Software CMAP TOOLS® para a construção de mapas conceituais: a avaliação dos estudantes de enfermagem

O mapeamento conceitual (MC) é uma estratégia de ensino que pode ser utilizada para resoluções de casos clínicos, porém de trabalhosa execução manuscrita. O estudo teve por objetivos descrever os desafios e as contribuições do software Cmap Tools® para a construção de mapas conceituais para resolução de caso clínico. Para isso, utilizou-se método descritivo, qualitativo, com estudantes da 3ª série de Graduação em Enfermagem da Universidade Federal de São Paulo. A estratégia de ensino foi aplicada e os dados foram coletados pela técnica do grupo focal. Os resultados evidenciaram que o software facilita e garante a organização, visualização e correlação dos dados, porém com dificuldades iniciais relacionadas ao manejo das ferramentas que dispõe. Concluiu-se que o software Cmap Tools® favoreceu a construção dos MC por seus recursos de formatação e autoformatação e que estratégias de orientação deveriam ser implantadas para a fase inicial de utilização.

[Archives de la Parole]. , The covetous Brahmin [and his reward] : in Oriya - Récit - Indo-Aryen - Ling[uistic] Survey of India / [Sir George Grierson], éd. ; Linguistic survey of India ; [Bepin Chandra Patra], voix

[Traditions. Asie. Inde. Orissa]

[Archives de la Parole]. , The rājā and his subjects : in Magathi (récit) / [Sir George Grierson], éd. ; Linguistic survey of India ; [Jadunandan Sahay], voix

[Traditions. Asie. Inde. Bihar]

Biana: a software framework for compiling biological interactions and analyzing networks

Background: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.Results: We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.Conclusions: BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Análise de riscos e dificuldades no desenvolvimento de software

O trabalho que ora apresentamos tem como tema “ O Ensino da Expressão e Educação Físico-Motora nas Escolas do Ensino Básico de São Lourenço dos Órgãos – Pós – Reforma.” O mesmo tem como horizonte temporal o ano lectivo 2006/07 e abarca as Escolas Básicas do novel Município de São Lourenço dos Órgãos. O ensino da Expressão e Educação Físico – Motora (E.E.F.M.) reveste-se de enorme importância e, nos últimos anos, tem-se emprestado muita atenção a esta área, quer seja a nível planetário, quer seja a nível nacional. Hoje, muitos estudiosos preocupam-se com o ensino desta área e, em consequência procuram fazer algo que permita a promoção da mesma e seja vista como as outras ditas académicas, isto porque todos estão cientes da sua importância e pertinência dentro do sistema escolar. Diante de tudo o que já foi dito e na possibilidade de poder dar um contributo a bem da educação no nosso pais, em geral, e no Município de São Lourenço dos Órgãos, em particular, pretendemos compreender, entretanto, porque é que muitos professores não leccionam ou raras vezes leccionam a área da Expressão e Educação Físico – Motora.

Sense and antisense transcripts in the histone H1 (HIS-1) locus of Leishmania major.

Histone H1 in the parasitic protozoan Leishmania is a developmentally regulated protein encoded by two genes, HIS-1.1 and HIS-1.2. These genes are separated by approximately 20 kb of sequence and are located on the same DNA strand of chromosome 27. When Northern blots of parasite RNA were probed with HIS-1 strand-specific riboprobes, we detected sense and antisense transcripts that were polyadenylated and developmentally regulated. When the HIS-1.2 coding region was replaced with the coding region of the neomycin phosphotransferase gene, antisense transcription of this gene was unaffected, indicating that the regulatory elements controlling antisense transcription were located outside of the HIS-1.2 gene, and that transcription in Leishmania can occur from both DNA strands even in the presence of transcription of a selectable marker in the complementary strand. A search for other antisense transcripts within the HIS-1 locus identified an additional transcript (SC-1) within the intervening HIS-1 sequence, downstream of adenine and thymine-rich sequences. These results show that gene expression in Leishmania is not only regulated polycistronically from the sense strand of genomic DNA, but that the complementary strand of DNA also contains sequences that could drive expression of open reading frames from the antisense strand of DNA. These findings suggest that the parasite has evolved in such a way as to maximise the transcription of its genome, a mechanism that might be important for it to maintain virulence.

Monitoring of NY-ESO-1 specific CD4+ T cells using molecularly defined MHC class II/His-tag-peptide tetramers.

MHC-peptide tetramers have become essential tools for T-cell analysis, but few MHC class II tetramers incorporating peptides from human tumor and self-antigens have been developed. Among limiting factors are the high polymorphism of class II molecules and the low binding capacity of the peptides. Here, we report the generation of molecularly defined tetramers using His-tagged peptides and isolation of folded MHC/peptide monomers by affinity purification. Using this strategy we generated tetramers of DR52b (DRB3*0202), an allele expressed by approximately half of Caucasians, incorporating an epitope from the tumor antigen NY-ESO-1. Molecularly defined tetramers avidly and stably bound to specific CD4(+) T cells with negligible background on nonspecific cells. Using molecularly defined DR52b/NY-ESO-1 tetramers, we could demonstrate that in DR52b(+) cancer patients immunized with a recombinant NY-ESO-1 vaccine, vaccine-induced tetramer-positive cells represent ex vivo in average 1:5,000 circulating CD4(+) T cells, include central and transitional memory polyfunctional populations, and do not include CD4(+)CD25(+)CD127(-) regulatory T cells. This approach may significantly accelerate the development of reliable MHC class II tetramers to monitor immune responses to tumor and self-antigens.

A scaled difference chi-square test statistic for moment structure analysis

A family of scaling corrections aimed to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model say ${\cal M}_0$ implies on a less restricted one ${\cal M}_1$. If $T_0$ and $T_1$ denote the goodness-of-fit test statistics associated to ${\cal M}_0$ and ${\cal M}_1$, respectively, then typically the difference $T_d = T_0 - T_1$ is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models ${\cal M}_0$ and ${\cal M}_1$. As in the case of the goodness-of-fit test, it is of interest to scale the statistic $T_d$ in order to improve its chi-square approximation in realistic, i.e., nonasymptotic and nonnormal, applications. In a recent paper, Satorra (1999) shows that the difference between two Satorra-Bentler scaled test statistics for overall model fit does not yield the correct SB scaled difference test statistic. Satorra developed an expression that permits scaling the difference test statistic, but his formula has some practical limitations, since it requires heavy computations that are notavailable in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference chi-square statistic from the scaled goodness-of-fit test statistics of models ${\cal M}_0$ and ${\cal M}_1$. A Monte Carlo study is provided to illustrate the performance of the competing statistics.