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2,139 results on '"Kang, Hui"'

1. Quantum error correction below the surface code threshold

Author

Acharya, Rajeev, Aghababaie-Beni, Laleh, Aleiner, Igor, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Bacon, Dave, Ballard, Brian, Bardin, Joseph C., Bausch, Johannes, Bengtsson, Andreas, Bilmes, Alexander, Blackwell, Sam, Boixo, Sergio, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Yu, Chen, Zijun, Chiaro, Ben, Chik, Desmond, Chou, Charina, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Davies, Alex, De Lorenzo, Laura, Debroy, Dripto M., Demura, Sean, Devoret, Michel, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Edlich, Thomas, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Farhi, Edward, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hall, John, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heras, Francisco J. H., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hill, Gordon, Hilton, Jeremy, Holland, George, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Iveland, Justin, Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Kelly, Julian, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Kohli, Pushmeet, Korotkov, Alexander N., Kostritsa, Fedor, Kothari, Robin, Kozlovskii, Borislav, Kreikebaum, John Mark, Kurilovich, Vladislav D., Lacroix, Nathan, Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Kenny, Lensky, Yuri D., Leon, Shannon, Lester, Brian J., Li, Wing Yan, Li, Yin, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Malone, Fionn D., Maloney, Ashley, Mandrá, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Mruczkiewicz, Wojciech, Naaman, Ofer, Neeley, Matthew, Neill, Charles, Nersisyan, Ani, Neven, Hartmut, Newman, Michael, Ng, Jiun How, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, O'Brien, Thomas E., Oliver, William D., Opremcak, Alex, Ottosson, Kristoffer, Petukhov, Andre, Pizzuto, Alex, Platt, John, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Roushan, Pedram, Rubin, Nicholas C., Saei, Negar, Sank, Daniel, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Senior, Andrew W., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Singh, Shraddha, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smelyanskiy, Vadim, Smith, W. Clarke, Somma, Rolando D., Springer, Sofia, Sterling, George, Strain, Doug, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, Weber, Kate, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, and Zobrist, Nicholas

Subjects
Quantum Physics
Abstract

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Published
2024

2. Thermalization and Criticality on an Analog-Digital Quantum Simulator

Author

Andersen, Trond I., Astrakhantsev, Nikita, Karamlou, Amir H., Berndtsson, Julia, Motruk, Johannes, Szasz, Aaron, Gross, Jonathan A., Schuckert, Alexander, Westerhout, Tom, Zhang, Yaxing, Forati, Ebrahim, Rossi, Dario, Kobrin, Bryce, Di Paolo, Agustin, Klots, Andrey R., Drozdov, Ilya, Kurilovich, Vladislav D., Petukhov, Andre, Ioffe, Lev B., Elben, Andreas, Rath, Aniket, Vitale, Vittorio, Vermersch, Benoit, Acharya, Rajeev, Beni, Laleh Aghababaie, Anderson, Kyle, Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Donohoe, Paul, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Giustina, Marissa, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hoffmann, Markus R., Huang, Hsin-Yuan, Huang, Trent, Huff, Ashley, Huggins, William J., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Kitaev, Alexei, Klimov, Paul V., Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Landhuis, David, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lensky, Yuri D., Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Martin, Steven, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rosenberg, Eliott, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Babbush, Ryan, Boixo, Sergio, Hilton, Jeremy, Lucero, Erik, Megrant, Anthony, Kelly, Julian, Chen, Yu, Smelyanskiy, Vadim, Vidal, Guifre, Roushan, Pedram, Lauchli, Andreas M., Abanin, Dmitry A., and Mi, Xiao

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

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Published
2024

3. Genomics of post-bottleneck recovery in the northern elephant seal.

Author

Beltran, Roxanne, Reiter, Joanne, Robinson, Patrick, McInerney, Nancy, Seim, Inge, Sun, Shuai, Fan, Guangyi, Li, Songhai, Hoelzel, A, Gkafas, Georgios, Kang, Hui, Sarigol, Fatih, Le Boeuf, Burney, and Costa, Daniel

Subjects
Male, Female, Humans, Animals, Base Sequence, Genomics, Seals, Earless
Abstract

Populations and species are threatened by human pressure, but their fate is variable. Some depleted populations, such as that of the northern elephant seal (Mirounga angustirostris), recover rapidly even when the surviving population was small. The northern elephant seal was hunted extensively and taken by collectors between the early 1800s and 1892, suffering an extreme population bottleneck as a consequence. Recovery was rapid and now there are over 200,000 individuals. We sequenced 260 modern and 8 historical northern elephant seal nuclear genomes to assess the impact of the population bottleneck on individual northern elephant seals and to better understand their recovery. Here we show that inbreeding, an increase in the frequency of alleles compromised by lost function, and allele frequency distortion, reduced the fitness of breeding males and females, as well as the performance of adult females on foraging migrations. We provide a detailed investigation of the impact of a severe bottleneck on fitness at the genomic level and report on the role of specific gene systems.

Published
2024

8. Channel-Wise Contrastive Learning for Learning with Noisy Labels

Author

Kang, Hui, Liu, Sheng, Huang, Huaxi, and Liu, Tongliang

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances. For this, the model must identify features indicative of the authentic labels. While research indicates that genuine label information is embedded in the learned features of even inaccurately labeled data, it's often intertwined with noise, complicating its direct application. Addressing this, we introduce channel-wise contrastive learning (CWCL). This method distinguishes authentic label information from noise by undertaking contrastive learning across diverse channels. Unlike conventional instance-wise contrastive learning (IWCL), CWCL tends to yield more nuanced and resilient features aligned with the authentic labels. Our strategy is twofold: firstly, using CWCL to extract pertinent features to identify cleanly labeled samples, and secondly, progressively fine-tuning using these samples. Evaluations on several benchmark datasets validate our method's superiority over existing approaches.

Published
2023

9. Hydrophobic Silica Microcavities with Sustainable Nonlinear Photonic Performance

Author

Xie, Jiadu, Wang, Yang, Kang, Hui, Cheng, Jinsong, and Shen, Xiaoqin

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

Ultrahigh quality factor (Q) microcavities have been emerging as an appealing compact photonic platform for various applications. The Q factor plays a critical role in determining the nonlinear optical performance of a microcavity. However, a silica microcavity suffers from severe degradation of its Q value over time during storage or use in air due to the accumulating surface absorption loss, which would deteriorate their nonlinear photonic performance. Here, we report a new type of ultrahigh Q silica microcavity that effectively prevents the Q degradation over time. The Q values of the devices remain unchanged over time under storage in air. Optical frequency combs are generated with sustainable ultralow threshold performance in the course of time from the devices in open air. This approach would greatly facilitate ultrahigh Q silica-based photonic devices for next generation photonic applications.

Published
2023
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10. Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

Author

Kang, Hui, Liu, Sheng, Huang, Huaxi, Yu, Jun, Han, Bo, Wang, Dadong, and Liu, Tongliang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination of regularization strategies can be more effective than intricate algorithms in tackling the challenges of learning with noisy labels. While some of these regularization strategies have been utilized in previous noisy label learning research, their full potential has not been thoroughly explored. Our results encourage a reevaluation of benchmarks for learning with noisy labels and prompt reconsideration of the role of specialized learning algorithms designed for training with noisy labels.

Published
2023

11. Research on the Impact of 5G Terminals on Electromagnetic Radiation of 5G Base Stations

Author

Tian, Ye, Kang, Hui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor

Published
2024
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16. PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels

Author

Huang, Huaxi, Kang, Hui, Liu, Sheng, Salvado, Olivier, Rakotoarivelo, Thierry, Wang, Dadong, and Liu, Tongliang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Convolutional Neural Networks (CNNs) have demonstrated superiority in learning patterns, but are sensitive to label noises and may overfit noisy labels during training. The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels. Motivated by biological findings that the amplitude spectrum (AS) and phase spectrum (PS) in the frequency domain play different roles in the animal's vision system, we observe that PS, which captures more semantic information, can increase the robustness of DNNs to label noise, more so than AS can. We thus propose early stops at different times for AS and PS by disentangling the features of some layer(s) into AS and PS using Discrete Fourier Transform (DFT) during training. Our proposed Phase-AmplituDe DisentangLed Early Stopping (PADDLES) method is shown to be effective on both synthetic and real-world label-noise datasets. PADDLES outperforms other early stopping methods and obtains state-of-the-art performance.

Published
2022

17. Research advances in the role of immune cells in the regulation of bone microenvironment in bone metastasis of tumor

Author

TANG Liang, ZHU Lijuan, LYU Chengwei, KANG Hui

Subjects
neoplasm metastasis, bone neoplasms, bone and bones, tumor microenvironment, tumor escape, immune system, review, Medicine
Abstract

The development and progression of tumor is closely associated with the microenvironment in which the tumor is located. In the process of bone metastasis, the numerous immune cells in bone marrow microenvironment play an important role in the colonization of tumor cells and the formation of metastases, and in particular, they can assist tumor cells with immune escape. Therefore, it is especially important to explore effective interventions to suppress the immune escape of tumor. This article reviews the role of macrophages, myeloid-derived suppressor cells, and lymphocytes in bone metastasis, so as to provide a refe-rence for the basic research on tumor immunotherapy and clinical treatment.

Published
2024
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20. Ni-based core-shell structured catalysts for efficient conversion of CH4 to H2: A review

Author

Yu Guan, Guoqiang Song, Claudia Li, Kang Hui Lim, Bo Wang, Lianghui Xia, Huchao Song, Yinhe Liu, Chunfei Wu, and Sibudjing Kawi

Subjects
Methane, Core-shell nanocatalyst, H2 production, Stability, Anti-sintering, Environmental technology. Sanitary engineering, TD1-1066
Abstract

Methane (CH4) serves as a primary constituent of natural gas and a potent greenhouse gas. Catalytic conversion of CH4 into H2 presents a promising avenue for mitigating CO2 emissions from direct CH4 combustion, while also facilitating the transition towards cleaner energy sources. Substantial efforts have been dedicated to enhancing the efficiency of methane conversion and the selectivity for hydrogen in catalyst design. Among these endeavors, core-shell structured Ni-based catalysts have emerged as highly advantageous catalysts for CH4 conversion, with a multitude of benefits including high catalytic activity, coking resistance, and longtime stability. In this review, we provide an overview of Ni-based core-shell catalysts. Furthermore, the synthesis methods of core-shell Ni-based catalysts are explored. We then delve into the myriad advantages these core-shell structured catalysts confer upon methane conversion, specifically emphasizing their remarkable stability and resistance to sintering, and CO2 activation capacity in dry reforming of methane. Moreover, this review comprehensively outlines the versatile applications of Ni-based core-shell structured catalysts in processes such as steam reforming of methane (SRM), dry reforming of methane (DRM), partial oxidation of methane (POM), and catalytic methane decomposition (CMD) for H2 production. Additionally, we delve into the recent advancements in the field of computational chemistry, with a particular focus on density functional theory (DFT) and machine learning (ML). These techniques play pivotal roles in screening Ni-based catalysts and offer insights into the intricate reaction mechanisms underlying methane conversion. Finally, we summarize the key findings of this review and provide forward-looking perspectives. This comprehensive review serves as a valuable reference in the realm of hydrogen production from methane on core-shell structured catalysts, fostering a deeper understanding of this promising area.

Published
2024
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21. A comprehensive review of carbon capture science and technologies

Author

Chunfei Wu, Qi Huang, Zhicheng Xu, Ayesha Tariq Sipra, Ningbo Gao, Luciana Porto de Souza Vandenberghe, Sabrina Vieira, Carlos Ricardo Soccol, Ruikai Zhao, Shuai Deng, Sandra K.S. Boetcher, Shijian Lu, Huancong Shi, Dongya Zhao, Yupeng Xing, Yongdong Chen, Jiamei Zhu, Dongdong Feng, Yu Zhang, Lihua Deng, Guoping Hu, Paul A. Webley, Daxin Liang, Zhichen Ba, Agata Mlonka-Mędrala, Aneta Magdziarz, Norbert Miskolczi, Szabina Tomasek, Su Shiung Lam, Shin Ying Foong, Hui Suan Ng, Long Jiang, Xinlong Yan, Yongzhuo Liu, Ying Ji, Hongman Sun, Haiping Yang, Xiong Zhang, Mingzhe Sun, Daniel C.W. Tsang, Jin Shang, Christoph Muller, Margarita Rekhtina, Maximilian Krödel, Alexander H. Bork, Felix Donat, Lina Liu, Xin Jin, Wen Liu, Syed Saqline, Xianyue Wu, Yongqing Xu, Asim Laeeq Khan, Zakawat Ali, Haiqing Lin, Leiqing Hu, Jun Huang, Rasmeet Singh, Kaifang Wang, Xuezhong He, Zhongde Dai, Shouliang Yi, Alar Konist, Mais Hanna Suleiman Baqain, Yijun Zhao, Shaozeng Sun, Guoxing Chen, Xin Tu, Anke Weidenkaff, Sibudjing Kawi, Kang Hui Lim, Chunfeng Song, Qing Yang, Zhenyu Zhao, Xin Gao, Xia Jiang, Haiyan Ji, Toluleke E. Akinola, Adekola Lawal, Olajide S. Otitoju, Meihong Wang, Guojun Zhang, Lin Ma, Baraka C. Sempuga, Xinying Liu, Eni Oko, Michael Daramola, Zewei Yu, Siming Chen, Guojun Kang, Qingfang Li, Li Gao, Ling Liu, and Hui Zhou

Subjects
Environmental technology. Sanitary engineering, TD1-1066
Published
2024
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28. miR-192 inhibits the activation of hepatic stellate cells by targeting Rictor

Author

Kang Hui, Luo Jie, Wang Chun, Hong Yinghui, Ye Mingliang, Ding Yang, Zhao Qiu, and Chang Ying

Subjects
liver fibrosis, microrna-192, rictor, hepatic stellate cell, smad, Medicine
Abstract

The activation of hepatic stellate cells (HSCs) is regarded as the primary driving factor of liver fibrosis. miR-192, a miRNA associated with hepatocellular carcinoma and enriched in HSCs, has an undisclosed role in HSC activation and liver fibrosis. In this study, a CCl4-induced rat liver fibrosis model and transforming growth factor-beta 1 (TGF-β1)-treated HSC lines (LX-2 and HSC-T6) were used to detect miR-192 and Rictor levels in vivo and in vitro. Bioinformatic analysis and a dual luciferase assay were used to predict and confirm the interaction of Rictor with miR-192. Gain- and/or loss-of-function methods evaluated molecular changes and HSC activation phenotypes, detected by quantitative real-time PCR, western blotting, and immunofluorescence. We observed a gradual downregulation of miR-192 and upregulation of Rictor during CCl4-induced liver fibrosis/cirrhosis in rats. Enriched miR-192 was downregulated, while Rictor was upregulated in TGF-β1-activated HSCs. miR-192 inhibited the activation of HSCs by directly targeting Rictor. High miR-192/low Rictor expression attenuated the fibrotic-related gene expression by AKT/mTORC2 signaling. In conclusion, miR-192 could inhibit the activation of HSCs by directly targeting Rictor in the AKT/mTORC2 signaling pathway. This study provides insights into potential therapeutic targets for liver fibrosis and cirrhosis.

Published
2023
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35. A comprehensive review of carbon capture science and technologies

Author

Wu, Chunfei, Huang, Qi, Xu, Zhicheng, Sipra, Ayesha Tariq, Gao, Ningbo, Vandenberghe, Luciana Porto de Souza, Vieira, Sabrina, Soccol, Carlos Ricardo, Zhao, Ruikai, Deng, Shuai, Boetcher, Sandra K.S., Lu, Shijian, Shi, Huancong, Zhao, Dongya, Xing, Yupeng, Chen, Yongdong, Zhu, Jiamei, Feng, Dongdong, Zhang, Yu, Deng, Lihua, Hu, Guoping, Webley, Paul A., Liang, Daxin, Ba, Zhichen, Mlonka-Mędrala, Agata, Magdziarz, Aneta, Miskolczi, Norbert, Tomasek, Szabina, Lam, Su Shiung, Foong, Shin Ying, Ng, Hui Suan, Jiang, Long, Yan, Xinlong, Liu, Yongzhuo, Ji, Ying, Sun, Hongman, Yang, Haiping, Zhang, Xiong, Sun, Mingzhe, Tsang, Daniel C.W., Shang, Jin, Muller, Christoph, Rekhtina, Margarita, Krödel, Maximilian, Bork, Alexander H., Donat, Felix, Liu, Lina, Jin, Xin, Liu, Wen, Saqline, Syed, Wu, Xianyue, Xu, Yongqing, Khan, Asim Laeeq, Ali, Zakawat, Lin, Haiqing, Hu, Leiqing, Huang, Jun, Singh, Rasmeet, Wang, Kaifang, He, Xuezhong, Dai, Zhongde, Yi, Shouliang, Konist, Alar, Baqain, Mais Hanna Suleiman, Zhao, Yijun, Sun, Shaozeng, Chen, Guoxing, Tu, Xin, Weidenkaff, Anke, Kawi, Sibudjing, Lim, Kang Hui, Song, Chunfeng, Yang, Qing, Zhao, Zhenyu, Gao, Xin, Jiang, Xia, Ji, Haiyan, Akinola, Toluleke E., Lawal, Adekola, Otitoju, Olajide S., Wang, Meihong, Zhang, Guojun, Ma, Lin, Sempuga, Baraka C., Liu, Xinying, Oko, Eni, Daramola, Michael, Yu, Zewei, Chen, Siming, Kang, Guojun, Li, Qingfang, Gao, Li, Liu, Ling, and Zhou, Hui

Published
2024
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46. Joint Feature Selection and Classifier Parameter Optimization: A Bio-Inspired Approach

Author

Wei, Zeqian, Kang, Hui, Li, Hongjuan, Sun, Geng, Li, Jiahui, Bao, Xinyu, Zhu, Bo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jin, Zhi, editor, Jiang, Yuncheng, editor, Buchmann, Robert Andrei, editor, Bi, Yaxin, editor, Ghiran, Ana-Maria, editor, and Ma, Wenjun, editor

Published
2023
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