Surface impedance anisotropy of YBa2_2Cu3_3O6.95_{6.95} single crystals: electrodynamic basis of the measurements

An electrodynamic technique is developed for determining the components of surface impedance and complex conductivity tensors of HTSC single crystals on the basis of measured quantities of a quality factor and a resonator frequency shift. A simple formula is obtained for a geometrical factor of a crystal in the form of a plate with dimensions $b\gg a>c$ in a microwave magnetic field ${\bf H_{\omega}}\perp ab$. To obtain the c-axis complex conductivity from measurements at ${\bf H_{\omega}}\parallel ab$ we propose a procedure which takes account of sample size effects. With the aid of the technique involved temperature dependences of all impedance and conductivity tensors components of YBa$_2$Cu$_3$O$_{6.95}$ single crystal, grown in BaZrO$_3$ crucible, are determined at a frequency of $f=9.4$ GHz in its normal and superconducting states. All of them proved to be linear at $T<T_c/2$, and their extrapolation to zero temperature gives the values of residual surface resistance $R_{ab}(0)\approx 40$ $\mu\Omega$ and $R_c(0)\approx 0.8$ m$\Omega$ and magnetic field penetration depth $\lambda_{ab}(0)\approx 150$ nm and $\lambda_c(0)\approx 1.55$ $\mu$m.Comment: 9 pages, 7 figures. Submitted to Phys.Rev.B 05Jun2002; accepted for publication 21Febr200

Superconductivity in the two dimensional Hubbard Model.

Quasiparticle bands of the two-dimensional Hubbard model are calculated using the Roth two-pole approximation to the one particle Green's function. Excellent agreement is obtained with recent Monte Carlo calculations, including an anomalous volume of the Fermi surface near half-filling, which can possibly be explained in terms of a breakdown of Fermi liquid theory. The calculated bands are very flat around the (pi,0) points of the Brillouin zone in agreement with photoemission measurements of cuprate superconductors. With doping there is a shift in spectral weight from the upper band to the lower band. The Roth method is extended to deal with superconductivity within a four-pole approximation allowing electron-hole mixing. It is shown that triplet p-wave pairing never occurs. Singlet d_{x^2-y^2}-wave pairing is strongly favoured and optimal doping occurs when the van Hove singularity, corresponding to the flat band part, lies at the Fermi level. Nearest neighbour antiferromagnetic correlations play an important role in flattening the bands near the Fermi level and in favouring superconductivity. However the mechanism for superconductivity is a local one, in contrast to spin fluctuation exchange models. For reasonable values of the hopping parameter the transition temperature T_c is in the range 10-100K. The optimum doping delta_c lies between 0.14 and 0.25, depending on the ratio U/t. The gap equation has a BCS-like form and (2*Delta_{max})/(kT_c) ~ 4.Comment: REVTeX, 35 pages, including 19 PostScript figures numbered 1a to 11. Uses epsf.sty (included). Everything in uuencoded gz-compressed .tar file, (self-unpacking, see header). Submitted to Phys. Rev. B (24-2-95

Principles of the Field Theory of High Temperature Superconductivity in Underdoped Copper-Oxides

Here I extend my last work about the origin of the pseudo-gaps in underdoped cuprates (arXiv: cond-mat. 1011.3206), to include the mechanism of superconductivity. This is done by adapting the formalism of the double correlations in systems with nested Fermi surfaces to the semi one dimensional system of strings of holes. It is proposed that magnetic interaction is crucial for the establishment of the pseudogap and the high temperature superconductivity. It is shown that superconductivity disturbs the completeness of the strings of holes, and creates fluctuations in their shapes. This, in turn, reduces the magnetic interaction and the pseudogap order.Comment: This paper has been withdrawn by the author. 27 page

InterPro in 2017-beyond protein family and domain annotations

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

Sport policy convergence: a framework for analysis

This is an Accepted Manuscript of an article published by Taylor & Francis Group in European Sport Management Quarterly on 30th April 2012, available online at: http://www.tandfonline.com/10.1080/16184742.2012.669390The growth in the comparative analysis of sport management processes and policy has led to an increased interest in the concept of convergence. However, the concept is too often treated as unproblematic in definition, measurement and operationalisation. It is argued in this paper that a more effective framework for examining claims of convergence is one that analyses the concept in terms of seven dimensions which can be explored through a mix of quantitative and qualitative methods of data collection. It is also argued that a deeper understanding of the process of convergence can be gained by operationalising the concept in the context of a selected range of meso-level theories of the policy process or of specific aspects of the process. The proposed analytic framework provides not only a definition of convergence but also the basis for a more nuanced investigation of hypotheses of convergence

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p