Measurement of the Proton's Neutral Weak Magnetic Form Factor

We report the first measurement of the parity-violating asymmetry in elastic electron scattering from the proton. The asymmetry depends on the neutral weak magnetic form factor of the proton which contains new information on the contribution of strange quark-antiquark pairs to the magnetic moment of the proton. We obtain the value $G_M^Z= 0.34 \pm 0.09 \pm 0.04 \pm 0.05$ n.m. at $Q^2=0.1$ (GeV/c)${}^2$.Comment: 4 pages TEX, text available at http://www.krl.caltech.edu/preprints/OAP.htm

Chemical equilibration of strangeness

Thermal models are very useful in the understanding of particle production in general and especially in the case of strangeness. We summarize the assumptions which go into a thermal model calculation and which differ in the application of various groups. We compare the different results to each other. Using our own calculation we discuss the validity of the thermal model and the amount of strangeness equilibration at CERN-SPS energies. Finally the implications of the thermal analysis on the reaction dynamics are discussed.Comment: 23 pages, LaTeX (figures included); Talk given at the Int. Symposium on Strangeness in Quark Matter 1997, Santorini (Greece), April 199

Modelling biological N fixation and grass-legume dynamics with process-based biogeochemical models of varying complexity

This work was conducted by the Models4Pastures consortium project under the auspices of FACCE-JPI. Funding was provided by: the New Zealand Government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases; AgResearch’s Strategic Science Investment Fund as a contribution to the Forages for Reduced Nitrate Leaching (FRNL) research programme; the input of UK partners was funded by DEFRA and also contributes to the RCUK-funded projects: N-Circle (BB/N013484/1), UGRASS (NE/M016900/1) and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE-JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc.Mobility fellowship (SNSF funded project: P1EZP2_172121). Lorenzo Brilli, Camilla Dibari and Marco Bindi acknowledge funding received from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF).Peer reviewedPublisher PD

Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe

The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.OA-hybri

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker

Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models

Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.Peer reviewe

Interventions for preventing or controlling health care–associated infection among health care workers or patients within primary care facilities: A scoping review

Background This review aimed to synthesize the evidence on infection prevention and control interventions for the prevention of health care–associated infection among health care workers or patients within primary care facilities. Methods PubMed, CINAHL, EMBASE, and CENTRAL databases were searched for quantitative studies published between 2011 and 2022. Study selection, data extraction, and quality assessment using Cochrane and Joanna Briggs tools, were conducted by independent review with additional sensitivity checking performed on study selection. Results Four studies were included. A randomized trial and a cross-sectional survey, respectively, found no statistical difference in laboratory-confirmed influenza in health care workers wearing N95 versus medical masks (P = .18) and a significant inverse association between the implementation of tuberculosis control measures and tuberculosis incidence (P = .02). For the prevention of surgical site infections following minor surgery, randomized trials found nonsterile gloves (8.7%; 95% confidence interval, 4.9%-12.6%) to be noninferior to sterile gloves (9.3%; 95% confidence interval, 7.4%-11.1%) and no significant difference between prophylactic antibiotics compared to placebo (P = .064). All studies had a high risk of bias. Conclusions Evidence for infection prevention and control interventions for the prevention of health care–associated infection in primary care is very limited and insufficient to make practice recommendations. Nevertheless, the findings highlight the need for future research