The spatial prediction sandbox - Investigating the use of spatially-explicit modelling and cross-validation strategies in spatial interpolation machine learning problems

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesMachine Learning (ML) methods are increasingly used for spatial interpolation and di erent strategies have been proposed to introduce space into the modelling and validation phases. Nevertheless, a comparison of these methods under di erent landscape autocorrelation ranges and sampling designs is still missing. This Master Thesis investigates under which scenarios spatially-explicit ML modelling and validation strategies are appropriate for spatial interpolation problems. We designed a framework that allowed us to simulate predictor and outcome spatial elds with di erent autocorrelation ranges, as well as samples with di erent number of points and distributions. With these data, we tested di erent non-spatial and spatially-explicit (coordinates, EDF, RFsp) Random Forest ML models and evaluated them using the simulated surfaces as well as di erent standard (Leave-One- Out, LOO) and spatially-explicit (spatial bu er LOO, sbLOO) Cross-Validation (CV) strategies. We developed a new method called Nearest Distance Matching (NDM) to estimate the appropriate radius for sbLOO CV for spatial interpolation based on sample distribution and landscape range, and compared it to state-of-the art methods for radius search, only based on range. While for short ranges non-spatial models were superior to spatially-explicit models regardless of the sample size and distribution; for long ranges, spatial models performed better under regular and random sampling designs, but not clustered and non-uniform. CV results indicated that although LOO correctly estimated model performance under random designs, it yielded overestimated errors for regular samples and underestimated errors for clustered and non-uniform designs under long ranges. Results of sbLOO combined with NDM correctly addressed error underestimation of LOO in clustered and non-uniform samples, whereas sbLOO based solely on the range resulted in error overestimation for all designs under long ranges. This Master Thesis provides important insights to the eld of predictive mapping: it elucidates in which cases spatially-explicit methods may be preferred, and establishes that state-of-the-art approaches for spatial CV designed to assess model transferability are not suited for spatial interpolation and proposes an alternative

Antecedentes históricos del mat-building: cinco ejemplos

La perspectiva histórica que nos proporcionan estos ejemplos, hace posible la formulación de la siguiente hipótesis: el concepto de mat-building encierra, precisamente, aquello que coloca de nuevo la arquitectura moderna dentro de la tradición de la forma urbana, rompiendo el drástico aislamiento al que las vanguardias de principios del siglo XX habían intentado someter a las formas arquitectónicas. Todo lo que caracteriza a los mat-building (continuidad, superposición, proliferación, anonimato, etc.) lo encontramos también en la realidad de los principales tejidos urbanos históricos. La idea de mat-building representa pues, en cierta medida, el retorno a la ciudad, tras esa peculiar travesía del desierto que, para la arquitectura, constituyó la experiencia de la vanguardia. Por ello, no es de extrañar que nuestros comentarios, de un modo impremeditado, hayan tendido a glosar algunas de las intuiciones contenidas en el libro La arquitectura de la ciudad, publicado por Aldo Rossi en 1966. Ya que, paradójicamente, lo que gentes como los Smithson o el grupo Candilis-Josic-Woods perseguían ansiosamente entre los escombros de las ciudades europeas bombardeadas durante la segunda guerra mundial, no era en realidad muy distinto de lo que Aldo Rossi hallaba en lo más profundo de los yacimientos materiales de la ciudad histórica.Postprint (author's final draft

Development of land-use regression models for fine particles and black carbon in peri-urban South India

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM2.5) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) mug/m(3) for PM2.5 and 2.7 (0.5) mug/m(3) for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies

rtimicropem: an R package supporting the analysis of RTI MicroPEM output files

rtmicropem (Salmon and Zhou 2017) is an R package (R Core Team 2017) that aims at supporting the analysis of PM2.5 measures made with RTI MicroPEM. RTI MicroPEM are personal monitoring devices (PM2.5 and PM10) developped by RTI international. They output csv files containing both settings and measurements corresponding to measurement sessions. These files are not tabular data, that the package transforms into tabular data

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-b, more advanced stages (AT) of AD pathology and APOE-e4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892 and the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033. FB is supported by the NIHR biomedical research center at UCLH. MSC receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 948677), the Instituto de Salud Carlos III (PI19/00155), and from a fellowship from ”la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (LCF/BQ/PR21/11840004).Report de recerca signat per 27 autors/es: Irene Cumplido-Mayoral 1,2; Marina García-Prat 1; Grégory Operto 1,3,4; Carles Falcon 1,3,5; Mahnaz Shekari 1,2,3; Raffaele Cacciaglia 1,3,4; Marta Milà-Alomà 1,2,3,4; Luigi Lorenzini 6; Silvia Ingala 6; Alle Meije Wink 6; Henk JMM Mutsaerts 6; Carolina Minguillón 1,3,4; Karine Fauria 1,4; José Luis Molinuevo 1; Sven Haller 7; Gael Chetelat 8,10; Adam Waldman 9; Adam Schwarz 10; Frederik Barkhof 6,11; Ivonne Suridjan 12, 11; Gwendlyn Kollmorgen 13; Anna Bayfield 13; Henrik Zetterberg 14,15,16,17,18; Kaj Blennow 14,15 12; Marc Suárez-Calvet 1,3,4,19; Verónica Vilaplana 20; Juan Domingo Gispert 1,3,5; ALFA study; EPAD study; ADNI study; OASIS study // 1) Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 2) Universitat Pompeu Fabra, Barcelona, Spain; 3) IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; 4) CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; 5) Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; 6) Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; 7) CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland; 8) Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France; 9) Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK; 10) Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA; 11) Institutes of Neurology and Healthcare Engineering, University College London, London, UK; 12) Roche Diagnostics International Ltd, Rotkreuz, Switzerland; 13) Roche Diagnostics GmbH, Penzberg, Germany; 14) Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; 15) Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; 16) Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom; 17) UK Dementia Research Institute at UCL, London, United Kingdom; 18) Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China; 19) Servei de Neurologia, Hospital del Mar, Barcelona, Spain; 20) Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain.Preprin

Who is more vulnerable to effects of long-term exposure to air pollution on COVID-19 hospitalisation?

Objective: Factors that shape individuals’ vulnerability to the effects of air pollution on COVID-19 severity remain poorly understood. We evaluated whether the association between long-term exposure to ambient NO2, PM2.5, and PM10 and COVID-19 hospitalisation differs by age, sex, individual income, area-level socioeconomic status, arterial hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. Methods: We analysed a population-based cohort of 4,639,184 adults in Catalonia, Spain, during 2020. We fitted Cox proportional hazard models adjusted for several potential confounding factors and evaluated the interaction effect between vulnerability indicators and the 2019 annual average of NO2, PM2.5, and PM10. We evaluated interaction on both additive and multiplicative scales. Results: Overall, the association was additive between air pollution and the vulnerable groups. Air pollution and vulnerability indicators had a synergistic (greater than additive) effect for males and individuals with low income or living in the most deprived neighbourhoods. The Relative Excess Risk due to Interaction (RERI) was 0.21, 95 % CI, 0.15 to 0.27 for NO2 and 0.16, 95 % CI, 0.11 to 0.22 for PM2.5 for males; 0.13, 95 % CI, 0.09 to 0.18 for NO2 and 0.10, 95 % CI, 0.05 to 0.14 for PM2.5 for lower individual income and 0.17, 95 % CI, 0.12 to 0.22 for NO2 and 0.09, 95 % CI, 0.05 to 0.14 for PM2.5 for lower area-level socioeconomic status. Results for PM10 were similar to PM2.5. Results on multiplicative scale were inconsistent. Conclusions: Long-term exposure to air pollution had a larger synergistic effect on COVID-19 hospitalisation for males and those with lower individual- and area-level socioeconomic status.</p

Who is more vulnerable to effects of long-term exposure to air pollution on COVID-19 hospitalisation?

Objective: Factors that shape individuals’ vulnerability to the effects of air pollution on COVID-19 severity remain poorly understood. We evaluated whether the association between long-term exposure to ambient NO2, PM2.5, and PM10 and COVID-19 hospitalisation differs by age, sex, individual income, area-level socioeconomic status, arterial hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. Methods: We analysed a population-based cohort of 4,639,184 adults in Catalonia, Spain, during 2020. We fitted Cox proportional hazard models adjusted for several potential confounding factors and evaluated the interaction effect between vulnerability indicators and the 2019 annual average of NO2, PM2.5, and PM10. We evaluated interaction on both additive and multiplicative scales. Results: Overall, the association was additive between air pollution and the vulnerable groups. Air pollution and vulnerability indicators had a synergistic (greater than additive) effect for males and individuals with low income or living in the most deprived neighbourhoods. The Relative Excess Risk due to Interaction (RERI) was 0.21, 95 % CI, 0.15 to 0.27 for NO2 and 0.16, 95 % CI, 0.11 to 0.22 for PM2.5 for males; 0.13, 95 % CI, 0.09 to 0.18 for NO2 and 0.10, 95 % CI, 0.05 to 0.14 for PM2.5 for lower individual income and 0.17, 95 % CI, 0.12 to 0.22 for NO2 and 0.09, 95 % CI, 0.05 to 0.14 for PM2.5 for lower area-level socioeconomic status. Results for PM10 were similar to PM2.5. Results on multiplicative scale were inconsistent. Conclusions: Long-term exposure to air pollution had a larger synergistic effect on COVID-19 hospitalisation for males and those with lower individual- and area-level socioeconomic status.</p

Land-Use Change and Cardiometabolic Risk Factors in an Urbanizing Area of South India: A Population-Based Cohort Study.

