Search

Background: The aim of this study was to examine publication bias associated with a failure to report research results of studies that were initially posted on the ClinicalTrials.gov registry and to examine factors associated with this phenomenon. Methods: A search was conducted in the ClinicalTrials.gov registry using six dental-related topics. Corresponding publications for trials completed between 2016 and 2019 were then searched using PUBMED, EMBASE and Google Scholar. For studies lacking matching publications, we emailed the primary investigator and received some additional data. For included studies, we recorded additional variables: industry funding, site setting (academic, private research facilities or private practice), design (single or multi-center), geographical location and commencement date vis a vis registration and publication dates. Results: A total of 744 entries were found, of which 7 duplicates were removed; an additional 67 entries just recently completed were removed. An additional 7 studies were in different fields and thus removed. Thus, 663 trials were included; of these, only 337 studies (50.8%) were published. The mean registration to publication interval was 29.01 ± 25.7 months, ranging from +142 to −34 months (post factum registration). Less than 1/3 of the studies were posted prior to commencement, of which much smaller proportions were published (37.3%). Studies that were posted after commencement (n = 462) had a much higher publication rate (56.7%), p < 0.001. Multi-center studies and those conducted in commercial facilities had much higher, though non-significant, publication rates (56.5% and 58.3%, respectively). Conclusions: With only half of the studies registered being published, a major source for publication bias is imminent. [ABSTRACT FROM AUTHOR]

In undercut-and-fill mining, backfills show weak tensile strength and poor ductility properties since they act as artificial pillars to support stope roofs. Hence, the enhancement of the stability of mining structures and backfills is a crucial requisite for underground mining backfill operations. This study addresses the reinforcing effect of polypropylene (PP) on the strength features of combined cement-based tailing backfill (CCTB) with varied cement/tail ratios (c/t: 1:8 to 1:4) at both macroscopic and microscopic levels. Fill specimens containing a fixed solid content of 70 wt% were reinforced with fiber (0.6 wt%) and with no fiber (classified as a reference sample). They were then cast in mold sizes of 160 × 40 × 40 mm3, and cured for 7 days. Following curing, some experiments covering three-point bending assisted by DIC and SEM were performed to inspect the microstructure and strength features of CCTB. The results illustrate that the flexural strength of fiber-oriented CCTB increases along with the c/t fraction, but it is not greater than that of specimens with a high c/t fraction without fiber. Adding PP fiber, the peak deflection of CCTB specimens was improved, and the increment of peak deflection increased linearly with rising c/t fraction, enhancing CCTB's bending characteristics. CCTB damage starts from the bottom to the middle, and the main cause of the damage is the stress distribution at the lowest section. The addition of fiber to CCTBs increases the ability to dissipate energy, which helps to hinder crack extension and prevent brittle damage from occurring. The microstructure shows that AFt and CSH were key hydrate materials in CCTB. As a result, this study develops the security of mining with backfill and helps to determine its design properties for safe production inputs and sustainable filling operations. [ABSTRACT FROM AUTHOR]

In order to locate forest fire smoke more precisely and expand existing forest fire monitoring methods, this research employed Himawari-8 data with a sub-pixel positioning concept in smoke detection. In this study, Himawari-8 data of forest fire smoke in Xichang and Linzhi were selected. An improved sub-pixel mapping method based on random forest results was proposed to realize the identification and sub-pixel positioning of smoke. More spatial details of forest fire smoke were restored in the final results. The continuous monitoring of smoke indicated the dynamic changes therein. The accuracy evaluation of smoke detection was realized using a confusion matrix. Based on the improved sub-pixel mapping method, the overall accuracies were 87.95% and 86.32%. Compared with the raw images, the smoke contours of the improved sub-pixel mapping results were clearer and smoother. The improved sub-pixel mapping method outperforms traditional classification methods in locating smoke range. Moreover, it especially made a breakthrough in the limitations of the pixel scale and in realizing sub-pixel positioning. Compared with the results of the classic PSA method, there were fewer "spots" and "holes" after correction. The final results of this study show higher accuracies of smoke discrimination, with it becoming the basis for another method of forest fire monitoring. [ABSTRACT FROM AUTHOR]

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium. [ABSTRACT FROM AUTHOR]

Background: With the knowledge of oligometastases, primary surgery plays an increasingly vital role in metastatic non-small cell lung cancer. We aimed to evaluate the survival benefit of primary surgery based on metastatic patterns. Materials and Methods: The selected patients with stage IV extrathoracic metastatic (m1b) non-small cell lung cancer between 2010 and 2015 were included in a retrospective cohort study from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple imputation was used for the missing data. Patients were divided into 2 groups depending on whether surgery was performed. After covariate balancing propensity score (CBPS) weighting, multivariate Cox regression models and Kaplan-Meier survival curve were built to identify the survival benefit of different metastatic patterns. Results: Surgery can potentially increase the overall survival (OS) (adjusted HR: 0.68, P < 0.001) of non-small cell lung cancer. The weighted 3-year OS in the surgical group was 16.9%, compared with 7.8% in the nonsurgical group. For single organ metastasis, surgery could improve the survival of metastatic non-small cell lung cancer. Meanwhile, no significant survival improvements in surgical group were observed in patients with multiple organ metastases. Conclusion: The surgical survival benefits for extrathoracic metastatic non-small cell lung cancer could be divided by metastatic pattern. [ABSTRACT FROM AUTHOR]

A major challenge for rare disease clinical trials is the limited amount of available information for making robust statistical inference. While external data present information integration opportunities to enhance statistical inference, conventional data combining methods, for example, meta-analysis, usually do not adequately address study population differences. Matching methods, on the other hand, directly account for population characteristics but often lead to inefficient use of data by underutilizing unmatched data points. Aiming at a better bias-variance tradeoff, we propose an intuitive integrated inference framework to borrow information from all relevant data sources and make inference on the response of interest over a target population precisely characterized by the joint distribution of baseline covariates. The method is easily implemented and can be complemented by modern statistical learning or machine learning tools. Statistical inference is facilitated by the bootstrap. We argue that the integrated inference framework not only provides an intuitive and coherent perspective for a variety of clinical trial inference problems but also has broad application areas in clinical trial settings and beyond, as a quantitative data integration tool for making robust inference in a target population precise manner for policy and decision makers. [ABSTRACT FROM AUTHOR]

Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori , are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study. [ABSTRACT FROM AUTHOR]

Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research. [ABSTRACT FROM AUTHOR]

Summary We developed the variant-Set Test for Association using Annotation infoRmation (STAAR) workflow description language (WDL) workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the WDL allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline. Availability and implementation The workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR%5fworkflow. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs. Wang and colleagues conducted rare-variant association analyses of long non-coding RNAs (lncRNAs) in 66,000 ancestrally diverse TOPMed participants with whole-genome sequencing and measurements of blood lipids and lipoproteins. The findings suggest an additional genomic element in known lipid gene regions that is distinct from the known lipid-associated genes. [ABSTRACT FROM AUTHOR]

The timestep of the finite-difference time-domain method (FDTD) is constrained by the stability limit known as the Courant–Friedrichs–Lewy (CFL) condition. This limit can make FDTD simulations quite time consuming for structures containing small geometrical details. Several methods have been proposed in the literature to extend the CFL limit, including implicit FDTD methods and filtering techniques. In this paper, we propose a novel approach, which combines model-order reduction and a perturbation algorithm to accelerate FDTD simulations beyond the CFL barrier. We compare the proposed algorithm against existing implicit and explicit CFL extension techniques, demonstrating increased accuracy and performance on a large number of test cases, including resonant cavities, a waveguide structure, a focusing metascreen, and a microstrip filter. [ABSTRACT FROM AUTHOR]

How do neuronal subtypes emerge during development? Recent molecular studies have profiled existing neuronal diversity, but neuronal subtype genesis remains elusive. The 15 types of mouse retinal bipolar interneurons are characterized by distinct functions, morphologies, and transcriptional profiles. Here, we develop a comprehensive spatiotemporal map of bipolar subtype genesis in the murine retina. Combining multiplexed detection of 16 RNA markers with timed delivery of 5-ethynyl uridine (EdU) and bromodeoxyuridine (BrdU), we analyze more than 30,000 single cells in full retinal sections to classify all bipolar subtypes and their birthdates. We find that bipolar subtype birthdates are ordered and follow a centrifugal developmental axis. Spatial analysis reveals a striking wave pattern of bipolar subtype birthdates, and lineage analyses suggest clonal restriction on homotypic subtype production. These results inspire a hierarchical developmental model, with ordered subtype genesis within lineages. Our results provide insight into neuronal subtype development and establish a framework for studying subtype diversification. [Display omitted] • Birthdates of retinal bipolar subtypes are patterned across space and time • Subtype birthdate patterns are consistent with a hierarchical genesis model • Late retinal lineages are biased against homotypic bipolar subtype production West et al. employ multiplexed detection of RNA markers to map the precise locations and birthdates of all bipolar interneuron subtypes in mouse retinal sections. They discover patterns among subtype birthdates in space and time and that retinal lineages are biased in subtype composition, together suggesting hierarchical development of subtype diversity. [ABSTRACT FROM AUTHOR]

The prediction and controlling of the solidification structure and macro-segregation in heavy steel casting, which is usually produced in limited quantities, was a conundrum in the foundry field. In this work, the cooling and solidification processes of a 16 t CB2 ferritic heat-resistant steel (FHRS) valve casting were reproduced by studying the solidification behavior of three typical units through a thermal simulation method. The results indicate that the types of casting without chilling have the most uneven distribution of solutes and hardness, while those types of casting in which parts are solidified by chilling are much more uniform. The macro-segregation degrees of B, C, Nb, P, Cr, Mo, Si, V and Mn decrease gradually during heavy casting of CB2 ferritic heat-resistant steel. Of them, B, C, Nb, and P are solutes prone to segregation, and the maximum macro-segregation index of B can even reach 15. The macro-segregation tendencies of Cr, Mo, Si, V, and Mn are relatively small. Further studies on the last solidification portion of samples taken by electron microprobe reveal that large-sized precipitates such as MnS and NbxC are easily formed due to solute enrichment, and the sizes of these precipitates were distributed from dozens to hundreds of micrometers. [ABSTRACT FROM AUTHOR]

The linear contraction and hot tearing behavior of 9Cr3Co3W heat-resistant steel during solidification were studied by combining in-situ casting experiments and the corresponding numerical simulations. The linear contraction, stress and temperature of the samples were detected during solidification. The results show that the linear contraction of 9Cr3Co3W heat-resistant steel was 1.7% in the sand mold. Meanwhile, the critical temperature and stress of the hot tearing that occurred at the hot spot were 1304 °C and 0.72 MPa, respectively. The occurrence of hot tearing was observed at the tip of secondary dendrites along the primary dendritic arms, where the solute elements were significantly enriched at the final solidification. [ABSTRACT FROM AUTHOR]

Abstract: The effect of synthetic diesel fuel made from natural gas (Gas to Liquid, GTL) on the engine performances (such as power, efficiency and emission) was carried out on one Euro III common rail (CR) heavy duty (HD) diesel engine without any modification. The results showed that the engine fueled with GTL had some variations compared with the one fueled with petroleum-based low sulfur diesel fuel (sulfur content less 50ppm). The maximum torque and power were decreased by 1.3% and 1.9%, respectively. The specific fuel consumption increased in volume but had no change in mass. Under the load characteristics, the NOx, CO and THC were reduced by 13%, 55% and 55%, respectively. During the ESC cycle test, the NOx, CO, THC and PM were reduced by 5.2%, 19.3%, 19.8% and 33%, respectively. [Copyright &y& Elsevier]

Abstract: Gas-to-liquids (GTL) as an alternative to diesel is considered to be one of the technical options to reduce petroleum consumption in the on-road transportation sector. Between May and August 2007, a joint demonstration program by Tsinghua University, Beijing Transit, Cummins Corporation and Shell Corporation was carried out in Beijing. The program focused on the supply systems and vehicle use of GTL fuel. The demonstration fleet was formed by four transit buses fueled with GTL and four with diesel. It was demonstrated that GTL has good compatibility with diesel in terms of fuel supply system and vehicle use. This paper compares the energy consumption and GHG emissions of diesel and GTL fuel supply chains by life cycle analysis based on demonstration results. The results indicate GTL’s large range (reported 54–70%) in synthesis efficiency, as the key factor in determining energy consumption and GHG emissions within the GTL fuel supply chain. For the probable case (GTL synthesis efficiency: 65%), the life cycle energy consumption and GHG emissions of GTL fuel are 42.5% and 12.6% higher than that of diesel. For two sensitivity analysis cases (GTL synthesis efficiency: 54% and70%), energy consumptions are 74.2% and 31.2% higher and GHG emissions are 27.3% and 7.4% higher than that of the diesel fuel supply chain. If the efficiency of the GTL synthesis process is improved to 75%, then the GHG emissions level of the GTL fuel supply chain can be reduced to the same level as the diesel fuel supply chain. [Copyright &y& Elsevier]

Linear mixed-effects models (LMMs) and ridge regression are commonly applied in genetic association studies to control for population structure and sample-relatedness. To control for sample-relatedness, the existing methods use empirical genetic relatedness matrices (GRM) either explicitly or conceptually. This works well with mostly homogeneous populations, however, in multi-ancestry heterogeneous populations, GRMs are confounded with population structure which leads to inflated type I error rates, massively increased computation, and reduced power. Here, we propose FastSparseGRM, a scalable pipeline for multi-ancestry Genome-Wide Association studies (GWAS) and Whole Genome Sequencing (WGS) studies. It utilizes a block-diagonal sparse ancestry-adjusted (BDSA) GRM to model sample-relatedness, and ancestry PCs as fixed effects to control for population structure. It is ~ 2540/4100/54 times faster than BOLT-LMM/fast-GWA/REGENIE for fitting the null LMM on 50,000 heterogeneous subjects. Through numerical simulations and both single-variant GWAS and rare variant WGS analyses of five biomarkers (Triglycerides, HDL, LDL, BMI, Total Bilirubin) on the entire UK Biobank data, we demonstrate that our approach scales to nearly half-a-million subjects and provides accurate p-value calibration and improved power compared to the existing methods.