Intake, digestibility, and growth by steers compared under continuous and frontal grazing

Last updated: 10/22/201

Structure of 55Sc and development of the N=34 subshell closure

The low-lying structure of $^{55}$Sc has been investigated using in-beam $\gamma$-ray spectroscopy with the $^{9}$Be($^{56}$Ti,$^{55}$Sc+$\gamma$)$X$ one-proton removal and $^{9}$Be($^{55}$Sc,$^{55}$Sc+$\gamma$)$X$ inelastic-scattering reactions at the RIKEN Radioactive Isotope Beam Factory. Transitions with energies of 572(4), 695(5), 1539(10), 1730(20), 1854(27), 2091(19), 2452(26), and 3241(39) keV are reported, and a level scheme has been constructed using $\gamma\gamma$ coincidence relationships and $\gamma$-ray relative intensities. The results are compared to large-scale shell-model calculations in the $sd$-$pf$ model space, which account for positive-parity states from proton-hole cross-shell excitations, and to it ab initio shell-model calculations from the in-medium similarity renormalization group that includes three-nucleon forces explicitly. The results of proton-removal reaction theory with the eikonal model approach were adopted to aid identification of positive-parity states in the level scheme; experimental counterparts of theoretical $1/2^{+}_{1}$ and $3/2^{+}_{1}$ states are suggested from measured decay patterns. The energy of the first $3/2^{-}$ state, which is sensitive to the neutron shell gap at the Fermi surface, was determined. The result indicates a rapid weakening of the $N=34$ subshell closure in $pf$-shell nuclei at $Z>20$, even when only a single proton occupies the $\pi f_{7/2}$ orbital

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty

In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories

Shape evolution in 116,118 Ru: Triaxiality and transition between the O(6) and U(5) dynamical symmetries

116Ru and 118Ru have been studied via β-delayed γ-ray spectroscopy of nuclei produced in fragmentation reactions at the Radioactive Ion-Beam Factory (RIBF) facility. Level schemes with positive-parity states up to spin J=6 have been constructed. The re

Microscale distribution patterns of terrestrial bryophytes in a subalpine forest: the use of logistic regression as an interpretive tool

This study investigated microhabitat relationships of terrestrial bryophytes in a subalpine forest of coastal British Columbia. Substratum affinities were characterized for dominant bryophytes. Logistic regression analysis was used to gain insight into the ecological determinants of fine scale (0.1 m2) bryophyte distribution by examining the predictive relationship between bryophyte species occurrence and localized environmental conditions, as well as the coverage of other bryophytes. The predictive relationships were compared to evaluate the relative importance of environmental factors versus interspecific interactions in structuring bryophyte communities. The results indicate that bryophytes show unique responses in their relationships to environmental conditions and other bryophytes. Positive feedback appears to be an important process among terrestrial bryophytes in subalpine forests

A primary care, multi-disciplinary disease management program for opioid-treated patients with chronic non-cancer pain and a high burden of psychiatric comorbidity

BACKGROUND: Chronic non-cancer pain is a common problem that is often accompanied by psychiatric comorbidity and disability. The effectiveness of a multi-disciplinary pain management program was tested in a 3 month before and after trial. METHODS: Providers in an academic general medicine clinic referred patients with chronic non-cancer pain for participation in a program that combined the skills of internists, clinical pharmacists, and a psychiatrist. Patients were either receiving opioids or being considered for opioid therapy. The intervention consisted of structured clinical assessments, monthly follow-up, pain contracts, medication titration, and psychiatric consultation. Pain, mood, and function were assessed at baseline and 3 months using the Brief Pain Inventory (BPI), the Center for Epidemiological Studies-Depression Scale scale (CESD) and the Pain Disability Index (PDI). Patients were monitored for substance misuse. RESULTS: Eighty-five patients were enrolled. Mean age was 51 years, 60% were male, 78% were Caucasian, and 93% were receiving opioids. Baseline average pain was 6.5 on an 11 point scale. The average CESD score was 24.0, and the mean PDI score was 47.0. Sixty-three patients (73%) completed 3 month follow-up. Fifteen withdrew from the program after identification of substance misuse. Among those completing 3 month follow-up, the average pain score improved to 5.5 (p = 0.003). The mean PDI score improved to 39.3 (p < 0.001). Mean CESD score was reduced to 18.0 (p < 0.001), and the proportion of depressed patients fell from 79% to 54% (p = 0.003). Substance misuse was identified in 27 patients (32%). CONCLUSIONS: A primary care disease management program improved pain, depression, and disability scores over three months in a cohort of opioid-treated patients with chronic non-cancer pain. Substance misuse and depression were common, and many patients who had substance misuse identified left the program when they were no longer prescribed opioids. Effective care of patients with chronic pain should include rigorous assessment and treatment of these comorbid disorders and intensive efforts to insure follow up