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Chinese Optics Letters 2018年第6期16卷 88-92页

作者： Shaofu Xu Weiwen Zou Guang Yang Jianping Chen State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China

We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser（AMLL）. The transmitted signal and sampling clock in the radar originate from the same AMLL to achieve a large instantaneous bandwidth. A Ka band linearly frequency modulated signal with a bandwidth up to 8 GHz is successfully generated and processed with the electro-optical upconversion and direct photonic sampling. The minor lobe suppression（MLS） algorithm is adopted to enhance the dynamic range at a cost of the range resolution. Two-target discrimination with the MLS algorithm proves the range resolution reaches 2.8 cm. The AMLL-based microwave-photonics radar shows promising applications in high-resolution imaging radars having the features of high-frequency band and large bandwidth.

关键词： Ultra-high range resolution demonstration of a photonics-based microwave radar using a high-repetition-rate mode-locked fiber laser

Bioinformatics Commons: The Cornerstone of Life and Health sciences

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Genomics,Proteomics & Bioinformatics 2018年第4期16卷 223-225页

作者： Zhang Zhang Yu Xue Fangqing Zhao BIG Data Center and CAS Key Laboratory of Genome Sciences & Information Beijing Institute of Genomics Chinese Academy of Sciences Department of Bioinformatics & Systems Biology MOE Key Laboratory of Molecular Biophysics College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering Huazhong University of Science and Technology Beijing Institutes of Life Science Chinese Academy of Sciences

Bioinformatics, an interdisciplinary field that combines biology, mathematics, computer science, medicine, and health science, to integrate, analyze, and interpret biological data, is now becoming increasingly data-intensive. To dig out the treasure from big data powered by high-throughput sequencing technologies, it is highly dependent on Bioinformatics Commons that involves a variety of fundamental resources, includ-

关键词： Bioinformatics Commons The Cornerstone of Life and Health sciences

A long-period transiting substellar companion in the super-Jupiters to brown dwarfs mass regime and a prototypical warm-Jupiter detected by TESS

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arXiv 2024年

作者： Jones, Matías I. Reinarz, Yared Brahm, Rafael Pinto, Marcelo Tala Eberhardt, Jan Rojas, Felipe Triaud, Amaury H.M.J. Gupta, Arvind F. Ziegler, Carl Hobson, Melissa J. Jordán, Andrés Henning, Thomas Trifonov, Trifon Schlecker, Martin Espinoza, Néstor Torres-Miranda, Pascal Sarkis, Paula Ulmer-Moll, Solène Lendl, Monika Uzundag, Murat Moyano, Maximiliano Hesse, Katharine Caldwell, Douglas A. Shporer, Avi Lund, Michael B. Jenkins, Jon M. Seager, Sara Winn, Joshua N. Ricker, George R. Burke, Christopher J. Figueira, Pedro Psaridi, Angelica Moulla, Khaled Al Mounzer, Dany Standing, Matthew R. Martin, David V. Dransfield, Georgina Baycroft, Thomas Dragomir, Diana Boyle, Gavin Suc, Vincent Mann, Andrew W. Timmermans, Mathilde Ducrot, Elsa Hooton, Matthew J. Sebastián, Zuñiga-Fernández Sebastian, Daniel Gillon, Michael Queloz, Didier Carson, Joe Lissauer, Jack J. European Southern Observatory Alonso de Córdova 3107 Vitacura Casilla Santiago19001 Chile Instituto de Astronomía Universidad Católica del Norte Angamos 0610 Antofagasta1270709 Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Peñalolén Santiago Chile Millennium Institute for Astrophysics Chile Data Observatory Foundation Chile Institute of Astronomy KU Leuven Celestijnenlaan 200D LeuvenB-3001 Belgium Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Department of Physics Engineering and Astronomy Stephen F. Austin State University 1936 North St NacogdochesTX75962 United States Department of Physics and Astronomy The University of North Carolina at Chapel Hill Chapel HillNC27599-3255 United States Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Santiago Chile Department of Physics & Astronomy College of Charleston 66 George Street CharlestonSC29424 United States School of Physics & Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany Department of Astronomy Sofia University "St Kliment Ohridski" 5 James Bourchier Blvd SofiaBG-1164 Bulgaria Steward Observatory Department of Astronomy The University of Arizona TucsonAZ85721 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Wesleyan University MiddletownCT06459 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Department of Astronomy The Ohio State Univers

We report on the confirmation and follow-up characterization of two long-period transiting substellar companions on low-eccentricity orbits around TIC 4672985 and TOI-2529, whose transit events were detected by the TESS space mission. Ground-based photometric and spectroscopic followup from different facilities, confirmed the substellar nature of TIC 4672985 b, a massive gas giant, in the transition between the super-Jupiters and brown-dwarfs mass regime. From the joint analysis we derived the following orbital parameters: P = 69.0480+−00.00040005 d, Mp = 12.74−+11.0101 MJ , Rp = 1.026+−00.065067 RJ and e = 0.018+−00.004004 . In addition, the RV time series revealed a significant trend at the ∼ 350 m s−1 yr−1 level, which is indicative of the presence of a massive outer companion in the system. TIC 4672985 b is a unique example of a transiting substellar companion with a mass above the deuterium-burning limit, located beyond 0.1 AU and in a nearly circular orbit. These planetary properties are difficult to reproduce from canonical planet formation and evolution models. For TOI-2529 b, we obtained the following orbital parameters: P = 64.5949+−00.00030003 d, Mp = 2.340+−00.197195 MJ , Rp = 1.030+−00.050050 RJ and e = 0.021+−00.024015 , making this object a new example of a growing population of transiting warm giant planets. © 2024, CC BY.

关键词： Planets

Protoglobin-Catalyzed Formation ofcis-Trifluoromethyl-Substituted Cyclopropanes by Carbene Transfer

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Angewandte Chemie International Edition 2022年第4期62卷

作者： Lucas Schaus Dr. Anuvab Das Dr. Anders M. Knight Dr. Gonzalo Jimenez-Osés Prof. Dr. K. N. Houk Dr. Marc Garcia-Borràs Division of Chemistry and Chemical Engineering California Institute of Technology 1200 E California Blvd. Pasadena CA 91125 USA Contribution: Data curation (lead) Formal analysis (equal) Investigation (lead) Methodology (lead) Validation (equal) Writing - original draft (equal) Contribution: Data curation (equal) Formal analysis (equal) Investigation (supporting) Methodology (supporting) Validation (equal) Writing - review & editing (supporting) Contribution: Investigation (supporting) Methodology (supporting) Center for Cooperative Research in Biosciences (CIC bioGUNE) Basque Research and Technology Alliance (BRTA) Bizkaia Technology Park Building 800 48160 Derio Spain Ikerbasque Basque Foundation for Science 48013 Bilbao Spain Contribution: Data curation (supporting) Formal analysis (supporting) Validation (supporting) Department of Chemistry and Biochemistry University of California Los Angeles CA 90095 USA Contribution: Resources (supporting) Supervision (supporting) Writing - original draft (supporting)

SWEET: a single-sample network inference method for deciphering individual features in disease

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Briefings in bioinformatics 2023年第2期24卷 bbad032页

作者： Hsin-Hua Chen Chun-Wei Hsueh Chia-Hwa Lee Ting-Yi Hao Tzu-Ying Tu Lan-Yun Chang Jih-Chin Lee Chun-Yu Lin Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan.

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

关键词： drug repurposing gene expression network inference network medicine precision medicine single-sample network

Corrigendum to “Reciprocal Recommender systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation” Information Fusion Volume 69 (2020) 103-127

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Information Fusion 2021年 75卷 101-101页

作者： Iván Palomares James Neve Carlos Porcel Luiz Pizzato Ido Guy Enrique Herrera-Viedma Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI) University of Granada Spain Intelligent Systems Laboratory University of Bristol Bristol United Kingdom AI Labs Commonwealth Bank of Australia Sydney Australia eBay Research Netanya Israel Department of Electrical and Computer Engineering from the Faculty of Engineering of the King Abdulaziz University Jeddah 21589 Saudi Arabia

Improved earthquake forecasting model based on long-term memory in earthquake

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arXiv 2020年

作者： Zhang, Yongwen Zhou, Dong Fan, Jingfang Marzocchi, Warner Ashkenazy, Yosef Havlin, Shlomo Department of Solar Energy and Environmental Physics Jacob Blaustein Institutes for Desert Research Ben-Gurion University of the Negev Midreshet Ben-Gurion84990 Israel Department of Physics Bar-Ilan University Ramat Gan52900 Israel Data Science Research Center Faculty of Science Kunming University of Science and Technology KunmingYunnan650500 China School of Reliability and Systems Engineering Beihang University Beijing100191 China Potsdam Institute for Climate Impact Research Potsdam14412 Germany Department of Earth Environmental and Resources Sciences University of Naples Federico II Complesso di Monte SantAngelo Via Cinthia 21 Napoli80126 Italy

A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model1,2, were developed based on earthquake empirical laws. Yet, a recent study3 showed that the ETAS model fails in reproducing significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) recently discovered in the data. Our generalized ETAS model reproduces accurately the short- and long-term/distance memory observed in the Italian and South California earthquake catalogs. The revised ETAS model is also found to significantly improve earthquake forecasting. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

Black box submodular maximization: Discrete and continuous settings

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arXiv 2019年

作者： Chen, Lin Zhang, Mingrui Hassani, Hamed Karbasi, Amin Department of Electrical Engineering Yale Institute for Network Science Yale University New HavenCT06520 United States Department of Statistics and Data Science Yale University New HavenCT06511 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Yale University New HavenCT06520 United States

In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided. For a monotone and continuous DR-submodular function, and subject to a bounded convex body constraint, we propose Black-box Continuous Greedy, a derivative-free algorithm that provably achieves the tight [(1 − 1/e)OPT − Ε] approximation guarantee with O(d/Ε3) function evaluations. We then extend our result to the stochastic setting where function values are subject to stochastic zero-mean noise. It is through this stochastic generalization that we revisit the discrete submodular maximization problem and use the multi-linear extension as a bridge between discrete and continuous settings. Finally, we extensively evaluate the performance of our algorithm on continuous and discrete submodular objective functions using both synthetic and real data. Copyright © 2019, The Authors. All rights reserved.

关键词： Function evaluation

Random walks on simplicial complexes and the normalized hodge 1-laplacian

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arXiv 2018年

作者： Schaub, Michael T. Benson, Austin R. Horn, Paul Lippner, Gabor Jadbabaiek, Ali Institute for Data Systems and Society Massachusetts Institute of Technology Department of Engineering Science University of Oxford Department of Computer Science Cornell University Department of Mathematics University of Denver Department of Mathematics Northeastern University Institute for Data Systems and Society

Graphs modeling pairwise relationships between entities have become a dominant framework to study complex systems and data. Simplicial complexes extend this dyadic model of graphs to polyadic relationships and have emerged as a model for multi-node relationships occurring in many complex systems. For instance, biological interactions occur between sets of molecules, and communication systems include group messages and not only pairwise interactions. While the graph Laplacian and Laplacian dynamics have been intensely studied, corresponding notions of Laplacian dynamics beyond the node-space have so far remained largely unexplored for simplicial complexes. In particular, di usion processes such as random walks and their relationship to the graph Laplacian, that underpin many methods such as centrality measures, ow-based rankings, community detection, and contagion models, lack a proper correspondence for general simplicial complexes Focusing on the coupling between edges, here we introduce a normalized Laplacian matrix for simplicial complexes and demonstrate its relationship to a random walk model on simplicial complexes as a foundational step towards translating many Laplacian-based analytics from graphs to simplicial complexes. Our key idea is to generalize the relationship between the normalized graph Laplacian and random walks on graphs by devising an appropriate normalization for the Hodge Laplacian, the analog of the graph Laplacian for simplicial complexes. We further discuss how this Hodge Laplacian gives rise to the Hodge decomposition, a decomposition of edge ows into intuitively interpretable components that are analogous to notions such as gradient ows or rotational ows from vector calculus. We demonstrate how these results can be leveraged for data analytics that extract information about the edge-space of a simplicial complex that complements and extends graph-based analysis. To illustrate the utility of these tools we derive spectral embeddings based

关键词： Random processes

Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology (vol 13, 634, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-13页

作者： Bjornsdottir, Gyda Stefansdottir, Lilja Thorleifsson, Gudmar Sulem, Patrick Norland, Kristjan Ferkingstad, Egil Oddsson, Asmundur Zink, Florian Lund, Sigrun H. Nawaz, Muhammad S. Walters, G. Bragi Skuladottir, Astros Th. Gudjonsson, Sigurjon A. Einarsson, Gudmundur Halldorsson, Gisli H. Bjarnadottir, Valgerdur Sveinbjornsson, Gardar Helgadottir, Anna Styrkarsdottir, Unnur Gudmundsson, Larus J. Pedersen, Ole B. Hansen, Thomas Folkmann Werge, Thomas Banasik, Karina Troelsen, Anders Skou, Soren T. Thorner, Lise Wegner Erikstrup, Christian Nielsen, Kaspar Rene Mikkelsen, Susan Jonsdottir, Ingileif Bjornsson, Aron Olafsson, Ingvar H. Ulfarsson, Elfar Blondal, Josep Vikingsson, Arnor Brunak, Soren Ostrowski, Sisse R. Ullum, Henrik Thorsteinsdottir, Unnur Stefansson, Hreinn Gudbjartsson, Daniel F. Thorgeirsson, Thorgeir E. Stefansson, Kari deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland Faculty of Medicine School of Health Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland School of Engineering and Natural Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) Landspitali University Hospital Reykjavik Iceland (GRID:grid.410540.4) (ISNI:0000 0000 9894 0842) Zealand University Hospital Department of Clinical Immunology Køge Denmark (GRID:grid.512923.e) (ISNI:0000 0004 7402 8188) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Rigshospitalet-Glostrup Danish Headache Center Dept. Neurology Glostrup Denmark (GRID:grid.475435.4) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Copenhagen University Hospital Institute of Biological Psychiatry Mental Health Services Copenhagen Denmark (GRID:grid.4973.9) (ISNI:0000 0004 0646 7373) University of Copenhagen Lundbeck Foundation for GeoGenetics GLOBE Institute Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001

Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD = 0.92, P = 1.6 × 10−39; ORdorsalgia = 0.92, P = 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) in CHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants in SLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44, P = 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory *** is known about the biology of back pain, a leading cause of disability. Here the authors report 30 new back pain loci, implicating genes involved in cartilage/bone biology, as well as neurological and inflammatory processes.

关键词： Bone Genome-wide association studies Pain