A microsatellite marker for yellow rust resistance in wheat

Bulk segregant analysis (BSA) was used to identify molecular markers associated with yellow rust disease resistance in wheat (Triticum aestivum L.). DNAs isolated from the selected yellow rust tolerant and susceptible F-2 individuals derived from a cross between yellow rust resistant and susceptible wheat genotypes were used to established a "tolerant" and a "susceptible" DNA pool. The BSA was then performed on these DNA pools using 230 markers that were previously mapped onto the individual wheat chromosomes. One of the SSR markers (Xgwm382) located on chromosome group 2 (A, B, D genomes) was present in the resistant parent and the resistant bulk but not in the susceptible parent and the susceptible bulk, suggesting that this marker is linked to a yellow rust resistance gene. The presence of Xgwm382 was also tested in 108 additional wheat genotypes differing in yellow rust resistance. This analysis showed that 81% of the wheat genotypes known to be yellow rust resistant had the Xgwm382 marker, further suggesting that the presence of this marker correlates with yellow rust resistance in diverse wheat germplasm. Therefore, Xgwm382 could be useful for marker assisted selection of yellow rust resistances genotypes in wheat breeding programs

Exploiting a wheat EST database to assess genetic diversity

Expressed sequence tag (EST) markers have been used to assess variety and genetic diversity in wheat (Triticum aestivum). In this study, 1549 ESTs from wheat infested with yellow rust were used to examine the genetic diversity of six susceptible and resistant wheat cultivars. The aim of using these cultivars was to improve the competitiveness of public wheat breeding programs through the intensive use of modern, particularly marker-assisted, selection technologies. The F2 individuals derived from cultivar crosses were screened for resistance to yellow rust at the seedling stage in greenhouses and adult stage in the field to identify DNA markers genetically linked to resistance. Five hundred and sixty ESTs were assembled into 136 contigs and 989 singletons. BlastX search results showed that 39 (29%) contigs and 96 (10%) singletons were homologous to wheat genes. The database-matched contigs and singletons were assigned to eight functional groups related to protein synthesis, photosynthesis, metabolism and energy, stress proteins, transporter proteins, protein breakdown and recycling, cell growth and division and reactive oxygen scavengers. PCR analyses with primers based on the contigs and singletons showed that the most polymorphic functional categories were photosynthesis (contigs) and metabolism and energy (singletons). EST analysis revealed considerable genetic variability among the Turkish wheat cultivars resistant and susceptible to yellow rust disease and allowed calculation of the mean genetic distance between cultivars, with the greatest similarity (0.725) being between Harmankaya99 and Sönmez2001, and the lowest (0.622) between Aytin98 and Izgi01

Assessment of genetic diversity of wheat genotypes by resistance gene analog-EST markers

Resistance gene analog-expressed sequence tag (RGA-EST)-based markers have been used for variety discrimination and studies of genetic diversity in wheat. Our aim is to increase the competitiveness of public wheat breeding programs through intensive use of modern selection technologies, mainly marker-assisted selection. The genetic diversity of 77 wheat nucleotide binding site (NBS)-containing RGA-ESTs was assessed. Resistant and susceptible bread wheat (Triticum aestivum) genotypes were used as sources of DNA for PCR amplifications. In our previous studies, the F(2) individuals derived from the combinations PI178383 x Harmankaya99, Izgi2001 x ES14, and Sonmez2001 x Aytin98 were evaluated for yellow rust resistance at both seedling and adult stages to identify DNA markers. We have now examined the genetic variability among the resistant and susceptible Turkish wheat cultivars for yellow rust disease and the mean genetic distance between the cultivars. The highest similarity was 0.500 between Harmankaya99 and Sonmez2001. The lowest similarity was 0.286 between Aytin98, PI178383 and Aytin98, ES14. A relatively high level (49.5%) of polymorphism was observed with 77 RGA-EST primers across the six wheat genotypes, despite the fact that all of them were local cultivars from geographically close locations. RGA-EST sequences were compared by BlastX algorithms for amino acid sequences to determine the polymorphic categories among the combinations. BlastX analyses of six RGA-ESTs that gave polymorphic patterns for all combinations were NBS-LRR class RGA, NB-ARC domain containing protein, NBS-type resistance protein RGC5, NBS-LRR-S/TPK stem rust resistance protein, and putative MLA1 proteins, while 38 RGA-EST gave a monomorphic pattern

Prediction of lateral effective stresses in sand using artificial neural network

Predicting the lateral effective stress and the coefficient of lateral earth pressure at rest values is an important task in geotechnical engineering since it is used in the design and analysis of earth retaining structures, slope stability, piles and pier foundations. It needs sophisticated test procedures. The laboratory and in situ tests are also expensive and time consuming. In this study, an artificial neural network model is developed to predict the ?h´, lateral effective stress in co-hesionless soils. Back propagation neural networks are used for function approximation and model has been trained by Levenberg-Marqurdt (LM) learning algorithm. The data used in the running of network models have been obtained from extensive series of oedometer tests on Kilyos, Ayvalik and Yalikoy sands. Tests were carried out on loose, medium dense and dense state of compactness in normal loading, unloading and reloading conditions. The test results demonstrate that there is a linear relationship between vertical and lateral stresses for normally loaded cohesionless soils under K0 conditions. K0 values obtained for the loose state of compactness are higher than for the dense state of compactness. The results of the artificial neural network model indicate that the model serves as simple and reliable tool to predict ? h´ and also K0 in cohesionless soils. The variation of K0 values with internal friction angles is obtained and a simple expression is derived from this relationship

IoT based smart office application for advanced indoor working environment and energy efficiency [Gelişmiş Çalişma Ortami ve Enerji Verimliliǧi için Nesnelerin İnterneti Bazli Akilli Ofis Uygulamasi]

Internet of Things is a complex network, consisting of many elements that communicate with each other constantly. The network includes various modules, sensors, and computers etc. which constantly share data with each other, and carry out independent actions. The system may include interfaces connecting the users to the system, such as mobile apps and websites. In our vision, a 'Smart Office' is an office which knows or can determine office users' needs, and acts according to this knowledge. Our main goal is to design an office which will make independent decisions to maintain optimal office environment. These decisions will be made according to manually set user preferences and sensor readings. With our smart office system, we are aiming to provide flexible and energy efficient working environment to the users. © 2017 IEEE