Genetic variability of muscle biological characteristics of young Limousin bulls

Genetic parameters of 4 muscle biological characteristics (protein to DNA ratio (Pro/DNA), lactate dehydrogenase (LDH) activity, isocitrate dehydrogenase (ICDH) activity and the proportion of type I myosin heavy chains (MHC I)), in the Semitendinosus and the Longissimus thoracis, were estimated simultaneously with average daily gain (ADG), 480-d final weight (FW), carcass lean and fat contents (CL% and CF% respectively) in a sample of young Limousin bulls tested in station. The data came from 144 animals, the progeny of 15 sires. Sire and residual variances and covariances were estimated u using an expectation maximization restricted maximum likelihood (EM-REML) procedure applied to a multitrait mixed model. Heritability coefficients of production traits, ADG, FW, CL% and CF%, were 0.19, 0.49, 0.39 and 0.43, respectively, while heritability coefficients of muscle characteristics, Pro/DNA, LDH, ICDH and MHC I, were 0.11, 0.26, 1.03 and 0.35 respectively, in the Semitendinosus muscle and 0.29, 0.31, 0.28 and 0.41, respectively, in the Longissimus thoracis muscle. In both muscles, the oxidative activity of the ICDH appeared to be genetically associated with the proportion of type I myosin heavy chains and opposed to the glycolytic activity of the LDH. The LDH activity was clearly associated with higher muscle-to-fat ratio, while the opposite relationship was observed between that ratio and the ICDH activity or the MHC I proportion.Les paramètres génétiques de 4 caractéristiques biologiques - le rapport protéines /ADN (Pro/DNA), les activités de la lactate déshydrogénase (LDH) et de l’isocitrate déshydrogénase (ICDH) et la proportion en chaînes lourdes de myosine lente (MHC I) - des muscles Semitendinosus et Longissimus thoracis, et ceux du gain moyen quotidien (ADG), du poids vif finaL à 480 j (FW) et des teneurs de la carcasse en muscles et en dépôts adipeux (CL% and CF% respectivement), ont été estimés simultanément à partir d’un échantillon de taurillons Limousins contrôlés en station. Le fichier comprenait 144 veaux issus de 15 pères testés sur descendance. Les variances et covariances paternelles et résiduelles ont été estimées par la méthode du maximum de vraisemblance restreinte, avec l’algorithme d’espérance-maximisation, appliquée à un modèle mixte multicaractère (EM-REML). Les coefficients d’héritabilité des variables de production, ADG, FW, CL% et CF%, s’élevaient respectivement à 0,19, 0,49, 0,39 et 0,43, tandis que les coefficients d’héritabilité des caractéristiques musculaires, Pro/DNA, LDH, ICDH et MHC I, valaient respectivement 0,11, 0,26, 1,03 et 0,35 dans le muscle Semitendinosus et 0,29, 0,31, 0,28 et 0,41 dans le muscle Longissimus thoracis. Dans les 2 muscles, l’activité oxidative de l’ICDH était génétiquement associée à la proportion de myosine lente et opposée à l’activité glycolytique du LDH. Cette activité du LDH était positivement corrélée avec le rapport muscles / dépôts adipeux, alors qu’une relation inverse était observée avec l’activité de l’ICDH et la proportion de MHC I

Accurate Object Detection with Deformable Shape Models Learnt from Images

International audienceWe present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the accurate boundaries of the objects, rather than just their bounding-boxes. This is made possible by 1) a novel technique for learning a shape model of an object class given images of example instances; 2) the combination of Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately, while needing no segmented examples for training (only bounding-boxes)

From Images to Shape Models for Object Detection

This research was supported by the EADS foundation, INRIA, CNRS, and SNSF. V. Ferrari was funded by a fellowship of the EADS foundation and by SNSF.International audienceWe present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes)

Evidence regarding the stock market's overreaction to management earning forecasts

Bibliography: p. [22-24]