Integration of end-of-life options as a design criterion in methods and tools for ecodesign

Ecodesigning a product consists (amongst other things) in assessing what its environmental impacts will be throughout its life (that is to say from its design phase to its end of life), in order to limit them. Some tools and methods exist to (eco)design a product, just like methods that assess its environmental impacts (more often, a posteriori). But it is now well accepted that these are the early design decisions that will initiate the greatest consequences on the product’s end-of-life options and their impacts. Thus, the present work aims at analysing traditional design tools, so as to integrate end-of-life possibilities in the form of recommendations for the design step. This proposal will be illustrated by means of a wind turbine design.EcoSD networ

Structure determination of Split-soret Cytochrome from a Desulfovibrio species isolated from a human abdominal abcess

The determined structure of the split-soret cytochrome (SSC) isolated from Desulfovibrio desulfuricans ATCC 27774 (D.d.) revealed a new Heme arrangement, which suggests that this protein constitutes a new cytochrome class.. SSC is a 52.6kDa homodimer containing four hemes at one end of the molecule. In each monomer the two hemes have their edges overlapped within van der Waals contacts. The polypeptide chain of each monomer supplies the sixth ligand to the heme-iron of the other monomer. A similar protein was recently purified from a homologous Desulfovibrio clinical strain isolated from an abdominal wall abscess in human patient2. Crystals of this SSC were grown using vapour diffusion method in the presence of agarose gel. Diffraction data were collected using X-ray synchrotron radiation at the ESRF, beamline, ID 14-1. The structure will be solved by molecular replacement using the structure of the D.d. as a starting model

Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions

Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs

L'entretien de recherche avec des journalistes Editorial

International audienceThis is true of our first issue on interviews, a methodology which cuts across the fields of human and social sciences. We invite you to read it and consider what this particular research approach yields from real people in different disciplinary—and cultural—contexts

L'entretien de recherche avec des journalistes Propos introductifs

International audienceL a mobilisation des entretiens dans les recherches sur le journalisme est une pratique courante, rapidement évoquée dans les écrits des chercheurs, comme si l'entre-tien était un outil transparent, sur lequel il ne semble pas, ou plus, nécessaire de s'arrêter. L'essentiel semble de produire puis de restituer un matériau, des données qui doivent faire levier dans des processus de dé-monstration et de dévoilement de réels médiatiques contrastés. L'analyse des spécificités des entretiens avec des professionnels des médias nous a semblé manquer aux recherches menées dans différentes disciplines ayant pour objet commun le journalisme. C'est ce manque et ce « creux épistémologique et méthodologique » que nous avons voulu interroger et chercher à combler. Le dossier est né d'une journée d'étude sur la méthodologie de recherche en journalisme, tenue à l'Université de Brasilia le 28 avril 2011. Intitulée L'entretien de recherche avec des journalistes : miroir, fiction et transferts ? cette rencontre clôturait un colloque international portant sur les mutations structurelles du journalisme (Actes du colloque, 2011). Elle entendait confronter des chercheurs de plusieurs disciplines issus de trois territoires (France, Canada, Brésil), spécialistes de la méthodologie de l'entretien, aux publics du colloque, experts pour leur part des études sur le journalisme. Ces travaux ont ensuite été complétés par un appel à proposition auprès de chercheurs de diverses disciplines

Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations

Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics.We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices.We further suggest possible development directions to enable such models to better project future fire activityinfo:eu-repo/semantics/publishedVersio

Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the output of the SE block is fed directly into subsequent bottleneck layers to reduce the number of channels. Finally, the calibrated 2D slices are concatenated to obtain multimodal features through a 2D convolutional neural network (CNN) for prediction of the central pixel. In our architecture, both local inter-slice and global intra-slice features are jointly exploited to predict class label of the central voxel in a given patch through the 2D CNN classifier. We implicitly apply all modalities through trainable parameters to assign weights to the contributions of each sequence for segmentation. Experimental results on the segmentation of brain tumors in multimodal MRI scans (BraTS'19) demonstrate that our proposed method can efficiently segment the tumor regions