Día Virtual CUDI de la Comunidad Ecología

Bienvenida y presentación de los participantes Oscar Gilberto Cárdenas Hernández Coordinador Comunidad Ecología CUDI, Universidad de Guadalajara Presentación CUDI José Antonio Ramírez Vidal CUDI Red Mexicana de Investigación Ecológica a Largo Plazo Manuel Maass Centro de Investigaciones en Ecosistemas Exóticos portuarios marinos Sergio Salazar ECOSUR-Chetumal Manejo de ecosistemas costeros mediante un Sistema de Bombeo por Energía de Oleaje (SIBEO) Steven Czitrom Baus Universidad Nacional Autónoma de México Biotecnología microbiana con impacto ambiental para el desarrollo económico y social del estado de Guanajuato Félix Gutiérrez Corona Universidad de Guanajuato Manejo integral del agua en cuencas contrastantes en México: de la investigación científica y formación a la toma de decisiones Ignacio Sánchez Cohen Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias Sistema de monitoreo de la dinámica del carbono en ecosistemas terrestres de México Felipe García Oliva Programa Mexicano de Carbono Unidad de Diversidad Genética en el Noroeste de México a través de la conformación del Instituto de Diversidad Genética Ricardo Rodríguez Estrella Centro de Investigaciones Biológicas del Noroeste Sociedad y desarrollo Reyna Moguel Viveros El Colegio de la Frontera Sur Conclusiones generales y propuesta de fecha próxima reunión Oscar Gilberto Cárdenas Hernández Coordinador Comunidad Ecología CUDI, Universidad de GuadalajaraCada grupo presentará información general sobre las Megapropuestas, con el fin de conocer qué se está haciendo en nuestro país con respecto a ecología, manejo de recursos naturales y sustentabilidad.ecologia29ene08.wmv/fl

Fungal Alcohol Dehydrogenases: Physiological Function, Molecular Properties, Regulation of Their Production, and Biotechnological Potential

Fungal alcohol dehydrogenases (ADHs) participate in growth under aerobic or anaerobic conditions, morphogenetic processes, and pathogenesis of diverse fungal genera. These processes are associated with metabolic operation routes related to alcohol, aldehyde, and acid production. The number of ADH enzymes, their metabolic roles, and their functions vary within fungal species. The most studied ADHs are associated with ethanol metabolism, either as fermentative enzymes involved in the production of this alcohol or as oxidative enzymes necessary for the use of ethanol as a carbon source; other enzymes participate in survival under microaerobic conditions. The fast generation of data using genome sequencing provides an excellent opportunity to determine a correlation between the number of ADHs and fungal lifestyle. Therefore, this review aims to summarize the latest knowledge about the importance of ADH enzymes in the physiology and metabolism of fungal cells, as well as their structure, regulation, evolutionary relationships, and biotechnological potential

Antología de poesía I

Con esta Antología de poesía damos inicio a la colección de estudiantes del PEC quienes verán aquí sus creaciones, mucho por vez primera, plasmadas en negro sobre blanco

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts