Explosive volcanism on the ultraslow-spreading Gakkel ridge, Arctic Ocean

Author Posting. © Nature Publishing Group, 2008. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 453 (2008): 1236-1238, doi:10.1038/nature07075.Roughly 60% of the Earth’s outer surface is comprised of oceanic crust formed by volcanic processes at mid-ocean ridges (MORs). Although only a small fraction of this vast volcanic terrain has been visually surveyed and/or sampled, the available evidence suggests that explosive eruptions are rare on MORs, particularly at depths below the critical point for steam (3000 m). A pyroclastic deposit has never been observed on the seafloor below 3000 m, presumably because the volatile content of mid-ocean ridge basalts is generally too low to produce the gas fractions required to fragment a magma at such high hydrostatic pressure. We employed new deep submergence technologies during an International Polar Year expedition to the Gakkel Ridge in the Arctic Basin at 85°E, to acquire the first-ever photographic images of ‘zero-age’ volcanic terrain on this remote, ice-covered MOR. Our imagery reveals that the axial valley at 4000 m water depth is blanketed with unconsolidated pyroclastic deposits, including bubble wall fragments (limu o Pele), covering a large area greater than 10 km2. At least 13.5 wt% CO2 is required to fragment magma at these depths, which is ~10x greater than the highest values measured to-date in a MOR basalt. These observations raise important questions regarding the accumulation and discharge of magmatic volatiles at ultra-slow spreading rates on the Gakkel Ridge (6- 14 mm yr-1, full-rate), and demonstrate that large-scale pyroclastic activity is possible along even the deepest portions of the global MOR volcanic system.This research was funded by the National Aeronautics and Space Administration, the National Science Foundation, and the Woods Hole Oceanographic Institution

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Intrusive Traumatic Re-Experiencing Domain (ITRED) – Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background Intrusive Traumatic Re-Experiencing Domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data was collected from nine sites taking part in the ENIGMA-PTSD Consortium (n=584) and included itemized PTSD symptoms scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and Trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. Random forest classification model was built on a training set using cross-validation (CV), and the averaged CV model performance for classification was evaluated using area-under-the-curve (AUC). The model was tested using a fully independent portion of the data (test dataset), and the test AUC was evaluated. Results RsFC signatures differentiated TE-only participants from PTSD and from ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and from ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontal-parietal network, differentiated TE-only participants from one group (PTSD or ITRED-only), but to a lesser extent from the other. Conclusion Neural network connectivity supports ITRED as a novel neurobiologically-based approach to classifying post-trauma psychopathology

HSF2BP Interacts with a Conserved Domain of BRCA2 and Is Required for Mouse Spermatogenesis

The tumor suppressor BRCA2 is essential for homologous recombination (HR), replication fork stability, and DNA interstrand crosslink repair in vertebrates. We identify HSF2BP, a protein previously described as testis specific and not characterized functionally, as an interactor of BRCA2 in mouse embryonic stem cells, where the 2 proteins form a constitutive complex. HSF2BP is transcribed in all cultured human cancer cell lines tested and elevated in some tumor samples. Inactivation of the mouse Hsf2bp gene results in male infertility due to a severe HR defect during spermatogenesis. The BRCA2-HSF2BP interaction is highly evolutionarily conserved and maps to armadillo repeats in HSF2BP and a 68-amino acid region between the BRC repeats and the DNA binding domain of human BRCA2 (Gly2270-Thr2337) encoded by exons 12 and 13. This region of BRCA2 does not harbor known cancer-associated missense mutations and may be involved in the reproductive rather than the tumor-suppressing function of BRCA2. BRCA2 is a key homologous recombination mediator in vertebrates. Brandsma et al. show that it directly interacts with a testis-expressed protein, HSF2BP, and that male mice deficient for HSF2BP are infertile due to a meiotic recombination defect. They also find that HSF2BP contributes to DNA repair in mouse embryonic stem cells

Pathogenetics of alveolar capillary dysplasia with misalignment of pulmonary veins.

Alveolar capillary dysplasia with misalignment of pulmonary veins (ACDMPV) is a lethal lung developmental disorder caused by heterozygous point mutations or genomic deletion copy-number variants (CNVs) of FOXF1 or its upstream enhancer involving fetal lung-expressed long noncoding RNA genes LINC01081 and LINC01082. Using custom-designed array comparative genomic hybridization, Sanger sequencing, whole exome sequencing (WES), and bioinformatic analyses, we studied 22 new unrelated families (20 postnatal and two prenatal) with clinically diagnosed ACDMPV. We describe novel deletion CNVs at the FOXF1 locus in 13 unrelated ACDMPV patients. Together with the previously reported cases, all 31 genomic deletions in 16q24.1, pathogenic for ACDMPV, for which parental origin was determined, arose de novo with 30 of them occurring on the maternally inherited chromosome 16, strongly implicating genomic imprinting of the FOXF1 locus in human lungs. Surprisingly, we have also identified four ACDMPV families with the pathogenic variants in the FOXF1 locus that arose on paternal chromosome 16. Interestingly, a combination of the severe cardiac defects, including hypoplastic left heart, and single umbilical artery were observed only in children with deletion CNVs involving FOXF1 and its upstream enhancer. Our data demonstrate that genomic imprinting at 16q24.1 plays an important role in variable ACDMPV manifestation likely through long-range regulation of FOXF1 expression, and may be also responsible for key phenotypic features of maternal uniparental disomy 16. Moreover, in one family, WES revealed a de novo missense variant in ESRP1, potentially implicating FGF signaling in the etiology of ACDMPV

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations