UC Riverside

Global groundwater depletion is a pressing issue, particularly in regions dependent on groundwater for agriculture. Agricultural Managed Aquifer Recharge (Ag-MAR), where farm fields are used as spreading grounds for flood water, is a promising strategy to replenish groundwater, but it raises concerns about pesticide leaching into aquifers, posing risks to both drinking water quality and ecosystems. This study employs a physically based unsaturated flow model, a Bayesian probabilistic approach and novel towed transient electromagnetic (tTEM) data to determine the fate and transport, especially the maximum transport depths (MTDs) of four pesticide residues (Imidacloprid, Thiamethoxam, Chlorantraniliprole, and Methoxyfenozide) in three 70-m-thick unsaturated zones (P1, P2, P3) of California's Central Valley alluvial aquifer. The results show that Ag-MAR significantly increased MTDs across all profiles for all pesticides and with higher variability in pesticide transport depths compared to the natural rainfall scenario. Profile P2, with the highest sand content exhibited the deepest MTDs under Ag-MAR, indicating a strong influence of soil texture on pesticide transport. While natural capillary barriers at the depth of 2.5-20 m impede water flow under natural rainfall conditions, the high-pressure infiltration during Ag-MAR overcomes these barriers, leading to deeper water and pesticide movement. Among various evaluated pesticides, Methoxyfenozide exhibited the smallest absolute MTDs but the largest relative increases in MTDs (RMTDs) under Ag-MAR due to its persistence and low mobility, posing a higher risk of deep transport during intensive recharge events. In contrast, Thiamethoxam showed the largest MTDs under both scenarios but smaller RMTDs due to its high mobility, suggesting a more consistent transport behavior regardless of recharge practices. The findings highlight the importance of understanding both site-specific and pesticide-specific behaviors to mitigate groundwater contamination risks during large water applications.

Millions of people move within the U.S. each year. We propose that people function as proxies for their locations, bringing the culture of their previous residence to their new homes. As a result, migration might systematically influence regional biases across geographic units over time. Using county-to-county migration data from the U.S. census and county-level racial attitude estimates from Project Implicit, the present research examined the impact of people relocating from one U.S. county to another on racial attitudes in their new county. Consistent with our prediction, the bias brought by the migrants positively predicts county-level racial bias after migration, even after controlling for county-level racial bias before migration. This finding remains robust across various sample inclusion criteria and spans three time periods (2006-2010, 2011-2015, and 2016-2020). These results highlight the significant role of migration in spreading and shaping regional racial attitudes, emphasizing the importance of considering macro-societal processes such as migration when studying changes in regional racial attitudes.

Abstract: Our interpretation of terrestrial exoplanet atmospheric spectra will always be limited by the accuracy of the data we use as input in our forward and retrieval models. Ultraviolet molecular absorption cross sections are one category of these essential model inputs; however, they are often poorly characterized at the longest wavelengths relevant to photodissociation. Photolysis reactions dominate the chemical kinetics of temperate terrestrial planet atmospheres. One molecule of particular importance is CO2, which is likely present in all terrestrial planet atmospheres. The photolysis of CO2 can introduce CO and O, as well as shield tropospheric water vapor from undergoing photolysis. This is important because H2O photolysis produces OH, which serves as a major reactive sink to many atmospheric trace gases. Here, we construct CO2 cross-section prescriptions at 195 K and 300 K extrapolated beyond 200 nm from measured cross sections. We compare results from the implementation of these new cross sections to the most commonly used CO2 prescriptions for temperate terrestrial planets with Archean-like atmospheres. We generally find that the observational consequences of CO2 dissociation beyond 200 nm are minimal so long as our least conservative (highest opacity) prescription can be ruled out. Moreover, implementing our recommended extended CO2 cross sections does not substantially alter previous results that show the consequential photochemical impact of extended H2O cross sections.

The Community Multiscale Air Quality (CMAQ) model simulates atmospheric phenomena, including advection, diffusion, gas-phase chemistry, aerosol physics and chemistry, and cloud processes. Gas-phase chemistry is often a major computational bottleneck due to its representation as large systems of coupled nonlinear stiff differential equations. We leverage the parallel computational performance of graphics processing unit (GPU) hardware to accelerate the numerical integration of these systems in CMAQs CHEM module. Our implementation, dubbed CMAQ-CUDA, in reference to its use in the Compute Unified Device Architecture (CUDA) general purpose GPU (GPGPU) computing solution, migrates CMAQs Rosenbrock solver from Fortran to CUDA Fortran. CMAQ-CUDA accelerates the Rosenbrock solver such that simulations using the chemical mechanisms RACM2, CB6R5, and SAPRC07 require only 51%, 50%, or 35% as much time, respectively, as CMAQv5.4 to complete a chemistry time step. Our results demonstrate that CMAQ is amenable to GPU acceleration and highlight a novel Rosenbrock solver implementation for reducing the computational burden imposed by the CHEM module.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants found in groundwater sources and a wide variety of consumer products. In recent years, electrochemical approaches for the degradation of these harmful contaminants have garnered a significant amount of attention due to their efficiency and chemical-free modular nature. However, these electrochemical processes occur in open, highly non-equilibrium systems, and a detailed understanding of PFAS degradation mechanisms in these promising technologies is still in its infancy. To shed mechanistic insight into these complex processes, we present the first constant-electrode potential (CEP) quantum calculations of PFAS degradation on electrified surfaces. These advanced CEP calculations provide new mechanistic details about the intricate electronic processes that occur during PFAS degradation in the presence of an electrochemical bias, which cannot be gleaned from conventional density functional theory calculations. We complement our CEP calculations with large-scale ab initio molecular dynamics simulations in the presence of an electrochemical bias to provide time scales for PFAS degradation on electrified surfaces. Taken together, our CEP-based quantum calculations provide critical reaction mechanisms for PFAS degradation in open electrochemical systems, which can be used to prescreen candidate material surfaces and optimal electrochemical conditions for remediating PFAS and other environmental contaminants.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants found in groundwater sources and a wide variety of consumer products. In recent years, electrochemical approaches for the degradation of these harmful contaminants have garnered a significant amount of attention due to their efficiency and chemical-free modular nature. However, these electrochemical processes occur in open, highly non-equilibrium systems, and a detailed understanding of PFAS degradation mechanisms in these promising technologies is still in its infancy. To shed mechanistic insight into these complex processes, we present the first constant-electrode potential (CEP) quantum calculations of PFAS degradation on electrified surfaces. These advanced CEP calculations provide new mechanistic details about the intricate electronic processes that occur during PFAS degradation in the presence of an electrochemical bias, which cannot be gleaned from conventional density functional theory calculations. We complement our CEP calculations with large-scale ab initio molecular dynamics simulations in the presence of an electrochemical bias to provide time scales for PFAS degradation on electrified surfaces. Taken together, our CEP-based quantum calculations provide critical reaction mechanisms for PFAS degradation in open electrochemical systems, which can be used to prescreen candidate material surfaces and optimal electrochemical conditions for remediating PFAS and other environmental contaminants.

xCas9 is an evolved variant of the CRISPR-Cas9 genome editing system, engineered to improve specificity and reduce undesired off-target effects. How xCas9 expands the DNA targeting capability of Cas9 by recognising a series of alternative protospacer adjacent motif (PAM) sequences while ignoring others is unknown. Here, we elucidate the molecular mechanism underlying xCas9s expanded PAM recognition and provide critical insights for expanding DNA targeting. We demonstrate that while wild-type Cas9 enforces stringent guanine selection through the rigidity of its interacting arginine dyad, xCas9 introduces flexibility in R1335, enabling selective recognition of specific PAM sequences. This increased flexibility confers a pronounced entropic preference, which also improves recognition of the canonical TGG PAM. Furthermore, xCas9 enhances DNA binding to alternative PAM sequences during the early evolution cycles, while favouring binding to the canonical PAM in the final evolution cycle. This dual functionality highlights how xCas9 broadens PAM recognition and underscores the importance of fine-tuning the flexibility of the PAM-interacting cleft as a key strategy for expanding the DNA targeting potential of CRISPR-Cas systems. These findings deepen our understanding of DNA recognition in xCas9 and may apply to other CRISPR-Cas systems with similar PAM recognition requirements.

Investigation of protein-drug recognition is key to understanding drug selectivity and binding affinity. In combination, the binding/unbinding free energy landscape and intermolecular interactions can be used to understand drug binding/unbinding mechanisms. This information is vital for the development of drugs with improved efficacy and explanation of mutation effects. This study investigated the dissociation processes of ritonavir unbinding from HIV protease (HIVp). Analyzing unbinding trajectories modeled by accelerated molecular dynamics (MD) simulations, three distinct pathways, pathways A-C, were characterized. Using a reduced dimensionality strategy with the principal component analysis, we carried out short classical MD runs with explicit water to sample local fluctuation during ritonavir dissociation and applied the milestoning theory to construct an unbinding free energy landscape. We found that each pathway showed similar values of binding free energy, albeit pathway A accounts for over 50% of dissociation trajectories. Interestingly, residue-residue correlation network analysis showed that in pathway A, a broad correlation network outside the flap region governs protein motions during ritonavir unbinding, which includes residues with reported mutation effects. However, the other two pathways showed limited correlation networks where no reported mutated residues were involved, explaining the favorability of pathway A. Guided by the free energy profile, we investigated each energy barrier and minimum, demonstrating that hydrogen bonding governed movement of the flap regions, directly impacting the calculated energy. Our study provided a new strategy to estimate ligand binding free energy and demonstrated the importance of the transient interactions during ligand-protein dissociation pathways in understanding drug unbinding.

Natural products have long been a viable source of therapeutic agents, providing unique structures and mechanisms that may be beneficial for cancer treatment. Herein we first report on the anticancer activity OST-01, a natural product from Baccharis Coridifolia, on breast cancer cells, including triple-negative breast cancer (TNBC). OST-01 significantly inhibited cell proliferation and oncogenic activities of TNBC cells in vitro. OST-01 also markedly inhibited TNBC tumor growth in vivo, with > 50% reduction in tumor size compared to vehicle control treatment in different in vivo models, i.e., cell line-derived (CDX), patient-derived (PDX), and mammary fat pad xenografts. Mechanistically, OST-01 induces ferroptosis by downregulating LRP8-regulated selenoproteins, i.e., GPX4. A shift from a basal-mesenchymal to a luminal-epithelial state of breast cancer stem cells (BCSCs) as supported by the downregulation of stemness (e.g., CD44) and mesenchymal (e.g., FN1 and vimentin) markers, along with the upregulation of differentiation markers (e.g., CD24) and luminal-epithelial markers (e.g., CK19), was also observed.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small many analyst study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.