Modelación numérica de la circulación costera y su aplicación al estudio de los procesos de transporte de sustancias y partículas contaminantes en el Litoral Caribe colombiano

IP 0140-09-347-956 v. Centro de documentacion tiene : Anexo 1 : Informes BancodeDatos y CIOH / Carlos Alberto Andrade. -- Anexo 2 : Publicaciones nacionales / Carlos Alberto Andrade. --Anexo 3 :Publicaciones internacionales. -- Anexo 4 : Manual usuario software amigable del modelo y DEMO enCD-Rom / Carlos Alberto Andrade. -- Anexo 5 : Informes cursos y otros eventos nacionales e internacionales /Carlos Alberto AndradeBogotá) -- Un sistema de pronostico de condiciones ambientalesydel impacto antropogenico en aguas costeras;del mar caribe colombiano / Lonin, Sergei A. ... [et.al].'-- en:Premio Nacional de Ciencias del Mar (1998) --;ARTICULO(S) EN REVISTA: Circulacion de las aguas y transportedecontaminantes en la Bahia interna de;Cartagena / S. Lonin, L. Giraldo. -- En: Bol. Cient. CIOH.no.16 (1995);p. 25-72. -- ISSN 01200542. --;Resultados preliminares del estudio de la capa limite mesometeorologica dela atmosfera en la Costa Norte;colombiana aplicando un modelo numerico / A. L. Kazakov...[etal.]. -- En: Noletin cientifico CIOH -- No. 17;(diciembre 1996); p. 17-56. -- ISSN 01200542. -- Resultados peliminares del estudio de la dinamica del sistema;de caños y lagunas de Cartagena / S. Lonin, L. Giraldo. --En:Bol.Cient.CIOH. No. 17 (1996); p. 57-63. --;PONENCIA(S) EN CONGRESO: Sobre la restitucion de los campos deviento a partir de observaciones en un area;determinada / Lonin y Giraldo. -- En: Congreso ColombianodeMeteorologia(4 : mar. 1996 : Santa Fe de;ISSN 01200542. -- Circulacion de las aguas en la Cienaga de lavirgen y formulacion de un modelo de su;calidad. -- En: Bol.Cient.CIOH. No. 17 (1996); p. 65-72. -'- isSN01200542.'-- Calculo de transparencia del;agua en la Bahia de Cartagena / S. Lonin. -- En: Bol. Cient.CIOH. no. 18 (1998); p. 85-92. -- ISSN 01200542.;'-- Hydrodynamic modelling and the problem of oil spills OntheColombian Caribbean Shoreline. -- En: Spill;Technol. -- vol. 22, no. 1-4 (Jan-Dic 1997); p. 1-6. -- ISSN3814459

Estructura y función de un ecosistema de manglar a lo largo de una trayectoria de restauración en diferentes niveles de perturbación

IP 2105-13-080-97Incluye anexos.por Jesus Garay Tinoco ... [et al.]. -- Santa Marta : Invemar,2004. -- 232 p. : il., fotografias ; 33 cm. --;(Publicaciones especiales ; no. 11). -- ISBN 9589734928.;LIBRO(S): Los manglares de la ecorregion Cienaga Grande deSantaMarta : pasado, presente y futuro / editad

Aplicación de la modelación numérica a la solución de problemas ambientales en lagunas costeras del caribe colombiano. Ciénaga grande de Santa Marta

IP 2119-09-023-99Incluye anexo

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications