ALBERT

Description: The Ser326Cys polymorphism in the human 8-oxogunaine DNA glycosylase ( hOGG1 ) gene had been implicated in cancer susceptibility. Studies investigating the associations between the Ser326Cys polymorphism and cancer susceptibility showed conflicting results. To derive a more precise estimation of the relationship, a meta-analysis was performed. This meta-analysis was performed from 83 case–control studies, including 27 918 cases and 33 399 controls. The fixed and random effect models were used to estimate the odds ratios (ORs) and their 95% confidence interval (CI) for various contrasts of this polymorphism. The combined results based on all studies showed that the hOGG1 Ser326Cys polymorphism was associated with an increased cancer susceptibility in different genetic models. In the stratified analyses, the association was significantly in head and neck cancer (homozygote comparison: OR = 2.19, 95% CI: 1.20–4.01, P heterogeneity = 0.002; heterozygote comparison: OR = 1.48, 95% CI: 1.11–1.99, P heterogeneity = 0.004; dominant model comparison: OR = 1.58, 95% CI: 1.14–2.19, P heterogeneity 〈 0.001; recessive model comparison: OR = 1.73, 95% CI: 1.02–2.94, P heterogeneity = 0.002; and additive model comparison: OR = 1.43, 95% CI: 1.09–1.88, P heterogeneity 〈 0.001) which remained for studies of the Asian populations and hospital-based of control sources. But it was not observed in other cancer types of the European population and population based of control sources. This meta-analysis suggested that the hOGG1 Ser326Cys polymorphism might contribute to an increased risk on cancer susceptibility. More studies based on larger sample size should be performed to confirm the findings.

Description: We present a study of timing properties of the accreting pulsar 2S 1417-624 observed during its 2018 outburst, based on Swift/BAT, Fermi/GBM, Insight-HXMT and NICER observations. We report a dramatic change of the pulse profiles with luminosity. The morphology of the profile in the range 0.2-10.0 keV switches from double to triple peaks at ∼2.5 $ m imes 10^{37}{it D}_{10}^2 erg s^{-1}$ and from triple to quadruple peaks at ∼7 $ m imes 10^{37}{it D}_{10}^2 erg s^{-1}$. The profile at high energies (25-100 keV) shows significant evolutions as well. We explain this phenomenon according to existing theoretical models. We argue that the first change is related to the transition from the sub to the super-critical accretion regime, while the second to the transition of the accretion disc from the gas-dominated to the radiation pressure-dominated state. Considering the spin-up as well due to the accretion torque, this interpretation allows to estimate the magnetic field self-consistently at ∼7 × 1012 G.

Description: We report on analysis of observations of the bright transient X-ray pulsar Swift J0243.6+6124 obtained during its 2017-2018 giant outburst with Insight-HXMT, NuSTAR, and Swift observatories. We focus on the discovery of a sharp state transition of the timing and spectral properties of the source at super-Eddington accretion rates, which we associate with the transition of the accretion disk to a radiation pressure dominated (RPD) state, the first ever directly observed for magnetized neutron star. This transition occurs at slightly higher luminosity compared to already reported transition of the source from sub- to super-critical accretion regime associate with onset of an accretion column. We argue that this scenario can only be realized for comparatively weakly magnetized neutron star, not dissimilar to other ultra-luminous X-ray pulsars (ULPs), which accrete at similar rates. Further evidence for this conclusion is provided by the non-detection of the transition to the propeller state in quiescence which strongly implies compact magnetosphere and thus rules out magnetar-like fields.

Description: Flowering time is one of the key determinants of crop adaptation to local environments during domestication. However, the genetic basis underlying flowering time is yet to be elucidated in most cereals. Although staple cereals, such as rice, maize, wheat, barley, and sorghum, have spread and adapted to a wide range of ecological environments during domestication, it is yet to be determined whether they have a common genetic basis for flowering time. In this study, we show, through map-based cloning, that flowering time in sorghum is controlled by a major quantitative trait locus (QTL) Heading Date 1 ( HD1 ), located on chromosome 10. The causal gene encodes the CONSTANS gene family which contains a CCT domain. A 5-bp deletion of a minor allele present in the coding sequence leads to a gene frameshift that delays flowering in sorghum. In contrast, in foxtail millet, association mapping of HD1 showed a common causal site with a splicing variant from "GT" to "AT" that was highly correlated with flowering time. In addition, the rice HD1 gene is known to harbor several causal variants controlling flowering time. These data indicate that the major flowering time QTL HD1 was under parallel domestication in sorghum, foxtail millet, and rice. The pattern of common mixed minor, or even rare, causal alleles in HD1 across different species may be representative of the genetic basis of the domestication syndrome. Furthermore, large DNA sequence analysis of HD1 revealed multiple origins for domesticated sorghum and a single origin for domesticated foxtail millet.