BACKGROUND: Land-use changes in city fringes due to urbanization can lead to a reduction of greenspace that may reduce its associated health benefits. OBJECTIVES: We evaluated the association between changes in residential surrounding built-up land use and cardiometabolic risk factors in an urbanizing peri-urban area of south India and explored the mediating roles of air pollution, physical activity, and stress in these associations. METHODS: We analyzed data on 6,039 adults from the third follow-up of the Andhra Pradesh Children and Parent Study (APCAPS) cohort (2010-2012). We generated trajectories of change in residential surrounding built-up land use (buffer areas) from 1995-2009 (stable, slow increase, fast increase) using remote sensing data and image classification methods. We estimated associations between built-up land use trajectories and natural log-transformed blood pressure, waist circumference, triglycerides, fasting glucose, and non-high-density lipoprotein (non-HDL) cholesterol using linear mixed models. We accounted for multiple mediators and the multilevel structure of the data in mediation analyses. RESULTS: We observed positive associations between a fast increase in built-up land use within 300m of the home and all cardiometabolic risk factors. Compared with participants with stable trajectories, those with the largest increase in built-up land use had 1.5% (95% CI: 0.1, 2.9) higher systolic blood pressure, 2.4% (95% CI: 0.6, 4.3) higher diastolic blood pressure, 2.1% (95% CI: 0.5, 3.8) higher waist circumference, and 1.6% (95% CI: -0.6, 3.8) higher fasting glucose in fully adjusted models. Associations were positive, but not statistically significant, for triglycerides, fasting glucose, and non-HDL cholesterol. Physical activity and ambient particulate matter ≤2.5μm in aerodynamic diameter (PM2.5) partially mediated the estimated associations. Associations between fast build-up and all cardiometabolic risk factors except non-HDL cholesterol were stronger in women than men. DISCUSSION: Increases in built-up land use surrounding residences were consistently associated with higher levels of cardiometabolic risk factors. Our findings support the need for better integration of health considerations in urban planning in rapidly urbanizing settings. https://doi.org/10.1289/EHP5445

Resistance to antiangiogenic therapies by metabolic symbiosis in renal cell carcinoma PDX models and patients

Antiangiogenic drugs are used clinically for treatment of renal cell carcinoma (RCC) as a standard first-line treatment. Nevertheless, these agents primarily serve to stabilize disease, and resistance eventually develops concomitant with progression. Here, we implicate metabolic symbiosis between tumor cells distal and proximal to remaining vessels as a mechanism of resistance to antiangiogenic therapies in patient-derived RCC orthoxenograft (PDX) models and in clinical samples. This metabolic patterning is regulated by the mTOR pathway, and its inhibition effectively blocks metabolic symbiosis in PDX models. Clinically, patients treated with antiangiogenics consistently present with histologic signatures of metabolic symbiosis that are exacerbated in resistant tumors. Furthermore, the mTOR pathway is also associated in clinical samples, and its inhibition eliminates symbiotic patterning in patient samples. Overall, these data support a mechanism of resistance to antiangiogenics involving metabolic compartmentalization of tumor cells that can be inhibited by mTOR-targeted drugs

Association of ambient and household air pollution with lung function in young adults in an peri-urban area of South-India: A cross-sectional study.

OBJECTIVE: Although there is evidence for the association between air pollution and decreased lung function in children, evidence for adolescents and young adults is scarce. For a peri-urban area in India, we evaluated the association of ambient PM2.5 and household air pollution with lung function for young adults who had recently attained their expected maximum lung function. METHODS: We measured, using a standardized protocol, forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) in participants aged 20-26 years from the third follow-up of the population-based APCAPCS cohort (2010-2012) in 28 Indian villages. We estimated annual average PM2.5outdoors at residence using land-use regression. Biomass cooking fuel (a proxy for levels of household air pollution) was self-reported. We fitted a within-between linear-mixed model with random intercepts by village, adjusting for potential confounders. RESULTS: We evaluated 1,044 participants with mean age of 22.8 (SD = 1) years (range 20-26 years); 327 participants (31%) were female. Only males reported use of tobacco smoking (9% of all participants, 13% of males). The mean ambient PM2.5 exposure was 32.9 (SD = 2.8) µg/m3; 76% reported use of biomass as cooking fuel. The adjusted association between 1 µg/m3 increase in PM2.5 was -27 ml (95% CI, -89 to 34) for FEV1 and -5 ml (95% CI, -93 to 76) for FVC. The adjusted association between use of biomass was -112 ml (95% CI, -211 to -13) for FEV1 and -142 ml (95% CI, -285 to 0) for FVC. The adjusted association was of greater magnitude for those with unvented stove (-158 ml, 95% CI, -279 to -36 for FEV1 and -211 ml, 95% CI, -386 to -36 for FVC). CONCLUSIONS: We observed negative associations between ambient PM2.5 and household air pollution and lung function in young adults who had recently attained their maximum lung function