Geostatistical Model-Based Estimates of Schistosomiasis Prevalence among Individuals Aged ≤20 Years in West Africa

Schistosomiasis is a parasitic disease caused by a blood fluke that mainly occurs in Africa. Current prevalence estimates of schistosomiasis are based on historical data, and hence might be outdated due to control programs, improved sanitation, and water resources development and management (e.g., construction of large dams and irrigation systems). To help planning, coordination, and evaluation of control activities, reliable schistosomiasis prevalence estimates are needed. We analyzed compiled survey data from 1980 onwards for West Africa, including Cameroon, focusing on individuals aged ≤20 years. Bayesian geostatistical models were implemented based on environmental and climatic predictors to take into account potential spatial clustering within the data. We created the first smooth data-driven prevalence maps for Schistosoma mansoni and S. haematobium at high spatial resolution throughout West Africa. We found that an estimated 50.8 million West Africans aged ≤20 years are infected with schistosome blood flukes. Country prevalence estimates ranged between 0.5% (in The Gambia) and 37.1% (in Liberia) for S. mansoni and between 17.6% (in The Gambia) and 51.6% (in Sierra Leone) for S. haematobium. Our results allow prioritization of areas where interventions are needed, and to monitor and evaluate the impact of control activities

Crop identification and area estimation through the combined use of satellite and field data for county Durham, northern England

This thesis investigates the use of combined field and satellite data for crop identification and area estimation in County Durham, Northeast England. The satellite data were obtained by the Thematic Mapper (TM) sensor onboard Landsat-5 on 31 May 1985. The TM data were geometrically corrected to the British National Grid and the county boundaries were digitized in order to apply the methodology used in this study on a county basis. The field data were obtained by applying a stratified random sampling strategy. The area was subdivided into five main strata and forty four 1km(_^2) sample units were randomly chosen and fully surveyed by the author using a pre-prepared questionnaire. The field area measurements were taken and the final hectarage estimates were obtained for each crop. The research demonstrated the ability of Landsat-TM data to discriminate between agricultural crops in the study area. Results obtained emphasised that satellite data can be used for identification of agricultural crops over large geographic areas with small field sizes and different environmental and physical features. A land-cover classification system appropriate to the study area was designed. Using the Landsat-TM data, the study produced a classification map of thirteen land-cover types with more than 80% accuracy. The classification accuracy was assessed quantitatively by using the known land-use information obtained from the sample units visited during the field survey. The study analysed the factors which influenced the degree of separability between different agricultural crops since some crops were more clearly identified than others. Using a double sampling method based on the combination of both Landsat- TM and field data in regression analysis, a hectarage estimate was produced for each crop type in County Durham. The results obtained showed that the regression estimator was always more efficient than the field estimator. Crop area estimated by regression reduced the imprecision in all strata and was more efficient in some strata than others. This indicated that a gain in precision was achieved by using Landsat- TM in conjunction with the field data. The results illustrated that stratification based on an environmental criterion was an efficient approach as far as the the application of agricultural remote sensing in County Durham is concerned. The stratified approach allowed each stratum to be analysed separately, thereby lessening the reliance on cloud free imagery for the whole county on any given date. Furthermore, the results obtained by this study suggest that it is possibile to link remote sensing data with existing county based information systems on agricultural and land-use

An evaluation of the effect of terrain normalization on classification accuracy of Landsat ETM+ imagery

More than 60% of land in New Zealand has been converted from native forests to residential areas, agriculture, or forest plantations. Settlers brought many species of plants and animals to New Zealand. Many native species were unable to protect themselves from these new predators, causing numerous extinctions. In light of this rapid decline in biodiversity, the New Zealand government has attempted to mitigate the destruction of endemic flora and fauna through both new environmental policies and intensive land management. Land management techniques include the restoration of developed land and the protection of remaining areas of native forest. Monitoring of restoration efforts is important to the government and organizations responsible for this work. Using remotely sensed data to perform change analysis is a powerful method for long-term monitoring of restoration areas. The accuracy of maps created from remotely sensed data may be limited by significant terrain variation within many of the restoration areas. Landcare Research New Zealand has developed a topographic suppression algorithm that reduces the effects of topography. Landsat ETM+ imagery from November 2000 was processed with this algorithm to produce two images, an orthorectified image and a terrain-flattened image of a 50-km by 60-km area near Wanganui, New Zealand. Using GLOBE reference data collected on the ground in September/October 2004 and additional reference data photointerpreted from aerial photography, thematic maps were created using unsupervised, supervised, and hybrid classification methods. The accuracy of the thematic maps was evaluated using error matrices and Kappa analysis. The different image processing techniques were statistically compared. It was determined that the topographic-flattening algorithm did not significantly improve map accuracy

Contamination

Soil contamination occurs when substances are added to soil, resulting in increases in concentrations above background or reference levels. Pollution may follow from contamination when contaminants are present in amounts that are detrimental to soil quality and become harmful to the environment or human health. Contamination can occur via a range of pathways including direct application to land and indirect application from atmospheric deposition. Contamination was identified by SEPA (2001) as a significant threat to soil quality in many parts of Scotland. Towers et al. (2006) identified four principal contamination threats to Scottish soils: acidification; eutrophication; metals; and pesticides. The Scottish Soil Framework (Scottish Government, 2009) set out the potential impact of these threats on the principal soil functions. Severe contamination can lead to “contaminated land” [as defined under Part IIA of the Environmental Protection Act (1990)]. This report does not consider the state and impacts of contaminated land on the wider environment in detail. For further information on contaminated land, see ‘Dealing with Land Contamination in Scotland’ (SEPA, 2009). This chapter considers the causes of soil contamination and their environmental and socio-economic impacts before going on to discuss the status of, and trends in, levels of contaminants in Scotland’s soils

Inflow and Loadings from Ground Water to the Great Bay Estuary, New Hampshire

This final report presents the results of a study to evaluate groundwater inflow and nutrient loadings to the Great Bay Estuary, New Hampshire. The evaluation of inflow was accomplished independently by two methods: one, used thermal imagery, and the other, piezometric mapping. The thermal imagery method assessed groundwater that was observed to discharge within the intertidal zone of an inland estuary. The groundwater piezometric mapping method used bedrock wells around the bay to create an overall piezometric map of the near-bay area. Groundwater discharge was evaluated with respect to flow, concentration, and ultimately nitrogen loading to coastal waters. The results represent a snapshot for these variables, examined by a thermal infrared aerial survey in the spring of 2000, and water quality, specific discharge, and piezometric surface maps in the summer of 2001. Monitoring wells upgradient of the Great Bay were analyzed for nitrogen as an indicator of potential discharge source waters. Total groundwater discharge to the estuary was calculated as 24.2 cubic feet per second (cfs) with an average of 0.81± 0.89 mg dissolved inorganic nitrogen (DIN)/L, with a maximum value of 2.7 mg DIN/L (n=20). Nutrient concentrations, averaging 0.83± 1.34 mg DIN/L, with a maximum value of 10.2 mg DIN/L, were observed in upgradient bedrock groundwater analyzed from 192 wells. Nutrient loading was calculated to be 19.3±21.2 tons of N per year for the total Great Bay Estuary, covering nearly 144 miles of shoreline. The groundwater derived nutrient loading accounts for approximately 5% of the total non-point source load to the estuary. The thermal imagery method was found to be an effective and affordable alternative to conventional groundwater exploration approaches

Prostate cancer biochemical recurrence prediction using bpMRI radiomics, clinical and histopathological data

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), Universidade de Lisboa, Faculdade de Ciências, 2021O cancro da próstata é a segunda doença oncológica mais frequente nos homens, sendo frequentemente tratado com remoção cirúrgica total do órgão, denominada prostatectomia radical. Apesar dos avanços no diagnóstico e da evolução das terapias cirúrgicas, 20–35% dos candidatos a prostatectomia radical com intuito curativo sofrem de recidiva bioquímica, uma condição que representa o insucesso do tratamento inicial e também o primeiro sinal de progressão da doença. Em particular, dois terços dos casos de recidiva bioquímica ocorrem dentro de um período de dois anos. Ocorrendo cedo, este estado implica uma maior agressividade biológica da doença e um pior prognóstico, uma vez que pode dever-se `a presença de doença oculta, localmente avançada ou metastática. Apesar de o prognóstico devido ao desenvolvimento de recidiva bioquímica variar, em geral está associado a um risco acrescido de desenvolvimento de doença metastática e de mortalidade específica por cancro da próstata, representando assim uma importante preocupação clínica após terapia definitiva. Contudo, os modelos preditivos de recidiva bioquímica actuais não só falham na explicação da variabilidade dos resultados pós-cirúrgicos, como não têm habilidade para intervir cedo no processo de decisão de tratamento, uma vez que dependem de informação provinda da avaliação histopatológica da peça cirúrgica da prostatectomia ou da biópsia. Actualmente, o exame padrão para diagnóstico e para estadiamento do cancro da próstata é a ressonância magnética multiparamétrica, e as características provindas da avaliação dessas imagens têm mostrado potencial na caracterização do(s) tumor(es) e para predição de recidiva bioquímica. “Radiomics”, a recente metodologia aplicada à análise quantitativa de imagens médicas tem mostrado ter capacidade de quantificar objectivamente a heterogeneidade macroscópica de tecidos biológicos como tumores. Esta heterogeneidade detectada tem vindo a sugerir associação a heterogeneidade genómica que, por sua vez, tem demonstrado correlação com resistência a tratamento e propensão metastática. Porém, o potencial da análise radiómica das imagens de ressonância magnética (MRI) multiparamétrica da próstata para previsão de recidiva bioquímica pós-prostatectomia radical ainda não foi totalmente aprofundado. Esta dissertação propôs explorar o potencial da análise radiómica aplicada a imagens pré-cirúrgicas de ressonância magnética biparamétrica da próstata para previsão de recidiva bioquímica, no período de dois anos após prostatectomia radical. Este potencial foi avaliado através de modelos predictivos com base em dados radiómicos e parâmetros clínico-histopatológicos comummente adquiridos em três fases clínicas: pré-biópsia, pré- e pós-cirúrgica. 93 pacientes, de um total de 250, foram eleitos para este estudo retrospectivo, dos quais 20 verificaram recidiva bioquímica. 33 parâmetros clínico-histopatológicos foram recolhidos e 2715 variáveis radiómicas baseadas em intensidade, forma e textura, foram extraídas de todo o volume da próstata caracterizado em imagens originais e filtradas de ressonância magnética biparamétrica, nomeadamente, ponderadas em T2, ponderadas em Difusão, e mapas de coeficiente de difusão aparente (ADC). Embora os pacientes elegíveis tenham sido examinados na mesma instituição, as características do conjunto de imagens eram heterogéneas, sendo necessário aplicar vários passos de processamento para possibilitar uma comparação mais justa. Foi feita correção do campo tendencial (do inglês, “bias”) e segmentação manual das imagens T2, registo tanto para transposição das delineações do volume de interesse entre as várias modalidades imagiológicas como para correção de movimento, cálculo de mapas ADC, regularização do campo de visão, quantização personalizada em tons cinza e reamostragem. Tendo os dados recolhidos uma alta dimensionalidade (número de variáveis maior que o número de observações), foi escolhida a regressão logística com penalização L1 (LASSO) para resolver o problema de classificação. O uso da penalização aliada à regressão logística, um método simples e commumente usado em estudos de classificação, permite impedir o sobreajuste provável neste cenário de alta dimensionalidade. Além do popular LASSO, recorremos também ao algoritmo Priority-LASSO, um método recente para lidar com dados “ómicos” e desenvolvido com base no LASSO. O Priority-LASSO tem como princípio a definição da hierarquia ou prioridade das variáveis de estudo, através do agrupamento dessas mesmas variáveis em blocos sequenciais. Neste trabalho explorámos duas maneiras de agrupar as variáveis (Clínico-histopatológicas vs. Radiómicas e Clínico-histopatológicas vs. T2 vs. Difusão vs. ADC). Além disso, quisemos perceber qual o impacto da ordem destes mesmos blocos no desempenho do modelo. Para tal, testámos todas as permutações de blocos possíveis (2 e 24, respectivamente) em cada um dos casos. Assim, uma estrutura de aprendizagem automática, composta por métodos de classificação, validação-cruzada k-fold estratificada e repetida, e análises estatísticas, foi desenvolvida para identificar os melhores classificadores, dentro um conjunto de configura¸c˜oes testado para cada um dos três cenários clínicos simulados. Os algoritmos de regressão logística penalizada com LASSO e o Priority-LASSO efectuaram conjuntamente a seleção de características e o ajuste de modelos. Os modelos foram desenvolvidos de forma a optimizar o n´umero de casos positivos de recidiva bioquímica através da maximização das métricas área sob a curva (AUC) e medida-F (Fmax), derivadas da análise de curva característica de operação do receptor (ROC). Além da comparação das implementações Priority-LASSO com o caso em que não houve agrupamento de variáveis (isto é, LASSO), foram também comparados dois métodos de normalização de imagens com base no desempenho dos modelos (avaliado por Fmax). Um dos métodos tinha em conta o sinal de intensidade proveniente da próstata e de tecidos imediatamente circundantes, e outro apenas da próstata. Paralelamente, também o efeito do método de amostragem SMOTE, que permite equilibrar o número de casos positivos e negativos durante o processo de aprendizagem do algoritmo, foi avaliado no desempenho dos modelos. Com este método, gerámos casos sintéticos para a classe positiva (classe minoritária) para recidiva bioquímica, a partir dos casos já existentes. O modelo de regressão logística com Priority-LASSO com a sequência de blocos de variáveis Clínico-histopatológicas, T2, Difusão, ADC e com restrição de esparsidade de cada bloco com o parâmetro pmax = (1,7,0,1), foi seleccionada como a melhor configuração em cada um dos cenários clínicos testados, superando os modelos de regressão logística LASSO. Durante o desenvolvimento dos modelos, e em todos os cenários clínicos, os modelos com melhor desempenho obtiveram bons valor médios de Fmax (mínimo–máximo: 0.702–0.754 e 0.910–0.925 para classe positiva e negativa de recidiva bioquímica, respectivamente). Contudo, na validação final com um conjunto de dados independentes, os modelos obtiveram valores Fmax muito baixos para a classe positiva (0.297–0.400), revelando um sobreajuste, apesar do uso de métodos de penalização. Também se verificou grande instabilidade nos atributos seleccionados. Contudo, os modelos obtiveram razoáveis valores de medida-F (0.779–0.833) e de Precisão (0.821–0.873) para a classe de recidiva bioquímica negativa durante as fases de treino e de validação, pelo que estes modelos poderão ter valor a ser explorado. Os modelos pré-biópsia tiveram desempenho inferior no treino, mas sofreram menos de sobreajuste. Os classificadores pré-operatórios foram excessivamente optimistas, e os modelos pós-operatórios foram os melhores a detectar correctamente casos negativos de recidiva bioquímica. Outros resultados observados incluem a superioridade no desempenho dos modelos baseados em imagens que usaram o método de normalização realizado apenas com o volume da próstata, e o inesperado resultado de que o uso método de amostragem SMOTE não ter trazido melhoria na classificação de casos positivos de recorrência bioquímica, nem nos casos negativos, durante a validação dos modelos. Tendo em contas às variáveis seleccionadas e a sequência de prioridade dos melhores modelos Priority-LASSO, concluímos que os atributos radiómicos provindos da análise de textura de imagens MRI ponderadas em T2 poderão ter potencial para distinguir pacientes que não irão sofrer recidiva bioquímica inicial, conjuntamente com níveis iniciais de antigénio específico da próstata, num cenário pré-biópsia. A inclusão de parâmetros pré- ou pós-operatórios não adicionou valor substancial para a classificação de casos positivos de recidiva bioquímica em conjunto com variáveis radiómicos de MRI biparamétrica. Estudos com alto poder estatístico serão necessários para elucidar acerca do papel de atributos de radiómica baseados em imagens de bpMRI como predictores de recidiva bioquímica.Primary prostate cancer is often treated with radical prostatectomy (RP). Yet, 20–35% of males undergoing RP with curative intent will experience biochemical recurrence (BCR). Of those, two-thirds happen within two years, implying a more aggressive disease and poorer prognosis. Current BCR risk stratification tools are bounded to biopsy- or to surgery-derived histopathological evaluation, having limited ability for early treatment decision-making. Magnetic resonance imaging (MRI) is acquired as part of the diagnostic procedure and imaging derived features have shown promise in tumour characterisation and BCR prediction. We investigated the value of imaging features extracted from preoperative biparametric MRI (bpMRI) combined with clinic-histopathological data to develop models to predict two-year post-prostatectomy BCR in three simulated clinical scenarios: pre-biopsy, pre- and postoperative. In a cohort of 20 BCR positive and 73 BCR negative RP-treated patients examined in the same institution, 33 clinico-histopathological variables were retrospectively collected, and 2715 radiomic features (based on intensity, shape and texture) were extracted from the whole-prostate volume imaged in original and filtered T2- and Diffusion-weighted MRI and ADC maps scans. A systematic machine-learning framework comprised of classification, stratified k-fold cross validation and statistical analyses was developed to identify the top performing BCR classifiers’ configurations within three clinical scenarios. LASSO and Priority-LASSO logistic regression algorithms were used for feature selection and model fitting, optimising the amount of correctly classified BCR positive cases through AUC and F-score maximisation (Fmax) derived from ROC curve analysis. We also investigated the impact of two image normalisation methods and SMOTE-based minority oversampling on model performance. Priority-LASSO logistic regression with four-block priority sequence Clinical, T2w, DWI, ADC, with block sparsity restriction pmax = (1,7,0,1) was selected as the best performing model configuration across all clinical scenarios, outperforming LASSO logistic regression models. During development and across the simulated clinical scenarios, top models achieved good median Fmax values (range: 0.702–0.754 and 0.910–0.925 for BCR positive and negative classes, respectively); yet, during validation with an independent set, the models obtained very low Fmax for the target BCR positive class (0.297–0.400), revealing model overfitting. We also observed instability in the selected features. However, models attained reasonably good F-score (0.779–0.833) and Precision (0.821–0.873) for BCR negative class during training and validation phases, making these models worth exploring. Pre-biopsy models had lower performances in training but suffered less from overfitting. Preoperative classifiers were overoptimistic, and postoperative models were the most successful in detecting BCR negative cases. T2w-MRI textured-based radiomic features may have potential to distinguish negative BCR patients together with baseline prostate-specific antigen (PSA) levels in a pre-biopsy scenario. The inclusion of pre- or postoperative variables did not substantially add value to BCR positive cases classification with bpMRI radiomic features. Highly powered studies with curated imaging data are needed to elucidate the role of bpMRI radiomic features as predictors of BCR

The development and application of statistical sampling regime to map out hydrocarbons in sediments.

This thesis investigates and develops a stratified random sampling design for sediments in an offshore oil field environment. The sampling area was partitioned equally into sixteen zones. Stratification was based on the near-field and far-field areas, and the number of samples in each zone was chosen by proportional allocation - i.e. proportional to the available appropriate area (far-field). Measurement techniques applied to the samples included: laser granulometry; ultraviolet fluorescence; gas chromatography using mass-selective detection or flame ionisation detection; elemental analysis. The total PAH concentrations (2- to 6-ring parent and alkylated PAHs, including the 16 US EPA PAHs) in sediments were relatively low ( < 100 pg kg dry weight). The PAH concentrations, Forties crude oil equivalent and diesel oil equivalent concentrations were generally higher in sediment of fine grain size and higher organic carbon loading. PAH distributions and concentration ratios indicated a predominantly pyrolytic input, being dominated by the heavier, more persistent, 5- and 6-ring compounds, and with a high proportion of parent PAHs. The nalkane profiles of a number of the sediments contained small, high-boiling UCMs, indicative of weathered oil arising from a limited petrogenic input. Spatial structure analysis shows the existence of a trend in the variogram. Additionally, the spatial pattern in the contour maps of the measured parameters shows that the regionalized variables exhibited non-stationarity and were non-ergodic. The stratified random sampling scheme showed significant advantages over a classical grid sampling scheme when applied to the same area. Specifically, the stratified random sampling design gave much more reliable mean concentrations for all the parameters, achieving a much lower variance than the grid sampling. A further composite random sampling scheme was designed for sediments in the near-shore. The aim is to estimate a within-stratum mean value for each of the chosen measurement parameters with more thorough coverage (i.e. better representation), better precision and less variance at lower analytical cost. This scheme was trialed in two near-shore environments, the Clyde Estuary and the Firth of Forth. The results show no significant differences between the mean and distribution profile of the individual samples and the composite samples for all the parameters measured. This work utilised the best modern chemical analytical methods for the quantification of a range of hydrocarbon species and utilised the results in a modem risk-based approach to environmental assessment. The new stratified random sampling design has been accepted for use in the national marine monitoring programme (NMMP) in the United Kingdom

Operational progression of digital soil assessment for agricultural growth in Tasmania, Australia

Tasmania, Australia, is currently undergoing a period of agricultural expansion through the development of new irrigation schemes across the State, primarily to stimulate the economy and ensure future food security. ‘Operational Progression of Digital Soil Assessment (DSA) for Agricultural Growth in Tasmania, Australia’ presents the adaptation and operationalisation of quantitative approaches for regional land evaluation within these schemes, specifically applied Digital Soil Mapping (DSM) to inform a land suitability evaluation for 20 different agricultural crops, and ultimately a spatial indication of the State’s agricultural versatility and capital. DSM had not previously been applied or tested in Tasmania; the research examines and validates DSM approaches with respect to the State’s unique and complex soils and biophysical interactions with climate and terrain, and how these apply to various agricultural land uses. The thesis is a major contribution to the methodology and development of one of the first major operational DSA programs in Australia, and forms a framework for this type of DSM approach to be used in future operational land evaluation elsewhere