Magnetic Properties of YBa_2Cu_3O_{7-\delta} in a self-consistent approach: Comparison with Quantum-Monte-Carlo Simulations and Experiments

We analyze single-particle electronic and two-particle magnetic properties of the Hubbard model in the underdoped and optimally-doped regime of \YBCO by means of a modified version of the fluctuation-exchange approximation, which only includes particle-hole fluctuations. Comparison of our results with Quantum-Monte Carlo (QMC) calculations at relatively high temperatures ($T\sim 1000 K$) suggests to introduce a temperature renormalization in order to improve the agreement between the two methods at intermediate and large values of the interaction $U$. We evaluate the temperature dependence of the spin-lattice relaxation time $T_1$ and of the spin-echo decay time $T_{2G}$ and compare it with the results of NMR measurements on an underdoped and an optimally doped \YBCO sample. For $U/t=4.5$ it is possible to consistently adjust the parameters of the Hubbard model in order to have a good {\it semi-quantitative} description of this temperature dependence for temperatures larger than the spin gap as obtained from NMR measurements. We also discuss the case $U/t\sim 8$, which is more appropriate to describe magnetic and single-particle properties close to half-filling. However, for this larger value of $U/t$ the agreement with QMC as well as with experiments at finite doping is less satisfactory.Comment: Final version, to appear in Phys. Rev. B (sched. Feb. 99

Acute cocoa flavanol supplementation improves muscle macro- and microvascular but not anabolic responses to amino acids in older men

The anabolic effects of nutrition on skeletal muscle may depend on adequate skeletal muscle perfusion, which is impaired in older people. Cocoa flavanols have been shown to improve flow-mediated dilation, an established measure of endothelial function. However, their effect on muscle microvascular blood flow is currently unknown. Therefore, the objective of this study was to explore links between the consumption of cocoa flavanols, muscle microvascular blood flow and muscle protein synthesis (MPS) in response to nutrition in older men. To achieve this objective leg blood flow (LBF), muscle microvascular blood volume (MBV) and MPS were measured under postabsorptive and postprandial (I.V glamin, dextrose to sustain glucose ~7.5 mmol·l-1) conditions in 20 older men. Ten of these men were studied with no cocoa flavanol intervention and a further 10 were studied with the addition of 350 mg of cocoa flavanols at the same time as nutrition began. Leg [femoral artery] blood flow was measured by Doppler ultrasound, muscle MBV by contrast-enhanced ultrasound (CEUS) using DefinityTM perflutren contrast agent and MPS using [1, 2-13C2] leucine tracer techniques. Our results show that although older individuals do not show an increase in LBF or MBV in response to feeding, these absent responses are apparent when cocoa flavanols are given acutely with nutrition. However this restoration in vascular responsiveness is not associated with improved MPS responses to nutrition. We conclude that acute cocoa flavanol supplementation improves muscle macro- and microvascular responses to nutrition, independently of modifying muscle protein anabolism

Monitoring Soil Quality to Assess the Sustainability of Harvesting Corn Stover

Harvesting feedstock for biofuel production must not degrade soil, water, or air resources. Our objective is to provide an overview of field research being conducted to quantify effects of harvesting corn (Zea mays L.) stover as a bioenergy feedstock. Coordinated field studies are being conducted near Ames, IA; St. Paul and Morris, MN; Mead, NE; University Park, PA; Florence, SC; and Brookings, SD., as part of the USDA-ARS Renewable Energy Assessment Project (REAP). A baseline soil quality assessment was made using the Soil Management Assessment Framework (SMAF). Corn grain and residue yield for two different stover harvest rates (∼50% and ∼90%) are being measured. Available soil data remains quite limited but sufficient for an initial SMAF analysis that confirms total organic carbon (TOC) is a soil quality indicator that needs to be closely monitored closely to quantify crop residue removal effects. Overall, grain yields averaged 9.7 and 11.7 Mg ha−1 (155 and 186 bu acre−1) in 2008 and 2009, values that are consistent with national averages for both years. The average amount of stover collected for the 50% treatment was 2.6 and 4.2 Mg ha−1 for 2008 and 2009, while the 90% treatment resulted in an average removal of 5.4 and 7.4 Mg ha−1, respectively. Based on a recent literature review, both stover harvest scenarios could result in a gradual decline in TOC. However, the literature value has a large standard error, so continuation of this long-term multi-location study for several years is warranted

Doping liquid argon with xenon in ProtoDUNE Single-Phase: effects on scintillation light

Doping of liquid argon TPCs (LArTPCs) with a small concentration of xenon is a technique for light-shifting and facilitates the detection of the liquid argon scintillation light. In this paper, we present the results of the first doping test ever performed in a kiloton-scale LArTPC. From February to May 2020, we carried out this special run in the single-phase DUNE Far Detector prototype (ProtoDUNE-SP) at CERN, featuring 720 t of total liquid argon mass with 410 t of fiducial mass. A 5.4 ppm nitrogen contamination was present during the xenon doping campaign. The goal of the run was to measure the light and charge response of the detector to the addition of xenon, up to a concentration of 18.8 ppm. The main purpose was to test the possibility for reduction of non-uniformities in light collection, caused by deployment of photon detectors only within the anode planes. Light collection was analysed as a function of the xenon concentration, by using the pre-existing photon detection system (PDS) of ProtoDUNE-SP and an additional smaller set-up installed specifically for this run. In this paper we first summarize our current understanding of the argon-xenon energy transfer process and the impact of the presence of nitrogen in argon with and without xenon dopant. We then describe the key elements of ProtoDUNE-SP and the injection method deployed. Two dedicated photon detectors were able to collect the light produced by xenon and the total light. The ratio of these components was measured to be about 0.65 as 18.8 ppm of xenon were injected. We performed studies of the collection efficiency as a function of the distance between tracks and light detectors, demonstrating enhanced uniformity of response for the anode-mounted PDS. We also show that xenon doping can substantially recover light losses due to contamination of the liquid argon by nitrogen

Perspective: Dietary Biomarkers of Intake and Exposure - Exploration with Omics Approaches

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research

Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for over-represented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer (GOTO), and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

Volume I. Introduction to DUNE

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE\u27s physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology