Quantifying spatial uncertainties in structure from motion snow depth mapping with drones in an alpine environment

Due to the heterogeneous nature of alpine snow distribution, advances in hydrological monitoring and forecasting for water resource management require an increase in the frequency, spatial resolution and coverage of field observations. Such detailed snow information is also needed to foster advances in our understanding of how snowpack affects local ecology and geomorphology. Although recent use of structure-from-motion multi-view stereo (SFM-MVS) 3D reconstruction techniques combined with aerial image collection using drones has shown promising potential to provide higher spatial and temporal resolution snow depth data for snowpack monitoring, there still remain challenges to produce high-quality data with this approach. These challenges, which include differentiating observations from noise and overcoming biases in the elevation data, are inherent in digital elevation model (DEM) differencing. A key issue to address these challenges is our ability to quantify measurement uncertainties in the SFM-MVS snow depths which can vary in space and time. The purpose of this thesis was to develop data-driven approaches for spatially quantifying, characterizing and reducing uncertainties in SFM-MVS snow depth mapping in alpine areas. Overall, this thesis provides a general framework for performing a detailed analysis of the spatial pattern of SFM-MVS snow depth uncertainties, as well as provides an approach for correction of snow depth errors due to changes in the sub-snow topography occurring between survey acquisition dates. It also contributes to the growing support of SFM-MVS combined with imagery acquired from drones as a suitable surveying technique for local scale snow distribution monitoring in alpine areas

Natural and anthropogenic controls of landslides on Vancouver Island

Empirically-based models of landslide distribution and susceptibility are currently the most commonly used approach for mapping probabilities of landslide initiation and analyzing their association with natural and anthropogenic environmental factors. In general, these models statistically estimate susceptibility based on the predisposition of an area to experience a landslide given a range of environmental factors, which may include land use, topography, hydrology and other spatial attributes. Novel statistical approaches include the generalized additive model (GAM), a non-parametric regression technique, which is used in this study to explore the relationship of landslide initiation to topography, rainfall and forest land cover and logging roads on Vancouver Island, British Columbia. The analysis is centered on an inventory of 639 landslides of winter 2006/07. Data sources representing potentially relevant environmental conditions of landslide initiation are based on: terrain analysis derived from a 20-m CDED digital elevation model; forest land cover classified from Landsat TM scenes for the summer before the 2006 rainy season; geostatistically interpolated antecedent rainfall patterns representing different temporal scales of rainfall (a major storm, winter and annual rainfall); and the main lithological units of surface geology. In order to assess the incremental effect of these data sources to predict landslide susceptibility, predictive performances of models based on GAMs are compared using spatial cross-validation estimates of the area under the ROC curve (AUROC), and variable selection frequencies are used to determine the prevalence of non-parametric associations to landslides. In addition to topographic variables, forest land cover (e.g., deforestation), and logging roads showed a strong association with landslide initiation, followed by rainfall patterns and the very general lithological classification as less important controls of landscape-scale landslide activity in this area. Annual rainfall patterns are found not to contribute significantly to model prediction improvement and may lead to model overfitting. Comparisons to generalized linear models (i.e., logistic regression) indicate that GAMs are significantly better for modeling landslide susceptibility. Overall, based on the model predictions, the most susceptible 4% of the study area had 29 times higher density of landslide initiation points than the least susceptible 73% of the study area (0.156 versus 0.005 landslides/km2)

Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term &ldquo;triggering&rdquo; precipitation, medium-term &ldquo;preparatory&rdquo; precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.</p

ERβ-mediated induction of cystatins results in suppression of TGFβ signaling and inhibition of triple-negative breast cancer metastasis.

Triple-negative breast cancer (TNBC) accounts for a disproportionately high number of deaths due to a lack of targeted therapies and an increased likelihood of distant recurrence. Estrogen receptor beta (ERβ), a well-characterized tumor suppressor, is expressed in 30% of TNBCs, and its expression is associated with improved patient outcomes. We demonstrate that therapeutic activation of ERβ elicits potent anticancer effects in TNBC through the induction of a family of secreted proteins known as the cystatins, which function to inhibit canonical TGFβ signaling and suppress metastatic phenotypes both in vitro and in vivo. These data reveal the involvement of cystatins in suppressing breast cancer progression and highlight the value of ERβ-targeted therapies for the treatment of TNBC patients

HIV RNA, CD4+ Percentage, and Risk of Hepatocellular Carcinoma by Cirrhosis Status.

