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Abstract Plant viruses both trigger and inhibit host plant defense responses, including defenses that target their insect vectors, such as aphids. Turnip mosaic viru (TuMV) infection and its protein, NIa-Pro (nuclear inclusion protease a), suppress aphid-induced plant defenses, however the mechanisms of this suppression are still largely unknown. In this study, we determined that NIa-Pro’s protease activity is required to increase aphid performance on host plants and that 40 transcripts with predicted NIa-Pro cleavage sequences are regulated in Arabidopsis plants challenged with aphids and/or virus compared to healthy controls. One of the candidates, MEDIATOR 16 (MED16), regulates the transcription of ethylene (ET)/jasmonic acid (JA)-dependent defense responses against necrotrophic pathogens. We show that a nuclear localization signal is removed from MED16 by specific proteolytic cleavage in virus-infected plants and in plants overexpressing NIa-Pro in the presence of aphids. Although some cleavage was occasionally detected in the absence of virus infection, it occurred at a much higher rate in plants that were virus-infected or overexpressing NIa-Pro, especially when aphids were also present. This suggests MED16 functions in the nucleus may be impacted in virus infected plants. Consistent with this, induction of the MED16-dependent transcript ofPLANT DEFENSIN 1.2 (PDF1.2), was reduced in virus-infected plants and in plants expressing NIa-Pro compared to controls, but not in plants expressing NIa-Pro C151A that lacks its protease activity. Finally, we show the performance of both the virus and the aphid vector was enhanced onmed16mutant Arabidopsis compared to controls. Overall, this study demonstrates MED16 regulates defense responses against both the virus and the aphid and provides insights into the mechanism by which TuMV suppresses anti-virus and anti-herbivore defenses. 

Freezing rain events, which have caused billions of dollars in damage in recent decades, are still one of the least understood forms of precipitation. These events affect both urban and rural regions, including cities and forests. However, the often short-lived and sporadic occurrence of these events poses significant challenges to accurate reporting, complicating analysis attempting to fill this knowledge gap. Through the utilization of ERA5 reanalysis from 1979 to 2020 as a proxy for lacking ground truth data, freezing rain events were identified and analyzed throughout eastern North America. These events were then sorted into full area and subarea regimes through a multivariate self-organizing map (SOM) analysis. This classification of regimes and their subsequent analysis showed the varying importances of the initial synoptic states of each event and provide examples of distinct synoptic structures associated with freezing rain events impacting different regions of eastern North America. In addition, our regime classification identified the prevalence of extreme events associated with each regime. This SOM approach can also be configured to more regional scales to provide more granularity to specific regions of eastern North America. 

Modern heavy vehicles rely on insecure protocols (CAN and SAE-J1939) to facilitate communication between the embedded devices that control their various subsys- tems. Due to the growing integration of wireless-enabled embedded devices, vehicles are becoming increasingly vulnerable to remote cyberattacks against their embedded networks. We propose an efficient deep-learning-based approach for mitigating such attacks through real-time J1939 signal reconstruction. Our approach uses random feature masking during training to build a generalized model of a vehicle’s network. To reduce the computa- tional and storage burden of the model, we employ 8-bit Quantization-Aware Training (QAT), enabling its deploy- ment on resource-constrained embedded devices while maintaining high performance. We evaluate Transformer and LSTM-based architectures, demonstrating that both effectively reconstruct signals with minimal computa- tional and storage overhead. Our approach achieves sig- nal reconstruction with error levels below 1% of their operating range while maintaining a very low storage footprint of under 1 MB, demonstrating that lightweight deep-learning models can enhance resiliency against real- time attacks in heavy vehicles. 

Abstract Astrophysically motivated population models for binary black hole (BBH) observables are often insufficient to capture the imprints of multiple formation channels. This is mainly due to the strongly parametrized nature of such investigations. Using a nonparametric model for the joint population-level distributions of BBH component masses and effective inspiral spins, we find hints of multiple subpopulations in the third gravitational-wave transient catalog. The higher (more positive) spin subpopulation is found to have a mass spectrum without any feature at in the 30–40M_⊙range, which is consistent with the predictions of isolated stellar binary evolution, simulations for which place the pileup due to pulsational pair-instability supernovae near 50M_⊙or higher. The other subpopulation with effective spins closer to zero shows a feature at 30–40M_⊙and is consistent with BBHs formed dynamically in globular clusters, which are expected to peak around 30M_⊙. We also compute merger rates for these two subpopulations and find that they are consistent with the theoretical predictions of the corresponding formation channels. We validate our results by checking their robustness against variations of several model configurations and by analyzing large simulated catalogs with the same model. 

Instant data deletion (or sanitization) in NAND flash devices is essential for achieving data privacy, but it remains challenging due to the mismatch between erase and write granularities, which leads to high overhead and accelerated wear. While page-overwrite-based instant data sanitization has proven effective for 2D NAND, its applicability to 3D NAND is limited due to the unique sub-block architecture. In this study, we experimentally evaluate page-overwrite-based sanitization on commercial 3D NAND flash memory chips and uncover significant threshold voltage disturbances in erased cells on adjacent pages within the same layer but across different sub-blocks. Our key findings reveal that page-overwrite sanitization increases the median raw bit error rate (RBER) beyond correction limits (exceeding 0.93%) in Floating-Gate (FG) Single-Level Cell (SLC) technology, whereas Charge-Trap (CT) SLC 3D NAND flash memories exhibit higher robustness. In Triple-Level Cell (TLC) 3D NAND, page-overwrite sanitization proves impractical, with the median RBER of ∼13% for FG and ∼5% for CT devices. To overcome these challenges, we proposePULSE, a low-disturbance sanitization technique that balances sanitization efficiency ({{\eta }_{san}}) and data integrity (RBER). Experimental results show that PULSE eliminates RBER increases in SLC devices and reduces the median RBER to below 0.57% for FG and 0.79% for CT in fresh TLC blocks, demonstrating its practical viability for 3D NAND flash sanitization. 

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development.