Identification of novel "pathologs" (human disease-related gene candidates) from the RIKEN full-length mouse cDNA data set

The majority of common diseases such as cancer, allergy, diabetes, or heart disease are characterized by complex genetic traits, in which genetic and environmental components contribute to disease susceptibility. Our knowledge of the genetic factors underlying most of such diseases is limited. A major goal in the post-genomic era is to identify and characterize disease susceptibility genes and to use this knowledge for disease treatment and prevention. More than 500 genes are conserved across the invertebrate and vertebrate genomes. Because of gene conservation, various organisms including yeast, fruitfly, zebrafish, rat, and mouse have been used as genetic models.

ICT AND PRODUCTIVITY IN DEVELOPING COUNTRIES: NEW FIRM-LEVEL EVIDENCE FROM BRAZIL AND INDIA

This paper uses a unique new data set on manufacturing firms in Brazil and India to estimate production functions, augmented by information and communications technology (ICT). We find a strong positive association between ICT capital and productivity in both countries that is robust to several different specification tests. The paper also breaks new ground when using the Indian data to investigate the effect of the institutional and policy environment on ICT capital investment and productivity. We find that poorer infrastructure quality and labor market policy are associated with lower levels of ICT adoption, while poorer infrastructure is also associated with lower returns to investment.

Stakeholders Theory and Theory of the Firm: a study on the hierarchy of objective-functions in Brazilian companies

Discussion opposing the Theory of the Firm to the Theory of Stakeholders are contemporaneous and polemical. One focal point of such debates refers to which objective-function companies, should choose, whether that of the shareholders or that of the stakeholders, and whether it is possible to opt for both simultaneously. Several empirical studies. have attempted-to test a possible correlation between both functions, and there has not been any consensus-so far. The objective of the present research is to examine a gap in such discussions: is there (or not) a subordination of the stakeholders` objective-function to that of the shareholders? The research is empirical,and analytical and employs quantitative methods. Hypotheses were tested and data analyzed by using non-parametrical (chi-square test) and parametrical procedures (frequency. correlation `coefficient). Secondary data was collected from he Economitica database and from the Brazilian Institute of Social and-Economic Analyses (IBASE) website, relative to public companies that have published their Social Balance Statements following the IBASE model from 1999 to 2006, whose sample amounted to 65 companies; In order to assess the objective-function of shareholders a proxy was created based on the following three indices: ROE (return on equity), EnterpriseValue and Tobin`s Q. In order to assess the objective-function of stakeholders a proxy was created by employing the following IBASE social balance indices: internal ones (ISI), external ones (ISE), and environmental ones (IAM). The results have shown no evidence of subordination of stakeholders` objective-function to that of the shareholders in analyzed companies, negating initial expectations and calling for deeper investigation of results. Its main conclusion, which states that the attempted subordination does not take place, is limited to the sample herein investigated and calls for ongoing research aiming at improvements which may lead to sample enlargement and, as a consequence, may make feasible the application of other statistical techniques which may yield a more thorough, analysis of the studied phenomehon.

On S-asymptotically omega-periodic functions on Banach spaces and applications

This paper is devoted to the study of the class of continuous and bounded functions f : [0, infinity] -> X for which exists omega > 0 such that lim(t ->infinity) (f (t + omega) - f (t)) = 0 (in the sequel called S-asymptotically omega-periodic functions). We discuss qualitative properties and establish some relationships between this type of functions and the class of asymptotically omega-periodic functions. We also study the existence of S-asymptotically omega-periodic mild solutions of the first-order abstract Cauchy problem in Banach spaces. (C) 2008 Elsevier Inc. All rights reserved.

Identification of a juvenile hormone esterase-like gene in the honey bee, Apis mellifera L. - Expression analysis and functional assays

Tight control over circulating juvenile hormone (JH) levels is of prime importance in an insect`s life cycle. Consequently, enzymes involved in JH metabolism, especially juvenile hormone esterases (JHEs), play major roles during metamorphosis and reproduction. In the highly eusocial Hymenoptera, JH has been co-opted into additional functions, primarily in the development of the queen and worker castes and in age-related behavioral development of workers. Within a set of 21 carboxylesterases predicted in the honey bee genome we identified one gene (Amjhe-like) that contained the main functional motifs of insect JHEs. Its transcript levels during larval development showed a maximum at the switch from feeding to spinning behavior, coinciding with a JH titer minimum. In adult workers, the highest levels were observed in nurse bees, where a low JH titer is required to prevent the switch to foraging. Functional assays showed that Amjhe-like expression is induced by JH-III and suppressed by 20-hydroxyecdysone. RNAi-mediated silencing of Amjhe-like gene function resulted in a six-fold increase in the JH titer in adult worker bees. The temporal profile of Amjhe-like expression in larval and adult workers, the pattern of hormonal regulation and the knockdown phenotype are consistent with the function of this gene as an authentic JHE. (C) 2008 Elsevier Inc. All rights reserved.

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting. Copyright (C) 2004 John Wiley Sons, Ltd.

A note on S-asymptotically periodic functions

In [Haiyin Gao, Ke Wang, Fengying Wei, Xiaohua Ding, Massera-type theorem and asymptotically periodic Logistic equations, Nonlinear Analysis: Real World Applications 7 (2006) 1268-1283, Lemma 2.1] it is established that a scalar S-asymptotically to-periodic function (that is, a continuous and bounded function f : [0, infinity) -> R such that lim(t ->infinity)(f (t + omega) - f (t)) = 0) is asymptotically omega-periodic. In this note we give two examples to show that this assertion is false. (C) 2008 Elsevier Ltd. Ail rights reserved.

Use of the q-Gaussian mutation in evolutionary algorithms

This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.

A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes. The AID functions allow for comparisons of feature vectors by choosing one of two parameterized expressions: one targeting weak attribute concurrence influence and the other for strong concurrence influence. This paper presents the mathematical definition and implementation of the AID family for a two-dimensional feature space and its extension to any dimension. The composition of the AID family with L (p) distance family is considered to propose a procedure to determine the best distance for a specific application. Experimental results involving several sets of medical images demonstrate that, taking as reference the perception of the specialist in the field (radiologist), the AID functions perform better than the general distance functions commonly used in CBIR.

Ant visitation to extrafloral nectaries decreases herbivory and increases fruit set in Chamaecrista debilis (Fabaceae) in a Neotropical savanna

Studies of ant-plant relationships elucidate how top-down effects of the third trophic level can affect the biomass, richness, and/or species composition of plants. Although widespread in the neotropics, few studies have so far examined the direct effects of ants on plant fitness. Here, through experimental manipulation (ant-exclusion) under natural conditions, we examined the effect of ant visitation to extrafloral nectaries on leaf herbivory and fruit set in Chamaecrista debilis in the Brazilian savanna. As opposed to other Chamaecrista species, our results showed that visiting ants (15 species) significantly reduce herbivory and increase fruit set by more than 50% compared to plants from which ants were excluded. This mutualistic system is facultative in nature, and corroborates the potential beneficial role of exudate-feeding ants as anti-herbivore agents of tropical plants. (C) 2010 Elsevier GmbH. All rights reserved.

Estimating remittance functions for Pacific island migrants

There is concern that Pacific island economies dependent on remittances of migrants will endure foreign exchange shortages and declining Living standards as remittance levels drop due to lower migration rates and the belief that migrants' willingness to remit decreases over time. The empirical validity of the remittance-decay hypothesis has never been tested. From survey data on Tongan and Western Samoan migrants in Sydney, this paper estimates remittance functions using Tobit regression analysis. It is found that the remittance-decay hypothesis has no empirical validity and migrants are motivated by factors other than altruistic family support, including asset accumulation and investment back home. (C) 1997 Elsevier Science Ltd.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Structure optimization combining soft-core interaction functions, the diffusion equation method, and molecular dynamics

Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.