Probing the anomalous extinction of four young star clusters: the use of colour-excess, main-sequence fitting and fractal analysis

Aims. We studied four young star clusters to characterise their anomalous extinction or variable reddening and asses whether they could be due to contamination by either dense clouds or circumstellar effects. Methods. We evaluated the extinction law (R-V) by adopting two methods: (i) the use of theoretical expressions based on the colour-excess of stars with known spectral type; and (ii) the analysis of two-colour diagrams, where the slope of the observed colour distribution was compared to the normal distribution. An algorithm to reproduce the zero-age main-sequence (ZAMS) reddened colours was developed to derive the average visual extinction (A(V)) that provides the closest fit to the observational data. The structure of the clouds was evaluated by means of a statistical fractal analysis, designed to compare their geometric structure with the spatial distribution of the cluster members. Results. The cluster NGC 6530 is the only object of our sample affected by anomalous extinction. On average, the other clusters suffer normal extinction, but several of their members, mainly in NGC 2264, seem to have high R-V, probably because of circumstellar effects. The ZAMS fitting provides A(V) values that are in good agreement with those found in the literature. The fractal analysis shows that NGC 6530 has a centrally concentrated distribution of stars that differs from the substructures found in the density distribution of the cloud projected in the A(V) map, suggesting that the original cloud was changed by the cluster formation. However, the fractal dimension and statistical parameters of Berkeley 86, NGC 2244, and NGC 2264 indicate that there is a good cloud-cluster correlation, when compared to other works based on an artificial distribution of points.

Lithium in M 67: From the main sequence to the red giant branch

Context. Lithium abundances in open clusters are a very effective probe of mixing processes, and their study can help us to understand the large depletion of lithium that occurs in the Sun. Owing to its age and metallicity, the open cluster M 67 is especially interesting on this respect. Many studies of lithium abundances in M 67 have been performed, but a homogeneous global analysis of lithium in stars from subsolar masses and extending to the most massive members, has yet to be accomplished for a large sample based on high-quality spectra. Aims. We test our non-standard models, which were calibrated using the Sun with observational data. Methods. We collect literature data to analyze, for the first time in a homogeneous way, the non-local thermal equilibrium lithium abundances of all observed single stars in M 67 more massive than similar to 0.9 M-circle dot. Our grid of evolutionary models is computed assuming a non-standard mixing at metallicity [Fe/H] = 0.01, using the Toulouse-Geneva evolution code. Our analysis starts from the entrance into the zero-age main-sequence. Results. Lithium in M 67 is a tight function of mass for stars more massive than the Sun, apart from a few outliers. A plateau in lithium abundances is observed for turn-off stars. Both less massive (M >= 1.10 M-circle dot) and more massive (M >= 1.28 M-circle dot) stars are more depleted than those in the plateau. There is a significant scatter in lithium abundances for any given mass M <= 1.1 M-circle dot. Conclusions. Our models qualitatively reproduce most of the features described above, although the predicted depletion of lithium is 0.45 dex smaller than observed for masses in the plateau region, i.e. between 1.1 and 1.28 solar masses. More work is clearly needed to accurately reproduce the observations. Despite hints that chromospheric activity and rotation play a role in lithium depletion, no firm conclusion can be drawn with the presently available data.

The zero temperature coefficient in junctionless nanowire transistors

This Letter presents an analysis of the zero temperature coefficient (ZTC) bias in junctionless nanowire transistors (JNTs). Unlike in previous works, which had shown that JNT did not present a ZTC point, this work shows that ZTC may occur in JNTs depending mainly on the series resistance of the devices and its dependence on the temperature. Experimental results of drain current, threshold voltage, and series resistance are presented for both long and short channel n and p-type devices. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4744965]

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.