Time Series Analysis of MODIS NDVI data with Cloudy Pixels: Frequency-domain and SiZer analyses of vegetation change in Western Rwanda

Remote sensing is a valuable source of data for the study of human ecology in rural areas. In this thesis, I attempt to analyze the presence of a long-term trend indicative of post-resettlement adaptation in the vegetation signals of Western Rwanda. There is a dearth of research utilizing medium resolution imagery to study difficult environments, such as tropical-montane regions, where complex topography and cloud cover diminish image accuracy. I attempt to add to the extant literature on frequency-domain smoothing methods as well as the literature on human-environment interaction in tropical-montane regions by applying a harmonic filtering and smoothing algorithm to the ‘MOD13Q1’, 16-day composite, 250m, NDVI, MODIS imagery. To create a more robust time-series, I combine Gaussian generalized additive models and discrete Fourier analysis of the residuals to impute values to a filtered time series, based on MODIS’s own pixel reliability data. These methods significantly improve the quality of the time-series being analyzed, compared with the raw data, or imputation of the mean signal. To control for conflating variables, I take a difference-in-differences (DD) approach (Abadie, 2005) comparing resettled regions to older regions, identified in Google Earth. Harmonic filtering and smoothing shows a definite long-term trend of post-resettlement changes in the vegetation signal, demonstrated by the DD approach, analyzed in SiZer maps (Chaudhuri & Marron, 1999). Further research will be needed to determine whether this is indicative of cropping changes, or other impacts of post-resettlement adaptation

Machine Learning with Topological Data Analysis

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with TDA and persistence homology offers greater interpretability and generalizability than can be achieved with current state of the art tools. My work explores several applications of TDA and persistence homology. TDA has previously been used to track the movements of intracellular bodies and it follows that it could also be used as a tool to examine the cytoskeleton of cells as a network captured in confocal microscopic images. I show recent and developing work in an application of persistence homology for the classification of intra-cellular networks. In addition to persistence homology, methods from TDA can be used to partition data into hierarchical clusters. I explore empirical and theoretical findings from topological clustering of images and graphs in order to propose a novel generalization on the convolutional neural network (CNN) and contrast its empirical results with the traditional CNN

Incidence of severe critical events in paediatric anaesthesia (APRICOT): a prospective multicentre observational study in 261 hospitals in Europe

Background Little is known about the incidence of severe critical events in children undergoing general anaesthesia in Europe. We aimed to identify the incidence, nature, and outcome of severe critical events in children undergoing anaesthesia, and the associated potential risk factors. Methods The APRICOT study was a prospective observational multicentre cohort study of children from birth to 15 years of age undergoing elective or urgent anaesthesia for diagnostic or surgical procedures. Children were eligible for inclusion during a 2-week period determined prospectively by each centre. There were 261 participating centres across 33 European countries. The primary endpoint was the occurence of perioperative severe critical events requiring immediate intervention. A severe critical event was defined as the occurrence of respiratory, cardiac, allergic, or neurological complications requiring immediate intervention and that led (or could have led) to major disability or death. This study is registered with ClinicalTrials.gov, number NCT01878760. Findings Between April 1, 2014, and Jan 31, 2015, 31â127 anaesthetic procedures in 30â874 children with a mean age of 6·35 years (SD 4·50) were included. The incidence of perioperative severe critical events was 5·2% (95% CI 5·0â5·5) with an incidence of respiratory critical events of 3·1% (2·9â3·3). Cardiovascular instability occurred in 1·9% (1·7â2·1), with an immediate poor outcome in 5·4% (3·7â7·5) of these cases. The all-cause 30-day in-hospital mortality rate was 10 in 10â000. This was independent of type of anaesthesia. Age (relative risk 0·88, 95% CI 0·86â0·90; p<0·0001), medical history, and physical condition (1·60, 1·40â1·82; p<0·0001) were the major risk factors for a serious critical event. Multivariate analysis revealed evidence for the beneficial effect of years of experience of the most senior anaesthesia team member (0·99, 0·981â0·997; p<0·0048 for respiratory critical events, and 0·98, 0·97â0·99; p=0·0039 for cardiovascular critical events), rather than the type of health institution or providers. Interpretation This study highlights a relatively high rate of severe critical events during the anaesthesia management of children for surgical or diagnostic procedures in Europe, and a large variability in the practice of paediatric anaesthesia. These findings are substantial enough to warrant attention from national, regional, and specialist societies to target education of anaesthesiologists and their teams and implement strategies for quality improvement in paediatric anaesthesia. Funding European Society of Anaesthesiology

Incidence of severe critical events in paediatric anaesthesia (APRICOT): a prospective multicentre observational study in 261 hospitals in Europe

Background Little is known about the incidence of severe critical events in children undergoing general anaesthesia in Europe. We aimed to identify the incidence, nature, and outcome of severe critical events in children undergoing anaesthesia, and the associated potential risk factors. Methods The APRICOT study was a prospective observational multicentre cohort study of children from birth to 15 years of age undergoing elective or urgent anaesthesia for diagnostic or surgical procedures. Children were eligible for inclusion during a 2-week period determined prospectively by each centre. There were 261 participating centres across 33 European countries. The primary endpoint was the occurence of perioperative severe critical events requiring immediate intervention. A severe critical event was defined as the occurrence of respiratory, cardiac, allergic, or neurological complications requiring immediate intervention and that led (or could have led) to major disability or death. This study is registered with ClinicalTrials.gov, number NCT01878760. Findings Between April 1, 2014, and Jan 31, 2015, 31 127 anaesthetic procedures in 30 874 children with a mean age of 6.35 years (SD 4.50) were included. The incidence of perioperative severe critical events was 5.2% (95% CI 5.0-5.5) with an incidence of respiratory critical events of 3.1% (2.9-3.3). Cardiovascular instability occurred in 1.9% (1.7-2.1), with an immediate poor outcome in 5.4% (3.7-7.5) of these cases. The all-cause 30-day in-hospital mortality rate was 10 in 10 000. This was independent of type of anaesthesia. Age (relative risk 0.88, 95% CI 0.86-0.90; p<0.0001), medical history, and physical condition (1.60, 1.40-1.82; p<0.0001) were the major risk factors for a serious critical event. Multivariate analysis revealed evidence for the beneficial effect of years of experience of the most senior anaesthesia team member (0.99, 0.981-0.997; p<0.0048 for respiratory critical events, and 0.98, 0.97-0.99; p=0.0039 for cardiovascular critical events), rather than the type of health institution or providers. Interpretation This study highlights a relatively high rate of severe critical events during the anaesthesia management of children for surgical or diagnostic procedures in Europe, and a large variability in the practice of paediatric anaesthesia. These findings are substantial enough to warrant attention from national, regional, and specialist societies to target education of anaesthesiologists and their teams and implement strategies for quality improvement in paediatric anaesthesia