Sohrab Salehi

EGFR -mutant lung adenocarcinoma (LUAD) represents 20% of all non-small cell lung carcinomas, with most patients presenting with incurable metastatic disease. Treatment with mutant-selective EGFR tyrosine kinase inhibitors (TKIs) results in initial tumor reduction, yet nearly all patients eventually relapse. The mechanisms driving drug resistance are incompletely understood, creating significant barriers to curing metastatic disease. We integrated clinical genomic and single-nuclei RNA (snRNA) sequencing from a cohort of 62 EGFR -mutant LUAD patients treated with the third generation EGFR TKI, osimertinib, and compared treatment-naïve (TN), minimal residual disease (MRD), and progressive disease (PD) tumors. We found that disease progression is associated with a marked decrease in alveolar lineage fidelity, coincident with reduced MAPK signaling and adenocarcinoma identity. PD tumors with sustained MAPK pathway activity, such as those with EGFR or MET amplifications, tended to retain adenocarcinoma identity. In contrast, MAPK-low tumors were more likely to undergo histological transformation to squamous or neuroendocrine lineages. Remarkably, we observed rare tumor cell populations prior to treatment that were poorly differentiated, in some cases with neuroendocrine or squamous features. At progression, these histologically divergent tumor cells increased in prevalence, both in cases with overt histological transformation, and in others with sub-clinical histological plasticity. These findings suggest that pre-existing capacity for histologic plasticity may be a substrate for therapy induced selection. Taken together, our results illuminate genomically encoded MAPK signaling and lineage plasticity as complementary mechanisms of acquired resistance to EGFR TKI in lung adenocarcinoma.

The Sacramento–San Joaquin Delta (Delta) is a critical hub of California’s statewide water distribution system. Located at the confluence of California’s two largest rivers, the Sacramento River and the San Joaquin River, the Delta features a complex network of braided channels and over a hundred islands, most of which are located below sea level. The Delta’s complex nature and low-lying topography make it a unique hydrological area pertinent to climate change studies. This paper aims to estimate and explore the potential effects of climate change on the hydrological features of the Delta, especially Net Channel Depletion (NCD), which is one of the main contributors to the Net Delta Outflow (NDO). Downscaled CMIP6 General Circulation Model outputs are used to generate plausible future climate data. The Delta Channel Depletion model (DCD) is used to simulate daily hydrological processes for 61 plausible future climate scenarios. Simulation models are applied to the historical period (1930–2014) and projected future periods (2016–2100). A thorough water balance is computed in the DCD simulation model, offering insights into various elements in the hydrological cycle. Key hydrological features such as crop evapotranspiration, seepage, drainage, and runoff are simulated. Potential changes in NCD, calculated as the sum of diversions and seepage minus drainage, are also examined. The study identified a wide range of increases in NCD across all scenarios in the future period relative to the average of the historical period. These increases are projected to vary from 0.3% up to 20%. Moreover, a spatial analysis conducted across diverse regions of the Delta highlights notable variations in depletion across these areas. The results of this research indicate an anticipated increased stress on water resources, necessitating the adoption of innovative strategies to manage extreme events effectively and ensure the sustainability and resilience of water resource management.

Estimation of extreme climate trends is a crucial, influential, and also controversial step in long-term water resources planning studies. One of the main approaches to capturing the variability of climate trends is to use a diverse set of General Circulation Models (GCMs). As climate change models refine following deepening climate knowledge, utilizing updated models is unavoidable. The California Central Valley (CCV), a key agricultural zone in the western U.S., derives the bulk of its surface water from the Sacramento and San Joaquin rivers. Moreover, this area serves as a water source for several megacities, including Los Angeles, San Francisco, San Diego, and Sacramento. On average, over 80% of the total Sacramento-San Joaquin Delta outflow comes from the north and eastern upgradient regions (called rim watersheds) surrounding the valley. In this study, the effect of climate change on extreme trends in precipitation and temperature is evaluated for 12 CCV rim watersheds using downscaled CMIP6 data. Downscaled data are derived from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), which were downscaled using the Bias-Correction Spatial Disaggregation (BCSD) statistical method. Based on the availability of precipitation and temperature data from historical and future time spans, 21 models were selected out of 35 available models. For comparison and consistency with previous studies, 1980–2010 is selected to represent the base period, and 2040–2070 is selected to represent the future period. Average daily temperature and precipitation are calculated for each period under historical and SSP126, SSP245, SSP370, and SSP585 scenarios at each grid point lying inside the rim watershed boundaries. Figure 1 shows the average changes in temperature and precipitation for each GCM and SSP scenario during the historical period. As shown in Figure 1, which is an average across all specified rim watersheds, extreme trends show a maximum of 10.75% decrease to a maximum of 28.25% increase in precipitation and a minimum of 0.7°C increase to a maximum of 5°C increase in temperature. The previous study, conducted using CMIP5 by Schwarz et al. in 2019, revealed that the changes in precipitation and temperature would range approximately from -13% to +25% and +0.6°C to +3.9°C, respectively. These findings show more severe temperature extremes when using CMIP6 compared to CMIP5. On the other hand, extreme precipitation trends were not significantly influenced by changing model generation and scenarios. These findings suggest that using the latest CMIP generation would take a more diverse set of climatological uncertainties into account. Another analysis was conducted by examining each of the 12 rim watersheds separately. The results of this section show that the temperature and precipitation extremes did not change significantly compared to those from the holistic analysis. Thus, it seems that a holistic analysis of all 12 rim watersheds could properly represent precipitation and temperature extreme trends for each of the rim watersheds.

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance.