A genome-wide association study (GWAS) was performed on a set of 260 lines which belong to three different bi-parental flax mapping populations. These lines were sequenced to an averaged genome coverage of 19× using the Illumina Hi-Seq platform. Phenotypic data for 11 seed yield and oil quality traits were collected in eight year/location environments. A total of 17,288 single nucleotide polymorphisms were identified, which explained more than 80% of the phenotypic variation for days to maturity (DTM), iodine value (IOD), palmitic (PAL), stearic, linoleic (LIO) and linolenic (LIN) acid contents. Twenty-three unique genomic regions associated with 33 quantitative trait loci (QTL) for the studied traits were detected, thereby validating four genomic regions previously identified. The 33 QTL explained 48–73% of the phenotypic variation for oil content, IOD, PAL, LIO and LIN but only 8–14% for plant height, DTM and seed yield. A genome-wide selective sweep scan for selection signatures detected 114 genomic regions that accounted for 7.82% of the flax pseudomolecule and overlapped with the 11 GWAS-detected genomic regions associated with 18 QTL for 11 traits. The results demonstrate the utility of GWAS combined with selection signatures for dissection of the genetic structure of traits and for pinpointing genomic regions for breeding improvement.

Waterlogging is a major abiotic stress causing oxygen depletion and carbon dioxide accumulation in the rhizosphere. Barley is more susceptible to waterlogging stress than other cereals. To gain a better understanding, the genome-wide gene expression responses in roots of waterlogged barley seedlings of Yerong and Deder2 were analyzed by RNA-Sequencing. A total of 6736, 5482, and 4538 differentially expressed genes (DEGs) were identified in waterlogged roots of Yerong at 72 h and Deder2 at 72 and 120 h, respectively, compared with the non-waterlogged control. Gene Ontology (GO) enrichment analyses showed that the most significant changes in GO terms, resulted from these DEGs observed under waterlogging stress, were related to primary and secondary metabolism, regulation, and oxygen carrier activity. In addition, more than 297 transcription factors, including members of MYB, AP2/EREBP, NAC, WRKY, bHLH, bZIP, and G2-like families, were identified as waterlogging responsive. Tentative important contributors to waterlogging tolerance in Deder2 might be the highest up-regulated DEGs: Trichome birefringence, α/β-Hydrolases, Xylanase inhibitor, MATE efflux, serine carboxypeptidase, and SAUR-like auxin-responsive protein. The study provides insights into the molecular mechanisms underlying the response to waterlogging in barley, which will be of benefit for future studies of molecular responses to waterlogging and will greatly assist barley genetic research and breeding.

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

This paper examines a novel method of regenerating saturated activated carbon after adsorption of complex phenolic, polycyclic aromatic hydrocarbons with low energy consumption by using superheated water pretreatment combined with CO₂ activation. The effects of the temperature of the superheated water, liquid–solid ratio, soaking time, activation temperature, activation time, and CO₂ flow rate of regeneration and adsorption of coal-powdered activated carbon (CPAC) were studied. The results show that the adsorption capacity of iodine values on CPAC recovers to 102.25% of the fresh activated carbon, and the recovery rate is 79.8% under optimal experimental conditions. The adsorption model and adsorption kinetics of methylene blue on regenerated activated carbon (RAC) showed that the adsorption process was in accordance with the Langmuir model and the pseudo-second-order kinetics model. Furthermore, the internal diffusion process was the main controlling step. The surface properties, Brunauer–Emmett–Teller (BET) surface area, and pore size distribution were characterized by Fourier transform infrared spectroscopy (FT-IR) and BET, which show that the RAC possesses more oxygen-containing functional groups with a specific surface area of 763.39 m² g⁻¹ and a total pore volume of 0.3039 cm³ g⁻¹. Micropores account for 79.8% and mesopores account for 20.2%.

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality, and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134, and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.