Personalized Risk Assessment in Never, Light, and Heavy Smokers in a prospective cohort in Taiwan.

The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives

A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application

Graph clustering, which aims to divide the nodes in the graph into several distinct clusters, is a fundamental and challenging task. In recent years, deep graph clustering methods have been increasingly proposed and achieved promising performance. However, the corresponding survey paper is scarce and it is imminent to make a summary in this field. From this motivation, this paper makes the first comprehensive survey of deep graph clustering. Firstly, the detailed definition of deep graph clustering and the important baseline methods are introduced. Besides, the taxonomy of deep graph clustering methods is proposed based on four different criteria including graph type, network architecture, learning paradigm, and clustering method. In addition, through the careful analysis of the existing works, the challenges and opportunities from five perspectives are summarized. At last, the applications of deep graph clustering in four domains are presented. It is worth mentioning that a collection of state-of-the-art deep graph clustering methods including papers, codes, and datasets is available on GitHub. We hope this work will serve as a quick guide and help researchers to overcome challenges in this vibrant field.Comment: 13 pages, 13 figure

Interactions of Oxide Surfaces with Water Revealed with Solid-State NMR Spectroscopy.

Hydrous materials are ubiquitous in the natural environment and efforts have previously been made to investigate the structures and dynamics of hydrated surfaces for their key roles in various chemical and physical applications, with the help of theoretical modeling and microscopy techniques. However, an overall atomic-scale understanding of the water-solid interface, including the effect of water on surface ions, is still lacking. Herein, we employ ceria nanorods with different amounts of water as an example and demonstrate a new approach to explore the water-surface interactions by using solid-state NMR in combination with density functional theory. NMR shifts and relaxation time analysis provide detailed information on the local structure of oxygen ions and the nature of water motion on the surface: the amount of molecularly adsorbed water decreases rapidly with increasing temperature (from room temperature to 150 °C), whereas hydroxyl groups are stable up to 150 °C, and dynamic water molecules are found to instantaneously coordinate to the surface oxygen ions. The applicability of dynamic nuclear polarization for selective detection of surface oxygen species is also compared to conventional NMR with surface selective isotopic-labeling: the optimal method depends on the feasibility of enrichment and the concentration of protons in the sample. These results provide new insight into the interfacial structure of hydrated oxide nanostructures, which is important to improve performance for various applications

Identification of a novel susceptibility locus at 13q34 and refinement of the 20p12.2 region as a multi-signal locus associated with bladder cancer risk in individuals of european ancestry

Candidate gene and genome-wide association studies (GWAS) have identified 15 independent genomic regions associated with bladder cancer risk. In search for additional susceptibility variants, we followed up on four promising single-nucleotide polymorphisms (SNPs) that had not achieved genome-wide significance in 6911 cases and 11 814 controls (rs6104690, rs4510656, rs5003154 and rs4907479, P < 1 7 10(-6)), using additional data from existing GWAS datasets and targeted genotyping for studies that did not have GWAS data. In a combined analysis, which included data on up to 15 058 cases and 286 270 controls, two SNPs achieved genome-wide statistical significance: rs6104690 in a gene desert at 20p12.2 (P = 2.19 7 10(-11)) and rs4907479 within the MCF2L gene at 13q34 (P = 3.3 7 10(-10)). Imputation and fine-mapping analyses were performed in these two regions for a subset of 5551 bladder cancer cases and 10 242 controls. Analyses at the 13q34 region suggest a single signal marked by rs4907479. In contrast, we detected two signals in the 20p12.2 region-the first signal is marked by rs6104690, and the second signal is marked by two moderately correlated SNPs (r(2) = 0.53), rs6108803 and the previously reported rs62185668. The second 20p12.2 signal is more strongly associated with the risk of muscle-invasive (T2-T4 stage) compared with non-muscle-invasive (Ta, T1 stage) bladder cancer (case-case P 64 0.02 for both rs62185668 and rs6108803). Functional analyses are needed to explore the biological mechanisms underlying these novel genetic associations with risk for bladder cancer

Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.

Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements