Project description:Despite the recent advances in high-throughput sequencing, metagenome analysis of microbial populations still remains a challenge. In particular, the metagenome-assembled genomes (MAGs) are often fragmented due to interspecies repeats, uneven coverage, and varying strain abundance. MAGs are constructed via a binning process that uses features of input data in order to cluster long contigs presumably belonging to the same species. In this work, we present BinSPreader-a binning refiner tool that exploits the assembly graph topology and other connectivity information to refine binning, correct binning errors, and propagate binning to shorter contigs. We show that BinSPreader could increase the completeness of the bins without sacrificing the purity and could predict contigs belonging to several MAGs. BinSPreader is effective in binning shorter contigs that often contain important conservative sequences that might be of great interest to researchers.
Project description:Wildlife trade is a major driver of biodiversity loss, yet whilst the impacts of trade in some species are relatively well-known, some taxa, such as many invertebrates are often overlooked. Here we explore global patterns of trade in the arachnids, and detected 1,264 species from 66 families and 371 genera in trade. Trade in these groups exceeds millions of individuals, with 67% coming directly from the wild, and up to 99% of individuals in some genera. For popular taxa, such as tarantulas up to 50% are in trade, including 25% of species described since 2000. CITES only covers 30 (2%) of the species potentially traded. We mapped the percentage and number of species native to each country in trade. To enable sustainable trade, better data on species distributions and better conservation status assessments are needed. The disparity between trade data sources highlights the need to expand monitoring if impacts on wild populations are to be accurately gauged and the impacts of trade minimised.
Project description:We rigorously extend the widely used wild bootstrap resampling technique to the multivariate Nelson-Aalen estimator under Aalen's multiplicative intensity model. Aalen's model covers general Markovian multistate models including competing risks subject to independent left-truncation and right-censoring. This leads to various statistical applications such as asymptotically valid confidence bands or tests for equivalence and proportional hazards. This is exemplified in a data analysis examining the impact of ventilation on the duration of intensive care unit stay. The finite sample properties of the new procedures are investigated in a simulation study.
Project description:We evaluated the impact of the influenza season on outcome of new lung nodules in a LDCT lung cancer screening trial population. NELSON-trial participants with ≥ 1 new nodule detected in screening rounds two and three were included. Outcome (resolution or persistence) of new nodules detected per season was calculated and compared. Winter (influenza season) was defined as 1st October to 31st March, and compared to the summer (hay-fever season), 1st April to 30th September. Overall, 820 new nodules were reported in 529 participants. Of the total new nodules, 482 (59%) were reported during winter. When considering the outcome of all new nodules, there was no statistically significant association between summer and resolving nodules (OR 1.07 [CI 1.00-1.15], p = 0.066), also when looking at the largest nodule per participant (OR 1.37 [CI 0.95-1.98], p = 0.094). Similarly, there was no statistically significant association between season and screen detected cancers (OR 0.47 [CI 0.18-1.23], p = 0.123). To conclude, in this lung cancer screening population, there was no statistically significant association between influenza season and outcome of new lung nodules. Hence, we recommend new nodule management strategy is not influenced by the season in which the nodule is detected.