838 resultados para Computer based training


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High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

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PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

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Background: We evaluated the effectiveness of a school-based intervention on the promotion of physical activity among high school students in Brazil: the Saude no Boa project. Methods: A school-based, randomized trial was carried out in 2 Brazilian cities: Recife (northeast) and Florianopolis (south). Ten schools in each city were matched by size and location, and randomized into intervention or control groups. The intervention included environmental/organizational changes, physical activity education, and personnel training and engagement. Students age 15 to 24 years were evaluated at baseline and 9 months later (end of school year). Results: Although similar at baseline, after the intervention, the control group reported significantly fewer d/wk accumulating 60 minutes+ moderate-to-vigorous physical activity (MVPA) in comparison with the intervention group (2.6 versus 3.3, P < .001). The prevalence of inactivity (0 days per week) rose in the control and decreased in the intervention group. The odds ratio for engaging at least once per week in physical activity associated with the intervention was 1.83 (95% CI = 1.24-2.71) in the unadjusted analysis and 1.88 (95% CI = 1.27-2.79) after controlling for gender. Conclusion: The Saude no Boa intervention was effective at reducing the prevalence of physical inactivity. The possibility of expanding the intervention to other locations should be considered.

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Currently, the identification of pollinators is a critical necessity of conservation programs. After it was found that features extracted from patterns of wing venation are sufficient to discriminate among insect species, various studies have focused on this structure. We examined wing venation patterns of males and workers of five stingless bee species in order to determine if there are differences between sexes and if these differences are greater within than between species. Geometric morphometric analyses were made of the forewings of males and workers of Nannotrigona testaceicornis, Melipona quadrifasciata, Frieseomelitta varia, and Scaptotrigona aff. depilis and Plebeia remota. The patterns of males and workers from the same species were more similar than the patterns of individuals of the same sex from different species, and the patterns of both males and workers, when analyzed alone, were sufficiently different to distinguish among these five species. This demonstrates that we can use this kind of analysis for the identification of stingless bee species and that the sex of the individual does not impede identification. Computer-assisted morphometric analysis of bee wing images can be a useful tool for biodiversity studies and conservation programs.

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This study aimed to compare maximal fat oxidation rate parameters between moderate-and low-performance runners. Eighteen runners performed an incremental treadmill test to estimate individual maximal fat oxidation rate (Fat(max)) based on gases measures and a 10,000-m run on a track. The subjects were then divided into a low and moderate performance group using two different criteria: 10,000-m time and VO(2)max values. When groups were divided using 10,000-m time, there was no significant difference in Fat(max) (0.41 +/- 0.16 and 0.27 +/- 0.12 g.min(-1), p = 0.07) or in the exercise intensity that elicited Fat(max) (59.9 +/- 16.5 and 68.7 +/- 10.3 % (V) over dotO(2max), p = 0.23) between the moderate and low performance groups, respectively (p > 0.05). When groups were divided using VO(2max) values, Fat(max) was significantly lower in the low VO(2max) group than in the high VO(2max) group (0.29 +/- 0.10 and 0.47 +/- 0.17 g.min(-1), respectively, p < 0.05) but the intensity that elicited Fat(max) did not differ between groups (64.4 +/- 14.9 and 61.6 +/- 15.4 % VO(2max)). Fat(max) or % VO(2max) that elicited Fat(max) was not associated with 10,000 m time. The only variable associated with 10,000-m running performance was % VO(2max) used during the run (p < 0.01). In conclusion, the criteria used for the division of groups according to training status might influence the identification of differences in Fat(max) or in the intensity that elicits Fat(max).

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Nowadays, digital computer systems and networks are the main engineering tools, being used in planning, design, operation, and control of all sizes of building, transportation, machinery, business, and life maintaining devices. Consequently, computer viruses became one of the most important sources of uncertainty, contributing to decrease the reliability of vital activities. A lot of antivirus programs have been developed, but they are limited to detecting and removing infections, based on previous knowledge of the virus code. In spite of having good adaptation capability, these programs work just as vaccines against diseases and are not able to prevent new infections based on the network state. Here, a trial on modeling computer viruses propagation dynamics relates it to other notable events occurring in the network permitting to establish preventive policies in the network management. Data from three different viruses are collected in the Internet and two different identification techniques, autoregressive and Fourier analyses, are applied showing that it is possible to forecast the dynamics of a new virus propagation by using the data collected from other viruses that formerly infected the network. Copyright (c) 2008 J. R. C. Piqueira and F. B. Cesar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Latin America is characterized by ethnic, geographical, cultural, and economic diversity; therefore, training in gastroenterology in the region must be considered in this context. The continent's medical education is characterized by a lack of standards and the volume of research continues to be relatively small. There is a multiplicity of events in general gastroenterology and in sub-disciplines, both at regional and local levels, which ensure that many colleagues have access to information. Medical education programs must be based on a clinical vision and be considered in close contact with the patients. The programs should be properly supervised, appropriately defined, and evaluated on a regular basis. The disparity between the patients' needs, the scarce resources available, and the pressures exerted by the health systems on doctors are frequent cited by those complaining of poor professionalism. Teaching development can play a critical role in ensuring the quality of teaching and learning in universities. Continuing professional development programs activities must be planned on the basis of the doctors' needs, with clearly defined objectives and using proper learning methodologies designed for adults. They must be evaluated and accredited by a competent body, so that they may become the basis of a professional regulatory system. The specialty has made progress in the last decades, offering doctors various possibilities for professional development. The world gastroenterology organization has contributed to the speciality through three distinctive, but closely inter-related, programs: Training Centers, Train-the-Trainers, and Global Guidelines, in which Latin America is deeply involved. (C) 2011 Baishideng. All rights reserved.

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In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.

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This paper presents SMarty, a variability management approach for UML-based software product lines (PL). SMarty is supported by a UML profile, the SMartyProfile, and a process for managing variabilities, the SMartyProcess. SMartyProfile aims at representing variabilities, variation points, and variants in UML models by applying a set of stereotypes. SMartyProcess consists of a set of activities that is systematically executed to trace, identify, and control variabilities in a PL based on SMarty. It also identifies variability implementation mechanisms and analyzes specific product configurations. In addition, a more comprehensive application of SMarty is presented using SEI's Arcade Game Maker PL. An evaluation of SMarty and related work are discussed.

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The purpose of the present study was to evaluate the effects of 8 weeks of strength and power training on the expression of genes related to the canonical WNT pathway and beta-catenin protein levels in physically active men. Twenty-five subjects (27.4 +/- A 4.6 years) were balanced based on their relative maximum strength in the squat exercise (squat 1RM/body mass) and randomly assigned to strength training (ST) (n = 10), power training (PT) (n = 10), and control (C) (n = 5) groups. The ST and the PT groups performed high and low intensity squats, respectively, thrice a week, for 8 weeks. Muscle biopsies from the vastus lateralis muscle were collected before and after the training period. Relative strength and power increased similarly in both ST and PT groups (P < 0.001). Fiber cross-sectional area also increased similarly in both ST and PT groups. Gene expression and beta-catenin protein expression levels were assessed by real-time PCR and Western blot. Certain genes were up-regulated in the ST group (WNT1: 6.4-fold, P < 0.0001; SFRP1: 3.3-fold, P < 0.0001 and LEF1: 7.3-fold, P < 0.0001) and also in the PT group (WNT1: 24.9-fold, P < 0.0001; SFRP1: 2.7-fold, P < 0.0001; LEF1: 34.1-fold, P < 0.0001 and Cyclin D1: 7.7-fold, P < 0.001). In addition, the expression of key WNT pathway genes was substantially more responsive to PT than to ST (WNT1: P < 0.0001; LEF1: P < 0.0001 and Cyclin D1: P < 0.001). Finally, the total beta-catenin protein content increased only in the PT group (P < 0.05). Our data indicate that a PT regimen triggers greater responses in key elements of the WNT pathway.

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Introduction: Internet users are increasingly using the worldwide web to search for information relating to their health. This situation makes it necessary to create specialized tools capable of supporting users in their searches. Objective: To apply and compare strategies that were developed to investigate the use of the Portuguese version of Medical Subject Headings (MeSH) for constructing an automated classifier for Brazilian Portuguese-language web-based content within or outside of the field of healthcare, focusing on the lay public. Methods: 3658 Brazilian web pages were used to train the classifier and 606 Brazilian web pages were used to validate it. The strategies proposed were constructed using content-based vector methods for text classification, such that Naive Bayes was used for the task of classifying vector patterns with characteristics obtained through the proposed strategies. Results: A strategy named InDeCS was developed specifically to adapt MeSH for the problem that was put forward. This approach achieved better accuracy for this pattern classification task (0.94 sensitivity, specificity and area under the ROC curve). Conclusions: Because of the significant results achieved by InDeCS, this tool has been successfully applied to the Brazilian healthcare search portal known as Busca Saude. Furthermore, it could be shown that MeSH presents important results when used for the task of classifying web-based content focusing on the lay public. It was also possible to show from this study that MeSH was able to map out mutable non-deterministic characteristics of the web. (c) 2010 Elsevier Inc. All rights reserved.

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This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools. Using the instantiation tool, the process of building applications is fast and easy. Therefore, computer programmers can obtain an initial application and adapt it to their needs. This tool allows the user to include, delete, and edit parameters in the functionalities chosen as well as storing these parameters for future use. In order to verify the efficiency of the framework, some case studies are presented.