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The chemical composition of snow provides insights on atmospheric transport of anthropogenic contaminants at different spatial scales. In this study, we assess how human activities influence the concentration of elements in the Andean mountain snow along a latitudinal transect throughout Chile. The concentration of seven elements (Al, Cu, Fe, Li, Mg, Mn and Zn) was associated to gaseous and particulate contaminants emitted at different spatial scales. Our results indicate carbon monoxide (CO) averaged at 20 km and nitrogen oxide (NOx) at 40 km as the main indicators of the chemical elements analyzed. CO was found to be a significant predictor of most element concentrations while concentrations of Cu, Mn, Mg and Zn were positively associated to emissions of NOx. Emission of 2.5 μm and 10 μm particulate matter averaged at different spatial scales was positively associated to concentration of Li. Finally, the concentration of Zn was positively associated to volatile organic compounds (VOC) averaged at 40 km around sampling sites. The association between air contaminants and chemical composition of snow suggests that regions with intensive anthropogenic pollution face reduced quality of freshwater originated from glacier and snow melting., (© 2021. The Author(s).)

We aimed to discover blood biomarkers associated with longitudinal changes in white matter hyperintensities (WMH). This study was divided into a discovery phase and a replication phase. Subjects in both studies were patients with hypertension, aged 50-70, who underwent two magnetic resonance imaging (MRI) sessions and blood extractions over a 4-year follow-up period. In the discovery phase, we screened 1305 proteins in 12 subjects with WMH progression and in 12 matched control subjects. We found that 41 proteins were differentially expressed: 13 were upregulated and 28 were downregulated. We subsequently selected three biomarkers for replication in baseline and follow-up samples in 80 subjects with WMH progression and in 80 control subjects. The selected protein candidates for the replication were MMP9 (matrix metalloproteinase-9), which was higher in cases, MET (hepatocyte growth factor receptor) and ASAH2 (neutral ceramidase), which were both lower in cases of WMH progression. Baseline biomarker concentrations did not predict WMH progression. In contrast, patients with WMH progression presented a steeper decline in MET over time. Furthermore, cases showed higher MMP9 and lower ASAH2 levels than controls at the follow-up. These results indicate that MMP9, MET, and ASAH2 are potentially associated with the progression of WMH, and could therefore be interesting candidates to validate in future studies., (© 2021. The Author(s).)

Predicting radiative forcing due to Antarctic stratospheric ozone recovery requires detecting changes in the ozone vertical distribution. In this endeavor, the Limb Profiler of the Ozone Mapping and Profiler Suite (OMPS-LP), aboard the Suomi NPP satellite, has played a key role providing ozone profiles over Antarctica since 2011. Here, we compare ozone profiles derived from OMPS-LP data (version 2.5 algorithm) with balloon-borne ozonesondes launched from 8 Antarctic stations over the period 2012-2020. Comparisons focus on the layer from 12.5 to 27.5 km and include ozone profiles retrieved during the Sudden Stratospheric Warming (SSW) event registered in Spring 2019. We found that, over the period December-January-February-March, the root mean square error (RMSE) tends to be larger (about 20%) in the lower stratosphere (12.5-17.5 km) and smaller (about 10%) within higher layers (17.5-27.5 km). During the ozone hole season (September-October-November), RMSE values rise up to 40% within the layer from 12.5 to 22 km. Nevertheless, relative to balloon-borne measurements, the mean bias error of OMPS-derived Antarctic ozone profiles is generally lower than 0.3 ppmv, regardless of the season.

To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We trained a 2D convolutional neural network (CNN) for automatic segmentation ventricular volume from 3D US of preterm infants with PHVD. The method was validated with the Dice similarity coefficient (DSC) and the intra-class coefficient (ICC) compared to manual segmentation. The mean birth weight of the included patients was 1233.1 g (SD 309.4) and mean gestational age was 28.1 weeks (SD 1.6). A total of 152 serial 3D US from 10 preterm infants with PHVD were analysed. 230 ventricles were manually segmented. Of these, 108 were used for training a 2D CNN and 122 for validating the methodology for automatic segmentation. The global agreement for manual versus automated measures in the validation data (n = 122) was excellent with an ICC of 0.944 (0.874-0.971). The Dice similarity coefficient was 0.8 (± 0.01). 3D US based ventricular volume estimation through an automatic segmentation software developed through deep learning improves the accuracy and reduces the processing time needed for manual segmentation using VOCAL. 3D US should be considered a promising tool to help deepen our current understanding of the complex evolution of PHVD.

The snowpack is an important source of water for many Andean communities. Because of its importance, elemental and mineralogical composition analysis of the Andean snow is a worthwhile effort. In this study, we conducted a chemical composition analysis (major and trace elements, mineralogy, and chemical enrichment) of surface snow sampled at 21 sites across a transect of about 2,500 km in the Chilean Andes (18-41°S). Our results enabled us to identify five depositional environments: (i) sites 1-3 (in the Atacama Desert, 18-26°S) with relatively high concentrations of metals, high abundance of quartz and low presence of arsenates, (ii) sites 4-8 (in northern Chile, 29-32°S) with relatively high abundance of quartz and low presence of metals and arsenates, (iii) sites 9-12 (in central Chile, 33-35°S) with anthropogenic enrichment of metals, relatively high values of quartz and low abundance of arsenates, (iv) sites 13-14 (also in central Chile, 35-37°S) with relatively high values of quartz and low presence of metals and arsenates, and v) sites 15-21 (in southern Chile, 37-41°S) with relatively high abundance of arsenates and low presence of metals and quartz. We found significant anthropogenic enrichment at sites close to Santiago (a major city of 6 million inhabitants) and in the Atacama Desert (that hosts several major copper mines).

Vertical profiles of black carbon (BC) and other light-absorbing impurities were measured in seasonal snow and permanent snowfields in the Chilean Andes during Austral winters 2015 and 2016, at 22 sites between latitudes 18°S and 41°S. The samples were analyzed for spectrally-resolved visible light absorption. For surface snow, the average mass mixing ratio of BC was 15 ng/g in northern Chile (18-33°S), 28 ng/g near Santiago (a major city near latitude 33°S, where urban pollution plays a significant role), and 13 ng/g in southern Chile (33-41°S). The regional average vertically-integrated loading of BC was 207 µg/m 2 in the north, 780 µg/m 2 near Santiago, and 2500 µg/m 2 in the south, where the snow season was longer and the snow was deeper. For samples collected at locations where there had been no new snowfall for a week or more, the BC concentration in surface snow was high (~10-100 ng/g) and the sub-surface snow was comparatively clean, indicating the dominance of dry deposition of BC. Mean albedo reductions due to light-absorbing impurities were 0.0150, 0.0160, and 0.0077 for snow grain radii of 100 µm for northern Chile, the region near Santiago, and southern Chile; respective mean radiative forcings for the winter months were 2.8, 1.4, and 0.6 W/m 2 . In northern Chile, our measurements indicate that light-absorption by impurities in snow was dominated by dust rather than BC.