Alternating magnetic anisotropy of Li2_2(Li1−xTx_{1-x}T_x)N with TT = Mn, Fe, Co, and Ni

Substantial amounts of the transition metals Mn, Fe, Co, and Ni can be substituted for Li in single crystalline Li$_2$(Li$_{1-x}T_x$)N. Isothermal and temperature-dependent magnetization measurements reveal local magnetic moments with magnitudes significantly exceeding the spin-only value. The additional contributions stem from unquenched orbital moments that lead to rare-earth-like behavior of the magnetic properties. Accordingly, extremely large magnetic anisotropies have been found. Most notably, the magnetic anisotropy alternates as easy-plane $\rightarrow$ easy-axis $\rightarrow$ easy-plane $\rightarrow$ easy-axis when progressing from $T$ = Mn $\rightarrow$ Fe $\rightarrow$ Co $\rightarrow$ Ni. This behavior can be understood based on a perturbation approach in an analytical, single-ion model. The calculated magnetic anisotropies show a surprisingly good agreement with the experiment and capture the basic features observed for the different transition metals.Comment: 5 pages, 3 figures, published as PRB Rapid Communication, Fig. 3 update

Decomposition of Algebraic Functions

AbstractFunctional decomposition—whether a functionf(x) can be written as a composition of functionsg(h(x)) in a non-trivial way—is an important primitive in symbolic computation systems. The problem of univariate polynomial decomposition was shown to have an efficient solution by Kozen and Landau (1989). Dickerson (1987) and Gathen (1990a) gave algorithms for certain multivariate cases. Zippel (1991) showed how to decompose rational functions. In this paper, we address the issue of decomposition of algebraic functions. We show that the problem is related to univariate resultants in algebraic function fields, and in fact can be reformulated as a problem ofresultant decomposition. We characterize all decompositions of a given algebraic function up to isomorphism, and give an exponential time algorithm for finding a non-trivial one if it exists. The algorithm involves genus calculations and constructing transcendental generators of fields of genus zero

MP20, the second most abundant lens membrane protein and member of the tetraspanin superfamily, joins the list of ligands of galectin-3

BACKGROUND: Although MP20 is the second most highly expressed membrane protein in the lens its function remains an enigma. Putative functions for MP20 have recently been inferred from its assignment to the tetraspanin superfamily of integral membrane proteins. Members of this family have been shown to be involved in cellular proliferation, differentiation, migration, and adhesion. In this study, we show that MP20 associates with galectin-3, a known adhesion modulator. RESULTS: MP20 and galectin-3 co-localized in selected areas of the lens fiber cell plasma membrane. Individually, these proteins purified with apparent molecular masses of 60 kDa and 22 kDa, respectively. A 104 kDa complex was formed in vitro upon mixing the purified proteins. A 102 kDa complex of MP20 and galectin-3 could also be isolated from detergent-solubilized native fiber cell membranes. Binding between MP20 and galectin-3 was disrupted by lactose suggesting the lectin site was involved in the interaction. CONCLUSIONS: MP20 adds to a growing list of ligands of galectin-3 and appears to be the first representative of the tetraspanin superfamily identified to possess this specificity

Characterization of Delayed Ettringite Formation Using Nonlinear Impact Resonance Spectroscopy Method

A nondestructive acoustic approach for the detection and quantification of damage in mortars affected by delayed ettringite formation (DEF) is used to study the degradation mechanism at micro- and macro-scales. The presentation considers a linear acoustic technique, dynamic elastic modulus, which measures the macro-scale damage, and a nonlinear acoustic technique, Nonlinear Impact ResonanceAcoustic Spectroscopy (NIRAS), which assesses the damage at the micro-scale. Both methods successfully differentiate the degree of DEF-damage in mortars experiencing various expansion levels. Variable Pressure Scanning Electron Microscopy (VP-SEM) images and Energy Dispersive X-Ray Spectroscopy (EDS) microanalysis are used to confirm the microstructural distress caused by DEF. Results indicate that mortars are damaged both during the early-age high-temperature curing and subsequent limewater exposure. However, the microcracking occurred during the early age high-temperature curing cycle is only detectable using nonlinear acoustic measurements. Furthermore, results from expansion, relative change in dynamic elastic modulus, standard deviation of average nonlinearity parameter, and corrected cumulative average nonlinearity parameter are in agreement with each other

Neuropsychological Characteristics of Preclinical Dementia in Parkinson's Disease

The goal of this study was to characterize the changes in cognition associated with the earliest, or preclinical, stages of dementia in Parkinson's disease (PD). We administered a comprehensive neuropsychological test battery to a group of initially nondemented PD patients participating in a longitudinal community-based epidemiologic study. We used Cox proportional hazards models to assess the relative risk of incident dementia associated with baseline scores on the neuropsychological tests. Baseline performance on two verbal fluency tasks (letter fluency and category fluency) was significantly and independently associated with incident dementia. Tests of memory, orientation, abstract reasoning, naming, and constructional skill were less sensitive predictors of subsequent dementia. The neuropsychological pattern characterizing the preclinical stages of dementia in PD differed from that described previously in preclinical Alzheimer's disease. Results suggest that poor performance on tests of verbal fluency may represent a distinct characteristic of the preclinical phase of dementia in PD

APOE Genotype Influences Functional Status among Elderly without Dementia

The presence of apolipoprotein-ϵ4 (APOE-ϵ4) significantly increases the risk of Alzheimer's disease (AD). The association between APOE-ϵ4 status and functional abilities was explored further in a multicultural sample of community-dwelling, non-demented elders. The sample was limited to cognitively-intact, community-dwelling elders, who were free of stroke or other neurologic disability. In 218 elders who met research criteria, the presence of APO-ϵ4 was associated with poorer functional status, apart from the effects of neuropsychological performance, gender, age, and education (OR = 2.5, 95% CI: 1.3, 4.9). In 158 subjects without an APOE-ϵ4 allele, 50% reported no functional limitation; in the 60 subjects with an ϵ4 allele, only 28% reported no functional limitation (P < .01). The relationship was not explained by the distribution of co-morbidities. The association between poorer function and the presence of an APOE-ϵ4 allele was evident in each ethnic group. In path analyses, the presence of an APOE-ϵ4 allele was associated with decreased functional ability in non-demented elders not simply through an association with poorer cognitive status, but also independently. These results suggest that the APOE-ϵ4 genotype is associated with functional deficit in people with normal neuropsychological profiles

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report