University College Cork

About: University College Cork is a education organization based out in Cork, Ireland. It is known for research contribution in the topics: Population & Context (language use). The organization has 12056 authors who have published 28452 publications receiving 958414 citations. The organization is also known as: Coláiste na hOllscoile Corcaigh & National University of Ireland, Cork.

Composition of the early intestinal microbiota: knowledge, knowledge gaps and the use of high-throughput sequencing to address these gaps.

Abstract: The colonization, development and maturation of the newborn gastrointestinal tract that begins immediately at birth and continues for two years, is modulated by numerous factors including mode of delivery, feeding regime, maternal diet/weight, probiotic and prebiotic use and antibiotic exposure pre-, peri- and post-natally. While in the past, culture-based approaches were used to assess the impact of these factors on the gut microbiota, these have now largely been replaced by culture-independent DNA-based approaches and most recently, high-throughput sequencing-based forms thereof. The aim of this review is to summarize recent research into the modulatory factors that impact on the acquisition and development of the infant gut microbiota, to outline the knowledge recently gained through the use of culture-independent techniques and, in particular, highlight advances in high-throughput sequencing and how these technologies have, and will continue to, fill gaps in our knowledge with respect to the human intestinal microbiota.

Algorithm Selection for Combinatorial Search Problems: A survey

Abstract: The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This chapter contrasts and compares different methods for solving the problem as well as ways of using these solutions.

Listeriolysin S, a novel peptide haemolysin associated with a subset of lineage I Listeria monocytogenes.

Abstract: Streptolysin S (SLS) is a bacteriocin-like haemolytic and cytotoxic virulence factor that plays a key role in the virulence of Group A Streptococcus (GAS), the causative agent of pharyngitis, impetigo, necrotizing fasciitis and streptococcal toxic shock syndrome. Although it has long been thought that SLS and related peptides are produced by GAS and related streptococci only, there is evidence to suggest that a number of the most notorious Gram-positive pathogenic bacteria, including Listeria monocytogenes, Clostridium botulinum and Staphylococcus aureus, produce related peptides. The distribution of the L. monocytogenes cluster is particularly noteworthy in that it is found exclusively among a subset of lineage I strains; i.e., those responsible for the majority of outbreaks of listeriosis. Expression of these genes results in the production of a haemolytic and cytotoxic factor, designated Listeriolysin S, which contributes to virulence of the pathogen as assessed by murine- and human polymorphonuclear neutrophil–based studies. Thus, in the process of establishing the existence of an extended family of SLS-like modified virulence peptides (MVPs), the genetic basis for the enhanced virulence of a proportion of lineage I L. monocytogenes may have been revealed.

DataSHIELD: taking the analysis to the data, not the data to the analysis

Abstract: Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.