Grey zone simulations of the morning convective boundary layer development

Numerical simulations of two cases of morning boundary layer development are conducted to investigate the impact of grid resolution on mean profiles and turbulent kinetic energy (TKE) partitioning from the large eddy simulation (LES) to the mesoscale limit. Idealized LES, using the 3-D Smagorinsky scheme, is shown to be capable of reproducing the boundary layer evolution when compared against measurements. However, increasing grid spacing results in the damping of resolved TKE and the production of superadiabatic temperature profiles in the boundary layer. Turbulence initiation is significantly delayed, exhibiting an abrupt onset at intermediate resolutions. Two approaches, the bounding of vertical diffusion coefficient and the blending of the 3-D Smagorinsky with a nonlocal 1D scheme, are used to model subgrid diffusion at grey zone resolutions. Simulations are compared against the coarse-grained fields from the validated LES results for each case. Both methods exhibit particular strengths and weaknesses, indicating the compromise that needs to be made currently in high-resolution numerical weather prediction. The blending scheme is able to reproduce the adiabatic profiles although turbulence is underestimated in favor of the parametrized heat flux, and the spin-up of TKE remains delayed. In contrast, the bounding approach gives an evolution of TKE that follows the coarse-grained LES very well, relying on the resolved motions for the nonlocal heat flux. However, bounding gives unrealistic static instability in the early morning temperature profiles (similar to the 3-D Smagorinsky scheme) because model dynamics are unable to resolve TKE when the boundary layer is too shallow compared to the grid spacing.This work has been funded by the Natural Environment Research Council (NERC) GREYBLS (Modelling Grey Zone Boundary Layers) project (grant NE/K011456/1). We acknowledge the use of the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the Met Office and the Natural Environment Research Council

Oilseed rape (Brassica napus) as a resource for farmland insect pollinators: quantifying floral traits in conventional varieties and breeding systems

This is the final version of the article. Available from the publisher via the DOI in this record.Oilseed rape (OSR; Brassica napus L.) is a major crop in temperate regions and provides an important source ofnutrition to many of the yield-enhancing insect flower visitors that consume floral nectar. The manipulation ofmechanisms that control various crop plant traits for the benefit of pollinators has been suggested in the bid toincrease food security, but little is known about inherent floral trait expression in contemporary OSR varieties orthe breeding systems used in OSR breeding programmes. We studied a range of floral traits in glasshouse-grown, certified conventional varieties of winter OSR to test for variation among and within breeding systems.We measured 24-h nectar secretion rate, amount, concentration and ratio of nectar sugars per flower, and sizesand number of flowers produced per plant from 24 varieties of OSR representing open-pollinated (OP), genicmale sterility (GMS) hybrid and cytoplasmic male sterility (CMS) hybrid breeding systems. Sugar concentrationwas consistent among and within the breeding systems; however, GMS hybrids produced more nectar and moresugar per flower than CMS hybrid or OP varieties. With the exception of ratio of fructose/glucose in OP vari-eties, we found that nectar traits were consistent within all the breeding systems. When scaled, GMS hybridsproduced 1.73 times more nectar resource per plant than OP varieties. Nectar production and amount of nectarsugar in OSR plants were independent of number and size of flowers. Our data show that floral traits of glass-house-grown OSR differed among breeding systems, suggesting that manipulation and enhancement of nectarrewards for insect flower visitors, including pollinators, could be included in future OSR breeding programmes.This work was fundedby the BBSRC, including support from an Insect Pollinators Ini-tiative grant awarded to GAW (BB/I000968/1) that was jointlyfunded by the BBSRC, NERC, the Wellcome Trust, Defra, andthe Scottish Government. Support was also received from HighWycombe Beekeepers’ Association. Rothamsted Researchreceives strategic funding from the Biotechnology and BiologicalSciences Research Council (BBSRC) of the UK

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spherically symmetric distribution within the ambient space (for simplicity we may assume that all distributions are uniform on their corresponding unit spheres). We also assume mixture weights for the different components. We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces. We study the recovery of the most significant subspace by minimizing the lp-averaged distances of data points from d-dimensional subspaces, where p>0. Unlike other lp minimization problems, this minimization is non-convex for all p>0 and thus requires different methods for its analysis. We show that if 0<p<=1, then for any fraction of outliers the most significant subspace can be recovered by lp minimization with overwhelming probability (which depends on the generating distribution and its parameters). We show that when adding small noise around the underlying subspaces the most significant subspace can be nearly recovered by lp minimization for any 0<p<=1 with an error proportional to the noise level. On the other hand, if p>1 and there is more than one underlying subspace, then with overwhelming probability the most significant subspace cannot be recovered or nearly recovered. This last result does not require spherically symmetric outliers.Comment: This is a revised version of the part of 1002.1994 that deals with single subspace recovery. V3: Improved estimates (in particular for Lemma 3.1 and for estimates relying on it), asymptotic dependence of probabilities and constants on D and d and further clarifications; for simplicity it assumes uniform distributions on spheres. V4: minor revision for the published versio

Estrogen receptor-β: why may it influence clinical outcome in estrogen receptor-α positive breast cancer?

In the previous issue of the journal, Lin and coworkers present data demonstrate that increased expression of estrogen receptor (ER)-β in ER-α-positive breast cancer cells antagonizes a defined group of ER-α/estrogen stimulated genes that are involved in cell cycle regulation and DNA replication. Similar expression patterns for these genes were found human ER-α positive breast tumors expressing higher levels or ER-β, and this correlated with better clinical outcome. The implications for these data, which suggest that ER-β is a positive actor and diagnostic marker for therapeutic outcome, are discussed

Lambda and Antilambda polarization from deep inelastic muon scattering

We report results of the first measurements of Lambda and Antilambda polarization produced in deep inelastic polarized muon scattering on the nucleon. The results are consistent with an expected trend towards positive polarization with increasing x_F. The polarizations of Lambda and Antilambda appear to have opposite signs. A large negative polarization for Lambda at low positive x_F is observed and is not explained by existing models.A possible interpretation is presented.Comment: 9 pages, 2 figure

Impacts of ground-level ozone on sugarcane production

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Sugarcane is a vital commodity crop often grown in (sub)tropical regions which have been experiencing a recent deterioration in air quality. Unlike for other commodity crops, the risk of air pollution, specifically ozone (O3), to this C4 crop has not yet been quantified. Yet, recent work has highlighted both the potential risks of O3 to C4 bioenergy crops, and the emergence of O3 exposure across the tropics as a vital factor determining global food security. Given the large extent, and planned expansion of sugarcane production in places like Brazil to meet global demand for biofuels, there is a pressing need to characterize the risk of O3 to the industry. In this study, we sought to a) derive sugarcane O3 dose-response functions across a range of realistic O3 exposure and b) model the implications of this across a globally important production area. We found a significant impact of O3 on biomass allocation (especially to leaves) and production across a range of sugarcane genotypes, including two commercially relevant varieties (e.g. CTC4, Q240). Using these data, we calculated dose-response functions for sugarcane and combined them with hourly O3 exposure across south-central Brazil derived from the UK Earth System Model (UKESM1) to simulate the current regional impact of O3 on sugarcane production using a dynamic global vegetation model (JULES vn 5.6). We found that between 5.6 % and 18.3 % of total crop productivity is likely lost across the region due to the direct impacts of current O3 exposure. However, impacts depended critically on the substantial differences in O3 susceptibility observed among sugarcane genotypes and how these were implemented in the model. Our work highlights not only the urgent need to fully elucidate the impacts of O3 in this important bioenergetic crop, but the potential implications air quality may have upon tropical food production more generally.Natural Environment Research Council (NERC)FAPESPCNRMet Office Hadley Centre Climate ProgrammeMet Offic

The impacts of climate change across the globe: a multi-sectoral assessment

The overall global-scale consequences of climate change are dependent on the distribution of impacts across regions, and there are multiple dimensions to these impacts.This paper presents a global assessment of the potential impacts of climate change across several sectors, using a harmonised set of impacts models forced by the same climate and socio-economic scenarios. Indicators of impact cover the water resources, river and coastal flooding, agriculture, natural environment and built environment sectors. Impacts are assessed under four SRES socio-economic and emissions scenarios, and the effects of uncertainty in the projected pattern of climate change are incorporated by constructing climate scenarios from 21 global climate models. There is considerable uncertainty in projected regional impacts across the climate model scenarios, and coherent assessments of impacts across sectors and regions therefore must be based on each model pattern separately; using ensemble means, for example, reduces variability between sectors and indicators. An example narrative assessment is presented in the paper. Under this narrative approximately 1 billion people would be exposed to increased water resources stress, around 450 million people exposed to increased river flooding, and 1.3 million extra people would be flooded in coastal floods each year. Crop productivity would fall in most regions, and residential energy demands would be reduced in most regions because reduced heating demands would offset higher cooling demands. Most of the global impacts on water stress and flooding would be in Asia, but the proportional impacts in the Middle East North Africa region would be larger. By 2050 there are emerging differences in impact between different emissions and socio-economic scenarios even though the changes in temperature and sea level are similar, and these differences are greater in 2080. However, for all the indicators, the range in projected impacts between different climate models is considerably greater than the range between emissions and socio-economic scenarios

Decline in Health-Related Quality of Life reported by more than half of those waiting for joint replacement surgery: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>In many healthcare systems, people with severe joint disease wait months to years for joint replacement surgery. There are little empirical data on the health consequences of this delay and it is unclear whether people with substantial morbidity at entry to the waiting list continue to deteriorate further while awaiting surgery. This study investigated changes in Health-Related Quality of Life (HRQoL), health status and psychological distress among people waiting for total hip (THR) and knee replacement (TKR) surgery at a major metropolitan Australian public hospital.</p> <p>Methods</p> <p>134 patients completed questionnaires including the Assessment of Quality of Life (AQoL) instrument, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Kessler Psychological Distress Scale after entering an orthopaedic waiting list (baseline) and before surgery (preadmission). To quantify potential decline in wellbeing, we calculated the proportion of people experiencing clinically important deterioration using published guidelines and compared HRQoL and psychological distress outcomes with population norms.</p> <p>Results</p> <p>Most participants (69%) waited ≥6 months for surgery (median 286 days, IQR 169-375 days). Despite poor physical and psychological wellbeing at baseline, there was an overall deterioration in HRQoL during the waiting period (mean AQoL change -0.04, 95%CI -0.08 to -0.01), with 53% of participants experiencing decline in HRQoL (≥0.04 AQoL units). HRQoL prior to surgery remained substantially lower than Australian population norms (mean sample AQoL 0.37, 95%CI 0.33 to 0.42 vs mean population AQoL 0.83, 95%CI 0.82 to 0.84). Twenty-five per cent of participants showed decline in health status (≥9.6 WOMAC units) over the waiting period and prevalence of high psychological distress remained high at preadmission (RR 3.5, 95%CI 2.8 to 4.5). Most participants considered their pain (84%), fatigue (76%), quality of life (73%) and confidence in managing their health (55%) had worsened while waiting for surgery.</p> <p>Conclusions</p> <p>Despite substantial initial morbidity, over half of the participants awaiting joint replacement experienced deterioration in HRQoL during the waiting period. These data provide much-needed evidence to guide health professionals and policymakers in the design of care pathways and resource allocation for people who require joint replacement surgery.</p

What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise