Energy levels, radiative rates and electron impact excitation rates for transitions in AlX

Energy levels, radiative rates and lifetimes are calculated among the lowest 98 levels of the n ≤4 configurations of Be-like Al X. The GRASP (General-purpose Relativistic Atomic Structure Package) is adopted and data are provided for all E1, E2, M1 and M2 transitions. Similar data are also obtained with the FAC (Flexible Atomic Code) to assess the accuracy of the calculations. Based on comparisons between calculations with the two codes as well as with available measurements, our listed energy levels are assessed to be accurate to better than 0.3 per cent. However, the accuracy for radiative rates and lifetimes is estimated to be about 20 per cent. Collision strengths are also calculated for which the DARC (Dirac Atomic R-matrix Code) is used. A wide energy range (up to 380 Ryd) is considered and resonances resolved in a fine energy mesh in the thresholds region. The collision strengths are subsequently averaged over a Maxwellian velocity distribution to determine effective collision strengths up to a temperature of 1.6 × 10⁷ K. Our results are compared with the previous (limited) atomic data and significant differences (up to a factor of 4) are noted for several transitions, particularly those which are not allowed in jj coupling. 

Novel polymorphic microsatellite loci for the protogynous hermaphrodite slinger sea bream (Chrysoblephus puniceus, Sparidae)

As a result of the global decline of fish stocks, an increasing number of fish species are becoming targets of heavy exploitation, often concomitantly with a lack of biological knowledge on their structure and demographics. Here we present 11 new polymorphic microsatellite loci, isolated from the slinger sea bream (Chrysoblephus puniceus, Sparidae), a relatively recent target of coastal fisheries in eastern South Africa. Levels of genetic diversity were assessed in 39 individuals collected from the KwaZulu-Natal coast (Park Rynie, South Africa). Observed and expected heterozygosities varied between 0.39 and 0.97 and between 0.53 and 0.96, respectively. One locus (SL35) showed significant heterozygote deficiency and linkage disequilibrium was detected between SL35 and SL1. Importantly, five of these microsatellites cross-amplify in Cheimerius nufar, a sympatric species also subjected to exploitation.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.