Spin Diode Based on Fe/MgO Double Tunnel Junction

We demonstrate a spin diode consisting of a semiconductor free nano-scale Fe/MgO-based double tunnel junction. The device exhibits a near perfect spin-valve effect combined with a strong diode effect. The mechanism consistent with our data is resonant tunneling through discrete states in the middle ferromagnetic layer sandwiched by tunnel barriers of different spin-dependent transparency. The observed magneto-resistance is record high, ~4000%, essentially making the structure an on/off spin-switch. This, combined with the strong diode effect, ~100, offers a new device that should be promising for such technologies as magnetic random access memory and re-programmable logic.Comment: 14 page

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

Background: Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. Methods: We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. Results: By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Conclusions: Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.X116sciescopu

High-throughput 18K SNP array to assess genetic variability of the main grapevine cultivars from Sicily

The viticulture of Sicily, for its vocation, is one of the most important and ancient forms in Italy. Autochthonous grapevine cultivars, many of which known throughout the world, have always been cultivated in the island from many centuries. With the aim to preserve this large grapevine diversity, previous studies have already started to assess the genetic variability among the Sicilian cultivars by using morphological and microsatellite markers. In this study, simple sequence repeat (SSR) were utilized to verify the true-to-typeness of a large clone collection (101) belonging to 21 biotypes of the most 10 cultivated Sicilian cultivars. Afterwards, 42 Organization Internationale de la Vigne et du Vin (OIV) descriptors and a high-throughput single nucleotide polymorphism (SNP) genotyping array (Vitis18kSNP) were applied to assess genetic variability among cultivars and biotypes of the same cultivar. Ampelographic traits and high-throughput SNP genotyping platforms provided an accuracy estimation of genetic diversity in the Sicilian germplasm, showing the relationships among cultivars by cluster and multivariate analyses. The large SNP panel defined sub-clusters unable to discern among biotypes, previously classified by ampelographic analysis, belonging to each cultivar. These results suggested that a very large number of SNP did not cover the genome regions harboring few morphological traits. Genetic structure of the collection revealed a clear optimum number of groups for K = 3, clustering in the same group a significant portion of family-related genotypes. Parentage analysis highlighted significant relationships among Sicilian grape cultivars and Sangiovese, as already reported, but also the first evidences of the relationships between Nero d’Avola and both Inzolia and Catarratto. Finally, a small panel of highly informative markers (12 SNPs) allowed us to isolate a private profile for each Sicilian cultivar, providing a new tool for cultivar identification

Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

Abstract Background The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The β value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions. Methods All PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of β values and γ factors in BPL were 50–600 and 2–20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRChot) of each hot sphere, the cold CRC (CRCcold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images. Results The CRChot and CRCcold for different β values and γ factors depended on the size of the small spheres. The CRChot, CRCcold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower β values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased. Conclusion High and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm