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An open-source, automated, and cost-effective platform for COVID-19 diagnosis and rapid portable genomic surveillance using nanopore sequencing

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Scientific Reports 2023年第1期13卷 1-12页

作者： Ramos-Mandujano, Gerardo Grünberg, Raik Zhang, Yingzi Bi, Chongwei Guzmán-Vega, Francisco J. Shuaib, Muhammad Gorchakov, Rodion V. Xu, Jinna Tehseen, Muhammad Takahashi, Masateru Takahashi, Etsuko Dada, Ashraf Ahmad, Adeel Nazir Hamdan, Samir M. Pain, Arnab Arold, Stefan T. Li, Mo Stem Cell and Regeneration Laboratory Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Structural Biology and Engineering Computational Biology Research Center. Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Pathogen Genomics Laboratory Bioscience Program Biological and Environmental Science and Engineering (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Health Safety and Environment Department King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Laboratory of DNA Replication and Recombination Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Pathology and Laboratory Medicine King Faisal Specialist Hospital and Research Center Jeddah Saudi Arabia College of Medicine Al Faisal University Riyadh Saudi Arabia KAUST Health King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Bioengineering Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

The COVID-19 pandemic, caused by SARS-CoV-2, has emphasized the necessity for scalable diagnostic workflows using locally produced reagents and basic laboratory equipment with minimal dependence on global supply chains. We introduce an open-source automated platform for high-throughput RNA extraction and pathogen diagnosis, which uses reagents almost entirely produced in-house. This platform integrates our methods for self-manufacturing magnetic nanoparticles and qRT-PCR reagents-both of which have received regulatory approval for clinical use–with an in-house, open-source robotic extraction protocol. It also incorporates our "Nanopore Sequencing of Isothermal Rapid Viral Amplification for Near Real-time Analysis" (NIRVANA) technology, designed for tracking SARS-CoV-2 mutations and variants. The platform exhibits high reproducibility and consistency without cross-contamination, and its limit of detection, sensitivity, and specificity are comparable to commercial assays. Automated NIRVANA effectively identifies circulating SARS-CoV-2 variants. Our in-house, cost-effective reagents, automated diagnostic workflows, and portable genomic surveillance strategies provide a scalable and rapid solution for COVID-19 diagnosis and variant tracking, essential for current and future pandemic responses. © 2023, The Author(s).

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On-chip frequency-bin quantum photonics

arXiv

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arXiv 2024年

作者： Myilswamy, Karthik V. Cohen, Lucas M. Seshadri, Suparna Lu, Hsuan-Hao Lukens, Joseph M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States National Institute of Standards and Technology BoulderCO80305 United States Sandia National Laboratories AlbuquerqueNM87123 United States Aliro Technologies Inc. BrightonMA02135 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office Quantum Collaborative Arizona State University TempeAZ85287 United States

Frequency-bin encoding furnishes a compelling pathway for quantum information processing systems compatible with established lightwave infrastructures based on fiber-optic transmission and wavelength-division multiplexing. Yet although significant progress has been realized in proof-of-principle tabletop demonstrations, ranging from arbitrary single-qubit gates to controllable multiphoton interference, challenges in scaling frequency-bin processors to larger systems remain. In this Perspective, we highlight recent advances at the intersection of frequency-bin encoding and integrated photonics that are fundamentally transforming the outlook for scalable frequency-based quantum information. Focusing specifically on results on sources, state manipulation, and hyperentanglement, we envision a possible future in which on-chip frequency-bin circuits fulfill critical roles in quantum information processing, particularly in communications and networking. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Hybrid classical-quantum communication networks

arXiv

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arXiv 2025年

作者： Lukens, Joseph M. Peters, Nicholas A. Qi, Bing Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute West LafayetteIN47907 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States New York University Shanghai NYU-ECNU Institute of Physics NYU Shanghai 567 West Yangsi Road Shanghai200126 China State Key Laboratory of Precision Spectroscopy School of Physical and Material Sciences East China Normal University Shanghai200062 China

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum-classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications. © 2025, CC BY.

关键词： Quantum communication

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Decision Trees with Hypotheses for Recognition of Monotone Boolean Functions and for Sorting

arXiv

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arXiv 2022年

作者： Azad, Mohammad Chikalov, Igor Hussain, Shahid Moshkov, Mikhail Zielosko, Beata Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka72441 Saudi Arabia Intel Corporation 5000 W Chandler Blvd ChandlerAZ85226 United States Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration University Road Karachi75270 Pakistan Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Institute of Computer Science Faculty of Science and Technology University of Silesia in Katowice B¸edzinska 39 Sosnowiec41-200 Poland

In this paper, we consider decision trees that use both queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to ones studied in exact learning, where not only membership but also equivalence queries are allowed. We investigate the problem of recognition of monotone Boolean functions with n variables, n = 2,..., 4, and the problem of sorting n pairwise different elements from linearly ordered set, n = 3,..., 6. For each of these problems, we compare the complexity of different types of optimal (relative to the depth or the number of realizable nodes) decision trees with hypotheses. We also study the complexity of decision trees constructed by entropy-based greedy algorithm and analyze the length of decision rules derived from these trees. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees

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A Communication-Efficient Decentralized Newton's Method with Provably Faster Convergence

arXiv

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arXiv 2022年

作者： Liu, Huikang Zhang, Jiaojiao So, Anthony Man-Cho Ling, Qing The Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University China The Pazhou Lab China

In this paper, we consider a strongly convex finite-sum minimization problem over a decentralized network and propose a communication-efficient decentralized Newton's method for solving it. The main challenges in designing such an algorithm come from three aspects: (i) mismatch between local gradients/ Hessians and the global ones;(ii) cost of sharing second-order information;(iii) tradeoff among computation and communication. To handle these challenges, we first apply dynamic average consensus (DAC) so that each node is able to use a local gradient approximation and a local Hessian approximation to track the global gradient and Hessian, respectively. Second, since exchanging Hessian approximations is far from communication-efficient, we require the nodes to exchange the compressed ones instead and then apply an error compensation mechanism to correct for the compression noise. Third, we introduce multistep consensus for exchanging local variables and local gradient approximations to balance between computation and communication. With novel analysis, we establish the globally linear (resp., asymptotically super-linear) convergence rate of the proposed method when m is constant (resp., tends to infinity), where m ≥ 1 is the number of consensus inner steps. To the best of our knowledge, this is the first super-linear convergence result for a communication-efficient decentralized Newton's method. Moreover, the rate we establish is provably faster than those of first-order methods. Our numerical results on various applications corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Newton-Raphson method

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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

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IEEE transactions on neural networks and learning systems 2021年第10期32卷 4278-4290页

作者： Ke Gu Hongyan Liu Zhifang Xia Junfei Qiao Weisi Lin Daniel Thalmann Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore EPFL CH Lausanne Switzerland

This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

关键词： Feature extraction Monitoring Atmospheric modeling COVID-19 Atmospheric measurements Transforms Temperature measurement

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Quantum oscillations of holes in GaN

arXiv

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arXiv 2025年

作者： Chang, Chuan F.C. Dill, Joseph E. Zhang, Zexuan Chen, Jie-Cheng Pieczulewski, Naomi Bader, Samuel J. Valenzuela, Oscar Ayala Crooker, Scott A. Balakirev, Fedor F. McDonald, Ross D. Encomendero, Jimy Muller, David A. Giustino, Feliciano Jena, Debdeep Xing, Huili Grace Department of Physics Cornell University IthacaNY United States School of Applied and Engineering Physics Cornell University IthacaNY United States School of Electrical and Computer Engineering Cornell University IthacaNY United States Department of Physics The University of Texas Austin AustinTX United States Oden Institute for Computational Engineering and Sciences The University of Texas Austin AustinTX United States Department of Materials Science and Engineering Cornell University IthacaNY United States Foundry Technology Research Intel Corporation HillsboroOR United States National High Magnetic Field Laboratory Los Alamos National Laboratory Los AlamosNM United States Kavli Institute at Cornell for Nanoscale Science IthacaNY United States

GaN has emerged to be a major semiconductor akin to silicon due to its revolutionary impacts in solid state lighting, critically enabled by p-type doping1, and high-performance radio-frequency and power electronics2–4. Suffering from inefficient hole doping and low hole mobility5, quantum oscillations in p-type GaN have not been observed, hindering fundamental studies of valence bands and hole transport in GaN. Here, we present the first observation of quantum oscillations of holes in GaN. Shubnikov-de Haas (SdH) oscillations in hole resistivity are observed in a quantum-confined two-dimensional hole gas at a GaN/AlN interface, where polarization-induced doping overcomes thermal freeze-out, and a sharp and clean interface boosts the hole mobility enough to unmask the quantum oscillations. These holes degenerately occupy the light and heavy hole bands of GaN and have record-high mobilities of ~1900 cm2/Vs and ~400 cm2/Vs at 3K, respectively. We use magnetic fields up to 72 T to resolve SdH oscillations of holes from both valence bands to extract their respective sheet densities, quantum scattering times, and the effective masses of light holes (0.5-0.7 m0) and heavy holes (1.9 m0). SdH oscillations of heavy and light holes in GaN constitute a direct metrology of valence bands and open new venues for quantum engineering in this technologically important semiconductor. Like strained silicon transistors, strain-engineering of the valence bands of GaN is predicted6,7 to dramatically improve hole mobilities by reducing the hole effective mass, a proposal that can now be explored experimentally, particularly in a fully fabricated transistor, using quantum oscillations. Furthermore, the findings of this work suggest a blueprint to create 2D hole gases and observe quantum oscillations of holes in related wide bandgap semiconductors such as SiC and ZnO in which such techniques are not yet possible. Copyright © 2025, The Authors. All rights reserved.

关键词： Gallium nitride

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Deep Learning Deepens the Analysis of Alternative Splicing

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Genomics, Proteomics & Bioinformatics 2019年第2期17卷 219-221页

作者： Xudong Zou Xin Gao Wei Chen Department of Biology Southern University of Science and Technology Shenzhen 518055 China Computational Bioscience Research Center (CBRC) Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955 Saudi Arabia

The ever-increasing high-volume and high-dimensional geno-mics data on the one hand challenge traditional data analysis approaches,and on the other hand provide ample opportuni-ties for developing novel analytic strat... 详细信息

关键词： Deep Learning Deepens the Analysis Alternative Splicing

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White spot syndrome virus (WSSV) modulates lipid metabolism in white shrimp

Communications Biology

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Communications Biology 2023年第1期6卷 546页

作者： Ng, Yen Siong Cheng, Cheng-Shun Ando, Masahiro Tseng, Yi-Ting He, Shu-Ting Li, Chun-Yuan Cheng, Shu-Wen Chen, Yi-Min Kumar, Ramya Liu, Chun-Hung Takeyama, Haruko Hirono, Ikuo Wang, Han-Ching Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan Research Organization for Nano and Life Innovations Waseda University Tokyo Japan International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan Department of Aquaculture National Pingtung University of Science and Technology Pingtung Taiwan Department of Life Science and Medical Bioscience Waseda University Tokyo Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Tokyo Japan Department of Marine Biosciences Tokyo University of Marine Science and Technology Tokyo Japan

In addition to the Warburg effect, which increases the availability of energy and biosynthetic building blocks in WSSV-infected shrimp, WSSV also induces both lipolysis at the viral genome replication stage (12 hpi) to provide material and energy for the virus replication, and lipogenesis at the viral late stage (24 hpi) to complete virus morphogenesis by supplying particular species of long-chain fatty acids (LCFAs). Here, we further show that WSSV causes a reduction in lipid droplets (LDs) in hemocytes at the viral genome replication stage, and an increase in LDs in the nuclei of WSSV-infected hemocytes at the viral late stage. In the hepatopancreas, lipolysis is triggered by WSSV infection, and this leads to fatty acids being released into the hemolymph. β-oxidation inhibition experiment reveals that the fatty acids generated by WSSV-induced lipolysis can be diverted into β-oxidation for energy production. At the viral late stage, WSSV infection leads to lipogenesis in both the stomach and hepatopancreas, suggesting that fatty acids are in high demand at this stage for virion morphogenesis. Our results demonstrate that WSSV modulates lipid metabolism specifically at different stages to facilitate its replication. © 2023, The Author(s).

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