A hybrid Bayesian hierarchical model combining cohort and case–control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented

Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China

AbstractThe visibility data from 2010 to 2012 were obtained at Shenyang in Northeast China and the relations between visibility, PM mass concentration and meteorological variables were statistically analyzed. These results demonstrate that the monthly–averaged visibility over Shenyang was higher in March and September with values of approximately 19.0±4.3 km and 17.1±4.3 km, respectively. Low visibility over Shenyang occurred in January at approximately 11.0±4.7 km. Among the meteorological variables considered, wind speed was the main meteorological factor that influenced visibility and PM mass concentrations. The relation between visibility and PM indicates that fine particles are already a main source of pollutants, the existence of which is the most important factor in the deterioration of visibility in an urban area of Northeast China. The study also shows an obvious diurnal variation and weekend effects of visibility and PM, which are mainly caused by human activities. Results of this study highlight the significant impact of fine particles on air pollution and visibility in an urban area of Northeast China

Statistical methods for multivariate meta-analysis of diagnostic tests: An overview and tutorial

In this article, we present an overview and tutorial of statistical methods for meta-analysis of diagnostic tests under two scenarios: 1) when the reference test can be considered a gold standard; and 2) when the reference test cannot be considered a gold standard. In the first scenario, we first review the conventional summary receiver operating characteristics (ROC) approach and a bivariate approach using linear mixed models (BLMM). Both approaches require direct calculations of study-specific sensitivities and specificities. We next discuss the hierarchical summary ROC curve approach for jointly modeling positivity criteria and accuracy parameters, and the bivariate generalized linear mixed models (GLMM) for jointly modeling sensitivities and specificities. We further discuss the trivariate GLMM for jointly modeling prevalence, sensitivities and specificities, which allows us to assess the correlations among the three parameters. These approaches are based on the exact binomial distribution and thus do not require an ad hoc continuity correction. Last, we discuss a latent class random effects model for meta-analysis of diagnostic tests when the reference test itself is imperfect for the second scenario. A number of case studies with detailed annotated SAS code in procedures MIXED and NLMIXED are presented to facilitate the implementation of these approaches

Comparative genomic analyses of Cutibacterium granulosum provide insights into genomic diversity

Cutibacterium granulosum, a commensal bacterium found on human skin, formerly known as Propionibacterium granulosum, rarely causes infections and is generally considered non-pathogenic. Recent research has revealed the transferability of the multidrug-resistant plasmid pTZC1 between C. granulosum and Cutibacterium acnes, the latter being an opportunistic pathogen in surgical site infections. However, there is a noticeable lack of research on the genome of C. granulosum, and the genetic landscape of this species remains largely uncharted. We investigated the genomic features and evolutionary structure of C. granulosum by analyzing a total of 30 Metagenome-Assembled Genomes (MAGs) and isolate genomes retrieved from public databases, as well as those generated in this study. A pan-genome of 6,077 genes was identified for C. granulosum. Remarkably, the ‘cloud genes’ constituted 62.38% of the pan-genome. Genes associated with mobilome: prophages, transposons [X], defense mechanisms [V] and replication, recombination and repair [L] were enriched in the cloud genome. Phylogenomic analysis revealed two distinct mono-clades, highlighting the genomic diversity of C. granulosum. The genomic diversity was further confirmed by the distribution of Average Nucleotide Identity (ANI) values. The functional profiles analysis of C. granulosum unveiled a wide range of potential Antibiotic Resistance Genes (ARGs) and virulence factors, suggesting its potential tolerance to various environmental challenges. Subtype I-E of the CRISPR-Cas system was the most abundant in these genomes, a feature also detected in C. acnes genomes. Given the widespread distribution of C. granulosum strains within skin microbiome, our findings make a substantial contribution to our broader understanding of the genetic diversity, which may open new avenues for investigating the mechanisms and treatment of conditions such as acne vulgaris

RMDAP: A Versatile, Ready-To-Use Toolbox for Multigene Genetic Transformation

Background: The use of transgenes to improve complex traits in crops has challenged current genetic transformation technology for multigene transfer. Therefore, a multigene transformation strategy for use in plant molecular biology and plant genetic breeding is thus needed. Methodology/Principal Findings: Here we describe a versatile, ready-to-use multigene genetic transformation method, named the Recombination-assisted Multifunctional DNA Assembly Platform (RMDAP), which combines many of the useful features of existing plant transformation systems. This platform incorporates three widely-used recombination systems, namely, Gateway technology, in vivo Cre/loxP and recombineering into a highly efficient and reliable approach for gene assembly. RMDAP proposes a strategy for gene stacking and contains a wide range of flexible, modular vectors offering a series of functionally validated genetic elements to manipulate transgene overexpression or gene silencing involved in a metabolic pathway. In particular, the ability to construct a multigene marker-free vector is another attractive feature. The built-in flexibility of original vectors has greatly increased the expansibility and applicability of the system. A proof-ofprinciple experiment was confirmed by successfully transferring several heterologous genes into the plant genome. Conclusions/Significance: This platform is a ready-to-use toolbox for full exploitation of the potential for coordinate regulation of metabolic pathways and molecular breeding, and will eventually achieve the aim of what we call ‘‘one-sto

Model and Algorithm for Dependent Activity Schedule Optimization Combining with BIM

The project duration can be shortened by overlapping construction activities. However, the continuous changing of the environment tends to cause problems such as rework and the failure of the overlapping plan. In order to solve these problems, communication strategies for the overlapping of dependent activities are first introduced and optimized from a revenue perspective. We first consider the different maturities of upstream activity before and after the overlapping, the downstream sensitivity which is decided by involving communication strategies, and the learning and error-correcting ability of workers. Then, the overlap and communication strategies are decided by calculating the maximum revenue using Monte Carlo simulation and MATLAB based on overlap cost, communication cost, rework cost, and reward amount. Finally, the algorithm and BIM are combined to provide a visual overlap plan and dynamic control platform framework. This research is valuable for practitioners as it provides a dynamic overlap plan which can maximize the revenue in changing the environment and ensure the duration of the project. This research also provides researchers a new insight into combining overlap problems and BIM technology

Research on the Operation of e-Commerce Enterprises Based on Blockchain Technology and Bilateral Platforms

E-commerce platform enterprises have typical bilateral market characteristics. The e-commerce platform provides real-time communication services for buyers and sellers. Different buyers and sellers form crossnetwork characteristics. The formulation and implementation of bilateral strategies affect both the merchants and consumers’ choice of platform and registration transactions. This impact will directly lead to the transaction value of the platform. Then, the article builds an econometric model and empirically analyses the impact of e-commerce platforms. The e-commerce chain is a complex structure that consists of logistics, information flow, and capital flow and connects suppliers, manufacturers, distributors, and users in the industry together. Blockchain technology can be used as a large-scale collaboration tool to adapt to supply chain management, the main factor that drives the market power of the enterprise. The research results show that the input costs of advertising, research and development, and employee training and the impact of long-term investment and taxation on market forces are quite different in different industries, both positive and negative, and subsidies, inventory, and state-owned holdings have a negative impact on the market power of companies in all industries. Finally, the competition strategy of e-commerce platform enterprises is summarized. On the basis of the conclusions of the theory and case study, the paper puts forward specific suggestions and countermeasures for the competition strategy of e-commerce platform enterprises in the bilateral network environment