An Evaluation of Unsaturated FlowModels in an Arid Climate by Jason DixonDr. James Cardle, Examination Committee Chair Professor of Civil and Environmental Engineering University of Nevada, Las VegasThe objective of this study was to evaluate the effectiveness of two unsaturated flow models in arid regions. The area selected for the study was the Area 5 Radioactive Waste Management Site (RWMS)at the Nevada Test Site in Nye County, Nevada. The two models selected for this evaluation were HYDRUS-1D [Simunek et al., 1998] and the SHAW model [Flerchinger and Saxton, 1989].Approximately 5 years of soil-water and atmospheric data collected from an instrumented weighing lysimeter site near the RWMS were used for building the models with actual initial and boundary conditions representative of the site. Physical processes affecting the site and model performance were explored. Model performance was based on a detailed sensitivity analysis and ultimately on storage comparisons. During the process of developing descriptive model input, procedures for converting hydraulic parameters for each model were explored. In addition, the compilation of atmospheric data collected at the site became a useful tool for developing predictive functions for future studies. The final model results were used to evaluate the capacities of the HYDRUS and SHAW models for predicting soilmoisture movement and variable surface phenomena for bare soil conditions in the arid vadose zone. The development of calibrated models along with the atmospheric and soil data collected at the site provide useful information for predicting future site performance at the RWMS.

Supplementary Data from Integrated DNA and RNA Sequencing Reveals Drivers of Endocrine Resistance in Estrogen Receptor–Positive Breast Cancer

<p>Supplemental methods on estradiol measurements and sample analysis. Supplemental tables: 1. Public datasets used; 2. Estrogen concentrations on breast tissue; 3. Association of baseline parameters and ERPAS (Edinburgh cohort); 4. Logistic regression analysis (Edinburgh cohort); 5. ERPAS association to response (TEAM IIa cohort); 6. Association of baseline parameters and ERPAS (TEAM IIa cohort); 7. Single nucleotide variants found in TEAM IIa resection samples. Supplemental Figures: 1. Consort diagram of tissue samples (TEAM IIa trial); 2. Agreement between AffyP2 models; 3. Improved performance of AffyP2 1 nM E2 model; 4. Agreement between AffyP2 and AffyA models; 5. Agreement between AffyP2 and TEAMII PCR models; 6. Correlation between baseline and decrease in ERPAS; 7. Non-synonymous ESR1 and PIK3CA mutations in TEAM IIa vs ERPAS; 8. ESR1 coverage on analyzed samples (TEAM IIa cohort); 9. Intra tumor variability of ERPAS resection samples of the TEAM IIa cohort.</p>

<p>Supplemental methods on estradiol measurements and sample analysis. Supplemental tables: 1. Public datasets used; 2. Estrogen concentrations on breast tissue; 3. Association of baseline parameters and ERPAS (Edinburgh cohort); 4. Logistic regression analysis (Edinburgh cohort); 5. ERPAS association to response (TEAM IIa cohort); 6. Association of baseline parameters and ERPAS (TEAM IIa cohort); 7. Single nucleotide variants found in TEAM IIa resection samples. Supplemental Figures: 1. Consort diagram of tissue samples (TEAM IIa trial); 2. Agreement between AffyP2 models; 3. Improved performance of AffyP2 1 nM E2 model; 4. Agreement between AffyP2 and AffyA models; 5. Agreement between AffyP2 and TEAMII PCR models; 6. Correlation between baseline and decrease in ERPAS; 7. Non-synonymous ESR1 and PIK3CA mutations in TEAM IIa vs ERPAS; 8. ESR1 coverage on analyzed samples (TEAM IIa cohort); 9. Intra tumor variability of ERPAS resection samples of the TEAM IIa cohort.</p>

<p>Supplementary Figure 1. ILC-specific and IDC-specific response genes identified from breast cancer cell lines are not significantly changed in response to letrozole in patient tumours. Heatmaps (A), gene set enrichment analysis (B) and specific highlighted genes (C) demonstrate that the reported ILC-specific and IDC-specific genes are not significantly changed between the histological cancer subtypes. Heatmap colours show changes in expression relative to respective patient-matched pre-treatment samples (Red=high, Green=low).</p>