Semantic information extraction from images of complex documents

Even though the digital processing of documents is increasingly widespread in industry, printed documents are still largely in use. In order to process electronically the contents of printed documents, information must be extracted from digital images of documents. When dealing with complex documents, in which the contents of different regions and fields can be highly heterogeneous with respect to layout, printing quality and the utilization of fonts and typing standards, the reconstruction of the contents of documents from digital images can be a difficult problem. In the present article we present an efficient solution for this problem, in which the semantic contents of fields in a complex document are extracted from a digital image.

Contextual Reading and Metalinguistic Processing in Brazilian Portuguese: A Longitudinal Study

Metalinguistic skill is the ability to reflect upon language as an object of thought. Amongst metalinguistic skills, two seem to be associated with reading and spelling: morphological awareness and phonological awareness. Phonological awareness is the ability of reflecting upon the phonemes that compose words, and morphological awareness is the ability of reflecting upon the morphemes that compose the words. The latter seems to be particularly important for reading comprehension and contextual reading, as beyond phonological information, syntactic and semantic information are required. This study is set to investigate - with a longitudinal design - the relation between those abilities and contextual reading measured by the Cloze test. The first part of the study explores the relationship between morphological awareness tasks and Cloze scores through simple correlations and, in the second part, the specificity of such relationship was inquired using multiple regressions. The results give some support to the hypothesis that morphological awareness offers an independent contribution regarding phonological awareness to contextual reading in Brazilian Portuguese.

The neuroimmune changes induced by cohabitation with an Ehrlich tumor-bearing cage mate rely on olfactory information

Cohabitation for 14 days with Ehrlich tumor-bearing mice was shown to increase locomotor activity, to decrease hypothalamic noradrenaline (NA) levels, to increase NA turnover and to decrease innate immune responses and decrease the animals' resistance to tumor growth. Cage mates of a B16F10 melanoma-bearer mice were also reported to show neuroimmune changes. Chemosignals released by Ehrlich tumor-bearing mice have been reported to be relevant for the neutrophil activity changes induced by cohabitation. The present experiment was designed to further analyze the effects of odor cues on neuroimmune changes induced by cohabitation with a sick cage mate. Specifically, the relevance of chemosignals released by an Ehrlich tumor-bearing mouse was assessed on the following: behavior (open-field and plus maze); hypothalamic NA levels and turnover; adrenaline (A) and NA plasmatic levels; and host resistance induced by tumor growth. To comply with such objectives, devices specifically constructed to analyze the influence of chemosignals released from tumor-bearing mice were employed. The results show that deprivation of odor cues released by Ehrlich tumor-bearing mice reversed the behavioral, neurochemical and immune changes induced by cohabitation. Mice use scents for intraspecies communication in many social contexts. Tumors produce volatile organic compounds released into the atmosphere through breath, sweat, and urine. Our results strongly suggest that volatile compounds released by Ehrlich tumor-injected mice are perceived by their conspecifics, inducing the neuroimmune changes reported for cohabitation with a sick companion. (C) 2011 Elsevier Inc. All rights reserved.

Antenatal depression strongly predicts postnatal depression in primary health care

Objective: To estimate the association between antenatal and postnatal depression and to examine the role of socioeconomic conditions in the risk of postnatal depression. Methods: A prospective cohort study, conducted between May 2005 and January 2006, with 831 pregnant women recruited from primary care clinics in the public sector in the city of Sao Paulo, Brazil. The presence of antenatal and postnatal depression was measured with the Self Report Questionnaire (SRQ-20). Sociodemographic and socioeconomic characteristics and obstetric information were obtained through a questionnaire. Crude and adjusted risk ratios (RR), with 95% CI, were calculated using a Poisson regression. Results: The prevalence of postnatal depressive symptoms was 31.2% (95% CI: 27.8-34.8%). Among the 219 mothers who had depressive symptoms, nearly 50% had already shown depressive symptoms during pregnancy. Women who had antenatal depression were 2.4 times more likely to present with postnatal depression than were women who did not have such symptoms during pregnancy. In the multivariate analysis, higher scores for assets (RR: 0.76, 95% CI 0.61-0.96), higher education (RR: 0.75 95% CI 0.59-0.96), daily contact with neighbors (RR: 0.68, 95% CI 0.51-0.90) and antenatal depression (RR: 2.44, 95% CI 1.93-3.08) remained independently associated with postnatal depression. Conclusions: Antenatal and postnatal depression are highly prevalent in the primary care setting.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society.