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

VeriFi:Towards Verifiable Federated Unlearning

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlearning, and propose VeriFi, a unified framework integrating federated unlearning and verification that allows systematic analysis of the unlearning and quantification of its effect, with different combinations of multiple unlearning and verification methods. In VeriFi, the leaving participant is granted the right to verify (RTV), that is, the participant notifies the server before leaving, then actively verifies the unlearning effect in the next few communication rounds. The unlearning is done at the server side immediately after receiving the leaving notification, while the verification is done locally by the leaving participant via two steps: marking (injecting carefully-designed markers to fingerprint the leaver) and checking (examining the change of the global model's performance on the markers). Based on VeriFi, we conduct the first systematic and large-scale study for verifiable federated unlearning, considering 7 unlearning methods and 5 verification methods. Particularly, we propose a more efficient and FL-friendly unlearning method, and two more effective and robust non-invasive-verification methods. We extensively evaluate VeriFi on 7 datasets and 4 types of deep learning models. Our analysis establishes important empirical understandings for more trustworthy federated unlearning

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

Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with cancer risk, several of which have shown pleiotropic effects across cancers. Therefore, we performed a systematic cross-cancer pleiotropic analysis to detect the effects of GWAS-identified variants from non-lung cancers on lung cancer risk in 12,843 cases and 12,639 controls from four lung cancer GWASs. The overall association between variants in each cancer and risk of lung cancer was explored using sequential kernel association test (SKAT) analysis. For single variant analysis, we combined the result of specific study using fixed-effect meta-analysis. We performed functional exploration of significant associations based on features from public databases. To further detect the biological mechanism underlying identified observations, pathway enrichment analysis were conducted with R package “clusterProfiler.” SNP-set analysis revealed the overall associations between variants of 8 cancer types and lung cancer risk. Single variant analysis identified 6 novel SNPs related to lung cancer risk after multiple correction (Pfdr &lt; 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90–0.97, P = 7.60 × 10−4), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03–1.11, P = 1.00 × 10−3), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86–0.96, P = 7.10 × 10−4), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87–0.94, P = 1.00 × 10−7), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90–0.96, P = 1.20 × 10−4), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03–1.11, P = 4.30 × 10−4). The eQTL analysis and functional annotation suggested that these variants might modify lung cancer susceptibility through regulating the expression of related genes. Pathway enrichment analysis showed that genes modulated by these variants play important roles in cancer carcinogenesis. Our findings demonstrate the pleiotropic associations between non-lung cancer susceptibility loci and lung cancer risk, providing important insights into the shared mechanisms of carcinogenesis across cancers.<br/

Exposición al SARS-CoV-2 y la percepción del riesgo de los trabajadores en entornos no sanitarios de Hong Kong, Nanjing y Wuhan: Un estudio cualitativo multisitio

Introducción: Understanding risk perception that hinges on health-protective behaviors is central to strategies for prevention. Aim: To classify the pattern of potential risk of worker exposure to SARS-CoV-2, and to assess association with risk perception among non-healthcare workers Methods: In a multi-site, qualitative study, we conducted individual in-depth interviews and mini focus group discussions with employees, managerial staff and self-employees from Hong Kong (n=87), Nanjing (n=60), and Wuhan (n=60) between June 2020 and March 2021. Audios were transcribed and categorized by themes following Grounded Theory approach. Results: We identified seven major types of potential risk exposure pattern by category of parameters. The risk perceptions decreased among Type A workers, working at fixed location in office, and no/little contacts with clients/customers, and increased among workers having the concern of asymptomatic characteristics of SAR-CoV-2, daily contact with large size of the unfamiliar crowds, unhygienic behaviors of clients/customers, and use of public transportation to commute to work. The notion that the sense of safety deriving from the implementation and adherence with safety measures despite stringency, and trust with the government was most frequently reported in Nanjing and Wuhan. Conclusion: Study examines COVID-19 risks and risk perceptions among non-healthcare workers in three cities. Variations in risk perceptions were found, influenced by factors such as work patterns and safety measures. Trust in government and concerns about international contacts were common themes. The findings emphasize the need for targeted interventions, mental health support, and inclusive policies to address occupational health disparities and promote workplace safety.Introducción: Comprender la percepción del riesgo que condiciona conductas de protección de la salud es fundamental para la prevención. Objetivo: Clasificar el patrón del riesgo de exposición de los trabajadores al SRAS-CoV-2 y su asociación con la percepción del riesgo entre los trabajadores no sanitarios. Métodos: Estudio cualitativo multisitio. Realizamos entrevistas en profundidad y minigrupos de discusión con empleados, personal directivo y autoempleados de Hong Kong (n=87), Nanjing (n=60) y Wuhan (n=60) -junio 2020-marzo 2021-. Los audios se transcribieron y clasificaron por temas con enfoque de la teoría fundamentada. Resultados: Se identificaron siete tipos de patrón de exposición (Tipo A-G) al riesgo potencial. Las percepciones de riesgo disminuyeron entre trabajadores de tipo A que trabajaban en oficina y sin contacto con clientes y aumentaron entre trabajadores preocupados por características asintomáticas del SAR-CoV-2, contacto diario con multitudes, comportamientos antihigiénicos de clientes y uso de transporte público para ir al trabajo. La sensación de seguridad por aplicación y cumplimiento de medidas de seguridad y la confianza en el gobierno fue más frecuente en Nanjing y Wuhan. Conclusiones: Se hallaron variaciones en las percepciones del riesgo, influidas por factores como pautas de trabajo y medidas de seguridad. Fueron comunes la confianza en el gobierno y la preocupación por los contactos internacionales. Se requieren intervenciones específicas, apoyo a la salud mental y políticas integradoras para abordar las disparidades en salud laboral y promover la seguridad en el lugar de trabajo

Inhibiting MARSs reduces hyperhomocysteinemia‐associated neural tube and congenital heart defects

Hyperhomocysteinemia is a common metabolic disorder that imposes major adverse health consequences. Reducing homocysteine levels, however, is not always effective against hyperhomocysteinemia‐associated pathologies. Herein, we report the potential roles of methionyl‐tRNA synthetase (MARS)‐generated homocysteine signals in neural tube defects (NTDs) and congenital heart defects (CHDs). Increased copy numbers of MARS and/or MARS2 were detected in NTD and CHD patients. MARSs sense homocysteine and transmit its signal by inducing protein lysine (N)‐homocysteinylation. Here, we identified hundreds of novel N‐homocysteinylated proteins. N‐homocysteinylation of superoxide dismutases (SOD1/2) provided new mechanistic insights for homocysteine‐induced oxidative stress, apoptosis and Wnt signalling deregulation. Elevated MARS expression in developing and proliferating cells sensitizes them to the effects of homocysteine. Targeting MARSs using the homocysteine analogue acetyl homocysteine thioether (AHT) reversed MARS efficacy. AHT lowered NTD and CHD onsets in retinoic acid‐induced and hyperhomocysteinemia‐induced animal models without affecting homocysteine levels. We provide genetic and biochemical evidence to show that MARSs are previously overlooked genetic determinants and key pathological factors of hyperhomocysteinemia, and suggest that MARS inhibition represents an important medicinal approach for controlling hyperhomocysteinemia‐associated 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 &gt; 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