Learning Abstract Words and Concepts: Insights from Developmental Language Disorder

Some explanations of abstract word learning suggest that these words are learnt primarily from linguistic input, using statistical co-occurrences of words in language whereas concrete words can also rely on non-linguistic, experiential information. According to this hypothesis, we expect that, if the learner is not able to fully exploit the information in the linguistic input, abstract words should be affected more than concrete ones. Embodied approaches, instead, argue that both abstract and concrete words can rely on experiential information and, therefore, there might not be any linguistic primacy. Here, we test the role of linguistic input in the development of abstract knowledge with children with Developmental Language Disorder (DLD) and Typically Developing (TD) children aged 8-13. We show that DLD children, who by definition have impoverished language, do not show a disproportionate impairment for abstract words in lexical decision and definition tasks. These results indicate that linguistic information does not have a primary role in the learning of abstract concepts and words, rather, it would play a significant role in semantic development across all domains of knowledge

R-Gada: a fast and flexible pipeline for copy number analysis in association studies

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) using Copy Number Variation (CNV) are becoming a central focus of genetic research. CNVs have successfully provided target genome regions for some disease conditions where simple genetic variation (i.e., SNPs) has previously failed to provide a clear association.</p> <p>Results</p> <p>Here we present a new R package, that integrates: (i) data import from most common formats of Affymetrix, Illumina and aCGH arrays; (ii) a fast and accurate segmentation algorithm to call CNVs based on Genome Alteration Detection Analysis (GADA); and (iii) functions for displaying and exporting the Copy Number calls, identification of recurrent CNVs, multivariate analysis of population structure, and tools for performing association studies. Using a large dataset containing 270 HapMap individuals (Affymetrix Human SNP Array 6.0 Sample Dataset) we demonstrate a flexible pipeline implemented with the package. It requires less than one minute per sample (3 million probe arrays) on a single core computer, and provides a flexible parallelization for very large datasets. Case-control data were generated from the HapMap dataset to demonstrate a GWAS analysis.</p> <p>Conclusions</p> <p>The package provides the tools for creating a complete integrated pipeline from data normalization to statistical association. It can effciently handle a massive volume of data consisting of millions of genetic markers and hundreds or thousands of samples with very accurate results.</p

Predicting the Lay Preventive Strategies in Response to Avian Influenza from Perceptions of the Threat

Background: The identification of patterns of behaviors that lay people would engage in to protect themselves from the risk of infection in the case of avian influenza outbreak, as well as the lay perceptions of the threat that underlie these risk reduction strategies. Methodology/Principal Findings: A population-based survey (N = 1003) was conducted in 2008 to understand and describe how the French public might respond to a possible outbreak. Factor analyses highlighted three main categories of risk reduction strategies consisting of food quality assurance, food avoidance, and animal avoidance. In combination with the fear of contracting avian influenza, mental representations associated with the manifestation and/or transmission of the disease were found to significantly and systematically shape the behavioral responses to the perceived threat. Conclusions/Significance: This survey provides insight into the nature and predictors of the protective patterns that might be expected from the general public during a novel domestic outbreak of avian influenza

Noncentral bimatrix variate generalised beta distributions

In this paper, we determine the density functions of nonsymmetrised doubly noncentral matrix variate beta type I and II distributions. The nonsymetrised density functions of doubly noncentral and noncentral bimatrix variate generalised beta type I and II distributions are also obtained.Comment: 14 page

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes

Types of the cerebral arterial circle (circle of Willis) in a Sri Lankan Population

<p>Abstract</p> <p>Background</p> <p>The variations of the circle of Willis (CW) are clinically important as patients with effective collateral circulations have a lower risk of transient ischemic attack and stroke than those with ineffective collaterals. The aim of the present cadaveric study was to investigate the anatomical variations of the CW and to compare the frequency of prevalence of the different variations with previous autopsy studies as variations in the anatomy of the CW as a whole have not been studied in the Indian subcontinent.</p> <p>Methods</p> <p>The external diameter of all the arteries forming the CW in 225 normal Sri Lankan adult cadaver brains was measured using a calibrated grid to determine the prevalence in the variation in CW. Chisquared tests and a correspondence analysis were performed to compare the relative frequencies of prevalence of anatomical variations in the CW across 6 studies of diverse ethnic populations.</p> <p>Results</p> <p>We report 15 types of variations of CW out of 22 types previously described and one additional type: hypoplastic precommunicating part of the anterior cerebral arteries (A1) and contralateral posterior communicating arteries (PcoA) 5(2%). Statistically significant differences (p < 0.0001) were found between most of the studies except for the Moroccan study. An especially notable difference was observed in the following 4 configurations: 1) hypoplastic precommunicating part of the posterior cerebral arteries (P1), and contralateral A1, 2) hypoplastic PcoA and contralateral P1, 3) hypoplastic PcoA, anterior communicating artery (AcoA) and contralateral P1, 4) bilateral hypoplastic P1s and AcoA in a Caucasian dominant study by Fisher versus the rest of the studies.</p> <p>Conclusion</p> <p>The present study reveals that there are significant variations in the CW among intra and inter ethnic groups (Caucasian, African and Asian: Iran and Sri Lanka dominant populations), and warrants further studies keeping the methods of measurements, data assessment, and the definitions of hypoplasia the same.</p

