Το λογισμικό SpatialAnalyzer & εφαρμογές του σε προβλήματα βιομηχανικής γεωδαισίας

This paper reports a first study exploring genomic prediction for adaptation of sorghum [Sorghum bicolor (L.) Moench] to drought-stress (D-ET) and nonstress (W-ET) environment types. The objective was to evaluate the impact of both modeling genotype × environment interaction (G×E) and accounting for heterogeneous variances of marker effects on genomic prediction of parental breeding values for grain yield within and across environment types (ETs). For this aim, different genetic covariance structures and different weights for individual markers were investigated in best linear unbiased prediction (BLUP)-based prediction models. The BLUP models used a kinship matrix combining pedigree and genomic information, termed K-BLUP. The dataset comprised testcross yield performances under D-ET and W-ET as well as pedigree and genomic data. In general, modeling G×E increased predictive ability and reduced empirical bias of genomic predictions for broad adaptation across both ETs vs. models that ignored G×E by fitting a main genetic effect only. Genomic predictions for specific adaptation to D-ET or W-ET were also improved by K-BLUP models that explicitly accommodated G×E and used data from both ETs relative to prediction models that used data from the targeted ET exclusively or models that used all the data but assumed no G×E. Allowing for heterogeneous marker variances through weighted K-BLUP produced clear increments (43–72%) in predictive ability of genomic prediction for grain yield in all adaptation scenarios. We conclude that G×E as well as locus-specific genetic variances should be accommodated in genomic prediction models to improve adaptability of sorghum to variable environmental conditions

In Situ Compatibilization of Biopolymer Ternary Blends by Reactive Extrusion with Low-Functionality Epoxy-Based Styrene Acrylic Oligomer

[EN] The present study reports on the use of low-functionality epoxy-based styrene¿acrylic oligomer (ESAO) to compatibilize immiscible ternary blends made of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), polylactide (PLA), and poly(butylene adipate-co-terephthalate) (PBAT). The addition during melt processing of low-functionality ESAO at two parts per hundred resin (phr) of biopolymer successfully changed the soften inclusion phase in the blend system to a thinner morphology, yielding biopolymer ternary blends with higher mechanical ductility and also improved oxygen barrier performance. The compatibilization achieved was ascribed to the in situ formation of a newly block terpolymer, i.e. PHBVb- PLA-b-PBAT, which was produced at the blend interface by the reaction of the multiple epoxy groups present in ESAO with the functional terminal groups of the biopolymers. This chemical reaction was mainly linear due to the inherently low functionality of ESAO and the more favorable reactivity of the epoxy groups with the carboxyl groups of the biopolymers, which avoided the formation of highly branched and/or cross-linked structures and thus facilitated the films processability. Therefore, the reactive blending of biopolymers at different mixing ratios with low-functionality ESAO represents a straightforward methodology to prepare sustainable plastics at industrial scale with different physical properties that can be of interest in, for instance, food packaging applications.This research was funded by the EU H2020 project YPACK (Reference number 773872) and by the Spanish Ministry of Science, Innovation, and Universities (MICIU) with project numbers MAT2017-84909-C2-2-R and AGL2015-63855-C2-1-R. L. Quiles-Carrillo wants to thank the Spanish Ministry of Education, Culture, and Sports (MECD) for financial support through his FPU Grant Number FPU15/03812. Torres-Giner also acknowledges the MICIU for his Juan de la Cierva contract (IJCI-2016-29675).Quiles-Carrillo, L.; Montanes, N.; Lagaron, J.; Balart, R.; Torres-Giner, S. (2019). In Situ Compatibilization of Biopolymer Ternary Blends by Reactive Extrusion with Low-Functionality Epoxy-Based Styrene Acrylic Oligomer. 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Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts

Contribution of reactive extrusion to technological and scientific challenges of eco-friendly circular economy

Nowadays, development and production in eco-friendly circular economy are themes of great importance. Concerning optimization of materials production, reactive extrusion (REX) is well established, using extruder as a chemical reactor. Biopolymers and recycling are two interesting fields in which REX can be used. In fact, through REX, biopolymers can be produced from monomers, modified (grafting…) or chemically blended with other polymers. Moreover, chemical reactions can occur between polymers and additives in order to optimize post-consumer polymer recycling. The first part of this chapter will focus on REX modellization, useful to optimize REX parameters, in agreement with eco-friendly circular economy domain. The second part of this chapter will focus on the production process of bio-derived polymers, their blends and composites, as well as recycling of post-consumer polymers