A dominant magnetic dipole for the evolved Ap star candidate EK Eridani

EK Eri is one of the most slowly rotating active giants known, and has been proposed to be the descendant of a strongly magnetic Ap star. We have performed a spectropolarimetric study of EK Eri over 4 photometric periods with the aim of inferring the topology of its magnetic field. We used the NARVAL spectropolarimeter at the Bernard Lyot telescope at the Pic du Midi Observatory, along with the least-squares deconvolution method, to extract high signal-to-noise ratio Stokes V profiles from a timeseries of 28 polarisation spectra. We have derived the surface-averaged longitudinal magnetic field Bl. We fit the Stokes V profiles with a model of the large-scale magnetic field and obtained Zeeman Doppler images of the surface magnetic strength and geometry. Bl variations of up to about 80 G are observed without any reversal of its sign, and which are in phase with photometric ephemeris. The activity indicators are shown to vary smoothly on a timescale compatible with the rotational period inferred from photometry (308.8 d.), however large deviations can occur from one rotation to another. The surface magnetic field variations of EK Eri appear to be dominated by a strong magnetic spot (of negative polarity) which is phased with the dark (cool) photometric spot. Our modeling shows that the large-scale magnetic field of EK Eri is strongly poloidal. For a rotational axis inclination of i = 60{\deg}, we obtain a model that is almost purely dipolar. In the dipolar model, the strong magnetic/photometric spot corresponds to the negative pole of the dipole, which could be the remnant of that of an Ap star progenitor of EK Eri. Our observations and modeling conceptually support this hypothesis, suggesting an explanation of the outstanding magnetic properties of EK Eri as the result of interaction between deep convection and the remnant of an Ap star magnetic dipole.Comment: 8 pages, 6 figures, accepted for publication in Astronomy & Astrophysic

The early-type galaxies NGC 1407 and NGC 1400 - I: spatially resolved radial kinematics and surface photometry

This is the first paper of a series focused on investigating the star formation and evolutionary history of the two early-type galaxies NGC 1407 and NGC 1400. They are the two brightest galaxies of the NGC 1407 (or Eridanus-A) group, one of the 60 groups studied as part of the Group Evolution Multi-wavelength Study (GEMS). Here we present new high signal-to-noise long-slit spectroscopic data obtained at the ESO 3.6m telescope and high-resolution multi-band imaging data from the HST/ACS and wide-field imaging from Subaru Suprime-Cam. We spatially resolved integrated spectra out to 0.6 (NGC 1407) and 1.3 (NGC 1400) effective radii. The radial profiles of the kinematic parameters v(rot), sigma, h3 and h4 are measured. The surface brightness profiles are fitted to different galaxy light models and the colour distributions analysed. The multi-band images are modelled to derive isophotal shape parameters and residual galaxy images. The parameters from the surface brightness profile fitting are used to estimate the mass of the possible central supermassive black hole in NGC 1407. The galaxies are found to be rotationally supported and to have a flat core in the surface brightness profiles. Elliptical isophotes are observed at all radii and no fine structures are detected in the residual galaxy images. From our results we can also discard a possible interaction between NGC 1400, NGC 1407 and the group intergalactic medium. We estimate a mass of 1.03x10^9 M(sun) for the supermassive black hole in NGC 1407 galaxy.Comment: 11 pages, 6 tables, 6 figures, Accepted for publication in MNRA

A database of chlorophyll a in Australian waters

© The Author(s) 2018. Chlorophyll a is the most commonly used indicator of phytoplankton biomass in the marine environment. It is relatively simple and cost effective to measure when compared to phytoplankton abundance and is thus routinely included in many surveys. Here we collate 173, 333 records of chlorophyll a collected since 1965 from Australian waters gathered from researchers on regular coastal monitoring surveys and ocean voyages into a single repository. This dataset includes the chlorophyll a values as measured from samples analysed using spectrophotometry, fluorometry and high performance liquid chromatography (HPLC). The Australian Chlorophyll a database is freely available through the Australian Ocean Data Network portal (https://portal.aodn.org.au/). These data can be used in isolation as an index of phytoplankton biomass or in combination with other data to provide insight into water quality, ecosystem state, and relationships with other trophic levels such as zooplankton or fish

A database of marine phytoplankton abundance, biomass and species composition in Australian waters

There have been many individual phytoplankton datasets collected across Australia since the mid 1900s, but most are unavailable to the research community. We have searched archives, contacted researchers, and scanned the primary and grey literature to collate 3,621,847 records of marine phytoplankton species from Australian waters from 1844 to the present. Many of these are small datasets collected for local questions, but combined they provide over 170 years of data on phytoplankton communities in Australian waters. Units and taxonomy have been standardised, obviously erroneous data removed, and all metadata included. We have lodged this dataset with the Australian Ocean Data Network (http://portal.aodn.org.au/) allowing public access. The Australian Phytoplankton Database will be invaluable for global change studies, as it allows analysis of ecological indicators of climate change and eutrophication (e.g., changes in distribution; diatom:dinoflagellate ratios). In addition, the standardised conversion of abundance records to biomass provides modellers with quantifiable data to initialise and validate ecosystem models of lower marine trophic levels

Classification schemes for knowledge translation interventions: a practical resource for researchers

