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It is widely known that some soils have strong levels of disease suppression and prevent the establishment of pathogens in the rhizosphere of plants. However, what soils are better suppressing disease, and how management can help us to boost disease suppression remain unclear. Here, we used field, greenhouse and laboratory experiments to investigate the effect of management (monocropping and rotation) on the capacity of rhizosphere microbiomes in suppressing peanut root rot disease. Compared with crop rotations, monocropping resulted in microbial assemblies that were less effective in suppressing root rot diseases. Further, the depletion of key rhizosphere taxa in monocropping, which were at a disadvantage in the competition for limited exudates resources, reduced capacity to protect plants against pathogen invasion. However, the supplementation of depleted strains restored rhizosphere resistance to pathogen. Taken together, our findings highlight the role of native soil microbes in fighting disease and supporting plant health, and indicate the potential of using microbial inocula to regenerate the natural capacity of soil to fight disease. © 2023, The Author(s).

Human brain is a natural ultimate-event-driven (UED) system with low power and real-time response-ability, thanks to the asynchronous propagation and processing of spikes. Power dissipation and latency are major concerns in AloT devices, usually operating in random-sparse-event (RSE) scenarios (Fig. 22.7.1, top). Being event-driven on the system level, an always-on wake-up system (WUS) detects the valid RSEs energy-efficiently and intelligently, and upon detection turns on the power-hungry high-performance system (HPS). Being event-driven on the module level, a prior WUS [1] uses asynchronous feature extraction and synchronous convolutional neural network to detect the RSEs, consuming 148nW-to-1.68µW with 348ms latency. On the circuit level, the Spiking Neural Network (SNN) gives natural event-driven property. However, the prior SNN works did not fully explore this nature. An SNN circuit [2] achieves keyword spotting task at 205nW-to-570nW, but the framing method causes 100ms latency and is not true real-time. The SNN core in [5] uses synchronous digital design, which consumes significant power by the clock tree. The asynchronous-in-global synchronous-in-local [3]–[4] SNN circuits use local clock signals. They need arbiters in each layer to sort the spikes, weakening the parallelism and timing; additionally, the separation of storage and computing consumes more energy for data movement.

Miniaturized wireless IoT sensor nodes stay mostly in their standby mode and wake up periodically to sense and store a small amount of data. To maximize battery life, the acquired data is usually accumulated in SRAM before being transmitted; requiring economic on-chip memory solutions with ultra-low standby power. Artificial-intelligence-of-things (AIoT) based sensing platforms aim to extend this concept further by using on-chip neural networks (NN) to detect valid events at the edge node; further reducing network traffic and overall power consumption by limiting the transmission of invalid events [1]. This edge intelligence has created an ever-increasing demand for on-chip SRAM: high capacity is required to store a large number of weights and accumulated features for accurate decisions; low leakage is critical for data retention as the edge nodes stay in standby for the majority of time; speed is important to deliver timely processing and fast propagation of emergent events; and area, as well as technology availability, are keys to keep the cost down. It is also preferred not to use a dedicated retention mode to eliminate the latency and power overhead of mode switching, which is more frequent when working with light and sparse data traffic in AIoT sensing scenarios.