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The polysaccharide purified from Undaria pinnatifida Suringar was phosphorylated using phosphate as a cross-linking agent. The structure of the phosphorylated polysaccharide was characterized by ultraviolet (UV) absorption spectroscopy, Fourier transform infrared (FTIR) spectroscopy, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), periodate oxidation and Smith degradation, Congo red test, β-elimination reaction, and iodine-potassium iodide test, and the free radical scavenging capacity and hypoglycemic activity of the raw and modified polysaccharide were evaluated. The results showed that the substitution degree of phosphate was 9.26, and the characteristic absorption peaks of P=O and P–O–C appeared at 1 216 and 886 cm-1, indicating that the phosphorylation modification was successful. The maximum relative molecular mass of the phosphorylated polysaccharide was 94.4 × 103. It had a triple-stranded helical structure and was composed of glucopyranose unis linked together by β-glycosidic bonds. The polysaccharide and amino acids were linked together mainly by –O– glycopeptide bonds. The main linkages between monosaccharides were 1→3, 1→2,1→4 and 1→6 glycosidic bonds, the molar ratio was 0.494:0.504:0.002, and the sugar chain had a branched structure. The scavenging capacity against 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical, superoxide anion radical (O2-·) and hydroxyl radical (·OH) and the α-glucosidase inhibitory activity of the polysaccharide were significantly increased by 34.76%, 12.30%, 76.05% and 3.70% after the phosphorylation modification (P < 0.05), indicating that phosphorylation modification enhanced the free radical scavenging capacity and hypoglycemic activity of the polysaccharide from Undaria pinnatifida Suringar.

This paper aims at extending the method of Zhang et al. (2023) to produce not only portrait bas-reliefs from single photographs, but also high-depth reliefs with reasonable depth ordering. We cast this task as a problem of style-aware photo-to-depth translation, where the input is a photograph conditioned by a style vector and the output is a portrait relief with desired depth style. To construct ground-truth data for network training, we first propose an optimization-based method to synthesize high-depth reliefs from 3D portraits. Then, we train a normal-to-depth network to learn the mapping from normal maps to relief depths. After that, we use the trained network to generate high-depth relief samples using the provided normal maps from Zhang et al. (2023). As each normal map has pixel-wise photograph, we are able to establish correspondences between photographs and high-depth reliefs. By taking the bas-reliefs of Zhang et al. (2023), the new high-depth reliefs and their mixtures as target ground-truths, we finally train a encoder-to-decoder network to achieve style-aware relief modeling. Specially, the network is based on a U-shaped architecture, consisting of Swin Transformer blocks to process hierarchical deep features. Extensive experiments have demonstrated the effectiveness of the proposed method. Comparisons with previous works have verified its flexibility and state-of-the-art performance.

Abstract Reliable detection of circulating small extracellular vesicles (SEVs) and their miRNA cargo has been needed to develop potential specific non-invasive diagnostic and therapeutic marker for cancer metastasis. Here, we detected miR-6750, the precise molecular function of which was largely unknown, was significantly enriched in serum-SEVs from normal volunteers vs. patients with nasopharyngeal carcinoma (NPC). And we determined that miR-6750-SEVs attenuated NPC metastasis. Subsequently, miR-6750-SEVs was proven to inhibit angiogenesis and activate macrophage toward to M1 phenotype to inhibit pre-metastatic niche formation. After analyzing the expression level of miR-6750 in NPC cells, HUVECs and macrophage, we found that once miR-6750 level in NPC cells was close to or higher than normal nasopharyngeal epithelial cells (NP69), miR-6750-SEVs would be transferred from NPC cells to macrophage and then to HUVECs to modulate metastatic niche. Moreover, in vitro assays and BALB/c mouse tumor models revealed that miR-6750 directly targeted mannose 6-phosphate receptor (M6PR). Taken together, our findings revealed that miR-6750-M6PR axis can mediate NPC metastasis by remodeling tumor microenvironment (TME) via SEVs, which give novel sights to pathogenesis of NPC.

To handle the task of pointer meter reading recognition, in this paper, we propose a deep network model that can accurately detect the pointer meter dial and segment the pointer as well as the reference points from the located meter dial. Specifically, our proposed model is composed of three stages: meter dial location, reference point segmentation, and dial number reading recognition. In the first stage, we translate the task of meter dial location into a regression task, which aims to separate bounding boxes by an object detection network. This results in the accurate and fast detection of meter dials. In the second stage, the dial region image determined by the bounding box is further processed by using a deep semantic segmentation network. After that, the segmented output is used to calculate the relative position between the pointer and reference points in the third stage, which results in the final output of reading recognition. Some experiments were conducted on our collected dataset, and the experimental results show the effectiveness of our method, with a lower computational burden compared to some existing works.