Exposure of thermoelectric power-plant workers to volatile organic compounds from fuel oil: Genotoxic and cytotoxic effects in buccal epithelial cells

Copyright © 2012 Elsevier B.V. All rights reserved.Thermoelectric power-plant workers are constantly exposed to high levels of potentially genotoxic gaseous substances, such as volatile organic compounds (VOCs) from the combustion of fuel oil or the processing of naphtha. The aim of the present study was to estimate the association between such occupational exposure and the frequency of micronucleated cells and cells with other nuclear anomalies. Buccal epithelial cells were collected from a total of 44 power-plant workers (exposed group) and 47 administrative workers (non-exposed group), and examined for the frequency of micronucleated cells (MNC) and of cells with other nuclear anomalies (ONA: pyknosis, karyolysis, and karyorrhexis) by means of the micronucleus assay. The frequencies of MNC and ONA per 1000 cells in the exposed group (1.8‰ and 82.4‰, respectively) were significantly higher than in the non-exposed group (0.2‰ and 58.3‰, respectively). The exposed group had a twelve-fold increase in risk for formation of MNC compared with non-exposed individuals (RR = 12.1; 95% CI, 5.0–29.2; P < 0.001). The confounding factors analyzed (age, smoking status, alcohol consumption, and mouthwash use) did not show any significant association with the frequency of MNC or ONA. The findings of this study show that workers from power plants exposed to VOCs have a significantly elevated risk for DNA damage. Therefore, bio-monitoring of DNA damage is recommended for this group of workers

Using EcoYieldSAFE to compare soil carbon dynamics under future climate in two contrasting agroforestry systems

N/

Keeping a parameter-sparse concept in agroforestry modeling while integrating new processes and dynamics: new developments in yield-safe

info:eu-repo/semantics/publishedVersio

Reproductive aspects of Macrobrachium amazonicum (Decapoda: Palaemonidae) in the State of Amapá, Amazon River mouth.

Macrobrachium amazonicum is an indigenous prawn vastly distributed in basins of South America, widely exploited by artisanal fisheries in northern and northeastern Brazil and, with great potential for aquaculture. This study aimed to investigate general aspects of population structure and reproductive characteristics (size at first maturity, fecundity and reproductive output) of M. amazonicum from two important areas to artisanal prawn fishing located at the mouth of the Amazon River, State of Amapá. The specimens were captured using 20 handcrafted traps called ?matapi?. A number of 5,179 prawns were captured, 2,975 females and 2,195 males resulting in 1.35:1 female to male ratio. Santana Island and Mazagão Velho showed females predominated in the population. A reproductive peak period was observed from January to April/2009 and in December/2010, coinciding with the period of higher rainfall. The recruitment peak occurred in June and July/2009. Egg-bearing females ranged in size (carapace length) from 11.10 to 29.6 mm. Fecundity increased with female size and reached up to 7,417 eggs. This amount of eggs is considered low if compared with other Macrobrachium estuarine species. Mean egg volume increased gradually from 0.121 to 0.24 mm3 during embryogenesis, representing 68.5% of overall increase from Stage I to Stage III. Eggs of M. amazonicum are small; this is typical for Macrobrachium species, which depends on brackish water to complete the larval development. Irrespective of female size, reproductive output of M. amazonicum varied between 4.8 and 21.85% of their body weight into eggs production

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Impact of climate change on maize grown in the brazilian cerrado.

Crops are subject to instabilities of climatic conditions that affect yield. Maize is very sensitive to factors like temperature, solar radiation and rainfall. The objective of this work was to evaluate, using crop growth models, the effects of climate change on maize grain yield produced under rainfed conditions. Two global circulation models, HadGEM2-ES and MIROC5, coupled to the regional model Eta, were used to generate projections of changes in maximum and minimum air temperature, solar radiation and rainfall for conditions in southeastern Brazil. The CSM-CERES-Maize model was then used to evaluate the effect of climate changes on rainfed maize grain yield. For each combination of global and regional circulation models, two greenhouse gas concentration scenarios were used: RCP4.5 and RCP8.5. The combined use of global circulation and crop growth models allowed us to estimate the expected average grain yield of corn as affected by future climate. The simulated results indicated that, even at best sowing dates, considerable reduction in maize grain yield may occur. Our simulated results also indicated that the largest grain yield reductions may occur for future climate scenarios from 2071 to the end of the 21st century

Transtornos psiquiátricos nos acadêmicos de medicina / Psychiatric disorders in medical students

Os problemas psicológicos com os quais se deparam os universitários, em particular os estudantes de medicina, são extremamente complexos e os acometem antes mesmo deles iniciarem o curso médico. Inicia-se com a escolha da profissão, competitividade no vestibular e os acompanham até o final do curso. Devido à fadiga, o cansaço e o estar constantemente sob pressão existem uma maior intensidade de problemas emocionais entre os estudantes de medicina se comparados com os outros estudantes universitários tornando os mais suscetíveis a desenvolver algum transtorno psiquiátrico maior ou menor. O objetivo dessa revisão de literatura consiste em identificar a etiologia, prevalência e elucidar os principais transtornos mentais que afetam esse grupo de risco. Foram selecionados artigos em português e inglês de caráter relevante para que pudessem contribuir com a pesquisa, tendo como base os bancos de dados Medline, Embase, LILACS e SciELO