900 resultados para biological systems


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Climate change poses special challenges for Caribbean decision makers related to the uncertainties inherent in future climate projections and the complex linkages between climate change, physical and biological systems, and socioeconomic sectors. At present, however, the Caribbean subregion lacks the adaptive capacity needed to address these challenges. The present report assesses the economic and social impacts of climate change on the coastal and marine sector in the Caribbean until 2050. It aims both to provide Caribbean decision makers with cutting edge information on the vulnerability to climate change of the subregion, and to facilitate the development of adaptation strategies informed by both local experience and expert knowledge.

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Caribbean policymakers are faced with special challenges from climate change and these are related to the uncertainties inherent in future climate projections and the complex linkages among climate change, physical and biological systems and socioeconomic sectors. The impacts of climate change threaten development in the Caribbean and may well erode previous gains in development as evidenced by the increased incidence of climate migrants internationally. This brief which is based on a recent study conducted by the Economic Commission for Latin America and the Caribbean (LC/CAR/L.395)1 provides a synthesis of the assessment of the economic and social impacts of climate change on the coastal and marine sector in the Caribbean which were undertaken. It provides Caribbean policymakers with cutting-edge information on the region’s vulnerability and encourages the development of adaptation strategies informed by both local experience and expert knowledge. It proceeds from an acknowledgement that the unique combination of natural resources, ecosystems, economic activities, and human population settlements of the Caribbean will not be immune to the impacts of climate change, and local communities, countries and the subregion as a whole need to plan for, and adapt to, these effects. Climate and extreme weather hazards related to the coastal and marine sector encompass the distinct but related factors of sea level rise, increasing coastal water temperatures, tropical storms and hurricanes. Potential vulnerabilities for coastal zones include increased shoreline erosion leading to alteration of the coastline, loss of coastal wetlands, and changes in the abundance and diversity of fish and other marine populations. The study examines four key themes in the analysis: climate, vulnerability, economic and social costs associated with climate change impacts, and adaptive measures.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Biologia Animal - IBILCE

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Ciências Biológicas (Genética) - IBB

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The Brazil´s Atlantic coastal forest biome is considered one of the richest and most exuberant biodiversity of the whole planet. Still, other data shows the reality of this Brazilian forest: more than 93% of its original formation has been already devastated, being the human actions the greatest threat to the perpetuation of the balance and diversity of this ecosystem. This destruction affects all the fauna and flora, with even greater impact on medium and large mammals, due to its high demand on the availability of resources, living area and sensitivity to disturbances. However, today it is known the vital importance of these mammals in the preservation and maintenance of biological systems. Thus, the purpose of this study was to survey the species richness of medium and large mammals in a remnant neotropical forest located in Itatinga, SP,Brazil, using the indirect method of recording (traces on sand-plots, marking territorial, faeces, carcasses, scratches on tree trunks and vocalizations) and camera trapping. Data were collected during ten (10) consecutive months in the year 2008, from February to November. Within twenty-five plots of sand installed and monitored during the ten months were found fifteen (15) species of terrestrial mammals of medium and large, five (5) orders and twelve (12) families. Among them, we highlight the presence of threated species like the puma (Puma concolor), the deer (Mazama gouazoubira) and the giant anteaters (Myrmecophaga tridactyla). The chosen method showed up effective in raising terrestrial mammalian species of medium and large neotropical forest area, having a good applicability and advantages over other existing methods. The study area presents a huge diversity of species and, therefore, this work it is recommended to be only the beginning of new researches in the area in order to enrich... (Complete abstract click electronic access below)

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Cancer biology is a complex and expanding field of science study. Due its complexity, there is a strong motivation to integrate many fields of knowledge to study cancer biology, and biological stoichiometry can make this. Biological stoichiometry is the study of the balance of multiple chemical elements in biological systems. A key idea in biological stoichiometry is the growth rate hypothesis, which states that variation in the carbon:nitrogen:phosphorus stoichiometry of living things is associated with growth rate because of the elevated demands for phosphorusrich ribosomal RNA and other elements necessary to protein synthesis. As tumor cells has high rate proliferation, the growth rate hypothesis can be used in cancer study. In this work the dynamic of two tumors (primary and secondary) and the chemical elements carbon and nitrogen are simulate and analyzed through mathematical models that utilize as central idea biological stoichiometry. Differential equations from mathematical model are solved by numerical method Runge-Kutta fourth order

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To understand how biological phenomena emerge, the nonlinear interactions among the components envolved in these and the correspondent connected elements, like genes, proteins, etc., can be represented by a mathematical object called graph or network, where interacting elements are represented by edges connecting pairs of nodes. The analysis of various graph-related properties of biological networks has revealed many clues about biological processes. Among these properties, the community structure, i.e. groups of nodes densely connected among themselves, but sparsely connected to other groups, are important for identifying separable functional modules within biological systems for the comprehension of the high-level organization of the cell. Communities' detection can be performed by many algorithms, but most of them are based on the density of interactions among nodes of the same community. So far, the detection and analysis of network communities in biological networks have only been pursued for networks composed by one type of interaction (e.g. protein-protein interactions or metabolic interactions). Since a real biological network is simultaneously composed by protein-protein, metabolic and transcriptional regulatory interactions, it would be interesting to investigate how communities are organized in this type of network. For this purpose, we detected the communities in an integrated biological network of the Escherichia coli and Saccharomyces cerevisiae by using the Clique Percolation Method and we veri ed, by calculating the frequency of each type of interaction and its related entropy, if components of communities... (Complete abstract click electronic access below)

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Communities are present on physical, chemical and biological systems and their identification is fundamental for the comprehension of the behavior of these systems. Recently, available data related to complex networks have grown exponentially, demanding more computational power. The Graphical Processing Unit (GPU) is a cost effective alternative suitable for this purpose. We investigate the convenience of this for network science by proposing a GPU based implementation of Newman community detection algorithm. We showed that the processing time of matrix multiplications of GPUs grow slower than CPUs in relation to the matrix size. It was proven, thus, that GPU processing power is a viable solution for community dentification simulation that demand high computational power. Our implementation was tested on an integrated biological network for the bacterium Escherichia coli