BackgroundDespite increasing incidence of hepatocellular carcinoma (HCC) among HIV-infected patients, it remains unclear if HIV-related factors contribute to development of HCC. We examined if higher or prolonged HIV viremia and lower CD4+ cell percentage were associated with HCC.MethodsWe conducted a cohort study of HIV-infected individuals who had HIV RNA, CD4+, and CD8+ cell counts and percentages assessed in the Veterans Aging Cohort Study (1999-2015). HCC was ascertained using Veterans Health Administration cancer registries and electronic records. Cox regression was used to determine hazard ratios (HR, 95% confidence interval [CI]) of HCC associated with higher current HIV RNA, longer duration of detectable HIV viremia (≥500 copies/mL), and current CD4+ cell percentage less than 14%, adjusting for traditional HCC risk factors. Analyses were stratified by previously validated diagnoses of cirrhosis prior to start of follow-up.ResultsAmong 35&nbsp;659 HIV-infected patients, 302 (0.8%) developed HCC over 281&nbsp;441 person-years (incidence rate = 107.3 per 100&nbsp;000 person-years). Among patients without baseline cirrhosis, higher HIV RNA (HR = 1.25, 95% CI = 1.12 to 1.40, per 1.0 log10 copies/mL) and 12 or more months of detectable HIV (HR = 1.47, 95% CI = 1.02 to 2.11) were independently associated with higher risk of HCC. CD4+ percentage less than 14% was not associated with HCC in any model. Hepatitis C coinfection was a statistically significant predictor of HCC regardless of baseline cirrhosis status.ConclusionAmong HIV-infected patients without baseline cirrhosis, higher HIV RNA and longer duration of HIV viremia increased risk of HCC, independent of traditional HCC risk factors. This is the strongest evidence to date that HIV viremia contributes to risk of HCC in this group

Foraging Behavior and Success of a Mesopelagic Predator in the Northeast Pacific Ocean: Insights from a Data-Rich Species, the Northern Elephant Seal

The mesopelagic zone of the northeast Pacific Ocean is an important foraging habitat for many predators, yet few studies have addressed the factors driving basin-scale predator distributions or inter-annual variability in foraging and breeding success. Understanding these processes is critical to reveal how conditions at sea cascade to population-level effects. To begin addressing these challenging questions, we collected diving, tracking, foraging success, and natality data for 297 adult female northern elephant seal migrations from 2004 to 2010. During the longer post-molting migration, individual energy gain rates were significant predictors of pregnancy. At sea, seals focused their foraging effort along a narrow band corresponding to the boundary between the sub-arctic and sub-tropical gyres. In contrast to shallow-diving predators, elephant seals target the gyre-gyre boundary throughout the year rather than follow the southward winter migration of surface features, such as the Transition Zone Chlorophyll Front. We also assessed the impact of added transit costs by studying seals at a colony near the southern extent of the species’ range, 1,150 km to the south. A much larger proportion of seals foraged locally, implying plasticity in foraging strategies and possibly prey type. While these findings are derived from a single species, the results may provide insight to the foraging patterns of many other meso-pelagic predators in the northeast Pacific Ocean

The clonal evolution of metastatic colorectal cancer

Tumor heterogeneity and evolution drive treatment resistance in metastatic colorectal cancer (mCRC). Patient-derived xenografts (PDXs) can model mCRC biology; however, their ability to accurately mimic human tumor heterogeneity is unclear. Current genomic studies in mCRC have limited scope and lack matched PDXs. Therefore, the landscape of tumor heterogeneity and its impact on the evolution of metastasis and PDXs remain undefined. We performed whole-genome, deep exome, and targeted validation sequencing of multiple primary regions, matched distant metastases, and PDXs from 11 patients with mCRC. We observed intricate clonal heterogeneity and evolution affecting metastasis dissemination and PDX clonal selection. Metastasis formation followed both monoclonal and polyclonal seeding models. In four cases, metastasis-seeding clones were not identified in any primary region, consistent with a metastasis-seeding-metastasis model. PDXs underrepresented the subclonal heterogeneity of parental tumors. These suggest that single sample tumor sequencing and current PDX models may be insufficient to guide precision medicine