Worldwide, urbanization constitutes a major and growing driver of global change and a distinctive feature of the Anthropocene. Thus, urban development paths present opportunities for technological and societal transformations towards energy efficiency and decarbonization, with benefits for both greenhouse gas (GHG) and air pollution mitigation. This requires a better understanding of the intertwined dynamics of urban energy and land use, emissions, demographics, governance, and societal and biophysical processes. In this study, we address several characteristics of urbanization in Santiago (33.5°S, 70.5°W, 500 m a.s.l.), the capital city of Chile. Specifically, we focus on the multiple links between mobility and air quality, describe the evolution of these two aspects over the past 30 years, and review the role scientific knowledge has played in policy-making. We show evidence of how technological measures (e.g., fuel quality, three-way catalytic converters, diesel particle filters) have been successful in decreasing coarse mode aerosol (PM10) concentrations in Santiago despite increasing urbanization (e.g., population, motorization, urban sprawl). However, we also show that such measures will likely be insufficient if behavioral changes do not achieve an increase in the use of public transportation. Our investigation seeks to inform urban development in the Anthropocene, and our results may be useful for other developing countries, particularly in Latin America and the Caribbean where more than 80% of the population is urban.

BackgroundCandida species are the most frequently found fungal pathogens causing nosocomial disease in a hospital setting. Such species must be correctly identified to ensure that appropriate control measures are taken and that suitable treatment is given for each species. Candida albicans is causing most fungal disease burden worldwide; the challenge lies in differentiating it from emerging atypical, minor and related species such as Candida dubliniensis and Candida africana. The purpose of this study was to compare identification based on MALDI-TOF MS to standard identification systems using a set of nosocomial isolates.MethodsEleven nosocomial samples were collected from 6 third-level hospitals in Bogotá, Colombia. All the samples were identified by combining MALDI-TOF MS with morphological characters, carbohydrate assimilation and molecular markers (D1/D2 and HWP1).ResultsThe present work describes the first collection of atypical Colombian Candida clinical isolates; these were identified as Candida albicans/Candida africana by their MALDI-TOF MS profile. Phenotypical characteristics showed that they were unable to produce chlamydospores, assimilate trehalose, glucosamine, N- acetyl-glucosamine and barely grew at 42 °C, as would be expected for Candida africana. The molecular identification of the D1/D2 region of large subunit ribosomal RNA and HWP1 hyphal cell wall protein 1 sequences from these isolates was consistent with those for Candida albicans. The mass spectra obtained by MALDI-TOF MS were analysed by multi-dimensional scaling (MDS) and cluster analysis, differences being revealed between Candida albicans, Candida africana, Candida dubliniensis reference spectra and two clinical isolate groups which clustered according to the clinical setting, one of them being clearly related to C. albicans.ConclusionThis study highlights the importance of using MALDI-TOF MS in combination with morphology, substrate assimilation and molecular markers for characterising Candida albicans-related and atypical C. albicans species, thereby overcoming conventional identification methods. This is the first report of hospital-obtained isolates of this type in Colombia; the approach followed might be useful for gathering knowledge regarding local epidemiology which could, in turn, have an impact on clinical management. The findings highlight the complexity of distinguishing between typical and atypical Candida albicans isolates in hospitals.

Abstract:The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m 2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.

Artículo de publicación ISIA multistructural object-based LiDAR approach to predict plant richness in complex structure forests is presented. A normalized LiDAR point cloud was split into four height ranges: 1) high canopies (points above 16 m); 2) middle-high canopies (8-16 m); 3) middle-low canopies (2-8 m); and 4) low canopies (0-2 m). A digital canopy model (DCM) was obtained from the full normalized LiDAR point cloud, and four pseudo-DCMs (pDCMs) were obtained from the split point clouds. We applied a multiresolution segmentation algorithm to the DCM and the four pDCMs to obtain crown objects. A partial least squares path model (PLS-PM) algorithm was applied to predict total vascular plant richness using object-based image analysis (OBIA) variables, derived from the delineated crown objects, and topographic variables, derived from a digital terrain model. Results showed that the object-based model was able to predict the total richness with an r(2) of 0.64 and a root-mean-square error of four species. Topographic variables showed to be more important than the OBIA variables to predict richness. Furthermore, high-medium canopies (8-16 m) showed the biggest correlation with the total plant richness within the structural segments of the forest.CONICYT through Integration of Advanced Human Capital into the Academy Project 79110001