Scaling of the morphology of African cities.

A large proportion of Africa's infrastructure is yet to be built. Where and how these new buildings are constructed matters since today's decisions will last for decades. The resulting morphology of cities has lasting implications for a city's energy needs. Estimating and projecting these needs has always been challenging in Africa due to the lack of data. Yet, given the sweeping urbanization expected in Africa over the next three decades, this obstacle must be overcome to guide cities toward a trajectory of sustainability and resilience. Based on the location and surface of nearly 200 million buildings on the continent, we estimate the interbuilding distance of almost six thousand cities. Buildings' footprint data enable the construction of urban form indicators to compare African cities' elongation, sprawl, and emptiness. We establish the BASE model, where the mean distance between buildings is a functional relation to the number of Buildings and their average Area, as well as the Sprawl and the Elongation of its spatial arrangement. The mean distance between structures in cities-our proxy for its energy demands related to mobility-grows faster than the square root of its population, resulting from the combined impact of a sublinear growth in the number of buildings and a sublinear increase in building size and sprawl. We estimate that when a city doubles its population, it triples its energy demand from transport.

Empiric recommendations for population disaggregation under different data scenarios.

High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping.

Cerebral blood flow is associated with markers of neurodegeneration in Huntington's disease.

INTRODUCTION: The ultimate cause of neuronal death in Huntington's disease (HD) is still uncertain. Apart from impairment in systems handling abnormal proteins, other mechanisms might contribute to neurodegeneration and progression of HD. Decreased cerebral blood flow (CBF) has been described in other neurodegenerative disorders and may play a role in HD. OBJECTIVES: To investigate CBF changes in HD gene carriers. METHODS: A group of 39 HD gene carriers (18 premanifest and 21 manifest HD) and 16 controls underwent a comprehensive clinical evaluation and a brain magnetic resonance imaging protocol that included pseudo-continuous arterial spin labeling to quantify CBF. Regions of interest (ROI) analyses were performed to compare CBF in controls vs premanifest HD vs manifest HD. Correlation analyses were performed to ascertain the relationship between CBF and clinical and biomarkers data. RESULTS: We found a decrease in CBF in bilateral caudate and putamen of patients with manifest HD in comparison with controls. CBF of premanifest HD carriers in the same ROIs was midway between controls and the HD patients, with differences not reaching statistical significance. Lower CBF in caudate and putamen was associated with worse motor symptoms, functionality, and cognitive performance. CBF was also associated with markers of neurodegeneration: higher CBF in caudate and putamen significantly correlated to higher volumes in the same ROI and to lower levels of neurofilament light chain. CONCLUSION: As CBF changes in caudate and putamen nuclei were associated with markers of neurodegeneration and with clinical outcomes, decreased CBF and oxygen supply could emerge as a relevant mechanism contributing to degeneration in HD.

Neighbourhood green space and health disparities in the global South: Evidence from Cali, Colombia.

Increasing attention has been given to the role of green space in reducing health disparities. However, robust evidence to support decision making is lacking in the global South. We investigate the relationship between green space and health as well as its underlying mechanism in Cali, Colombia. Results indicate that neighbourhood greenness is associated with enhanced self-rated 'good' health and reduced physical and mental distress. The health benefits of green space appear to be stronger for people living in wealthier neighbourhoods than those in poor neighbourhoods. Results highlight the importance of considering health disparities for future green infrastructure planning in the global South context.

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

Neuropad for the detection of cardiovascular autonomic neuropathy in patients with type 2 diabetes.

BACKGROUND: Cardiovascular autonomic neuropathy (CAN) is a prevalent and neglected chronic complication of diabetes, with a large impact on morbidity and mortality. Part of the reason why it is not detected and treated opportunely is because of the complexity of the tests required for its diagnosis. We evaluated the Neuropad®, a test based on sudomotor function, as a screening tool for CAN in adult patients with type 2 diabetes in Bogotá, Colombia. METHODS: This was a cross-sectional evaluation of Neuropad® for the detection of CAN. Patients were 20-75years of age and did not suffer from any other type of neuropathy. CAN was diagnosed using the Ewing battery of tests for R-R variability during deep breathing, Valsalva and lying-to-standing maneuvers. Additionally, distal symmetric polyneuropathy (DSP) was diagnosed using a sign-based scale (Michigan Neuropathy Disability Score - NDS) and a symptom-based score (Total Symptom Score - TSS). The primary outcome was the sensitivity and specificity of the Neuropad® for the diagnosis of CAN, and secondary outcomes were the sensitivity and specificity of Neuropad® for DSP. RESULTS: We studied 154 patients (74 men and 80 women). Prevalence of CAN was extremely high (68.0% of study participants), but also DSP was prevalent, particularly according to the signs-based definition (45%). The sensitivity of the Neuropad® for any degree of CAN was 70.1%, being slightly higher for the deep breathing and Valsalva tests than for lying-to-standing. The specificity of the Neuropad® for any type of CAN was only 37.0%, as expected for a screening exam. The negative predictive value was higher for the deep breathing and Valsalva tests (69.4 and 81.6%, respectively). Neuropad showed also a good sensitivity and negative predictive value for DSP. The sensitivity and specificity of Neuropad were better among men, and among patients with diabetes duration above the group median. CONCLUSIONS: The Neuropad is a simple and inexpensive device that demonstrated an adequate performance as a screening tool for cardiovascular autonomic neuropathy in Latin American patients with DM2.