Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.
A large genetic variation, moderately high heritability, and promising prediction ability for genomic selection show that wheat breeding can substantially reduce the acrylamide forming potential in bread wheat by a reduction in its precursor asparagine. Acrylamide is a potentially carcinogenic substance that is formed in baked products of wheat via the Maillard reaction from carbonyl sources and asparagine. In bread, the acrylamide content increases almost linearly with the asparagine content of the wheat grains. Our objective was, therefore, to investigate the potential of wheat breeding to contribute to a reduction in acrylamide by decreasing the asparagine content in wheat grains. To this end, we evaluated 149 wheat varieties from Central Europe at three locations for asparagine content, as well as for sulfur content, and five important quality traits regularly assessed in bread wheat breeding. The mean asparagine content ranged from 143.25 to 392.75 mg/kg for the different wheat varieties, thus underlining the possibility to reduce the acrylamide content of baked wheat products considerably by selecting appropriate varieties. Furthermore, a moderately high heritability of 0.65 and no negative correlations with quality traits like protein content, sedimentation volume and falling number show that breeding of quality wheat with low asparagine content is feasible. Genome-wide association mapping identified few QTL for asparagine content, the largest explaining 18% of the genotypic variance. Combining these QTL with a genome-wide prediction approach yielded a mean cross-validated prediction ability of 0.62. As we observed a high genotype-by-environment interaction for asparagine content, we recommend the costly and slow laboratory analysis only for late breeding generations, while selection in early generations could be based on marker-assisted or genomic selection.
In robotic applications gridmaps are a common representation of the environment. For the automotive field, radar as sensing technology is suitable due to its robustness. This paper presents two radar-based grid-mapping algorithms for automotive applications like self-localization. These algorithms involve first an amplitude-based approach, which gains information about the RCS of all targets, and second an occupancy grid-mapping approach with an adapted inverse sensor measurement model. Experiments show that both gridmapping algorithms result in adequate representations of the environment.
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