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.
Abstract: Some sub-Neptune planets may host habitable conditions; for example “Hycean” worlds with H2 envelopes over liquid water oceans can maintain potentially hospitable pressures and temperatures at their surface. Recent JWST observations of K2-18b and TOI-270d have shown that such worlds could be compelling targets for biosignature searches, given their extended scale heights and therefore large atmospheric signatures. Methylated biosignatures, a broad group of gases that can be generated by biological attachment of a CH3 group to an environmental substrate, have been proposed as candidate signs of life for Earth-like exoplanets. However, methyl halides (CH3 + halogen) have not yet been robustly examined with self-consistent photochemical and spectral models for planets with H2-dominated atmospheres. Here we demonstrate that methyl chloride (CH3Cl), predominantly produced by marine microbes, could be detected using JWST in tens of transits or fewer for Hycean planets, comparable to detection requirements for other potential atmospheric biosignatures. The threshold atmospheric mixing ratio for detectability is ∼10 ppm, which can accumulate with global fluxes comparable to moderately productive local environments on Earth.
The mosquito Aedes aegypti is an emerging model insect for invertebrate neurobiology. We detail the application of a dual transgenesis marker system that reports the nature of transgene integration with circular donor template for CRISPR-Cas9-mediated homology-directed repair at target mosquito chemoreceptor genes. Employing this approach, we demonstrate the establishment of cell-type-specific T2A-QF2 driver lines for the A. aegypti olfactory co-receptor genes Ir8a and orco via canonical homology-directed repair and the CO2 receptor complex gene Gr1 via noncanonical homology-directed repair involving duplication of the intended T2A-QF2 integration cassette separated by intervening donor plasmid sequence. Using Gr1+ olfactory sensory neurons as an example, we show that introgression of such T2A-QF2 driver and QUAS responder transgenes into a yellow cuticular pigmentation mutant strain facilitates transcuticular calcium imaging of CO2-evoked neural activity on the maxillary palps with enhanced sensitivity relative to wild-type mosquitoes enveloped by dark melanized cuticle. We further apply Cre-loxP excision to derive marker-free T2A-QF2 in-frame fusions to clearly map axonal projection patterns from olfactory sensory neurons expressing these 3 chemoreceptors into the A. aegypti antennal lobe devoid of background interference from 3xP3-based fluorescent transgenesis markers. The marker-free Gr1 T2A-QF2 driver facilitates clear recording of CO2-evoked responses in this central brain region using the genetically encoded calcium indicators GCaMP6s and CaMPARI2. Systematic application of these optimized methods to different chemoreceptors stands to enable mapping A. aegypti olfactory circuits at peripheral and central levels of olfactory coding at high resolution.
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.
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.
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.
We describe the difficulties of measuring variability in performance, a critical but largely ignored problem in studies of risk perception. The problem seems intractable if a large number of successful and unsuccessful trials are infeasible. We offer a solution based on estimates of task-specific variability pooled across the sample. Using a dataset of adult performance in throwing and walking tasks, we show that mischaracterizing the slope leads to unacceptably large errors in estimates of performance levels that undermine analyses of risk perception. We introduce a pooled-slope solution that approximates estimates of individual variability in performance and outperforms arbitrary assumptions about performance variability within and across tasks. We discuss the advantages of objectively measuring performance based on the rate of successful attempts-modeled via psychometric functions-for improving comparisons of risk across participants, tasks, and studies.
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.
