Field-cycling imaging in ovarian cancer : a novel technology

Peer reviewedPostprin

Incidence and recognition of acute respiratory distress syndrome in a UK intensive care unit.

The reported incidence of ARDS is highly variable (2.5%-19% of intensive care unit (ICU) patients) and varies depending on study patient population used. We undertook a 6-month, prospective study to determine the incidence and outcome of ARDS in a UK adult University Hospital ICU. 344 patients were admitted during the study period, of these 43 (12.5%) were determined to have ARDS. Patients with ARDS had increased mortality at 28 days and 2 years post-diagnosis, and there was under-recognition of ARDS in both medical records and death certificattion. Our findings have implications for critical care resource planning.This is the final version of the article. It first appeared from BMJ Thorax via ://dx.doi.org/10.1136/thoraxjnl-2016-20840

PoolHap: Inferring Haplotype Frequencies from Pooled Samples by Next Generation Sequencing

With the advance of next-generation sequencing (NGS) technologies, increasingly ambitious applications are becoming feasible. A particularly powerful one is the sequencing of polymorphic, pooled samples. The pool can be naturally occurring, as in the case of multiple pathogen strains in a blood sample, multiple types of cells in a cancerous tissue sample, or multiple isoforms of mRNA in a cell. In these cases, it's difficult or impossible to partition the subtypes experimentally before sequencing, and those subtype frequencies must hence be inferred. In addition, investigators may occasionally want to artificially pool the sample of a large number of individuals for reasons of cost-efficiency, e. g., when carrying out genetic mapping using bulked segregant analysis. Here we describe PoolHap, a computational tool for inferring haplotype frequencies from pooled samples when haplotypes are known. The key insight into why PoolHap works is that the large number of SNPs that come with genome-wide coverage can compensate for the uneven coverage across the genome. The performance of PoolHap is illustrated and discussed using simulated and real data. We show that PoolHap is able to accurately estimate the proportions of haplotypes with less than 2% error for 34-strain mixtures with 2X total coverage Arabidopsis thaliana whole genome polymorphism data. This method should facilitate greater biological insight into heterogeneous samples that are difficult or impossible to isolate experimentally. Software and users manual are freely available at http://arabidopsis.gmi.oeaw.ac.at/quan/poolhap/