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JD
Jean-Baptiste Durand
Jean-Baptiste Durand - Statistics and stochastic modelling of plant growth
58
Documents
Researcher identifiers
jean-baptiste-durand
-
0000-0001-6800-1438
- IdRef : 073764817
Presentation
### Webpage: [http://amap-collaboratif.cirad.fr/pages-chercheurs/?page\_id=32153](http://amap-collaboratif.cirad.fr/pages-chercheurs/?page_id=32153)
### Short biography / Research topics
Jean-Baptiste Durand received the engineering degree in applied mathematics and computer science from the "Institut National Polytechnique de Grenoble" [Grenoble INP](http://www.grenoble-inp.fr/grenoble-institute-of-technology-9224.kjsp?RH=INPG) (Grenoble Institute of Technology) in 1999. He received the Ph.D. degree in applied mathematics in 2003 from the University of Grenoble I (France), now [Université Grenoble Alpes](https://www.univ-grenoble-alpes.fr).
From 1999 to 2003, he worked on statistical modelling at the French National Institute for Research in Computer Science and Control ([Inria](http://www.inria.fr/en/)), as a member of the [IS2 project](http://www.inrialpes.fr/is2) (Statistical Inference for Health and Industry). Then, he was a Postdoctoral Research Fellow at [AMAP](http://amap.cirad.fr/) "Plant Modelling" mixed research unit, Montpellier (France), with a grant from [CIRAD](http://www.cirad.fr/en).
From 2004 to 2022, he was a teaching assistant at [Grenoble INP](http://www.grenoble-inp.fr/92723626/1/fiche___pagelibre/), member of the [Statify](https://team.inria.fr/statify/) team of [Laboratoire Jean Kuntzmann](http://www-ljk.imag.fr/). He was the head of the statistics and data science tracks of the international Master's programme MSIAM <http://msiam.imag.fr/>.
He taught the fundamentals of statistics and computational statistics at the "École Nationale Supérieure d'Informatique et de Mathématiques Appliquées de Grenoble" [Ensimag](http://ensimag.grenoble-inp.fr/ecole-nationale-superieure-d-informatique-et-de-mathematiques-appliquees-74488.kjsp?RH=ENSIMAG_FR) (engineering school of applied mathematics and computer science).
He was delegated to Inria team [Virtual Plants](http://team.inria.fr/virtualplants/) (Inria Sophia) between September 2009 and August 2011 and to Inria team [Statify](https://team.inria.fr/statify/) between September 2019 and August 2021 .
His research interests include computational methods for hidden Markov models, statistical analysis of tree-structured processes, discrete multivariate distributions and applications to signal processing and botany.
He has returned to [AMAP](http://amap.cirad.fr/) as a [CIRAD](http://www.cirad.fr/en) researcher since 2022 and now aims at developing statistical models and approaches for the analysis of structured data issued from plant phenotyping.

### Related working groups, research teams and pages
- Research teams:
- formerly [Virtual Plants](http://team.inria.fr/virtualplants/) (Inria Sophia), now [MOSAIC](https://www.inria.fr/fr/mosaic) in Lyon, see also their [research program](http://www.ens-lyon.fr/RDP/spip.php?rubrique52).
- CIRAD [PhenoMEn](https://umr-agap.cirad.fr/nos-recherches/equipes-scientifiques/phenotypage-et-modelisation-des-plantes-dans-leur-environnement-agro-climatique/organisation-de-l-equipe) - mainly, the former M2P2 team (Montpellier)
- AGAP [AFEF](https://umr-agap.cirad.fr/nos-recherches/equipes-scientifiques/architecture-et-floraison-des-especes-fruitieres/contexte-et-enjeux) (Montpellier)

### <a name="publis"></a>Publications (See [copyryight notice](http://amap-collaboratif.cirad.fr/pages-chercheurs/?page_id=32360))
#### [Publications on HAL](https://hal.archives-ouvertes.fr/search/index/q/*/authIdHal_s/jean-baptiste-durand)
#### PhD thesis (defended the 31st of january 2003)
### Modèles à structure cachée : inférence, estimation, sélection de modèles et applications (in French)
([Compressed PDF](http://amap-collaboratif.cirad.fr/pages-chercheurs/wp-content/uploads/JDURAND_these.pdf.gz))
#### [Habilitation](https://en.wikipedia.org/wiki/Habilitation) thesis (defended the 19th of October 2020)
### Contributions to hidden Markov models and applications to plant structure analysis
([Compressed PDF](http://amap-collaboratif.cirad.fr/pages-chercheurs/wp-content/uploads/JDURAND_habilitation.pdf.gz))
#### Book
- [Data Science. Cours et exercices.](https://www.eyrolles.com/Informatique/Livre/data-science-9782212674101) M.-R. Amini, R. Blanch, M. Clausel, J.-B. Durand, E. Gaussier, J. Malick, C. Picard, V. Quéma et G. Quénot. Eyrolles (Éd.), 2018.

### <a name="logiciels"></a>Software
[OpenAlea](https://github.com/openalea/) - Author of package [TREE\_STATISTIC](https://github.com/openalea/StructureAnalysis/tree/master/tree_statistic) in [StructureAnalysis](https://github.com/openalea/StructureAnalysis)
<!--
### <a name="phd"></a>PhD Proposal
#### Computational methods for hidden semi-Markov models with mixed effects -- application to plant and root branching patterns
Profile and required skills required: Skills in statistics (modelling, parametric estimation) and if possible, random processes. Taste for applications and programming.
##### Abstract
In the framework of plant development modelling, statistical models can be divided into two categories. The first one, referred to as "genotype x environment", is based on mixed models, which do not account for time dependencies existing in the considered processes. The second category is based on sequence analysis models that are funded on biological models, but currently do not account for genotype or environmental effects.
More specifically, we are here focused on hidden semi-Markov models, introduced about 20 years ago (Guédon *et al.*, 2001) to model dynamical aspect of plant structure development. These models allow modellers to account for different development phases of either plants or their components (branches, roots, etc) through hidden states. The PhD proposal aims at including fixed and random effects within this category of models, the former aiming at characterising the effects of targeted covariates (genotype and environment) ant the latter, to account for constraints related to experimental design. The work to be accomplished, beyond model specification, is to develop inference algorithms suited to the specific complexity of these models .
This PhD proposal opens avenues in two fields. Firstly, the newly developed family of models will be included as basic components within more global models, which is a major concern in model agronomic methods. Moreover, the methodological advances obtained in the hidden semi-Markov framework will enrich this family of models and offer new possibilities for addressing scientific questions in various domains of application (health, seismology, reliability, ecology, etc).
##### Context and aims
The fast development of automatic platforms for plant phenotypic, achieving dynamical measurements of plant characteristics, is now leading to time and spatially dependent data. These data can lead to precious information to guide crop management, provided adequate models are built to analyse them. Most common plant development models are, one the one hand, deterministic approaches based on complex biological models, which hardly account for the observed phenotypic variability. On the other hand, statistical regression models offer possibilities to test genotype and environment effects, but they rely on over-simplistic biological time processes, for example summarized through an unique coefficient. Between both approaches, statistical methods including dynamical aspects of plant development were proposed by Guédon *et al.* (2001), in the framework of hidden Markov or semi-Markov models. These are based on plant representations motivated by biological knowledge (Barthélémy and Caraglio, 2007) and include a hidden variable, which represents plant development phases. However, such models do not account for genotypic and climatic effects..
This PhD proposal consists in extending the work by Guédon *et al.* (2001) by introducing fixed and random effects in the different distribution that define hidden semi-Markov models. Then, the aim is to propose inference algorithm with adequate accounting for model complexity. The latter is due to two levels of latent variables: hidden states corresponding to development phases ( categorical variables) and latent variables associated with random effects (continuous variables).
##### Methods and expected results
Several methods are considered for inference. The first one relies on quadrature methods, whose specification and validity require a detailed study of the functions to be integrated, as in INLA (Rue *et al.*, 2009). The second one is Monte-Carlo approximation. The third one consists in Variational EM approximations (Jordan *et al.*, 1999, with VBEM as a variant in Bayesian analysis), based on an approximation of the log-likelihood with parametric functions being factorised with respect to some random variables.
The work to be achieved includes a comparison of these methods regarding their abilities to accurately estimate parameters and to achieve the best possible compromise between accuracy and computation time. The issue of model selection wit usual criteria will have to be addressed, so as to determine which relevant covariates deserve to be part of the model.
The selected methods for model inference will be applied on one or several available data sets in cereal root architecture, issued from research projects by Bertrand Muller [LEPSE](https://www6.montpellier.inrae.fr/lepse). Other potential applications are in fruit trees, with data issued from various agronomic contexts and projects from the [AFEF team](https://umr-agap.cirad.fr/nos-recherches/equipes-scientifiques/architecture-et-floraison-des-especes-fruitieres/liste-des-agents) (AGAP laboratory, INRAE).
##### Supervision
The PhD will be co-supervised by Jean-Baptiste Durand (computational statistics), [AMAP](https://amap.cirad.fr/) Laboratory, CIRAD, and Bertrand Muller (ecophysiology), [LEPSE](https://www6.montpellier.inrae.fr/lepse). Nathalie Peyrard (computational statistics) at [MIAT research](https://miat.inra.fr/site/SCIDYN(english)) unit by INRAE Toulouse, Sandra Plancade (statistics and modelling) at MIAT and Evelyne Costes (genetics, botany and biostatistics) at AFEF team (AGAP laboratory, INRAE, Montpellier) will also take part to supervision.
##### Location and funding
The PhD will take place at AMAP Laboratory, Cirad, in Montpellier (Agropolis district) https://amap.cirad.fr/. Visits to MIAT, INRAE Toulouse, will be planned: https://miat.inrae.fr/
Moreover, co-supervisers belong to [INCA](https://groupes.renater.fr/wiki/hsmm-inca/public/index) consortium (headed by N. Peyrard), which gathers most statisticians working on hidden semi-Markov models (both from theoretical and applied aspects) in France. Close collaborations, through regular working groups, will offer opportunities to interact with the community in statistics.
Applicants are requested to join their full grade transcripts in third year of Bachelor, first year of Master and first semester, second year of Master (and even second semester if available), together with a resume and motivation letter.
##### Valorisation objectives
The obtained results will lead to publications in computational statistics journals, in agronomy journals, and in journals in mathematical modelling for biology.
The developed algorithm will be included in software packages for the statistical community in hidden semi-Markov models and adapted to the community in plant structure modelling.
The PhD student will have the possibity to present his or her work in national and international workshops.
##### International collaborations
The application to fruit specices (apple tree) is part of a collaboration with Martin Mészáros (Research and Breeding Institute of Pomology in Holovousy - VŠÚO - Czech Republic).
An international workshop on Markovian and semi-Markovian models will be co-organised by INCA consortium in 2024. This will be an opportunity to meet international researchers with related topics and may lead to new collaborations.
##### References
* Barthélémy, D. and Caraglio, Y. Plant Architecture : A Dynamic, Multilevel and Comprehensive Approach to Plant Form, Structure and Ontogeny. *Annals of Botany* **99**(3), 375–407 (2007)
* Guédon, Y., Barthélémy, D., Caraglio, Y. and Costes, E. Pattern Analysis in Branching and Axillary Flowering Sequences. *Journal of theoretical biology*, **212**(4), 481–520 (2001)
* Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K. An Introduction to Variational Methods for Graphical Models. *Machine Learning* **37**, 183–233 (1999)
* Rue, H., Martino, S. and Chopin, N., Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. *Journal of the Royal Statistical Society: Series B (Statistical Methodology*), **71**: 319-392 (2009)
* Yu, S.-Z. Hidden semi-Markov models. *Artificial intelligence*, **174(2)**, 215–243 (2010)
-->
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Analysing the architecture of Corylus avellana and parametrizing L-HAZELNUT FSPMFSPM 2023 - 10th International Conference on Functional-Structural Plant Models, Dr. Susann Müller, Mar 2023, Berlin, Germany. pp.1-2
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Architecture and yield relationship in hazelnut treeXXXI International Horticultural Congress (IHC2022): International Symposium on Innovative Perennial Crops Management, Aug 2022, Angers, France. pp.331-336, ⟨10.17660/ActaHortic.2023.1366.40⟩
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Approximation of trees by self-nested treesALENEX 2019 - Algorithm Engineering and Experiments, Jan 2019, San Diego, United States. pp.39-53, ⟨10.1137/1.9781611975499.4⟩
Conference papers
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Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequencesJdS 2019 - 51èmes Journées de Statistique, Jun 2019, Vandœuvre-lès-Nancy, France. pp.1-7
Conference papers
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Compétitions d’analyse des données à l’Université Grenoble Alpes : motivations, organisation et retours d’expérienceCFIES 2019 - Colloque francophone international sur l'enseignement de la statistique, Sep 2019, Strasbourg, France. pp.1-6
Conference papers
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Genetic determinism of flowering regularity over years in an apple multi-family populationInternational Symposium on Flowering, Fruit Set and Alternate Bearing, Jun 2017, Palermo, Italy. pp.15-22, ⟨10.17660/ActaHortic.2018.1229.3⟩
Conference papers
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Challenges d'analyse de données : une formation par la pratique transversale et multidisciplinaire en science des donnéesCFIES2017 - Colloque Francophone International sur l’Enseignement de la Statistique, Sep 2017, Grenoble, France
Conference papers
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Eye-tracking data analysis using hidden semi-Markovian models to identify and characterize reading strategiesECM 2017 - 19th European Conference on Eye Movements, Aug 2017, Wuppertal, Germany
Conference papers
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Characterization of mango tree patchiness using a tree-segmentation/clustering approach2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA 2016), Nov 2016, Qingdao, China. pp.68-74, ⟨10.1109/FSPMA.2016.7818290⟩
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Impact of Long Term Water Deficit on Production and Flowering Occurrence in the 'Granny Smith' Apple Tree CultivarXI International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems, Prof. Dr. Luca Corelli-Grappadelli, Department of Agricultural Sciences, Università di Bologna, Aug 2016, Bologna, Italy
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Analysis of the Impact of Carbon Source-sink Relationships on Flowering Patterns Reveals That Apple Tree Growth and Functioning are Determined by Mechanisms Occurring at the Tree and Shoot ScalesXI International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems, L. Corelli Grappadelli, Aug 2016, Bologne, Italy. pp.405-412, ⟨10.17660/ActaHortic.2018.1228.60⟩
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Impact of long term water deficit on production and flowering occurrence in the ‘Granny Smith’ apple tree cultivar11. Symposium on Integrating Canopy, Rootstockand Environmental Physiology in Orchard Systems, Aug 2016, Bologna, Italy
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Analyse de séquences oculométriques et d'électroencéphalogrammes par modèles markoviens cachésJdS 2016 - 48èmes Journées de la Statistique, May 2016, Montpellier, France
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Détection de motifs disruptifs au sein de plantes : une approche de quotientement/classification d'arborescences47èmes Journées de Statistique, Société Française de Statistique, Jun 2015, Lille, France
Conference papers
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Estimation of Discrete Partially Directed Acyclic Graphical Models in Multitype Branching ProcessesCOMPSTAT 2014, 21st International Conference on Computational Statistics, The International Association for Statistical Computing (IASC), Aug 2014, Geneva, Switzerland. pp.561-568
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Modèles graphiques paramétriques pour la modélisation des lois de génération dans des processus de branchement multitypes46èmes Journées de Statistique, Jun 2014, Rennes, France. 6 p
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Quantifying and localizing state uncertainty in hidden Markov models using conditional entropy profilesCOMPSTAT 2014 - 21st International Conference on Computational Statistics, The International Association for Statistical Computing (IASC), Aug 2014, Genève, Switzerland. pp.213-221
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Quantification de l'incertitude sur la structure latente dans des modèles de Markov cachés46èmes Journées de Statistique, Société Française de Statistique (SFdS). FRA., Jun 2014, Rennes, France
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Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical ModelsAIGM13 - 3rd Workshop on Algorithmic issues for Inference in Graphical Models, Sep 2013, Paris, France
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Approche graphique pour la modélisation statistique de la dépendance entre activités journalièresWorkshop "Statistique, Transport et Activités", Nov 2013, Grenoble, France
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Deciphering mango tree asynchronisms using Markov tree and probabilistic graphical modelsFSPM2013 - 7th International Workshop on Functional-Structural Plant Models, Jun 2013, Saariselkä, Finland. pp.210-212
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Estimating the genetic value of F1 apple progenies for irregular bearing during first years of productionFSPM2013 - 7th International Workshop on Functional-Structural Plant Models, Jun 2013, Saariselkä, Finland. pp.13-15
Conference papers
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Reconstructing Plant Architecture from 3D Laser scanner data6th International Workshop on Functional-Structural Plant Models, Sep 2010, Davis, United States. pp.12--17
Conference papers
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A statistical model for optimizing power consumption of printersXIG R & T Conference, Xerox Corporation, May 2008, Webster, United States
Conference papers
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A Statistical Model for Optimizing Power Consumption of Printers40èmes Journées de Statistique, Congrès conjoint de la Société Statistique du Canada et de la Société Française de Statistique, May 2008, Ottawa, Canada. pp.227
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Calcul de probabilités et estimation dans des modèles de Markov cachés graphiques39èmes Journées de Statistique, Société Française de Statistique, Jun 2007, Angers, France
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Segmentation-based approaches for characterising plant architecture and assessing its plasticity at different scalesFSPM07 - 5th International Workshop on Functional-Structural Plant Models, Nov 2007, Napier, New Zealand. pp.39:1-3
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Exploring morphogenetical gradients plasticity using hidden Markov tree models in young individuals of the tropical specie Symphonia globulifera (Clusiaceae)FSPM07 - 5th International Workshop on Functional-Structural Plant Models, Nov 2007, Napier, New Zealand. pp.P19:1-1
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OpenAlea: An open-source platform for the integration of heterogeneous FSPM componentsFSPM07 - 5th International Workshop on Functional-Structural Plant Models, Nov 2007, Napier, New Zealand. pp.P36:1-2
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OpenAlea : a platform for plant modelling, analysis and simulationPython Conference Europython, 2006, Genève, Switzerland. 16 p
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Arbres de Markov cachés : inférence et applicationsProceedings of XXXVI èmes Journées de Statistique, May 2004, Montpellier, France
Conference papers
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Analysis of the plant architecture via tree-structured statistical models: the hidden Markov tree models4th International workshop on functional-structural plant models (FSPM), Jun 2004, Montpellier, France. pp.813-825, ⟨10.1111/j.1469-8137.2005.01405.x⟩
Conference papers
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Lossy compression of unordered rooted treesDCC 2016 - Data Compression Conference, Mar 2016, Snowbird, Utah, United States. ⟨10.1109/DCC.2016.73⟩
Conference poster
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Data ScienceEyrolles, 2018, 9782212674101
Books
hal-01978692v1
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Modèles génératifsData Science. Cours et exercices, Eyrolles, 2018, 978-2-212-67410-1
Book sections
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Multinomial distributions for the parametric modeling of multivariate count data2016
Preprints, Working Papers, ...
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Semi-parametric Markov Tree for cell lineage analysis2016
Preprints, Working Papers, ...
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Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models[Research Report] RR-7896, Inria. 2012, pp.43
Reports
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Selecting Hidden Markov Chain States Number with Cross-Validated Likelihood[Research Report] RR-5877, INRIA. 2006
Reports
inria-00071392v1
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Software Reliability Modelling and Prediction with Hidden Markov Chain[Research Report] RR-4747, INRIA. 2003
Reports
inria-00071840v1
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Analyse de courbes de consommation électrique par chaines de Markov cachées[Rapport de recherche] RR-4858, INRIA. 2003
Reports
inria-00071725v1
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Statistical Inference for Hidden Markov Tree Models and Application to Wavelet Trees[Research Report] RR-4248, INRIA. 2001
Reports
inria-00072339v1
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Modèles à structure cachée : inférence, estimation, sélection de modèles et applicationsMathématiques [math]. Université Joseph-Fourier - Grenoble I, 2003. Français. ⟨NNT : ⟩
Theses
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