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1,069 results on '"Wang, Shouyu"'

4. Registration-Free Hybrid Learning Empowers Simple Multimodal Imaging System for High-quality Fusion Detection

Author

Guan, Yinghan, Dai, Haoran, Yu, Zekuan, Wang, Shouyu, and Gu, Yuanjie

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multimodal fusion detection always places high demands on the imaging system and image pre-processing, while either a high-quality pre-registration system or image registration processing is costly. Unfortunately, the existing fusion methods are designed for registered source images, and the fusion of inhomogeneous features, which denotes a pair of features at the same spatial location that expresses different semantic information, cannot achieve satisfactory performance via these methods. As a result, we propose IA-VFDnet, a CNN-Transformer hybrid learning framework with a unified high-quality multimodal feature matching module (AKM) and a fusion module (WDAF), in which AKM and DWDAF work in synergy to perform high-quality infrared-aware visible fusion detection, which can be applied to smoke and wildfire detection. Furthermore, experiments on the M3FD dataset validate the superiority of the proposed method, with IA-VFDnet achieving the best detection performance than other state-of-the-art methods under conventional registered conditions. In addition, the first unregistered multimodal smoke and wildfire detection benchmark is openly available in this letter.

Published
2023

7. Lnc-BM Promotes Gastric Cancer Progression by Regulating Mitochondrial Respiratory Function Through FASTK/MT-ND6 Axis

Author

ZHANG Mingyue, CHEN Chen, WANG Meng, and WANG Shouyu

Subjects
gastric cancer, lnc-bm, fastk, mitochondrion, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Objective To explore the role and molecular mechanism of Lnc-BM in the occurrence and development of gastric cancer (GC). Methods GC tissues and paired adjacent normal tissues of 36 GC patients were collected, and the expression of Lnc-BM was detected by RT-qPCR. Colony formation and CCK-8 assays were used to investigate the proliferation of GC cells. The migration and invasion properties of GC cells were investigated via Transwell assay. RNA pull-down assay was applied to confirm the interaction between FASTK and Lnc-BM. Western blot assay was used to detect FASTK protein level in Lnc-BM overexpressing or knockdown cells. Mitochondrial respiratory capacity and the related proteins expression levels were detected by Seahorse and Western blot assays, respectively. Lnc-BM stably overexpressing GC cells were constructed and then injected subcutaneously into nude mice. The tumor growth was observed. Results Lnc-BM was highly expressed in GC tissues compared with their paired adjacent normal tissues. Lnc-BM overexpression significantly promoted GC cells proliferation migration and invasion, while Lnc-BM knockdown inhibited GC cells proliferation, migration and invasion (P < 0.05). RNA pull-down experiment demonstrated that Lnc-BM can directly bind to FASTK. Western blot results indicated that overexpression of Lnc-BM increased the protein levels of FASTK, while knockdown of Lnc-BM inhibited the expression of FASTK (P < 0.05). Compared to the control group, overexpression of Lnc-BM increased the levels of mitochondria associated proteins, such as MT-ND6 and TOM20 (P < 0.05). Seahorse results indicated that overexpression of Lnc-BM enhanced mitochondrial respiratory capacity (P < 0.05). Knocking down FASTK in Lnc-BM stably overexpressing cells can reverse the increase in mitochondrial respiratory capacity caused by Lnc-BM overexpression (P < 0.05). In vivo, the results of subcutaneously implanted tumor model in nude mouse showed that Lnc-BM overexpression promoted the tumor growth (P < 0.05). Conclusion Lnc-BM promotes GC progression by regulating mitochondrial respiratory function through the FASTK/MT-ND6 axis.

Published
2024
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8. Astrocyte endfoot formation controls the termination of oligodendrocyte precursor cell perivascular migration during development.

Author

Su, Yixun, Wang, Xiaorui, Yang, Yujian, Chen, Liang, Xia, Wenlong, Hoi, Kimberly, Li, Hui, Wang, Qi, Yu, Guangdan, Chen, Xiaoying, Wang, Shouyu, Wang, Yuxin, Xiao, Lan, Verkhratsky, Alexei, Fancy, Stephen, Yi, Chenju, and Niu, Jianqin

Subjects
astrocyte endfoot, myelination, oligodendrocyte precursor cells, perivascular migration, semaphorins, Oligodendrocyte Precursor Cells, Astrocytes, Oligodendroglia, Cell Differentiation, Cell Movement
Abstract

Oligodendrocyte precursor cells (OPCs) undergo an extensive and coordinated migration in the developing CNS, using the pre-formed scaffold of developed blood vessels as their physical substrate for migration. While OPC association with vasculature is critical for dispersal, equally important for permitting differentiation and proper myelination of target axons is their appropriate and timely detachment, but regulation of this process remains unclear. Here we demonstrate a correlation between the developmental formation of astrocytic endfeet on vessels and the termination of OPC perivascular migration. Ex vivo and in vivo live imaging shows that astrocyte endfeet physically displace OPCs from vasculature, and genetic abrogation of endfoot formation hinders both OPC detachment from vessels and subsequent differentiation. Astrocyte-derived semaphorins 3a and 6a act to repel OPCs from blood vessels at the cessation of their perivascular migration and, in so doing, permit subsequent OPC differentiation by insulating them from a maturation inhibitory endothelial niche.

Published
2023

11. Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion

Author

Gu, Yuanjie, Xiao, Zhibo, Guan, Yinghan, Dai, Haoran, Liu, Cheng, Xue, Liang, and Wang, Shouyu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Convolutional neural networks have turned into an illustrious tool for image fusion and super-resolution. However, their excellent performance cannot work without large fixed-paired datasets; and additionally, these high-demanded ground truth data always cannot be obtained easily in fusion tasks. In this study, we show that, the structures of generative networks capture a great deal of image feature priors, and then these priors are sufficient to reconstruct high-quality fused super-resolution result using only low-resolution inputs. By this way, we propose a novel self-supervised dataset-free method for adaptive infrared (IR) and visible (VIS) image super-resolution fusion named Deep Retinex Fusion (DRF). The key idea of DRF is first generating component priors which are disentangled from physical model using our designed generative networks ZipperNet, LightingNet and AdjustingNet, then combining these priors which captured by networks via adaptive fusion loss functions based on Retinex theory, and finally reconstructing the super-resolution fusion results. Furthermore, in order to verify the effectiveness of our reported DRF, both qualitative and quantitative experiments via comparing with other state-of-the-art methods are performed using different test sets. These results prove that, comparing with large datasets trained methods, DRF which works without any dataset achieves the best super-resolution fusion performance; and more importantly, DRF can adaptively balance IR and VIS information and has good noise immunity. DRF codes are open source available at https://github.com/GuYuanjie/Deep-Retinex-fusion., Comment: 20 pages, 9 figures

Published
2021

12. Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging

Author

Gu, Yuanjie, Guan, Yinghan, Xiao, Zhibo, Dai, Haoran, Liu, Cheng, and Wang, Shouyu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Multi-focus image fusion (MFIF) and super-resolution (SR) are the inverse problem of imaging model, purposes of MFIF and SR are obtaining all-in-focus and high-resolution 2D mapping of targets. Though various MFIF and SR methods have been designed; almost all the them deal with MFIF and SR separately. This paper unifies MFIF and SR problems in the physical perspective as the multi-focus image super resolution fusion (MFISRF), and we propose a novel unified dataset-free unsupervised framework named deep fusion prior (DFP) based-on deep image prior (DIP) to address such MFISRF with single model. Experiments have proved that our proposed DFP approaches or even outperforms those state-of-art MFIF and SR method combinations. To our best knowledge, our proposed work is a dataset-free unsupervised method to simultaneously implement the multi-focus fusion and super-resolution task for the first time. Additionally, DFP is a general framework, thus its networks and focus measurement tactics can be continuously updated to further improve the MFISRF performance. DFP codes are open source available at http://github.com/GuYuanjie/DeepFusionPrior., Comment: 24 pages

Published
2021
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18. RNA Modification in Carcinogenesis and Development of Gastric Cancer

Author

XU Jiawen, WANG Qiang, and WANG Shouyu

Subjects
rna modification, gastric cancer, m6a, m5c, ac4c, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

The occurrence of gastric cancer is closely related to environmental, genetic, and epigenetic factors. Currently, RNA modification is a research frontier and hotspot in the field of epigenetics. With the advancements in analytical chemistry and high-throughput sequencing technologies, new technologies and methods of exploring RNA modification are constantly being presented. Numerous studies have confirmed the involvement of RNA modifications in the occurrence and development of various diseases. Recent studies have shown that RNA modifications such as m6A, m5C, and ac4C regulate the malignant progression of various tumors, including gastric cancer, liver cancer, colorectal cancer, and leukemia. This article systematically reviews the research status and mechanism of different RNA modifications in the occurrence and development of gastric cancer, as well as discusses its potential value in the diagnosis and treatment of gastric cancer.

Published
2023
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28. Qualitative Phase Imaging

Author

Liu, Cheng, Wang, Shouyu, Veetil, Suhas P., Atai, Javid, Series Editor, Liang, Rongguang, Series Editor, Dinish, U.S., Series Editor, Liu, Cheng, Wang, Shouyu, and Veetil, Suhas P.

Published
2022
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33. Object Detection in Specific Traffic Scenes using YOLOv2

Author

Wang, Shouyu and Tang, Weitao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

object detection framework plays crucial role in autonomous driving. In this paper, we introduce the real-time object detection framework called You Only Look Once (YOLOv1) and the related improvements of YOLOv2. We further explore the capability of YOLOv2 by implementing its pre-trained model to do the object detecting tasks in some specific traffic scenes. The four artificially designed traffic scenes include single-car, single-person, frontperson-rearcar and frontcar-rearperson.

Published
2019

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