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See detailDiscriminative stimulus effects of ethanol with a conditioned taste aversion procedure: lack of acetaldehyde substitution
Quertemont, Etienne ULg

in Behavioural Pharmacology (2003), 14(4), 343-350

Acetaldehyde has been suggested to mediate a number of the pharmacological and behavioural effects of ethanol. Recently, several studies investigated the role of acetaldehyde in the subjective effects of ... [more ▼]

Acetaldehyde has been suggested to mediate a number of the pharmacological and behavioural effects of ethanol. Recently, several studies investigated the role of acetaldehyde in the subjective effects of ethanol, but obtained conflicting results. With the discriminative taste aversion (DTA) procedure, high acetaldehyde doses were shown to substitute for the discriminative stimulus effects of ethanol. In contrast, the operant drug discrimination protocol failed to show any substitution effect of acetaldehyde. Several methodological differences between the two procedures could explain these discrepancies, and particularly the absence of an individual discrimination criterion in the DTA procedure. In the present study, the DTA procedure was adapted to introduce such a criterion. In addition, the effects of acetaldehyde were compared with those of other drugs, for which the substitution effects for ethanol are well known. Rats were trained to discriminate 1.0 g/kg ethanol from saline in a DTA protocol. When the rats met the criterion of ethanol discrimination, various doses of several drugs were tested for their ethanol stimulus substitution effects: ethanol, acetaldehyde, dizocilpine, diazepam and nicotine. The results showed a clear dose-dependent discrimination of ethanol stimulus effects. In addition, dizocilpine fully substituted for ethanol, while diazepam only partially substituted. In contrast, both acetaldehyde and nicotine failed to substitute for ethanol. These results show that acetaldehyde is not significantly involved in the subjective and discriminative stimulus effects of ethanol. Acetaldehyde up to toxic doses did not substitute for the ethanol discriminative stimulus in the DTA protocol, when non-specific effects were carefully controlled. [less ▲]

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See detailDiscriminative stimulus effects of ethanol: Lack of interaction with taurine
Quertemont, Etienne ULg; Grant, Kathleen A.

in Behavioural Pharmacology (2004), 15(7), 495-501

Recent microdialysis studies showed that ethanol administration increases the release of taurine in various rat brain regions, and it was suggested that this increase in extracellular concentrations of ... [more ▼]

Recent microdialysis studies showed that ethanol administration increases the release of taurine in various rat brain regions, and it was suggested that this increase in extracellular concentrations of taurine might mediate some of the neurochemical effects of ethanol. Previous drug discrimination studies showed that positive modulators of the GABA(A) receptor consistently substituted for ethanol discriminative stimulus effects. Since taurine is also believed to modulate GABA(A) receptor activity, this study addressed the hypothesis that taurine mediates the discriminative stimulus effects of ethanol due to GABA(A) activation. Male Long-Evans rats were trained to discriminate water from either 1 or 2 g/kg ethanol. In a first experiment, various taurine doses (0-500 mg/kg) were tested to investigate whether taurine substitutes for ethanol. In a second experiment, rats were pretreated with either 500 mg/kg taurine or an equivalent volume of saline before testing for ethanol discrimination with various ethanol doses (0-2.0 g/kg). The results showed that taurine does not substitute for ethanol at any tested doses. In addition, taurine pretreatments failed to modify the dose-response curve for ethanol discrimination. These results demonstrate that taurine is not directly involved in mediating the discriminative stimulus effects of ethanol. It is therefore very unlikely that the brain release of taurine observed after ethanol administration is implicated in the major pharmacological effects of ethanol, i.e. positive modulation of GABA(A) receptor, that mediate its discriminative stimulus effects. [less ▲]

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See detailDiscussant
Martiniello, Marco ULg

Scientific conference (2015)

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See detailDiscussant
Martiniello, Marco ULg

Scientific conference (2009, January 28)

