Evaluation of the inspiratory and expiratory muscular force in active and sedentary elderly women

O presente estudo teve como objetivo verificar a influência da prática de atividade física no incremento da força muscular inspiratória e expiratória em mulheres idosas. Para tanto foram selecionadas 136 idosas nas faixas etárias de 65 a 80 anos. Nenhuma era fumante e não apresentava doenças neuromusculares ou pulmonares. Foram comparados dois grupos de idosas, sendo que um grupo era praticante de atividade física (caminhadas livres 2 vezes por semana) e o outro de sedentárias (controle). Na avaliação foi utilizado um manovacuômetro aneróide para investigar as pressões inspiratórias e expiratórias. Os resultados demonstraram que o grupo de praticantes de atividade física apresentou aumento significativo da Pressão Inspiratória Máxima nas faixas etárias compreendidas entre 65-69 anos (p=0,0001), 70-74 anos (p=0,0046) e 75-80 anos (p=0,0240) e, da Pressão Expiratória Máxima nas faixas etárias entre 70-74 anos (p=0,0114) e 75-80 anos (p=0,0101). Além disto estes resultados foram comparados as Tabelas de Neder et al. (1999) e Black & Hyatt (1969) em que se observou estarem abaixo dos índices indicativos de normalidade. Conclui-se que a atividade física não específica para músculos respiratórios resultou em aumento da força muscular respiratória na maioria das mulheres idosas participantes do estudo, sugerindose ainda que para uma melhor performance respiratória poderiam ser associados treinamentos específicos para a musculatura respiratória. _________________________________________________________________________________ ABSTRACTThe present study aimed to verify the influence of physical activity in the increment of the inspiratory and expiratory muscular force in elderly women. In order to do that, 136 women were selected between 65 to 80 years old. No one of them was a smoker and no presented neuromuscular or lung diseases. Two groups of elderly women were compared, one group was practicing physical activity (free walks twice a week) and the other group was sedentary (control). In the evaluation a aneroid manovacuometro was used in order to investigate the inspiratory and expiratory pressures. The results demonstrated that the group which has done physical activity presented significant increase of the Inspiratory Pressure Maximum in age group between 65-69 years old (p=0,0001), 70-74 years old (p=0,0046) and 75-80 years old (p=0,0240) and, increase of the Expiratory Pressure Maximum in age group between 70-74 years old (p=0,0114) and 75-80 years old (p=0,0101). Besides, these results were compared to the Tables of Neder (1999) and Black & Hyatt (1969) which breathing pressures were under the indicative indexes of normality. One concluded that the physical activity which is not specific for breathing muscles can contribute to the increment of the inspiratory and expiratory muscular force in elderly women. Moreover, one suggests specific training to the respiratory musculature in order to have a better respiratory performance

Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data

Peer reviewedPublisher PD

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

Troubleshooting is a labor-intensive task that includes repetitive solutions to similar problems. This task can be partially or fully automated using text-similarity matching to find previous solutions, lowering the workload of technicians. We develop a systematic literature review to identify the best approaches to solve the problem of troubleshooting automation and classify incidents effectively. We identify promising approaches and point in the direction of a comprehensive set of solutions that could be employed in solving the troubleshooting automation problem

Jogando oportunidades no lixo: uma estimativa dos benefícios potenciais da reciclagem em Porto Alegre

A produção de resíduos encontra-se em constante elevação, seja em decorrência do modo de vida ocidental baseado no consumo material, seja pelo aumento populacional e pela urbanização crescente. É exatamente por esse cenário que a questão da gestão dos resíduos passou a ganhar força nos estudos sobre a sustentabilidade do desenvolvimento. Tendo em vista a importância desse tema, o presente trabalho busca estimar os possíveis benefícios que podem ser gerados a partir da reciclagem de resíduos sólidos, tendo como base o Município de Porto Alegre, Rio Grande do Sul. Isso é calculado através de uma análise de custo-benefício, empregada inicialmente por Motta (2006), que mostra, em valores monetários, os benefícios potenciais decorrentes de uma gestão adequada dos resíduos sólidos, com ganhos sociais, econômicos e ambientais. Entende-se que esse tipo de análise é essencial para otimizar o emprego de recursos públicos, auxiliando na determinação de prioridades e na avaliação de políticas públicas. TÍTULO EM INGLÊS Throwing opportunities away: an estimate of the potential benefits of recycling in Porto Alegre, Brazil Abstract The management of waste produced by the population has become an important issue in discussions about the sustainable development of countries, since the production of such waste is constantly rising as a result of a Western lifestyle based on material consumption, population growth and an increasing urbanization. Given the importance of this issue, this paper seeks to estimate the possible benefits that can be generated from the recycling of solid waste in Porto Alegre, State of Rio Grande do Sul, by means of a Cost-Benefit Analysis initially employed by Motta (2006), which shows the potential benefits, in monetary values, of an appropriate solid waste management system, in terms of social, economic and environmental gains. Using this analysis is essential to optimize the use of public resources, assisting in setting priorities and evaluating public policies. Keywords: solid waste; socioeconomic and environmental benefits; Porto Alegre. Artigo recebido em 28 nov. 2014

