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Material extrusion additive manufacturing enables us to combine more materials in the same nozzle during the deposition process. This technology, called material coextrusion, generates an expanded range of material properties, which can gradually change in the design domain, ensuring blending or higher bonding/interlocking among the different materials. To exploit the opportunities offered by these technologies, it is necessary to know the behavior of the combined materials according to the materials fractions. In this work, two compatible pairs of materials, namely Polylactic Acid (PLA)-Thermoplastic Polyurethane (TPU) and Acrylonitrile Styrene Acrylate (ASA)-TPU, were investigated by changing the material fractions in the coextrusion process. An original model describing the distribution of the materials is proposed. Based on this, the mechanical properties were investigated by analytical and numerical approaches. The analytical model was developed on the simplified assumption that the coextruded materials are a set of rods, whereas the more realistic numerical model is based on homogenization theory, adopting the finite element analysis of a representative volume element. To verify the deposition model, a specific experimental test was developed, and the modeled material deposition was superimposed and qualitatively compared with the actual microscope images regarding the different deposition directions and material fractions. The analytical and numerical models show similar trends, and it can be assumed that the finite element model has a more realistic behavior due to the higher accuracy of the model description. The elastic moduli obtained by the models was verified in experimental tensile tests. The tensile tests show Young's moduli of 3425 MPa for PLA, 1812 MPa for ASA, and 162 MPa for TPU. At the intermediate material fraction, the Young's modulus shows an almost linear trend between PLA and TPU and between ASA and TPU. The ultimate tensile strength values are 63.9 MPa for PLA, 35.7 MPa for ASA, and 63.5 MPa for TPU, whereas at the intermediate material fraction, they assume lower values. In this initial work, the results show a good agreement between models and experiments, providing useful tools for designers and contributing to a new branch in additive manufacturing research.

Rhizoctonia solani , causing Rhizoctonia crown and root rot, is a major risk to sugar beet ( Beta vulgaris L.) cultivation. The development of resistant varieties accelerated by marker-assisted selection is a priority of breeding programs. We report the identification of a single-nucleotide polymorphism (SNP) marker linked to Rhizoctonia resistance using restriction site-associated DNA (RAD) sequencing of two geographically discrete sets of plant materials with different degrees of resistance/susceptibility to enable a wider selection of superior genotypes. The variant calling pipeline utilized SAMtools for variant calling and the resulting raw SNPs from RAD sequencing (15,988 and 22,439 SNPs) were able to explain 13.40% and 25.45% of the phenotypic variation in the two sets of material from different sources of origin, respectively. An association analysis was carried out independently on both the datasets and mutually occurring significant SNPs were filtered depending on their contribution to the phenotype using principal component analysis (PCA) biplots. To provide a ready-to-use marker for the breeding community, a systematic molecular validation of significant SNPs distributed across the genome was undertaken to combine high-resolution melting, Sanger sequencing, and rhAmp SNP genotyping. We report that RsBv1 located on Chromosome 6 (9,000,093 bp) is significantly associated with Rhizoctonia resistance ( p < 0.01) and able to explain 10% of the phenotypic disease variance. The related SNP assay is thus ready for marker-assisted selection in sugar beet breeding for Rhizoctonia resistance.

Sulfur is an essential plant macronutrient, and its adequate supply allows an efficient root storage and sugar extractability in sugar beets ( Beta &nbsp; vulgaris L.). In this study, we investigated the effect of changes in sulfur availability on the endophytic community structure of sugar beets. Plants were hydroponically grown in a complete nutrient solution (S-supplied), a nutrient solution without MgSO 4 (S-deprived), and a nutrient solution without MgSO 4 for six days and resupplied with 100 μM MgSO 4 for 48 h (S-resupplied). The sulfur status was monitored by inductively coupled plasma ICP-OES, and combustion analysis together with the evaluation of microRNA395 as a biomarker for sulfate status. Metabarcoding of the bacterial 16S rRNA gene was carried out in order to determine leaf endophytic community structure. The Shannon diversity index significantly differed ( p < 0.05) between sulfate-supplied and sulfate-deprived seedlings. Validation by Real-Time PCR showed a significant increase ( p < 0.05) of Burkholderia spp. in sulfate-deprived plants as compared to sulfate-supplied ones. The study sheds new light on the effects of nutrient deficiency on the microbiome of sugar beet plants.

Leonardite-based biostimulants are a large class of compounds, including humic acid substances. Foliar application of biostimulants at field level improves plant growth, yield and quality through metabolic changes and stimulation of plant proton pumps. The present study aimed at identifying optimum dosage of BLACKJAK, a humic acid-based substance, which is able to modify genes involved in sugar beet growth. Thirty-three genes belonging to various biochemical pathway categories were tested in leaves of treated sugar beet ( Beta vulgaris L.) samples to assess gene expression profiling in response to BLACKJAK. Seedlings of a diploid and multigerm variety were grown in plastic pots and sprayed with two dilutions of BLACKJAK (dilution 1:500-1.0 mg C L -1 and dilution 1:1000-0.5 mg C L -1 ). Leaf samples were collected after 24, 48, and 72 h treatment with BLACKJAK for each dilution. RNA was extracted and the quantification of gene expression was performed while using an OpenArray platform. Results of analysis of variance demonstrated that, 15 genes out of a total of 33 genes tested with OpenArray qPCR were significantly affected by treatment and exposure time. Analysis for annotation of gene products and pathways revealed that genes belonging to the mitochondrial respiratory pathways, nitrogen and hormone metabolisms, and nutrient uptake were up-regulated in the BLACKJAK treated samples. Among the up-regulated genes, Bv&#95;PHT2;1 and Bv&#95;GLN1 expression exerted a 2-fold change in 1:1000 and 1:500 BLACKJAK concentrations. Overall, the gene expression data in the BLACKJAK treated leaves demonstrated the induction of plant growth-related genes that were contributed almost to amino acid and nitrogen metabolism, plant defense system, and plant growth.

Resistance gene analogs (RGAs) were searched bioinformatically in the sugar beet ( Beta vulgaris L.) genome as potential candidates for improving resistance against different diseases. In the present study, Ion Torrent sequencing technology was used to identify mutations in 21 RGAs. The DNA samples of ninety-six individuals from six sea beets ( Beta vulgaris L. subsp. maritima ) and six sugar beet pollinators (eight individuals each) were used for the discovery of single-nucleotide polymorphisms (SNPs). Target amplicons of about 200 bp in length were designed with the Ion AmpliSeq Designer system in order to cover the DNA sequences of the RGAs. The number of SNPs ranged from 0 in four individuals to 278 in the pollinator R740 (which is resistant to rhizomania infection). Among different groups of beets, cytoplasmic male sterile lines had the highest number of SNPs (132) whereas the lowest number of SNPs belonged to O-types (95). The principal coordinates analysis (PCoA) showed that the polymorphisms inside the gene Bv8&#95;184910&#95;pkon (including the CCCTCC sequence) can effectively differentiate wild from cultivated beets, pointing at a possible mutation associated to rhizomania resistance that originated directly from cultivated beets. This is unlike other resistance sources that are introgressed from wild beets. This gene belongs to the receptor-like kinase (RLK) class of RGAs, and is associated to a hypothetical protein. In conclusion, this first report of using Ion Torrent sequencing technology in beet germplasm suggests that the identified sequence CCCTCC can be used in marker-assisted programs to differentiate wild from domestic beets and to identify other unknown disease resistance genes in beet., Competing Interests: The authors declare no conflict of interest.