Search

Your search keyword '"Liu, Zhou"' showing total 860 results
Start Over Author "Liu, Zhou" Search Limiters Full Text
860 results on '"Liu, Zhou"'

1. Organic solvent boosts charge storage and charging dynamics of conductive MOF supercapacitors

Author

Chen, Ming, Wu, Taizheng, Niu, Liang, Ye, Ting, Dai, Wenlei, Zeng, Liang, Kornyshev, Alexei A., Wang, Zhenxiang, Liu, Zhou, and Feng, Guang

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

Conductive metal-organic frameworks (c-MOFs) and ionic liquids (ILs) have emerged as auspicious combinations for high-performance supercapacitors. However, the nanoconfinement from c-MOFs and high viscosity of ILs slow down the charging process. This hindrance can, however, be resolved by adding solvent. Here, we performed constant-potential molecular simulations to scrutinize the solvent impact on charge storage and charging dynamics of MOF-IL-based supercapacitors. We find conditions for >100% enhancement in capacity and ~6 times increase in charging speed. These improvements were confirmed by synthesizing near-ideal c-MOFs and developing multiscale models linking molecular simulations to electrochemical measurements. Fundamentally, our findings elucidate that the solvent acts as an ionophobic agent to induce a substantial enhancement in charge storage, and as an ion traffic police to eliminate convoluted counterion and co-ion motion paths and create two distinct ion transport highways to accelerate charging dynamics. This work paves the way for the optimal design of MOF supercapacitors.

Published
2024

2. BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images

Author

Chen, Zhanghao, Sun, Yifei, Qin, Wenjian, Ge, Ruiquan, Pan, Cheng, Deng, Wenming, Liu, Zhou, Min, Wenwen, Elazab, Ahmed, Wan, Xiang, and Wang, Changmiao

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

Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung screening. Nonetheless, the efficacy of CXRs is somewhat impeded, given that approximately 75% of the lung area overlaps with bone, which in turn hampers the detection and diagnosis of diseases. As a remedial measure, bone suppression techniques have been introduced. The current dual-energy subtraction imaging technique in the clinic requires costly equipment and subjects being exposed to high radiation. To circumvent these issues, deep learning-based image generation algorithms have been proposed. However, existing methods fall short in terms of producing high-quality images and capturing texture details, particularly with pulmonary vessels. To address these issues, this paper proposes a new bone suppression framework, termed BS-Diff, that comprises a conditional diffusion model equipped with a U-Net architecture and a simple enhancement module to incorporate an autoencoder. Our proposed network cannot only generate soft tissue images with a high bone suppression rate but also possesses the capability to capture fine image details. Additionally, we compiled the largest dataset since 2010, including data from 120 patients with high-definition, high-resolution paired CXRs and soft tissue images collected by our affiliated hospital. Extensive experiments, comparative analyses, ablation studies, and clinical evaluations indicate that the proposed BS-Diff outperforms several bone-suppression models across multiple metrics. Our code can be accessed at https://github.com/Benny0323/BS-Diff., Comment: 5 pages, 2 figures, accepted by IEEE ISBI 2024

Published
2023

6. Solvent engineering for scalable fabrication of perovskite/silicon tandem solar cells in air

Author

Zheng, Xuntian, Kong, Wenchi, Wen, Jin, Hong, Jiajia, Luo, Haowen, Xia, Rui, Huang, Zilong, Luo, Xin, Liu, Zhou, Li, Hongjiang, Sun, Hongfei, Wang, Yurui, Liu, Chenshuaiyu, Wu, Pu, Gao, Han, Li, Manya, Bui, Anh Dinh, Mo, Yi, Zhang, Xueling, Yang, Guangtao, Chen, Yifeng, Feng, Zhiqiang, Nguyen, Hieu T., Lin, Renxing, Li, Ludong, Gao, Jifan, and Tan, Hairen

Published
2024
Full Text
View/download PDF

11. TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

Author

Xiaozhi Zhang, Liu Zhou, Yaping Wan, Bingo Wing‐Kuen Ling, and Dongping Xiong

Subjects
image reconstruction, magnetic resonance imaging, texture transfer, Transformer, Photography, TR1-1050, Computer software, QA76.75-76.765
Abstract

Abstract Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

Published
2024
Full Text
View/download PDF

12. An allocentric human odometer for perceiving distances on the ground plane

Author

Liu Zhou, Wei Wei, Teng Leng Ooi, and Zijiang J He

Subjects
visual space perception, allocentric reference frame, depth perception, path integration, slef-motion, hippocampal formation, Medicine, Science, Biology (General), QH301-705.5
Abstract

We reliably judge locations of static objects when we walk despite the retinal images of these objects moving with every step we take. Here, we showed our brains solve this optical illusion by adopting an allocentric spatial reference frame. We measured perceived target location after the observer walked a short distance from the home base. Supporting the allocentric coding scheme, we found the intrinsic bias , which acts as a spatial reference frame for perceiving location of a dimly lit target in the dark, remained grounded at the home base rather than traveled along with the observer. The path-integration mechanism responsible for this can utilize both active and passive (vestibular) translational motion signals, but only along the horizontal direction. This asymmetric path-integration finding in human visual space perception is reminiscent of the asymmetric spatial memory finding in desert ants, pointing to nature’s wondrous and logically simple design for terrestrial creatures.

Published
2024
Full Text
View/download PDF

13. View-Disentangled Transformer for Brain Lesion Detection

Author

Li, Haofeng, Huang, Junjia, Li, Guanbin, Liu, Zhou, Zhong, Yihong, Chen, Yingying, Wang, Yunfei, and Wan, Xiang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to balance between the granularity of 3D context aggregation and the computational complexity. In this paper, we propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view, which approximately achieves the 3D correlation computing in an efficient way. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions. The experimental results show that our proposed view-disentangled transformer performs well for brain lesion detection on a challenging brain MRI dataset., Comment: International Symposium on Biomedical Imaging (ISBI) 2022, code: https://github.com/lhaof/ISBI-VDFormer

Published
2022
Full Text
View/download PDF

14. Learning to Prove Trigonometric Identities

Author

Liu, Zhou, Li, Yujun, Liu, Zhengying, Li, Lin, and Li, Zhenguo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Automatic theorem proving with deep learning methods has attracted attentions recently. In this paper, we construct an automatic proof system for trigonometric identities. We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities. Our goal is not only to complete the proof, but to complete the proof in as few steps as possible. For this reason, we design a model to learn proof data generated by random BFS (rBFS), and it is proved theoretically and experimentally that the model can outperform rBFS after a simple imitation learning. After further improvement through reinforcement learning, we get AutoTrig, which can give proof steps for identities in almost as short steps as BFS (theoretically shortest method), with a time cost of only one-thousandth. In addition, AutoTrig also beats Sympy, Matlab and human in the synthetic dataset, and performs well in many generalization tasks.

