Introduction to Diffraction and low x Dynamics

An attempt is made to illustrate the relation between low x process es and diffraction. ep scattering provides a unique laboratory, a single hadronic target probed by a point like lepton, where one can try to understand diffraction in terms of a colourless exchange in QCD. Low x processes eventually involve aspects of QCD which cannot be described perturbatively. The HERA inclusive measurements are examined in this respect and compared to results of the Tevatron.Comment: 8 pages, 7 figures in eps. talk given at XXXI International Symposium on Multiparticle Dynamics, Sep. 1-7, 2001, Datong China See http://ismd31 .ccnu.edu.cn

Potential gain from including major gene information in breeding value estimation

Two indexes were compared for the selection of a quantitative trait in the case of a mixed inheritance. The first index did not consider the major genotype information (standard method) whereas the second index took this information into account (modified method). Two types of selection scheme were considered: individual selection and selection based on a progeny test. The model for the estimation of genetic progress and evolution of allele frequencies takes overlapping generations into account. All of the effects studied suggested a large number of interactions. However, it can be concluded that information about the major gene should be put into the selection indexes when the heritability is low, the major gene effect high and its initial frequency small, in particular for a recessive major gene. The selection pressure has little influence on the results. In the short term, the modified method is of more value in the case of individual selection than in the case of selection based on a progeny test. On the whole, the extra genetic gain of the modified method is limited and considering the major genotypes in the selection indexes without any change of the selection scheme is probably not the best way to use this information.Le but de l’étude est de comparer l’application de deux indices dans le cas d’une sélection sur un caractère quantitatif soumis à l’effet d’un gène majeur. Dans le premier cas, l’indice ne prend pas en compte l’information sur le génotype au locus majeur (méthode standard) alors que le deuxième indice prend en compte cette information (méthode modifiée). Deux types de schémas sont considérés : sélection individuelle et sélection sur descendance. Le calcul du progrès génétique et de l’évolution des fréquences alléliques est réalisé pas à pas en considérant des générations chevauchantes. Tous les effets étudiés sur la supériorité de la méthode modifiée sur la méthode standard suggèrent de nombreuses interactions. Cependant, il ressort que la prise en compte de l’information sur le gène majeur dans l’indexation est avantageuse dans les cas de faible héritabilité, de fort effet du gène majeur et de faible proportion initiale de l’allèle favorable surtout lorsque cet allèle est récessif. Le taux de sélection n’a que peu d’influence sur les résultats. Enfin, l’intérêt de la méthode modifiée est plus visible et plus rapide dans la sélection individuelle que dans la sélection sur descendance. Il n’en demeure pas moins qu’en dehors des conditions extrêmes précédemment citées, l’intérêt de la méthode modifiée sur la méthode standard reste pour le moins limité et la prise en compte de l’information sur les génotypes au locus majeur dans l’indice de sélection, sans modification du schéma de sélection, ne constitue sûrement pas le meilleur outil de valorisation de cette information pour la sélection

A Hybrid Approach Support Vector Machine (SVM) – Neuro Fuzzy for Fast Data Classification

In recent decade, support vector machine (SVM) was a machine learning method that widely used in several application domains. It was due to SVM has a good performance for solving data classification problems, particularly in non-linear case. Nevertheless, several studies indicated that SVM still has some inadequacies, especially the high time complexity in testing phase that is caused by increasing the number of support vector for high dimensional data. To address this problem, we propose a hybrid approach SVM – Neuro Fuzzy (SVMNF), which neuro fuzzy here is used to avoid influence of support vector in testing phase of SVM. Moreover, our approach is also equipped with a feature selection that can reduce data attributes in testing phase, so that it can improve the effectiveness of time computation. Based on our evaluation in real benchmark datasets, our approach outperformed SVM in testing phase for solving data classification problems without significantly affecting the accuracy of SVM

Effect of including major gene information in mass selection: a stochastic simulation in a small population

Using a system of recurrence equations, best linear unbiased prediction applied to a reduced animal model (RAM) is presented for marker-assisted selection. This approach is a RAM version of the method with the animal model to reduce the number of equations per animal to one. The current RAM approach allows simultaneous evaluation of fixed effects and total additive genetic merit which is expressed as the sum of the additive genetic effects due to quantitative trait loci (QTL) unlinked to the marker locus (ML) and the additive effects due to the QTL linked to the ML. The total additive genetic merits for animals with no progeny are predicted by the formulae derived for backsolving. A numerical example is given to illustrate the current RAM approach.Sur la base d’un système d’équations de récurrence, la méthode du meilleur prédicteur linéaire sans biais appliquée à un modèle animal réduit (MAR) est présentée pour la sélection assistée par marqueur. Cette méthode est une version MAR de celle du modèle animal pour réduire à un le nombre d’équations par animal. Cette méthode MAR permet d’estimer simultanément les effets fixés et la valeur génétique globale, qui est la somme des effets génétiques additifs des locus de caractère quantitatif (QTL) non liés au locus marqueur et des effets additifs des QTL liés au locus marqueur. La valeur génétique globale des animaux sans descendance est prédite par un système d’équations reconstitué à partir du système principal. Un exemple numérique est donné pour illustrer la méthode MAR présentée ici

Structural analysis of Salmonella enterica effector protein SopD

Salmonella outer protein D (SopD) is a type III secreted virulence effector protein from Salmonella enterica. Full-length SopD and SopD lacking 16 amino acids at the N-terminus (SopDDeltaN) have been expressed as fusions with GST in Escherichia coli, purified with a typical yield of 20-30 mg per litre of cell culture and crystallized. Biophysical characterization has been carried out mainly on SopDDeltaN. Analytical size exclusion chromatography shows that SopDDeltaN is monomeric and probably globular in aqueous solution. The secondary structure composition, calculated from the CD spectrum, is mixed (38% alpha-helix and 26% beta-strand). Sequence analysis indicates that SopD contains a coiled coil motif, as found in numerous other type III secretion system-associated proteins. This suggests that SopD has the potential for one or more heterotypic protein-protein interactions. Limited trypsin digestion of SopDDeltaN, monitored by both one-dimensional proton NMR spectroscopy and SDS-PAGE, shows that the protein has a large, protease-resistant core domain of 286 amino acid residues. This single-domain architecture suggests that SopD lacks a cognate chaperone. In crystallization trials, SopDDeltaN produced better crystals than either full-length SopD or trypsin-digested SopDDeltaN. Diffraction to 3.0 Angstrom resolution has so far been obtained from crystals of SopDDeltaN