Evaluation des projets de télématique routière : contribution des programmes européens "DRIVE" et "4ème PCRD" à la réflexion méthodologique

Ce document a été rédigé par Eric Mouline, du CETE de l’Est, avec des contributions des membres du groupe de suivi CERTU-SETRA. Il s’attache dans un premier temps à définir ce que recouvre le terme évaluation, à préciser quelles sont les différentes « catégories » d’évaluation que l’on peut être amené à conduire, et enfin quelles sont les différentes étapes que l’on rencontre dans tout travail d’évaluation. La suite du document présente les différents apports méthodologiques notables à travers les différents programmes de recherche européens. La méthode d’évaluation socio-économique élaborée lors du projet EVA, très utile quant à la définition de critères d’évaluation « universels » ainsi que la méthode des « 12 ETAPES », ensemble de recommandations facilitant les évaluations nécessitant des essais de terrain, issue du projet FIELD TRIALS, deux projets du programme DRIVE I sont tout d’abord exposées. Le rapport se consacre ensuite à l’étude des travaux menés lors du programme DRIVE II et présente les résultats du projet CORD, qui a élaboré un certain nombre de recommandations adaptées à chacun des thèmes explorés par les projets DRIVE II, ainsi que du projet HOPES, proposant des recommandations utiles à l’appréciation de l’impact des projets en termes de sécurité. Enfin, l’actuel programme cadre de Recherche et Développement est à son tour étudié, et les apports du projet CONVERGE, notamment le guide conçu en 1996 destiné à aider les projets dans leurs tâches d’évaluation, ainsi que de l’étude APAS/ROAD 3 consacrée par la Direction Générale des Transports (DGVII) à l’évaluation sont présentés

Evaluation des projets de télématique routière : contribution des programmes européens "DRIVE" et "4ème PCRD" à la réflexion méthodologique

Ce document a été rédigé par Eric Mouline, du CETE de l’Est, avec des contributions des membres du groupe de suivi CERTU-SETRA. Il s’attache dans un premier temps à définir ce que recouvre le terme évaluation, à préciser quelles sont les différentes « catégories » d’évaluation que l’on peut être amené à conduire, et enfin quelles sont les différentes étapes que l’on rencontre dans tout travail d’évaluation. La suite du document présente les différents apports méthodologiques notables à travers les différents programmes de recherche européens. La méthode d’évaluation socio-économique élaborée lors du projet EVA, très utile quant à la définition de critères d’évaluation « universels » ainsi que la méthode des « 12 ETAPES », ensemble de recommandations facilitant les évaluations nécessitant des essais de terrain, issue du projet FIELD TRIALS, deux projets du programme DRIVE I sont tout d’abord exposées.Le rapport se consacre ensuite à l’étude des travaux menés lors du programme DRIVE II et présente les résultats du projet CORD, qui a élaboré un certain nombre de recommandations adaptées à chacun des thèmes explorés par les projets DRIVE II, ainsi que du projet HOPES, proposant des recommandations utiles à l’appréciation de l’impact des projets en termes de sécurité.Enfin, l’actuel programme cadre de Recherche et Développement est à son tour étudié, et les apports du projet CONVERGE, notamment le guide conçu en 1996 destiné à aider les projets dans leurs tâches d’évaluation, ainsi que de l’étude APAS/ROAD 3 consacrée par la Direction Générale des Transports (DGVII) à l’évaluation sont présentés

The 'PUCE CAFE' Project: the First 15K Coffee Microarray, a New Tool for Discovering Candidate Genes correlated to Agronomic and Quality Traits

Background: Understanding the genetic elements that contribute to key aspects of coffee biology will have an impact on future agronomical improvements for this economically important tree. During the past years, EST collections were generated in Coffee, opening the possibility to create new tools for functional genomics. Results: The "PUCE CAFE" Project, organized by the scientific consortium NESTLE/IRD/CIRAD, has developed an oligo-based microarray using 15,721 unigenes derived from published coffee EST sequences mostly obtained from different stages of fruit development and leaves in Coffea Canephora (Robusta). Hybridizations for two independent experiments served to compare global gene expression profiles in three types of tissue matter (mature beans, leaves and flowers) in C. canephora as well as in the leaves of three different coffee species (C. canephora, C. eugenoides and C. arabica). Microarray construction, statistical analyses and validation by Q-PCR analysis are presented in this study. Conclusion: We have generated the first 15 K coffee array during this PUCE CAFE project, granted by Genoplante (the French consortium for plant genomics). This new tool will help study functional genomics in a wide range of experiments on various plant tissues, such as analyzing bean maturation or resistance to pathogens or drought. Furthermore, the use of this array has proven to be valid in different coffee species (diploid or tetraploid), drastically enlarging its impact for high-throughput gene expression in the community of coffee research

Organization and molecular evolution of a disease-resistance gene cluster in coffee trees

<p>Abstract</p> <p>Background</p> <p>Most disease-resistance (R) genes in plants encode NBS-LRR proteins and belong to one of the largest and most variable gene families among plant genomes. However, the specific evolutionary routes of NBS-LRR encoding genes remain elusive. Recently in coffee tree (<it>Coffea arabica</it>), a region spanning the <it>S</it>_<it>H</it><it>3 </it>locus that confers resistance to coffee leaf rust, one of the most serious coffee diseases, was identified and characterized. Using comparative sequence analysis, the purpose of the present study was to gain insight into the genomic organization and evolution of the <it>S</it>_<it>H</it><it>3 </it>locus.</p> <p>Results</p> <p>Sequence analysis of the <it>S</it>_<it>H</it><it>3 </it>region in three coffee genomes, E^aand C^asubgenomes from the allotetraploid <it>C. arabica </it>and C^cgenome from the diploid <it>C. canephora</it>, revealed the presence of 5, 3 and 4 R genes in E^a, C^a, and C^cgenomes, respectively. All these R-gene sequences appeared to be members of a CC-NBS-LRR (CNL) gene family that was only found at the <it>S</it>_<it>H</it><it>3 </it>locus in <it>C. arabica</it>. Furthermore, while homologs were found in several dicot species, comparative genomic analysis failed to find any CNL R-gene in the orthologous regions of other eudicot species. The orthology relationship among the <it>S</it>_<it>H</it><it>3</it>-CNL copies in the three analyzed genomes was determined and the duplication/deletion events that shaped the <it>S</it>_<it>H</it><it>3 </it>locus were traced back. Gene conversion events were detected between paralogs in all three genomes and also between the two sub-genomes of <it>C. arabica</it>. Significant positive selection was detected in the solvent-exposed residues of the <it>S</it>_<it>H</it><it>3</it>-CNL copies.</p> <p>Conclusion</p> <p>The ancestral <it>S</it>_<it>H</it><it>3</it>-CNL copy was inserted in the <it>S</it>_<it>H</it><it>3 </it>locus after the divergence between Solanales and Rubiales lineages. Moreover, the origin of most of the <it>S</it>_<it>H</it><it>3</it>-CNL copies predates the divergence between <it>Coffea </it>species. The <it>S</it>_<it>H</it><it>3</it>-CNL family appeared to evolve following the birth-and-death model, since duplications and deletions were inferred in the evolution of the <it>S</it>_<it>H</it><it>3 </it>locus. Gene conversion between paralog members, inter-subgenome sequence exchanges and positive selection appear to be the major forces acting on the evolution of <it>S</it>_<it>H</it><it>3</it>-CNL in coffee trees.</p

Development of microsatellite markers for identifying Brazilian Coffea arabica varieties

Microsatellite markers, also known as SSRs (Simple Sequence Repeats), have proved to be excellent tools for identifying variety and determining genetic relationships. A set of 127 SSR markers was used to analyze genetic similarity in twenty five Coffea arabica varieties. These were composed of nineteen commercially important Brazilians and six interspecific hybrids of Coffea arabica, Coffea canephora and Coffealiberica. The set used comprised 52 newly developed SSR markers derived from microsatellite enriched libraries, 56 designed on the basis of coffee SSR sequences available from public databases, 6 already published, and 13 universal chloroplast microsatellite markers. Only 22 were polymorphic, these detecting 2-7 alleles per marker, an average of 2.5. Based on the banding patterns generated by polymorphic SSR loci, the set of twenty-five coffee varieties were clustered into two main groups, one composed of only Brazilian varieties, and the other of interspecific hybrids, with a few Brazilians. Color mutants could not be separated. Clustering was in accordance with material genealogy thereby revealing high similarity

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests

The coffee genome provides insight into the convergent evolution of caffeine biosynthesis.

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