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Nonlocality in photonic materials and metamaterials: roadmap

Optical Materials Express 2025年第7期15卷 1544-1709页

作者： Francesco Monticone N Asger Mortensen Antonio I Fernández-Domínguez Yu Luo Xuezhi Zheng Christos Tserkezis Jacob B Khurgin Tigran V Shahbazyan André J Chaves Nuno M R Peres Gino Wegner Kurt Busch Huatian Hu Fabio Della Sala Pu Zhang Cristian Ciracì Javier Aizpurua Antton Babaze Andrei G Borisov Xue-Wen Chen Thomas Christensen Wei Yan Yi Yang Ulrich Hohenester Lorenz Huber Martijn Wubs Simone De Liberato P A D Gonçalves F Javier García de Abajo Ortwin Hess Illya Tarasenko Joel D Cox Line Jelver Eduardo J C Dias Miguel Sánchez Sánchez Dionisios Margetis Guillermo Gómez-Santos Igor M Vasilevskiy Tobias Stauber Sergei Tretyakov Constantin Simovski Samaneh Pakniyat J Sebastián Gómez-Díaz Igor V Bondarev Svend-Age Biehs Alexra Boltasseva Vladimir M Shalaev Alexey V Krasavin Anatoly V Zayats Andrea Alù Jung-Hwan Song Mark L Brongersma Uriel Levy Olivia Y Long Cheng Guo Shanhui Fan Sergey I Bozhevolnyi Adam Overvig Filipa R Prudêncio Mário G Silveirinha S Ali Hassani Gangaraj Christos Argyropoulos Paloma A Huidobro Emanuele Galiffi Fan Yang John B Pendry David A B Miller School of Electrical and Computer Engineering Cornell University Ithaca New York 14853 USA POLIMA—Center for Polariton-driven Light-Matter Interactions University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid E-28049 Madrid Spain Condensed Matter Physics Center (IFIMAC) Universidad Autónoma de Madrid E-28049 Madrid Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China WaveCoRE Division Department of Electrical Engineering KU Leuven Leuven B-3001 Belgium Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland 21218 USA Department of Physics Jackson State University Jackson Mississippi 39217 USA Department of Physics Aeronautics Institute of Technology 12228-900 So Jos dos Campos SP Brazil Centro de Física (CF-UM-UP) and Departamento de Física Universidade do Minho P-4710-057 Braga Portugal International Iberian Nanotechnology Laboratory (INL) Av Mestre José Veiga 4715-330 Braga Portugal Max-Born-Institut 12489 Berlin Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik 12489 Berlin Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 73010 Arnesano Italy Institute for Microelectronics and Microsystems (CNR-IMM) Via Monteroni Campus Unisalento 73100 Lecce Italy School of Physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Donostia International Physics Center DIPC 20018 Donostia-San Sebastián Spain Ikerbasque Basque Foundation fo

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally adopted in optical material modeling. The growing interest in plasmonic, polaritonic, and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces—can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In metasurfaces, in particular, nonlocality engineering has emerged as a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials and metamaterials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward—toward new scientific discoveries and technological *** by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License . Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

关键词： Chemical elements Localized surface plasmon resonance Material properties Near field scanning optical microscopy Optical materials Thin films

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Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

SSRN

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SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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Global, regional, and national burden of asthma and atopic dermatitis, 1990–2021, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

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The Lancet Respiratory Medicine 2025年第5期13卷 425-446页

作者： Oh, Jiyeon Kim, Soeun Kim, Min Seo Abate, Yohannes Habtegiorgis Abd ElHafeez, Samar Abdelkader, Atef Abdi, Parsa Abdulah, Deldar Morad Aboagye, Richard Gyan Abolhassani, Hassan Abtahi, Dariush Abualruz, Hasan Abu-Gharbieh, Eman Aburuz, Salahdein Adane, Mesafint Molla Addo, Isaac Yeboah Adeleke, Olumide Thomas Aden, Bashir Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Afzal, Muhammad Sohail Ahmad, Sajjad Ahmad, Tauseef Ahmadi, Ali Ahmed, Syed Anees Al Awaidy, Salah Al Bakour, Mohamad Anas Alam, Khurshid Albashtawy, Mohammed Al-Eyadhy, Ayman Al-Gheethi, Adel Ali Saeed Alhalaiqa, Fadwa Naji Ali, Syed Shujait Ali, Waad Alif, Sheikh Mohammad Al-Jabi, Samah W. Alqahtani, Jaber S. AlQudah, Mohammad Alrawashdeh, Ahmad Alshahrani, Najim Z. Altaany, Zaid Altaf, Awais Al-Tammemi, Alaa B. Altirkawi, Khalid A. Alvis-Guzman, Nelson Alwafi, Hassan Al-Wardat, Mohammad Al-Worafi, Yaser Mohammed Aly, Hany Alyahya, Mohammad Sharif Ibrahim Alzoubi, Karem H. Amusa, Ganiyu Adeniyi Ansar, Adnan Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Asghari-Jafarabadi, Mohammad Ashraf, Tahira Athari, Seyyed Shamsadin Aujayeb, Avinash Ayana, Lemessa Assefa A. Aziz, Shahkaar Azzam, Ahmed Y. Barqawi, Hiba Jawdat Barrow, Amadou Bastan, Mohammad-Mahdi Batra, Kavita Behera, Priyamadhaba Behzadi, Payam Bell, Michelle L. Beneke, Alice A. Berhie, Alemshet Yirga Beyene, Kebede A. Bhattacharjee, Priyadarshini Bhatti, Jasvinder Singh Bolarinwa, Obasanjo Afolabi Bouaoud, Souad Bustanji, Yasser Butt, Nadeem Shafique Camargos, Paulo Cámera, Luis Alberto Carugno, Andrea Cenderadewi, Muthia Cerrai, Sonia Chakraborty, Sandip Chan, Jeffrey Shi Kai Chandika, Rama Mohan Chattu, Vijay Kumar Chaudhary, Anis Ahmad Cheng, Esther T. W. Chichagi, Fatemeh Chimoriya, Ritesh Ching, Patrick R. Chirinos-Caceres, Jesus Lorenzo Chong, Yuen Yu Chopra, Hitesh Chu, Dinh-Toi Corlateanu, Alexandru Cruz-Martins, Natalia da Silva, Alanna Gomes Dababo, Nour Dadras, Omid Dai, Xiaochen Damiani Department of Medicine Kyung Hee University College of Medicine Seoul South Korea Center for Digital Health Kyung Hee University College of Medicine Seoul South Korea Broad Institute of MIT and Harvard Cambridge MA United States Massachusetts General Hospital Boston MA United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia Department of Epidemiology Alexandria University Alexandria Egypt Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St John's NL Canada Community and Maternity Nursing Unit University of Duhok Duhok Iraq Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden Department of Anesthesiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan College of Pharmacy University of Jordan Amman Jordan Department of Pharmacology and Therapeutics Al Ain United Arab Emirates College of Medicine and Health Sciences Bahir Dar University Bahir Dar Ethiopia School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Department of Family Medicine Bowen University Iwo Nigeria Bowen University Teaching Hospital Ogbomoso Nigeria United Arab Emirates University Al Ain United Arab Emirates Institute of Public Health Walden University Al Ain United Arab Emirates Department of Public Health Padjadjaran University (Universitas Padjadjaran) Bandung Indonesia Clinical Sciences Department Sharjah United Arab Emirates Department

Background: Asthma and atopic dermatitis are common allergic conditions that contribute to substantial health loss, economic burden, and pain across individuals of all ages worldwide. Therefore, as a component of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we present updated estimates of the prevalence, disability-adjusted life-years (DALYs), incidence, and deaths due to asthma and atopic dermatitis and the burden attributable to modifiable risk factors, with forecasted prevalence up to 2050. Methods: Asthma and atopic dermatitis prevalence, incidence, DALYs, and mortality, with corresponding 95% uncertainty intervals (UIs), were estimated for 204 countries and territories from 1990 to 2021. A systematic review identified data from 389 sources for asthma and 316 for atopic dermatitis, which were further pooled using the Bayesian meta-regression tool. We also described the age-standardised DALY rates of asthma attributable to four modifiable risk factors: high BMI, occupational asthmagens, smoking, and nitrogen dioxide pollution. Furthermore, as a secondary analysis, prevalence was forecasted to 2050 using the Socio-demographic Index (SDI), air pollution, and smoking as predictors for asthma and atopic dermatitis. To assess trends in the burden of asthma and atopic dermatitis before (2010–19) and during (2019–21) the COVID-19 pandemic, we compared their average annual percentage changes (AAPCs). Findings: In 2021, there were an estimated 260 million (95% UI 227–298) individuals with asthma and 129 million (124–134) individuals with atopic dermatitis worldwide. Asthma cases declined from 287 million (250–331) in 1990 to 238 million (209–272) in 2005 but increased to 260 million in 2021. Atopic dermatitis cases consistently rose from 107 million (103–112) in 1990 to 129 million (124–134) in 2021. However, age-standardised prevalence rates decreased—by 40·0% (from 5568·3 per 100 000 to 3340·1 per 100 000) for asthma and 8·3% (from 1885·4

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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Nitrate-mediated anaerobic microorganism-sponge iron system promoting simultaneous nitrogen and phosphate removal from piggery tail water

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Journal of Environmental sciences 2025年

作者： Qin Liao Cong Jin Ping Li Jinhua Wu Chiqian Zhang Zhiqiang Hu School of Environment and Energy South China University of Technology Guangzhou 510006 China Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling South China University of Technology Guangzhou 510006 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University Jonesboro AR 72467 USA Department of Civil and Environmental Engineering University of Missouri-Columbia 65211 USA

Piggery tail water (PTW), the biochemical unit effluent rich in nitrogen and phosphorus, causes eutrophication when directly discharged, threatening the ecological environment. Stricter standards and environmental regulations necessitate the urgent development of green, low-carbon advanced treatment technologies. This study introduces a nitrate-mediated anaerobic microorganism-sponge iron (mic-Fe 0 ) system, which leverages microbial metabolism, in situ iron oxidation, and iron-phosphorus biomineralization to enhance nitrogen and phosphorus removal. A 262-days column experiment was conducted to compare three systems (abiotic sponge iron (s-Fe 0 ), microbial s-Fe 0 , and nitrate-mediated mic-Fe 0 ) and to optimize s-Fe 0 dosage (5 %-15 %, V/V ). The results indicated that when the s-Fe 0 dosage was 10 %, the system achieved 83.55 % nitrate removal and 87.53 % total phosphorus (TP) removal, reducing the effluent TP concentration to 2.55 ± 1.06 mg/L, a 60 % decrease compared to the abiotic s-Fe 0 system (6.33 ± 1.83 mg/L). Several analytical techniques revealed that the nitrate-mediated mic-Fe 0 system exhibited the highest iron corrosion among all experimental groups and generated substantial iron-phosphorus minerals, such as vivianite and strengite. This confirmed that microbial-induced biocorrosion accelerated iron dissolution and promoted phosphorus fixation. Simultaneously, nitrate mediation further enhanced the corrosion process, facilitating the sustained dissolution of s-Fe 0 and thereby achieving efficient phosphorus removal. Additionally, the enrichment of autotrophic and heterotrophic denitrifying bacteria expanded the nitrogen cycling pathway, enabling efficient nitrogen removal under low carbon-to-nitrogen ratios. This study elucidates the mechanism of enhanced nitrogen and phosphorus removal in the nitrate-mediated mic-Fe 0 system, offering a novel strategy for the advanced treatment of PTW.

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How framelets enhance graph neural networks

arXiv

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arXiv 2021年

作者： Zheng, Xuebin Zhou, Bingxin Gao, Junbin Wang, Yu Guang Liò, Pietro Li, Ming Montúfar, Guido The University of Sydney Business School The University of Sydney CamperdownNSW2006 Australia Max Planck Institute for Mathematics in the Sciences Leipzig Germany Institute of Natural Sciences School of Mathematical Sciences Shanghai Jiao Tong University China School of Mathematics and Statistics The University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Department of Mathematics Department of Statistics University of California Los Angeles United States

This paper presents a new approach for assembling graph neural networks based on framelet transforms. The latter provides a multi-scale representation for graph-structured data. We decompose an input graph into low-pass and high-pass frequencies coefficients for network training, which then defines a framelet-based graph convolution. The framelet decomposition naturally induces a graph pooling strategy by aggregating the graph feature into low-pass and high-pass spectra, which considers both the feature values and geometry of the graph data and conserves the total information. The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks. Moreover, we propose shrinkage as a new activation for the framelet convolution, which thresholds high-frequency information at different scales. Compared to ReLU, shrinkage activation improves model performance on denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with well-preserved prediction *** Codes 68T07, 05C85, 42C40 © 2021, CC BY.

关键词： Graph neural networks

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One-pass multi-view clustering based on adaptive multiple projection regression and graph learning

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Expert Systems with applications 2025年 294卷

作者： Yongxin Duan Hongmei Chen Biao Xiang Zhong Yuan Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of SichuanProvince Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

In graph-based multi-view clustering algorithms, performing K -means post-processing on the consensus representation is common to generate the final partition result. This may lead to a decrease and oscillation in clustering performance because consensus representation learning and label generation cannot be closely related. Additionally, existing multi-view clustering methods often overlook the importance of sample features, failing to leverage discriminative features to guide the learning of the consensus representation effectively. This paper proposes a one-pass multi-view clustering method based on adaptive multiple projection regression and graph learning, named OMCAG, to address the above problems. First, OMCAG utilizes multiple projection regression to select discriminative features, explore the consensus indication matrix, and maintain the local structure of the data through adaptive graph learning in the projection space to eliminate the effects of noise and redundant information in the original space. Next, multi-view spectral embedding is combined with regression learning to jointly guide the generation of the consensus indication matrix to exploit the consistent information among views fully. Then, a spectral rotation strategy is employed to extract the final discrete labels from the consensus indication matrix to obtain the clustering result. Moreover, an adaptive weighting strategy is introduced for Laplacian graph fusion and regression learning to enhance the information of important views. Experimental results on many real-world datasets verify the effectiveness of the proposed method.

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SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

TechRxiv

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TechRxiv 2022年

作者： Li, Yongfu Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Ma, Hui Liu, Lu Yuan, Shuhua Zhou, Hongyuan Zhao, Jian Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Department of Micro-Nano Electronics and MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Pediatric AI Clinical Application and Research Center Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine China Department of Respiratory Medicine Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine China China School of Computer Engineering and Science Shanghai University China Sanya Maternity and Child Care Hospital China Department of Paediatrics Taicang Affiliated Hospital of Soochow University The First People’s Hospital of Taicang China Linyi City People Hospital China Department of Paediatrics Fengcheng Hospital China Department of Pediatrics Fifth Affiliated Hospital of Harbin Medical University China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians’ workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and © 2022, CC BY.

关键词： Pediatrics

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Finding high posterior density phylogenies by systematically extending a directed acyclic graph

arXiv

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arXiv 2024年

作者： Jennings-Shaffer, Chris Rich, David H. Macaulay, Matthew Karcher, Michael D. Ganapathy, Tanvi Kiami, Shosuke Kooperberg, Anna Zhang, Cheng Suchard, Marc A. Matsen, Frederick A. Public Health Sciences Division Fred Hutchinson Cancer Research Center SeattleWA United States University of Technology Sydney Australian Institute for Microbiology & Infection Sydney Australia Department of Math & Computer Science Muhlenberg College AllentownPA United States School of Mathematical Sciences Peking University Beijing China Department of Human Genetics University of California Los Angeles United States Department of Computational Medicine University of California Los Angeles United States Department of Biostatistics University of California Los Angeles United States Department of Genome Sciences University of Washington Seattle United States Howard Hughes Medical Institute Fred Hutchinson Cancer Research Center SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. Mail stop: S2-140 SeattleWA98109-1024 United States

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees. © 2024, CC BY.

关键词： Markov chains

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