Service evaluation to establish the sensitivity, specificity and additional value of broad-range 16S rDNA PCR for the diagnosis of infective endocarditis from resected endocardial material in patients from eight UK and Ireland hospitals

Infective endocarditis (IE) can be diagnosed in the clinical microbiology laboratory by culturing explanted heart valve material. We present a service evaluation that examines the sensitivity and specificity of a broad-range 16S rDNA polymerase chain reaction (PCR) assay for the detection of the causative microbe in culture-proven and culture-negative cases of IE. A clinical case-note review was performed for 151 patients, from eight UK and Ireland hospitals, whose endocardial specimens were referred to the Microbiology Laboratory at Great Ormond Street Hospital (GOSH) for broad-range 16S rDNA PCR over a 12-year period. PCR detects the causative microbe in 35/47 cases of culture-proven IE and provides an aetiological agent in 43/69 cases of culture-negative IE. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the 16S rDNA PCR assay were calculated for this series of selected samples using the clinical diagnosis of IE as the reference standard. The values obtained are as follows: sensitivity = 67 %, specificity = 91 %, PPV = 96 % and NPV = 46 %. A wide range of organisms are detected by PCR, with Streptococcus spp. detected most frequently and a relatively large number of cases of Bartonella spp. and Tropheryma whipplei IE. PCR testing of explanted heart valves is recommended in addition to culture techniques to increase diagnostic yield. The data describing the aetiological agents in a large UK and Ireland series of culture-negative IE will allow future development of the diagnostic algorithm to include real-time PCR assays targeted at specific organisms

Use of fat mass and fat free mass standard deviation scores obtained using simple measurement methods in healthy children and patients: comparison with the reference 4-component model

Background Clinical application of body composition (BC) measurements for individual children has been limited by lack of appropriate reference data. Objectives (1) To compare fat mass (FM) and fat free mass (FFM) standard deviation scores (SDS) generated using new body composition reference data and obtained using simple measurement methods in healthy children and patients with those obtained using the reference 4-component (4-C) model; (2) To determine the extent to which scores from simple methods agree with those from the 4-C model in identification of abnormal body composition. Design FM SDS were calculated for 4-C model, dual-energy X-ray absorptiometry (DXA; GE Lunar Prodigy), BMI and skinfold thicknesses (SFT); and FFM SDS for 4CM, DXA and bioelectrical impedance analysis (BIA; height2/Z)) in 927 subjects aged 3.8–22.0 y (211 healthy, 716 patients). Results DXA was the most accurate method for both FM and FFM SDS in healthy subjects and patients (mean bias (limits of agreement) FM SDS 0.03 (±0.62); FFM SDS −0.04 (±0.72)), and provided best agreement with the 4-C model in identifying abnormal BC (SDS ≤−2 or ≥2). BMI and SFTs were reasonable predictors of abnormal FM SDS, but poor in providing an absolute value. BIA was comparable to DXA for FFM SDS and in identifying abnormal subjects

Biophotonic Tools in Cell and Tissue Diagnostics.

In order to maintain the rapid advance of biophotonics in the U.S. and enhance our competitiveness worldwide, key measurement tools must be in place. As part of a wide-reaching effort to improve the U.S. technology base, the National Institute of Standards and Technology sponsored a workshop titled "Biophotonic tools for cell and tissue diagnostics." The workshop focused on diagnostic techniques involving the interaction between biological systems and photons. Through invited presentations by industry representatives and panel discussion, near- and far-term measurement needs were evaluated. As a result of this workshop, this document has been prepared on the measurement tools needed for biophotonic cell and tissue diagnostics. This will become a part of the larger measurement road-mapping effort to be presented to the Nation as an assessment of the U.S. Measurement System. The information will be used to highlight measurement needs to the community and to facilitate solutions

Development of a novel walkability index for London, United Kingdom: cross-sectional application to the Whitehall II study

BACKGROUND: Physical activity is essential for health; walking is the easiest way to incorporate activity into everyday life. Previous studies report positive associations between neighbourhood walkability and walking but most focused on cities in North America and Australasia. Urban form with respect to street connectivity, residential density and land use mix-common components of walkability indices-differs in European cities. The objective of this study was to develop a walkability index for London and test the index using walking data from the Whitehall II Study.  METHODS: A neighbourhood walkability index for London was constructed, comprising factors associated with walking behaviours: residential dwelling density, street connectivity and land use mix. Three models were produced that differed in the land uses included. Neighbourhoods were operationalised at three levels of administrative geography: (i) 21,140 output areas, (ii) 633 wards and (iii) 33 local authorities. A neighbourhood walkability score was assigned to each London-dwelling Whitehall II Study participant (2003-04, N = 3020, mean ± SD age = 61.0 years ± 6.0) based on residential postcode. The effect of changing the model specification and the units of enumeration on spatial variation in walkability was examined. RESULTS: There was a radial decay in walkability from the centre to the periphery of London. There was high inter-model correlation in walkability scores for any given neighbourhood operationalisation (0.92-0.98), and moderate-high correlation between neighbourhood operationalisations for any given model (0.39-0.70). After adjustment for individual level factors and area deprivation, individuals in the most walkable neighbourhoods operationalised as wards were more likely to walk >6 h/week (OR = 1.4; 95 % CI: 1.1-1.9) than those in the least walkable. CONCLUSIONS: Walkability was associated with walking time in adults. This walkability index could help urban planners identify and design neighbourhoods in London with characteristics more supportive of walking, thereby promoting public health

