AbstractThis study used the first detailed radar measurements of the vertical structure of precipitation obtained in the central Andes of southern Peru and Bolivia to investigate the diurnal cycle and vertical structure of precipitation and melting-layer heights in the tropical Andes. Vertically pointing 24.1-GHz Micro Rain Radars in Cusco, Peru (3350 m MSL, August 2014–February 2015), and La Paz, Bolivia (3440 m MSL, October 2015–February 2017), provided continuous 1-min profiles of reflectivity and Doppler velocity. The time–height data enabled the determination of precipitation timing, melting-layer heights, and the identification of convective and stratiform precipitation features. Rawinsonde data, hourly observations of meteorological variables, and satellite and reanalysis data provided additional insight into the characteristics of these precipitation events. The radar data revealed a diurnal cycle with frequent precipitation and higher rain rates in the afternoon and overnight. Short periods with strong convective cells occurred in several storms. Longer-duration events with stratiform precipitation structures were more common at night than in the afternoon. Backward air trajectories confirmed previous work indicating an Amazon basin origin of storm moisture. For the entire dataset, median melting-layer heights were above the altitude of nearby glacier termini approximately 17% of the time in Cusco and 30% of the time in La Paz, indicating that some precipitation was falling as rain rather than snow on nearby glacier surfaces. During the 2015–16 El Niño, almost half of storms in La Paz had melting layers above 5000 m MSL.

AbstractBrain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.