Whole-genome sequencing for national surveillance of Shiga toxin–producing Escherichia coli O157

Background. National surveillance of gastrointestinal pathogens, such as Shiga toxin–producing Escherichia coli O157 (STEC O157), is key to rapidly identifying linked cases in the distributed food network to facilitate public health interventions. In this study, we used whole-genome sequencing (WGS) as a tool to inform national surveillance of STEC O157 in terms of identifying linked cases and clusters and guiding epidemiological investigation. Methods. We retrospectively analyzed 334 isolates randomly sampled from 1002 strains of STEC O157 received by the Gastrointestinal Bacteria Reference Unit at Public Health England, Colindale, in 2012. The genetic distance between each isolate, as estimated by WGS, was calculated and phylogenetic methods were used to place strains in an evolutionary context. Results. Estimates of linked clusters representing STEC O157 outbreaks in England and Wales increased by 2-fold when WGS was used instead of traditional typing techniques. The previously unidentified clusters were often widely geographically distributed and small in size. Phylogenetic analysis facilitated identification of temporally distinct cases sharing common exposures and delineating those that shared epidemiological and temporal links. Comparison with multi locus variable number tandem repeat analysis (MLVA) showed that although MLVA is as sensitive as WGS, WGS provides a more timely resolution to outbreak clustering. Conclusions. WGS has come of age as a molecular typing tool to inform national surveillance of STEC O157; it can be used in real time to provide the highest strain-level resolution for outbreak investigation. WGS allows linked cases to be identified with unprecedented specificity and sensitivity that will facilitate targeted and appropriate public health investigations

Comparison of Regional Body Composition Estimates Obtained from Dual-energy X-ray Absorptiometry and Single-frequency Bioelectrical Impedance Analysis

The anatomical distribution of fat mass (FM) and lean mass (LM) is significant for health and athletic performance. Dual-energy x-ray absorptiometry (DXA) is often used for regional body composition analysis but is not portable, often inaccessible, and costly, while single-frequency bioelectrical impedance analysis (SFBIA) is a more affordable and accessible alternative. PURPOSE: The purpose of this analysis was to compare regional body composition estimates obtained via DXA and SFBIA. METHODS: After an overnight food and fluid fast, 102 adults (64 F, 38 M; age: 29.2 ± 13.4 y; BMI: 24.3 ± 3.9 kg/m2; BF%: 24.6 ± 8.3%) underwent assessments via DXA and SBFIA, each of which provided estimates of FM and LM for the whole body, torso, legs, and arms. DXA scans were performed using custom-made foam blocks to enhance accuracy of regional body composition estimates. SFBIA was performed using an 8-lead device with a 12-channel multiplexer. Both DXA and SFBIA were performed in the supine position. DXA was designated as the criterion method, and body composition estimates were compared using paired-samples t-tests using a Bonferroni-corrected significance level of p ≤ 0.00625. Additional evaluations were conducted using the correlation coefficient (r), constant error (CE), standard error of the estimate (SEE), and total error (TE). RESULTS: Correlations between DXA and SFBIA were high, and the magnitude of errors was generally small: LMTOTAL (r: 0.97; CE: 1.4 kg; SEE: 2.7 kg; TE: 2.9 kg), LMLEGS (r: 0.85; CE: -0.3 kg; SEE: 2.0 kg; TE: 2.1 kg), LMTORSO (r: 0.92; CE: 1.0 kg; SEE: 2.2 kg; TE: 2.5 kg), LMARMS (r: 0.96; CE: 0.6 kg; SEE: 0.6 kg; TE: 0.8 kg), FMTOTAL (r: 0.95; CE: -2.3 kg; SEE: 2.6 kg; TE: 3.5 kg), FMLEGS (r: 0.83; CE: -1.0 kg; SEE: 1.2 kg; TE: 2.0 kg), FMTORSO (r: 0.90; CE: -1.3 kg; SEE: 2.2 kg; TE: 2.6 kg), and FMARMS (r: 0.89; CE: -0.1 kg; SEE: 0.5 kg; TE: 0.5 kg). Despite the relatively small magnitude of differences in FM and LM estimates between DXA and SFBIA, results of paired-samples t-tests indicated that all differences were statistically significant (p \u3c 0.0001), with the exception of LMLEGS (p=0.13) and FMARMS (p=0.11). CONCLUSION: Despite the fact that body composition estimates for most regions exhibited statistically significant differences between DXA and SFBIA, the strong correlations (r: 0.83 to 0.97) and relatively low magnitude of error (CE: -2.3 to 1.4 kg; TE: 0.8 to 3.5 kg) indicate that SFBIA may be an acceptable alternative to DXA when regional body composition is being evaluated and DXA is unavailable. However, additional research is needed to determine the ability of SFBIA to accurately track changes in regional body composition over time. Due to its low cost, portability, and ease of use, the presently examined SFBIA device may represent a useful tool for the evaluation of regional body composition when more advanced methods are unavailable

Geochemical approaches to the quantification of dispersed volcanic ash in marine sediment

Volcanic ash has long been recognized in marine sediment, and given the prevalence of oceanic and continental arc volcanism around the globe in regard to widespread transport of ash, its presence is nearly ubiquitous. However, the presence/absence of very fine-grained ash material, and identification of its composition in particular, is challenging given its broad classification as an “aluminosilicate” component in sediment. Given this challenge, many studies of ash have focused on discrete layers (that is, layers of ash that are of millimeter-to-centimeter or greater thickness, and their respective glass shards) found in sequences at a variety of locations and timescales and how to link their presence with a number of Earth processes. The ash that has been mixed into the bulk sediment, known as dispersed ash, has been relatively unstudied, yet represents a large fraction of the total ash in a given sequence. The application of a combined geochemical and statistical technique has allowed identification of this dispersed ash as part of the original ash contribution to the sediment. In this paper, we summarize the development of these geochemical/statistical techniques and provide case studies from the quantification of dispersed ash in the Caribbean Sea, equatorial Pacific Ocean, and northwest Pacific Ocean. These geochemical studies (and their sedimentological precursors of smear slides) collectively demonstrate that local and regional arc-related ash can be an important component of sedimentary sequences throughout large regions of the ocean

Validity of Four-Compartment Model Body Fat Using Single- or Multi-frequency Bioelectrical Impedance Analysis to Estimate Body Water

Most common body composition assessment techniques make assumptions about the body, including the density and hydration of fat-free mass (FFM). An advantage of the four-compartment (4C) model is the ability to take these FFM characteristics into account when assessing body composition, thus reducing potential error. The total body water (TBW) estimate utilized in 4C models is particularly important due to the large contribution of water to an adult human’s total body mass (~40 - 70%) and FFM (~68 - 81%); however, the impact of utilizing different estimates of TBW within 4C model has not been fully explored. PURPOSE: The purpose of this investigation was to examine the validity of body fat percentage (BF%) estimates produced by 4C models utilizing single- or multi-frequency bioelectrical impedance analysis (BIA) TBW estimates as compared to a criterion 4C with TBW from bioimpedance spectroscopy (BIS). METHODS: After an overnight food and fluid fast, a sample of 101 adults (63 F, 38 M; age: 29.3 ± 13.5 y; BMI: 24.3 ± 4.0 kg/m2; BF%: 24.5 ± 8.3%) completed assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), BIS, single-frequency BIA (SFBIA), multi-frequency BIA (MFBIA) and a body mass scale. A criterion 4C model (4CBIS) estimate of BF% was obtained using DXA for bone mineral, ADP for body volume, scale for body mass, and BIS for TBW. BIS was used as the reference TBW method due to its more direct estimation of TBW via mathematical procedures (i.e. Cole modeling and mixture theories) as compared to the prediction equations used by BIA. Alternate 4C estimates of BF% were produced using TBW values from MFBIA (4CMFBIA) and SFBIA (4CSFBIA). BF% estimates were compared using one-way ANOVA, and additional evaluations were conducted using the coefficient of determination (R2), constant error (CE), total error (TE), and 95% limits of agreement (LOA). RESULTS: BF% did not differ between 4CBIS (24.5 ± 8.3%), 4CMFBIA (24.4 ± 8.9%), and 4CSFBIA (25.7 ± 8.3%; p=0.52). 4CMFBIA exhibited negligible CE (-0.1 ± 2.3%), R2 of 0.97, TE of 2.3%, and LOA of 4.4%. 4CSFBIA exhibited a small CE (1.2 ± 1.2%), R2 of 0.98, TE of 1.6%, and LOA of 2.3%. CONCLUSION: At the group level, BF% estimates did not differ between any 4C model, indicating that both SFBIA and MFBIA can serve as viable alternatives to BIS for TBW estimation. Although the magnitude of group error (i.e. CE) was slightly smaller in 4CMFBIA, the individual error (i.e. LOA) and total error were smaller in 4CSFBIA,indicating that SFBIA TBW estimates may be more appropriate when tracking body composition changes within individuals using a 4C model. While the MFBIA and SFBIA technologies employed in the present study exhibited good validity, these results may not be attributable to all BIA analyzers. The quality of assessment device, affordability, portability and ease of use should be considered when utilizing an impedance-based technology for TBW estimation in a 4C model

Validity of Infrared 3-dimensional Scanning for Estimation of Body Composition: A 4-Compartment Model Comparison

Multiple infrared 3-dimensional (3D) scanning technologies exist, including time of flight (ToF) scanners and structured light scanners with static (SL-S) and dynamic (SL-D) configurations. ToF scanners measure depth by using the round-trip time of reflected photons, whereas SL scanners measure deformations in light patterns and allow for creation of a depth image using geometric triangulation. Recently, 3D scanning technologies have been proposed as novel methods of body composition assessment. PURPOSE: The purpose of this analysis was to examine the validity of four different commercially-available 3D scanners for estimation of body fat percentage (BF%) as compared to a 4-compartment (4C) model criterion. METHODS: After an overnight fast, 101 adults (63 F, 38 M; age: 29.3 ± 13.5 y; BMI: 24.3 ± 3.9 kg/m2; BF%: 24.6 ± 8.3%) completed assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), bioimpedance spectroscopy (BIS), a standard body mass scale, and four infrared 3D scanners. Two scanners (3DSSL-D1; 3DSSL-D2) utilized structured light scanning with a dynamic configuration, one utilized structured light scanning with a static configuration (3DSSL-S), and one utilized time-of-flight technology (3DSToF). Using the equation of Wang et al. (2002), a criterion 4C estimate of BF% was obtained using DXA for bone mineral, ADP for body volume, scale for body mass, and BIS for total body water. BF% estimates were compared using one-way ANOVA with Bonferroni adjustment for multiple comparisons, and additional evaluations were conducted using the correlation coefficient (r), constant error (CE), standard error of the estimate (SEE), total error (TE), and 95% limits of agreement (LOA). RESULTS: Estimates of BF% did not significantly differ between 4C and any of the 3D scanners. However, metrics of group, individual, and prediction errors varied between scanners: 3DSSL-D1: p=1.0; CE: 0.4%; r: 0.91; SEE: 2.5%; TE: 3.6%; LOA: ±7.0%; 3DSSL-D2: p= 1.0; CE: 0.8%; r: 0.86; SEE: 4.2%; TE: 4.7%; LOA: ±9.2%; 3DSSL-S: p= 1.0; CE: 1.0%; r: 0.81; SEE: 4.0%; TE: 5.0%; LOA: ±9.7%; 3DSToF: p=0.08; CE: -2.9%; r: 0.86, SEE: 2.5%; TE: 5.2%; LOA: ±8.6%. CONCLUSION: All three structured light scanners exhibited low magnitudes of group error (CE ≤ 1%) and may be valid assessment methods when analyzing the body composition of groups. 3DSSL-D1 exhibited the lowest group-level error (i.e. CE), prediction errors (i.e. SEE; TE), and individual error (i.e. LOA) of all scanners. Therefore, this device was deemed the most valid 3D scanner for body composition assessment. 3DSSL-D2, 3DSSL-S, and 3DSToF exhibited comparable TE, although group-level error was lower in 3DSSL-D2 and 3DSSL-S, while the SEE and individual-level error was lower for 3DSToF. However, individual-level errors were relatively high with all scanners (LOA ≥ 7%), which calls into question the utility of these methods for assessing the body composition of individuals. Nonetheless, additional research is needed regarding the ability of 3DS to successfully detect changes in body composition over time

Regional-scale input of dispersed and discrete volcanic ash to the Izu-Bonin and Mariana subduction zones

We have geochemically and statistically characterized bulk marine sediment and ash layers at Ocean Drilling Program Site 1149 (Izu-Bonin Arc) and Deep Sea Drilling Project Site 52 (Mariana Arc), and have quantified that multiple dispersed ash sources collectively comprise ~30-35% of the hemipelagic sediment mass entering the Izu-Bonin-Mariana subduction system. Multivariate statistical analyses indicate that the bulk sediment at Site 1149 is a mixture of Chinese Loess, a second compositionally distinct eolian source, a dispersed mafic ash, and a dispersed felsic ash. We interpret the source of these ashes as respectively being basalt from the Izu-Bonin Front Arc (IBFA) and rhyolite from the Honshu Arc. Sr-, Nd-, and Pb isotopic analyses of the bulk sediment are consistent with the chemical/statistical-based interpretations. Comparison of the mass accumulation rate of the dispersed ash component to discrete ash layer parameters (thickness, sedimentation rate, and number of layers) suggests that eruption frequency, rather than eruption size, drives the dispersed ash record. At Site 52, the geochemistry and statistical modeling indicates that Chinese Loess, IBFA, dispersed BNN (boninite from Izu-Bonin), and a dispersed felsic ash of unknown origin are the sources. At Site 1149 the ash layers and the dispersed ash are compositionally coupled, whereas at Site 52 they are decoupled in that there are no boninite layers, yet boninite is dispersed within the sediment. Changes in the volcanic and eolian inputs through time indicate strong arc- and climate-related controls

Eu-Social Science: The Role of Internet Social Networks in the Collection of Bee Biodiversity Data

Background Monitoring change in species diversity, community composition and phenology is vital to assess the impacts of anthropogenic activity and natural change. However, monitoring by trained scientists is time consuming and expensive. Methodology/Principal Findings Using social networks, we assess whether it is possible to obtain accurate data on bee distribution across the UK from photographic records submitted by untrained members of the public, and if these data are in sufficient quantity for ecological studies. We used Flickr and Facebook as social networks and Flickr for the storage of photographs and associated data on date, time and location linked to them. Within six weeks, the number of pictures uploaded to the Flickr BeeID group exceeded 200. Geographic coverage was excellent; the distribution of photographs covered most of the British Isles, from the south coast of England to the Highlands of Scotland. However, only 59% of photographs were properly uploaded according to instructions, with vital information such as ‘tags’ or location information missing from the remainder. Nevertheless, this incorporation of information on location of photographs was much higher than general usage on Flickr (∼13%), indicating the need for dedicated projects to collect spatial ecological data. Furthermore, we found identification of bees is not possible from all photographs, especially those excluding lower abdomen detail. This suggests that giving details regarding specific anatomical features to include on photographs would be useful to maximise success. Conclusions/Significance The study demonstrates the power of social network sites to generate public interest in a project and details the advantages of using a group within an existing popular social network site over a traditional (specifically-designed) web-based or paper-based submission process. Some advantages include the ability to network with other individuals or groups with similar interests, and thus increasing the size of the dataset and participation in the project

Enteroaggregative escherichia coli have evolved independently as distinct complexes within the E. Coli population with varying ability to cause disease

Enteroaggregative E. Coli (EAEC) is an established diarrhoeagenic pathotype. The association with virulence gene content and ability to cause disease has been studied but little is known about the population structure of EAEC and how this pathotype evolved. Analysis by Multi Locus Sequence Typing of 564 EAEC isolates from cases and controls in Bangladesh, Nigeria and the UK spanning the past 29 years, revealed multiple successful lineages of EAEC. The population structure of EAEC indicates some clusters are statistically associated with disease or carriage, further highlighting the heterogeneous nature of this group of organisms. Different clusters have evolved independently as a result of both mutational and recombination events; the EAEC phenotype is distributed throughout the population of E. coli

Identification of GSK3186899/DDD853651 as a Preclinical Development Candidate for the Treatment of Visceral Leishmaniasis

The leishmaniases are diseases that affect millions of people across the world, in particular visceral leishmaniasis (VL) which is fatal unless treated. Current standard of care for VL suffers from multiple issues and there is a limited pipeline of new candidate drugs. As such, there is a clear unmet medical need to identify new treatments. This paper describes the optimization of a phenotypic hit against Leishmania donovani, the major causative organism of VL. The key challenges were to balance solubility and metabolic stability while maintaining potency. Herein, strategies to address these shortcomings and enhance efficacy are discussed, culminating in the discovery of preclinical development candidate GSK3186899/DDD853651 (<b>1</b>) for VL

Polymorphisms near TBX5 and GDF7 are associated with increased risk for Barrett's esophagus.

BACKGROUND & AIMS: Barrett's esophagus (BE) increases the risk of esophageal adenocarcinoma (EAC). We found the risk to be BE has been associated with single nucleotide polymorphisms (SNPs) on chromosome 6p21 (within the HLA region) and on 16q23, where the closest protein-coding gene is FOXF1. Subsequently, the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) identified risk loci for BE and esophageal adenocarcinoma near CRTC1 and BARX1, and within 100 kb of FOXP1. We aimed to identify further SNPs that increased BE risk and to validate previously reported associations. METHODS: We performed a genome-wide association study (GWAS) to identify variants associated with BE and further analyzed promising variants identified by BEACON by genotyping 10,158 patients with BE and 21,062 controls. RESULTS: We identified 2 SNPs not previously associated with BE: rs3072 (2p24.1; odds ratio [OR] = 1.14; 95% CI: 1.09-1.18; P = 1.8 × 10(-11)) and rs2701108 (12q24.21; OR = 0.90; 95% CI: 0.86-0.93; P = 7.5 × 10(-9)). The closest protein-coding genes were respectively GDF7 (rs3072), which encodes a ligand in the bone morphogenetic protein pathway, and TBX5 (rs2701108), which encodes a transcription factor that regulates esophageal and cardiac development. Our data also supported in BE cases 3 risk SNPs identified by BEACON (rs2687201, rs11789015, and rs10423674). Meta-analysis of all data identified another SNP associated with BE and esophageal adenocarcinoma: rs3784262, within ALDH1A2 (OR = 0.90; 95% CI: 0.87-0.93; P = 3.72 × 10(-9)). CONCLUSIONS: We identified 2 loci associated with risk of BE and provided data to support a further locus. The genes we found to be associated with risk for BE encode transcription factors involved in thoracic, diaphragmatic, and esophageal development or proteins involved in the inflammatory response