Seasonality Influence on Biochemical and Hematological Indicators of Stress and Growth of Pirarucu ( Arapaima gigas

Environmental factors such as seasonal cycles are the main chronic stress cause in fish increasing incidence of disease and mortality and affecting productive performance. Arapaima gigas (pirarucu) is an Amazonian air-breathing and largest freshwater fish with scales in the world. The captivity development of pirarucu is expanding since it can fatten up over 1 kg per month reaching 10 kg body mass in the first year of fattening. This work was conducted in three periods (April to July 2010, August to November 2010, and December 2010 to March 2011) defined according to rainfall and medium temperatures. Seasonality effect analysis was performed on biochemical (lectin activity, lactate dehydrogenase, and alkaline phosphatase activities) and hematological (total count of red blood cells, hematocrit, hemoglobin, and hematimetric Wintrobe indexes) stress indicators, as well as on growth and wellbeing degree expressed by pirarucu condition factor developed in captivity. All biochemical and hematological stress indicators showed seasonal variations. However, the fish growth was allometrically positive; condition factor high values indicated good state of healthiness in cultivation. These results reinforce the robust feature of pirarucu and represent a starting point for understanding stress physiology and environmental changes during cultivation enabling identification and prevention of fish adverse health conditions

CLASSIFICAÇÃO DO USO E COBERTURA DA TERRA A PARTIR DE IMAGENS RapidEye PARA O MUNICÍPIO DE SEGREDO - RS - BRASIL

Devido à importância das florestas nativas e da produção de alimentos surgiu a necessidade de desenvolver pesquisas alicerçadas em ferramentas de geoprocessamento e sensoriamento remoto com vistas no monitoramento do uso e cobertura do solo. Nesse contexto, o estudo objetivou o processamento digital de imagens e a determinação de classes de uso e cobertura da terra através da utilização de dados multiespectrais do sensor REIS (RapidEye Earth Imaging System) com alta resolução espacial, possibilitando assim a produção de um mapa temático de uso e cobertura do solo. Para tanto, foi utilizado o Software Spring versão 5.2 no qual foram realizados os processos: recorte dos limites do município, segmentação, definição de padrões de uso e cobertura da terra e classificação da área. Constatou-se que o município de Segredo possui uma área total de 285,18 km2. A classe mais representativa a Agricultura (110,23 km2), seguida da Floresta Nativa (98,29 km2), Campo (65,09 km2), Floresta Plantada (7,32 km2), Corpos d.água (3,51 km2) e em último a Área Urbana (0,74 km2). A produção de mapas através do processamento digital de imagens de alta resolução demonstra-se uma potencial ferramenta de monitoramento ambiental municipal, pois possibilita o entendimento da distribuição espacial do uso e cobertura dos município

Early sedation and clinical outcomes of mechanically ventilated patients: a prospective multicenter cohort study

Introduction: Sedation overuse is frequent and possibly associated with poor outcomes in the intensive care unit (ICU) patients. However, the association of early oversedation with clinical outcomes has not been thoroughly evaluated. the aim of this study was to assess the association of early sedation strategies with outcomes of critically ill adult patients under mechanical ventilation (MV).Methods: A secondary analysis of a multicenter prospective cohort conducted in 45 Brazilian ICUs, including adult patients requiring ventilatory support and sedation in the first 48 hours of ICU admissions, was performed. Sedation depth was evaluated after 48 hours of MV. Multivariate analysis was used to identify variables associated with hospital mortality.Results: A total of 322 patients were evaluated. Overall, ICU and hospital mortality rates were 30.4% and 38.8%, respectively. Deep sedation was observed in 113 patients (35.1%). Longer duration of ventilatory support was observed (7 (4 to 10) versus 5 (3 to 9) days, P = 0.041) and more tracheostomies were performed in the deep sedation group (38.9% versus 22%, P=0.001) despite similar PaO2/FiO(2) ratios and acute respiratory distress syndrome (ARDS) severity. in a multivariate analysis, age (Odds Ratio (OR) 1.02; 95% confidence interval (CI) 1.00 to 1.03), Charlson Comorbidity Index >2 (OR 2.06; 95% Cl, 1.44 to 2.94), Simplified Acute Physiology Score 3 (SAPS 3) score (OR 1.02; Cl 95%, 1.00 to 1.04), severe ARDS (OR 1.44; Cl 95%, 1.09 to 1.91) and deep sedation (OR 2.36; Cl 9596, 1.31 to 4.25) were independently associated with increased hospital mortality.Conclusions: Early deep sedation is associated with adverse outcomes and constitutes an independent predictor of hospital mortality in mechanically ventilated patients.Research and Education Institute from Hospital Sirio-Libanes, São PauloD'Or Institute for Research and Education, Rio de Janeiro, BrazilBrazilian Research in Intensive Care NetworkHosp Copa DOr, BR-22031010 Rio de Janeiro, BrazilHosp Sirio Libanes, Res & Educ Inst, BR-01308060 São Paulo, BrazilUniv São Paulo, Fac Med, Hosp Clin, ICU,Emergency Med Dept, BR-05403000 São Paulo, BrazilHosp Sao Camilo Pompeia, ICU, BR-05022000 São Paulo, BrazilCEPETI, BR-82530200 Curitiba, Parana, BrazilHosp Canc I, Inst Nacl Canc, ICU, BR-20230130 Rio de Janeiro, BrazilPasteur Hosp, ICU, BR-20735040 Rio de Janeiro, BrazilIrmandade Santa Casa Misericordia Porto Alegre, RIPIMI, BR-90020090 Porto Alegre, RS, BrazilVitoria Apart Hosp, ICU, BR-29161900 Serra, ES, BrazilHosp Mater Dei, ICU, BR-30140093 Belo Horizonte, MG, BrazilHosp Santa Luzia, ICU, BR-70390902 Brasilia, DF, BrazilHosp Sao Luiz, ICU, BR-04544000 São Paulo, BrazilUniversidade Federal de São Paulo, Anesthesiol Pain & Intens Care Dept, ICU, BR-04024900 São Paulo, BrazilHosp Sao Jose Criciuma, ICU, BR-88801250 Criciuma, BrazilUDI Hosp, ICU, BR-65076820 Sao Luis, BrazilUniv São Paulo, Univ Hosp, ICU, BR-05508000 São Paulo, BrazilUniv São Paulo, Fac Med, Hosp Clin, ICU,Surg Emergency Dept, BR-05403000 São Paulo, BrazilIDOR DOr Inst Res & Educ, BR-22281100 Rio de Janeiro, BrazilInst Nacl Canc, Postgrad Program, BR-20230130 Rio de Janeiro, BrazilUniversidade Federal de São Paulo, Anesthesiol Pain & Intens Care Dept, ICU, BR-04024900 São Paulo, BrazilWeb of Scienc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Study of rapidity gap and characterization of diffractive processes in minimum bias events at 7TeV in CMS/LHC

Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorNeste trabalho estudamos as características das distribuições da lacuna de rapidez em amostras de eventos de minimum bias de colisões pp a ps=7 TeV no CMS/LHC. Tais eventos são constituídos por processos difrativos, além de processos de QCD mole. São investigados o tamanho e a localização das lacunas, assim como as correlações entre as distribuições obtidas a partir dos objetos reconstruídos no detector e as distribuições obtidas a partir das partículas geradas via simulação Monte Carlo. Uma boa compreensão dessas distribuições pode, eventualmente, possibilitar a caracterização de eventos difrativos nos dados.Rapidity gap distributions in minimum bias events from pp collisions at ps = 7TeV are studied. Minimum bias events are composed by diffractive processes and soft QCD processes. Gap size and position, as well as correlations between reconstructed distributions and Monte Carlo simulated distributions are investigated. A good understanding of such distributions may, eventually, make it possible characterize diffractive events in real data

Epidemiologic and Clinical Characteristics of Human Bocavirus Infection in Children with or without Acute Gastroenteritis in Acre, Northern Brazil

Human bocavirus (HBoV) is an emerging virus detected around the world that may be associated with cases of acute gastroenteritis (AGE). However, its contribution to AGE has not been elucidated. This study aimed to describe the frequency, clinical features, and HBoV species circulation in children up to 5 years with or without AGE symptoms in Acre, Northern Brazil. A total of 480 stool samples were collected between January and December 2012. Fecal samples were used for extraction, nested PCR amplification, and sequencing for genotyping. Statistical analysis was applied to verify the association between epidemiological and clinical characteristics. Overall, HBoV-positivity was 10% (48/480), with HBoV-positive rates of 8.4% (19/226) and 11.4% (29/254) recorded in diarrheic and non-diarrheic children, respectively. The most affected children were in the age group ranging between 7 and 24 months (50%). HBoV infection was more frequent in children who live in urban areas (85.4%), use water from public networks (56.2%), and live with adequate sewage facilities (50%). Co-detection with other enteric viruses was 16.7% (8/48) and the most prevalent coinfection was RVA+ HBoV (50%, 4/8). HBoV-1 was the most frequent species detected in diarrheic and non-diarrheic children, responsible for 43.8% (21/48) of cases, followed by HBoV-3 (29.2%, 14/48) and HBoV-2 (25%, 12/48). In this study, HBoV infection was not always associated with AGE, as most HBoV cases belonged to the non-diarrheal group. Future studies are warranted in order to determine the role of HBoV in causing acute diarrhea disease