Predicting Brain Age at Slice Level : Convolutional Neural Networks and Consequences for Interpretability

Funding Information: NE was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior— Brasil (CAPES)—Finance Code 001. MM was financed in part by the Conselho Nacional de Pesquisa—Brasil (CNPq). Funding Information: Conflict of Interest: BF had a research grant from Pfizer outside of this study.Peer reviewe

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with "expert-based" clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy

Caracterización de sistemas silvopastoriles con eucalipto en el Paraguay

Introducción: Paraguay presenta un alto déficit de oferta de madera y busca fomentar las plantaciones forestales. Según UNIQUE (2014) la mayor parte de las tierras del Paraguay son aptas para una producción forestal, ya sea de madera de valor o de biomasa. Por ello, el potencial de implementación de los sistemas silvopastoriles es muy alto. La integración del componente forestal permite a los productores diversificar su producción y aumentarlos ingresos sin necesidad de cambiar el uso actual de la tierra. Objetivo: El objetivo del trabajo fue realizar un diagnóstico de las plantaciones forestales bajo sistema silvopastoril en Paraguay. Metodología: Fue aplicado un cuestionario para la caracterización de las plantaciones, identificación de los factores críticos y la estimación de la viabilidad económica, mientras que para la estimación del crecimiento de plantaciones fueron instaladas parcelas permanentes de 1.000 m² para realizar la medición de DAP y altura de los individuos dentro de las parcelas y estimar el IMA (m³/ha/año). Para la caracterización fueron encuestadas 7 empresas, y se realizó inventario en 3 de las mismas.Consejo Nacional de Ciencia y TecnologíaPrograma Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarroll

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with “expert-based” clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy

Viability of forest projects in three management regime in the center-oriental region of Parana State

O objetivo do presente trabalho foi analisar a viabilidade econômica de projetos florestais de Pinus taeda L. com base em dados de uma empresa florestal localizada no estado do Paraná. A análise de rentabilidade foi feita contemplando oito projetos, subdivididos em três diferentes regimes de manejo, localizados na mesorregião centro-oriental do Paraná. A viabilidade econômica foi avaliada por meio de Valor Presente Líquido (VPL), Taxa Interna de Retorno (TIR), Valor Periódico Equivalente (VPE) e Valor Esperado da Terra (VET), calculados com base no fluxo de caixa de cada projeto, utilizando-se a taxa de juros real de 6,75% ao ano. Os volumes utilizados foram obtidos de acordo com os sortimentos adotados para a espécie no Paraná, e os custos foram obtidos junto ao setor de planejamento da empresa. Os preços de venda da madeira em pé utilizados na análise foram oriundos do preço médio de mercado no estado, de acordo com o sortimento. O regime de manejo que apresentou melhores resultados foi o multiple use, com VPL de R$2.606,32; TIR de 10,2$ 241,26 e VET de R$4.193,39.Palavras-chave: Rentabilidade; economia florestal; indicadores de viabilidade econômica. AbstractViability of forest projects in three management regime in the center-oriental region of Parana State. The objective of this research was to analyze the economic viability of the forest projects of Pinus taeda based on data from a forest enterprise located on the Parana State. The profit analyze was developed contemplating eight projects, subdivided in three different management regime, located in the Center-Oriental region of the Parana State. The economic viability was evaluated based on the Net Present Value (NPV), Internal Return Rate (IRR), Periodic Equivalent Value (PEV) and the Soil Expected Value (SEV), determined by cash flow, with a real interest rate of 6.75$2,606.32; IRR of 10.2%; PEV of R$241.26 and SEV of R$4,193.39.Keywords: Profit; forest economy; economic viability indicators.AbstractThe objective of this research was to analyze the economic viability of the forest projects of Pinus taeda based on data from a forest enterprise located on the Parana State. The profit analyze was developed contemplating eight projects, subdivided in three different management regime, located in the Center-Oriental region of the Parana State. The economic viability was evaluated based on the Net Present Value (NPV), Internal Return Rate (IRR), Periodic Equivalent Value (PEV) and the Soil Expected Value (SEV), determined by cash flow, with a real interest rate of 6.75% per year. The volumes used were described based on assortment used for the species in Paraná, and the costs were from the planning department of the enterprise. The selling price of the stand-up wood used in the analyze was obtained by the average market price in the state, according to the assortment. The management regime that presented the best results was the multiple use, with NPV of R$2,606.32; IRR of 10.2$241.26 and SEV of R$4,193.39.Keywords: Profit; forest economy; economic viability indicators