential target antigen for any future vaccine designed to control these malignancies. However, the high degree of genetic variation in the LMP1 sequence has been considered a major impediment for its use as a potential immunotherapeutic target for the treatment of EBV-associated malignancies. In the present study, we have employed a highly efficient strategy, based on ex vivo functional assays, to conduct an extensive sequence-wide analysis of LMP1-specific T-cell responses in a large panel of healthy virus carriers of diverse ethnic origin and nasopharyngeal carcinoma patients. By comparing the frequencies of T cells specific for overlapping peptides spanning LMP1, we mapped a number of novel HLA class I- and class II-restricted LMP1 T-cell epitopes, including an epitope with dual HLA class I restriction. More importantly, extensive sequence analysis of LMP1 revealed that the majority of the T-cell epitopes were highly conserved in EBV isolates from Caucasian, Papua New Guinean, African, and Southeast Asian populations, while unique geographically constrained genetic variation was observed within one HLA A2 supertype-restricted epitope. These findings indicate that conserved LMP1 epitopes should be considered in designing epitope-based immunotherapeutic strategies against EBV-associated malignancies in different ethnic populations.

Semantic social network analysis

Mestrado em Engenharia Informática

Response Time Analysis for Fixed-Priority Tasks with Multiple Probabilistic Parameters

Demo presented in 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2015). 8 to 12, Jun, 2015. La Roche-en-Ardenne, Belgium. Extended abstract.

The Future of services: Uncovering latent needs through trend analysis

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Métodos para el análisis de programas probabilistas y no-deterministas utilizando probadores de teoremas. Methods for the analysis of probabilistic and nondeterministic programs using theorem provers.

La verificación y el análisis de programas con características probabilistas es una tarea necesaria del quehacer científico y tecnológico actual. El éxito y su posterior masificación de las implementaciones de protocolos de comunicación a nivel hardware y soluciones probabilistas a problemas distribuidos hacen más que interesante el uso de agentes estocásticos como elementos de programación. En muchos de estos casos el uso de agentes aleatorios produce soluciones mejores y más eficientes; en otros proveen soluciones donde es imposible encontrarlas por métodos tradicionales. Estos algoritmos se encuentran generalmente embebidos en múltiples mecanismos de hardware, por lo que un error en los mismos puede llegar a producir una multiplicación no deseada de sus efectos nocivos.Actualmente el mayor esfuerzo en el análisis de programas probabilísticos se lleva a cabo en el estudio y desarrollo de herramientas denominadas chequeadores de modelos probabilísticos. Las mismas, dado un modelo finito del sistema estocástico, obtienen de forma automática varias medidas de performance del mismo. Aunque esto puede ser bastante útil a la hora de verificar programas, para sistemas de uso general se hace necesario poder chequear especificaciones más completas que hacen a la corrección del algoritmo. Incluso sería interesante poder obtener automáticamente las propiedades del sistema, en forma de invariantes y contraejemplos.En este proyecto se pretende abordar el problema de análisis estático de programas probabilísticos mediante el uso de herramientas deductivas como probadores de teoremas y SMT solvers. Las mismas han mostrado su madurez y eficacia en atacar problemas de la programación tradicional. Con el fin de no perder automaticidad en los métodos, trabajaremos dentro del marco de "Interpretación Abstracta" el cual nos brinda un delineamiento para nuestro desarrollo teórico. Al mismo tiempo pondremos en práctica estos fundamentos mediante implementaciones concretas que utilicen aquellas herramientas.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Incidence of active mycobacterial infections in Brazilian patients with chronic inflammatory arthritis and negative evaluation for latent tuberculosis infection at baseline - A longitudinal analysis after using TNFa blockers

Several studies point to the increased risk of reactivation of latent tuberculosis infection (LTBI) in patients with chronic inflammatory arthritis (CIAs) after using tumour necrosis factor (TNF)a blockers. To study the incidence of active mycobacterial infections (aMI) in patients starting TNFa blockers, 262 patients were included in this study: 109 with rheumatoid arthritis (RA), 93 with ankylosing spondylitis (AS), 44 with juvenile idiopathic arthritis (JIA) and 16 with psoriatic arthritis (PsA). All patients had indication for anti-TNFa therapy. Epidemiologic and clinical data were evaluated and a simple X-ray and tuberculin skin test (TST) were performed. The control group included 215 healthy individuals. The follow-up was 48 months to identify cases of aMI. TST positivity was higher in patients with AS (37.6%) than in RA (12.8%), PsA (18.8%) and JIA (6.8%) (p < 0.001). In the control group, TST positivity was 32.7%. Nine (3.43%) patients were diagnosed with aMI. The overall incidence rate of aMI was 86.93/100,000 person-years [95% confidence interval (CI) 23.6-217.9] for patients and 35.79/100,000 person-years (95% CI 12.4-69.6) for control group (p < 0.001). All patients who developed aMI had no evidence of LTBI at the baseline evaluation. Patients with CIA starting TNFa blockers and no evidence of LTBI at baseline, particularly with nonreactive TST, may have higher risk of aMI.

Consumers’ Demand for Pork Quality: Applying Semantic Network Analysis, May 2006

Abstract Consideration of consumers’ demand for food quality entails several aspects. Quality itself is a complex and dynamic concept, and constantly evolving technical progress may cause changes in consumers’ judgment of quality. To improve our understanding of the factors influencing the demand for quality, food quality must be defined and measured from the consumer’s perspective (Cardello, 1995). The present analysis addresses the issue of food quality, focusing on pork—the food that respondents were concerned about. To gain insight into consumers’ demand, we analyzed their perception and evaluation and focused on their cognitive structures concerning pork quality. In order to more fully account for consumers’ concerns about the origin of pork, in 2004 we conducted a consumer survey of private households. The qualitative approach of concept mapping was used to uncover the cognitive structures. Network analysis was applied to interpret the results. In order to make recommendations to enterprises, we needed to know what kind of demand emerges from the given food quality schema. By establishing the importance and relative positions of the attributes, we find that the country of origin and butcher may be the two factors that have the biggest influence on consumers’ decisions about the purchase of pork.

Recent misconceptions about the "database search problem": a probabilistic analysis using Bayesian networks

This paper analyses and discusses arguments that emerge from a recent discussion about the proper assessment of the evidential value of correspondences observed between the characteristics of a crime stain and those of a sample from a suspect when (i) this latter individual is found as a result of a database search and (ii) remaining database members are excluded as potential sources (because of different analytical characteristics). Using a graphical probability approach (i.e., Bayesian networks), the paper here intends to clarify that there is no need to (i) introduce a correction factor equal to the size of the searched database (i.e., to reduce a likelihood ratio), nor to (ii) adopt a propositional level not directly related to the suspect matching the crime stain (i.e., a proposition of the kind 'some person in (outside) the database is the source of the crime stain' rather than 'the suspect (some other person) is the source of the crime stain'). The present research thus confirms existing literature on the topic that has repeatedly demonstrated that the latter two requirements (i) and (ii) should not be a cause of concern.