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Introduction: The classification of the four tobacco shred varieties, tobacco silk, cut stem, expanded tobacco silk, and reconstituted tobacco shred, and the subsequent determination of tobacco shred components, are the primary tasks involved in calculating the tobacco shred blending ratio. The identification accuracy and subsequent component area calculation error directly affect the composition determination and quality of the tobacco shred. However, tiny tobacco shreds have complex physical and morphological characteristics; in particular, there is substantial similarity between the expanded tobacco silk and tobacco silk varieties, and this complicates their classification. There must be a certain amount of overlap and stacking in the distribution of tobacco shreds on the actual tobacco quality inspection line. There are 24 types of overlap alone, not to mention the stacking phenomenon. Self-winding does not make it easier to distinguish such varieties from the overlapped types, posing significant difficulties for machine vision-based tobacco shred classification and component area calculation tasks. Methods: This study focuses on two significant challenges associated with identifying various types of overlapping tobacco shreds and acquiring overlapping regions to calculate overlapping areas. It develops a new segmentation model for tobacco shred images based on an improved Mask region-based convolutional neural network (RCNN). Mask RCNN is used as the segmentation network's mainframe. Convolutional network and feature pyramid network (FPN) in the backbone are replaced with Densenet121 and U-FPN, respectively. The size and aspect ratios of anchors parameters in region proposal network (RPN) are optimized. An algorithm for the area calculation of the overlapped tobacco shred region (COT) is also proposed, which is applied to overlapped tobacco shred mask images to obtain overlapped regions and calculate the overlapped area. Results: The experimental results showed that the final segmentation accuracy and recall rates are 89.1% and 73.2%, respectively. The average area detection rate of 24 overlapped tobacco shred samples increases from 81.2% to 90%, achieving high segmentation accuracy and overlapped area calculation accuracy. Discussion: This study provides a new implementation method for the type identification and component area calculation of overlapped tobacco shreds and a new approach for other similar overlapped image segmentation tasks.
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Histones are intracellular nucleosomal components and extracellular damage-associated molecular pattern molecules that modulate chromatin remodeling, as well as the immune response. However, their extracellular roles in cell migration and invasion remain undefined. Here, we demonstrate that histones are novel regulators of tumor metastasis with chemokine-like activities. Indeed, exogenous histones promote both hepatocellular carcinoma (HCC) cell migration and invasion through toll-like receptor (TLR)4, but not TLR2 or the receptor for advanced glycosylation end product. TLR4-mediated activation of nuclear factor-κB (NF-κB) by extracellular signal-regulated kinase (ERK) is required for histone-induced chemokine (e.g., C-C motif ligand 9/10) production. Pharmacological and genetic inhibition of TLR4-ERK-NF-κB signaling impairs histone-induced chemokine production and HCC cell migration. Additionally, TLR4 depletion (by using TLR4-/- mice and TLR4-shRNA) or inhibition of histone release/activity (by administration of heparin and H3 neutralizing antibody) attenuates lung metastasis of HCC cells injected via the tail vein of mice. Thus, histones promote tumor metastasis of HCC cells through the TLR4-NF-κB pathway and represent novel targets for treating patients with HCC.