Measurement of the antineutrino neutral-current elastic differential cross section

arXiv:1309.7257v1 [hep-ex

Sarcopenia Exacerbates Obesity-Associated Insulin Resistance and Dysglycemia: Findings from the National Health and Nutrition Examination Survey III

Sarcopenia often co-exists with obesity, and may have additive effects on insulin resistance. Sarcopenic obese individuals could be at increased risk for type 2 diabetes. We performed a study to determine whether sarcopenia is associated with impairment in insulin sensitivity and glucose homeostasis in obese and non-obese individuals.We performed a cross-sectional analysis of National Health and Nutrition Examination Survey III data utilizing subjects of 20 years or older, non-pregnant (N = 14,528). Sarcopenia was identified from bioelectrical impedance measurement of muscle mass. Obesity was identified from body mass index. Outcomes were homeostasis model assessment of insulin resistance (HOMA IR), glycosylated hemoglobin level (HbA1C), and prevalence of pre-diabetes (6.0≤ HbA1C<6.5 and not on medication) and type 2 diabetes. Covariates in multiple regression were age, educational level, ethnicity and sex.Sarcopenia was associated with insulin resistance in non-obese (HOMA IR ratio 1.39, 95% confidence interval (CI) 1.26 to 1.52) and obese individuals (HOMA-IR ratio 1.16, 95% CI 1.12 to 1.18). Sarcopenia was associated with dysglycemia in obese individuals (HbA1C ratio 1.021, 95% CI 1.011 to 1.043) but not in non-obese individuals. Associations were stronger in those under 60 years of age. We acknowledge that the cross-sectional study design limits our ability to draw causal inferences.Sarcopenia, independent of obesity, is associated with adverse glucose metabolism, and the association is strongest in individuals under 60 years of age, which suggests that low muscle mass may be an early predictor of diabetes susceptibility. Given the increasing prevalence of obesity, further research is urgently needed to develop interventions to prevent sarcopenic obesity and its metabolic consequences

A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

A Proposal for a Near Detector Experiment on the Booster Neutrino Beamline: FINeSSE: Fermilab Intense Neutrino Scattering Scintillator Experiment

219 pages219 pagesUnderstanding the quark and gluon substructure of the nucleon has been a prime goal of both nuclear and particle physics for more than thirty years and has led to much of the progress in strong interaction physics. Still the flavor dependence of the nucleon's spin is a significant fundamental question that is not understood. Experiments measuring the spin content of the nucleon have reported conflicting results on the amount of nucleon spin carried by strange quarks. Quasi-elastic neutrino scattering, observed using a novel detection technique, provides a theoretically clean measure of this quantity. The optimum neutrino beam energy needed to measure the strange spin of the nucleon is 1 GeV. This is also an ideal energy to search for neutrino oscillations at high $\Delta m^2$ in an astrophysically interesting region. Models of the r-process in supernovae which include high-mass sterile neutrinos may explain the abundance of neutron-rich heavy metals in the universe. These high-mass sterile neutrinos are outside the sensitivity region of any previous neutrino oscillation experiments. The Booster neutrino beamline at Fermilab provides the world's highest intensity neutrino beam in the 0.5-1.0 GeV energy range, a range ideal for both of these measurements. A small detector located upstream of the MiniBooNE detector, 100 m from the recently commissioned Booster neutrino source, could definitively measure the strange quark contribution to the nucleon spin. This detector, in conjunction with the MiniBooNE detector, could also investigate $\nu_{\mu}$ disappearance in a currently unexplored, cosmologically interesting region

Changes in Weight, Waist Circumference and Compensatory Responses with Different Doses of Exercise among Sedentary, Overweight Postmenopausal Women

It has been suggested that exercise training results in compensatory mechanisms that attenuate weight loss. However, this has only been examined with large doses of exercise. The goal of this analysis was to examine actual weight loss compared to predicted weight loss (compensation) across different doses of exercise in a controlled trial of sedentary, overweight or obese postmenopausal women (n = 411).Participants were randomized to a non-exercise control (n = 94) or 1 of 3 exercise groups; exercise energy expenditure of 4 (n = 139), 8 (n = 85), or 12 (n = 93) kcal/kg/week (KKW). Training intensity was set at the heart rate associated with 50% of each woman's peak VO(2) and the intervention period was 6 months. All exercise was supervised. The main outcomes were actual weight loss, predicted weight loss (exercise energy expenditure/ 7700 kcal per kg), compensation (actual minus predicted weight loss) and waist circumference. The study sample had a mean (SD) age 57.2 (6.3) years, BMI of 31.7 (3.8) kg/m(2), and was 63.5% Caucasian. The adherence to the intervention was >99% in all exercise groups. The mean (95% CI) weight loss in the 4, 8 and 12 KKW groups was -1.4 (-2.0, -0.8), -2.1 (-2.9, -1.4) and -1.5 (-2.2, -0.8) kg, respectively. In the 4 and 8 KKW groups the actual weight loss closely matched the predicted weight loss of -1.0 and -2.0 kg, respectively, resulting in no significant compensation. In the 12 KKW group the actual weight loss was less than the predicted weight loss (-2.7 kg) resulting in 1.2 (0.5, 1.9) kg of compensation (P<0.05 compared to 4 and 8 KKW groups). All exercise groups had a significant reduction in waist circumference which was independent of changes in weight.In this study of previously sedentary, overweight or obese, postmenopausal women we observed no difference in the actual and predicted weight loss with 4 and 8 KKW of exercise (72 and 136 minutes respectively), while the 12 KKW (194 minutes) produced only about half of the predicted weight loss. However, all exercise groups had a significant reduction in waist circumference which was independent of changes in weight.(ClinicalTrials.gov) NCT00011193

Improved Search for νˉμ→νˉe\bar ν_μ\rightarrow \bar ν_e Oscillations in the MiniBooNE Experiment

Submitted to PRL. Further information provided in arXiv:1207.4809Submitted to PRL. Further information provided in arXiv:1207.4809The MiniBooNE experiment at Fermilab reports results from an analysis of $\bar \nu_e$ appearance data from $11.27 \times 10^{20}$ protons on target in antineutrino mode, an increase of approximately a factor of two over the previously reported results. An event excess of $78.4 \pm 28.5$ events ($2.8 \sigma$) is observed in the energy range $200<E_\nu^{QE}<1250$ MeV. If interpreted in a two-neutrino oscillation model, $\bar{\nu}_{\mu}\rightarrow\bar{\nu}_e$, the best oscillation fit to the excess has a probability of 66% while the background-only fit has a $\chi^2$-probability of 0.5% relative to the best fit. The data are consistent with antineutrino oscillations in the $0.01 < \Delta m^2 < 1.0$ eV$^2$ range and have some overlap with the evidence for antineutrino oscillations from the Liquid Scintillator Neutrino Detector (LSND). All of the major backgrounds are constrained by in-situ event measurements so non-oscillation explanations would need to invoke new anomalous background processes. The neutrino mode running also shows an excess at low energy of $162.0 \pm 47.8$ events ($3.4 \sigma$) but the energy distribution of the excess is marginally compatible with a simple two neutrino oscillation formalism. Expanded models with several sterile neutrinos can reduce the incompatibility by allowing for CP violating effects between neutrino and antineutrino oscillations