Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.Skull, neoplasms, logistic regression, neural networks, receiver operating characteristic curve, statistics, resampling

Margalef: el último naturalista

No resulta frecuente encontrar científicos con acreditada capacidad de excelencia y, al mismo tiempo, que hayan consolidado un cuerpo de doctrina significativo en su área de conocimiento, además de ostentar una completa formación humanística. El profesor Ramón Margalef ha cumplido sobradamente con ambos requisitos, demostrando al mismo tiempo gran eficacia y eficiencia, sirviendo como referencia a la generación de ecólogos del cambio de siglo

Gaudeamus Igitur: protocolo universitario X. Distinciones y condecoraciones

Análisis del protocolo para las autoridades académicas de la Universidad de Leó

Implicaciones ecológicas de la pandemia COVID-19

La irrupción de la pandemia COVID-19 ha acrecentado y certificado los posibles efectos de la insostenible intervención que pretende ejercer la especie humana sobre todos los ambientes, provocando reacciones de respuesta que se escapan de ese ansiado control en la explotación de los recursos del planeta. La presión cada vez más aguda sobre los ecosistemas silvestres, la merma de biodiversidad y la simplificación del funcionamiento de los sistemas biológicos, la degradación de los ambientes ecológicos, la destrucción de los recursos, el incremento de riesgos naturales destructivos y el aumento de la contaminación global, con proyecciones hacia un cambio climático jamás expresado en nuestro planeta con esa acelerada velocidad, pueden ser la causa de que un virus haya pasado desde un animal a la especie human

Emociones positivas y negativas en la predicción del burnout y engagement en el trabajo

En el presente estudio se prueba un modelo donde las emociones positivas presentan una relación directa y positiva con el engagement (hipótesis 1) y directa y negativa con el burnout (hipótesis 2). Para ello se cuenta con la participación de 407 trabajadores de empresas privadas de Argentina. Los resultados de Modelos de Ecuaciones Estructurales muestran que el modelo presenta un buen ajus- te a los datos (CFI = .97; GFI= .97 RMSEA= 0.09) y un considerable valor explicativo (66% del engagement y 60% del burnout). Finalmente, se discuten los desarrollos teóricos y las aplicaciones prácticas de estos resultados para generar modos de intervenciones prácticas tendientes a disminuir los niveles de burnout y aumentar los niveles de engagement en los trabajadores.This study tests a model in which positive emotions have a direct and positive relationship with engagement (hypothesis 1) and a direct and negative relationship with burnout (hypothesis 2). In order to do this, 407 workers from private companies in Argentina participated. Structural Equa- tion Modeling results show that the model presents a good adjustment of the data (CFI = .97; GFI= .97 RMSEA= 0.09) and a considerable explanatory value (66% of engagement and 60% of burnout). Finally, the theoretical developments and practical applications of these results are dis- cussed to develop practical interventions aiming to reduce the levels of burnout and to increase the levels of engagement in the workers

Agua

Conferencia sobre el agua, impartida en la Facultad de Ciencias Biológicas y Ambientales, con motivo de la festividad de San Alberto en noviembre de 201

Effects of wildfires on environmental variability: a comparative analysis using different spectral indices, patch metrics and thematic resolutions

P. 697-710Knowledge on environmental variability and how it is affected by disturbances is crucial for understanding patterns of biodiversity and determining adequate conservation strategies. The aim of this study is to assess environmental variability in patches undergoing post-fire vegetation recovery, identifying trends of change and their relevant drivers. We particularly evaluate: the value of three spectral indices derived from Landsat satellite data [Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI) and Wetness Component of the Tasseled Cap Transformation (TCW)] for describing secondary succession; the effectiveness of three metrics (diversity, evenness and richness) as indicators of patch variability; and how thematic resolution can affect the perception of environmental variability patterns. While the system was previously characterised as highly resilient from estimations of vegetation cover, here we noted that more time is required to fully recover pre-fire environmental variability. Using mean diversity as indicator of patch variability, we found similar patterns of temporal change for the three spectral indices (NBR, NDVI and TCW). Analogous conclusions could be drawn for richness and evenness. Patch variability, measured as diversity, showed consistent patterns across thematic resolutions, although values increased with the number of spectral classes. However, when the variance of diversity was plotted against thematic resolution, different scale dependencies were detected for those three spectral indices, yielding a dissimilar perception of patch variability. In general terms, NDVI was the best performing spectral index to assess patterns of vegetation recovery, while TCW was the worst. Finally, burned patches were classified into three classes with similar trends of change in environmental variability, which were strongly related to fire severity, elevation and vegetation type.S