Pre-main-sequence Lithium Depletion

In this review I briefly discuss the theory of pre-main-sequence (PMS) Li depletion in low-mass (0.075<M<1.2 Msun) stars and highlight those uncertain parameters which lead to substantial differences in model predictions. I then summarise observations of PMS stars in very young open clusters, clusters that have just reached the ZAMS and briefly highlight recent developments in the observation of Li in very low-mass PMS stars.Comment: 8 pages, invited review at "Chemical abundances and mixing in stars in the Milky Way and its satellites", eds. L. Pasquini, S. Randich. ESO Astrophysics Symposium (Springer-Verlag

Tidal Dwarf Galaxies at Intermediate Redshifts

We present the first attempt at measuring the production rate of tidal dwarf galaxies (TDGs) and estimating their contribution to the overall dwarf population. Using HST/ACS deep imaging data from GOODS and GEMS surveys in conjunction with photometric redshifts from COMBO-17 survey, we performed a morphological analysis for a sample of merging/interacting galaxies in the Extended Chandra Deep Field South and identified tidal dwarf candidates in the rest-frame optical bands. We estimated a production rate about 1.4 {\times} 10^{-5} per Gyr per comoving volume for long-lived TDGs with stellar mass 3 {\times} 10^{8-9} solar mass at 0.5<z<1.1. Together with galaxy merger rates and TDG survival rate from the literature, our results suggest that only a marginal fraction (less than 10%) of dwarf galaxies in the local universe could be tidally-originated. TDGs in our sample are on average bluer than their host galaxies in the optical. Stellar population modelling of optical to near-infrared spectral energy distributions (SEDs) for two TDGs favors a burst component with age 400/200 Myr and stellar mass 40%/26% of the total, indicating that a young stellar population newly formed in TDGs. This is consistent with the episodic star formation histories found for nearby TDGs.Comment: 9 pages, 5 figures, Accepted for publication in Astrophysics & Space Scienc

Loss of mothers against decapentaplegic homolog 4 (SMAD4) expression and its correlation with clinicopathological parameters in pancreatic carcinoma

Abstract: Pancreatic cancer (PC) has an important role in the clinical and research area representing one of the lowest five-year rates as well as a global mortality rate of 4.8% due to its late and poor diagnosis. Therapeutic strategies have also an unsatisfactory response. Even after surgery, the recurrence or appearance of metastasis are frequent, leading to a poor overall survival. The PC has been related with several mutations, including K-RAS; P16; TP53; HER2. Besides, it is also associated with the deleted in pancreatic cancer locus 4 (DPC4), also known as the suppressor mothers against decapentaplegic homolog 4 (SMAD4) which is present in nearly 50% of the diagnosed patients with PC. Preceding studies proved that SMAD4 loss expression plays an important role in tumorigenesis and in the promotion of pancreatic carcinoma´s growth. Therefore, it is highly relevant in late stages suggesting that SMAD4 may be a molecular biomarker in prognostic results. The main goal of this review is to highlight the foregoing findings focused on SMAD4 deletion and its influence in clinicopathological parameters in pancreatic carcinoma by referring some of the investigations and clinical trials made in this field. Furthermore, it is also required to contemplate some of the therapeutical strategies and the influence of SMAD4 in future therapiesinfo:eu-repo/semantics/publishedVersio

Measurement of the ttbar Production Cross Section in ppbar Collisions at sqrt(s)=1.96 TeV using Lepton + Jets Events with Lifetime b-tagging

We present a measurement of the top quark pair ($t\bar{t}$) production cross section ($\sigma_{t\bar{t}}$) in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV using 230 pb$^{-1}$ of data collected by the D0 experiment at the Fermilab Tevatron Collider. We select events with one charged lepton (electron or muon), missing transverse energy, and jets in the final state. We employ lifetime-based b-jet identification techniques to further enhance the $t\bar{t}$ purity of the selected sample. For a top quark mass of 175 GeV, we measure $\sigma_{t\bar{t}}=8.6^{+1.6}_{-1.5}(stat.+syst.)\pm 0.6(lumi.)$ pb, in agreement with the standard model expectation.Comment: 7 pages, 2 figures, 3 tables Submitted to Phys.Rev.Let

Search for W' bosons decaying to an electron and a neutrino with the D0 detector

This Letter describes the search for a new heavy charged gauge boson W' decaying into an electron and a neutrino. The data were collected with the D0 detector at the Fermilab Tevatron proton-antiproton Collider at a center-of-mass energy of 1.96 TeV, and correspond to an integrated luminosity of about 1 inverse femtobarn. Lacking any significant excess in the data in comparison with known processes, an upper limit is set on the production cross section times branching fraction, and a W' boson with mass below 1.00 TeV can be excluded at the 95% C.L., assuming standard-model-like couplings to fermions. This result significantly improves upon previous limits, and is the most stringent to date.Comment: submitted to Phys. Rev. Let

Genetic parameters for milk yield, lactation length and calving intervals of Murrah buffaloes from Brazil

The major objective of this study was to estimate heritability and genetic correlations between milk yield (MY) and calving interval (CI) and lactation length (LL) in Murrah buffaloes using Bayesian inference. The database used belongs to the genetic improvement program of four buffalo herds from Brazil. To obtain the estimates of variance and covariance, bivariate analyses were performed with the Gibbs sampler, using the program MTGSAM. The heritability coefficient estimates were 0.28, 0.03 and 0.15 for MY, CI and LL, respectively. The genetic correlations between MY and LL was moderate (0.48). However, the genetic correlation between MY and CI showed large HPD regions (highest posterior density interval). Milk yield was the only trait with clear potential for genetic improvement by direct mass selection. The genetic correlation between MY and LL indicates that indirect selection using milk yield is a potentially beneficialstrategy.Theinterpretation of the estimated genetic correlation between MY and CI is difficult and could be spurious. ©2013 Sociedade Brasileira de Zootecnia.Universidade Estadual do Sudoeste da BahiaUniversidade Federal do AlagoasUniversidade Estadual PaulistaUniversidade Federal do Mato Grosso do SulCanakkale Onsekiz Mart University Ziraat FakUniversidade Estadual Paulist

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown

The southern photometric local universe survey (S-PLUS): Improved SEDs, morphologies, and redshifts with 12 optical filters

The Southern Photometric Local Universe Survey (S-PLUS) is imaging ~9300 deg2 of the celestial sphere in 12 optical bands using a dedicated 0.8mrobotic telescope, the T80-South, at the Cerro Tololo Inter-american Observatory, Chile. The telescope is equipped with a 9.2k × 9.2k e2v detector with 10 μm pixels, resulting in a field of view of 2 deg2 with a plate scale of 0.55 arcsec pixel-1. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| > 30° , 8000 deg2) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, Hd, G band, Mg b triplet, Hα, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δz/(1 + z) = 0.02 or better) for galaxies with r < 19.7 AB mag and z < 0.4, thus producing a 3D map of the local Universe over a volume of more than 1 (Gpc/h)3. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ~336 deg2 of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.Fil: De Oliveira, C. Mendes. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Ribeiro, T.. Universidade Federal de Sergipe; Brasil. National Optical Astronomy Observatory; Estados UnidosFil: Schoenell, W.. Universidade Federal do Rio Grande do Sul; BrasilFil: Kanaan, A.. Universidade Federal de Santa Catarina; BrasilFil: Overzier, R.A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Ministério da Ciência, Tecnologia, Inovação e Comunicações. Observatório Nacional; BrasilFil: Molino, A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Sampedro, L.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Coelho, P.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Barbosa, C.E.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Cortesi, A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Costa Duarte, M.V.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Herpich, F.R.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Universidade Federal de Santa Catarina; BrasilFil: Hernandez Jimenez, J.A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Placco, V.M.. University of Notre Dame; Estados Unidos. JINA Center for the Evolution of the Elements ; Estados UnidosFil: Xavier, H.S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Abramo, L.R.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Saito, R.K.. Universidade Federal de Santa Catarina; BrasilFil: Chies Santos, A.L.. Universidade Federal do Rio Grande do Sul; BrasilFil: Ederoclite, A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Centro de Estudios de Física del Cosmo de Aragon; EspañaFil: De Oliveira, R. Lopes. Universidade Federal de Sergipe; Brasil. Ministério da Ciência, Tecnologia, Inovação e Comunicações. Observatório Nacional; Brasil. University of Maryland; Estados UnidosFil: Goncalves, D.R.. Universidade Federal do Rio de Janeiro; BrasilFil: Akras, S.. Ministério da Ciência, Tecnologia, Inovação e Comunicações. Observatório Nacional; Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Almeida, L.A.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Universidade Federal do Rio Grande do Norte; BrasilFil: Almeida Fernandes, F.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Beers, T.C.. University of Notre Dame; Estados Unidos. JINA Center for the Evolution of the Elements ; Estados UnidosFil: Bonatto, C.. Universidade Federal do Rio Grande do Sul; BrasilFil: Bonoli, S.. Centro de Estudios de Física del Cosmo de Aragon; EspañaFil: Cypriano, E.S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Vinicius Lima, E.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Smith Castelli, Analia Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentin