Variabilidade espacial da condutividade elétrica aparente ao longo do perfil do solo de vinhedos comerciais.

A caracterização da variabilidade espacial da condutividade elétrica aparente do solo (CEa) de vinhedos comerciais a partir do sensoriamento proximal dessa propriedade em diferentes intervalos de profundidade do perfil é apresentada nesse estudo. Medidas de CEa, representativas dos intervalos de profundidade de 0-0,20, 0-0,40, 0-0,50 e 0-1,00 m, foram feitas a partir da superfície do solo em dois vinhedos comerciais utilizando um medidor portátil desenvolvido pela Embrapa Instrumentação e o sensor EM38-MK2. A dependência espacial da CEa foi avaliada por meio de análise geoestatística, seguida da predição espacial por krigagem ordinária. Os valores estimados da CEa foram normalizados e classificados para delimitação de zonas homogêneas (ZH) e elaboração de mapas categóricos deste atributo. A magnitude da associação espacial entre as regionalizações da CEa em intervalos de profundidades distintos foi realizada pelo cálculo de indicadores de concordância implementados para comparação de mapas temáticos. A CEa apresenta variabilidade espacial ao longo do perfil do solo, porém o padrão de distribuição de ZH desse atributo nos vinhedos é variável conforme o intervalo de profundidade e a área avaliada. A maior concordância entre os mapas categóricos de CEa correspondentes aos intervalos de profundidades superficiais do solo (0-0,20 e 0- 0,40 m) indicam que essas camadas podem apresentar semelhança quanto aos seus atributos físico-químico

Gestión de datos espacio-temporales de imágenes satelitales

El manejo de datos de largas series temporales del índice de vegetación de diferencia normalizada (NDVI) en territorios extensos demanda un uso eficiente del recurso computacional. En este trabajo se discuten e ilustran estrategias para la construcción y procesamiento estadístico de bases de datos masivos espacio-temporales provenientes de imágenes satelitales. Se detalla la implementación de un protocolo de manejo de datos en el software R, con implementación de cómputos paralelizada. Los resultados muestran que el concepto dividir-aplicar-combinar resultó adecuado para filtrar y clasificar largas series de tiempo de NDVI distribuidas territorialmente a escala regional.Fil: Castillo Moine, Matías Alejandro. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentina. Instituto Nacional de Tecnologia Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatologia y Modelizacion Agricola. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Unidad de Fitopatologia y Modelizacion Agricola.; Argentin

Distributed hydrological model using machine learning algorithm for assessing climate change impact

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatio-temporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. An integrated statistical index coupled with a classification optimisation algorithm was used to select coupled model intercomparison project (CMIP6) global climate model (GCMs). Several bias-correction methods were evaluated to identify the best method for downscaling GCM simulations. The study also evaluated the performance of different Satellite-Based Products (SBPs) in replicating observed rainfall to select the best product. A novel two-stage bias correction method were used to correct the bias of the selected SBP. Besides, four widely used bias correction methods were compared to select the best method for downscaling GCM simulations at SBP grid locations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff, and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) located at the south of Peninsular Malaysia was considered as the case study area. The results showed that three GCMs, namely EC-Earth, EC-Earth-Veg and MRI-ESM-2, were the best in replicating the precipitation climatology in mainland Southeast Asia. IMERG was the best among five SBPs with an R2 of 0.56 compared to SM2RAIN-ASCAT (0.15), GSMap (0.18), PERSIANN-CDR (0.14), PERSIANN-CSS (0.10) and CHIRPS (0.13). The two-step bias correction approach improved the performance of IMERG, which reduced the mean bias up to 140 % compared to the other conventional bias correction methods. The method also successfully simulates the historical high rainfall events that caused floods in Peninsular Malaysia. The distributed hydrological model developed using ML showed NSE values of 0.96 and 0.78 and RMSE of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020 - 2059) and the far future (2060 - 2099) for different SSPs. The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as R95TOT, R99TOT, Rx1day, Rx5day and RI, were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The ML based distributed hydrological model developed using the novel two-step bias corrected SBP showed sufficient capability to simulate runoff from satellite rainfall. Application of the ML-based distributed model in JRB indicated that climate change and socio-economic development would cause an increase in the frequency streamflow extremes, causing larger flood events. The modelling framework developed in this study can be used for near-real time monitoring of flood through bias correction near-real time satellite rainfall

SINGLE CELL LINEAGE TRACING REVEALS MECHANISMS OF TUMOR INITIATION AND CHEMORESISTANCE IN SMALL CELL LUNG CANCER

Small Cell Lung Cancer (SCLC) is a devastating disease characterized by a very low two-year survival rate and almost universal acquisition of chemoresistance. Nearly all patients have tumors driven by functional inactivation of the tumor suppressors Rb and p53, but despite the uniform origins of this tumor, not all patients are genetically or phenotypically identical. SCLC can be subtyped into four unique molecular subtypes, determined by the expression of ASCL1, NEUROD1, POU2F3, or YAP1. These subtypes are plastic, and subtype switching after chemotherapy has been documented. Without the understanding of how tumor heterogeneity arises, we cannot solve the challenge of chemoresistance in SCLC. In recent years, a powerful new tool in studying tumor heterogeneity has emerged. Genetic barcoding allows for the identification and tracking of individual tumor populations by inserting a small genetic sequence (“barcode”) into the genome of tumor cells. As the cells divide, the barcode is passed on and a high-resolution lineage map is constructed. Here, genetic barcoding is used for the first time in SCLC, combined with single-cell RNA sequencing in a genetically engineered mouse model and a xenograft model of SCLC. In the mouse model of SCLC, tumors were sequenced at early, middle, and late stages of tumor development, as well as chemoresistant tumors. While no barcodes were detected by scRNA-seq, valuable information about the process of tumor development in SCLC is observed. I identify two cellular populations (“early” and “late”) that arise during tumor development. A notable difference in the two populations is the expression of genes corresponding to members of the AP-1 network. The AP-1 network was validated to be critical for tumorigenesis in SCLC. Barcoded SCLC xenografts and chemoresistant xenografts belonging to two SCLC subtypes were generated. scRNA-seq revealed increased transcriptomic plasticity following chemotherapy treatment in SCLC-A xenografts but not SCLC-N xenografts. The Cancer Testis Antigens PAGE5 and GAGE2A were identified and validated as mediators of chemoresistance in SCLC. This work represents the first application of genetic barcoding in SCLC and identifies actionable drug targets for future development

Change detection and landscape similarity comparison using computer vision methods

Human-induced disturbances of terrestrial and aquatic ecosystems continue at alarming rates. With the advent of both raw sensor and analysis-ready datasets, the need to monitor ecosystem disturbances is now more imperative than ever; yet the task is becoming increasingly complex with increasing sources and varieties of earth observation data. In this research, computer vision methods and tools are interrogated to understand their capability for comparing spatial patterns. A critical survey of literature provides evidence that computer vision methods are relatively robust to scale and highlights issues involved in parameterization of computer vision models for characterizing significant pattern information in a geographic context. Utilizing two widely used pattern indices to compare spatial patterns in simulated and real-world datasets revealed their potential to detect subtle changes in spatial patterns which would not otherwise be feasible using traditional pixel-level techniques. A texture-based CNN model was developed to extract spatially relevant information for landscape similarity comparison; the CNN feature maps proved to be effective in distinguishing agriculture landscapes from other landscape types (e.g., forest and mountainous landscapes). For real-world human disturbance monitoring, a U-Net CNN was developed and compared with a random forest model. Both modeling frameworks exhibit promising potential to map placer mining disturbance; however, random forests proved simple to train and deploy for placer mapping, while the U-Net may be used to augment RF as it is capable of reducing misclassification errors and will benefit from increasing availability of detailed training data

A Homogeneity-based Zone Delineation Model for Land Use and Transportation Interaction Analysis: Investigating the Case of Light Rail Transit (LRT) Development in Kitchener – Waterloo

In an ever-increasingly urbanized world, planning policies bring direct and indirect societal and environmental impacts affecting quality of life for millions of people. Policy decisions are often complex, involving trade-offs between competing interests and high degrees of uncertainties. Quantitative methods have been used to understand the complexity of urban dynamics, to evaluate the alternative future scenarios and ultimately to help make more informed decisions. Despite the advantages these methods offer, they have been criticized for being ad-hoc, complicated and sensitive to the arbitrary choice of the indicators and the spatial scales of analysis. In particular, transportation analysis and modeling often rely on pre-set structures of Traffic (or Transportation) Analysis Zones (TAZs) to conceptualize geographic space as it relates to urban activities and transportation flows. Theory suggests that appropriately created spatial structures for transportation analysis should represent areas with homogeneous characteristics in terms of land uses and activities. Reviewing literature indicates that conventional TAZs do not necessarily provide satisfactory levels of homogeneity due primarily to the insufficiency of density as the primary measure to create these zones and the arbitrary use of roadways in breaking the zones boundaries. As we move towards an era in which new mobility modes emerge and modern data sources open up great opportunities, it is necessary to rethink the way we conceptualize space within land use and transportation system interactions (LUTI) studies. This research is motivated by the idea that land use diversity is equally important as densities (and other attributes) to define the spatial unit of analysis. The research aims to advance understanding of the impacts caused by the choice of analysis zones on the travel behavior and land use development analysis outcomes. This dissertation develops an enhanced measure of heterogeneity (i.e., land use diversity) and applies this measure to create a dynamic zonal structure through an iterative spatial aggregation method. This algorithm combines the input disaggregate zones that have similar diversity levels but also assembled from similar disaggregate land uses that make up their diversity. The developed spatial models are examined and validated using a set of disaggregate land use, travel behavior and the building permits data from Waterloo Region in southern Ontario, Canada. This research examines the effects of land use heterogeneity and access to rapid transit on an ongoing urban dynamic in this fast-growing mid-size metropolitan region. The first set of analyses explores the suitability of the proposed zonal structure – called Dynamic Activity Cluster Zones (DACZs) – compared to a commonly used pre-defined TAZ system and a graph-based spatial clustering model. The results indicate the advantages of the DACZ model in terms of concurrently creating more homogeneous zones with balanced size distribution. A sensitivity analysis is then performed to evaluate the robustness of the DACZ model in producing reliable zonal structures as a function of three parameters including aggregation heterogeneity threshold, levels of adjacency, and the original (input) spatial disaggregation. The results show that the model is effective in generating zones for which the size is defined as a function of homogeneity, as a result, these zones will generate more predictable outcomes in travel behavior modeling and analysis. The second work investigates the regional daily travel behavior data aggregated and compared for both the DACZ and a conventional TAZ structure used in the regional planning called PLUM (an acronym for Population and Land Use Model). The comparisons reveal that the impacts of built environment homogeneity on travel behavior are more pronounced within DACZs, where the dynamic zones effectively capture variations of the active transportation and public transit mode shares. This analysis also uncovers a varying pattern of mode share and the average travel times across the built environment categories identified based on the population density and land use diversity levels; by increasing the levels of population density and land use diversity more trips are shown to be made by non-auto modes. This outcome supports the LUTI theories which contend that areas with diverse land uses and high population density are more conducive to active transportation and public transit trips. The third investigation seeks to understand how the introduction of proposed and actual rapid transit investments are related to land use development trends. In a temporal analysis, the historical building permit data from 2000 to 2019 are analyzed focusing on two periods before and after the LRT project funding announcement (2010-2011). The adjusted permits construction values are calculated and compared across multiple scales including the study area, relative to the Regions’ Central Transit Corridor (CTC) and within different heterogeneous built environment categories. The results identify areas that have disproportionately attracted more and higher valued developments, especially after announcement of the LRT project funding. The outcomes also confirm the role of higher levels of land use diversity and access to rapid transit on attracting greater scale of land use developments, while the density is found to have minimal association with this trend. In summary, this study advances the research on land use and transportation system interactions by (i) articulating a novel spatial unit of analysis through developing and applying an enhanced homogeneity index and a spatial aggregation model, (ii) examining the associations between travel behavior patterns and heterogeneous built environment characteristics, (iii) providing insights on the development trends across Waterloo Region at multiple spatial-temporal scales that can be used in ongoing regional policy and planning evaluations, (iv) more generally facilitating the land use and transportation integration in planning and policy development through assessment and dissemination of a set of rigorous spatial modeling methods