A divergent role for estrogen receptor-beta in node-positive and node-negative breast cancer classified according to molecular subtypes: an observational prospective study

Introduction: Estrogen receptor-alpha (ER-alpha) and progesterone receptor (PgR) are consolidated predictors of response to hormonal therapy (HT). In contrast, little information regarding the role of estrogen receptor-beta (ER-beta) in various breast cancer risk groups treated with different therapeutic regimens is available. In particular, there are no data concerning ER-beta distribution within the novel molecular breast cancer subtypes luminal A (LA) and luminal B (LB), HER2 (HS), and triple-negative (TN). Methods: We conducted an observational prospective study using immunohistochemistry to evaluate ER-beta expression in 936 breast carcinomas. Associations with conventional biopathological factors and with molecular subtypes were analyzed by multiple correspondence analysis (MCA), while univariate and multivariate Cox regression analysis and classification and regression tree analysis were applied to determine the impact of ER-beta on disease-free survival in the 728 patients with complete follow-up data. Results: ER-beta evenly distributes (55.5%) across the four molecular breast cancer subtypes, confirming the lack of correlation between ER-beta and classical prognosticators. However, the relationships among the biopathological factors, analyzed by MCA, showed that ER-beta positivity is located in the quadrant containing more aggressive phenotypes such as HER2 and TN or ER-alpha/PgR/Bcl2- tumors. Kaplan-Meier curves and Cox regression analysis identified ER-beta as a significant discriminating factor for disease-free survival both in the node-negative LA (P = 0.02) subgroup, where it is predictive of response to HT, and in the node-positive LB (P = 0.04) group, where, in association with PgR negativity, it conveys a higher risk of relapse. Conclusion: Our data indicated that, in contrast to node-negative patients, in node-positive breast cancer patients, ER-beta positivity appears to be a biomarker related to a more aggressive clinical course. In this context, further investigations are necessary to better assess the role of the different ER-beta isoforms

Defective Innate Cell Response and Lymph Node Infiltration Specify Yersinia pestis Infection

Since its recent emergence from the enteropathogen Yersinia pseudotuberculosis, Y. pestis, the plague agent, has acquired an intradermal (id) route of entry and an extreme virulence. To identify pathophysiological events associated with the Y. pestis high degree of pathogenicity, we compared disease progression and evolution in mice after id inoculation of the two Yersinia species. Mortality studies showed that the id portal was not in itself sufficient to provide Y. pseudotuberculosis with the high virulence power of its descendant. Surprisingly, Y. pseudotuberculosis multiplied even more efficiently than Y. pestis in the dermis, and generated comparable histological lesions. Likewise, Y. pseudotuberculosis translocated to the draining lymph node (DLN) and similar numbers of the two bacterial species were found at 24 h post infection (pi) in this organ. However, on day 2 pi, bacterial loads were higher in Y. pestis-infected than in Y. pseudotuberculosis-infected DLNs. Clustering and multiple correspondence analyses showed that the DLN pathologies induced by the two species were statistically significantly different and identified the most discriminating elementary lesions. Y. pseudotuberculosis infection was accompanied by abscess-type polymorphonuclear cell infiltrates containing the infection, while Y. pestis-infected DLNs exhibited an altered tissue density and a vascular congestion, and were typified by an invasion of the tissue by free floating bacteria. Therefore, Y. pestis exceptional virulence is not due to its recently acquired portal of entry into the host, but is associated with a distinct ability to massively infiltrate the DLN, without inducing in this organ an organized polymorphonuclear cell reaction. These results shed light on pathophysiological processes that draw the line between a virulent and a hypervirulent pathogen