Abstract Background As implementation science advances, the number of interventions to promote the translation of evidence into healthcare, health systems, or health policy is growing. Accordingly, classification schemes for these knowledge translation (KT) interventions have emerged. A recent scoping review identified 51 classification schemes of KT interventions to integrate evidence into healthcare practice; however, the review did not evaluate the quality of the classification schemes or provide detailed information to assist researchers in selecting a scheme for their context and purpose. This study aimed to further examine and assess the quality of these classification schemes of KT interventions, and provide information to aid researchers when selecting a classification scheme. Methods We abstracted the following information from each of the original 51 classification scheme articles: authors’ objectives; purpose of the scheme and field of application; socioecologic level (individual, organizational, community, system); adaptability (broad versus specific); target group (patients, providers, policy-makers), intent (policy, education, practice), and purpose (dissemination versus implementation). Two reviewers independently evaluated the methodological quality of the development of each classification scheme using an adapted version of the AGREE II tool. Based on these assessments, two independent reviewers reached consensus about whether to recommend each scheme for researcher use, or not. Results Of the 51 original classification schemes, we excluded seven that were not specific classification schemes, not accessible or duplicates. Of the remaining 44 classification schemes, nine were not recommended. Of the 35 recommended classification schemes, ten focused on behaviour change and six focused on population health. Many schemes (n = 29) addressed practice considerations. Fewer schemes addressed educational or policy objectives. Twenty-five classification schemes had broad applicability, six were specific, and four had elements of both. Twenty-three schemes targeted health providers, nine targeted both patients and providers and one targeted policy-makers. Most classification schemes were intended for implementation rather than dissemination. Conclusions Thirty-five classification schemes of KT interventions were developed and reported with sufficient rigour to be recommended for use by researchers interested in KT in healthcare. Our additional categorization and quality analysis will aid in selecting suitable classification schemes for research initiatives in the field of implementation science

Sex, Gender and Work Segregation in the Cultural Industries

This chapter addresses work ‘segregation’ by sex in the cultural industries. We outline some of the main forms this takes, according to our observations: the high presence of women in marketing and public relations roles; the high numbers of women in production co-ordination and similar roles; the domination of men of more prestigious creative roles; and the domination by men of technical jobs. We then turn to explanation: what gender dynamics drive such patterns of work segregation according to sex? Drawing on interviews, we claim that the following stereotypes or prevailing discourses, concerning the distinctive attributes of women and men, may influence such segregation: that women are more caring, supportive and nurturing; that women are better communicators; that women are ‘better organized’; and that men are more creative because they are less bound by rules

Implementation salvage experiences from the Melbourne diabetes prevention study

Background Many public health interventions based on apparently sound evidence from randomised controlled trials encounter difficulties when being scaled up within health systems. Even under the best of circumstances, implementation is exceedingly difficult. In this paper we will describe the implementation salvage experiences from the Melbourne Diabetes Prevention Study, which is a randomised controlled trial of the effectiveness and cost-effectiveness nested in the state-wide Life! Taking Action on Diabetes program in Victoria, Australia.Discussion The Melbourne Diabetes Prevention Study sits within an evolving larger scale implementation project, the Life! program. Changes that occurred during the roll-out of that program had a direct impact on the process of conducting this trial. The issues and methods of recovery the study team encountered were conceptualised using an implementation salvage strategies framework. The specific issues the study team came across included continuity of the state funding for Life! program and structural changes to the Life! program which consisted of adjustments to eligibility criteria, referral processes, structure and content, as well as alternative program delivery for different population groups. Staff turnover, recruitment problems, setting and venue concerns, availability of potential participants and participant characteristics were also identified as evaluation roadblocks. Each issue and corresponding salvage strategy is presented.Summary The experiences of conducting such a novel trial as the preliminary Melbourne Diabetes Prevention Study have been invaluable. The lessons learnt and knowledge gained will inform the future execution of this trial in the coming years. We anticipate that these results will also be beneficial to other researchers conducting similar trials in the public health field. We recommend that researchers openly share their experiences, barriers and challenges when conducting randomised controlled trials and implementation research. We encourage them to describe the factors that may have inhibited or enhanced the desired outcomes so that the academic community can learn and expand the research foundation of implementation salvage.<br /

Finding a Needle in the Virus Metagenome Haystack - Micro-Metagenome Analysis Captures a Snapshot of the Diversity of a Bacteriophage Armoire

Viruses are ubiquitous in the oceans and critical components of marine microbial communities, regulating nutrient transfer to higher trophic levels or to the dissolved organic pool through lysis of host cells. Hydrothermal vent systems are oases of biological activity in the deep oceans, for which knowledge of biodiversity and its impact on global ocean biogeochemical cycling is still in its infancy. In order to gain biological insight into viral communities present in hydrothermal vent systems, we developed a method based on deep-sequencing of pulsed field gel electrophoretic bands representing key viral fractions present in seawater within and surrounding a hydrothermal plume derived from Loki's Castle vent field at the Arctic Mid-Ocean Ridge. The reduction in virus community complexity afforded by this novel approach enabled the near-complete reconstruction of a lambda-like phage genome from the virus fraction of the plume. Phylogenetic examination of distinct gene regions in this lambdoid phage genome unveiled diversity at loci encoding superinfection exclusion- and integrase-like proteins. This suggests the importance of fine-tuning lyosgenic conversion as a viral survival strategy, and provides insights into the nature of host-virus and virus-virus interactions, within hydrothermal plumes. By reducing the complexity of the viral community through targeted sequencing of prominent dsDNA viral fractions, this method has selectively mimicked virus dominance approaching that hitherto achieved only through culturing, thus enabling bioinformatic analysis to locate a lambdoid viral “needle" within the greater viral community “haystack". Such targeted analyses have great potential for accelerating the extraction of biological knowledge from diverse and poorly understood environmental viral communities

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers