Velasquezroed7688

Conclusion This dataset provides an overview on dental aspects in patients with dementia in nursing homes. The accuracy of the assessment of a given dental situation by nursing staff is to be questioned. The results indicated an underdetection of oral illnesses by nurses.Both SH and BHA weedy rice genotypes evolved independently and have distinct genomic composition. Different genetic mechanisms may be responsible for their competitiveness and adaptation to diverse environmental conditions. Two major types of weedy rice are recognized in the USA based on morphology straw-hull (SH) and black-hull awned (BHA) weedy rice. We performed whole-genome resequencing of a SH weedy rice 'PSRR-1', a BHA weedy rice 'BHA1115', and a japonica cultivar 'Cypress' to delineate genome-wide differences and their relevance to genetics and evolution of weedy attributes. The high-quality reads were uniformly distributed with 82-88% genome coverage. The number of genotype-specific SNPs and InDels was highest in Cypress, followed by BHA1115 and PSRR-1. However, more genes were affected in BHA1115 compared with other two genotypes which is evident from the number of high-impact SNPs and InDels. Haplotype analysis of selected genes involved in domestication, adaptation, and agronomic performance not only differentiated SH from BHA weedy rice and supported evolution of weedy rice through de-domestication, but also validated the function of several genes such as qAn-1, qAn-2, Bh4, Rc, SD1, OsLG1, and OsC1. MSDC-0160 manufacturer Several candidate genes were identified for previously reported seed dormancy and seed shattering QTLs. The SH and BHA weedy rice have distinct genomic composition, and the BHA weedy rice likely diverged earlier than SH weedy rice. The accumulation of plant development, reproduction, and defense-related genes in weedy rice possibly helped them to compete, survive, and spread under a wide range of environmental conditions by employing novel and diverse mechanisms. The genomic resources will be useful for both weed management and rice improvement by exploring the molecular basis of key agronomic, adaptive, and domestication attributes.Wheat blast resistance in Caninde#1 is controlled by a major QTL on 2NS/2AS translocation and multiple minor QTL in an additive mode. Wheat blast (WB) is a devastating disease in South America, and it recently also emerged in Bangladesh. Host resistance to WB has relied heavily on the 2NS/2AS translocation, but the responsible QTL has not been mapped and its phenotypic effects in different environments have not been reported. In the current study, a recombinant inbred line population with 298 progenies was generated, with the female and male parents being Caninde#1 (with 2NS) and Alondra (without 2NS), respectively. Phenotyping was carried out in two locations in Bolivia, namely Quirusillas and Okinawa, and one location in Bangladesh, Jashore, with two sowing dates in each of the two cropping seasons in each location, during the years 2017-2019. Genotyping was performed with the DArTseq® technology along with five previously reported STS markers in the 2NS region. QTL mapping identified a major and consistent QTL on 2NS/2AS region, explaining between 22.4 and 50.1% of the phenotypic variation in different environments. Additional QTL were detected on chromosomes 1AS, 2BL, 3AL, 4BS, 4DL and 7BS, all additive to the 2NS QTL and showing phenotypic effects less than 10%. Two codominant STS markers, WGGB156 and WGGB159, were linked proximally to the 2NS/2AS QTL with a genetic distance of 0.9 cM, being potentially useful in marker-assisted selection.This study dissected and validated a QTL cluster associated with thousand grain weight on chromosome 4B using multiple near-isogenic lines in common wheat. Grain size and weight are crucial components of wheat yield. Previously, we identified a QTL cluster for thousand grain weight (TGW) on chromosome 4B using the Nongda3338 (ND3338)/Jingdong6 (JD6) doubled haploid population. Here, near-isogenic lines (NILs) in the ND3338 background were developed to dissect and validate the QTL cluster. Based on six independent BC3F34 heterogeneous inbred families, the 4B QTL cluster was divided into two linked QTL intervals (designated 4B.1 and 4B.2 QTL). For the 4B.1 QTL, the Rht-B1 gene, of which Rht-B1b allele reduces plant height (PH) by 21.18-29.34 cm (34.34-53.71%), was demonstrated to be the most likely candidate gene with pleiotropic effects on grain size and TGW. For the 4B.2 QTL, the NILJD6 consistently showed an increase in TGW of 3.51-7.68 g (8.84-22.77%) compared with NILND3338 across different field trials, along with a significant increase in PH of 2.26-6.71 cm (3.92-12.01%). Moreover, both QTL intervals had a larger effect on grain width than on grain length. Additionally, the first significant difference in 100-grain fresh weight and 100-grain dry weight between the NIL pairs of the 4B.1 QTL interval (Rht-B1) was observed at 6 days after pollination (DAP), while the differences were first visible at 30 DAP for the 4B.2 QTL interval. Collectively, our work provides a new example of QTL dissection for grain weight in wheat and lays a foundation for further map-based cloning of the major QTL that have potential applications in wheat molecular breeding for high yield.Genomic selection using data from an on-going breeding program can improve gain from selection, relative to phenotypic selection, by significantly increasing the number of lines that can be evaluated. The early stages of phenotyping involve few observations and can be quite inaccurate. Genomic selection (GS) could improve selection accuracy and alter resource allocation. Our objectives were (1) to compare the prediction accuracy of GS and phenotyping in stage-1 and stage-2 field evaluations and (2) to assess the value of stage-1 phenotyping for advancing lines to stage-2 testing. We built training populations from 1769 wheat breeding lines that were genotyped and phenotyped for yield, test weight, Fusarium head blight resistance, heading date, and height. The lines were in cohorts, and analyses were done by cohort. Phenotypes or GS estimated breeding values were used to determine the trait value of stage-1 lines, and these values were correlated with their phenotypes from stage-2 trials. This was repeated for stage-2 to stage-3 trials.