Coherent spin valve phenomena and electrical spin injection in ferromagnetic/semiconductor/ferromagnetic junctions

Coherent quantum transport in ferromagnetic/ semiconductor/ ferromagnetic junctions is studied theoretically within the Landauer framework of ballistic transport. We show that quantum coherence can have unexpected implications for spin injection and that some intuitive spintronic concepts which are founded in semi-classical physics no longer apply: A quantum spin-valve (QSV) effect occurs even in the absence of a net spin polarized current flowing through the device, unlike in the classical regime. The converse effect also arises, i.e. a zero spin-valve signal for a non-vanishing spin-current. We introduce new criteria useful for analyzing quantum and classical spin transport phenomena and the relationships between them. The effects on QSV behavior of spin-dependent electron transmission at the interfaces, interface Schottky barriers, Rashba spin-orbit coupling and temperature, are systematically investigated. While the signature of the QSV is found to be sensitive to temperature, interestingly, that of its converse is not. We argue that the QSV phenomenon can have important implications for the interpretation of spin-injection in quantum spintronic experiments with spin-valve geometries.Comment: 15 pages including 11 figures. To appear in PR

Chemical Changes of Flattened Enamel Surfaces with in Vitro Weak Acid Demineralization

Infrared internal reflection spectroscopy (IRS) was used to study changes in surface chemistry of enamel after in vitro weak acid demineralization. Hypotheses that relate previous histomorphologic observations to current IRS findings are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68276/2/10.1177_00220345710500064001.pd

Powder Compaction: Compression Properties of Cellulose Ethers

Effective development of matrix tablets requires a comprehensive understanding of different raw material attributes and their impact on process parameters. Cellulose ethers (CE) are the most commonly used pharmaceutical excipients in the fabrication of hydrophilic matrices. The innate good compression and binding properties of CE enable matrices to be prepared using economical direct compression (DC) techniques. However, DC is sensitive to raw material attributes, thus, impacting the compaction process. This article critically reviews prior knowledge on the mechanism of powder compaction and the compression properties of cellulose ethers, giving timely insight into new developments in this field

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Complex personality disorder in bulimia nervosa

Objective: Recent research has suggested a move toward a dimensional system for the classification of personality disorders (PDs). Tyrer's dimensional model using severity as a form of categorizing PDs was used to compare eating disorder outcome in women with bulimia nervosa (BN) over 3 years. Method: One hundred thirty-four women with BN were divided into 4 groups based on PD severity: no PD (n = 32), personality difficulty (n = 27), simple PD (n = 29), and complex PD (n = 46). Eating disorder symptoms and attitudes, general psychosocial functioning, and depressive symptoms were examined at pretreatment and at 1-year and 3-year follow-up (posttreatment). Results: The complex PD group had greater Axis I comorbidity and psychopathology than the remaining 3 groups at pretreatment. At 1-year and 3-year follow-up, there were no differences in eating disorder outcome, general psychosocial functioning, and depressive symptoms across the 4 groups. Conclusion: These results suggest that having an increased number of PDs comorbid with BN does not influence eating disorder outcome up to 3 years after treatment

Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana

Open Access Journal; Published online: 2 March 2018Improving the efficiency of selection in conventional crossbreeding is a major priority in banana (Musa spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47– 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding

Stratification of asthma phenotypes by airway proteomic signatures

© 2019 Background: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. Objective: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. Methods: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. Results: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. Conclusion: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies