Large-scale analysis of the SDSS-III DR8 photometric luminous galaxies angular correlation function

We analyse the large-scale angular correlation function (ACF) of the CMASS luminous galaxies (LGs), a photometric-redshift catalogue based on the Data Release 8 (DR8) of the Sloan Digital Sky Survey-III. This catalogue contains over $600 \, \, 000$ LGs in the range $0.45 \leq z \leq 0.65$, which was split into four redshift shells of constant width. First, we estimate the constraints on the redshift-space distortion (RSD) parameters $b\sigma_8$ and $f\sigma_8$, where $b$ is the galaxy bias, $f$ the growth rate and $\sigma_8$ is the normalization of the perturbations, finding that they vary appreciably among different redshift shells, in agreement with previous results using DR7 data. When assuming constant RSD parameters over the survey redshift range, we obtain $f\sigma_8 = 0.69 \pm 0.21$, which agrees at the $1.5\sigma$ level with Baryon Oscillation Spectroscopic Survey DR9 spectroscopic results. Next, we performed two cosmological analyses, where relevant parameters not fitted were kept fixed at their fiducial values. In the first analysis, we extracted the baryon acoustic oscillation peak position for the four redshift shells, and combined with the sound horizon scale from 7-year \textit{Wilkinson Microwave Anisotropy Probe} $(WMAP7)$ to produce the constraints $\Omega_{m}=0.249 \pm 0.031$ and $w=-0.885 \pm 0.145$. In the second analysis, we used the ACF full shape information to constrain cosmology using real data for the first time, finding $\Omega_{m} = 0.280 \pm 0.022$ and $f_b = \Omega_b/\Omega_m = 0.211 \pm 0.026$. These results are in good agreement with $WMAP7$ findings, showing that the ACF can be efficiently applied to constrain cosmology in future photometric galaxy surveys.Comment: MNRAS accepted. Minor corrections to match publish versio

Investigação epidemiológica e controle da Esquistossomose e demais parasitoses intestinais na Zona da Mata Mineira

-O presente trabalho avalia a cobertura e a qualidade do cadastro das famílias nos municípios mineiros de Coronel Pacheco, Goianá, e Piau, através da comparação entre a ficha A aplicada pelo agente comunitário de saúde e os dados obtidos de investigações realizadas diretamente nos domicílios. Foram entrevistadas, no período de novembro a dezembro de 2006, 192 famílias selecionadas de forma aleatória. A alta cobertura do cadastro das famílias verificada (97,9%) assim como a fidedignidade para os campos “família corresponde ao endereço” e “família está completa” (95,7% e 93,1%), observados nos três municípios, indicaram a confiabilidade do cadastro realizado pelo PSF. Os dados referentes à atualização das gestantes, hipertensos, e crianças menores de um ano, não tiveram a mesma confiabilidade. Quanto ao perfil dos agentes comunitários de saúde, que atuaram nos três municípios, a maioria dos entrevistados eram mulheres, com menos de 25 anos e estavam estudando ou já haviam completado o segundo grau. O tempo médio de permanência dos profissionais no PSF foi de 10 meses, sendo que 45% dos agentes foram submetidos a concurso municipal e os demais contratados. Os autores concluem que o cadastro do PSF (ficha A/SIAB) nos três municípios é confiável como base populacional para o cálculo amostral, justificando sua utilização como ferramenta legítima para a realização de pesquisas de campo nas áreas básicas e clínicas

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation for maximising their effectiveness.We carry out a comparison between several common machine learning methods for galaxy classification (Convolutional Neural Network (CNN), K-nearest neighbour, LogisticRegression, Support Vector Machine, Random Forest, and Neural Networks) by using DarkEnergy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project(GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of _2,800 galaxies with visual classification from GZ1, we reach an accuracy of _0.99 for the morphological classification of Ellipticals and Spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals an the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually Lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both Es and Spirals.We confirm that _2.5% galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result)

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