Modification of classical electron transport due to collisions between electrons and fast ions

A Fokker-Planck model for the interaction of fast ions with the thermal electrons in a quasi-neutral plasma is developed. When the fast ion population has a net flux (i.e. the distribution of the fast ions is anisotropic in velocity space) the electron distribution function is significantly perturbed from Maxwellian by collisions with the fast ions, even if the fast ion density is orders of magnitude smaller than the electron density. The Fokker-Planck model is used to derive classical electron transport equations (a generalized Ohm's law and a heat flow equation) that include the effects of the electron-fast ion collisions. It is found that these collisions result in a current term in the transport equations which can be significant even when total current is zero. The new transport equations are analyzed in the context of a number of scenarios including $\alpha$ particle heating in ICF and MIF plasmas and ion beam heating of dense plasmas

The legacy of cover crops on the soil habitat and ecosystem services in a heavy clay, minimum tillage rotation

Abstract Cover crops are grown as potential ways to improve soil fertility, soil structure, and biodiversity, while reducing weed/pest burdens. Yet, increased costs (in both time and fuel), farmer knowledge requirements, and yield uncertainty (green bridge effect and variable crop establishment) have led to hesitation among farmers. This study was conducted at the field scale (covering an area of nearly 20 hectares) to determine whether different cover crop mixtures affected soil properties and ecosystem services on a heavy clay soil. Measurements of soil chemistry, physics, biology, weed abundance, and subsequent crop performance were taken within a minimum tillage management system, across three cover crop mixtures (commonly sold to UK farmers). The cover crop mixtures included oats (Avena sativa), radish (Raphanus sativus), phacelia (Phacelia tanacetifolia), vetch (Vicia sativa), legumes, buckwheat (Fagopyrum esculentum) and a bare stubble control followed by a spring oat crop. Soil physics (penetrometer and bulk density) and chemistry (N, P, K, Mg, Ca, and organic matter) varied little across treatments, although there was significantly lower Mg in the cover crop including legumes and an increase in NO3 within this treatment. Soil biology and botanical composition were also assessed, monitoring earthworm and mesofauna abundance; and sown and unsown (weed) biomass. Epigeic earthworms were found to have significantly larger abundance in cover crop mixtures with radish present, although other meso- and macrofauna did not differ. Significant weed suppression was found during both the cover crop growing period and as a legacy in the subsequent crop, leading to significant yield increases and economic benefits in some treatments. Our study confirms that cover crops are providing benefits, even on heavy clay soils, including improvements in nutrient leaching risk reduction, weed suppression, and crop yield, coupled with wider ecosystem benefits. We therefore consider cover crops to have a role in sustainable management of arable rotations

CASNET2: Evaluation of an Electronic Safety Netting cancer toolkit for the primary care electronic health record: protocol for a pragmatic stepped-wedge RCT

Introduction: Safety-netting in primary care is the best practice in cancer diagnosis, ensuring that patients are followed up until symptoms are explained or have resolved. Currently, clinicians use haphazard manual solutions. The ubiquitous use of electronic health records provides an opportunity to standardise safety-netting practices. A new electronic safety-netting toolkit has been introduced to provide systematic ways to track and follow up patients. We will evaluate the effectiveness of this toolkit, which is embedded in a major primary care clinical system in England:Egerton Medical Information System(EMIS)-Web. Methods and analysis: We will conduct a stepped-wedge cluster RCT in 60 general practices within the RCGP Research and Surveillance Centre (RSC) network. Groups of 10 practices will be randomised into the active phase at 2-monthly intervals over 12 months. All practices will be activated for at least 2 months. The primary outcome is the primary care interval measured as days between the first recorded symptom of cancer (within the year prior to diagnosis) and the subsequent referral to secondary care. Other outcomes include referrals rates and rates of direct access cancer investigation. Analysis of the clustered stepped-wedge design will model associations using a fixed effect for intervention condition of the cluster at each time step, a fixed effect for time and other covariates, and then include a random effect for practice and for patient to account for correlation between observations from the same centre and from the same participant. Ethics and dissemination: Ethical approval has been obtained from the North West—Greater Manchester West National Health Service Research Ethics Committee (REC Reference 19/NW/0692). Results will be disseminated in peer-reviewed journals and conferences, and sent to participating practices. They will be published on the University of Oxford Nuffield Department of Primary Care and RCGP RSC websites

The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO₂ and CH₄ retrieval algorithm products with measurements from the TCCON

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO₂, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH₄, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH₄ precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively

11 beta-hydroxysteroid dehydrogenase type 1 regulates glucocorticoid-induced insulin resistance in skeletal muscle

OBJECTIVE: Glucocorticoid excess is characterized by increased adiposity, skeletal myopathy, and insulin resistance, but the precise molecular mechanisms are unknown. Within skeletal muscle, 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1) converts cortisone (11-dehydrocorticosterone in rodents) to active cortisol (corticosterone in rodents). We aimed to determine the mechanisms underpinning glucocorticoid-induced insulin resistance in skeletal muscle and indentify how 11beta-HSD1 inhibitors improve insulin sensitivity. \ud RESEARCH DESIGN AND METHODS: Rodent and human cell cultures, whole-tissue explants, and animal models were used to determine the impact of glucocorticoids and selective 11beta-HSD1 inhibition upon insulin signaling and action. \ud RESULTS: Dexamethasone decreased insulin-stimulated glucose uptake, decreased IRS1 mRNA and protein expression, and increased inactivating pSer$^{307}$ insulin receptor substrate (IRS)-1. 11beta-HSD1 activity and expression were observed in human and rodent myotubes and muscle explants. Activity was predominantly oxo-reductase, generating active glucocorticoid. A1 (selective 11beta-HSD1 inhibitor) abolished enzyme activity and blocked the increase in pSer$^{307}$ IRS1 and reduction in total IRS1 protein after treatment with 11DHC but not corticosterone. In C57Bl6/J mice, the selective 11beta-HSD1 inhibitor, A2, decreased fasting blood glucose levels and improved insulin sensitivity. In KK mice treated with A2, skeletal muscle pSer$^{307}$ IRS1 decreased and pThr$^{308}$ Akt/PKB increased. In addition, A2 decreased both lipogenic and lipolytic gene expression.\ud CONCLUSIONS: Prereceptor facilitation of glucocorticoid action via 11beta-HSD1 increases pSer$^{307}$ IRS1 and may be crucial in mediating insulin resistance in skeletal muscle. Selective 11beta-HSD1 inhibition decreases pSer$^{307}$ IRS1, increases pThr$^{308}$ Akt/PKB, and decreases lipogenic and lipolytic gene expression that may represent an important mechanism underpinning their insulin-sensitizing action

Boron-rich, cytocompatible block copolymer nanoparticles by polymerization-induced self-assembly

Core-shell nanoparticles (NPs) with a boron-rich core were synthesized by RAFT-mediated polymerization-induced self-assembly using a new methacrylic boronate ester monomer. Under specific conditions, sub-100 nm spherical NPs could be obtained at high conversions by either emulsion or dispersion RAFT polymerization using poly(oligo(ethylene glycol) methacrylate) (POEGMA) dithiobenozate-based chain transfer agents. Phenylboronic acid surface-functionalized NPs were obtained using a telechelic POEGMA. Primary data on biocompatibility is provided and suggests suitability as boron delivery agent for boron neutron capture therapy

Technical Note: Latitude-time variations of atmospheric column-average dry air mole fractions of CO_2, CH_4 and N_2O

We present a comparison of an atmospheric general circulation model (AGCM)-based chemistry-transport model (ACTM) simulation with total column measurements of CO_2, CH_4 and N_2O from the Total Carbon Column Observing Network (TCCON). The model is able to capture observed trends, seasonal cycles and inter hemispheric gradients at most sampled locations for all three species. The model-observation agreements are best for CO_2, because the simulation uses fossil fuel inventories and an inverse model estimate of non-fossil fuel fluxes. The ACTM captures much of the observed seasonal variability in CO_2 and N_2O total columns (~81 % variance, R>0.9 between ACTM and TCCON for 19 out of 22 cases). These results suggest that the transport processes in troposphere and stratosphere are well represented in ACTM. Thus the poor correlation between simulated and observed CH4 total columns, particularly at tropical and extra-tropical sites, have been attributed to the uncertainties in surface emissions and loss by hydroxyl radicals. While the upward-looking total column measurements of CO_2 contains surface flux signals at various spatial and temporal scales, the N_2O measurements are strongly affected by the concentration variations in the upper troposphere and stratosphere

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Safeguarding people living in vulnerable conditions in the COVID-19 era through universal health coverage and social protection

The COVID-19 pandemic is unprecedented. The pandemic not only induced a public health crisis, but has led to severe economic, social, and educational crises. Across economies and societies, the distributional consequences of the pandemic have been uneven. Among groups living in vulnerable conditions, the pandemic substantially magnified the inequality gaps, with possible negative implications for these individuals' long-term physical, socioeconomic, and mental wellbeing. This Viewpoint proposes priority, programmatic, and policy recommendations that governments, resource partners, and relevant stakeholders should consider in formulating medium-term to long-term strategies for preventing the spread of COVID-19, addressing the virus's impacts, and decreasing health inequalities. The world is at a never more crucial moment, requiring collaboration and cooperation from all sectors to mitigate the inequality gaps and improve people's health and wellbeing with universal health coverage and social protection, in addition to implementation of the health in all policies approach