Cosmological simulations with rare and frequent dark matter self-interactions

Dark matter (DM) with self-interactions is a promising solution for the small-scale problems of the standard cosmological model. Here we perform the first cosmological simulation of frequent DM self-interactions, corresponding to small-angle DM scatterings. The focus of our analysis lies in finding and understanding differences to the traditionally assumed rare DM (large-angle) self scatterings. For this purpose, we compute the distribution of DM densities, the matter power spectrum, the two-point correlation function and the halo and subhalo mass functions. Furthermore, we investigate the density profiles of the DM haloes and their shapes. We find that overall large-angle and small-angle scatterings behave fairly similarly with a few exceptions. In particular, the number of satellites is considerably suppressed for frequent compared to rare self-interactions with the same cross-section. Overall we observe that while differences between the two cases may be difficult to establish using a single measure, the degeneracy may be broken through a combination of multiple ones. For instance, the combination of satellite counts with halo density or shape profiles could allow discriminating between rare and frequent self-interactions. As a by-product of our analysis, we provide - for the first time - upper limits on the cross-section for frequent self-interactions

Climate impact and adaptation to heat and drought stress of regional and global wheat production

Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.info:eu-repo/semantics/publishedVersio

The frequency distribution of presenting symptoms in children aged six months to six years to primary care.

Primary care providers and researchers wishing to estimate study recruitment rates need estimates of illness frequency in primary care. Previous studies of children's symptoms have found that presentations are most common for the symptoms: cough, fever, earache, rash, diarrhoea and vomiting. Since 2000, primary care provision in the United Kingdom has changed with the introduction of Walk-in-Centres (WICs) and new Out of Hours (OoHs) providers. To describe the type and frequency of parent-reported presenting symptoms at a range of primary care sites between 2005 and 2007. Parent-reported presenting symptoms, recorded in their own words, were extracted from data collected from all children aged six months to six years during recruitment to a randomised controlled trial. Presenting symptoms were coded and presented as frequency per 100 'consulting sessions' by type of primary care site. Results were evaluated from 2491 episodes of illness at 35 sites. When grouped by primary care site, respiratory symptoms were the most common at OoHs centres, the WIC and general practitioner (GP) surgeries. Trauma symptoms were common in the Emergency Department, but unexpectedly, diarrhoea and vomiting were more common in the Emergency Department and skin presenting symptoms more common at the WIC than at GP sites. We report the relative frequency of acute symptoms by type of primary care provider. These data may be useful to those planning recruitment to primary care paediatric studies and policy makers for planning primary care service provision

Improving primary care identification of familial breast cancer risk using proactive invitation and decision support

Family history of breast cancer is a key risk factor, accounting for up to 10% of cancers. We evaluated the proactive assessment of familial breast cancer (FBC) risk in primary care. Eligible women (30 to 60 years) were recruited from eight English general practices. Practices were trained on familial breast cancer risk assessment. In four randomly-assigned practices, women were invited to complete a validated, postal family history questionnaire, which practice staff inputted into decision support software to determine cancer risk. Those with increased risk were offered specialist referral. Usual care was observed in the other four practices. In intervention practices, 1127/7012 women (16.1%) returned family history questionnaires, comprising 1105 (98%) self-reported white ethnicity and 446 (39.6%) educated to University undergraduate or equivalent qualification, with 119 (10.6%) identified at increased breast cancer risk and offered referral. Sixty-seven (56%) women recommended referral were less than 50 years old. From 66 women attending specialists, 26 (39.4%) were confirmed to have high risk and recommended annual surveillance (40-60 years) and surgical prevention; while 30 (45.5%) were confirmed at moderate risk, with 19 offered annual surveillance (40–50 years). The remaining 10 (15.2%) managed in primary care. None were recommended chemoprevention. In usual care practices, only ten women consulted with concerns about breast cancer family history. This study demonstrated proactive risk assessment in primary care enables accurate identification of women, including many younger women, at increased risk of breast cancer. To improve generalisability across the population, more active methods of engagement need to be explored

Impactful and measurable progress on climate-smart agriculture in corporate value chains

The World Business Council for Sustainable Development (WBCSD) and its partners have an ambition to reduce greenhouse gas (GHG) emissions from agriculture and land use change by 50% and make 50% more nutritious food available by 2030 (including by reducing food loss and waste), while strengthening the climate resilience of agricultural landscapes and farming communities. Companies must accelerate progress to meet these ambitions, but measurement of progress has been limited by the availability of data, particularly on upstream and downstream GHG emissions in supply chains, climate resilience and food loss and waste. To address these gaps, WBCSD and CCAFS convened a workshop at the University of Vermont, in partnership with the International Center for Tropical Agriculture (CIAT), World Resources Institute (WRI) and PricewaterhouseCoopers (PwC). This info note captures the key lessons which emerged from the workshop

Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions

We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many similarity metrics (e.g. covariance) needs very long period historic data (e.g. 3K days) which cannot represent current market effectively; (c) They cannot capture translation-invariance. To solve these problems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned representation and modularity optimisation to cluster stocks and identify diverse sectors, (ii) picking stocks within each cluster according to their Sharpe ratio (Sharpe 1994). Overall this strategy provides low-risk high-return portfolios. We use the Financial Times Stock Exchange 100 Index (FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100 index and many well known funds in terms of total return in 2000 trading days.Comment: Accepted to International Conference on Acoustics, Speech and Signal Processing (ICASSP) 201

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Molecular dynamics simulation of the effects of swift heavy ion irradiation on multilayer graphene and diamond-like carbon

As a promising material used in accelerators and in space in the future, it is important to study the property and structural changes of graphene and diamond-like carbon on the surface as a protective layer before and after swift heavy ion irradiation, although this layer could have a loose structure due to the intrinsic sp(2) surrounding environment of graphene during its deposition period. In this study, by utilizing inelastic thermal spike model and molecular dynamics, we simulated swift heavy ion irradiation and examined the track radius in the vertical direction, as well as temperature, density, and sp(3) fraction distribution along the radius from the irradiation center at different time after irradiation. The temperature in the irradiation center can reach over 11000 K at the beginning of irradiation while there would be a low density and sp(3) fraction area left in the central region after 100 ps. Ring analysis also demonstrated a more chaotic cylindrical region in the center after irradiation. After comprehensive consideration, diamond-like carbon deposited by 70 eV carbon bombardment provided the best protection.Peer reviewe