Cu_{2}O as nonmagnetic semiconductor for spin transport in crystalline oxide electronics

We probe spin transport in Cu_{2}O by measuring spin valve effect in La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co and La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/La_{0.7}Sr_{0.3}MnO_{3} epitaxial heterostructures. In La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co systems we find that a fraction of out-of-equilibrium spin polarized carrier actually travel across the Cu_{2}O layer up to distances of almost 100 nm at low temperature. The corresponding spin diffusion length dspin is estimated around 40 nm. Furthermore, we find that the insertion of a SrTiO_{3} tunneling barrier does not improve spin injection, likely due to the matching of resistances at the interfaces. Our result on dspin may be likely improved, both in terms of Cu_{2}O crystalline quality and sub-micrometric morphology and in terms of device geometry, indicating that Cu_{2}O is a potential material for efficient spin transport in devices based on crystalline oxides.Comment: 15 pages, 10 figure

Comparison of Lipoprotein Based Insulin Resistance Score and Traditional Risk Factors in Adolescents with Obesity

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Origin of interface magnetism in BiMnO3/SrTiO3 and LaAlO3/SrTiO3 heterostructures

Possible ferromagnetism induced in otherwise non-magnetic materials has been motivating intense research in complex oxide heterostructures. Here we show that a confined magnetism is realized at the interface between SrTiO3 and two insulating polar oxides, BiMnO3 and LaAlO3. By using polarization dependent x-ray absorption spectroscopy, we find that in both cases the magnetic order is stabilized by a negative exchange interaction between the electrons transferred to the interface and local magnetic moments. These local magnetic moments are associated to Ti3+ ions at the interface itself for LaAlO3/SrTiO3 and to Mn3+ ions in the overlayer for BiMnO3/SrTiO3. In LaAlO3/SrTiO3 the induced magnetic moments are quenched by annealing in oxygen, suggesting a decisive role of oxygen vacancies in the stabilization of interfacial magnetism.Comment: 5 pages, 4 figure

Integrating computational methods to predict mutagenicity of aromatic azo compounds

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds

Assessment of the hyperspectral data analysis as a tool to diagnose xylella fastidiosa in the asymptomatic leaves of olive plants

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources

Deciphering the nuclear bile acid receptor FXR paradigm

Originally called retinoid X receptor interacting protein 14 (RIP14), the farnesoid X receptor (FXR) was renamed after the ability of its rat form to bind supra-physiological concentrations of farnesol. In 1999 FXR was de-orphanized since primary bile acids were identified as natural ligands. Strongly expressed in the liver and intestine, FXR has been shown to be the master transcriptional regulator of several entero-hepatic metabolic pathways with relevance to the pathophysiology of conditions such as cholestasis, fatty liver disease, cholesterol gallstone disease, intestinal inflammation and tumors. Furthermore, given the importance of FXR in the gut-liver axis feedbacks regulating lipid and glucose homeostasis, FXR modulation appears to have great input in diseases such as metabolic syndrome and diabetes. Exciting results from several cellular and animal models have provided the impetus to develop synthetic FXR ligands as novel pharmacological agents. Fourteen years from its discovery, FXR has gone from bench to bedside; a novel nuclear receptor ligand is going into clinical use

Genetic mapping of loci for resistance to stem rust in a tetraploid wheat collection

Stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is a major biotic constraint to wheat production worldwide. Disease resistant cultivars are a sustainable means for the efficient control of this disease. To identify quantitative trait loci (QTLs) conferring resistance to stem rust at the seedling stage, an association mapping panel consisting of 230 tetraploid wheat accessions were evaluated for reaction to five Pgt races under greenhouse conditions. A high level of phenotypic variation was observed in the panel in response to all of the races, allowing for genome-wide association mapping of resistance QTLs in wild, landrace, and cultivated tetraploid wheats. Twenty-two resistance QTLs were identified, which were characterized by at least two marker-trait associations. Most of the identified resistance loci were coincident with previously identified rust resistance genes/QTLs; however, six regions detected on chromosomes 1B, 5A, 5B, 6B, and 7B may be novel. Availability of the reference genome sequence of wild emmer wheat accession Zavitan facilitated the search for candidate resistance genes in the regions where QTLs were identified, and many of them were annotated as NOD (nucleotide binding oligomerization domain)-like receptor (NLR) genes or genes related to broad spectrum resistance

A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees

Xylella fastidiosa is one of the most destructive plant pathogenic bacteria worldwide, affecting more than 500 plant species. In Apulia region (southeastern Italy), X. fastidiosa subsp. pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism, and the cultural heritage of this region. An early detection of the infected plants would hinder the rapid spread of the disease. The main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk. The study site was an olive grove located at Oria (province of Brindisi, Italy), where at the time of monitoring (September 2017) only few plants showed initial symptoms of the disease. The measurements included: 1) acquisitions of reflected electromagnetic radiation with UAV (Unmanned Aerial Vehicle) equipped with a multi-spectral camera; 2) geophysical surveys on the trunks of 49 plants with Ground Penetrating Radar (GPR); 3) disease severity rating, by visual inspection of the proportion of canopy with symptoms; 4) qPCR (real time-quantitative Polymerase Chain Reaction) data from tests on 61 plants. The data were submitted to a set of processing techniques to define a “data fusion” procedure, based on non-parametric multivariate geostatistics. The approach allowed marking those areas where the risk of infection was higher, and identifying the possible infection entry routes into the field. The probability map of infection risk could be used as an effective tool for a preventive action and for a better organization of the monitoring plans