2,315 research outputs found

    Enhanced skin carcinogenesis and lack of thymus hyperplasia in transgenic mice expressing human cyclin D1b (CCND1b)

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    Cyclin D1b is an alternative transcript of the cyclin D1 gene (CCND1) expressed in human tumors. Its abundance is regulated by a single base pair polymorphism at the exon 4/intron 4 boundary (nucleotide 870). Epidemiological studies have shown a correlation between the presence of the G870A allele (that favors the splicing for cyclin D1b) with increased risk and less favorable outcome in several forms of cancer. More recently, it has been shown that, unlike cyclin D1a, the alternative transcript D1b by itself has the capacity to transform fibroblasts in vitro. In order to study the oncogenic potential of cyclin D1b, we developed transgenic mice expressing human cyclin D1b under the control of the bovine K5 promoter (K5D1b mice). Seven founders were obtained and none of them presented any significant phenotype or developed spontaneous tumors. Interestingly, K5D1b mice do not develop the fatal thymic hyperplasia, which is characteristic of the cyclin D1a transgenic mice (K5D1a). Susceptibility to skin carcinogenesis was tested in K5D1b mice using two-stage carcinogenesis protocols. In two independent experiments, K5D1b mice developed higher papilloma multiplicity as compared with wild-type littermates. However, when K5D1b mice were crossed with cyclin D1KO mice, the expression of cyclin D1b was unable to rescue the carcinogenesis-resistant phenotype of the cyclin D1 KO mice. To further explore the role of cyclin D1b in mouse models of carcinogenesis we carried out in silico analysis and in vitro experiments to evaluate the existence of a mouse homologous of the human cyclin D1b transcript. We were unable to find any evidence of an alternatively spliced transcript in mouse Ccnd1. These results show that human cyclin D1b has different biological functions than cyclin D1a and confirm its oncogenic properties.Fil: Rojas, Paola Andrea. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Instituto de BiologĂ­a y Medicina Experimental. FundaciĂłn de Instituto de BiologĂ­a y Medicina Experimental. Instituto de BiologĂ­a y Medicina Experimental; Argentina. University of Texas; Estados UnidosFil: Benavides, Fernando. University of Texas; Estados UnidosFil: Blando, Jorge. University of Texas; Estados UnidosFil: PĂ©rez, Carlos. University of Texas; Estados UnidosFil: Cardenas, Kim. University of Texas; Estados UnidosFil: Richie, Ellen. University of Texas; Estados UnidosFil: Knudsen, Erik S.. Thomas Jefferson University; Estados UnidosFil: Johnson, David G.. University of Texas; Estados UnidosFil: Senderowicz, Adrian M.. Department of Health and Human Services. Food and Drug Administration. Center for Drug Evaluation and Research; Estados UnidosFil: Rodriguez Puebla, Marcelo L.. University of North Carolina; Estados UnidosFil: Conti, Claudio. University of Texas; Estados Unido

    Spatiotemporal determinants of seasonal gleaning

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    1. Many coastal communities depend on ecosystems for goods and services that contribute to human well-being. As long-standing interactions between people and nature are modified by global environmental change, dynamic and diversified livelihood strategies that enable seasonal adaptation will be critical for vulnerable coastal communities. However, the success of such strategies depends on a range of poorly understood influences. 2. Gleaning, the hand-based collection of marine organisms from littoral habitats, provides an interesting case study of dynamic change in social-ecological interac- tions. It is an important coastal livelihood strategy, yet seasonal gleaning dynamics have not been empirically explored in contemporary communities. We examined seasonal gleaning in eight coastal communities on Atauro Island, Timor-Leste, using household surveys and satellite-derived maps of shallow-water benthic habitats. Our analysis explored the factors affecting household decisions to glean in each season, the relationship between gleaning and seafood consumption, and seasonal gleaning pressure on near-shore coastal resources. 3. Dynamic marine harvesting strategies differed among households and gleaning activity was seasonally heterogeneous. Not all gleaning households gleaned dur- ing the season characterised by rough sea conditions despite rough season glean- ing being associated with greater seafood consumption stability among seasons. Households also gleaned less regularly, and catches were smaller, in the rough season. 4. Differences in seasonal participation in gleaning were explained mostly by type and extent of shallow habitat proximate to a community. In the calm season, household gleaning was positively related to the total area of shallow habitat, and in the rough season the percentage of hard-bottom shallow habitat was also an important predictor of gleaning activity. 5.Our findings illustrate how changes in the biophysical environment mediate human–nature interactions at fine scales through time and space. Consequently, this research highlights the importance of context-specific perspectives for under- standing drivers and dynamics in fishing pressure on littoral ecosystems, access to ecosystem benefits and limits to adaptation. Factors influencing when livelihood activities are feasible and desirable are important for evaluating the social impacts of climate change, particularly in the context of rural communities in the Global South