Spontaneous Stratification in Granular Mixtures

Granular materials size segregate when exposed to external periodic perturbations such as vibrations. Moreover, mixtures of grains of different sizes spontaneously segregate in the absence of external perturbations: when a mixture is simply poured onto a pile, the large grains are more likely to be found near the base, while the small grains are more likely to be near the top. Here, we report a spontaneous phenomenon arising when we pour a mixture between two vertical plates: the mixture spontaneously stratifies into alternating layers of small and large grains whenever the large grains are rougher than the small grains. In contrast, we find only spontaneous segregation when the large grains are more rounded than the small grains. The stratification is related to the occurrence of avalanches; during each avalanche the grains comprising the avalanche spontaneously stratify into a pair of layers through a "kink" mechanism, with the small grains forming a sublayer underneath the layer of large grains.Comment: 4 pages, 6 figures, http://polymer.bu.edu/~hmakse/Home.htm

Genes Suggest Ancestral Colour Polymorphisms Are Shared across Morphologically Cryptic Species in Arctic Bumblebees

email Suzanne orcd idCopyright: © 2015 Williams et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A novel walkability index for London predicts walking time in adults

Objective: To develop a novel walkability index for London and test it through measurement of associations between neighbourhood walkability and walking among adults using data from the Whitehall II Study. Background: Physical activity is essential for health; walking is the easiest way to incorporate it into everyday life. Many studies have reported positive associations between neighbourhood walkability and walking but the majority have focused on cities in North America and Australasia. Urban form with respect to street connectivity, residential density and land use mix – common components of walkability indices – is likely to differ in European cities. Methods: A walkability index for the 633 spatially contiguous census area statistics wards of London was constructed, comprising three core dimensions associated with walking behaviours: residential dwelling density, street connectivity and land use mix. Walkability was expressed as quartile scores, with wards scoring 1 being in the bottom 25% in terms of walkability, and those scoring 4 in the top 25%. A neighbourhood walkability score was assigned to each London-dwelling Whitehall II Study participant (2003-04, N=3020, mean +/-SD age=61.0y +/-6.0) as the walkability score of the ward in which their residential postcode fell. Associations between neighbourhood walkability and weekly walking time were measured using multiple logistic regression. Results: After adjustment for individual level factors and area deprivation, people in the most walkable neighbourhoods were significantly more likely to spend ≥6hr/wk (Odds Ratio 1.4; 95%Confidence Interval 1.1-1.9), than those in the least walkable. Conclusions: The walkability index constructed can predict walking time in adults: living in a more walkable neighbourhood is associated with longer weekly walking time. The index may help urban planners identify and design neighbourhoods in London with characteristics that are potentially more supportive of walking and, thereby, promote public health

Classifying atopic dermatitis: protocol for a systematic review of subtypes (phenotypes) and associated characteristics.

INTRODUCTION: Atopic dermatitis is a complex disease with differing clinical presentations. Many attempts have been made to identify uniform subtypes, or phenotypes, of atopic dermatitis in order to identify different aetiologies, improve diagnosis, estimate more accurate clinical prognoses, inform treatment andmanagement or predict treatment efficacy andeffectiveness. However, no consensus yet exists on exactly what defines these phenotypes or how many there are and whether they are genuine or statistical artefacts. This review aims to identify previously reported phenotypes of atopic dermatitis, the features used to define them and any characteristics or clinical outcomes significantly associated with them. METHODS AND ANALYSIS: We will search Ovid Embase, Ovid MEDLINE and Web of Science from inception to the latest available date at the time of the search for studies attempting to classify atopic dermatitis in humans using any cross-sectional or longitudinal epidemiological or interventional design. Primary outcomes are atopic dermatitis phenotypes, features used to define them and characteristics associated with them in subsequent analyses. A secondary outcome is the methodological approach used to derive them. Two reviewers will independently screen titles and abstracts for inclusion, extract data and assess study quality. We will present the results of this review descriptively and with frequencies where possible. ETHICS AND DISSEMINATION: Ethical approval is not required for this study as it is a systematic review. We will report results from this systematic review in a peer-reviewed journal. The main value of this study will be to inform further research. PROSPERO REGISTRATION NUMBER: CRD42018087500

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning