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.

DNA methylation is an epigenetic marker that directly or indirectly regulates several critical cellular processes. While cytosines in mammalian genomes generally maintain stable methylation patterns over time, other cytosines that belong to specific regulatory regions, such as promoters and enhancers, can exhibit dynamic changes. These changes in methylation are driven by a complex cellular machinery, in which the enzymes DNMT3 and TET play key roles. The objective of this study is to design a machine learning model capable of accurately predicting which cytosines have a fluctuating methylation level [hereafter called differentially methylated cytosines (DMCs)] from the surrounding DNA sequence. Here, we introduce L-MAP, a transformer-based large language model that is trained on DNMT3-knockout and TET-knockout data in human and mouse embryonic stem cells. Our extensive experimental results demonstrate the high accuracy of L-MAP in predicting DMCs. Our experiments also explore whether a classifier trained on human knockout data could predict DMCs in the mouse genome (and vice versa), and whether a classifier trained on DNMT3 knockout data could predict DMCs in TET knockouts (and vice versa). L-MAP enables the identification of sequence motifs associated with the enzymatic activity of DNMT3 and TET, which include known motifs but also novel binding sites that could provide new insights into DNA methylation in stem cells. L-MAP is available at https://github.com/ucrbioinfo/dmc_prediction.

Repetitive regions in eukaryotic genomes often contain important functional or regulatory elements. Despite significant algorithmic and technological advancements in genome sequencing and assembly over the past three decades, modern de novo assemblers still struggle to accurately reconstruct highly repetitive regions. In this work, we introduce RAmbler (Repeat Assembler), a reference-guided assembler specialized for the assembly of complex repetitive regions exclusively from PacBio HiFi reads. RAmbler (i) identifies repetitive regions by detecting unusually high coverage regions after mapping HiFi reads to the draft genome assembly, (ii) finds single-copy k-mers from the HiFi reads, (i.e., k-mers that are expected to occur only once in the genome), (iii) uses the relative location of single-copy k-mers to barcode each HiFi read, (iv) clusters HiFi reads based on their shared bar-codes, (v) generates contigs by assembling the reads in each cluster, and (vi) generates a consensus assembly from the overlap graph of the assembled contigs. Here we show that RAmbler can reconstruct human centromeres and other complex repeats to a quality comparable to the manually-curated telomere-to-telomere human genome assembly. Across over 250 synthetic datasets, RAmbler outperforms hifiasm, LJA, HiCANU, and Verkko across various parameters such as repeat lengths, number of repeats, heterozygosity rates and depth of sequencing.

Abstract: Coastal wetlands, including seagrass meadows, emergent marshes, mangroves, and temperate tidal swamps, can efficiently sequester and store large quantities of sediment organic carbon (SOC). However, SOC stocks may vary by ecosystem type and along environmental or climate gradients at different scales. Quantifying such variability is needed to improve blue carbon accounting, conservation effectiveness, and restoration planning. We analyzed SOC stocks in 1,284 sediment cores along >6,500 km of the Pacific coast of North America that included large environmental gradients and multiple ecosystem types. Tidal wetlands with woody vegetation (mangroves and swamps) had the highest mean stocks to 1 m depth (357 and 355 Mg ha−1, respectively), 45% higher than marshes (245 Mg ha−1), and more than 500% higher than seagrass (68 Mg ha−1). Unvegetated tideflats, though not often considered a blue carbon ecosystem, had noteworthy stocks (148 Mg ha−1). Stocks increased with tidal elevation and with fine (<63 μm) sediment content in several ecosystems. Stocks also varied by dominant plant species within individual ecosystem types. At larger scales, marsh stocks were lowest in the Sonoran Desert region of Mexico, and swamp stocks differed among climate zones; otherwise stocks showed little correlation with ecoregion or latitude. More variability in SOC occurred among ecosystem types, and at smaller spatial scales (such as individual estuaries), than across regional climate gradients. These patterns can inform coastal conservation and restoration priorities across scales where preserving stored carbon and enhancing sequestration helps avert greenhouse gas emissions and maintains other vital ecosystem services.

Methamphetamine (METH) use is frequent among people with HIV (PWH) and appears to increase the risk of neuronal injury and neurocognitive impairment (NCI). This study explored in vivo the effects of a 12 week (long-term), low-dose METH regimen in a transgenic animal model of neuroHIV with inducible expression of HIV-1 transactivator of transcription (Tat). Seven months after transient Tat induction and five months after METH exposure ended, we detected behavioral changes in the Barnes maze (BM) spatial memory task in the Tat and METH groups but not the combined Tat + METH group. The novel object recognition (NOR) task revealed that Tat extinguished discrimination in female animals with and without METH, although METH alone slightly improved NOR. In contrast, in males, Tat, METH, and Tat + METH all compromised NOR. Neuropathological examination detected sex-dependent and brain region-specific changes of pre-synaptic terminals, neurites, and activation of astrocytes and microglia. RNA-sequencing and quantitative reverse transcription polymerase chain reaction indicated that METH and Tat significantly altered gene expression, including factors linked to Alzheimer's disease-like NCI. In summary, chronic low-dose METH exerts long-term effects on behavioral function, neuropathology, and mRNA expression, and modulates the effects of Tat, suggesting sex-dependent and -independent mechanisms may converge in HIV brain injury and NCI.

Cancer, a significant cause of mortality, necessitates improved drug delivery strategies. Exosomes, as natural drug carriers, offer a more efficient, targeted, and less toxic drug delivery system compared to direct dispersal methods via ingestion or injection. To be successfully implemented as drug carriers, efficient loading of drugs into exosomes is crucial, and a deeper understanding of the loading mechanism remains to be solved. This study introduces surface-enhanced Raman scattering (SERS) to monitor drug loading efficacy at the single vesicle level. By enhancing the Raman signal, SERS overcomes limitations in Raman spectroscopy. A gold nanopyramids array-based SERS substrate assesses exosome heterogeneity in drug-loading capabilities with the help of single-layer graphene for precise quantification. This research advances targeted drug delivery by presenting a more efficient method of evaluating drug-loading efficiency into individual exosomes through SERS-based monitoring. Furthermore, the study explores leveraging osmotic pressure variations, enhancing the efficiency of drug loading into exosomes.

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.