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1,882 results on '"Zhang, Shuyu"'

202. Tirzepatide once weekly for the treatment of obesity in people with type 2 diabetes (SURMOUNT-2): a double-blind, randomised, multicentre, placebo-controlled, phase 3 trial

Author

Garvey, W Timothy, primary, Frias, Juan P, additional, Jastreboff, Ania M, additional, le Roux, Carel W, additional, Sattar, Naveed, additional, Aizenberg, Diego, additional, Mao, Huzhang, additional, Zhang, Shuyu, additional, Ahmad, Nadia N, additional, Bunck, Mathijs C, additional, Benabbad, Imane, additional, Zhang, Xiaotian M, additional, Abalos, Franklin H., additional, Manghi, Federico C.P., additional, Zaidman, Cesar J., additional, Vico, Marisa L., additional, Costanzo, Pablo R., additional, Serra, Leonardo P., additional, MacKinnon, Ignacio J., additional, Hissa, Miguel N., additional, Vidotti, Maria H., additional, Kerr Saraiva, Jose F., additional, Alves, Breno B., additional, Franco, Denise R., additional, Moratto, Otavio, additional, Murthy, Sreenivasa, additional, Goyal, Ghanshyam, additional, Yamasaki, Yoshimitsu, additional, Sato, Nobuyuki, additional, Inoue, Satoshi, additional, Asakura, Taro, additional, Shestakova, Marina, additional, Khaykina, Elena, additional, Troshina, Ekaterina, additional, Vorokhobina, Natalia, additional, Ametov, Alexander, additional, Tu, Shih-Te, additional, Yang, Chwen-Yi, additional, Lee, I-Te, additional, Huang, Chien-Ning, additional, Ou, Horng-Yih, additional, Freeman, George, additional, Machineni, Sriram, additional, Klein, Klara, additional, Sultan, Senan, additional, Parsa, Alan, additional, Otero-Martinez, Juan, additional, Gonzalez, Alex, additional, Bhargava, Anuj, additional, Brian, Susan, additional, Ince, Carlos, additional, Plantholt, Stephen, additional, Cole, Jeremy, additional, Lacour, Audrey, additional, Vega, Damaris, additional, de Souza, Jose, additional, Rohlf, Jane L., additional, St. John, Roy C., additional, Horowitz, Barry, additional, Audish, Hanid, additional, Galindo, Rodolfo, additional, Umpiperrez, Guillermo, additional, Ard, Jamy, additional, Curtis, Brian, additional, Garvey, William T., additional, Fraser, Neil J., additional, Mandry, Jose, additional, Mohseni, Rizwana, additional, Mayfield, Ronald, additional, Powell, Talessa, additional, Vance, Carl, additional, Ong, Stephen, additional, Lewy-Alterbaum, Ana L., additional, Murray, Alexander, additional, Al-Karadsheh, Amer, additional, Yacoub, Tamer, additional, Roberts, Kevin, additional, Fried, David L., additional, Rosenstock, Julio, additional, Pulla, Bharathi, additional, Bode, Bruce, additional, Frias, Juan, additional, Klaff, Leslie, additional, Brazg, Ronald, additional, Van, Joanna, additional, Tan, Anjanette, additional, Briskin, Toby, additional, Rhee, Margaret, additional, Chaicha-Brom, Tira, additional, Hartley, Paul A., additional, Nunez, Lazaro, additional, Cortes-Maisonet, Gregorio, additional, Soucie, Gary, additional, Hsia, Stanley, additional, and Jones, Thomas, additional

Published
2023
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210. Compositional representation of protein sequences and the number of Eulerian loops

Author

Hao, Bailin, Xie, Huimin, and Zhang, Shuyu

Subjects
Physics - Biological Physics, Quantitative Biology
Abstract

An amino acid sequence of a protein may be decomposed into consecutive overlapping strings of length K. How unique is the converse, i.e., reconstruction of amino acid sequences using the set of K-strings obtained in the decomposition? This problem may be transformed into the problem of counting the number of Eulerian loops in an Euler graph, though the well-known formula must be modified. By exhaustive enumeration and by using the modified formula we show that the reconstruction is unique at K equal or greater than 5 for an overwhelming majority of the proteins in the PDB.seq database. The corresponding Euler graphs provide a means to study the structure of repeated segments in protein sequences., Comment: 5 pages, no figure, 2 tables, RevTex3.1

Published
2001

213. Effects of Altitude on Thermal Flying-Height Control Actuation

Author

Zheng, Jinglin, Bogy, David B., Zhang, Shuyu, and Yan, Wentao

Subjects
Material Science, Nanotechnology, Physical Chemistry, Theoretical and Applied Mechanics, Surfaces and Interfaces, Thin Films, Tribology, Corrosion and Coatings, Thermal flying-height control, Altitude, Protrusion, Pushback
Abstract

Thermal flying-height control (TFC) is now a key technology used in hard-disk drives (HDD) as an effective way to push the magnetic spacing to sub-5 nm. Precise control of the TFC sliders’ actuated flying-height (FH) is a major consideration in improving the read/write capability as well as reducing the reliability problem. In this paper, we investigate the response of TFC sliders to altitude changes with a focus on the variation of actuated FH. Numerical and experimental results both indicate an increase in the actuated FH at higher altitudes. Simulations show that the increased TFC protrusion and less air bearing pushback on sliders at higher altitudes contribute to this increase. This study is of practical importance for improving heater and air bearing surface design to reduce the TFC sliders’ sensitivities to altitude changes.

Published
2010

228. Detection of redox potential evolution during the initial stage of an acute wound based on a redox-sensitive SERS-active optical fiberElectronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4ay00095a

Author

Zhang, Shuyu, Ji, Lingling, Xu, Kun, Xiong, Xiulei, Ai, Bingwei, Qian, Weiping, and Dong, Jian

Abstract

To detect redox potential evolution during the initial stage of an acute wound, a redox-sensitive SERS-active optical fiber was fabricated by integrating redox-sensitive SERS probes in a hole of an optical fiber. The redox-sensitive SERS-active optical fibers carried redox-sensitive SERS probes into the inside of a wound to sense its redox potential. The laser was transmitted to the redox-sensitive SERS probes in the body by optical fibers, and the SERS signals of the redox-sensitive SERS probes were transferred out of the body by optical fibers to indicate the redox potentials in the wound. The redox-sensitive SERS probes dynamically sensed the redox potential in vivo, and their SERS signals were collected constantly to indicate the redox potentials. The assessments in vivoand in vitroproved the responsiveness of redox-sensitive SERS-active optical fibers. The redox potential evolution during the initial stage of an acute wound with the treatments of different concentrations of glucose was detected to verify the feasibility of redox-sensitive SERS-active optical fibers to dynamically detect redox potentials in vivo. The redox-sensitive SERS-active optical fiber would be a versatile tool to explore the roles of redox potentials in living organisms.

Published
2024
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229. Graph-in-Graph Convolutional Network for Hyperspectral Image Classification

Author

Jia, Sen, Jiang, Shuguo, Zhang, Shuyu, Xu, Meng, and Jia, Xiuping

Abstract

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation. To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG representation covers information inside and outside superpixels, respectively, corresponding to the local and global characteristics of ground objects. Concretely, after segmenting HSI into disjoint superpixels, each one is converted to an internal graph. Meanwhile, an external graph is constructed according to the spatial adjacent relationships among superpixels. Significantly, each node in the external graph embeds a corresponding internal graph, forming the so-called GiG structure. Then, GiGCN composed of internal and External graph convolution (EGC) is designed to extract hierarchical features and integrate them into multiple scales, improving the discriminability of GiGCN. Ensemble learning is incorporated to further boost the robustness of GiGCN. It is worth noting that we are the first to propose the GiG framework from the superpixel point and the GiGCN scheme for HSI classification. Experiment results on four benchmark datasets demonstrate that our proposed method is effective and feasible for HSI classification with limited labeled samples. For study replication, the code developed for this study is available at https://github.com/ShuGuoJ/GiGCN.git.

Published
2024
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230. Vertical Attention-Based Siamese ConvLSTM Network for Argo Data Error Detection

Author

Zhang, Shuyu, Gao, Fan, Shi, Zhaoji, Wu, Chuhong, Zhang, Zhiyuan, Li, Yan, Liao, Xiaomei, Mu, Lin, and Jia, Sen

Abstract

The international array for real-time geostrophic oceanography (Argo) project is committed to rapidly and precisely acquiring comprehensive 3-D data on ocean temperature and salinity, which is crucial for monitoring ocean climate change and natural phenomena. During the buoy observation, environmental factors, human mistakes, and equipment malfunctions can cause abnormalities such as density inversion and spike, and thus detecting the errors in Argo data is significant to ensure its reliability and applicability. Traditional methods mainly rely on the knowledge and judgment of marine experts, ensuring high accuracy but requiring large amounts of effort. Machine-learning methods are used for automatic Argo data error detection, while they still struggle with extracting deep and discriminative features from profiles. Recently, deep-learning methods have received increasing attention in this field, yet their effectiveness have not been widely explored, faced with challenges of imbalanced samples, joint detection, and complicated patterns. In this article, a novel vertical attention-based siamese ConvLSTM (VAS-CLSTM) network is proposed for the accurate error detection of Argo data. First, an oversampling approach with optimized deep clustering based on inheritance theory and Mahalanobis distance is designed to effectively augment the error samples. Second, a siamese convolutional long–short-term memory (ConvLSTM) network with contextual connection and spatial–temporal adjacent profile search is built to learn interactively from temperature and salinity profiles. Third, a depth-based vertical attention mechanism with grouped weights and vertical trends is proposed for adaptive modeling and flexible learning. Experimental results of North and South Atlantic datasets show that the proposed VAS-CLSTM method effectively improves the accuracy and reliability of error detection in Argo observation data.

Published
2024
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231. Spatiotemporal Data Fusion of Index-Based VTCI Using Sentinel-2 and -3 Satellite Data for Field-Scale Drought Monitoring

Author

Zhang, Yue, Wang, Pengxin, Tansey, Kevin, Li, Mingqi, Guo, Fengwei, Liu, Junming, and Zhang, Shuyu

Abstract

Due to climate change, the impact of drought on field crop production is extremely important. This study focuses on the vegetation temperature condition index (VTCI), an index-based drought monitoring index that can characterize drought conditions in near real time (at ten-day intervals), and explores the applicability of different spatial and temporal data fusion schemes to it. It also proposes a field-scale VTCI fusion framework based on the Sentinel-3 VTCI calculation and the land surface temperature (LST) downscaling. First, based on analyzing the computational characteristics of VTCI, multiyear VTCI based on Sentinel data sources was obtained, which further expands the diversity of data sources for VTCI. On this basis, a combination of qualitative and quantitative methods was used to compare the applicability of two schemes: Scheme 1, based on the “blend-then-index” (BI) strategy, which first fuses normalized difference vegetation index (NDVI) and LST, and then calculated the fused VTCIs; and Scheme 2, based on the “index-then-blend” (IB) strategy, which directly fuses the VTCIs based on the calculated VTCIs. It was found that all the fused VTCIs remained highly correlated with the ten-day cumulative precipitation. Compared with the fused VTCIs obtained by Scheme 2, the VTCIs obtained by Scheme 1 were able to display more spatial details. In addition, the VTCIs of Scheme 1 were more consistent with the Sentinel-3 VTCIs, and the accuracy of field yield estimation using the fused VTCIs was higher ( $r$ of 0.58 and root-mean-square error (RMSE) of 783.27 kg/ha).

Published
2024
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234. Single-atom infrared emission in doped silicon nanocrystals.

Author

Wang, Feilong, Ou, Qiongrong, and Zhang, Shuyu

Abstract

Silicon luminescence, due to silicon being abundant, non-toxic and harmless, is a topic of pivotal importance in optoelectronics and biological imaging. However, a major challenge in developing high-efficiency silicon light sources is the relatively weak allowable transitions. This study focuses on single atom-doped silicon nanocrystals (Si NCs) and theoretically investigates the emission behavior of single atoms within a tetrahedral coordination field. Doping a single atom in Si NCs can result in a ∼102 times improvement at least in the squared transition dipole moment (TDM2), and induce a spectral shift towards near- and mid-infrared wavelengths. These findings offer a strong foundation for designing Si NCs for on-chip optical communication and single photon emitters. [ABSTRACT FROM AUTHOR]

Published
2023
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235. Research on Luggage Package Extraction of X-ray Images Based on Edge Sensitive Multi-Channel Background Difference Algorithm.

Author

Song, Xueping, Zhang, Shuyu, Yang, Jianming, and Zhang, Jicun

Subjects
X-ray imaging, MACHINE learning, ALGORITHMS, LUGGAGE, GRAYSCALE model, DEEP learning
Abstract

Many security detectors do not have the ability to output individual luggage package images and are not compatible with deep learning algorithms. In this paper, a luggage package extraction of X-ray images based on the ES-MBD (Edge Sensitive Multi-channel Background Difference Algorithm) method is proposed, which is aiming at the problem that background difference binarization is insensitive to texture features and edge detection binarization is insensitive to smooth areas. In this method, X-ray luggage package images from complex original video images are used as a key target, the RGB three-channel background difference is calculated from the original X-ray image, the edge detection of the grayscale map is performed using the Sobel operator optimized by local gradient enhancement, and the morphological expansion process is performed on the combined results to obtain the complete wrapping target. The Suzuki algorithm is used to detect the outline of the binarized package image, match the package frame area and determine the key target. The ES-MBD method solves the problem of information loss in the traditional binarization method, and retains the information of insensitive regions while reducing noise. Through experimental comparison, the accuracy of ES-MBD binarization method reaches 97.3%, the recall rate reaches 96.5%, and ES-MBD method has obvious advantages in key target extraction of X-ray images. [ABSTRACT FROM AUTHOR]

Published
2023
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