Published
2022

15. Single-cell RNA sequencing reveals recruitment of the M2-like CCL8high macrophages in Lewis lung carcinoma-bearing mice following hypofractionated radiotherapy

Author

Haonan Yang, Zheng Lei, Jiang He, Lu Zhang, Tangmin Lai, Liu Zhou, Nuohan Wang, Zheng Tang, Jiangdong Sui, and Yongzhong Wu

Subjects
Hypofractionated radiotherapy, Macrophages, Chemokines, CCL8, CCL signaling pathway, Medicine
Abstract

Abstract Background Tumor-associated macrophages (TAMs) play a pivotal role in reshaping the tumor microenvironment following radiotherapy. The mechanisms underlying this reprogramming process remain to be elucidated. Methods Subcutaneous Lewis lung carcinoma (LLC) murine model was treated with hypofrationated radiotherapy (8 Gy × 3F). Single-cell RNA sequencing was utilized to identify subclusters and functions of TAMs. Multiplex assay and enzyme-linked immunosorbent assay (ELISA) were employed to measure serum chemokine levels. Bindarit was used to inhibit CCL8, CCL7, and CCL2. The infiltration of TAMs after combination treatment with hypofractionated radiotherapy and Bindarit was quantified with flow cytometry, while the influx of CD206 and CCL8 was assessed by immunostaining. Results Transcriptome analysis identified a distinct subset of M2-like macrophages characterized by elevated Ccl8 expression level following hypofractionated radiotherapy in LLC-bearing mice. Remarkbly, hypofractionated radiotherapy not only promoted CCL8high macrophages infiltration but also reprogrammed them by upregulating immunosuppressive genes, thereby fostering an immunosuppressive tumor microenvironment. Additioinally, hypofractionated radiotherapy enhanced the CCL signaling pathway, augmenting the pro-tumorigenic functions of CCL8high macrophages and boosting TAMs recruitment. The adjunctive treatment combining hypofractionated radiotherapy with Bindarit effectively reduced M2 macrophages infiltration and prolonged the duration of local tumor control. Conclusions Hypofractionated radiotherapy enhances the infiltration of CCL8high macrophages and amplifies their roles in macrophage recruitment through the CCL signaling pathway, leading to an immunosuppressive tumor microenvironment. These findings highlight the potential of targeting TAMs and introduces a novel combination to improve the efficacy of hypofractionated radiotherapy.

Published
2024
Full Text
View/download PDF

16. Integration of morphology and electronic structure modulation on cobalt phosphide nanosheets to boost photocatalytic hydrogen evolution from ammonia borane hydrolysis

Author

Chao Wan, Yu Liang, Liu Zhou, Jindou Huang, Jiapei Wang, Fengqiu Chen, Xiaoli Zhan, and Dang-guo Cheng

Subjects
Ammonia borane, Hydrogen generation, Hydrolysis, Cobalt phosphide nanosheets, Photocatalysis, Renewable energy sources, TJ807-830, Ecology, QH540-549.5
Abstract

The controllable and safe hydrogen storage technologies are widely recognized as the main bottleneck for the accomplishment of sustainable hydrogen energy. Ammonia borane (AB) has regarded as a competitive candidate for chemical hydrogen storage. However, developing efficient yet high-performance catalysts towards hydrogen evolution from AB hydrolysis remains an enormous challenge. Herein, cobalt phosphide nanosheets are synthesized by a facile salt-assisted along with low-temperature phosphidation strategy for simultaneously modulating its morphology and electronic structure, and function as hydrogen evolution photocatalysts. Impressively, the Co2P nanosheets display extraordinary performance with a record high turnover frequency of 44.9 min−1, outperforming most of the noble-metal-free catalysts reported to date. This remarkable performance is attributed to its desired nanosheets structure, featuring with high specific surface area, abundant exposed active sites, and short charge diffusion paths. Our findings provide a novel strategy for regulating metal phosphides with desired phase structure and morphology for energy-related applications and beyond.

Published
2024
Full Text
View/download PDF

23. Metamagnetic Transitions in Few-Layer CrOCl Controlled by Magnetic Anisotropy Flipping

Author

Zhang, Minjie, Hu, Qifeng, Hua, Chenqiang, Cheng, Man, Liu, Zhou, Song, Shijie, Wang, Fanggui, He, Pimo, Cao, Guang-Han, Xu, Zhu-An, Lu, Yunhao, Yang, Jinbo, and Zheng, Yi

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The pivotal role of magnetic anisotropy in stabilising two-dimensional (2D) magnetism has been widely accepted, however, direct correlation between magnetic anisotropy and long-range magnetic ordering in the 2D limit is yet to be explored. Here, using angle- and temperature-dependent tunnelling magnetoresistance, we report unprecedented metamagnetic phase transitions in atomically-thin CrOCl, triggered by magnetic easy-axis flipping instead of the conventional spin flop mechanism. Few-layer CrOCl tunnelling devices of various thicknesses consistently show an in-plane antiferromagnetic (AFM) ground state with the easy axis aligned along the Cr-O-Cr direction (b-axis). Strikingly, with the presence of a magnetic field perpendicular to the easy-axis (H||c), magnetization of CrOCl does not follow the prevalent spin rotation and saturation pattern, but rather exhibits an easy-axis flipping from the in-plane to out-of-plane directions. Such magnetic anisotropy controlled metamagnetic phase transitions are manifested by a drastic upturn in tun- nelling current, which shows anomalous shifts towards higher H when temperature increases. By 2D mapping of tunnelling currents as a function of both temperature and H, we determine a unique ferrimagnetic state with a superstructure periodicity of five unit cells after the field-induced metam- agnetic transitions. The feasibility to control 2D magnetism by manipulating magnetic anisotropy may open enormous opportunities in spin-based device applications., Comment: 7 pages, 4 figures

Published
2021

33. Refining the rheological characteristics of high drug loading ointment via SDS and machine learning.

Author

Xilong Qian, Kewei Wang, Yulu Ma, Fang Fang, Xiangsong Meng, Liu Zhou, Yanqiong Pan, Yang Zhang, Yehuang Wang, Xiuxiu Wang, Jing Zhao, Bin Jiang, and Shengjin Liu

Subjects
Medicine, Science
Abstract

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes.

