999 resultados para 1470
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Ubiquitin ligases play a pivotal role in substrate recognition and ubiquitin transfer, yet little is known about the regulation of their catalytic activity. Nedd4 (neural-precursor-cell-expressed, developmentally down-regulated 4)-2 is an E3 ubiquitin ligase composed of a C2 domain, four WW domains (protein-protein interaction domains containing two conserved tryptophan residues) that bind PY motifs (L/PPXY) and a ubiquitin ligase HECT (homologous with E6-associated protein C-terminus) domain. In the present paper we show that the WW domains of Nedd4-2 bind (weakly) to a PY motif (LPXY) located within its own HECT domain and inhibit auto-ubiquitination. Pulse-chase experiments demonstrated that mutation of the HECT PY-motif decreases the stability of Nedd4-2, suggesting that it is involved in stabilization of this E3 ligase. Interestingly, the HECT PY-motif mutation does not affect ubiquitination or down-regulation of a known Nedd4-2 substrate, ENaC (epithelial sodium channel). ENaC ubiquitination, in turn, appears to promote Nedd4-2 self-ubiquitination. These results support a model in which the inter- or intra-molecular WW-domain-HECT PY-motif interaction stabilizes Nedd4-2 by preventing self-ubiquitination. Substrate binding disrupts this interaction, allowing self-ubiquitination of Nedd4-2 and subsequent degradation, resulting in down-regulation of Nedd4-2 once it has ubiquitinated its target. These findings also point to a novel mechanism employed by a ubiquitin ligase to regulate itself differentially compared with substrate ubiquitination and stability.
Resumo:
This article extends existing discussion in literature on probabilistic inference and decision making with respect to continuous hypotheses that are prevalent in forensic toxicology. As a main aim, this research investigates the properties of a widely followed approach for quantifying the level of toxic substances in blood samples, and to compare this procedure with a Bayesian probabilistic approach. As an example, attention is confined to the presence of toxic substances, such as THC, in blood from car drivers. In this context, the interpretation of results from laboratory analyses needs to take into account legal requirements for establishing the 'presence' of target substances in blood. In a first part, the performance of the proposed Bayesian model for the estimation of an unknown parameter (here, the amount of a toxic substance) is illustrated and compared with the currently used method. The model is then used in a second part to approach-in a rational way-the decision component of the problem, that is judicial questions of the kind 'Is the quantity of THC measured in the blood over the legal threshold of 1.5 μg/l?'. This is pointed out through a practical example.
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1944/07/14 (N25).
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1943/02/04 (N7).
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1944/03-1944/04.
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1943/02/17 (N8).
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1943/11/05 (N19).
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1943/12/25 (N20).
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1943/01/13 (N5).
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1943/09/15 (N17).
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1942/11/14 (N2).
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Périodicité : Quotid. à partir du n° 27 (1944, 2 sept.)
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1942/10/21 (N1).
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1943/06/23 (N14).
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1943/01/25 (N6).