Vitimização por homicídios segundo características de raça no Brasil

OBJETIVO: Descrever a tendência temporal da mortalidade por homicídio no Brasil. MÉTODOS: Estudo de série temporal dos homicídios no Brasil de 2000 a 2009. As variáveis explicativas foram raça/cor, sexo e escolaridade. Os óbitos foram provenientes do Sistema de Informações de Mortalidade. A análise de tendência foi realizada por meio de regressão polinomial para séries históricas (p < 0,05; intervalo de 95% de confiança). RESULTADOS: A população negra representou 69% das vítimas de homicídios em 2009. O número de homicídios aumentou entre a população negra e diminuiu entre a branca, com tendência de crescimento da taxa nos negros e de redução nos brancos no período. As taxas aumentaram nos grupos de maior e menor escolaridade entre negros, enquanto, entre brancos, reduziram para os de menor nível escolar e mantiveram-se estáveis no grupo com maior nível de escolaridade. Em 2009 negros tiveram maior risco de morte por homicídios do que a população branca, independentemente do nível de escolaridade. Entre 2004 e 2009, as taxas de homicídios na população branca diminuíram e aumentaram na negra. CONCLUSÕES: O risco relativo de homicídios cresce na população negra, sugerindo o aumento das desigualdades. A repercussão das medidas antiarmas no Brasil, implantada em 2004, foi positiva na população branca e discreta na população negra. Raça/cor pode predizer a ocorrência de homicídio.OBJETIVO: Describir la tendencia temporal de la mortalidad por homicidio en Brasil. MÉTODOS: Estudio de serie temporal de los homicidios en Brasil de 2000 a 2009. Las variables explicativas fueron raza/color, sexo y escolaridad. Los óbitos fueron provenientes del Sistema de Informaciones de Mortalidad. El análisis de tendencia fue realizada por medio de regresión polinomial para series históricas (pOBJECTIVE: To describe the temporal patterns of mortality by homicide in Brazil. METHODS: A series of homicides in Brazil from 2000 to 2009 were studied. The explanatory variables were race/skin color, gender and education. The death statistics were obtained from the Mortality Information System. A trend analysis was performed by means of a polynomial regression for a historic time series (p < 0.05, 95% confidence interval). RESULTS: The black population represented 69% of the homicide victims in 2009. The homicide rate increased in the black population, while it decreased in the white population in the period studied. The homicide rate increased in groups with both higher and lower education among blacks; among whites, the rate decreased for those with the lowest level of schooling and remained stable in the group with higher educational levels. In 2009, blacks had a higher risk of death than whites from homicide, regardless of education level. Between 2004 and 2009, the homicide rate decreased in the white population, while it increased in the black population. CONCLUSIONS: The relative risk of falling victim to homicide increased in the black population, suggesting an increase in inequality. The effect of the anti-gun measures implemented in Brazil in 2004 was positive in the white population and less pronounced in the black population. Overall, race/skin color predicted the occurrence of homicide

Modelling the structure and dynamics of biological pathways

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system's dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems

Tools for visualization and analysis of molecular networks, pathways, and -omics data

Jose M Villaveces, Prasanna Koti, Bianca H Habermann Max Planck Institute of Biochemistry, Research Group Computational Biology, Martinsried, Germany Abstract: Biological pathways have become the standard way to represent the coordinated reactions and actions of a series of molecules in a cell. A series of interconnected pathways is referred to as a biological network, which denotes a more holistic view on the entanglement of cellular reactions. Biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization. Here, we review a set of pathway and network visualization and analysis methods and take a look at potential future developments in the field. Keywords: biological networks, reactions, proteins, genes, signaling, protein-protein interactions, organism

Tools for visualization and analysis of molecular networks, pathways, and -omics data

Jose M Villaveces, Prasanna Koti, Bianca H Habermann Max Planck Institute of Biochemistry, Research Group Computational Biology, Martinsried, Germany Abstract: Biological pathways have become the standard way to represent the coordinated reactions and actions of a series of molecules in a cell. A series of interconnected pathways is referred to as a biological network, which denotes a more holistic view on the entanglement of cellular reactions. Biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization. Here, we review a set of pathway and network visualization and analysis methods and take a look at potential future developments in the field. Keywords: biological networks, reactions, proteins, genes, signaling, protein-protein interactions, organism

Anatomy of BioJS, an open source community for the life sciences

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects