A total-evidence approach to dating with fossils, applied to the early radiation of the Hymenoptera

unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease : a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7.8 million single nucleotide polymorphisms in 37688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1.4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0 .0035 for intracranial volume, p=0.024 for putamen volume), smoking status (p=0.024), and educational attainment (p=0.038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8.00 x10 -7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Copyright (C) 2019 Elsevier Ltd. All rights reserved.Peer reviewe

PAPER Special Section on Statistical Modeling for Speech Processing Trigger-Based Language Model Adaptation for Automatic Transcription of Panel Discussions

SUMMARY We present a novel trigger-based language model adaptation method oriented to the transcription of meetings. In meetings, the topic is focused and consistent throughout the whole session, therefore keywords can be correlated over long distances. The trigger-based language model is designed to capture such long-distance dependencies, but it is typically constructed from a large corpus, which is usually too general to derive taskdependent trigger pairs. In the proposed method, we make use of the initial speech recognition results to extract task-dependent trigger pairs and to estimate their statistics. Moreover, we introduce a back-off scheme that also exploits the statistics estimated from a large corpus. The proposed model reduced the test-set perplexity considerably more than the typical triggerbased language model constructed from a large corpus, and achieved a remarkable perplexity reduction of 44% over the baseline when combined with an adapted trigram language model. In addition, a reduction in word error rate was obtained when using the proposed language model to rescore word graphs. key words: speech recognition, language model, trigger-based language model, TF/ID

Numerical Study of Hydrodynamic Process in Chaohu Lake

In this paper, the hydrodynamic characteristics of water flow in Chaohu Lake are studied by using the finite volume coastal ocean model (FVCOM), which is verified by the observed data. The typical flow field and the 3-D flow structure are obtained for the lake. The flow fields under extreme conditions are analyzed to provide a prospective knowledge of the water exchange and the transport process.The influence of the wind on the flow is determined by the cross spectrum method. The results show that the wind-driven flow dominates most area of the lake. Under prevailing winds in summer and winter, the water flows towards the downwind side at the upper layer while towards the upwind side at the lower layer in most area except that around the Chaohu Sluice. The extreme wind speed is not favorable for the water exchange while the sluice's releasing water accelerates the process. The water velocity in the lake is closely related with the wind speed

Baseline-free damage identification of metallic sandwich panels with truss core based on vibration characteristics

A baseline-free damage identification method is proposed to identify damages in metallic sandwich panels with truss core in the article. The method is based on flexibility matrix and gapped smoothing method, with damage index defined DIm. The weight coefficient m is introduced to consider the effect of damages on both low-order modes and high-order modes. Numerical simulations and experiments are conducted to evaluate the present method. Besides, damage index DIm* is also defined by processing DIm with Teager energy operator, and comparisons between DIm and DIm* are also carried out. Results show that the proposed method is effective in detecting single damage and multiple damages of the same or different extent. The weight coefficient m plays a very important role in identification of multiple damages of different styles. When comparing with DIm*, it is found that the present index DIm is better at suppressing the singularity caused by contact nodes and detecting of multiple damages which contain small or slight damages.</p

Comparative analyses of the scaling diversity index and its applicability

As well as the newly developed scaling diversity index, there are also eleven traditional diversity indices to be found in the literature. Analyses show that these eleven traditional indices are unable to formulate the richness component of diversity. In particular, the most widely used index, the Shannon-Weiner index, cannot express the evenness component. On the contrary, the scaling diversity index is able to formulate both the richness aspect and the evenness aspect of diversity. The scaling diversity index has been applied to developing scenarios of ecological diversity at different spatial resolutions and spatial scales. A case study in Fukang in the Xinjiang Uygur Autonomous Region in China shows that the scaling diversity index is sensitive to spatial resolution and is easy to understand. It is scientifically sound and could be operated at affordable cost

Simulating the Hydraulic Characteristics of the Lower Yellow River By the Finite-Volume Technique

The finite-volume technique is used to solve the two-dimensional shallow-water equations on unstructured mesh consisting of quadrilateral elements. In this paper the algorithm of the finite-volume method is discussed in detail and particular attention is paid to accurately representing the complex irregular computational domain. The lower Yellow River reach from Huayuankou to Jiahetan is a typical meandering river. The generation of the computational mesh, which is used to simulate the flood, is affected by the distribution of water works in the river channel. The spatial information about the two Yellow River levee, the protecting dykes, and those roads that are obviously higher than the ground, need to be used to generate the computational mesh. As a result these dykes and roads locate the element interfaces of the computational mesh. In the model the finite-volume method is used to solve the shallow-wave equations, and the Osher scheme of the empirical function is used to calculate the flux through the interface between the neighbouring elements. The finite-volume method has the advantage of using computational domain with complex geometry, and the Osher scheme is a method based on characteristic theory and is a monotone upwind numerical scheme with high resolution. The flood event with peak discharge of 15 300 m(3)/s, occurring in the period from 30 July to 10 August 1982, is simulated. The estimated result indicates that the simulation method is good for routing the flood in a region with complex geometry. Copyright (C) 2002 John Wiley Sons, Ltd

Parametric design and optimization of high speed train nose

Aiming at shortening the design period and improve the design efficiency of the nose shape of high speed trains, a parametric shape optimization method is developed for the design of the nose shape has been proposed in the present paper based on the VMF parametric approach, NURBS curves and discrete control point method. 33 design variables have been utilized to control the nose shape, and totally different shapes could be obtained by varying the values of design variables. Based on the above parametric method, multi-objective particle swarm algorithm, CFD numerical simulation and supported vector machine regression model, multi-objective aerodynamic shape optimization has been performed. Results reveal that the parametric shape design method proposed here could precisely describe the three-dimensional nose shape of high speed trains and could be applied to the concept design and optimization of the nose shape. Besides, the SVM regression model based the multi-points criterion could accurately describe the non-linear relationship between the design variables and objectives, and could be generally utilized in other fields. No matter the simplified model or the real model, the aerodynamic performance of the model after optimization has been greatly improved. Based on the SVR model, the nonlinear relation between the aerodynamic drag and the design variables is obtained, which could provide guidance for the engineering design and optimization

Parametric design and optimization of high speed train nose

Aiming at shortening the design period and improve the design efficiency of the nose shape of high speed trains, a parametric shape optimization method is developed for the design of the nose shape has been proposed in the present paper based on the VMF parametric approach, NURBS curves and discrete control point method. 33 design variables have been utilized to control the nose shape, and totally different shapes could be obtained by varying the values of design variables. Based on the above parametric method, multi-objective particle swarm algorithm, CFD numerical simulation and supported vector machine regression model, multi-objective aerodynamic shape optimization has been performed. Results reveal that the parametric shape design method proposed here could precisely describe the three-dimensional nose shape of high speed trains and could be applied to the concept design and optimization of the nose shape. Besides, the SVM regression model based the multi-points criterion could accurately describe the non-linear relationship between the design variables and objectives, and could be generally utilized in other fields. No matter the simplified model or the real model, the aerodynamic performance of the model after optimization has been greatly improved. Based on the SVR model, the nonlinear relation between the aerodynamic drag and the design variables is obtained, which could provide guidance for the engineering design and optimization

MHC associations with clinical and autoantibody manifestations in European SLE

Systemic lupus erythematosus (SLE) is a clinically heterogeneous disease affecting multiple organ systems and characterized by autoantibody formation to nuclear components. Although genetic variation within the major histocompatibility complex (MHC) is associated with SLE, its role in the development of clinical manifestations and autoantibody production is not well defined. We conducted a meta-analysis- of four independent European SLE case collections for associations between SLE sub-phenotypes and MHC single-nucleotide polymorphism genotypes, human leukocyte antigen (HLA) alleles and variant HLA amino acids. Of the 11 American College of Rheumatology criteria and 7 autoantibody sub-phenotypes examined, anti-Ro/SSA and anti-La/SSB antibody subsets exhibited the highest number and most statistically significant associations. HLA-DRB1*03:01 was significantly associated with both sub-phenotypes. We found evidence of associations independent of MHC class II variants in the anti-Ro subset alone. Conditional analyses showed that anti-Ro and anti-La subsets are independently associated with HLA-DRB1*0301, and that the HLA-DRB1*03:01 association with SLE is largely but not completely driven by the association of this allele with these sub-phenotypes. Our results provide strong evidence for a multilevel risk model for HLA-DRB1*03:01 in SLE, where the association with anti-Ro and anti-La antibody-positive SLE is much stronger than SLE without these autoantibodies