Evaluacion del modelo aerodinamico simplificado para determinar la evapotranspiracion en soya (Glycine max cv. Paoki)

Resumen (Spanish, English)39 p.La evapotranspiración (ET) es importante en la determinación de las necesidades de agua de los cultivos, el diseño y operación de los sistemas de riego. Para estimarla se han desarrollado varios métodos los que además de presentar diferentes grados de error deben ser calibrados localmente. El Modelo Aerodinámico Simplificado (MAS), estima la ET en forma directa (no necesita de calibración) y se basa en el principio físico del balance de energía para un cultivo. La energía que recibe un sistema cultivo desde el sol se denomina radiación neta (Rn), y es la que emplea dicho sistema para realizar 3 procesos: incrementar la temperatura del suelo (flujo de calor del suelo, G), producir el calentamiento del ambiente (flujo de calor sensible, H), y el cambio de agua líquida a gaseosa (flujo de calor latente, LE), proceso conocido como ET. El objetivo de esta investigación fue evaluar el Modelo Aerodinámico Simplificado como estimador del flujo de calor latente (LE) sobre un cultivo de soya, empleando datos meteorológicos registrados por estaciones automáticas pertenecientes al Instituto Nacional de Investigación Agraria (INRA) en Francia; y comparados con los obtenidos por el modelo de Bowen, que es uno de los más exactos y simples usados en investigación. Los resultados mostraron que, en promedio, el MAS presentó una línea de regresión que pasa por el origen con pendiente estadísticamente uno (α = 0,05) y una desviación estándar del error de 46 W m-2. La principal dificultad del modelo se refiere a las propiedades aerodinámicas de los cultivos, las que debieron ser estimadas. Lo anterior revela que este modelo podría ser una herramienta eficaz en la estimación del calor latente y de la evapotranspiración

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Joint effect among p53, CYP1A1, GSTM1 polymorphism combinations and smoking on prostate cancer risk: an exploratory genotype-environment interaction study

Aim: To assess the role of several genetic factors in combination with an environmental factor as modulators of prostate cancer risk. We focus on allele variants of low-penetrance genes associated with cell control, the detoxification processes and smoking. Methods: In a case-control study we compared people carrying p53cd72 Pro allele, CYP1A1 M1 allele and GSTM1 null genotypes with their prostate cancer risk. Results: The joint risk for smokers carrying Pro* and M1*, Pro* and GSTM1null or GSTM1 null and CYP1A1 M1* variants was significantly higher (odds ratio [OR]: 13.13, 95% confidence interval [CI]: 2.41-71.36; OR: 3.97, 95% CI: 1.13-13.95 and OR: 6.87, 95% Cl: 1.68-27.97, respectively) compared with that for the reference group, and for non-smokers was not significant. OR for combinations among p53cd72, GSTM1 and CYP1A1 M I in smokers were positively and significantly associated with prostate cancer risk compared with non-smokers and compared with the Putative lowest risk group (OR: 8.87, 95% CI: 1.25-62.71). Conclusion: Our results suggest that a combination of p53cd72, CYP1A1, GSTM1 alleles and smoking plays a significant role in modified prostate cancer risk on the study population, which means that smokers carrying susceptible genotypes might have a significantly higher risk than those carrying non-susceptible genotypes