Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

On Fermat's principle for causal curves in time oriented Finsler spacetimes

In this work, a version of Fermat's principle for causal curves with the same energy in time orientable Finsler spacetimes is proved. We calculate the second variation of the time arrival functional along a geodesic in terms of the index form associated with the Finsler spacetime Lagrangian. Then the character of the critical points of the time arrival functional is investigated and a Morse index theorem in the context of Finsler spacetime is presented. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4765066]

Mayaro Virus Infection in Amazonia: A Multimodel Inference Approach to Risk Factor Assessment

Background: Arboviral diseases are major global public health threats. Yet, our understanding of infection risk factors is, with a few exceptions, considerably limited. A crucial shortcoming is the widespread use of analytical methods generally not suited for observational data - particularly null hypothesis-testing (NHT) and step-wise regression (SWR). Using Mayaro virus (MAYV) as a case study, here we compare information theory-based multimodel inference (MMI) with conventional analyses for arboviral infection risk factor assessment. Methodology/Principal Findings: A cross-sectional survey of anti-MAYV antibodies revealed 44% prevalence (n = 270 subjects) in a central Amazon rural settlement. NHT suggested that residents of village-like household clusters and those using closed toilet/latrines were at higher risk, while living in non-village-like areas, using bednets, and owning fowl, pigs or dogs were protective. The "minimum adequate" SWR model retained only residence area and bednet use. Using MMI, we identified relevant covariates, quantified their relative importance, and estimated effect-sizes (beta +/- SE) on which to base inference. Residence area (beta(Village) = 2.93 +/- 0.41; beta(Upland) = -0.56 +/- 0.33, beta(Riverbanks) = -2.37 +/- 0.55) and bednet use (beta = -0.95 +/- 0.28) were the most important factors, followed by crop-plot ownership (beta = 0.39 +/- 0.22) and regular use of a closed toilet/latrine (beta = 0.19 +/- 0.13); domestic animals had insignificant protective effects and were relatively unimportant. The SWR model ranked fifth among the 128 models in the final MMI set. Conclusions/Significance: Our analyses illustrate how MMI can enhance inference on infection risk factors when compared with NHT or SWR. MMI indicates that forest crop-plot workers are likely exposed to typical MAYV cycles maintained by diurnal, forest dwelling vectors; however, MAYV might also be circulating in nocturnal, domestic-peridomestic cycles in village-like areas. This suggests either a vector shift (synanthropic mosquitoes vectoring MAYV) or a habitat/habits shift (classical MAYV vectors adapting to densely populated landscapes and nocturnal biting); any such ecological/adaptive novelty could increase the likelihood of MAYV emergence in Amazonia.

Social phobia in obsessive-compulsive disorder: Prevalence and correlates

Background: Social Phobia (SP) is an anxiety disorder that frequently co-occurs with obsessive-compulsive disorder (OCD); however, studies that evaluate clinical factors associated with this specific comorbidity are rare. The aim was to estimate the prevalence of SP in a large multicenter sample of OCD patients and compare the characteristics of individuals with and without SP. Method: A cross-sectional study with 1001 patients of the Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders using several assessment instruments, including the Dimensional Yale-Brown Obsessive-Compulsive Scale and the Structured Clinical Interview for DSM-IV Axis I Disorders. Univariate analyses were followed by logistic regression. Results: Lifetime prevalence of SP was 34.6% (N=346). The following variables remained associated with SP comorbidity after logistic regression: male sex, lower socioeconomic status, body dysmorphic disorder, specific phobia, dysthymia, generalized anxiety disorder, agoraphobia, Tourette syndrome and binge eating disorder. Limitations: The cross-sectional design does not permit the inference of causal relationships; some retrospective information may have been subject to recall bias; all patients were being treated in tertiary services, therefore generalization of the results to other samples of OCD sufferers should be cautious. Despite the large sample size, some hypotheses may not have been confirmed due to the small number of cases with these characteristics (type 2 error). Conclusion: SP is frequent among OCD patients and co-occurs with other disorders that have common phenomenological features. These findings have important implications for clinical practice, indicating the need for broader treatment approaches for individuals with this profile. (C) 2012 Elsevier B.V. All rights reserved.

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results". The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.

Bioelectrical impedance with different equations versus deuterium oxide dilution method for the inference of body composition in healthy older persons

There is no consensus regarding the accuracy of bioimpedance for the determination of body composition in older persons. This study aimed to compare the assessment of lean body mass of healthy older volunteers obtained by the deuterium dilution method (reference) with those obtained by two frequently used bioelectrical impedance formulas and one formula specifically developed for a Latin-American population. A cross-sectional study. Twenty one volunteers were studied, 12 women, with mean age 72 +/- 6.7 years. Urban community, Ribeiro Preto, Brazil. Fat free mass was determined, simultaneously, by the deuterium dilution method and bioelectrical impedance; results were compared. In bioelectrical impedance, body composition was calculated by the formulas of Deuremberg, Lukaski and Bolonchuck and Valencia et al. Lean body mass of the studied volunteers, as determined by bioelectrical impedance was 37.8 +/- 9.2 kg by the application of the Lukaski e Bolonchuk formula, 37.4 +/- 9.3 kg (Deuremberg) and 43.2 +/- 8.9 kg (Valencia et. al.). The results were significantly correlated to those obtained by the deuterium dilution method (41.6 +/- 9.3 Kg), with r=0.963, 0.932 and 0.971, respectively. Lean body mass obtained by the Valencia formula was the most accurate. In this study, lean body mass of older persons obtained by the bioelectrical impedance method showed good correlation with the values obtained by the deuterium dilution method. The formula of Valencia et al., developed for a Latin-American population, showed the best accuracy.

Likelihood-based inference for power distributions

This paper considers likelihood-based inference for the family of power distributions. Widely applicable results are presented which can be used to conduct inference for all three parameters of the general location-scale extension of the family. More specific results are given for the special case of the power normal model. The analysis of a large data set, formed from density measurements for a certain type of pollen, illustrates the application of the family and the results for likelihood-based inference. Throughout, comparisons are made with analogous results for the direct parametrisation of the skew-normal distribution.

Causal amplitudes in the Schwinger model at finite temperature

We show, in the imaginary time formalism, that the temperature dependent parts of all the retarded (advanced) amplitudes vanish in the Schwinger model. We trace this behavior to the CPT invariance of the theory and give a physical interpretation of this result in terms of forward scattering amplitudes of on-shell thermal particles.

Mycobacterium Bovis como agente causal da tuberculose humana

O Mycobacterium bovis incluído no complexo Mycobacterium tuberculosis pode infectar várias espécies de animais domésticos e silvestres. Embora acometa principalmente animais da espécie bovina também pode infectar outros mamíferos e inclusive os seres humanos, nos quais determina um quadro clínico indistinguível do causado pelo M. tuberculosis. Em diversos países desenvolvidos, devido à aplicação de rigorosas medidas de controle e consequente redução da prevalência da tuberculose bovina, bem como de infecções em outras espécies de animais pelo M. bovis houve em decréscimo dos níveis de ocorrência desta patologia e o tema passou a ser considerado de menor importância. No entanto, nos países em desenvolvimento, a infecção por M. bovis ainda representa um importante risco para a saúde pública, pois tem sido observada nos animais domésticos, silvestres e em seres humanos. O presente trabalho analisa a importância da infecção pelo Mycobacterium bovis em termos de saúde pública.