American oil palm from Brazil: genetic diversity, population structure, and core collection.

The American oil palm [Elaeis oleifera (Knuth) Cortés] has pronounced importance in oil palm breeding programs. Here, a germplasm bank (GB) of E. oleifera plants collected in the Amazon rainforest in Brazil was submitted to single nucleotide polymorphism (SNP) marker identification, selection, and use, aiming to characterize genetic diversity and population structure and to design a core collection (CC). Five hundred and fifty-three plants from 206 subsamples, collected at 19 localities spread throughout six geographic regions, were submitted to genotyping-by-sequencing analysis. A set of 1,827 high-quality SNP markers was then selected and used to run the genetic diversity and population structure analysis. The genetic diversity found is of moderate degree, and probably only a small portion of the species diversity is represented in the collection. The possible reason for that is the collecting strategy used, which collected subsamples only around the most prominent watercourses in the region. The average degree of genetic differentiation among subsamples is very high, indicating the presence of high interpopulation differentiation. The collection showed a low level of endogamy. The low average gene flow found indicates that genetic isolation caused by drift is occurring, and there is a need to review the conservation strategy. A set of 245 SNPs distributed throughout all 16 chromosomes was used to design CC based on maximizing the strategy of diversity. The optimal adjustment of the validated parameters, maintained while taking fewest subsamples, led to the choice of a model containing 20% of the entire collection as the ideal to form the CC

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

© 2018 The Author(s). Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling

Report from the Annual Conference of the British Society of Echocardiography, November 2017, Edinburgh International Conference Centre, Edinburgh

No abstract available

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society

Relative language proficiency modulates BOLD signal change when bilinguals perform semantic judgments

The effect of relative language proficiency on the spatial distribution and magnitude of BOLD signal change was evaluated by studying two groups of righthanded English–Mandarin bilingual participants with contrasting language proficiencies as they made semantic judgments with words and characters. Greater language proficiency corresponded to shorter response times and greater accuracy in the semantic judgment task. Within the left prefrontal and parietal regions, the change in BOLD signal was smaller in a participant’s more proficient language. The least proficient performance was associated with right, in addition to left, inferior frontal activation. The results highlight the importance of taking into consideration nature of task and relative language proficiency when drawing inferences from functional imaging studies of bilinguals. © 2001 Academic Pres