To determine if there is any change with age in the distribution of Bi dobacterium and Enterobacteriaceae species in human intestinal micro ora, strains were isolated from a total of 54 samples of human faeces (15 children, 3-15 years old; 17 adults, 30-46 years old; 22 elderly, 69-89 years old). The bi dobacteria were identi ed at species level using a numerical phenotypic identi cation system developed on the Internet (http:::kounou.lille.inra.fr) and DNA-DNA hyridization. The enterobacteria were identi ed using the API 20E system (Biomérieux, France). Escherichia coli was present in 93% of samples. Enterobacter and Klebsiella species were more frequently found in children while Proteus and Pro×idencia species were typically found in the elderly. The species Bi dobacterium longum was the most frequently species isolated in children and adults, whereas Bi dobacterium adolescentis was the most encountered species in the elderly. The distribution of B. adolescentis was signi cantly split between the children and the elderly age groups. There is a noticeable trend whereby Bi dobacterium pseudocatenulatu m occurs more frequently in children than in the elderly. The species Bi dobacterium infantis, Bi dobacterium gallicum and Bi dobacterium angulatum have never been isolated, while Bi dobacterium bre×e has only been isolated on one occasion.

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The intelligent habitat is made of fixed components (movements detectors and intelligent electric household appliances) and small mobile processors worn by the elder. Fixed and mobile components communicate to assist the elder in performing his tasks and to intervene in case of risk. The system has two types of features: those carried out inside the residence (information acquisition, cognitive help like sound or visual cues when everyday life activity is carried out in an incomplete or dangerous way) and those reporting to the relatives and the external care network major risk events or evolution of the elder health state. The system intervention with the elder must be personalized according to the incurred risk gravity, his health state, his life habits and his preferred interaction mode: image, text, sound, voice ...

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Miace as a human cognitive architecture is a computational model that explains how a student acquires, encodes and uses domain knowledge. Because Miace takes into account the cognitive psychological laws and the environment in which the student works, it can be used as a virtual student in help systems dedicated to pedagogical formation, in intelligent tutoring systems, in cooperative learning applications and for the conception of didactic material. This paper describes the implementation of Miace and discusses the Miace theoretical components from three point of view: temporal, their roles in cognitive activity and their generic or functional forms. A comparison is done to show the originality and the contribution of Miace in user modeling.

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A prototype of an expert system for the identification of beta-galactosidase positive Enterobacteriaceae has been developed, for use with the API 20 EC kit. The system is implemented in Prolog on an IBM PC AT with 640 K of central memory and 20 megabytes of secondary memory. Its objectives are to highlight errors that can occur when the kit is in use. It can indicate the presence of new groups or species and give advice or suggest additional tests for the differentiation of the new species from those included in the kit.