Tecnologias digitais no processo de alfabetização: analisando o uso do laboratório de informática nos anos iniciais

Este artigo discute tecnologias digitais na alfabetização, onde os estudantes as utilizam para desenvolver seus conhecimentos. Iniciamos com breves considerações sobre o uso das tecnologias na educação e as implicações na alfabetização. Em seguida, apresentamos resultados de uma pesquisa qualitativa, realizada com 15 professores que atuam nas salas de aula de alfabetização de 1º e 2º anos do Ensino Fundamental e professores de laboratório de informática de seis escolas no Município de Curitiba – PR. O propósito deste estudo foi analisar e investigar o impacto do uso do laboratório de informática no processo de alfabetização dos educandos nos anos iniciais. Este trabalho de pesquisa junto aos professores alfabetizadores permitiu que se elencassem duas categorias de análise preliminar: Uso e Planejamento para o Laboratório de Informática; Contribuições para Aprendizagem e Alfabetização no Laboratório de Informática. As reflexões baseiam-se nos estudos de Almeida (2005; 1998); Kenski (2012), Leite, Colello e Arantes (2010), Lucena (2002), Masetto(2000), Moran (2007), Nóvoa (2010), Pretto (2000), Sancho e Hernandez (2006), Soares (2004; 1998), Valente (1999; 1998). O resultado apontou que os professores alfabetizadores utilizam o laboratório de informática nas práticas educativas e que há saberes e habilidades que os alfabetizandos adquirem fazendo uso deste ambiente como melhora na leitura e na oralidade,reconhecimento de letras, registro de letras, palavras e textos, coordenação motora, atenção, raciocínio e nas suas produções

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis : a pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/)

Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2 = 0.53–0.90, p < 10− 5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter:Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability

CellML 2.0

We present here CellML 2.0, an XML-based language for describing and exchanging mathematical models of physiological systems. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of reusable components, each with variables and equations giving relationships between those variables. Models may import other models to create systems of increasing complexity. CellML 2.0 is defined by the normative specification presented here, prescribing the CellML syntax and the rules by which it should be used. The normative specification is intended primarily for the developers of software tools which directly consume CellML syntax. Users of CellML models may prefer to browse the informative rendering of the specification (https://cellml.org/specifications/cellml_2.0/) which extends the normative specification with explanations of the rules combined with examples of their usage

White matter hyperintensities are positively associated with cortical thickness in Alzheimer's disease.

White matter hyperintensities are associated with an increased risk of Alzheimer's disease (AD). White matter hyperintensities are believed to disconnect brain areas. We examined the topographical association between white matter hyperintensities and cortical thickness in controls, mild cognitive impairment (MCI), and AD patients. We examined associations between white matter hyperintensities and cortical thickness among 18 older cognitively healthy participants, 18 amnestic MCI, and 17 mild AD patients. These associations were cluster-size corrected for multiple comparisons. In controls, a positive association between white matter hyperintensities and cortical thickness was found in lateral temporal gyri. In MCI patients, white matter hyperintensities were positively related to cortical thickness in frontal, temporal, and parietal areas. Positive associations between white matter hyperintensities and cortical thickness in AD patients were confined to parietal areas. The results of the interaction group by white matter hyperintensities on cortical thickness were consistent with the findings of positive associations in the parietal lobe for MCI and AD patients separately. In the frontal areas, controls and AD patients showed inverse associations between white matter hyperintensities and cortical thickness, while MCI patients still showed a positive association. These results suggest that a paradoxical relationship between white matter hyperintensities and cortical thickness could be a consequence of neuroinflammatory processes induced by AD-pathology and white matter hyperintensities. Alternatively, it might reflect a region-specific and disease-stage dependent compensatory hypertrophy in response to a compromised network