    Model-As-A-Service (MaaS) Using the Cloud Services Innovation Platform (CSIP)

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    Cloud infrastructures for modelling activities such as data processing, performing environmental simulations, or conducting model calibrations/optimizations provide a cost effective alternative to traditional high performance computing approaches. Cloud - based modelling examples emerged into the m ore formal notion: \u27Model - as - a - Service\u27 (MaaS). This paper presents the Cloud Services Innovation Platform (CSIP) as a software framework offering MaaS. It describes both the internal CSIP infrastructure and software architecture that manages cloud resources for typical modelling tasks, and the use of CSIP\u27s \u27 ModelServices API \u27 for a modelling application . CSIP\u27s architecture supports fast and resource aware auto - scaling of computational resources. An example model service is presented: the USDA hydrograph model EFH2 used in the desktop - based \u27engineering field tools\u27 is deployed as a CSIP service. This and other MaaS CSIP examples benefit from the use of cloud resources to enable straightforward scalable model deployment into cloud environments

    A Unified Model Representation of Machine Learning Knowledge

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    Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and (3) a traceability links maintenance. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct.2019-2

    Monitoring Self-Perceived Occupational Health inequities in Central america, 2011 and 2018

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    Objectives. to analyze changes in occupational health inequity between 2011 and 2018 among workers in Central America. Methods. Data were collected by face-to-face interviews at the workers\u27 homes for the 2 Central America Working Conditions Surveys (n = 12 024 in 2011 and n = 9030 in 2018). We estimated health inequity gaps by means of absolute and relative population attributable risks and the weighted Keppel index. We stratified all analyses by gender. Results. Between 2011 and 2018, the proportion of workers reporting poor self-perceived health decreased both in women (from 32% to 29%) and men (from 33% to 30%). However, the health inequity gaps remained wide in the 4 stratifiers. Measured by the Keppel index, health inequity gaps between countries increased from 22% to 39% in women and from 20% to 29% in men. Conclusions. While health improved between 2011 and 2018, health inequity gaps remained wide. Wider health inequity gaps were observed between countries than by gender, age, occupation, or education. Public Health Implications. This first benchmark of occupational health inequities in Central America could be useful when developing and evaluating the impact of public policies on work

    Microglial inhibition of neuroprotection by antagonists of the EP1 prostaglandin E2 receptor

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    © 2009 Carlson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

    CARGO: a web portal to integrate customized biological information

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    There is a huge quantity of information generated in Life Sciences, and it is dispersed in many databases and repositories. Despite the broad availability of the information, there is a great demand for methods that are able to look for, gather and display distributed data in a standardized and friendly way. CARGO (Cancer And Related Genes Online) is a configurable biological web portal designed as a tool to facilitate, integrate and visualize results from Internet resources, independently of their native format or access method. Through the use of small agents, called widgets, supported by a Rich Internet Application (RIA) paradigm based on AJAX, CARGO provides pieces of minimal, relevant and descriptive biological information. The tool is designed to be used by experimental biologists with no training in bioinformatics. In the current state, the system presents a list of human cancer genes. Available at http://cargo.bioinfo.cnio.e
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