University College London

About: University College London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 81105 authors who have published 210603 publications receiving 9868552 citations. The organization is also known as: UCL & University College, London.

Voluntary action and conscious awareness.

Abstract: Humans have the conscious experience of 'free will': we feel we can generate our actions, and thus affect our environment. Here we used the perceived time of intentional actions and of their sensory consequences as a means to study consciousness of action. These perceived times were attracted together in conscious awareness, so that subjects perceived voluntary movements as occurring later and their sensory consequences as occurring earlier than they actually did. Comparable involuntary movements caused by magnetic brain stimulation reversed this attraction effect. We conclude that the CNS applies a specific neural mechanism to produce intentional binding of actions and their effects in conscious awareness.

Olaparib Maintenance Therapy in Patients With Platinum-Sensitive Relapsed Serous Ovarian Cancer: A Preplanned Retrospective Analysis of Outcomes by BRCA Status in a Randomised Phase 2 Trial

Abstract: Summary Background Maintenance monotherapy with the PARP inhibitor olaparib significantly prolonged progression-free survival (PFS) versus placebo in patients with platinum-sensitive recurrent serous ovarian cancer. We aimed to explore the hypothesis that olaparib is most likely to benefit patients with a BRCA mutation. Methods We present data from the second interim analysis of overall survival and a retrospective, preplanned analysis of data by BRCA mutation status from our randomised, double-blind, phase 2 study that assessed maintenance treatment with olaparib 400 mg twice daily (capsules) versus placebo in patients with platinum-sensitive recurrent serous ovarian cancer who had received two or more platinum-based regimens and who had a partial or complete response to their most recent platinum-based regimen. Randomisation was by an interactive voice response system, stratified by time to progression on penultimate platinum-based regimen, response to the most recent platinum-based regimen before randomisation, and ethnic descent. The primary endpoint was PFS, analysed for the overall population and by BRCA status. This study is registered with ClinicalTrials.gov, number NCT00753545. Findings Between Aug 28, 2008, and Feb 9, 2010, 136 patients were assigned to olaparib and 129 to placebo. BRCA status was known for 131 (96%) patients in the olaparib group versus 123 (95%) in the placebo group, of whom 74 (56%) versus 62 (50%) had a deleterious or suspected deleterious germline or tumour BRCA mutation. Of patients with a BRCA mutation, median PFS was significantly longer in the olaparib group than in the placebo group (11·2 months [95% CI 8·3–not calculable] vs 4·3 months [3·0–5·4]; HR 0·18 [0·10–0·31]; p BRCA , although the difference between groups was lower (7·4 months [5·5–10·3] vs 5·5 months [3·7–5·6]; HR 0·54 [0·34–0·85]; p=0·0075). At the second interim analysis of overall survival (58% maturity), overall survival did not significantly differ between the groups (HR 0·88 [95% CI 0·64–1·21]; p=0·44); similar findings were noted for patients with mutated BRCA (HR 0·73 [0·45–1·17]; p=0·19) and wild-type BRCA (HR 0·99 [0·63–1·55]; p=0·96). The most common grade 3 or worse adverse events in the olaparib group were fatigue (in ten [7%] patients in the olaparib group vs four [3%] in the placebo group) and anaemia (seven [5%] vs one [ BRCA and the overall population. Interpretation These results support the hypothesis that patients with platinum-sensitive recurrent serous ovarian cancer with a BRCA mutation have the greatest likelihood of benefiting from olaparib treatment. Funding AstraZeneca.

InterPro in 2019: improving coverage, classification and access to protein sequence annotations.

Abstract: The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Epigenome-wide association studies for common human diseases

Abstract: Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.