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While considerable knowledge exists about the enzymes pivotal for C₄photosynthesis, much less is known about thecis-regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C₄enzymes for five different grass species. This study spans four C₄species, covering three distinct photosynthetic subtypes:Zea maysandSorghum bicolor(NADP-dependent malic enzyme),Panicum miliaceum(NAD-dependent malic enzyme),Urochloa fusca(phosphoenolpyruvate carboxykinase), along with the C₃outgroupOryza sativa. We studied thecis-regulatory landscape of enzymes essential across all C₄species and those unique to C₄subtypes, measuring cell-type-specific biases for C₄enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C₄evolution. Besides promoter proximal ACRs, we found that, on average, C₄genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C₄evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution ofcis-regulation at these C₄loci. This study illuminates the dynamic and complex nature ofcis-regulatory elements evolution in C₄photosynthesis, particularly highlighting the intricatecis-regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C₃crop performance under changing climatic conditions. 

Physical computation devices, including CPUs, FPGAs, and GPUs, are integral to cloud computing but face unique security challenges. While cloud infrastructures are pivotal for service delivery, they are susceptible to threats. This paper introduces a novel hardware security framework to bolster cloud infrastructure resilience. Utilizing sidechannel measurements from the power distribution network (PDN), the framework detects anomalies in computational devices. Leveraging Ring Oscillators and Time-to-Digital Converters, we design PDN sensors, further enhancing security with a co-processor for real-time checks based on Neural Network analysis. 

The development of FPGA-based applications using HLS is fraught with performance pitfalls and large design space exploration times. These issues are exacerbated when the application is complicated and its performance is dependent on the input data set, as is often the case with graph neural network approaches to machine learning. Here, we introduce HLPerf, an open-source, simulation-based performance evaluation framework for dataflow architectures that both supports early exploration of the design space and shortens the performance evaluation cycle. We apply the methodology to GNNHLS, an HLS-based graph neural network benchmark containing 6 commonly used graph neural network models and 4 datasets with distinct topologies and scales. The results show that HLPerf achieves over 10 000 × average simulation acceleration relative to RTL simulation and over 400 × acceleration relative to state-of-the-art cycle-accurate tools at the cost of 7% mean error rate relative to actual FPGA implementation performance. This acceleration positions HLPerf as a viable component in the design cycle.