Don Suero de Quiñones : Paso Honroso, 1434-1934

Descripción basada en la cub.Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 201

High prevalence of secondary resistance mutations in Venezuelan HIV-1 isolates.

The genetic variability was studied in HIV-1 from Venezuelan patients with and without treatment, in order to evaluate the presence of polymorphisms and drug resistance mutations. Proviral DNA from peripheral blood mononuclear cells or viral RNA from plasma was extracted from the blood of 30 patients. Two regions from the polymerase gene, protease (Pr) and reverse transcriptase (RT) and one genomic fragment from the envelope (Env) gene were amplified and sequenced. All HIV-1 samples analyzed were classified as subtype B, without evidence of recombination. Although no primary protease mutations were detected, a high frequency of secondary mutations (86%, 19/22), associated to restoration of viral replicative fitness, was observed in strains circulating both in treated and non-treated patients. Resistance mutations to nucleoside RT inhibitors (NRTI) and non-nucleoside RT inhibitors (NNRTI) were detected in 35% (6/17) and 12% (2/17) of the viruses circulating in treated patients, respectively. Resistance mutations were also present in the virus infecting one antiretroviral naive individual (7.7%), suggesting that local screening for resistant mutation in naive patient might be important to minimize therapy failure. Future studies are warranted to assess the role of secondary mutation in the success of viral infection

Maximum diffusion reinforcement learning

Robots and animals both experience the world through their bodies and senses. Their embodiment constrains their experiences, ensuring they unfold continuously in space and time. As a result, the experiences of embodied agents are intrinsically correlated. Correlations create fundamental challenges for machine learning, as most techniques rely on the assumption that data are independent and identically distributed. In reinforcement learning, where data are directly collected from an agent's sequential experiences, violations of this assumption are often unavoidable. Here, we derive a method that overcomes this issue by exploiting the statistical mechanics of ergodic processes, which we term maximum diffusion reinforcement learning. By decorrelating agent experiences, our approach provably enables single-shot learning in continuous deployments over the course of individual task attempts. Moreover, we prove our approach generalizes well-known maximum entropy techniques, and robustly exceeds state-of-the-art performance across popular benchmarks. Our results at the nexus of physics, learning, and control form a foundation for transparent and reliable decision-making in embodied reinforcement learning agents.Comment: The PDF file contains the collated main text and supplementary information. For supplementary movies, see https://www.youtube.com/playlist?list=PLO5AGPa3klrCTSO-t7HZsVNQinHXFQmn

Título: Crónicas del campo

En anteport. y lom. : Crónicas del campo

Del cancionero leonés

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201

[Fiestas en honor de San Isidoro, 1941]

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201

Valdejimena : cuento novelesco de gentes y costumbres

Precede al tít.: Tierra SalamanquinaCopia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201

Monumentos leoneses

Tít. de la cub.: LeónCopia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201