Effect of sonication on the viscosity of reconstituted skim milk powder and milk protein concentrate as influenced by solids concentration, temperature and sonication

Skim milk powder (SMP) and milk protein concentrates (MPCs) are manufactured by evaporation followed by spray drying and are widely used as functional and nutritional ingredients. This study investigated the effects of temperature (40–60 °C) and total solids content (TS) on the viscosity of reconstituted MPC (rMPC) (≥30% TS) and SMP (rSMP) (≥46% TS) in laboratory conditions. Additionally, the influence of sonication in batch (70% amplitude) and flow through systems (90% amplitude) was studied in a laboratory setting. The viscosity increased for all treatments with an increase in TS and decreased with an increase in temperature. Overall, sonication in both batch (30 s) and flow through systems (10.1, 20.2, and 30.2 s) resulted in significant decreases in viscosity for both rSMP and rMPC. An increase in viscosity was observed after post-sonication circulation; however, the viscosity did not return to the pre-sonication values

Hierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data

A numerical classifi cation problem encountered by breeders and gene-bank curators is how to partition the original heterogeneous population of genotypes into non-overlapping homogeneous subpopulations. The measure of distance that may be defi ned depends on the type of variables measured (i.e., continuous and/or discrete). The key points are whether and how a distance may be defi ned using all types of variables to achieve effective classifi cation. The objective of this research was to propose an approach that combines the use of hierarchical multiple-factor analysis (HMFA) and the two-stage Ward Modifi ed Location Model (Ward-MLM) classifi cation strategy that allows (i) combining different types of phenotypic and genetic data simultaneously; (ii) balancing out the effects of the different phenotypic, genetic, continuous, and discrete variables; and (iii) measuring the contribution of each original variable to the new principal axes (PAs). Of the two strategies applied for developing PA scores to be used for clustering genotypes, the strategy that used the fi rst few PA scores to which phenotypic and genetic variables each contributed 50% (i.e., a balanced contribution) formed better groups than those formed by the strategy that used a large number of PA scores explaining 95% of total variability. Phenotypic variables account for much variability in the initial PA; then their contributions decrease. The importance of genetic variables increases in later PAs. Results showed that various phenotypic and genetic variables made important contributions to the new PA. The HMFA uses all phenotypic and genetic variables simultaneously and, in conjunction with the Ward-MLM method, it offers an effective unifying approach for the classifi cation of breeding genotypes into homogeneous groups and for the formation of core subsets for genetic resource conservation

Calculation of two-loop virtual corrections to b --> s l+ l- in the standard model

We present in detail the calculation of the virtual O(alpha_s) corrections to the inclusive semi-leptonic rare decay b --> s l+ l-. We also include those O(alpha_s) bremsstrahlung contributions which cancel the infrared and mass singularities showing up in the virtual corrections. In order to avoid large resonant contributions, we restrict the invariant mass squared s of the lepton pair to the range 0.05 < s/mb^2 < 0.25. The analytic results are represented as expansions in the small parameters s/mb^2, z = mc^2/mb^2 and s/(4 mc^2). The new contributions drastically reduce the renormalization scale dependence of the decay spectrum. For the corresponding branching ratio (restricted to the above s-range) the renormalization scale uncertainty gets reduced from +/-13% to +/-6.5%.Comment: 41 pages including 9 postscript figures; in version 2 some typos and inconsistent notation correcte

Image quality and diagnostic accuracy of unenhanced SSFP MR angiography compared with conventional contrast-enhanced MR angiography for the assessment of thoracic aortic diseases

The purpose of this study was to determine the image quality and diagnostic accuracy of three-dimensional (3D) unenhanced steady state free precession (SSFP) magnetic resonance angiography (MRA) for the evaluation of thoracic aortic diseases. Fifty consecutive patients with known or suspected thoracic aortic disease underwent free-breathing ECG-gated unenhanced SSFP MRA with non-selective radiofrequency excitation and contrast-enhanced (CE) MRA of the thorax at 1.5 T. Two readers independently evaluated the two datasets for image quality in the aortic root, ascending aorta, aortic arch, descending aorta, and origins of supra-aortic arteries, and for abnormal findings. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were determined for both datasets. Sensitivity, specificity, and diagnostic accuracy of unenhanced SSFP MRA for the diagnosis of aortic abnormalities were determined. Abnormal aortic findings, including aneurysm (n = 47), coarctation (n = 14), dissection (n = 12), aortic graft (n = 6), intramural hematoma (n = 11), mural thrombus in the aortic arch (n = 1), and penetrating aortic ulcer (n = 9), were confidently detected on both datasets. Sensitivity, specificity, and diagnostic accuracy of SSFP MRA for the detection of aortic disease were 100% with CE-MRA serving as a reference standard. Image quality of the aortic root was significantly higher on SSFP MRA (P &lt; 0.001) with no significant difference for other aortic segments (P &gt; 0.05). SNR and CNR values were higher for all segments on SSFP MRA (P &lt; 0.01). Our results suggest that free-breathing navigator-gated 3D SSFP MRA with non-selective radiofrequency excitation is a promising technique that provides high image quality and diagnostic accuracy for the assessment of thoracic aortic disease without the need for intravenous contrast material

THE DATA REDUCTION PIPELINE FOR THE APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT

The Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey III, explores the stellar populations of the Milky Way using the Sloan 2.5-m telescope linked to a high resolution (R ~ 22,500), near-infrared (1.51–1.70 µm) spectrograph with 300 optical fibers. For over 150,000 predominantly red giant branch stars that APOGEE targeted across the Galactic bulge, disks and halo, the collected high signal-to-noise ratio (>100 per half-resolution element) spectra provide accurate (~0.1 km s-1) RVs, stellar atmospheric parameters, and precise (lesssim0.1 dex) chemical abundances for about 15 chemical species. Here we describe the basic APOGEE data reduction software that reduces multiple 3D raw data cubes into calibrated, well-sampled, combined 1D spectra, as implemented for the SDSS-III/APOGEE data releases (DR10, DR11 and DR12). The processing of the near-IR spectral data of APOGEE presents some challenges for reduction, including automated sky subtraction and telluric correction over a 3°-diameter field and the combination of spectrally dithered spectra. We also discuss areas for future improvement