Genetic structure of community acquired methicillin-resistant Staphylococcus aureus USA300.

BackgroundCommunity-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is a significant bacterial pathogen that poses considerable clinical and public health challenges. The majority of the CA-MRSA disease burden consists of skin and soft tissue infections (SSTI) not associated with significant morbidity; however, CA-MRSA also causes severe, invasive infections resulting in significant morbidity and mortality. The broad range of disease severity may be influenced by bacterial genetic variation.ResultsWe sequenced the complete genomes of 36 CA-MRSA clinical isolates from the predominant North American community acquired clonal type USA300 (18 SSTI and 18 severe infection-associated isolates). While all 36 isolates shared remarkable genetic similarity, we found greater overall time-dependent sequence diversity among SSTI isolates. In addition, pathway analysis of non-synonymous variations revealed increased sequence diversity in the putative virulence genes of SSTI isolates.ConclusionsHere we report the first whole genome survey of diverse clinical isolates of the USA300 lineage and describe the evolution of the pathogen over time within a defined geographic area. The results demonstrate the close relatedness of clinically independent CA-MRSA isolates, which carry implications for understanding CA-MRSA epidemiology and combating its spread

Statistical Analysis of Functions on Surfaces, With an Application to Medical Imaging

In functional data analysis, data are commonly assumed to be smooth functions on a fixed interval of the real line. In this work, we introduce a comprehensive framework for the analysis of functional data, whose domain is a two-dimensional manifold and the domain itself is subject to variability from sample to sample. We formulate a statistical model for such data, here called functions on surfaces, which enables a joint representation of the geometric and functional aspects, and propose an associated estimation framework. We assess the validity of the framework by performing a simulation study and we finally apply it to the analysis of neuroimaging data of cortical thickness, acquired from the brains of different subjects, and thus lying on domains with different geometries. Supplementary materials for this article are available online

On A. D. Smith's constancy based defence of direct realism

This paper presents an argument against A D Smith's Direct Realist theory of perception, which attempts to defend Direct Realism against the argument from illusion by appealing to conscious perceptual states that are structured by the perceptual constancies. Smith's contention is that the immediate objects of perceptual awareness are characterised by these constancies, which removes any difficulty there may be in identifying them with the external, or normal, objects of awareness. It is here argued that Smith's theory does not provide an adequate defence of Direct Realism because it does not adequately deal with the difficulties posed by the possibility of perceptual illusion. It is argued that there remain possible illusory experiences where the immediate objects of awareness, which in Smith's account are those characterised by perceptual constancies, cannot be identified with the external objects of awareness, contrary to Direct Realism. A further argument is offered to extend this conclusion to all non-illusory cases, by adapting an argument of Smith's own for the generalising step of the Argument from Illusion. The result is that Smith's theory does not provide an adequate Direct Realist account of the possibility of perceptual illusion. © 2011 Springer Science+Business Media B.V

Representation and reconstruction of covariance operators in linear inverse problems

Abstract We introduce a framework for the reconstruction and representation of functions in a setting where these objects cannot be directly observed, but only indirect and noisy measurements are available, namely an inverse problem setting. The proposed methodology can be applied either to the analysis of indirectly observed functional images or to the associated covariance operators, representing second-order information, and thus lying on a non-Euclidean space. To deal with the ill-posedness of the inverse problem, we exploit the spatial structure of the sample data by introducing a flexible regularizing term embedded in the model. Thanks to its efficiency, the proposed model is applied to MEG data, leading to a novel approach to the investigation of functional connectivity.</jats:p

Process innovation and performance : the role of divergence

Process innovation is a key determinant of performance. While extant literature paints a clear picture of the drivers of process innovation, the effect of process innovation on performance has received little attention. This paper contributes to theory building in this important area and examines how divergence of process innovation impacts performance. Divergence concerns the extent to which the observed level of process innovation diverges from the expected level of process innovation. Positive (negative) divergence occurs when the observed level of process innovation is higher (lower) than expected. In turn, we consider how divergence acts as a driver of performance. This approach is useful and important for managers and theory development as it provides insight into situations where a firm may have “too little” or “too much” process innovation. We use survey and archival data from 5,594 firms across 15 countries and find negative divergence to reduce performance under high competitive intensity, whereas positive divergence is detrimental under high environmental uncertainty. Thus, divergence advances understanding as, in contrast with previous work, we do not suggest that more innovation is always better. These findings contribute to understanding the process innovation-performance relationship and has important implications for strategic management research and practice alike

A Functional Approach to Deconvolve Dynamic Neuroimaging Data.

Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis. This yields flexibility in the possible deconvolved functions while still performing well when a linear compartmental model setup is the true data generating mechanism. As the deconvolution needs to be performed on only a relative small number of basis functions rather than voxel by voxel in the entire three-dimensional volume, the methodology is both robust to typical brain imaging noise levels while also being computationally efficient. The new methodology is investigated through simulations in both one-dimensional functions and 2D images and also applied to a neuroimaging study whose goal is the quantification of opioid receptor concentration in the brain.The research of Ci-Ren Jiang is supported in part by NSC 101-2118-M-001-013-MY2 (Taiwan); the research of Jane-Ling Wang is supported by NSF grants, DMS-09-06813 and DMS-12-28369. JA is supported by EPSRC grant EP/K021672/2. The authors would like to thank SAMSI and the NDA programme where some of this research was carried out.This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.1080/01621459.2015.106024

An Introduction to Applications of Wavelet Benchmarking with Seasonal Adjustment

Summary Before adjustment, low and high frequency data sets from national accounts are frequently inconsistent. Benchmarking is the procedure used by economic agencies to make such data sets consistent. It typically involves adjusting the high frequency time series (e.g. quarterly data) so that they become consistent with the lower frequency version (e.g. annual data). Various methods have been developed to approach this problem of inconsistency between data sets. The paper introduces a new statistical procedure, namely wavelet benchmarking. Wavelet properties allow high and low frequency processes to be jointly analysed and we show that benchmarking can be formulated and approached succinctly in the wavelet domain. Furthermore the time and frequency localization properties of wavelets are ideal for handling more complicated benchmarking problems. The versatility of the procedure is demonstrated by using simulation studies where we provide evidence showing that it substantially outperforms currently used methods. Finally, we apply this novel method of wavelet benchmarking to official data from the UK's Office for National Statistics.Engineering and Physical Sciences Research CouncilThis is the final version of the article. It first appeared from Wiley via https://doi.org/10.1111/rssa.1224

The Structural Determinants of Insulin-Like Peptide 3 Activity

Insulin-like peptide 3 (INSL3) is a hormone and/or paracrine factor which is a member of the relaxin peptide family. It has key roles as a fertility regulator in both males and females. The receptor for INSL3 is the leucine rich repeat (LRR) containing G-protein coupled receptor 8 (LGR8) which is now known as relaxin family peptide receptor 2 (RXFP2). Receptor activation by INSL3 involves binding to the LRRs in the large ectodomain of RXFP2 by residues within the B-chain of INSL3 as well as an interaction with the transmembrane exoloops of the receptor. Although the binding to the LRRs is well characterized the features of the peptide and receptor involved in the exoloop interaction are currently unknown. This study was designed to determine the key INSL3 determinants for RXFP2 activation. A chimeric peptide approach was first utilized to demonstrate that the A-chain is critical for receptor activation. Replacement of the INSL3 A-chain with that from the related peptides INSL5 and INSL6 resulted in complete loss of activity despite only minor changes in binding affinity. Subsequent replacement of specific A-chain residues with those from the INSL5 peptide highlighted that the N-terminus of the A-chain of INSL3 is critical for its activity. Remarkably, replacement of the entire N-terminus with four or five alanine residues resulted in peptides with near native activity suggesting that specific residues are not necessary for activity. Additionally removal of two amino acids at the C-terminus of the A-chain and mutation of Lys-8 in the B-chain also resulted in minor decreases in peptide activity. Therefore we have demonstrated that the activity of the INSL3 peptide is driven predominantly by residues 5–9 in the A-chain, with minor additional contributions from the two C-terminal A-chain residues and Lys-8 in the B-chain. Using this new knowledge, we were able to produce a truncated INSL3 peptide structure which retained native activity, despite having 14 fewer residues than the parent peptide

Ecopsychosocial Interventions in Cognitive Decline and Dementia:A New Terminology and a New Paradigm

Dementia is a major medical and social scourge. Neither pharmacological nor nonpharmacological interventions and treatments have received sufficient funding to be meaningful in combatting this tsunami. Because the term—“nonpharmacological”—refers to what these interventions are not, rather than what they are, nonpharmacological treatments face a special set of challenges to be recognized, accepted, funded, and implemented. In some ways, the current situation is analogous to using the term “nonhate” to mean “love.” This article presents a carefully reasoned argument for using the terminology “ecopsychosocial” to describe the full range of approaches and interventions that fall into this category. These include interventions such as educational efforts with care partners, social support programs for individuals with various levels of dementia, efforts to improve community awareness of dementia, an intergenerational school where persons with dementia teach young children, and the design of residential and community settings that improve functioning and can reduce behavioral symptoms of dementia. The proposed terminology relates to the nature of the interventions themselves, rather than their outcomes, and reflects the broadest range of interventions possible under the present rubric—nonpharmacological. The goal of this new label is to be better able to compare interventions and their outcomes and to be able to see the connections between data sets presently not seen as fitting together, thereby encouraging greater focus on developing new ecopsychosocial interventions and approaches that can improve the lives of those with dementia, their care partners, and the broader society. </jats:p