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Objective The Ordos block, with typical boundary zone activity, is located in the center of mainland China. Influenced by the remote action of the Tibetan Plateau in the southwest and the Pacific Plate in the east, each boundary zone of the Ordos Block exhibits distinct tectonic activity and deformation characteristics. This study aims to provide insights into the characteristics of strong earthquake generation and the future seismic risk along the boundaries of the Ordos active block. Methods This study systematically summarizes research achievements made in the study of active faults and the seismogenic mechanism of strong earthquakes around the Ordos Block over the past few decades, providing a systematic overview of the characteristics of fault activity and seismic generation mechanisms along the block' s periphery. Conslusion The differences in fault activity in each boundary zone determined the differences in the earthquake-breeding strong tectonic environments. In the southern section of the western boundary, the faults are mainly characterized by strike-slip, reverse strike-slip, and thrust owing to the influence of the northward compression and expansion of the Qinghai-Tibet Plateau, resulting in complex structural deformation styles within the boundary zone. In the northern section of the western boundary, the latest expansion boundary of the Qinghai-Tibet Plateau is characterized by dextral movement along the Sanguankou-Niushoushan fault. The Yinchuan Basin in the northern section of the western boundary zone is a typical fault basin with basin-controlling faults exhibiting dextral strike-slip characteristics, and earthquakes are primarily of the standard strike-slip type. The Hetao Basin on the northern boundary is controlled by normal faults on its northern side, with historical and ancient earthquakes concentrated on the northern boundary faults. The Weihe Basin on the southern boundary exhibits relatively complex structural features, comprising two sets of normal faults. Historical major earthquakes mostly occur at the southern edge of the basin, with moderate seismic activity in the central-northern part of the basin. The Shanxi Graben system on the eastern boundary comprises multiple rift-type basins. Historical major earthquakes exhibit a pattern of stronger activity in the south and weaker activity in the north. The northern basins are influenced by the Zhang-Bo tectonic belt, resulting in significant changes in basin trend and fault movement characteristics, often possessing the structural conditions necessary for earthquakes of around M 7.0. Overall, in the typical fault activity zones surrounding the Ordos active block, future strong earthquakes are more likely to occur in seismic gaps or transition zones of fault systems, where significant time has passed since the occurrence of major earthquakes or at the intersections of tectonic zones.

The role of prokaryotic CRISPR/Cas system proteins as a defensive shield against invasive nucleic acids has been studied extensively. Non-canonical roles in pathogenesis involving intracellular targeting of certain virulence-associated endogenous mRNA have also been reported for some Type I and Type II CRISPR/Cas proteins, but no such roles have yet been established for Type III system proteins. Here, we demonstrate that M. tuberculosis (Type III-A system) CRISPR/Cas proteins Csm1, Csm3, Csm5, Csm6, and Cas6 are secreted and induce host immune responses. Using cell and animal experiments, we show that Cas6, in particular, provokes IFN-γ release from PBMCs from active tuberculosis (TB) patients, and its deletion markedly attenuates virulence in a murine M. tuberculosis challenge model. Recombinant MTBCas6 induces apoptosis of macrophages and lung fibroblasts, and interacts with the surface of cells in a caspase and TLR-2 independent manner. Transcriptomic and signal pathway studies using THP-1 macrophages stimulated with MTBCas6 indicated that MTBCas6 upregulates expression of genes associated with the NF-κB pathway leading to higher levels of IL-6, IL-1β, and TNF-α release, cytokines known to activate immune system cells in response to M. tuberculosis infection. Our findings suggest that, in addition to their intracellular shielding role, M. tuberculosis CRISPR/Cas proteins have non-canonical extracellular roles, functioning like a virulent sword, and activating host immune responses.

Summary: Convolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field‐programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co‐optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware‐friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row‐wise input‐stationary dataflow, which is tightly coupled with the algorithm. A row‐wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar‐vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co‐optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co‐optimized method implements a 10.9×$$ \times $$ speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss. [ABSTRACT FROM AUTHOR]