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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 U.S. 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 concern – 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 atmospheric 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. 

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types. 

Abstract Using nearly simultaneous radio, near-infrared, optical, and ultraviolet (UV) data collected since 2009, we constructed 106 spectral energy distributions (SEDs) of the blazar OJ 287. These SEDs are well fitted by a log-parabolic model. By classifying the data into “flare” and “quiescent” segments, we find that the median flux at the peak frequency of the SEDs during the flare segments is 0.37 ± 0.22 dex higher compared to the quiescent segments, while no significant differences are observed in the median values of the curvature parameterbor the peak frequency $\log \nu \mathrm{p}$. A significant bluer-when-brighter trend is confirmed through the relation between theVmagnitude andB − Vcolor index, with this trend being stronger in the flare segments. Additionally, a significant anticorrelation is detected between $\log \nu \mathrm{p}$andb, with a slope of 5.79 in the relation between 1/band $\log \nu \mathrm{p}$, closer to the prediction from a statistical acceleration model than a stochastic acceleration interpretation, though a notable discrepancy persists. This discrepancy indicates that additional factors—such as deviations from idealized conditions or radiative contributions, such as the thermal emission from the accretion disk in the optical–UV range during quiescent states—may play a role in producing the observed steeper slope. Within the framework of the statistical acceleration mechanism, the lack of correlation between the change in the peak intensity and the change in the peak frequency suggests that the change in the electron energy distribution is unlikely to be responsible for the time-dependent SED changes. Instead, changes in Doppler boosting or magnetic fields may have a greater influence.