Published
2024
Full Text
View/download PDF

34. Observation of the Orbital Rashba-Edelstein Magnetoresistance

Author

Ding, Shilei, Liang, Zhongyu, Go, Dongwook, Yun, Chao, Xue, Mingzhu, Liu, Zhou, Becker, Sven, Yang, Wenyun, Du, Honglin, Wang, Changsheng, Yang, Yingchang, Jakob, Gerhard, Kläui, Mathias, Mokrousov, Yuriy, and Yang, Jinbo

Subjects
Condensed Matter - Materials Science
Abstract

We report the observation of magnetoresistance (MR) originating from the orbital angular momentum transport (OAM) in a Permalloy (Py) / oxidized Cu (Cu*) heterostructure: the orbital Rashba-Edelstein magnetoresistance. The angular dependence of the MR depends on the relative angle between the induced OAM and the magnetization in a similar fashion as the spin Hall magnetoresistance (SMR). Despite the absence of elements with large spin-orbit coupling, we find a sizable MR ratio, which is in contrast to the conventional SMR which requires heavy elements. By varying the thickness of the Cu* layer, we confirm that the interface is responsible for the MR, suggesting that the orbital Rashba-Edelstein effect is responsible for the generation of the OAM. Through Py thickness-dependence studies, we find that the effective values for the spin diffusion and spin dephasing lengths of Py are significantly larger than the values measured in Py / Pt bilayers, approximately by the factor of 2 and 4, respectively. This implies that another mechanism beyond the conventional spin-based scenario is responsible for the MR observed in Py / Cu* structures originated in a sizeable transport of OAM. Our findings not only unambiguously demonstrate the current-induced torque without using any heavy element via the OAM channel but also provide an important clue towards the microscopic understanding of the role that OAM transport can play for magnetization dynamics., Comment: 11 pages,3 figures

Published
2021
Full Text
View/download PDF

35. 顶空-气质联用法鉴别压榨植物油和浸出植物油Identification of pressed vegetable oil and extracted vegetable oil by headspace-gas chromatography-mass spectrometry

Author

杨柳，周侃侃，尚素微，朱杰丽，欧菊芳，王朝仁，王衍彬 YANG Liu, ZHOU Kankan, SHANG Suwei, ZHU Jieli, OU Jufang, WANG Chaoren, WANG Yanbin

Subjects
顶空-气质联用；鉴别；压榨；浸出；植物油, headspace-gas chromatography-mass spectrometry, identification, press, extraction, vegetable oil, Oils, fats, and waxes, TP670-699
Abstract

旨在为探明植物油出油方式提供技术支持，建立了一种鉴别压榨植物油和浸出植物油的方法。采用顶空-气质联用法测定植物油的残留溶剂，对顶空进样器平衡温度进行了优化，并探究植物油残留溶剂的特征性成分，通过对压榨植物油和浸出植物油样品中特征性成分的分析确定植物油出油方式的鉴别方法。结果表明：六号溶剂主要成分的出峰面积均随着平衡温度的升高而增加，优化的平衡温度为150 ℃；六号溶剂中5种主要成分在0~1.00 mg/kg范围内线性关系良好，相关系数均可达0.999以上，检出限为0.001 0~0.014 4 mg/kg；六号溶剂中不仅含有烷烃、环烷烃，还含有烯烃、芳香烃（包括多环芳烃）等，成分较为复杂，浸出植物油中溶剂残留物主要为2-甲基戊烷、3-甲基戊烷 、正己烷、甲基环戊烷和环己烷，占比达97.5%，与六号溶剂中这5种物质含量（占比达96.6%）相似，因此确定这5种物质为植物油残留溶剂的特征性成分；实际样品测定表明压榨植物油中没有溶剂残留或残留量较低，浸出植物油中均有溶剂残留且残留量较高；总结出鉴别压榨植物油和浸出植物油的方法为若同时检出4种及以上六号溶剂特征性成分或特征性成分总含量大于或等于0.50 mg/kg的样品可判定为浸出植物油，若同时检出小于或等于3种六号溶剂特征性成分且特征性成分总含量小于 0.50 mg/kg的样品可判定为压榨植物油。综上，采用顶空-气质联用法对植物油中六号溶剂的特征性成分进行分析，以特征性成分有无和含量可以鉴别压榨植物油和浸出植物油。In order to provide technical support for the exploration of vegetable oil production mode, a method for identifying pressed vegetable oil and extracted vegetable oil was established. The residual solvent in vegetable oil was determined by headspace-gas chromatography-mass spectrometry. The equilibrium temperature of headspace injection was optimized, and the characteristic components of residual solvent in vegetable oil was identified. By analyzing the characteristic components in pressed and extracted vegetable oil samples, the extraction method of vegetable oil was identified. The results showed that the peak areas of main components of No. 6 solvent increased with the increase of equilibrium temperature, and the optimal equilibrium temperature was 150 ℃. The linearity of five main components was good in the range of 0-1.00 mg/kg, the correlation coefficient was more than 0.999, and the detection limits was 0.001 0-0.014 4 mg/kg. The components of No.6 solvent were relatively complex, including not only alkane and cycloalkanes, but also olefins, aromatic hydrocarbons (including polycyclic aromatic hydrocarbons). The solvent residues in the extracted vegetable oil were mainly 2-methylpentane, 3-methylpentane, n-hexane, methylcyclopentane and cyclohexane, accounting for 97.5%, which were similar to the contents of these five substances in No. 6 solvent(accouting for 96.6%). Therefore, it was determined that these five substances were characteristic components of residual solvents in vegetable oil. The determination of practical samples showed that there was no or low solvent residue in pressed vegetable oil, while there was solvent residue in the extracted vegetable oil and the residue amount was relative high. The methods for identifying pressed vegetable oil and extracted vegetable oil were summarized as follows: if four or more characteristic components of No. 6 solvent at the same time or the total content of characteristic components greater than or equal to 0.50 mg/kg were detected, the sample could be determined as extracted vegetable oil. If less than or equal to three main components of No. 6 solvent at the same time and the total content of characteristic components less than 0.50 mg/kg were detected, the sample could be judged as pressed vegetable oil. By analyzing characteristic components of No. 6 solvent in vegetable oil using headspace-gas chromatography-mass spectrometry, the pressed vegetable oil and extracted vegetable oil can be identified based on the presence or absence and content of its characteristic components.

Published
2023
Full Text
View/download PDF

38. Regional Practice Variation and Outcomes in the Standard Versus Accelerated Initiation of Renal Replacement Therapy in Acute Kidney Injury (STARRT-AKI) Trial: A Post Hoc Secondary Analysis

Author

Vaara, Suvi T., Serpa Neto, Ary, Bellomo, Rinaldo, Adhikari, Neill K. J., Dreyfuss, Didier, Gallagher, Martin, Gaudry, Stephane, Hoste, Eric, Joannidis, Michael, Pettilä, Ville, Wang, Amanda Y., Kashani, Kianoush, Wald, Ron, Bagshaw, Sean M., Ostermann, Marlies, Bagshaw, Sean M, Wald, Ron, Adhikari, Neill K.J., Bellomo, Rinaldo, Dreyfuss, Didier, Du, Bin, Gallagher, Martin P., Gaudry, Stéphane, Hoste, Eric A., Lamontagne, François, Joannidis, Michael, Liu, Kathleen D., McAuley, Daniel F., McGuinness, Shay P., Nichol, Alistair D., Ostermann, Marlies, Palevsky, Paul M., Qiu, Haibo, Pettilä, Ville, Schneider, Antoine G., Smith, Orla M., Vaara, Suvi T., Weir, Matthew, Bellomo, Rinaldo, Eastwood, Glenn M., Peck, Leah, Young, Helen, Kruger, Peter, Laurie, Gordon, Saylor, Emma, Meyer, Jason, Venz, Ellen, Wetzig, Krista, French, Craig, McGain, Forbes, Mulder, John, Fennessy, Gerard, Koottayi, Sathyajith, Bates, Samantha, Towns, Miriam, Morgan, Rebecca, Tippett, Anna, Udy, Andrew, Mason, Chris, Licari, Elisa, Gantner, Dashiell, McClure, Jason, Nichol, Alistair, McCracken, Phoebe, Board, Jasmin, Martin, Emma, Vallance, Shirley, Young, Meredith, Vladic, Chelsey, McGloughlin, Steve, Gattas, David, Buhr, Heidi, Coles, Jennifer, Hutch, Debra, Wun, James, Cole, Louise, Whitehead, Christina, Lowrey, Julie, Masters, Kristy, Gresham, Rebecca, Campbell, Victoria, Gutierrez, David, Brailsford, Jane, Forbes, Loretta, Murray, Lauren, Maguire, Teena, NiChonghaile, Martina, Orford, Neil, Bone, Allison, Elderkin, Tania, Salerno, Tania, Chimunda, Tim, Fletcher, Jason, Broadfield, Emma, Porwal, Sanjay, Knott, Cameron, Boschert, Catherine, Smith, Julie, Richardson, Angus, Hill, Dianne, Duke, Graeme, Oziemski, Peter, Cegarra, Santiago, Chan, Peter, Welsh, Deborah, Hunter, Stephanie, Roodenburg, Owen, Dyett, John, Kokotsis, Nicos, Moser, Max, Yang, Yang, Padayachee, Laven, Vetro, Joseph, Gangopadhyay, Himangsu, Kaufman, Melissa, Ghosh, Angaj, Said, Simone, Patel, Alpesh, Bihari, Shailesh, Matheson, Elisha, Jin, Xia, Shrestha, Tapaswi, Schwartz, Kate, Gallagher, Martin P., Cross, Rosalba, Cheung, Winston, Wong, Helen, Kol, Mark, Shah, Asim, Wang, Amanda Y., Endre, Zoltan, Bradford, Celia, Janin, Pierre, Finfer, Simon, Diel, Naomi, Gatward, Jonathan, Hammond, Naomi, Delaney, Anthony, Bass, Frances, Yarad, Elizabeth, Buscher, Hergen, Reynolds, Claire, Baker, Nerilee, Joannidis, Michael, Bellmann, Romuald, Peer, Andreas, Hasslacher, Julia, Koglberger, Paul, Klein, Sebastian, Zotter, Klemens, Brandtner, Anna, Finkenstedt, Armin, Ditlbacher, Adelheid, Hartig, Frank, Fries, Dietmar, Bachler, Mirjam, Schenk, Bettina, Wagner, Martin, Staudinger, Thomas, Tiller, Esther, Schellongowski, Peter, Bojic, Andja, Hoste, Eric A., Bracke, Stephanie, De Crop, Luc, Vermeiren, Daisy, Thome, Fernando, Chiella, Bianca, Fendt, Lucia, Antunes, Veronica, Maisonneuve-Rosemont, Lafrance, Jean-Philippe, Lamontagne, François, D’Aragon, Frédérick, St-Arnaud, Charles, Mayette, Michael, Carbonnaeu, Élaine, Marchand, Joannie, Masse, Marie-Hélène, Ladouceur, Marilène, Turgeon, Alexis F., Lauzier, François, Bellemare, David, Langis Francoeur, Charles, LeBlanc, Guillaume, Guilbault, Gabrielle, Grenier, Stéphanie, Cloutier, Eve, Boivin, Annick, Delisle-Thibault, Charles, Giannakouros, Panagiota, Costerousse, Olivier, Cailhier, Jean-François, Carrier, François-Martin, Ghamraoui, Ali, Lebrasseur, Martine, Benettaib, Fatna, Salamé, Maya, Boumahni, Dounia, Tung Sia, Ying, Naud, Jean-François, Roy, Isabelle, Stelfox, Henry T., Ruddell, Stacey, Manns, Braden J., Duggan, Shelley, Carney, Dominic, Barchard, Jennifer, Whitlock, Richard P., Belley-Cote, Emilie, Savija, Nevena, Sabev, Alexandra, Campbell, Troy, Creary, Thais, Devereaux, Kelson, Brodutch, Shira, Rigatto, Claudio, Paunovic, Bojan, Mooney, Owen, Glybina, Anna, Harasemiw, Oksana, Di Nella, Michelle, Harmon, John, Mehta, Navdeep, Lakatos, Louis, Haslam, Nicole, Lellouche, Francois, Simon, Mathieu, Tung, Ying, Lizotte, Patricia, Bourchard, Pierre-Alexandre, Rochwerg, Bram, Karachi, Tim, Millen, Tina, Muscedere, John, Maslove, David, Gordon Boyd, J., Sibley, Stephanie, Drover, John, Hunt, Miranda, Georgescu, Ilinca, Wax, Randy, Lenga, Ilan, Sridhar, Kavita, Steele, Andrew, Fusco, Kelly, Ghate, Taneera, Tolibas, Michael, Robinson, Holly, Weir, Matthew A., Taneja, Ravi, Ball, Ian M., Garg, Amit, Campbell, Eileen, Ovsenek, Athena, Bagshaw, Sean M., van Diepen, Sean, Baig, Nadia, Magder, Sheldon, Yao, Han, Alam, Ahsan, Campisi, Josie, MacIntyre, Erika, Rokosh, Ella, Scherr, Kimberly, Lapinsky, Stephen, Mehta, Sangeeta, Shah, Sumesh, Niven, Daniel J., Stelfox, Henry T., Ruddell, Stacey, Russell, Michael, Jim, Kym, Brown, Gillian, Oxtoby, Kerry, Hall, Adam, Benoit, Luc, Sokolowski, Colleen, Prasad, Bhanu, Rao, Jag, Giebel, Shelley, Kutsogiannis, Demetrios J., Thompson, Patricia, Thompson, Tayne, Cirone, Robert, Kavikondala, Kanthi, Soth, Mark, Clarke, France, Takaoka, Alyson, Wald, Ron, Mazer, David, Burns, Karen, Friedrich, Jan, Klein, David, Sandhu, Gyan, Santos, Marlene, Khalid, Imrana, Hodder, Jennifer, Dodek, Peter, Ayas, Najib, Alcuaz, Victoria, Suen, Gabriel, Rewa, Oleksa, Singh, Gurmeet, Norris, Sean, Gibson, Neil, Arias, Castro, Shami, Aysha, Pelletier, Celine, Adhikari, Neill K.J., Zahirieh, Alireza, Amaral, Andre, Marinoff, Nicole, Kaur, Navjot, Perez, Adic, Wang, Jane, Haljan, Gregory, Condin, Christopher, McIntyre, Lauralyn, Gomes, Brigette, Porteous, Rebecca, Watpool, Irene, Hiremath, Swapnil, Clark, Edward, Herridge, Margaret S., Backhouse, Felicity, Elizabeth Wilcox, M., Walczak, Karolina, Ki, Vincent, Sharman, Asheer, Romano, Martin, Bagshaw, Sean M., Noel Gibney, R.T., Romanovsky, Adam S., Rewa, Oleksa, McCoshen, Lorena, Baig, Nadia, Wood, Gordon, Ovakim, Daniel, Auld, Fiona, Carney, Gayle, Duan, Meili, Ji, Xiaojun, Guo, Dongchen, Qi, Zhili, Lin, Jin, Zhang, Meng, Dong, Lei, Liu, Jingfeng, Liu, Pei, Zhi, Deyuan, Bai, Guoqiang, Qiu, Yu, Yang, Ziqi, Bai, Jing, Liu, Zhuang, Zhuang, Haizhou, Wang, Haiman, Li, Jian, Zhao, Mengya, Zhou, Xiao, Shi, Xianqing, Ye, Baning, Liu, Manli, Wu, Jing, Fu, Yongjian, Long, Dali, Pan, Yu, Wang, Jinlong, Mei, Huaxian, Zhang, Songsong, Wen, Mingxiang, Yang, Enyu, Mu, Sijie, Li, Jianquan, Hu, Tingting, Qin, Bingyu, Li, Min, Wang, Cunzhen, Dong, Xin, Wang, Kaiwu, Wang, Haibo, Yang, Jianxu, Du, Bin, Wang, Chuanyao, Wang, Dongxin, Li, Nan, Yu, Zhui, Xu, Song, Yao, Lan, Hou, Guo, Liu, Zhou, Lu, Liping, Lian, Yingtao, Wang, Chunting, Zhang, Jichen, Ding, Ruiqi, Qi, Guoqing, Wang, Qizhi, Wang, Peng, Meng, Zhaoli, Chen, Man, Hu, Xiaobo, He, Xiandi, Zhao, Shibing, Hang, Lele, Li, Rui, Qin, Suhui, Lu, Kun, Dun, Shijuan, Liu, Cheng, Zhou, Qi, Chen, Zhenzhen, Mei, Jing, Zhang, Minwei, Xu, Hao, Lin, Jincan, Shi, Qindong, Fu, Lijuan, Zeng, Qinjing, Ma, Hongye, Yan, Jinqi, Gao, Lan, Liu, Hongjuan, Zhang, Lei, Li, Hao, He, Xiaona, Fan, Jingqun, Guo, Litao, Liu, Yu, Wang, Xue, Sun, Jingjing, Liu, Zhongmin, Yang, Juan, Ding, Lili, Sheng, Lulu, Liu, Xingang, Yan, Jie, Wang, Quihui, Wang, Yifeng, Zhao, Dan, Zhao, Shuangping, Hu, Chenghuan, Li, Jing, Deng, Fuxing, Qiu, Haibo, Yang, Yi, Mo, Min, Pan, Chun, Wu, Changde, Huang, Yingzi, Huang, Lili, Liu, Airan, Pettilä, Ville, Vaara, Suvi T., Korhonen, Anna-Maija, Törnblom, Sanna, Sutinen, Sari, Pettilä, Leena, Heinonen, Jonna, Lappi, Eliria, Suhonen, Taria, Karlsson, Sari, Hoppu, Sanna, Jalkanen, Ville, Kuitunen, Anne, Levoranta, Markus, Långsjö, Jaakko, Ristimäki, Sanna, Malila, Kaisa, Wootten, Anna, Varila, Simo, Järvisalo, Mikko J, Inkinen, Outi, Kentala, Satu, Leivo, Keijo, Haltia, Paivi, Dreyfuss, Didier, Ricard, Jean-Damien, Messika, Jonathan, Tiagarajah, Abirami, Emery, Malo, Dechanet, Aline, Gernez, Coralie, Roux, Damien, Martin-Lefevre, Laurent, Fiancette, Maud, Vinatier, Isabelle, Claude Lacherade, Jean, Colin, Gwenhaël, Lebert, Christine, Azais, Marie-Ange, Yehia, Aihem, Pouplet, Caroline, Henry- Lagarrigue, Matthieu, Seguin, Amélie, Crosby, Laura, Maizel, Julien, Titeca-Beauport, Dimitri, Combes, Alain, Nieszkowska, Ania, Masi, Paul, Demoule, Alexandre, Mayaux, Julien, Dres, Martin, Morawiec, Elise, Decalvele, Maxens, Demiri, Suela, Faure, Morgane, Marios, Clémence, Mallet, Maxime, Amélie Ordon, Marie, Morizot, Laura, Cantien, Marie, Pousset, François, Gaudry, Stéphane, Poirson, Florent, Cohen, Yves, Argaud, Laurent, Cour, Martin, Bitker, Laurent, Simon, Marie, Hernu, Romain, Baudry, Thomas, De La Salle, Sylvie, Robine, Adrien, Sedillot, Nicholas, Tchenio, Xavier, Bouisse, Camille, Roux, Sylvie, Barbar, Davide, Trusson, Rémi, Tamion, Fabienne, Grangé, Steven, Carpentier, Dorothée, Chevrel, Guillaume, Ensenyat-Martin, Luis, Marque, Sophie, Quenot, Jean-Pierre, Andreu, Pascal, Dargent, Auguste, Large, Audrey, Chudeau, Nicolas, Landais, Mickael, Derrien, Benoit, Christophe Callahan, Jean, Guitton, Christophe, Le Moal, Charlène, Robert, Alain, Asehnoune, Karim, Cinotti, Raphaël, Grillot, Nicolas, Demeure, Dominique, Vinsonneau, Christophe, Rahmani, Imen, Marzouk, Mehdi, Dekeyser, Thibault, Sejourne, Caroline, Verlay, Mélanie, Thevenin, Fabienne, Delecolle, Lucie, Didier Thevenin, Lens, Souweine, Bertrand, Coupez, Elisabeth, Adda, Mireille, Eraldi, Jean-Pierre, Marchalot, Antoine, De Prost, Nicolas, Mekontso Dessap, Armand, Razazi, Keyvan, Meziani, Ferhat, Boisrame-Helms, Julie, Clere-Jehl, Raphael, Delabranche, Xavier, Kummerlen, Christine, Merdji, Hamid, Monnier, Alexandra, Rabouel, Yannick, Rahmani, Hassene, Allam, Hayat, Chenaf, Samir, Franja, Vincenta, Pons, Bertrand, Carles, Michel, Martino, Frédéric, Richard, Régine, Zuber, Benjamin, Lacave, Guillaume, Lakhal, Karim, Rozec, Bertrand, Dang Van, Hoa, Boulet, Éric, Dubos, René, Fadel, Fouad, Cleophax, Cedric, Dufour, Nicolas, Grant, Caroline, Thuong, Marie, Reignier, Jean, Canet, Emmanuel, Nicolet, Laurent, Boulain, Thierry, Nay, Mai-Anh, Benzekri, Dalila, Barbier, François, Bretagnol, Anne, Kamel, Toufik, Mathonnet, Armelle, Muller, Grégoire, Skarzynski, Marie, Rossi, Julie, Pradet, Amandine, Dos Santos, Sandra, Guery, Aurore, Muller, Lucie, Felix, Luis, Bohé, Julien, Thiéry, Guillaume, Aissaoui, Nadia, Vimpere, Damien, Commeureuc, Morgane, Diehl, Jean-Luc, Guerot, Emmanuel, Liangos, Orfeas, Wittig, Monika, Zarbock, Alexander, Küllmar, Mira, van Waegeningh, Thomas, Rosenow, Nadine, Nichol, Alistair D., Brickell, Kathy, Doran, Peter, Murray, Patrick T., Landoni, Giovanni, Lembo, Rosalba, Zangrillo, Alberto, Monti, Giacomo, Tozzi, Margherita, Marzaroli, Matteo, Lombardi, Gaetano, Paternoster, Gianluca, Vitiello, Michelangelo, McGuinness, Shay, Parke, Rachael, Butler, Magdalena, Gilder, Eileen, Cowdrey, Keri-Anne, Wallace, Samantha, Hallion, Jane, Woolett, Melissa, Neal, Philippa, Duffy, Karina, Long, Stephanie, McArthur, Colin, Simmonds, Catherine, Chen, Yan, McConnochie, Rachael, Newby, Lynette, Knight, David, Henderson, Seton, Mehrtens, Jan, Morgan, Stacey, Morris, Anna, Vander Hayden, Kymbalee, Burke, Tara, Bailey, Matthew, Freebairn, Ross, Chadwick, Lesley, Park, Penelope, Rolls, Christine, Thomas, Liz, Buehner, Ulrike, Williams, Erin, Albrett, Jonathan, Kirkham, Simon, Jackson, Carolyn, Browne, Troy, Goodson, Jennifer, Jackson, David, Houghton, James, Callender, Owen, Higson, Vicki, Keet, Owen, Dominy, Clive, Young, Paul, Hunt, Anna, Judd, Harriet, Lawrence, Cassie, Olatunji, Shaanti, Robertson, Yvonne, Latimer-Bell, Charlotte, Hendry, Deborah, Mckay-Vucago, Agnes, Beehre, Nina, Lesona, Eden, Navarra, Leanlove, Robinson, Chelsea, Jang, Ryan, Junge, Andrea, Lambert, Bridget, Schneider, Antoine G., Thibault, Michel, Eckert, Philippe, Kissling, Sébastien, Polychronopoulos, Erietta, Poli, Elettra, Altarelli, Marco, Schnorf, Madeleine, Abed Mallaird, Samia, Heidegger, Claudia, Perret, Aurelie, Montillier, Philippe, Sangla, Frederic, Neils, Seigenthaller, De Watteville, Aude, Phull, Mandeep-Kaur, George, Aparna, Hussain, Nauman, Pogreban, Tatiana, Lobaz, Steve, Daniels, Alison, Cunningham, Mishell, Kerr, Deborah, Nicholson, Alice, Shanmugasundaram, Pradeep, Abrams, Judith, Manso, Katarina, Hambrook, Geraldine, McKerrow, Elizabeth, Salva, Juvy, Foulkes, Stephen, Wise, Matthew, Morgan, Matt, Brooks, Jenny, Cole, Jade, Michelle Davies, Tracy, Hill, Helen, Thomas, Emma, Vizcaychipi, Marcela, Baharlo, Behrad, Carungcong, Jaime, Costa, Patricia, Martins, Laura, Kapoor, Ritoo, Hazelton, Tracy, Moon, Angela, Musselwhite, Janine, Shelley, Ben, McCall, Philip, Ostermann, Marlies, Arbane, Gill, Bociek, Aneta, Marotti, Martina, Lim, Rosario, Campos, Sara, Grau Novellas, Neus, Cennamo, Armando, Slack, Andrew, Wyncoll, Duncan, Camporota, Luigi, Sparkes, Simon, Tilley, Rosalinde, Rattray, Austin, Moreland, Gayle, Duffy, Jane, McGonigal, Elizabeth, Hopkins, Philip, Finney, Clare, Smith, John, Noble, Harriet, Watson, Hayley, Harris, Claire-Louise, Clarey, Emma, Corcoran, Eleanor, Beck, James, Howcroft, Clare, Youngs, Nora, Wilby, Elizabeth, Ogg, Bethan, Wolverson, Adam, Lee, Sandra, Butler, Susie, Okubanjo, Maryanne, Hindle, Julia, Welters, Ingeborg, Williams, Karen, Johnson, Emily, Patrick-Heselton, Julie, Shaw, David, Waugh, Victoria, Stewart, Richard, Mwaura, Esther, Wren, Lynn, Mew, Louise, Sutherland, Sara-Beth, Adderley, Jane, Ruddy, Jim, Harkins, Margaret, Kaye, Callum, Scott, Teresa, Mitchell, Wendy, Anderson, Felicity, Willox, Fiona, Jagannathan, Vijay, Clark, Michele, Purv, Sarah, Sharman, Andrew, Meredith, Megan, Ryan, Lucy, Conner, Louise, Peters, Cecilia, Harvey, Dan, Roshdy, Ashraf, Collins, Amy, Sim, Malcolm, Henderson, Steven, Chee, Nigel, Pitts, Sally, Bowman, Katie, Dilawershah, Maria, Vamplew, Luke, Howe, Elizabeth, Rogers, Paula, Hernandez, Clara, Prendergast, Clara, Benton, Jane, Rosenberg, Alex, Forni, Lui G., Grant, Alice, Carvelli, Paula, Raithatha, Ajay, Bird, Sarah, Richardson, Max, Needham, Matthew, Hirst, Claire, Ball, Jonathan, Leaver, Susannah, Howlett, Luisa, Castro Delgado, Carlos, Farnell-Ward, Sarah, Farrah, Helen, Gray, Geraldine, Joseph, Gipsy, Robinson, Francesca, Tridente, Ascanio, Harrop, Clare, Shuker, Karen, McLaughlan, Derek, Ramsey, Judith, Meehan, Sharon, Oliver Rose, Bernd, Reece-Anthony, Rosie, Gurung, Babita, Whitehouse, Tony, Snelson, Catherine, Veenith, Tonny, Johnston, Andy, Cooper, Lauren, Carrera, Ron, Ellis, Karen, Fellows, Emma, Harkett, Samanth, Bergin, Colin, Spruce, Elaine, Despy, Liesl, Goundry, Stephanie, Dooley, Natalie, Mason, Tracy, Clark, Amy, Dignam, Gemma, Ward, Geraldine, Attwood, Ben, Parsons, Penny, Mason, Sophie, Margarson, Michael, Lord, Jenny, McGlone, Philip, Hodgson, Luke E., Chadbourn, Indra, Gomez, Raquel, Margalef, Jordi, Pretorius, Rinus, Hamshere, Alexandra, Carter, Joseph, Cahill, Hazel, Grainger, Lia, Howard, Kate, Forshaw, Greg, Guy, Zoe, Kashani, Kianoush B., Albright, Robert C., Amsbaugh, Amy, Stoltenberg, Anita, Niven, Alexander S., Lynch, Matthew, O’Mara, AnnMarie, Naeem, Syed, Sharif, Sairah, McKenney Goulart, Joyce, Lynch, Matthew, O’Mara, AnnMarie, Naeem, Syed, Sharif, Sairah, McKenney Goulart, Joyce, Tolwani, Ashita, Lyas, Claretha, Latta, Laura, Bihorac, Azra, Hashemighouchani, Haleh, Efron, Philip, Ruppert, Matthew, Cupka, Julie, Kiley, Sean, Carson, Joshua, White, Peggy, Omalay, George, Brown, Sherry, Velez, Laura, Marceron, Alina, Neyra, Javier A., Carlos Aycinena, Juan, Elias, Madona, Ortiz-Soriano, Victor M., Hauschild, Caroline, and Dorfman, Robert

Published
2024
Full Text
View/download PDF

39. Covariant propagator and chiral power counting

Author

Liu, Zhou, Wen, Li-Hong, and Yang, J. -F.

Subjects
High Energy Physics - Theory, High Energy Physics - Phenomenology, Nuclear Theory
Abstract

Some one-loop diagrams with one and two external baryons/nucleons are revisited using covariant baryon propagators in chiral effective theory. We showed that it is enough to separate and subtract all the local terms that violate chiral power counting to recover chiral power counting, no need to introduce extra operations. The structures of leading chiral corrections and IR enhancement or threshold effects are 'stable' or persist as long as covariant propagators are employed for all particles., Comment: 9 pages, 11 figures, final version, accepted for publication in Nuclear Physics B

Published
2020
Full Text
View/download PDF

40. Mixed Noise Removal with Pareto Prior

Author

Liu, Zhou, Yu, Lei, Xia, Gui-Song, and Sun, Hong

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

Denoising images contaminated by the mixture of additive white Gaussian noise (AWGN) and impulse noise (IN) is an essential but challenging problem. The presence of impulsive disturbances inevitably affects the distribution of noises and thus largely degrades the performance of traditional AWGN denoisers. Existing methods target to compensate the effects of IN by introducing a weighting matrix, which, however, is lack of proper priori and thus hard to be accurately estimated. To address this problem, we exploit the Pareto distribution as the priori of the weighting matrix, based on which an accurate and robust weight estimator is proposed for mixed noise removal. Particularly, a relatively small portion of pixels are assumed to be contaminated with IN, which should have weights with small values and then be penalized out. This phenomenon can be properly described by the Pareto distribution of type 1. Therefore, armed with the Pareto distribution, we formulate the problem of mixed noise removal in the Bayesian framework, where nonlocal self-similarity priori is further exploited by adopting nonlocal low rank approximation. Compared to existing methods, the proposed method can estimate the weighting matrix adaptively, accurately, and robust for different level of noises, thus can boost the denoising performance. Experimental results on widely used image datasets demonstrate the superiority of our proposed method to the state-of-the-arts.

Published
2020

41. Learning Cluster Structured Sparsity by Reweighting

Author

Jiang, Yulun, Yu, Lei, Zhang, Haijian, and Liu, Zhou

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery. Benefit from available training data, the learned networks have achieved state-of-the-art performance in respect of both speed and accuracy. However, the structure behind sparsity, imposing constraint on the support of sparse signals, is often an essential prior knowledge but seldom considered in the existing networks. In this paper, we aim at bridging this gap. Specifically, exploiting the iterative reweighted $\ell_1$ minimization (IRL1) algorithm, we propose to learn the cluster structured sparsity (CSS) by rewegihting adaptively. In particular, we first unfold the Reweighted Iterative Shrinkage Algorithm (RwISTA) into an end-to-end trainable deep architecture termed as RW-LISTA. Then instead of the element-wise reweighting, the global and local reweighting manner are proposed for the cluster structured sparse learning. Numerical experiments further show the superiority of our algorithm against both classical algorithms and learning-based networks on different tasks.

Published
2019

47. Glycyrrhizic acid glycosides reduces extensive tripterygium glycosides-induced lipid deposition in hepatocytes

Author

Yifei Yang, Xiaotong Fu, Bing Xia, Liu Zhou, Haijing Zhang, Chun Li, Xiao Ye, and Ting Liu

Subjects
Tripterygium glycosides, Glycyrrhizic acid glycosides, Liver, Hepatotoxicity, Lipid metabolism, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Aim: Tripterygium glycosides (TG) extracted from the plant Tripterygium wilfordii Hook F has been used to treat chronic kidney diseases for many years. However, hepatotoxicity limits its clinical application. Glycyrrhizic acid glycosides (GA) can reduce TG hepatotoxicity, however, further investigation into the underlying molecular mechanisms by which GA attenuates TG-induced hepatotoxicity is required. Methods: Sprague‒Dawley rats were randomly divided into the control group, the TG groups (TG189 mg/kg group, TG472.5 mg/kg group), and the TG + GA groups (TG189 mg/kg + GA20.25 mg/kg group, TG472.5 mg/kg + GA20.25 mg/kg group). After 21 consecutive days of intragastric administration, structural and molecular changes in hepatocytes were detected. Results: After 21 days of TG treatment, the serum level of the total bilirubin, triglyceride, total cholesterol, and low-density lipoprotein cholesterol increased in the TG189 mg/kg and TG472.5 mg/kg groups when compared to the control group. High-density lipoprotein cholesterol levels were reduced in both TG groups. The ultrastructure of hepatocytes and the structural integrity of the liver were compromised. In addition, the relevant molecular level of the peroxisome proliferators-activated receptor α (PPARα) and acyl-CoA synthetase long-chain family members (ACSLs) pathway was modulated. With the addition of 20.25 mg/kg GA, the serum biochemical indexes and liver tissue structure ultrastructure of hepatocytes were improved, and the PPARα-ACSLs pathway was corrected. Conclusion: The combined application of GA and TG improved abnormal lipid metabolism, repaired liver structure, reduced lipid deposition in hepatocytes, and reduced TG-induced hepatotoxicity.

Published
2023
Full Text
View/download PDF

48. CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network

Author

Zhang, Menglei, Liu, Zhou, and Yu, Lei

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

Convolutional Sparse Coding (CSC) has been attracting more and more attention in recent years, for making full use of image global correlation to improve performance on various computer vision applications. However, very few studies focus on solving CSC based image Super-Resolution (SR) problem. As a consequence, there is no significant progress in this area over a period of time. In this paper, we exploit the natural connection between CSC and Convolutional Neural Networks (CNN) to address CSC based image SR. Specifically, Convolutional Iterative Soft Thresholding Algorithm (CISTA) is introduced to solve CSC problem and it can be implemented using CNN architectures. Then we develop a novel CSC based SR framework analogy to the traditional SC based SR methods. Two models inspired by this framework are proposed for pre-/post-upsampling SR, respectively. Compared with recent state-of-the-art SR methods, both of our proposed models show superior performance in terms of both quantitative and qualitative measurements., Comment: 10 pages, 12 figures, 3 tables

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results