Haplotype association analysis of meat quality traits at the bovine PRKAG3 locus

The current study presents the results of a preliminary haplotype association analysis at the bovine PRKAG3 locus with meat quality traits in the Chianina breed. No significant association was shown between haploid haplotypes (or diplotypes) and phenotypical traits after applying a Bonferroni correction for multiple comparison. Nonetheless, data from Longissimus dorsi muscle suggest the presence of a statistically non-significant trend toward an influence of the PRKAG3 haploid haplotypes on meat colour (a*) and water holding capacity (M/T) traits, as confirmed also by diplotype-based association analysis. A less clear set of results was observed for the Triceps brachii and Semitendinosus muscles

Rhizobium gallicum sp. nov. and Rhizobium giardinii sp. nov. from Phaseolus vulgaris nodules

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A multi-experts methodology for early detection and diagnosis of mechanical defects

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Industrial Application of Artificial Neural Networks: Optical Surfaces Defects’ Detection and Classification

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Problem Decomposition Driven by Local Intrinsic Dimension for Neural Network Based Classification

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Hybrid Intelligent Diagnosis Approach Based on Neural Pattern Recognition and Fuzzy Decision-Making

International audienceFault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach

A Human-like Visual-Attention-based Artificial Vision System for Wildland Firefighting Assistance

International audienceIn this work we contribute to development of a “Human-like Visual-Attention-based Artificial Vision” system for boosting firefighters’ awareness about the hostile environment in which they are supposed to move along. Taking advantage from artificial visual-attention, the investigated system’s conduct may be adapted to firefighter’s way of gazing by acquiring some kind of human-like artificial visual neatness supporting firefighters in interventional conditions’ evaluation or in their appraisal of the rescue conditions of people in distress dying out within the disaster. We achieve such a challenging goal by combining a statistically-founded bio-inspired saliency detection model with a Machine-Learning-based human-eye-fixation model. Hybridization of the two above-mentioned models leads to a system able to tune its parameters in order to fit human-like gazing of the inspected environment. It opens appealing perspectives in computer-aided firefighters’ assistance boosting their awareness about the hostile environment in which they are supposed to evolve. Using as well various available wildland fires images’ databases as an implementation of the investigated concept on a 6-wheeled mobile robot equipped with communication facilities, we provide experimental results showing the plausibility as well as the efficiency of the proposed system

A polymorphic porcine dinucleotide repeat S0559 (BHT0107) at chromosome 6q28-q31.

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