Plantio em clareiras de exploração: uma opção para o uso e conservação do mogno (Swietenia macrophylla King).

bitstream/item/39944/1/ComTec46.pd

Identificação, distribuição e abundância dos caranguejos do gênero Uca na península de Ajuruteua, nordeste do Pará, Brasil.

Resumo P-2207

Outcome-based reimbursement in Central-Eastern Europe and Middle-East

Outcome-based reimbursement models can effectively reduce the financial risk to health care payers in cases when there is important uncertainty or heterogeneity regarding the clinical value of health technologies. Still, health care payers in lower income countries rely mainly on financial based agreements to manage uncertainties associated with new therapies. We performed a survey, an exploratory literature review and an iterative brainstorming in parallel about potential barriers and solutions to outcome-based agreements in Central and Eastern Europe (CEE) and in the Middle East (ME). A draft list of recommendations deriving from these steps was validated in a follow-up workshop with payer experts from these regions. 20 different barriers were identified in five groups, including transaction costs and administrative burden, measurement issues, information technology and data infrastructure, governance, and perverse policy outcomes. Though implementing outcome-based reimbursement models is challenging, especially in lower income countries, those challenges can be mitigated by conducting pilot agreements and preparing for predictable barriers. Our guidance paper provides an initial step in this process. The generalizability of our recommendations can be improved by monitoring experiences from pilot reimbursement models in CEE and ME countries and continuing the multistakeholder dialogue at national levels

Testing the Validity of Social Capital Measures in the Study of Information and Communication Technologies

Social capital has been considered a cause and consequence of various uses of new information and communication technologies (ICTs). However, there is a growing divergence between how social capital is commonly measured in the study of ICTs and how it is measured in other fields. This departure raises questions about the validity of some of the most widely cited studies of social capital and ICTs. We compare the Internet Social Capital Scales (ISCS) developed by Williams [2006. On and off the ’net: scales for social capital in an online era. Journal of Computer-Mediated Communication, 11(2), 593–628. doi: 10.1111/j.1083-6101.2006.00029.x] – a series of psychometric scales commonly used to measure ‘social capital’ – to established, structural measures of social capital: name, position, and resource generators. Based on a survey of 880 undergraduate students (the population to which the ISCS has been most frequently administered), we find that, unlike structural measures, the ISCS does not distinguish between the distinct constructs of bonding and bridging social capital. The ISCS does not have convergent validity with structural measures of bonding or bridging social capital; it does not measure the same concept as structural measures. The ISCS conflates social capital with the related constructs of social support and attachment. The ISCS does not measure perceived or actual social capital. These findings raise concerns about the interpretations of existing studies of ‘social capital’ and ICTs that are based on the ISCS. Given the absence of measurement validity, we urge those studying social capital to abandon the ISCS in favor of alternative approaches

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution.

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control