On groups with a class-preserving outer automorphism

In 1911, Burnside asked whether or not there exist groups that have an outer automorphism which preserves conjugacy classes. Two years later he answered his own question by constructing a family of such groups. Using the small group library in MAGMA we determine all of the groups of order n < 512 that possess such an automorphism. Our investigations led to the discovery of four new infinite families of such groups, all of which are 2-groups of coclass 4

Persistent Homology Over Directed Acyclic Graphs

We define persistent homology groups over any set of spaces which have inclusions defined so that the corresponding directed graph between the spaces is acyclic, as well as along any subgraph of this directed graph. This method simultaneously generalizes standard persistent homology, zigzag persistence and multidimensional persistence to arbitrary directed acyclic graphs, and it also allows the study of more general families of topological spaces or point-cloud data. We give an algorithm to compute the persistent homology groups simultaneously for all subgraphs which contain a single source and a single sink in $O(n^4)$ arithmetic operations, where $n$ is the number of vertices in the graph. We then demonstrate as an application of these tools a method to overlay two distinct filtrations of the same underlying space, which allows us to detect the most significant barcodes using considerably fewer points than standard persistence.Comment: Revised versio

Rationale and design of the genotype-blinded trial of torasemide for the treatment of hypertension (BHF UMOD)

Background: There is evidence from genome wide association study that single-nucleotide polymorphisms (SNPs) in the 5' end of the uromodulin gene (UMOD) affect uromodulin excretion and blood pressure (BP). Uromodulin is almost exclusively expressed in the thick ascending limb of the loop of Henle (TAL) and its effect on BP appear to be mediated via the TAL sodium transporter, NKCC2. Loop-diuretics block NKCC2 but are not commonly used in hypertension management. As volume overload is considered as one of the primary drivers for uncontrolled hypertension, targeting loop-diuretics to individuals who are more likely to respond to this drug class, using UMOD genotype, could be an efficient precision medicine strategy. Methods: A genotype-blinded, multi-centre trial comparing the BP response to torasemide between individuals possessing the AA genotype of the SNP rs13333226 and those possessing the G allele. 240 participants with uncontrolled BP aged ≥18 years, on ≥1 antihypertensive agent for ≥3 months, will be included. Uncontrolled BP is average systolic BP (SBP) &gt;135mmHg and/or diastolic BP &gt;85mmHg on home monitoring. Torasemide, 5mg daily, is taken for 16 weeks. The primary outcome is the change in 24h ambulatory SBP area under the curve between baseline and end of treatment. Sample size was calculated to detect a 4mmHg difference between groups at 90% power. Approval by West of Scotland Research Ethics Committee 5 (16/WS/0160). Registration at https://clinicaltrials.gov/ct2/show/NCT03354897. Results: The study should conclude August 2021. Conclusions: If hypothesis confirmed, a targeted strategy will improve BP control and could reduce the burden of uncontrolled hypertension

Constructing female entrepreneurship policy in the UK : is the US a relevant benchmark?

Successive UK governments have introduced a range of policy initiatives designed to encourage more women to start new firms. Underpinning these policies has been an explicit ambition for the UK to achieve similar participation rates as those in the US where it is widely reported that women own nearly half the stock of businesses. The data underlying these objectives are critically evaluated and it is argued that the definitions and measures of female enterprise used in the UK and the US restrict meaningful comparisons between the two. It is suggested that the expansion of female entrepreneurship in the US is historically and culturally specific to that country. UK policy goals should reflect the national socioeconomic context, while drawing upon good practice examples from a range of other countries. The paper concludes by discussing the economic and social viability of encouraging more women in the UK to enter self-employment without fully recognising the intensely competitive sectors in which they are often located

DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs

DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data ‘depth’). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug–drug and food–drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications

ChemBank: a small-molecule screening and cheminformatics resource database

ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector

GLIDA: GPCR—ligand database for chemical genomics drug discovery—database and tools update

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery

Classificatory Theory in Data-Intensive Science: The Case of Open Biomedical Ontologies

publication-status: Publishedtypes: ArticleThis is the author's version of a paper that was subsequently published in International Studies in the Philosophy of Science. Please cite the published version by following the DOI link.Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online.Economic and Social Research Council (ESRC)The British AcademyLeverhulme Trus

GOBLET: the Global Organisation for Bioinformatics Learning, Education and Training

In recent years, high-throughput technologies have brought big data to the life sciences. The march of progress has been rapid, leaving in its wake a demand for courses in data analysis, data stewardship, computing fundamentals, etc., a need that universities have not yet been able to satisfy--paradoxically, many are actually closing "niche" bioinformatics courses at a time of critical need. The impact of this is being felt across continents, as many students and early-stage researchers are being left without appropriate skills to manage, analyse, and interpret their data with confidence. This situation has galvanised a group of scientists to address the problems on an international scale. For the first time, bioinformatics educators and trainers across the globe have come together to address common needs, rising above institutional and international boundaries to cooperate in sharing bioinformatics training expertise, experience, and resources, aiming to put ad hoc training practices on a more professional footing for the benefit of all