Testing allele homogeneity: the problem of nested hypotheses

Background: The evaluation of associations between genotypes and diseases in a case-control framework plays an important role in genetic epidemiology. This paper focuses on the evaluation of the homogeneity of both genotypic and allelic frequencies. The traditional test that is used to check allelic homogeneity is known to be valid only under Hardy-Weinberg equilibrium, a property that may not hold in practice. Results: We first describe the flaws of the traditional (chi-squared) tests for both allelic and genotypic homogeneity. Besides the known problem of the allelic procedure, we show that whenever these tests are used, an incoherence may arise: sometimes the genotypic homogeneity hypothesis is not rejected, but the allelic hypothesis is. As we argue, this is logically impossible. Some methods that were recently proposed implicitly rely on the idea that this does not happen. In an attempt to correct this incoherence, we describe an alternative frequentist approach that is appropriate even when Hardy-Weinberg equilibrium does not hold. It is then shown that the problem remains and is intrinsic of frequentist procedures. Finally, we introduce the Full Bayesian Significance Test to test both hypotheses and prove that the incoherence cannot happen with these new tests. To illustrate this, all five tests are applied to real and simulated datasets. Using the celebrated power analysis, we show that the Bayesian method is comparable to the frequentist one and has the advantage of being coherent. Conclusions: Contrary to more traditional approaches, the Full Bayesian Significance Test for association studies provides a simple, coherent and powerful tool for detecting associations.

RegCM3 nested in HadAM3 scenarios A2 and B2: projected changes in extratropical cyclogenesis, temperature and precipitation over the South Atlantic Ocean

The RegCM3 (Regional Climate Model-version 3) was nested in HadAM3 model to simulate present (1975-1989, referred hereafter as RegHad) and two future climate scenarios (A2 and B2 from 2071 to 2085, referred as RegA2 and RegB2) over the South America (SA) and South Atlantic Ocean (SAO). Projected changes in the air temperature, precipitation, low level circulation and cyclogenesis climatology were investigated. The cyclogenesis were identified using an automatic scheme for tracking based on the minimum of relative vorticity (zeta) from 10-m height wind. During summer, a general decrease (increase) in the precipitation is projected by RegA2 and RegB2 over the northeastern SA (center-west and south Brazil, north Argentina and Uruguay). For winter, an anomalous low level anticyclonic circulation is associated with the reduction in the rainfall over the central part of southern Brazil in RegA2 and RegB2 scenarios. Similar to HadAM3, RegCM3 projects larger warming in A2 scenario. For the present climate, when compared to HadAM3, RegHad defines better both the location of the main cyclogenetic areas and its annual cycle near southwestern SAO. The projections indicate a reduction in the total number of cyclones of -7.2% and -4.7% for RegA2 and RegB2, respectively, while HadAM3 reduction is -4.5% for both scenarios. The decrease is larger for initially intense cyclones (zeta <=-<= 2.5 x 10(-5) s-(1)): -20.9% (RegA2) and -11.3% (RegB2). For the lifetime, distance traveled and mean velocity of the cyclones, the A2 and B2 scenarios present mean values close to the present climate ( 3 days, 1900 km, and 9 m s(-1), respectively). Regarding the initial mean vorticity of the systems, RegB2 simulates values similar to the present climate, but they are initially weaker in RegA2. In general, RegA2 and RegB2 show a large decrease in the number of cyclones over the southern SAO due to an anticyclonic anomaly covering SAO between 30-55A degrees S. The reduction is larger in the scenario with higher concentrations of greenhouse gases (RegA2).

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Decisions on Temporal Sampling Protocol Influence the Detection of Ecological Patterns

Within-site variability in species detectability is a problem common to many biodiversity assessments and can strongly bias the results. Such variability can be caused by many factors, including simple counting inaccuracies, which can be solved by increasing sample size, or by temporal changes in species behavior, meaning that the way the temporal sampling protocol is designed is also very important. Here we use the example of mist-netted tropical birds to determine how design decisions in the temporal sampling protocol can alter the data collected and how these changes might affect the detection of ecological patterns, such as the species-area relationship (SAR). Using data from almost 3400 birds captured from 21,000 net-hours at 31 sites in the Brazilian Atlantic Forest, we found that the magnitude of ecological trends remained fairly stable, but the probability of detecting statistically significant ecological patterns varied depending on sampling effort, time of day and season in which sampling was conducted. For example, more species were detected in the wet season, but the SAR was strongest in the dry season. We found that the temporal distribution of sampling effort was more important than its total amount, discovering that similar ecological results could have been obtained with one-third of the total effort, as long as each site had been equally sampled over 2 yr. We discuss that projects with the same sampling effort and spatial design, but with different temporal sampling protocol are likely to report different ecological patterns, which may ultimately lead to inappropriate conservation strategies.

Work activities as a resource for work organization design and for strategic decisions?

This paper proposes a discussion about the possibilities to link strategic decisions to working activities performed by workers. In that sense contributions from activities analysis could be considered in order to design and manage production.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Management practices and influences on IT architecture decisions: a case study in a telecom company

The study aims to analyze the IT architecture management practices associated with their degree of maturity and the influence of institutional and strategic factors on the decisions involved through a case study in a large telecom organization. The case study allowed us to identify practices that led the company to its current stage of maturity and identify practices that can lead the company to the next stage. The strategic influence was mentioned by most respondents and the institutional influence was present in decisions related to innovation and those dealing with a higher level of uncertainties.

Diagnosis of rabies and eastern and western equine viral encephalitides in equids by multiplex Hemi-Nested RT-PCR technique

Financial support: CNPq and Pasteur Institute of São Paulo