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Using artificial intelligence to document the hidden RNA virosphere

四川生理科学杂志 2024年第10期46卷 2345-2345页

作者： Xin Hou National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases State Key Laboratory for Biocontrol School of Medicine Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Apsara Lab Alibaba Cloud Intelligence Alibaba Group Hangzhou China Wuhan Centers for Disease Control and Prevention Wuhan China Polar Research Institute of China Shanghai China Department of Nursing The Fifth Affiliated Hospital Sun Yat-sen University Zhuhai China School of Geospatial Engineering and Science Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Sun Yat-sen University Zhuhai China Key Laboratory of Comprehensive Observation of Polar Environment Ministry of Education Sun Yat-sen University Zhuhai China Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Institute of Biodiversity Science and Institute of Eco-Chongming School of Life Sciences Fudan University Shanghai Shanghai China Centre for Virus Research Westmead Institute for Medical Research Westmead NSW Australia School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia College of Life Sciences Zhejiang University Hangzhou China School of Life Science Guangzhou University Guangzhou China Key Laboratory of Pathogen Infection Prevention and Control (MOE) State Key Laboratory of Respiratory Health and Multimorbidity National Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China School of Public Health (Shenzhen) Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Guangzhou National Laboratory Guangzhou International Bio-Island Guangzhou China Department of Infectious Diseases and Public Health Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong SAR China Laboratory of Data Disco

Current metagenomic tools can fail to identify highly divergent RNA *** developed a deep learning algorithm,termed LucaProt,to discover highly divergent RNA-dependent RNA polymerase(RdRP)sequences in 10,487 metatranscriptomes generated from diverse global *** integrates both sequence and predicted structural information,enabling the accurate detection of RdRP *** this approach,we identified 161,979 potential RNA virus species and 180 RNA virus supergroups,including many previously poorly studied groups,as well as RNA virus genomes of exceptional length(up to 47,250 nucleotides)and genomic complexity.

关键词： artificial exceptional integrate

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Interactive wearable digital devices for blind and partially sighted people

Nature Reviews Electrical Engineering

引用

Nature Reviews Electrical engineering 2025年 1-15页

作者： Wenhao Xue ( Tianfang Zhang ( Xiaonan Dong ( Xuezhi Ma ( Heng Xu ( Jiaqing Zhao ( Yuan Ma ( Department of Mechanical Engineering and Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University Hong Kong S.A.R China Department of Rehabilitation Medicine First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China Technology Innovation Center Zhe Jiang Key Laboratory of Intelligent Rehabilitation and Translational Neuroelectronics Hangzhou China Life and Health Institute China Resources Research Institute of Science and Technology Company Ltd Hong Kong S.A.R. China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong S.A.R. China A*STAR Quantum Innovation Centre (Q. InC) Agency for Science Technology and Research Singapore Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore Singapore Department of Haptic Intelligence CVTE Guangzhou Shiyuan Electronics Company Ltd Guangzhou China Institute of Nuclear and New Energy Technology Tsinghua University Beijing China Research Center for Green and Smart Manufacturing PolyU-Daya Bay Technology and Innovation Research Institute Huizhou China

Digital information has permeated all aspects of life, and diverse forms of information exert a profound influence on social interactions and cognitive perceptions. In contrast to the flourishing of digital interaction devices for sighted users, the needs of blind and partially sighted users for digital interaction devices have not been adequately addressed. Current assistive devices often cause frustration in blind and partially sighted users owing to the limited efficiency and reliability of information delivery and the high cognitive load associated with their use. The expected rise in the prevalence of blindness and visual impairment due to global population ageing drives an urgent need for assistive devices that can deliver information effectively and non-visually, and thereby overcome the challenges faced by this community. This Perspective presents three potential directions in assistive device design: multisensory learning and integration; gestural interaction control; and the synchronization of tactile feedback with large-scale visual language models. Future trends in assistive devices for use by blind and partially sighted people are also explored, focusing on metrics for text delivery efficiency and the enhancement of image content delivery. Such devices promise to greatly enrich the lives of blind and partially sighted individuals in the digital age.

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Recycling qubits for the generation of Bell nonlocality between independent sequential observers

arXiv

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arXiv 2021年

作者： Cheng, Shuming Liu, Lijun Baker, Travis J. Hall, Michael J.W. The Department of Control Science and Engineering Tongji University Shanghai 201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 201804 China Institute for Advanced Study Tongji University Shanghai 200092 China College of Mathematics and Computer Science Shanxi Normal University Linfen041000 China Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Department of Theoretical Physics Research School of Physics Australian National University CanberraACT0200 Australia

There is currently much interest in the recycling of entangled systems, for use in quantum information protocols by sequential observers. In this work, we study the sequential generation of Bell nonlocality via recycling one or both components of two-qubit states. We first give a description of two-valued qubit measurements in terms of measurement bias, strength, and reversibility, and derive useful tradeoff relations between them. Then, we derive one-sided monogamy relations for unbiased observables, that strengthen the recent Conjecture in [S. Cheng et al., Phys. Rev. A 104, L060201 (2021) ] that if the first pair of observers violate Bell nonlocality then a subsequent independent pair cannot, and give semi-analytic results for the best possible monogamy relation. We also extend the construction in [P. J. Brown and R. Colbeck, Phys. Rev. Lett. 125, 090401 (2020)] to obtain (i) a broader class of two-qubit states that allow the recycling of one qubit by a given number of observers on one side, and (ii) a scheme for generating Bell nonlocality between arbitrarily many independent observers on each side, via the two-sided recycling of multiqubit states. Our results are based on a formalism that is applicable to more general problems in recycling entanglement, and hence is expected to aid progress in this field. Copyright © 2021, The Authors. All rights reserved.

关键词： Recycling

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Functional Synthetic Biology

arXiv

引用

arXiv 2022年

作者： Aldulijan, Ibrahim Beal, Jacob Billerbeck, Sonja Bouffard, Jeff Chambonnier, Gaël Delkis, Nikolaos Guerreiro, Isaac Holub, Martin Ross, Paul Selvarajah, Vinoo Sprent, Noah Vidal, Gonzalo Vignoni, Alejandro Systems Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States Intelligent Software & Systems Raytheon BBN Technologies 10 Moulton Street CambridgeMA02138 United States Molecular Microbiology Groningen Biomolecular Sciences and Biotechnology Institute University of Groningen Nijenborgh 7 Groningen9747 AG Netherlands Centre for Applied Synthetic Biology Department of Biology Concordia University 7141 Sherbrooke Street West MontréalQCH4B 1R6 Canada Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Plant and Environmental Biotechnology Laboratory Department of Biochemistry and Biotechnology University of Thessaly Viopolis Mezourlo Larissa41500 Greece iGEM Foundation 45 Prospect Street CambridgeMA02139 United States Delft University of Technology Van der Maasweg 9 2629 HZ Netherlands BioStrat Marketing 9965 Harbour Lake Circle Boynton BeachFL33437 United States Department of Chemical Engineering Imperial College London South Kensington Campus Exhibition Road SW7 2AZ United Kingdom Research Group School of Computing Newcastle University Devonshire Building Devonshire Terrace Newcastle Upon TyneNE1 7RU United Kingdom Synthetic Biology and Biosystems Control Lab Instituto de Automatica e Informatica Industrial Universitat Politecnica de Valencia Camino de Vera s/n Valencia46022 Spain

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost. © 2022, CC BY.

关键词： Biological systems

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Tailoringd-Band Center of Single-Atom Nickel Sites for Boosted Photocatalytic Reduction of Diluted CO2from Flue Gas

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Angewandte Chemie 2024年第5期137卷

作者： Jiahui Liu Prof. Bin Han Prof. Xueming Liu Dr. Shujie Liang Yang Fu Prof. Jun He Lai-Hon Chung Yuanfang Lin Yupeng Wei Sibo Wang Tianyi Ma Prof. Zhifeng Yang Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Collaborative Innovation Institute of Carbon Neutrality and Green Development School of Ecology Environment and Resources Guangdong University of Technology Guangzhou 510006 P. R. China School of Environment and Energy Key Laboratory of Pollution Control and Ecosystem Restoration in Industry Clusters (Ministry of Education) South China University of Technology Guangzhou 510006 P. R. China Centre for Atomaterials and Nanomanufacturing (CAN) School of Science RMIT University Melbourne VIC 3000 Australia ARC Industrial Transformation Research Hub for Intelligent Energy Efficiency in Future Protected Cropping (E2Crop) Melbourne VIC 3000 Australia School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou Guangdong 510006 P. R. China State Key Laboratory of Photocatalysis on Energy and Environment College of Chemistry Fuzhou University Fujian 350116 P. R. China

Photocatalytic reduction of diluted CO 2 from anthropogenic sources holds tremendous potential for achieving carbon neutrality, while the huge barrier to forming *COOH key intermediate considerably limits catalytic effectiveness. Herein, via coordination engineering of atomically scattered Ni sites in conductive metal–organic frameworks (CMOFs), we propose a facile strategy for tailoring the d-band center of metal active sites towards high-efficiency photoreduction of diluted CO 2 . Under visible-light irradiation in pure CO 2 , CMOFs with Ni-O 4 sites (Ni-O 4 CMOFs) exhibits an outstanding rate for CO generation of 13.3 μmol h −1 with a selectivity of 94.5 %, which is almost double that of its isostructural counterpart with traditional Ni-N 4 sites (Ni-N 4 CMOFs), outperforming most reported systems under comparable conditions. Interestingly, in simulated flue gas, the CO selectivity of Ni-N 4 CMOFs decreases significantly while that of Ni-O 4 CMOFs is mostly unchanged, signifying the supremacy for Ni-O 4 CMOFs in leveraging anthropogenic diluted CO 2 . In situ spectroscopy and density functional theory (DFT) investigations demonstrate that O coordination can move the center of the Ni sites′ d-band closer to the Fermi level, benefiting the generation of *COOH key intermediate as well as the desorption of *CO and hence leading to significantly boosted activity and selectivity for CO 2 -to-CO photoreduction.

关键词： single-atom catalysts CO2 photoreduction d-band centers coordination engineering conductive MOFs

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

引用

arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： Data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature ecology & evolution 2025年 2025 May 22页

作者： Stephen Pringle Martin Dallimer Mark A Goddard Léni K Le Goff Emma Hart Simon J Langdale Jessica C Fisher Sara-Adela Abad Marc Ancrenaz Fabio Angeoletto Fernando Auat Cheein Gail E Austen Joseph J Bailey Katherine C R Baldock Lindsay F Banin Cristina Banks-Leite Aliyu S Barau Reshu Bashyal Adam J Bates Jake E Bicknell Jon Bielby Petra Bosilj Emma R Bush Simon J Butler Dan Carpenter Christopher F Clements Antoine Cully Kendi F Davies Nicolas J Deere Michael Dodd Rosie Drinkwater Don A Driscoll Guillaume Dutilleux Mads Dyrmann David P Edwards Mohammad S Farhadinia Aisyah Faruk Richard Field Robert J Fletcher Chris W Foster Richard Fox Richard M Francksen Aldina M A Franco Alison M Gainsbury Charlie J Gardner Ioanna Giorgi Richard A Griffiths Salua Hamaza Marc Hanheide Matt W Hayward Marcus Hedblom Thorunn Helgason Sui P Heon Kevin A Hughes Edmund R Hunt Daniel J Ingram George Jackson-Mills Kelly Jowett Timothy H Keitt Laura N Kloepper Stephanie Kramer-Schadt Jim Labisko Frédéric Labrosse Jenna Lawson Nicolas Lecomte Ricardo F de Lima Nick A Littlewood Harry H Marshall Giovanni L Masala Lindsay C Maskell Eleni Matechou Barbara Mazzolai Alistair McConnell Brett A Melbourne Aslan Miriyev Eric Djomo Nana Alessandro Ossola Sarah Papworth Catherine L Parr Ana Payo-Payo Gad Perry Nathalie Pettorelli Rajeev Pillay Simon G Potts Miranda T Prendergast-Miller Lan Qie Persie Rolley-Parnell Stephen J Rossiter Marcus Rowcliffe Heather Rumble Jon P Sadler Christopher J Sandom Asiem Sanyal Franziska Schrodt Sarab S Sethi Adi Shabrani Robert Siddall Simón C Smith Robbert P H Snep Carl D Soulsbury Margaret C Stanley Philip A Stephens P J Stephenson Matthew J Struebig Matthew Studley Martin Svátek Gilbert Tang Nicholas K Taylor Kate D L Umbers Robert J Ward Patrick J C White Mark J Whittingham Serge Wich Christopher D Williams Ibrahim B Yakubu Natalie Yoh Syed A R Zaidi Anna Zmarz Joeri A Zwerts Zoe G Davies Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK. s.pringle@kent.ac.uk. Centre for Environmental Policy Imperial College London London UK. Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK. Synthotech Ltd Milner Court Hornbeam Square Harrogate UK. Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK. Mechanical Engineering Department University College London London UK. HUTAN SWD Kota Kinabalu Malaysia. Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brasil. Department of Engineering Harper Adams University Newport UK. Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge UK. Centre for Ecology and Hydrology Penicuik UK. Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK. Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria. Greenhood Nepal Kathmandu Nepal. Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire UK. School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK. Lincoln Centre for Autonomous Systems University of Lincoln Lincoln UK. Royal Botanic Garden Edinburgh Edinburgh UK. School of Biological Sciences University of East Anglia Norwich Research Park Norwich UK. Digital Ecology Limited Bristol UK. School of Biological Sciences University of Bristol Bristol UK. Department of Computing Imperial College London London UK. Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA. Faculty of Science Technology Engineering and Mathematics (STEM) The Open University Walton Hall Milt

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more 'difficult' taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

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Three-dimensional super-resolution longitudinal magnetization spot arrays

引用

Light(Science & Applications) 2017年第1期6卷 630-637页

作者： Zhong-Quan Nie Han Lin Xiao-Fei Liu Ai-Ping Zhai Yan-Ting Tian Wen-Jie Wang Dong-Yu Li Wei-Qiang Ding Xue-Ru Zhang Ying-Lin Song Bao-Hua Jia Key Lab of Advanced Transducers and Intelligent Control Systems Ministry of Education of Shanxi ProvinceCollege of Physics and OptoelectronicsTaiyuan University of TechnologyTaiyuan 030024China Centre for Micro-Photonics Faculty of ScienceEngineering and TechnologySwinburne University of TechnologyHawthornVictoria 3122Australia Department of Science Taiyuan Institute of TechnologyTaiyuan 030008China Department of Physics Lingnan Normal UniversityZhanjiang 524048China Department of Physics Harbin Institute of TechnologyHarbin 150001China

We demonstrate an all-optical strategy for realizing spherical three-dimensional(3D)super-resolution(∼λ3/22)spot arrays of pure longitudinal magnetization by exploiting a 4πoptical microscopic setup with two high numerical aperture(NA)objective lenses,which focus and interfere two modulated vectorial *** phase filters(MPFs)are designed via an analytical approach derived from the vectorial Debye diffraction theory to modulate the two circularly polarized *** system is tailored to constructively interfere the longitudinal magnetization components,while simultaneously destructively interfering the azimuthal *** a result,the magnetization field is not only purely longitudinal but also super-resolved in all three ***,the MPFs can be designed analytically to control the number and locations of the super-resolved magnetization spots to produce both uniform and nonuniform arrays in a 3D ***,an all-optical control of all the properties of light-induced magnetization spot arrays has been demonstrated for the first *** results open up broad applications in magnetic-optical devices such as confocal and multifocal magnetic resonance microscopy,3D ultrahigh-density magneto-optic memory,and light-induced magneto-lithography.

关键词： inverse Faraday effect longitudinal magnetization magnetic-optical devices magneto-optics vectorial beams vectorial Debye diffraction theory

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Development of a global road safety performance function using deep neural networks

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International Journal of Transportation Science and technology 2017年第3期6卷 159-173页

作者： Pan, Guangyuan Fu, Liping Thakali, Lalita Department of Civil & Environmental Engineering University of Waterloo Waterloo N2L 3G1 ON Canada Intelligent Transportation Systems Research Centre Wuhan University of Technology Mailbox 125 No. 1040 Heping Road Wuhan 430063 Hubei China

This paper explores the idea of applying a machine learning approach to develop a global road safety performance function (SFP) that can be used to predict the expected crash frequencies of different highways from different regions. A deep belief network (DBN) – one of the most popular deep learning models is introduced as an alternative to the traditional regression models for crash modelling. An extensive empirical study is conducted using three real world crash data sets covering six classes of highways as defined by location (urban vs. rural), number of lanes, access control, and region. The study involves a number of experiments aiming at addressing several critical questions pertaining to the relative performance of the DBN in terms of network structure, training method, data size, and generalization ability, as compared to the traditional regression models. The experimental results have shown that a DBN model could be trained with different crash datasets with prediction performance being at least comparable to that of the locally calibrated negative binomial (NB) model. © 2017

关键词： Collision model Deep learning Global model Road safety

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