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See detailDiscussant and Reporter at the Colloquium: “Multiculturalism, Minorities and Citizenship”
Martiniello, Marco ULg

Scientific conference (1997, April 18)

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See detailDiscussant in the Colloquium: “Rethinking Ethnic and Racial Studies”
Martiniello, Marco ULg

Scientific conference (1997, May 16)

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Peer Reviewed
See detailDiscussant Panel on "Public-Private Partnerships: New Governance Arrangements"
Remy, Céline ULg

Conference (2014, November 27)

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Peer Reviewed
See detailDiscussing the validation of high-dimensional probability distribution learning with mixtures of graphical models for inference
Schnitzler, François ULg

Poster (2010, October 06)

Exact inference on probabilistic graphical models quickly becomes intractable when the dimension of the problem increases. A weighted average (or mixture) of different simple graphical models can be used ... [more ▼]

Exact inference on probabilistic graphical models quickly becomes intractable when the dimension of the problem increases. A weighted average (or mixture) of different simple graphical models can be used instead of a more complicated model to learn a distribution, allowing probabilistic inference to be much more efficient. I hope to discuss issues related to the validation of algorithms for learning such mixtures of models and to high-dimensional learning of probabilistic graphical models in general, and to gather valuable feedback and comments on my approach. The main problems are the difficulties to assess the accuracy of the algorithms and to choose a representative set of target distributions. The accuracy of algorithms for learning probabilistic graphical models is often evaluated by comparing the structure of the resulting model to the target (e.g. Number of similar/dissimilar edges, score BDe etc). This approach however falls short when studying methods using a mixture of simple models : individually, these lack the representation power to model the true distribution, and only their combination allows them to compete with more sophisticated models. The Kullback-Leibler divergence is a measure of the difference between two probability densities, and can be used to compare any model learned from a dataset to the data generating distribution. For computational reasons, I however had to resort to a Monte Carlo estimation of this quantity for large problems (starting at around 200 variables). Since probabilistic inference is the ultimate motivation for building these models, and not probability modelling, a more meaningful measure of accuracy could be obtained by comparing mixtures against a combination of state of the art model learning and approximate inference algorithms. However, the exact inference result cannot be easily assessed for interesting target distributions, since the use of mixtures is precisely considered because exact inference is not possible on said targets, and approximate inference would introduce a bias. Selecting a target distribution used to generate the data sets on which the algorithms are evaluated also proved a challenge. The easiest solution was to generate them at random (although different approaches can be designed). These models are however likely to be rather different from real problems, and thus constitute a poor choice to assess the practical interest of mixture of models. Methods (e.g. linking multiple copies of a given network) have been developed to increase the size of models known by the community (e.g. the alarm network), and the obtained graphical models have been made available. These could however still be far from the kind of interactions present in a real setting. A better way to proceed could be to generate samples based on the equations describing a physical problem, to learn a probabilistic model as best as possible from this high-dimensional dataset, and to use it as target distribution. [less ▲]

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See detailDiscussion
Richelle, Marc ULg

in Angelergue, André (Ed.) Psychologie de la connaissance de soi (1975)

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See detailDiscussion : l'astronomie actuelle
Swings, Polydore ULg; Bruhat, M.; Ulmo, M. et al

in Berr, Henri (Ed.) Le ciel dans l'histoire et la science : exposés de la huitième semaine internationale de synthèse (1936)

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See detailDiscussion : Pratiques évaluatives en classe et épreuves externes
Lafontaine, Dominique ULg

in Congrès international. Actualité de al recherche en éducation et en formation. AREF 2010 (2010)

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See detailLa discussion à visée démocratique et philosophique au prisme de la critique deleuzienne de la discussion. Analyse d'un verbatim.
Herla, Anne ULg; Jeanmart, Gaëlle

in Diotime. Revue internationale de didactique de la philosophie. (2014, April), 60

Detailed reference viewed: 52 (5 ULg)