Five things I wish I knew when I left school

First paragraph: Dear class of 2016, Finishing school can be a daunting experience but you are young, bright and have your future ahead of you — easy for me to say, you might think.  Access article on The Conversation website at: https://theconversation.com/five-things-i-wish-i-knew-when-i-left-school-6067

Echinochrome A Release by Red Spherule Cells Is an Iron-Withholding Strategy of Sea Urchin Innate Immunity

Cellular immune defences in sea urchins are shared amongst the coelomocytes – a heterogeneous population of cells residing in the coelomic fluid (blood equivalent) and tissues. The most iconic coelomocyte morphotype is the red spherule cell (or amebocyte), so named due to the abundance of cytoplasmic vesicles containing the naphthoquinone pigment, echinochrome A. Despite their identification over a century ago, and evidence of anti-septic properties, little progress has been made in characterising the immune-competence of these cells. Upon exposure of red spherule cells from sea urchins, Paracentrotus lividus and Psammechinus miliaris, to microbial ligands, intact microbes and damage signals, we observed cellular degranulation and increased detection of cell-free echinochrome in the coelomic fluid ex vivo. Treatment of the cells with ionomycin, a calcium-specific ionophore, confirmed that an increase in intracellular levels of Ca2+ ¬is a trigger of echinochrome release. Incubating Gram-positive/negative bacteria as well as yeast with lysates of red spherule cells led to significant reductions in colony-forming units. Such antimicrobial properties were counteracted by the addition of ferric iron (Fe3+), suggesting that echinochrome acts as a primitive iron chelator in echinoid biological defences

Multiple AMPK activators inhibit L-Carnitine uptake in C2C12 skeletal muscle myotubes

Mutations in the gene that encodes the principal L-Carnitine transporter, OCTN2, can lead to a reduced intracellular L-Carnitine pool and the disease Primary Carnitine Deficiency. L-Carnitine supplementation is used therapeutically to increase intracellular L-Carnitine. As AMPK and insulin regulate fat metabolism and substrate uptake we hypothesised that AMPK activating compounds and insulin would increase L-Carnitine uptake in C2C12myotubes. The cells express all three OCTN transporters at the mRNA level and immunohistochemistry confirmed expression at the protein level. Contrary to our hypothesis, despite significant activation of PKB and 2DG uptake, insulin did not increase L-Carnitine uptake at 100nM. However, L-Carnitine uptake was modestly increased at a dose of 150nM insulin. A range of AMPK activators that increase intracellular calcium content [caffeine (10mM, 5mM, 1mM, 0.5mM), A23187 (10μM)], inhibit mitochondrial function [Sodium Azide (75μM), Rotenone (1μM), Berberine (100μM), DNP (500μM)] or directly activate AMPK [AICAR (250μM)] were assessed for their ability to regulate L-Carnitine uptake. All compounds tested significantly inhibited L-Carnitine uptake. Inhibition by caffeine was not dantrolene (10μM) sensitive. Saturation curve analysis suggested that caffeine did not competitively inhibit L-Carnitine transport. However, the AMPK inhibitor Compound C (10μM) partially rescued the effect of caffeine suggesting that AMPK may play a role in the inhibitory effects of caffeine. However, caffeine likely inhibits L-Carnitine uptake by alternative mechanisms independently of calcium release. PKA activation or direct interference with transporter function may play a role

MadGraph/MadEvent v4: The New Web Generation

We present the latest developments of the MadGraph/MadEvent Monte Carlo event generator and several applications to hadron collider physics. In the current version events at the parton, hadron and detector level can be generated directly from a web interface, for arbitrary processes in the Standard Model and in several physics scenarios beyond it (HEFT, MSSM, 2HDM). The most important additions are: a new framework for implementing user-defined new physics models; a standalone running mode for creating and testing matrix elements; generation of events corresponding to different processes, such as signal(s) and backgrounds, in the same run; two platforms for data analysis, where events are accessible at the parton, hadron and detector level; and the generation of inclusive multi-jet samples by combining parton-level events with parton showers. To illustrate the new capabilities of the package some applications to hadron collider physics are presented: 1) Higgs search in pp \to H \to W^+W^-: signal and backgrounds. 2) Higgs CP properties: pp \to H jj$in the HEFT. 3) Spin of a new resonance from lepton angular distributions. 4) Single-top and Higgs associated production in a generic 2HDM. 5) Comparison of strong SUSY pair production at the SPS points. 6) Inclusive W+jets matched samples: comparison with the Tevatron data.Comment: 38 pages, 15 figure

Screening for atrial fibrillation – a cross-sectional survey of healthcare professionals in primary care

Introduction: Screening for atrial fibrillation (AF) in primary care has been recommended; however, the views of healthcare professionals (HCPs) are not known. This study aimed to determine the opinions of HCP about the feasibility of implementing screening within a primary care setting. Methods: A cross-sectional mixed methods census survey of 418 HCPs from 59 inner-city practices (Nottingham, UK) was conducted between October-December 2014. Postal and web-surveys ascertained data on existing methods, knowledge, skills, attitudes, barriers and facilitators to AF screening using Likert scale and open-ended questions. Responses, categorized according to HCP group, were summarized using proportions, adjusting for clustering by practice, with 95% C.Is and free-text responses using thematic analysis. Results: At least one General Practitioner (GP) responded from 48 (81%) practices. There were 212/418 (51%) respondents; 118/229 GPs, 67/129 nurses [50 practice nurses; 17 Nurse Practitioners (NPs)], 27/60 healthcare assistants (HCAs). 39/48 (81%) practices had an ECG machine and diagnosed AF in-house. Non-GP HCPs reported having less knowledge about ECG interpretation, diagnosing and treating AF than GPs. A greater proportion of non-GP HCPs reported they would benefit from ECG training specifically for AF diagnosis than GPs [proportion (95% CI) GPs: 11.9% (6.8–20.0); HCAs: 37.0% (21.7–55.5); nurses: 44.0% (30.0–59.0); NPs 41.2% (21.9–63.7)]. Barriers included time, workload and capacity to undertake screening activities, although training to diagnose and manage AF was a required facilitator. Conclusion: Inner-city general practices were found to have adequate access to resources for AF screening. There is enthusiasm by non-GP HCPs to up-skill in the diagnosis and management of AF and they may have a role in future AF screening. However, organisational barriers, such as lack of time, staff and capacity, should be overcome for AF screening to be feasibly implemented within primary care

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist