The present-day chemical composition of the SMC from UVES spectra of the sharp-lined, B-type dwarf AV 304

High-resolution spectroscopic VLT/UVES observations are presented for the B-type main-sequence star, AV 304, in the Small Magellanic Cloud (SMC). These spectra have been analysed using LTE model-atmosphere techniques, to derive stellar atmospheric parameters and chemical compositions. As AV 304 is located within the hydrogen burning main-sequence band, its chemical composition should reflect that of the SMC interstellar medium (ISM). A detailed line-by-line differential analysis has been undertaken relative to a Galactic comparison star. A general metal deficiency for the a-process elements O, Si & S of -0.43 +/- 0.05 dex is found for AV 304, with iron having a similar underabundance. Oxygen may be relatively over- abundant by similar to0.1 dex and carbon and aluminium underabundant by similar to0.2 dex. A large nitrogen underabundance (of -1.2 dex relative to hydrogen and -0.7 dex relative to iron) is found. This is interpreted in terms of the CNO bi-cycle having been suppressed in the SMC. Furthermore, the large nitrogen deficiency is in excellent agreement with that found for SMC H II regions. Indeed, this represents a first for stellar astrophysics - confirming the low base-line nitrogen composition of the SMC ISM (viz. 12+log(N/H) similar to 6.66 +/- 0.10 dex).

The chemical composition towards the galactic anti-centre .1. Observations and model atmosphere analyses

High resolution spectra of six early B-type main-sequence stars having galactocentric distances of between 10 and 18 kpc are presented. We List the equivalent widths for the metal lines and illustrate their hydrogen and helium line profiles. The stars are analysed using LTE line-blanketed model atmosphere techniques to derive atmospheric parameters and surface chemical compositions. All six stars have similar effective temperatures and surface gravities, allowing a reliable comparison of their metal abundances and distances. Significant variations in the photospheric abundances are evident and are discuss the need for a more detailed line-by-line differential analysis to exactly quantify the differences. This will be presented in a companion paper (Smartt et al. 1996).

Differential power analysis resistance of Camellia and countermeasure strategy on FPGAs

Security devices are vulnerable to Differential Power Analysis (DPA) that reveals the key by monitoring the power consumption of the circuits. In this paper, we present the first DPA attack against an FPGA implementation of the Camellia encryption algorithm with all key sizes and evaluate the DPA resistance of the algorithm. The Camellia cryptographic algorithm involves several different key-dependent intermediate operations including S-Box operations. In previous research, it was believed that the Camellia is stronger than AES due to the additional Whitening phase protecting the S-Box operation. However, we propose an attack that bypasses the Whitening phase and targets the S-Box. In this paper, we also discuss a lowcost countermeasure strategy to protect the Pre-whitening / Post-whitening and FL function of Camellia using Dual-rail Precharged Logic and to protect against attacks of the S-Box using Random Delay Insertion. © 2009 IEEE.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

Motivation: To date, Gene Set Analysis (GSA) approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations, or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

Results: In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA, indeed, captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses.

BRCA1 Functions as a Differential Modulator of Chemotherapy-induced Apoptosis

We have evaluated the role played by BRCA1 in mediating the phenotypic response to a range of chemotherapeutic agents commonly used in cancer treatment. Here we provide evidence that BRCA1 functions as a differential mediator of chemotherapy-induced apoptosis. Specifically, we demonstrate that BRCA1 mediates sensitivity to apoptosis induced by antimicrotubule agents but conversely induces resistance to DNA-damaging agents. These data are supported by a variety of experimental models including cells with inducible expression of BRCA1, siRNA-mediated inactivation of endogenous BRCA1, and reconstitution of BRCA1-deficient cells with wild-type BRCA1. Most notably we demonstrate that BRCA1 induces a 10–1000-fold increase in resistance to a range of DNA-damaging agents, in particular those that give rise to double-strand breaks such as etoposide or bleomycin. In contrast, BRCA1 induces a >1000-fold increase in sensitivity to the spindle poisons, paclitaxel and vinorelbine. Fluorescence-activated cell sorter analysis demonstrated that BRCA1 mediates G2/M arrest in response to both antimicrotubule and DNA-damaging agents. However, poly(ADP-ribose) polymerase and caspase-3 cleavage assays indicate that the differential effect mediated by BRCA1 in response to these agents occurs through the inhibition or induction of apoptosis. Therefore, our data suggest that BRCA1 acts as a differential modulator of apoptosis depending on the nature of the cellular insult.