188,743 research outputs found
COSSIO (Manuel B.)
No full textDubois Patrick. COSSIO (Manuel B.). In: Le dictionnaire de pédagogie et d'instruction primaire de Ferdinand Buisson : répertoire biographique des auteurs. Paris : Institut national de recherche pédagogique, 2002. p. 57. (Bibliothèque de l'Histoire de l'Education, 17
Relación de las honras, que la Capilla Real de S. Marcos de la ciudad de Salamanca consagró por la sacra catholica, y real Magestad de el Rey N.S.D. Phelipe IV...a 22 de noviembre de el año pasado de 1665
No full text[14], 29 p. ; 4º(19 cm)Sign.[calderón]4, [calderón]4,A-C4, D2Autor tomado das aprobaciónsPort.e iniciais con orlas tip.Texto en castelán, notas marxinais en latí
Conformations of the Huntingtin N-term in aqueous solution from atomistic simulations
No full textThe first 17 amino acids of Huntingtin protein (N17) play a crucial role in the protein’s aggregation. Here we predict its free energy landscape in aqueous solution by using bias exchange metadynamics. All our findings are consistent with experimental data. N17 populates four main kinetic basins, which interconvert on the microsecond time-scale. The most populated basin (about 75%) is a random coil, with an extended flat exposed hydrophobic surface. This might create a hydrophobic seed promoting Huntingtin aggregation. The other main populated basins contain helical conformations, which could facilitate N17 binding on its cellular targets
Predicting the Affinity of Peptides to Major Histocompatibility Complex Class II by Scoring Molecular Dynamics Simulations
No full textPredicting the binding affinity of peptides able to interact with major histocompatibility complex (MHC) molecules is a priority for researchers working in the identification of novel vaccines candidates. Most available approaches are based on the analysis of the sequence of peptides of known experimental affinity. However, for MHC class II receptors, these approaches are not very accurate, due to the intrinsic flexibility of the complex. To overcome these limitations, we propose to estimate the binding affinity of peptides bound to an MHC class II by averaging the score of the configurations from finite-temperature molecular dynamics simulations. The score is estimated for 18 different scoring functions, and we explored the optimal manner for combining them. To test the predictions, we considered eight peptides of known binding affinity. We found that six scoring functions correlate with the experimental ranking of the peptides significantly better than the others. We then assessed a set of techniques for combining the scoring functions by linear regression and logistic regression. We obtained a maximum accuracy of 82% for the predicted sign of the binding affinity using a logistic regression with optimized weights. These results are potentially useful to improve the reliability of in silico protocols to design high-affinity binding peptides for MHC class II receptors
Optimizing the performance of bias-exchange metadynamics: Folding a 48-residue LysM domain using a coarse-grained model
No full textAuthor-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Consultacion, iuridica, moral, canonica, en que se funda, que el impedimento, que para el matrimonio nace del primer grado de affinidad en la linea obliqua, es dispensable ...
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Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
PARCE: Protocol for Amino acid Refinement through Computational Evolution
No full textThe in silico design of peptides and proteins as binders is useful for diagnosis and therapeutics due to their low adverse effects and major specificity. To select the most promising candidates, a key matter is to understand their interactions with protein targets. In this work, we present PARCE, an open source Protocol for Amino acid Refinement through Computational Evolution that implements an advanced and promising method for the design of peptides and proteins. The protocol performs a random mutation in the binder sequence, then samples the bound conformations using molecular dynamics simulations, and evaluates the protein–protein interactions using multiple scoring functions. Finally, it accepts or rejects the mutation by applying a consensus criterion based on the binding scores. The procedure is iterated with the aim to explore efficiently novel sequences with potential better affinities towards their targets. We also provide a tutorial for running and reproducing the methodology. Program summary: Program Title: PARCE CPC Library link to program files: http://dx.doi.org/10.17632/jcpj3j83rt.1 Developer's repository link: https://github.com/PARCE-project/PARCE-1 Licensing provisions: MIT License Programming language: Python 3 Nature of problem: Computational design of peptides and proteins as binders for diagnosis and therapeutics. Solution method: A protocol that performs random mutations in the binder sequence, samples the bound conformations using molecular dynamics simulations, and evaluates the protein–protein interactions from multiple scoring predictions in order to accept or reject the mutations. Additional comments including restrictions and unusual features: Subprograms used: Gromacs 5.1.4, Scwrl4, FASPR, PDB2PQR, Scoring functions source code. This article describes version 1.0. PARCE is available at: https://github.com/PARCE-project/PARCE-1, and a Docker container can be downloaded from: https://hub.docker.com/r/rochoa85/parce-1
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