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The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study

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The Lancet Planetary Health 2022年第4期6卷 e350-e358页

作者： Wang, Zengmiao Liu, Yonghong Li, Yapin Wang, Guangze Lourenço, José Kraemer, Moritz He, Qixin Cazelles, Bernard Li, Yidan Wang, Ruixue Gao, Dongqi Li, Yuchun Song, Wenjing Sun, Dingwei Dong, Lu Pybus, Oliver G Stenseth, Nils Chr Tian, Huaiyu State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing China Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering College of Life Sciences Beijing Normal University Beijing China School of National Security and Emergency Management Beijing Normal University Beijing China Central Theater Center for Disease Control and Prevention of PLA Beijing China Hainan Center for Disease Control and Prevention Haikou China Department of Zoology University of Oxford UK Oxford United Kingdom Harvard Medical School Harvard University Boston MA United States Boston Children's Hospital Boston MA United States Department of Ecology and Evolution University of Chicago Chicago IL United States Institut de Biologie de l’École Normale Supérieure Unité Mixte de Recherche 8197 Centre National de la Recherche Scientifique et École Normale Supérieure Paris France Unité Mixte Internationnale 209 Mathematical and Computational Modeling of Complex Systems Institut de Recherche pour le Développement et Sorbonne Université Bondy France Centre for Ecological and Evolutionary Synthesis Department of Biosciences University of Oslo Oslo Norway Biosystems and Integrative Sciences Institute University of Lisbon Lisbon Portugal Department of Pathobiology and Population Science The Royal Veterinary College UK London United Kingdom

Background: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. Methods: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984–2010 and the WorldClim dataset. A temperature-dependent R₀ model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. Findings: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from –5·6% (95% CI –4·5 to –6·6) to –9·2% (95% CI –7·6 to –10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. Interpretation: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. Funding: S

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Author Correction: Malaria elimination on Hainan Island despite climate change

Communications Medicine

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Communications Medicine 2022年第1期2卷 1页

作者： Huaiyu Tian Naizhe Li Yonghong Liu Yidan Li Ben Wang Peiyi Wu Yapin Li Dongqi Gao Wenjing Song Moritz U. G. Kraemer José Lourenço Oliver G. Pybus Christopher Dye Hua Tan Bernard Cazelles Dingwei Sun Yuchun Li Guangze Wang State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing China Central Theater Center for Disease Control and Prevention of PLA Beijing China Department of Zoology University of Oxford Oxford UK Department of Pathobiology and Population Science The Royal Veterinary College London UK Oxford Martin School University of Oxford Oxford UK School of Biomedical Informatics the University of Texas Health Science Center at Houston Houston TX USA Institut de Biologie de l’École Normale Supérieure Unité Mixte de Recherche Centre National de la Recherche Scientifique et École Normale Supérieure Paris France Unité Mixte Internationnale Mathematical and Computational Modeling of Complex Systems Institut de Recherche pour le Développement et Sorbonne Université Bondy France Hainan Center for Disease Control and Prevention Haikou China

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A dynamic model of two-beam tuning fork

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Key Engineering Materials 2008年 381-382卷 337-340页

作者： Dong, Z.G. Zhang, Y. Soh, Y.C. Precision Measurements Group Singapore Institute of Manufacturing Technology 71 Nanyang Drive Singapore 638075 Singapore Centre for Modeling and Control of Complex Systems School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore

The shear force detection by using a tuning fork plays a key role in the implement all kinds of scanning probe microscopes. This paper presents primary results of modeling dynamics of a tuning fork. The obtained model considers not only the piezoelectric properties and mechanical properties of the tuning fork, but also the electric-mechanical coupling between the two prongs of the tuning fork. It has been shown by theoretical studies and experiment results that the theoretical model can fit the amplitude and phase responses of a tuning fork.

关键词： Tuning

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Rate control for Spatial/CGS Scalable Extension of H.264/AVC

Rate Control for Spatial/CGS Scalable Extension of H.264/AVC

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Y. Liu Y. C. Soh Z. G. Li Centre for Modeling and Control of Complex Systems Nanyang Technological University Singapore Institute for Infocomm Research Singapore

This paper presents a rate control scheme for H.264/AVC spatial/ coarse-gain-SNR (CGS) scalable extension. A switched model is proposed to predict mean absolute difference (MAD) either from the previous frame of the current layer or from the current frame of the previous layer. This way, abrupt MAD fluctuations could be predicted properly in the enhancement layer. Moreover, a sum bits R-Q model is formulated to describe the relationship between the total amount of bits for texture and non-texture information and quantization parameter (QP) so as to reduce the negative effect caused by inaccurate non-texture bits estimation of the previous rate control schemes. With the relation between PSNR and QP value of H.264/AVC, our proposed sum bits R-Q model could further optimize the QP calculation at the MB level. With our algorithm, the rate control for all the coarse-grain-scalability (CGS) and spatial enhancement layer could be realized, and the encoder could achieve fixed bitrate encoding for each scalable layer. Our method encodes the sequence only once and the buffer is well controlled to prevent it from overflowing and underflowing.

关键词： Automatic voltage control Predictive models Scalability Quadratic programming Quantization Bit rate Encoding Application software Video coding Programmable control

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MRI brain image segmentation by adaptive spatial deterministic annealing clustering

MRI brain image segmentation by adaptive spatial determinist...

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IEEE International Symposium on Biomedical Imaging

作者： Zhi Min Wang Qing Song Yeng Chai Soh Centre for Modeling and Control of Complex Systems School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)078039576x

This paper describes a novel image segmentation algorithm suitable for MRI image segmentation. We introduce a new dissimilarity measure which incorporates the spatial connectivity. A fully automatic technique is developed to obtain the segmentation result and the new clustering objective function incorporates the spatial information. The weighting factor for neighborhood effect is adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous region and reduces the edge-blurring effect. The experimental results with synthetic and MRI brain images demonstrate that the proposed method is effective in improving the segmentation and it outperforms the popular Markov random field algorithm

关键词： Magnetic resonance imaging Brain Image segmentation Annealing Clustering algorithms Electrical resistance measurement Pixel control system synthesis Automatic control Markov random fields

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Adaptive Mad Prediction and Refined R-Q Model for H.264/AVC Rate control

Adaptive Mad Prediction and Refined R-Q Model for H.264/AVC ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Y. Liu Z.G. Li Y.C. Soh Centre for Modeling and Control of Complex Systems School of Electrical and Electronic Engineering Nanyang Technological University Singapore Media Division Institute for Infocomm Research Singapore

This paper presents an improved rate control scheme for the H.264/AVC video coding scheme. By analyzing the relationship between direct mean absolute difference (MAD) and actual MAD, a new MAD prediction scheme is introduced to enhance traditional linear MAD prediction model, which is unable to predict abrupt MAD fluctuations. Our proposed adaptive model could reduce MAD prediction error by up to 34%. One simple sum bit quadratic R-Q model is also presented to solve the problem caused by inaccurate texture bits estimation of H.264/AVC. With the new MAD prediction model and R-Q model, our proposed scheme could reduce the mismatch between actual frame bits and target frame bits by up to 32%, and the buffer occupancy is much closer to the ideal status. Meanwhile, reconstructed video quality is also improved by up to 0.21 dB at low bitrate

关键词： Programmable control Adaptive control Predictive models Automatic voltage control Quadratic programming Quantization Video compression Motion estimation Video coding Motion compensation

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Conversational Video Communication of H.264/AVC with Region-of-Interest Concern

Conversational Video Communication of H.264/AVC with Region-...

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IEEE International Conference on Image Processing

作者： Y. Liu Z. G. Li Y. C. Soh M. H. Loke School of Electrical and Electronic Engineering Centre for Modeling and Control of Complex Systems Nanyang Technological University Singapore Media Division Institute for Infocomm Research Singapore

Although region-of-interest (ROI) based video coding has been well studied for some other video coding standards, its application for H.264 is still of significant interest because there exists a dilemma in the detection of the ROI due to the rate distortion optimization. In this paper, the ROI is first detected by using the direct MAD that is determined without motion information. In this way, the ROI-motion dilemma of H.264/AVC is solved. The rate control scheme with ROI concern can then adjust the quantization parameter (QP) to allocate more bits to the ROI, so the overall subjective visual quality is improved.

关键词： Automatic voltage control Communication system control Motion detection Videoconference Quadratic programming Rate-distortion Humans Face detection Color Video coding

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Speech-like Analysis of Snore Signals for the Detection of Obstructive Sleep Apnea

Speech-like Analysis of Snore Signals for the Detection of O...

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International Conference on Biomedical and Pharmaceutical Engineering, ICBPE

作者： Andrew K. Ng T.S. Koh Eugene Baey K. Puvanendran Centre for Modeling and Control of Complex Systems School of Electrical and Electronic Engineering Nanyang Technological University Singapore Respironics Incorporated Sleep Disorders Unit Singapore General Hospital Singapore

Correlation between snoring and obstructive sleep apnea (OSA) has been studied over the past years using techniques such as sound intensity computation, power spectrum estimation, counting of snores, and more recently, pitch and jitter analysis. Although these studies have ascertained the relationship between snoring and OSA, they gave mixed outcomes in diagnosing OSA. In this paper, snore signals are analyzed using linear predictive coding (LPC), which is a powerful speech analytic tool. The noisy snore signals were first preprocessed using a Modified Normalized Least-Mean-Square (MNLMS) adaptive filter for noise cancellation, followed by the extraction of formant structures from the denoised snore signals to discriminate between benign and apneic snores. This initial study demonstrates that the formant structures of snore signals contain useful information for OSA detection, and suggests the feasibility of using snore signal analysis as a noninvasive and inexpensive diagnostic tool for mass screening of OSA.

关键词： Sleep apnea Signal analysis Speech analysis Linear predictive coding Noise cancellation Spectral analysis Jitter Speech coding Acoustic noise Adaptive filters

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