The evolution of GX 339-4 in the low-hard state as seen by NuSTAR and Swift

We analyze eleven NuSTAR and Swift observations of the black hole X-ray binary GX 339-4 in the hard state, six of which were taken during the end of the 2015 outburst, five during a failed outburst in 2013. These observations cover luminosities from 0.5%-5% of the Eddington luminosity. Implementing the most recent version of the reflection model relxillCp, we perform simultaneous spectral fits on both datasets to track the evolution of the properties in the accretion disk including the inner edge radius, the ionization, and temperature of the thermal emission. We also constrain the photon index and electron temperature of the primary source (the "corona"). We find the disk becomes more truncated when the luminosity decreases, and observe a maximum truncation radius of $37R_g$. We also explore a self-consistent model under the framework of coronal Comptonization, and find consistent results regarding the disk truncation in the 2015 data, providing a more physical preferred fit for the 2013 observations.Comment: 15 pages, 8 figures, 6 tables, accepted for publication in The Astrophysical Journa

RELXILL_NK: a relativistic reflection model for testing Einstein's gravity

Einstein's theory of general relativity was proposed over 100 years ago and has successfully passed a large number of observational tests in the weak field regime. However, the strong field regime is largely unexplored, and there are many modified and alternative theories that have the same predictions as Einstein's gravity for weak fields and present deviations when gravity becomes strong. RELXILL_NK is the first relativistic reflection model for probing the spacetime metric in the vicinity of astrophysical black holes and testing Einstein's gravity in the strong field regime. Here we present our current constraints on possible deviations from Einstein's gravity obtained from the black holes in 1H0707-495, Ark 564, GX 339-4, and GS 1354-645.Comment: 8 pages, 6 figures. Talk given at the "International Conference on Quantum Gravity" (26-28 March 2018, Shenzhen, China). To appear in the conference proceeding

Integrated gene-based and pathway analyses using UK Biobank data identify novel genes for chronic respiratory diseases

BackgroundChronic respiratory diseases have become a non-negligible cause of death globally. Although smoking and environmental exposures are primary risk factors for chronic respiratory diseases, genetic factors also play an important role in determining individual’s susceptibility to diseases. Here we performed integrated gene-based and pathway analyses to systematically illuminate the heritable characteristics of chronic respiratory diseases.MethodsUK (United Kingdom) Biobank is a very large, population-based prospective study with over 500,000 participants, established to allow detailed investigations of the genetic and nongenetic determinants of the diseases. Utilizing the GWAS-summarized data downloaded from UK Biobank, we conducted gene-based analysis to obtain associations of susceptibility genes with asthma, chronic obstructive pulmonary disease (COPD) and pneumonia using FUSION and MAGMA software. Across the identified susceptibility regions, functional annotation integrating multiple functional data sources was performed to explore potential regulatory mechanisms with INQUISIT algorithm. To further detect the biological process involved in the development of chronic respiratory diseases, we undertook pathway enrichment analysis with the R package (clusterProfiler).ResultsA total of 195 susceptibility genes were identified significantly associated with chronic respiratory diseases (Pbonferroni < 0.05), and 24/195 located out of known susceptibility regions (e.g. WDPCP in 2p15). Within the identified susceptibility regions, functional annotation revealed an aggregation of credible variants in promoter-like and enhancer-like histone modification regions and such regulatory mechanisms were specific to lung tissues. Furthermore, 110 genes with INQUISIT score ≥1 may influence diseases susceptibility through exerting effects on coding sequences, proximal promoter and distal enhancer regulations. Pathway enrichment results showed that these genes were enriched in immune-related processes and nicotinic acetylcholine receptors pathways.ConclusionsThis study implemented an integrated gene-based and pathway strategy to explore the underlying biological mechanisms and our findings may serve as promising targets for future clinical treatments of chronic respiratory diseases

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

BackgroundSelecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.MethodsParticipants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.ResultsWe identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).ConclusionsKey features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

The global contribution of soil mosses to ecosystem services

Soil mosses are among the most widely distributed organisms on land. Experiments and observations suggest that they contribute to terrestrial soil biodiversity and function, yet their ecological contribution to soil has never been assessed globally under natural conditions. Here we conducted the most comprehensive global standardized field study to quantify how soil mosses influence 8 ecosystem services associated with 24 soil biodiversity and functional attributes across wide environmental gradients from all continents. We found that soil mosses are associated with greater carbon sequestration, pool sizes for key nutrients and organic matter decomposition rates but a lower proportion of soil-borne plant pathogens than unvegetated soils. Mosses are especially important for supporting multiple ecosystem services where vascular-plant cover is low. Globally, soil mosses potentially support 6.43 Gt more carbon in the soil layer than do bare soils. The amount of soil carbon associated with mosses is up to six times the annual global carbon emissions from any altered land use globally. The largest positive contribution of mosses to soils occurs under a high cover of mat and turf mosses, in less-productive ecosystems and on sandy and salty soils. Our results highlight the contribution of mosses to soil life and functions and the need to conserve these important organisms to support healthy soils.The study work associated with this paper was funded by a Large Research Grant from the British Ecological Society (no. LRB17\1019; MUSGONET). D.J.E. is supported by the Hermon Slade Foundation. M.D.-B. was supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I), a project from the Spanish Ministry of Science and Innovation for the I + D + i (PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033a) and a project PAIDI 2020 from the Junta de Andalucía (P20_00879). E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT). M.B. is supported by a Ramón y Cajal grant from Spanish Ministry of Science (RYC2021-031797-I). A.d.l.R is supported by the AEI project PID2019-105469RB-C22. L.W. and Jianyong Wang are supported by the Program for Introducing Talents to Universities (B16011) and the Ministry of Education Innovation Team Development Plan (2013-373). The contributions of T.G. and T.U.N. were supported by the Research Program in Forest Biology, Ecology and Technology (P4-0107) and the research projects J4-3098 and J4-4547 of the Slovenian Research Agency. The contribution of P.B.R. was supported by the NSF Biological Integration Institutes grant DBI-2021898. J. Durán and A. Rodríguez acknowledge support from the FCT (2020.03670.CEECIND and SFRH/BDP/108913/2015, respectively), as well as from the MCTES, FSE, UE and the CFE (UIDB/04004/2021) research unit financed by FCT/MCTES through national funds (PIDDAC)

Differences in perceived causes of childhood obesity between migrant and local communities in China: a qualitative study

In developing countries, obesity traditionally affectsmore affluent children, butis spreading to a wider social group. Understanding the perceivedcontributors can provide valuable insights to plan preventive interventions. We exploreddifferences in the perceived causes of childhood obesity between local and migrant communities in a major Chinese city. We conducted 20 focus groups (137 parents, grandparents, school teachers) and 11semi-structured interviews with school Principals from migrant and local communities in Guangzhou. Data were transcribed and analysed using a thematic approach. We found that Lack of influence from grandparents, who were perceived to promote obesogenic behaviorin local children, fewer opportunities for unhealthy snacking and less pressure for academic attainment leading to moreactive play were interpreted as potential "protective" factors among migrant children. Nevertheless, two perceived causes of obesity were more pronounced in migrant than local children: lack of parental monitoring after-school andunsafe neighborhoods limiting physical-activity. Two barriers that restricted child physical activity were only found in the migrant community: limited home space, and cultural differences, inhabitinginteractive play with local children. Future interventions should consider uniquedeterminants of obesity in children from different social backgrounds, with tailored strategies to prevent further rise of the epidemic

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p