Description: Loosely coupled and cross-platform features make Web services accessible and increasingly popular on the Internet. However, efficient service discovery and automated service composition are still challenges under the conventional practice where services are organized into categories. In this paper, we propose a graph-based method to organize Web services into a service ecosystem interlaced with service relationships at the semantic level. First, Web services are modelled as a set of interfaces, whose input and output parameters are annotated with well-defined ontologies. Secondly, semantic associations and interactions between Web services are mined, and services are constructed into a Web services network (SN), a variant of bipartite graph, by projecting the functional aspects of concrete Web services onto the abstract service layer. Thirdly, from the complex network perspective, the services relations are investigated and the structure of SN is analysed. To demonstrate the basic topological properties of SN, an empirical study is conducted on two data sets for comparative purposes, 10 000+ Web services collected from the Internet and 1231 Web services provided by Titan system of Zhejiang University. The experimental results reveal that SNs, which are built by different data sets on the semantic level, exhibit the same features such as small-world and scale-free. In addition, our results yield valuable insight for developing service discovery and automated composition algorithms, and characterizing the evolution of the entire Web service ecosystem.

Description: Motivation : Genome-wide association studies (GWASs) are commonly applied on human genomic data to understand the causal gene combinations statistically connected to certain diseases. Patients involved in these GWASs could be re-identified when the studies release statistical information on a large number of single-nucleotide polymorphisms. Subsequent work, however, found that such privacy attacks are theoretically possible but unsuccessful and unconvincing in real settings. Results : We derive the first practical privacy attack that can successfully identify specific individuals from limited published associations from the Wellcome Trust Case Control Consortium (WTCCC) dataset. For GWAS results computed over 25 randomly selected loci, our algorithm always pinpoints at least one patient from the WTCCC dataset. Moreover, the number of re-identified patients grows rapidly with the number of published genotypes. Finally, we discuss prevention methods to disable the attack, thus providing a solution for enhancing patient privacy. Availability and implementation : Proofs of the theorems and additional experimental results are available in the support online documents. The attack algorithm codes are publicly available at https://sites.google.com/site/zhangzhenjie/GWAS_attack.zip . The genomic dataset used in the experiments is available at http://www.wtccc.org.uk/ on request. Contact : winslett@illinois.edu or zhenjie@adsc.com.sg Supplementary information: Supplementary data are available from Bioinformatics online.

Description: Motivation: Identifying microRNAs associated with diseases (disease miRNAs) is helpful for exploring the pathogenesis of diseases. Because miRNAs fulfill function via the regulation of their target genes and because the current number of experimentally validated targets is insufficient, some existing methods have inferred potential disease miRNAs based on the predicted targets. It is difficult for these methods to achieve excellent performance due to the high false-positive and false-negative rates for the target prediction results. Alternatively, several methods have constructed a network composed of miRNAs based on their associated diseases and have exploited the information within the network to predict the disease miRNAs. However, these methods have failed to take into account the prior information regarding the network nodes and the respective local topological structures of the different categories of nodes. Therefore, it is essential to develop a method that exploits the more useful information to predict reliable disease miRNA candidates. Results: miRNAs with similar functions are normally associated with similar diseases and vice versa. Therefore, the functional similarity between a pair of miRNAs is calculated based on their associated diseases to construct a miRNA network. We present a new prediction method based on random walk on the network. For the diseases with some known related miRNAs, the network nodes are divided into labeled nodes and unlabeled nodes, and the transition matrices are established for the two categories of nodes. Furthermore, different categories of nodes have different transition weights. In this way, the prior information of nodes can be completely exploited. Simultaneously, the various ranges of topologies around the different categories of nodes are integrated. In addition, how far the walker can go away from the labeled nodes is controlled by restarting the walking. This is helpful for relieving the negative effect of noisy data. For the diseases without any known related miRNAs, we extend the walking on a miRNA-disease bilayer network. During the prediction process, the similarity between diseases, the similarity between miRNAs, the known miRNA-disease associations and the topology information of the bilayer network are exploited. Moreover, the importance of information from different layers of network is considered. Our method achieves superior performance for 18 human diseases with AUC values ranging from 0.786 to 0.945. Moreover, case studies on breast neoplasms, lung neoplasms, prostatic neoplasms and 32 diseases further confirm the ability of our method to discover potential disease miRNAs. Availability and implementation: A web service for the prediction and analysis of disease miRNAs is available at http://bioinfolab.stx.hk/midp/ . Contact: guoyahong_hlju@163.com or lixia@hrbmu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.