Reentrant behavior of the phase stiffness in Josephson junction arrays

The phase diagram of a 2D Josephson junction array with large substrate resistance, described by a quantum XY model, is studied by means of Fourier path-integral Monte Carlo. A genuine Berezinskii-Kosterlitz-Thouless transition is found up to a threshold value g* of the quantum coupling, beyond which no phase coherence is established. Slightly below g* the phase stiffness shows a reentrant behavior with temperature, in connection with a low-temperature disappearance of the superconducting phase, driven by strong nonlinear quantum fluctuations.Comment: 4 pages, 7 figures, to appear in Phys.Rev.Let

Suppression of Dimer Correlations in the Two-Dimensional J1J_1-J2J_2 Heisenberg Model: an Exact Diagonalization Study

We present an exact diagonalization study of the ground state of the spin-half $J_1{-}J_2$ model. Dimer correlation functions and the susceptibility associated to the breaking of the translational invariance are calculated for the $4\times 4$ and the $6\times 6$ clusters. These results -- especially when compared to the one dimensional case, where the occurrence of a dimerized phase for large enough frustration is well established -- suggest either a homogeneous spin liquid or, possibly, a dimerized state with a rather small order parameter

Treatment responses to antiangiogenetic therapy and chemotherapy in nonsecreting paraganglioma (PGL4) of urinary bladder with SDHB mutation: a case report

Paraganglioma (PGL) is a rare neuroendocrine tumor. Currently, the malignancy is defined as the presence of metastatic spread at presentation or during follow-up. Several gene mutations are listed in the pathogenesis of PGL, among which succinate dehydrogenase (SDHX), particularly the SDHB isoform, is the main gene involved in malignancy. A 55-year-old male without evidence of catecholamine secretion had surgery for PGL of the urinary bladder. After 1 year, he showed a relapse of disease and demonstrated malignant PGL without evidence of catecholamine secretion with a germline heterozygous mutation of succinate dehydrogenase B (SDHB). After failure of a second surgery for relapse, he started medical treatment with sunitinib daily but discontinued due to serious side effects. Cyclophosphamide, vincristine, and dacarbazine (CVD) chemotherapeutic regimen stopped the disease progression for 7 months. Conclusion: Malignant PGL is a very rare tumor, and SDHB mutations must be always considered in molecular diagnosis because they represent a critical event in the progression of the oncological disease. Currently, there are few therapeutic protocols, and it is often difficult, as this case demonstrates, to decide on a treatment option according to a reasoned set of choices. Abbreviations: CVD = cyclophosphamide, vincristine and dacarbazine, HIF-1a = hypoxia inducible factor 1 alpha, PGL = paraganglioma, SDH = succinate dehydrogenase, VEGF = vasoendothelial growth factor

Disposable chlorine dioxide wipes for high-level disinfection in the ENT department: A systematic review

Background: Nasopharyngoscope reprocessing methods should be effective, rapid and reproducible with moderate cost. Tristel Trio Wipes system (TTWS) is a manual reprocessing method based on chlorine dioxide that has lately emerged in ENT department. This review aims to collect evidence on this system. Methods: The PubMed, Web of Science and Cochrane Library databases were searched for all the studies on TTWS or one of its components. Data were grouped according to the study type. Results: Ten articles were included in the review. TTWS ensured high-level disinfection in laboratory and clinical setting. Although the limitations of the manual systems, TTWS proved to be faster than automated endoscope reprocessing (AER) and safe for patients and health-care workers. TTWS represented cheaper system than AER or sheaths in low- and medium-volume centers. Conclusion: TTWS could be a valid, safe and fast HLD method for nasopharyngoscopes, with reasonable costs for medium-low reprocessing volumes

Nonequilibrium relaxation study of the anisotropic antiferromagnetic Heisenberg model on the triangular lattice

Effect of exchange anisotropy on the relaxation time of spin and vector chirality is studied for the antiferromagnetic classical Heisenberg model on the triangular lattice by using the nonequilibrium relaxation Monte Carlo method. We identify the Berezinskii-Kosterlitz-Thouless (BKT) transition and the chiral transition in a wide range of the anisotropy, even for very small anisotropy of $\sim$ 0.01%. As the anisotropy decreases, both the critical temperatures steeply decrease, while the BKT critical region becomes divergently wide. We elucidate a sharp "V shape" of the phase diagram around the isotropic Heisenberg point, which suggests that the isotropic case is exceptionally singular and the associated Z vortex transition will be isolated from the BKT and chiral transitions. We discuss the relevance of our results to peculiar behavior of the spin relaxation time observed experimentally in triangular antiferromagnets.Comment: 5 pages, 4 figures, accepted for publication in J. Phys. Soc. Jp

Improving the prediction of disease-related variants using protein three-dimensional structure

Background: Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.Results: In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.Conclusion: This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available. \ua9 2011 Capriotti; licensee BioMed Central Ltd

Analysis and interpretation of the impact of missense variants in cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategie

A deep-learning sequence-based method to predict protein stability changes upon genetic variations

Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools have been developed to predict the free energy changes upon protein residue variations. Most of these methods require both sequence and structure information to obtain reliable predictions. However, the lower number of protein structures available with respect to their sequences, due to experimental issues, drastically limits the application of these tools. In addition, current methodologies ignore the antisymmetric property characterizing the thermodynamics of the protein stability: a variation from wild-type to a mutated form of the protein structure (XW→XM) and its reverse process (XM→XW) must have opposite values of the free energy difference (ΔΔGWM=−ΔΔGMW). Here we propose ACDC-NN-Seq, a deep neural network system that exploits the sequence information and is able to incorporate into its architecture the antisymmetry property. To our knowledge, this is the first convolutional neural network to predict protein stability changes relying solely on the protein sequence. We show that ACDC-NN-Seq compares favorably with the existing sequence-based methods

Resonating Valence Bond Wave Functions for Strongly Frustrated Spin Systems

The Resonating Valence Bond (RVB) theory for two-dimensional quantum antiferromagnets is shown to be the correct paradigm for large enough ``quantum frustration''. This scenario, proposed long time ago but never confirmed by microscopic calculations, is very strongly supported by a new type of variational wave function, which is extremely close to the exact ground state of the $J_1{-}J_2$ Heisenberg model for $0.4 \lesssim J_2/J_1\lesssim 0.5$. This wave function is proposed to represent the generic spin-half RVB ground state in spin liquids.Comment: 4 Pages, 5 figures, accepted for publication in PR

Quasiparticle interference patterns as a test for the nature of the pseudogap phase in the cuprate superconductors

Electrons, when scattered by static random disorder, form standing waves that can be imaged using scanning tunneling microscopy. Such interference patterns, observable by the recently developed technique of Fourier transform scanning tunneling spectroscopy (FT-STS), are shown to carry unique fingerprints characteristic of the electronic order present in a material. We exploit this feature of the FT-STS technique to propose a test for the nature of the enigmatic pseudogap phase in the high-$T_c$ cuprate superconductors. Through their sensitivity to the quasiparticle spectra and coherence factors, the FT-STS patterns in principle carry enough information to unambiguously determine the nature of the condensate responsible for the pseudogap phenomenon. We argue that the next generation of FT-STS experiments, currently underway, should be able to distinguish between the pseudogap dominated by the remnants of superconducting order from the pseudogap dominated by some competing order in the particle-hole channel. Using general arguments and detailed numerical calculations, we point to certain fundamental differences between the two scenarios and discuss the prospects for future experiments.Comment: 15 pages REVTeX + 9 ps figures. For related work and info visit http://www.physics.ubc.ca/~franz; version 2 to appear in IJMP