University of Oxford

About: University of Oxford is a education organization based out in Oxford, Oxfordshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 99713 authors who have published 258108 publications receiving 12972806 citations. The organization is also known as: Oxford University & Oxon..

Abstract reward and punishment representations in the human orbitofrontal cortex.

Abstract: The orbitofrontal cortex (OFC) is implicated in emotion and emotion-related learning. Using event-related functional magnetic resonance imaging (fMRI), we measured brain activation in human subjects doing an emotion-related visual reversal-learning task in which choice of the correct stimulus led to a probabilistically determined 'monetary' reward and choice of the incorrect stimulus led to a monetary loss. Distinct areas of the OFC were activated by monetary rewards and punishments. Moreover, in these areas, we found a correlation between the magnitude of the brain activation and the magnitude of the rewards and punishments received. These findings indicate that one emotional involvement of the human orbitofrontal cortex is its representation of the magnitudes of abstract rewards and punishments, such as receiving or losing money.

The Phylogenetic Regression

Abstract: A new statistical method called the phylogenetic regression is proposed that applies multiple regression techniques to cross-species data. It allows continuous and categorical variables to be tested for and controlled for. The new method is valid despite the problem that phylogenetically close species tend to be similar, and is designed to be used when information about the phylogeny is incomplete. Information about the phylogeny of the species is assumed to be available in the form of a working phylogeny, which contains multiple nodes representing ignorance about the order of splitting of taxa. The non-independence between species is divided into that due to recognized phylogeny, that is, to phylogenetic associations represented in the working phylogeny; and that due to unrecognized phylogeny. The new method uses one linear contrast for each higher node in the working phylogeny, thus applying the ‘radiation principle’. For binary phylogenies the method is similar to an existing method. A criterion is suggested in the form of a simulation test for deciding on the acceptability of proposed statistical methods for analysing cross-species data with a continuous y-variable. This criterion is applied to the phylogenetic regression and to some other methods. The phylogenetic regression passes this test; the other methods tested fail it. Arbitrary choices have to be made about the covariance structure of the error in order to implement the method. It is argued that error results from omitted but relevant variables, and the implications for those arbitrary choices are discussed. One conclusion is that the dates of splits between taxa, even supplemented by rates of neutral gene evolution, do not provide the ‘ true ’ covariance structure. A pragmatic approach is adopted. Several analytical results about the phylogenetic regression are given, without proof, in a mathematical appendix. A computer program has been written in GLIM to implement the phylogenetic regression, and readers are informed how to obtain a copy.

BIGSdb: Scalable analysis of bacterial genome variation at the population level

Abstract: The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches LIMS functionality of the software enables linkage to and organisation of laboratory samples The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database Some of the applications of BIGSDB are illustrated with the genera Neisseria and Streptococcus The BIGSDB source code and documentation are available at http://pubmlstorg/software/database/bigsdb/ Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies BIGSDB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach

Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

Abstract: Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05