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Abstract Irrigation modifies land–atmosphere interactions by altering surface fluxes, boundary-layer thermodynamics, and moisture transport, yet its influence on precipitation remains highly dependent on synoptic influence. Building on recent modeling studies that quantify irrigation impacts within irrigated regions, we investigate how irrigation affects precipitation and its thermodynamic and dynamical controls in downwind areas using convection-permitting WRF simulations. A wind-aligned composite framework is applied to isolate coherent flow-relative responses under three synoptic conditions: quiescent, transitional, and strong synoptic disturbance. Across all regimes, irrigation induces surface cooling, enhanced latent heat flux, and low-level moistening that is advected downwind, yet the precipitation response differs fundamentally across synoptic conditions. Critically, we find that the LFC–LCL deficit, which measures thermodynamic resistance to deep parcel ascent, is a key discriminating variable explaining why irrigation-induced CAPE enhancement under quiescent conditions fails to produce precipitation, while comparable or lesser thermodynamic instability under transitional forcing yields organized downwind rainfall enhancement. These results establish a robust and rigorous regime-dependent framework for understanding how large-scale irrigation modifies downwind hydroclimate. They demonstrate that irrigation can substantially enhance atmospheric instability and reduce thermodynamic resistance to deep parcel ascent, but its impact on precipitation depends on background synoptic conditions. Responses are maximized when irrigation-induced thermodynamic enhancements, including reductions in LFC − LCL deficit, are organized by synoptic-scale flow, highlighting the role of synoptic conditions in modulating land–atmosphere interaction and hydroclimate responses. 

The ability to precisely engineerAgrobacteriumstrains is crucial for advancing their utility in plant biotechnology. We recently implemented the CRISPR RNA-guided transposase system, INTEGRATE, as an efficient tool for genetic modification inAgrobacterium. Despite its promise, the practical application of INTEGRATE inAgrobacteriumstrain engineering remains underexplored. Here, we present a standardized and optimized workflow that enables researchers to harness INTEGRATE for targeted genome modifications. By addressing common challenges, such as crRNA design, transformation efficiency, and vector eviction, this protocol expands the genetic toolkit available forAgrobacterium, facilitating both functional genomics and strain development for plant transformation. As a demonstration, we domesticatedAgrobacterium rhizogenesK599 strain by deleting the 15-kb T-DNA region from its root-inducing plasmid pRi2659 and inactivating a thymidylate synthase gene to render the strain auxotrophic for thymidine. The protocol provides detailed guidance for each step, including target site selection, crRNA spacer cloning,Agrobacteriumtransformation, screening for targeted insertion and Cre/loxP-mediated deletion, and vector removal. This resource will empower new users to perform efficient and reproducible genome engineering inAgrobacteriumusing the INTEGRATE system, paving the way for broader adoption and innovation in plant biotechnology. 

Genome function requires regulated genome motion. However, tools to directly observe this motion in vivo have been limited in coverage and resolution. Here we introduce an approach to tile mammalian chromosomes with self-mapping fluorescent labels and track them at ultraresolution. We find that sequences separated by submegabase distances transition to proximity in tens of seconds. This rapid search is dependent on cohesin and is exhibited only within domains. Domain borders act as kinetic impediments to this search process, rather than structural boundaries. The genomic separation–dependent scaling of the search time for cis interactions violated predictions of diffusion, suggesting motor-driven folding. We also uncover cohesin-dependent processive motion at 2.7 kilobases per second. Together, these multiscale dynamics reveal the organization of the genome into kinetically associated domains. 

Abstract Irrigation plays a crucial role in agricultural production across the US Great Plains. Meanwhile, it is a key driver of local and regional climate due to its influence on energy and water exchange between land surface and atmosphere. Despite the irrigation-induced evaporative cooling on temperature alone, how irrigation affects summer heat stress—a combination of temperature and humidity can become a concern to public health—is not well understood. This study examines the potential impacts of irrigation practices on summer temperature and heat extremes in the Great Plains using a set of sensitivity experiments conducted with the Weather Research & Forecasting (WRF) model for 10 growing seasons. Results show that intensive irrigation lowers the ambient temperature, but the increased humidity from enhanced evapotranspiration, especially during the extreme hot and dry summers, can possibly elevate the risks of heat stress in the heavily irrigated area and its surroundings. The response of humid heat extremes to irrigation depends on the heat metrics used in the assessment. For variables like wet-bulb temperature, wet-bulb globe temperature, and equivalent temperature, irrigation leads to significantly intensified humid heat extremes by up to 5 °C and increased heatwave frequency by 3 events year⁻¹. In contrast, metrics like the heat index and environmental stress index suggest that irrigation mitigates heat intensity by decreasing the temperature metrics by up to 1 °C. Given the importance of irrigation in Great Plains agriculture in a changing climate, these uncertainties underscore the urgent need to connect heat metrics with health outcomes to better address heat mitigation in rural communities. 

Abstract The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, and negative binomial (NB)-based loss functions, to infer GRNs. GRANGER was evaluated using multiple popular benchmarking datasets, where it demonstrated superior performance compared to eight well-known GRN inference methods. The integration of a NB-based loss function and sparsity-inducing penalties in GRANGER significantly enhanced its capacity to address dropout noise and sparsity in scRNA-seq data. Additionally, GRANGER exhibited robustness against high levels of dropout noise. We applied GRANGER to scRNA-seq data from the whole mouse brain obtained through the BRAIN Initiative project and identified GRNs for five transcription regulators: E2f7, Gbx1, Sox10, Prox1, and Onecut2, which play crucial roles in diverse brain cell types. The inferred GRNs not only recalled many known regulatory relationships but also revealed sets of novel regulatory interactions with functional potential. These findings demonstrate that GRANGER is a highly effective tool for real-world applications in discovering novel gene regulatory relationships. 

The 3D organization of chromatin in the nucleus plays a critical role in regulating gene expression and maintaining cellular functions in eukaryotic cells. High-throughput chromosome conformation capture (Hi-C) and its derivative technologies have been developed to map genome-wide chromatin interactions at the population and single-cell levels. However, insufficient sequencing depth and high noise levels in bulk Hi-C data, particularly in single-cell Hi-C (scHi-C) data, result in low-resolution contact matrices, thereby limiting diverse downstream computational analyses in identifying complex chromosomal organizations. To address these challenges, we developed a transformer-based deep learning model, HiCENT, to impute and enhance both scHi-C and Hi-C contact matrices. Validation experiments on large-scale bulk Hi-C and scHi-C datasets demonstrated that HiCENT achieves superior enhancement effects compared to five popular methods. When applied to real Hi-C data from the GM12878 cell line, HiCENT effectively enhanced 3D structural features at the scales of topologically associated domains and chromosomal loops. Furthermore, when applied to scHi-C data from five human cell lines, it significantly improved clustering performance, outperforming five widely used methods. The adaptability of HiCENT across different datasets and its capacity to improve the quality of chromatin interaction data will facilitate diverse downstream computational analyses in 3D genome research, single-cell studies and other large-scale omics investigations.