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Abstract In plant immunity, a well-orchestrated cascade is initiated by the dimerization of receptor-like kinases (RLKs), followed by the phosphorylation of receptor-like cytoplasmic kinases (RLCKs) and subsequent activation of NADPH oxidases for ROS generation. Recent findings by Zhong et al. illustrated that a maize signaling module comprising ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 governs quantitative disease resistance to grey leaf spot, a pervasive fungal disease in maize worldwide, unveiling the conservation of this signaling quartet in plant immunity. 

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non‐ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X‐space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence‐based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. Level of Evidence5 Technical EfficacyStage 1 

In Fall of 2024, central Florida was impacted by Hurricane Helene (landfall in Perry, FL as a Cat 4 hurricane on Sept 27) and by Hurricane Milton (landfall in Siesta Key, FL as Cat 3 on Oct 9). The hurricanes led to damages of an estimated value > $200billion. The Nearshore Extreme Events Reconnaissance Association (NEER) and the Geotechnical Extreme Events Reconnaissance Association (GEER) represented by their members from more than 10 academic institutions, federal agencies, and industry and supported by technical staff from the NHERI RAPID facility and the UF Center for Coastal Solutions initiated on Sept 23 a data collection effort that included pre-, during-, and post-storm multi-disciplinary data collections efforts. The field data collection effort was concluded on Nov 22. Data includes hydraulic information on storm surge, waves, and currents, topographic and bathymetric data sets, terrestrial and seabed mapping, and geotechnical site characterization including in-situ testing, sediment sampling, and seismic testing. Data was collected in four focus areas in Florida (Cedar Key; Horseshoe Beach; Midnight Pass and Milton Pass, both near Venice) and observational data and limited data products were collected in other areas in Florida including Orchid, Ponte Vedra, Suwannee, Panama City, and others. Data is organized by site (four primary sites and others); data collection phase with respect to the two hurricanes; and instruments or data collection method. This work included support from both the UF Center for Coastal Solutions and the NHERI RAPID facility.