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Enhancing the sustainability and effectiveness of non-governmental organizations: An integrated neutrosophic decision-making model for determining the most effective corporate governance strategies

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Emrah Ayhan Hakan Gökhan Gündoğdu Ahmet Aytekin Ahmet Furkan Karakaya Vladimir Simic Dragan Pamucar Department of Political Science and Public Administration Faculty of Economics and Administrative Sciences Anadolu University Tepebaşı 26470 Eskişehir Türkiye Department of Business Administration Hopa Faculty of Economics and Administrative Sciences Artvin Çoruh University Hopa 08100 Artvin Türkiye Public Administration Master Program Graduate Education Institute Anadolu University Tepebaşı 26470 Eskişehir Türkiye University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 11010 Belgrade Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City 320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer 34396 İstanbul Türkiye

This research explores the development of effective corporate governance strategies for non-governmental organizations (NGOs) by evaluating both critical decision criteria and innovative organizational models. Based on an extensive literature review, seven governance criteria were identified: credibility and non-profit orientation, regular information flow and transparency, level of institutionalization, frequency of leadership turnover, internal and external stakeholder relations, annual budget, and ideological or religious motivation. In addition, five innovative organizational models were selected as strategic alternatives: continuous learning organization, technology-oriented policy development, income diversification, pluralistic governance, and transformational leadership. The empirical data were gathered through interviews with 16 experts from academia, civil society, and public institutions in Türkiye. To address the complexity and uncertainty of decision-making processes, a hybrid type-2 neutrosophic number (T2NN) multi-criteria decision-making methodology is introduced. The T2NN-ranking comparison and T2NN-criteria importance through intercriteria correlation methods are used to determine the weight of criteria, while T2NN-stable preference ordering towards ideal solution is utilized to rank the alternative models. Results show that the level of institutionalization is the most significant criterion, and income diversification is the most effective organizational model for improving NGO sustainability. The study not only provides practical guidance for NGO leaders but also introduces a robust methodological approach applicable to governance challenges under uncertain conditions.

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Bayesian classification of multiclass functional data

arXiv

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arXiv 2018年

作者： Li, Xiuqi Ghosal, Subhashis Operations Research Graduate Program North Carolina State University RaleighNC27695 United States Department of Statistics North Carolina State University RaleighNC27695 United States

We propose a Bayesian approach to estimating parameters in multiclass functional models. Unordered multinomial probit, ordered multinomial probit and multinomial logistic models are considered. We use finite random series priors based on a suitable basis such as B-splines in these three multinomial models, and classify the functional data using the Bayes rule. We average over models based on the marginal likelihood estimated from Markov Chain Monte Carlo (MCMC) output. Posterior contraction rates for the three multinomial models are computed. We also consider Bayesian linear and quadratic discriminant analyses on the multivariate data obtained by applying a functional principal component technique on the original functional data. A simulation study is conducted to compare these methods on different types of data. We also apply these methods to a phoneme dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Discriminant analysis

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Covariance estimation via fiducial inference

arXiv

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arXiv 2017年

作者： Shi, W. Jenny Hannig, Jan Lai, Randy C.S. Lee, Thomas C.M. Computational Bioscience Program University of Colorado Department of Statistics & Operations Research University of North Carolina Department of Mathematics & Statistics University of Maine Department of Statistics University of California

As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and the Bayesian frameworks. Aiming to quantify the uncertainty of the estimators without having to choose a prior, we have developed a fiducial approach to the estimation of covariance matrix. Built upon the Fiducial Berstein-von Mises Theorem (Sonderegger and Hannig 2014), we show that the fiducial distribution of the covariate matrix is consistent under our framework. Consequently, the samples generated from this fiducial distribution are good estimators to the true covariance matrix, which enable us to define a meaningful confidence region for the covariance matrix. Lastly, we also show that the fiducial approach can be a powerful tool for identifying clique structures in covariance matrices.62J10, 62E20, 62F25, 62F12 Copyright © 2017, The Authors. All rights reserved.

关键词： Covariance matrix

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CEMRACS 2017: numerical probabilistic approach to MFG

arXiv

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arXiv 2018年

作者： Angiuli, Andrea Graves, Christy V. Li, Houzhi Chassagneux, Jean-François Delarue, François Carmona, René Department of Statistics and Applied Probability University of California Santa Barbara Program in Applied and Computational Mathematics Princeton University Laboratoire de Probabilités Modélisation et Statistiques Université Paris Diderot Laboratoire Jean-Alexandre Dieudonné Université de Nice Sophia-Antipolis Operations Research and Financial Engineering Bendheim Center for Finance Princeton University

This project investigates numerical methods for solving fully coupled forward-backward stochastic differential equations (FBSDEs) of McKean-Vlasov type. Having numerical solvers for such mean field FBSDEs is of interest because of the potential application of these equations to optimization problems over a large population, say for instance mean field games (MFG) and optimal mean field control problems. Theory for this kind of problems has met with great success since the early works on mean field games by Lasry and Lions, see [27], and by Huang, Caines, and Malhamé, see [24]. Generally speaking, the purpose is to understand the continuum limit of optimizers or of equilibria (say in Nash sense) as the number of underlying players tends to infinity. When approached from the probabilistic viewpoint, solutions to these control problems (or games) can be described by coupled mean field FBSDEs, meaning that the coefficients depend upon the own marginal laws of the solution. In this note, we detail two methods for solving such FBSDEs which we implement and apply to five benchmark problems. The first method uses a tree structure to represent the pathwise laws of the solution, whereas the second method uses a grid discretization to represent the time marginal laws of the solutions. Both are based on a Picard scheme;importantly, we combine each of them with a generic continuation method that permits to extend the time horizon (or equivalently the coupling strength between the two equations) for which the Picard iteration converges. Copyright © 2018, The Authors. All rights reserved.

关键词： Numerical methods

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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Public goods games in populations with fluctuating size

arXiv

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arXiv 2017年

作者： McAvoy, Alex Fraiman, Nicolas Hauert, Christoph Wakeley, John Nowak, Martin A. Program for Evolutionary Dynamics Harvard University CambridgeMA02138 Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel HillNC27599 Department of Mathematics University of British Columbia 1984 Mathematics Road VancouverBCV6T 1Z2 Canada Department of Organismic and Evolutionary Biology Harvard University CambridgeMA02138 Department of Mathematics Harvard University CambridgeMA02138

Many mathematical frameworks of evolutionary game dynamics assume that the total population size is constant and that selection affects only the relative frequency of strategies. Here, we consider evolutionary game dynamics in an extended Wright-Fisher process with variable population size. In such a scenario, it is possible that the entire population becomes extinct. Survival of the population may depend on which strategy prevails in the game dynamics. Studying cooperative dilemmas, it is a natural feature of such a model that cooperators enable survival, while defectors drive extinction. Although defectors are favored for any mixed population, random drift could lead to their elimination and the resulting pure-cooperator population could survive. On the other hand, if the defectors remain, then the population will quickly go extinct because the frequency of cooperators steadily declines and defectors alone cannot survive. In a mutation-selection model, we find that (i) a steady supply of cooperators can enable long-term population survival, provided selection is sufficiently strong, and (ii) selection can increase the abundance of cooperators but reduce their relative frequency. Thus, evolutionary game dynamics in populations with variable size generate a multifaceted notion of what constitutes a trait’s long-term success. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamics

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Author Correction: Universal DNA methylation age across mammalian tissues

Nature aging

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Nature aging 2023年第11期3卷 1462页

作者： A T Lu Z Fei A Haghani T R Robeck J A Zoller C Z Li R Lowe Q Yan J Zhang H Vu J Ablaeva V A Acosta-Rodriguez D M Adams J Almunia A Aloysius R Ardehali A Arneson C S Baker G Banks K Belov N C Bennett P Black D T Blumstein E K Bors C E Breeze R T Brooke J L Brown G G Carter A Caulton J M Cavin L Chakrabarti I Chatzistamou H Chen K Cheng P Chiavellini O W Choi S M Clarke L N Cooper M L Cossette J Day J DeYoung S DiRocco C Dold E E Ehmke C K Emmons S Emmrich E Erbay C Erlacher-Reid C G Faulkes S H Ferguson C J Finno J E Flower J M Gaillard E Garde L Gerber V N Gladyshev V Gorbunova R G Goya M J Grant C B Green E N Hales M B Hanson D W Hart M Haulena K Herrick A N Hogan C J Hogg T A Hore T Huang J C Izpisua Belmonte A J Jasinska G Jones E Jourdain O Kashpur H Katcher E Katsumata V Kaza H Kiaris M S Kobor P Kordowitzki W R Koski M Krützen S B Kwon B Larison S G Lee M Lehmann J F Lemaitre A J Levine C Li X Li A R Lim D T S Lin D M Lindemann T J Little N Macoretta D Maddox C O Matkin J A Mattison M McClure J Mergl J J Meudt G A Montano K Mozhui J Munshi-South A Naderi M Nagy P Narayan P W Nathanielsz N B Nguyen C Niehrs J K O'Brien P O'Tierney Ginn D T Odom A G Ophir S Osborn E A Ostrander K M Parsons K C Paul M Pellegrini K J Peters A B Pedersen J L Petersen D W Pietersen G M Pinho J Plassais J R Poganik N A Prado P Reddy B Rey B R Ritz J Robbins M Rodriguez J Russell E Rydkina L L Sailer A B Salmon A Sanghavi K M Schachtschneider D Schmitt T Schmitt L Schomacher L B Schook K E Sears A W Seifert A Seluanov A B A Shafer D Shanmuganayagam A V Shindyapina M Simmons K Singh I Sinha J Slone R G Snell E Soltanmaohammadi M L Spangler M C Spriggs L Staggs N Stedman K J Steinman D T Stewart V J Sugrue B Szladovits J S Takahashi M Takasugi E C Teeling M J Thompson B Van Bonn S C Vernes D Villar H V Vinters M C Wallingford N Wang R K Wayne G S Wilkinson C K Williams R W Williams X W Yang M Yao B G Young B Zhang Z Zhang P Zhao Y Zhao W Zhou J Zimmermann J Ernst K Raj S Horvath Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Altos Labs San Diego Institute of Science San Diego CA USA. Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA USA. Department of Statistics University of California Riverside Riverside CA USA. Zoological SeaWorld Parks and Entertainment Orlando FL USA. Altos Labs Cambridge Institute of Science Cambridge UK. Bioinformatics Interdepartmental Program University of California Los Angeles CA USA. Department of Biological Chemistry University of California Los Angeles Los Angeles CA USA. Department of Biology University of Rochester Rochester NY USA. Department of Neuroscience Peter O'Donnell Jr. Brain Institute University of Texas Southwestern Medical Center Dallas TX USA. Department of Biology University of Maryland College Park MD USA. Loro Parque Fundacion Puerto de la Cruz Spain. Department of Biology University of Kentucky Lexington KY USA. Division of Cardiology Department of Internal Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Marine Mammal Institute Oregon State University Newport OR USA. School of Science and Technology Clifton Campus Nottingham Trent University Nottingham UK. School of Life and Environmental Sciences the University of Sydney Sydney New South Wales Australia. Department of Zoology and Entomology University of Pretoria Hatfield South Africa. Busch Gardens Tampa Tampa FL USA. Department of Ecology and Evolutionary Biology University of California Los Angeles Los Angeles CA USA. Rocky Mountain Biological Laboratory Crested Butte CO USA. Altius Institute for Biomedical Sciences Seattle WA USA. Epigenetic Clock Development Foundation Los Angeles CA USA. Center for Species Survival Smithsonian Conservation Biology Institute Front Royal VA USA. Department of Evolution Eco

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Getting Undergraduates Ready for China’s Belt and Road Initiative (BRI) Through an Overseas Experiential Learning Project

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China and the World 2021年第3期4卷

作者： Andrew Min Han Chin Thomas Menkhoff Hans-Dieter Evers Hoong Hui Daniel Gn Kevin Koh Chester Wey Lee Patrick H. M. Loh Linda Low Sebastian Tan Teng Seng Teo Natalie Yap Lee Kong Chian School of Business Singapore Management University (SMU) 81 Victoria Street Singapore 188065 Singapore Andrew Chin Min Han graduated from Tsinghua University in Beijing China with a Tsinghua–MIT Sloan International Master of Business Administration degree under the Singapore Government’s Asian Business Fellowship (ABF) scholarship program. He is the Program Director of Singapore Management University (SMU) Executive Certificate in China Business and of the SMU Internationalisation Series. He teaches as an Adjunct Faculty of Strategy and Organisation at the SMU Lee Kong Chian School of Business since January 2017. He teaches effectively in English and Chinese. With over 23 years of experience in education and publishing industry in Asia Andrew Chin’s multi-sector career has enabled him to experience the full spectrum of the education industry in his roles as the Singapore EDB Officer handling the World-Class University portfolio and the Specialist Information and Publishing Industry portfolio Chief Representative and Country Manager (China) at Thomson Learning (Fortune 500 company) and Co-founder and CEO for an early childhood education business. Andrew was a Business Development Director (Universities) at Sodexo Group (Fortune 500 company) for the Asia-Pacific region. After working 10 years in Beijing China Andrew returned to Singapore in 2012 to work closely with the top leadership at Singapore Management University on the SMU China & East Asia Initiatives. Thomas Menkhoff is the Professor of Organisational Behaviour & Human Resources (Education) at the Lee Kong Chian School of Business Singapore Management University (SMU). Two of his recent publications include: (i) Menkhoff Thomas Ning Kan Siew Evers Hans-Dieter and Chay Yue Wah eds. Living in Smart Cities: Innovation and Sustainability (New Jersey: World Scientific Publishing 2018) and (ii) Chay Yue Wah Menkhoff Thomas and Low Linda eds. China’s Belt and Road Initiative — Understanding the Dynamics of a Global Transfor

In this paper, we explain how an experiential learning course and study tour to Gansu Province (People’s Republic of China) enabled undergraduates at the Singapore Management University (SMU) to acquire 21st-century competencies and higher-order thinking skills by analyzing and evaluating specific aspects of China’s Belt and Road Initiative (BRI) and China–Singapore (Chongqing) Connectivity Initiative — New International Land–Sea Trade Corridor (CCI-ILSTC) with emphasis on developing viable Go-To-Market (GTM) strategies aimed at selling Gansu produce in four Southeast Asian markets. We share how the course was designed to support the attainment of key learning goals and discuss how we turned pedagogical aspirations into concrete learning outcomes. We introduce key aspects of the so-called “SMU-XO” project that the students conducted in partnership with an industry partner, Pacific International Lines (PIL), and discuss how the project work helped learners to gain global competency by (i) examining critical issues related to BRI such as multi-modal infrastructure connectivity, (ii) appreciating the local perspectives of project stakeholders in Lanzhou and Shanghai and (iii) successfully interacting with people from different cultures, namely China, Indonesia, Vietnam, Malaysia and Thailand.

关键词： Experiential learning Singapore China Belt and Road Initiative (BRI)

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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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Erratum to: A survey of best practices for RNA-seq data analysis

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Genome biology 2016年第1期17卷 181页

作者： Ana Conesa Pedro Madrigal Sonia Tarazona David Gomez-Cabrero Alejandra Cervera Andrew McPherson Michal Wojciech Szcześniak Daniel J Gaffney Laura L Elo Xuegong Zhang Ali Mortazavi Institute for Food and Agricultural Sciences Department of Microbiology and Cell Science University of Florida Gainesville FL 32603 USA. aconesa@ufl.edu. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. aconesa@ufl.edu. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. pm12@sanger.ac.uk. Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute Anne McLaren Laboratory for Regenerative Medicine Department of Surgery University of Cambridge Cambridge CB2 0SZ UK. pm12@sanger.ac.uk. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. Department of Applied Statistics Operations Research and Quality Universidad Politécnica de Valencia 46020 Valencia Spain. Unit of Computational Medicine Department of Medicine Karolinska Institutet Karolinska University Hospital 171 77 Stockholm Sweden. Center for Molecular Medicine Karolinska Institutet 17177 Stockholm Sweden. Unit of Clinical Epidemiology Department of Medicine Karolinska University Hospital L8 17176 Stockholm Sweden. Science for Life Laboratory 17121 Solna Sweden. Systems Biology Laboratory Institute of Biomedicine and Genome-Scale Biology Research Program University of Helsinki 00014 Helsinki Finland. School of Computing Science Simon Fraser University Burnaby V5A 1S6BC Canada. Department of Bioinformatics Institute of Molecular Biology and Biotechnology Adam Mickiewicz University in Poznań 61-614 Poznań Poland. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. Turku Centre for Biotechnology University of Turku and Åbo Akademi University FI-20520 Turku Finland. Key Lab of Bioinformatics/Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China. School of Life Sciences Tsinghua University Beijing 100084 China. Department of Developmental and Cell Biolo

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