Background Considering its pandemicity and absence of effective treatment, authorities across the globe have designed various mitigation strategies to combat the spread of COVID-19. Although adherence towards preventive measures is the only means to tackle the virus, reluctance to do so has been reported to be a major problem everywhere. Thus, this study aimed to assess the community’s adherence towards COVID-19 mitigation strategies and its associated factors among Gondar City residents, Northwest Ethiopia. Methods A community-based cross-sectional study was employed among 635 respondents from April 20–27, 2020. Cluster sampling technique was used to select the study participants. Data were collected using an interviewer-administered structured questionnaire. Epi-Data version 4.6 and STATA version 14 were used for data entry and analysis, respectively. Binary logistic regressions (Bivariable and multivariable) were performed to identify statistically significant variables. Adjusted odds ratio with 95% CI was used to declare statistically significant variables on the basis of p < 0.05 in the multivariable logistic regression model. Results The overall prevalence of good adherence towards COVID-19 mitigation measures was 51.04% (95%CI: 47.11, 54.96). Female respondents [AOR: 2.39; 95%CI (1.66, 3.45)], receiving adequate information about COVID-19 [AOR: 1.58; 95%CI (1.03, 2.43)], and favorable attitude towards COVID-19 preventive measures were significantly associated with good adherence towards COVID-19 mitigation measures. Whereas, those respondents who had high risk perception of COVID-19 were less likely to adhere towards COVID-19 mitigation measures [AOR: 0.61; 95% CI (0.41, 0.92)]. Conclusions The findings have indicated that nearly half of the study participants had poor adherence towards COVID-19 mitigation measures. Sex, level of information exposure, attitude towards COVID-19 preventive measures, and risk perception of COVID-19 were factors which significantly influenced the adherence of the community towards COVID-19 mitigation measures. Therefore, it is crucial to track adherence responses towards the COVID-19 preventive measures, scale up the community’s awareness of COVID-19 prevention and mitigation strategies through appropriate information outlets, mainstream media, and rely on updating information from TV, radio, and health care workers about COVID-19.

Two hundred one hexaploid wheat accessions, representing 200 years of selection and breeding history, were sampled from the National Small Grains Collection in Aberdeen, ID, and evaluated for five root traits at the seedling stage. A paper roll-supported hydroponic system was used for seedling growth. Replicated roots samples were analyzed by WinRHIZO. We observed accessions with nearly no branching and accessions with up to 132 cm of branching. Total seminal root length ranged from 70 to 248 cm, a 3.5-fold difference. Next-generation sequencing was used to produce single-nucleotide polymorphism (SNP) markers and genomic libraries that were aligned to the wheat reference genome IWGSCv1 and were called single-nucleotide polymorphism (SNP) markers. After filtering and imputation, a total of 20,881 polymorphic sites were used to perform association mapping in TASSEL. Gene annotations were conducted for identified marker-trait associations (MTAs) with - logP > 3.5 (p value < 0.003). In total, we identified 63 MTAs with seven for seminal axis root length (SAR), 24 for branching (BR), four for total seminal root length (TSR), eight for root dry matter (RDM), and 20 for root diameter (RD). Putative proteins of interest that we identified include chalcone synthase, aquaporin, and chymotrypsin inhibitor for SAR, MYB transcription factor and peroxidase for BR, zinc fingers and amino acid transporters for RDM, and cinnamoyl-CoA reductase for RD. We evaluated the effects of height-reducing Rht alleles and the 1B/1R translocation event on root traits and found presence of the Rht-B1b allele decreased RDM, while presence of the Rht-D1b allele increased TSR and decreased RD.

We report genomic regions that significantly control resistance to scald, net form (NFNB) and spot form net blotch (SFNB) in barley. Barley genotypes from Ethiopia, ICARDA, and the United States were evaluated in Ethiopia and North Dakota State University (NDSU). Genome-wide association studies (GWAS) were conducted using 23,549 single nucleotide polymorphism (SNP) markers for disease resistance in five environments in Ethiopia. For NFNB and SFNB, we assessed seedling resistance in a glasshouse at NDSU. A large proportion of the Ethiopian landraces and breeding genotypes were resistant to scald and NFNB. Most of genotypes resistant to SFNB were from NDSU. We identified 17, 26, 7, and 1 marker-trait associations (MTAs) for field-scored scald, field-scored net blotch, greenhouse-scored NFNB, and greenhouse-scored SFNB diseases, respectively. Using the genome sequence and the existing literature, we compared the MTAs with previously reported loci and genes for these diseases. For leaf scald, only a few of our MTAs overlap with previous reports. However, the MTAs found for field-scored net blotch as well as NFNB and SFNB mostly overlap with previous reports. We scanned the barley genome for identification of candidate genes within 250 kb of the MTAs, resulting in the identification of 307 barley genes for the 51 MTAs. Some of these genes are related to plant defense responses such as subtilisin-like protease, chalcone synthase, lipoxygenase, and defensin-like proteins.

Market changes in the malting and brewing industries have increased the demand for locally produced barley (Hordeum vulgare L.) in many regions across North America. Breeding for productive barley cultivars in diverse growing environments is complicated by genotype × environment interactions (GEIs), which can make selection for broad adaptation diicult but may be exploited to select optimal cultivars for each environment. Genomewide selection has recently become a useful tool to make eicient selections on individuals using genomewide marker data. To support the use of genomewide selection to breed locally adapted barley cultivars, the University of Minnesota barley breeding program is publicly releasing a panel of two-row barley lines, and accompanying data, called the S2MET (Spring Two-Row Multi-Environment Trial) (Reg. No. MP-2, NSL 526938 MAP). The S2MET includes 233 breeding lines grouped into a 183line training population and a 50-line validation population. The entire panel was genotyped using genotyping-bysequencing and phenotyped for 14 important traits in 44 location-year environments between 2015 and 2017. All data are freely available at the Triticeae Toolbox (https:// triticeaetoolbox.org/barley/), and we describe several ontap projects and breeding advances that are exploiting this resource. We believe this panel and dataset will be useful for answering important breeding questions related to genomewide selection and GEIs and developing locally superior barley cultivars.

Background and objectives Field pea is a leading crop for plant‐based protein. Expanded market demand may necessitate targeted breeding and a deeper understanding of the level of variation and the factors affecting pea protein content, composition, and functionality. Findings Pea protein content varies from 13% to 38% and is influenced by environmental and genetic factors. Protein‐regulating genes per se directly and genes involved in starch biosynthesis indirectly play key roles in determining pea protein content and composition. Protein content is often negatively correlated with starch content and seed yield. A thorough analysis of the potential variation in the functional properties of pea protein is needed. Conclusions Since multiple genes and environmental variables influence pea protein, critical designing of a breeding scheme is essential to improve pea for protein isolation. The economics of protein isolation necessitates a high value for the starch‐rich by‐product. Improving protein and starch contents together may be complicated due to a negative association between these two traits. Two strategies to improve pea seed quality may be suggested: improving protein and starch together, or targeting varieties for specific end‐uses. Large‐scale evaluation of the functional properties of protein in broader germplasm and the inclusion of molecular breeding approaches are also relevant. Significance and novelty The variation in pea protein content, composition, and functional properties as well as the contributing environmental and genetic factors are discussed in this study. The review also explores the influence of starch biosynthesis genes in pea protein composition and provides a brief overview of protein fractionation methods. Research needs and breeding strategies are suggested for improving pea protein.

Although kernel weight (KW) is a major component of grain yield, its contribution to yield genetic gain during breeding history has been minimal. This highlights an untapped potential for further increases in yield via improving KW. We investigated variation and genetics of KW and kernel length (KL) via genome-wide association studies (GWAS) using a historical and contemporary soft red winter wheat population representing 200 years of selection and breeding history in the United States. The observed changes of KW and KL over time did not show any conclusive trend. The population showed a structure, which was mainly explained by the time and location of germplasm development. Cluster sharing by germplasm from more than one breeding population was suggestive of episodes of germplasm exchange. Using 2 years of field-based phenotyping, we detected 26 quantitative trait loci (QTL) for KW and 27 QTL for KL with –log10(p) > 3.5. The search for candidate genes near the QTL on the wheat genome version IWGSCv1.0 has resulted in over 500 genes. The predicted functions of several of these genes are related to kernel development, photosynthesis, sucrose and starch synthesis, and assimilate remobilization and transport. We also evaluated the effect of allelic polymorphism of genes previously reported for KW and KL by using Kompetitive Allele Specific PCR (KASP) markers. Only TaGW2 showed significant association with KW. Two genes, i.e., TaSus2-2B and TaGS-D1 showed significant association with KL. Further physiological studies are needed to decipher the involvement of these genes in KW and KL development.

Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important trait to plant breeders and agronomists. Label-free quantitative proteomics was used to analyze yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary, and three successive kernel development stages at five, ten, and fifteen days after anthesis. We identified 3,182 proteins across all samples. The largest number was obtained for spike initiation (1673), while the smallest was kernel sample at fifteen days after anthesis (709). Of the 3,182 proteins, 296 of them were common to all seven organs. Organ-specific proteins ranged from 148 in ovary to 561 in spike initiation. When relative protein abundances were compared to that of leaf sample, 347 and 519 proteins were identified as differentially abundant in tiller initiation and spike initiation, respectively. When compared with ovary, 81, 35, and 96 proteins were identified as differentially abundant in kernels sampled at five, ten, and fifteen days after anthesis, respectively. Our study indicated that two argonaute proteins were solely expressed in spike initiation. Of the four expansin proteins detected, three of them were mainly expressed during the first 10 days of kernel development after anthesis. We also detected cell wall invertases and sucrose and starch synthases mainly during the kernel development period. The manipulation of these proteins could lead to increases in tillers, kernels per spike, or final grain weight, and is worth exploring in future studies.

No abstract