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Meta-analysis is a powerful method used to discern reliable genomic regions associated with quantitative traits. The objective of this study was to use a meta-QTL (MQTL) analysis method to identify the most reliable and accurate QTL controlling morphological traits and terpenoid indole alkaloids (TIAs); serpentine (S), vinblastine (VB), ajmalicine (A) and vincristine (VC) in Catharanthus roseus. A quantitative trait loci (QTL) database containing 40 QTLs retrieved from two individual mapping studies associated with 13 phytochemical and morphological traits. The results of projection of 40 putative QTLs on to the consensus genetic map showed that 18 MQTLs had a 25.88% reduction in confidence interval (CI) on C. roseus chromosomes. Co-localisation results suggested that leaves TAL and serpentine in roots (SR) with 50% and 60% frequencies were the most common QTLs associated with vindoline (V) and catharanthine (C). The MQTL.LG1.5, MQTL.LG2.2, MQTL.LG1.1, MQTL.LG1.2, MQTL.LG1.7 and MQTL.LG3.3 were the most important MQTLs and the most reliable chromosome regions for refining genetic control of the tested traits in C. roseus. The consensus map provided information for the identification of the QTL-dense genomic regions that will assist in improving the efficiency of genomic and marker-assisted selection (MAS) in pharmacological researches.

Water shortage and drought stress in the reproductive stage of wheat (Triticum aestivum L.) considerably affect grain yield (GY). Mapping genes for drought tolerance assists in selection for drought improvement. In the present study, we evaluated breeding values based on best linear unbiased predictions (BLUPs) and drought tolerance/susceptibility indices (DT/SIs), and identified marker–phenotype associations in 220 doubled haploid (DH) lines. The DH lines were evaluated for GY and 1000-grain weight (TGW) under drought stress and well-watered conditions at the heading stage in 2015 and 2016. The linkage map comprised 1333 SSR, DArT and SNP markers with an average density of 2.18 markers per cM. The BLUPs were significantly correlated with GY of the lines. Significant correlations were found between stress tolerance score (STS) and drought response index, yield index, yield stability index, geometric mean productivity and stress tolerance index. The lines DH_R295 and DH_R360 had the highest breeding values for GY and STS. Major QTLs, one main effect and eight epistatic, were identified for the DT/SIs. A major QTL was identified for STS-GY, which explained 11.39% of the STS-GY variation in 2015. This QTL was co-located with QTLs for yield index-GY and yield stability index-GY within the BS00066932_51–gwm0314b marker interval (48 cM on 3B). The search for gene annotation showed that BS00066932_51 overlapped with protein-encoding genes. In conclusion, the QTL-linked markers help genotype selection for the improvement of drought tolerance, and they are good candidates for use in genomic selection.

Highlights • comparing Iranian coronavirus data with other countries. • predicting the trends of deaths from COVID-19 using regression. • spatial modelling, risk mapping, and change detection of COVID-19 using the random forest (RF) machine learning technique. • validation of the modelled risk maps., Objectives Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries and 2 international conveyance with more than 432,902 recorded deaths and 7,898,442 confirmed global worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods This is the first comprehensive study of COVID-19 in Iran and it undertakes spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends; prediction of mortality trends using regression modelling; spatial modelling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT); and validation of the modelled risk map. Results The results show that from February 19 to June 14, 2020 the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on World Health Organisation (WHO) data, Iran’s fatality (deaths/0.1 M pop) is 10.53. Other countries’ fatality rates were, for comparison, Belgium – 83.32, UK – 61.39, Spain – 58.04, Italy – 56.73, Sweden – 48.28, France – 45.04, USA – 35.52, Canada – 21.49, Brazil – 20.10, Peru – 19.70, Chile – 16.20, Mexico– 12.80, and Germany – 10.58. This fatality rate for China is 0.32 (deaths/0.1 M pop). The heatmap of the infected areas over time identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks that were separate from others. The heatmap of countries of the world show that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidences of turning. A polynomial relationship was identified between coronavirus infection rate and province population density. In addition, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the worlds, but it shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11th to March 18th showed an increasing trend in COVID-19 in Iran’s provinces. It is worth noting that using the LASSO MLT to evaluate variables’ importance indicated that the most important variables were distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. Conclusions We believe that the risk maps provided by this study is the primary, fundamental step for managing and controlling COVID-19 in Iran and its provinces.

Wheat is the most important crop in the world which faces the global problem of drought. Its production is affected by water deficit after pollination in arid and semi-arid regions. An experiment was conducted to assess tolerance of 39 bread wheat genotypes to end-season drought. The experimental design was Randomized Complete Block in three replications and the drought tolerance indices (SSI, STI, TOL, MP and GMP) were calculated for grain yield. The cultivar Cambin produced the highest grain yield under normal irrigation by 369.19 g m-2 while Arina had the highest yield (223.35 g m-2) under drought stress conditions. Stress tolerance (TOL) introduced Hindukesh, Iran2355 and Iran6476 as drought tolerant genotypes. Also, results showed that grain yield under stress and non-stress environments were highly correlated with the mean productivity (MP), the geometric mean productivity (GMP) and tolerance index (TOL). These genotypes could be further used in crosses for genetic studies and breeding programs for improvement tolerance to drought.

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.

Genotype, growth stages, and moisture regimes affect polyphenols as beneficial compounds in rapeseed with edible and medicinal properties. The aims of this study were to assess the effects of tissue, genotype background and moisture on growth, pigment composition, phenolic acids, flavonoids, antioxidant, and antiglycation activities in rapeseed. Treatments included two moisture regimes (10% field capacity as drought-treated and 30% field capacity as control), tissue (leaf, flower and seed), and 12 rapeseed genotypes. The range of loss in growth traits under drought compared with control was between 23% and 47%. Drought reduced number of leaves, leaf area, fresh and dry weights by 23%, 31%, 37%, and 36%, respectively whilst increased chlorophylls, carotenoids, total pigment, phenolic compounds, flavonoids, and antioxidant activities. Analysis for antiglycation properties in two genotypes (G01 and G08) which accumulated higher phenolic compounds showed that higher antiglycation property was associated with higher epicatechin, and caffeic, and syringic acids. Flower extracts showed higher phenolics than leaf and seed suggesting flowering stage is a preferred timing to harvest a higher polyphenols from rapeseed. Overall, our results demonstrated role of specific polyphenols in antiglycation activities and the importance of growth stage and genotype in attaining higher polyphenols and antioxidants that affect edible and medicinal values of rapeseed under water limited conditions.

Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways. Identifying ionome contributing genomic regions provides a practical framework to dissect the genetic architecture of ionomic traits for use in biofortification. Meta-QTL (MQTL) analysis is a robust method to discover stable genomic regions for traits regardless of the genetic background. This study used information of 483 QTLs for ionomic traits identified from 12 populations for MQTL analysis in Arabidopsis thaliana. The selected QTLs were projected onto the newly constructed genetic consensus map and 33 MQTLs distributed on A. thaliana chromosomes were identified. The average confidence interval (CI) of the drafted MQTLs was 1.30 cM, reduced eight folds from a mean CI of 10.88 cM for the original QTLs. Four MQTLs were considered as stable MQTLs over different genetic backgrounds and environments. In parallel to the gene density over the A. thaliana genome, the genomic distribution of MQTLs over the genetic and physical maps indicated the highest density at non- and sub-telomeric chromosomal regions, respectively. Several candidate genes identified in the MQTLs intervals were associated with ion transportation, tolerance, and homeostasis. The genomic context of the identified MQTLs suggested nine chromosomal regions for Zn, Mn, and Fe control. The QTLs for potassium (K) and phosphorus (P) were the most frequently co-located with Zn (78.3%), Mn (76.2%), and Fe (88.2% and 70.6%) QTLs. The current MQTL analysis demonstrates that meta-QTL analysis is cheaper than, and as informative as genome-wide association study (GWAS) in refining the known QTLs.

Drought stress is one of the most significant environmental factors restricting plant production all over the world. In arid and semi-arid regions where drought often causes serious problems, wheat is usually grown as a major crop and faces water stress. In order to study drought tolerance of wheat, an experiment with 34 genotypes including 11 local and commercial cultivars, 17 landraces, and six genotypes from International Maize and Wheat Improvement Center (CIMMYT) was conducted at the experimental station, School of Agriculture, Shiraz University, Iran in 2010-2011 growing season. Three different irrigation regimes (100%, 75% and 50% Field Capacity) were applied and physiological and biochemical traits were measured for which a significant difference was observed in genotypes. Under severe water stress, proline content and enzymes’ activities increased while the relative water content (RWC) and chlorophyll index decreased significantly in all genotypes. Of these indices, superoxide dismutase (SOD) and RWC were able to distinguish tolerant genotypes from sensitives. Moreover, yield index (YI) was useful in detecting tolerant genotypes. The drought susceptibility index (DSI) varied from 0.40 to 1.71 in genotypes. These results indicated that drought-tolerant genotypes could be selected based on high YI, RWC and SOD and low DSI. On the whole, the genotypes 31 (30ESWYT200), 29 (30ESWYT173) and 25 (Akbari) were identified to be tolerant and could be further used in downstream breeding programs for the improvement of wheat tolerance under water limited conditions.

Propagation and maintaining cytoplasmic male-sterile (CMS) lines are prerequisite of hybrid production programs in sugar beet. The identification of Owen-type (O-type) source materials is important to maintain CMS plants that accelerate crosses between single plants. The objectives of the present study were to assess a base beet germplasm (SB19) to identify O-type plants for use in hybrid production programs and test for resistance against rhizoctonia crown and root rot (Rcrr) disease. A family was developed from each two identified candidate monogerm O-types. A number of 100 plants of each family were crossed with FC708 (male sterile) in insulated cages, and the progenies of hybrids were tested for the frequency of male sterility. Progenies of candidate O-types crossed with FC708 revealed various levels of male sterility. Type I and Type II male sterility had lowest frequency, while completely sterile type was the most frequent. The results demonstrated that 6 plants in each family were fully male sterile and their parallels S1 were selected as O-type and male sterility maintainer. The mean for male sterility frequency was 10.5%. The mean disease index (DI) in the selected O-types was 2.22 that was lower than DI in SB19 as a resistant check. Mean comparison for Rcrr demonstrated that all O-types and SB19 plants were discriminated form Jolgeh as Rcrr-susceptible check. In conclusion, the identified O-types can be tested for combining ability for the development of Rcrr-resistant sugar beet hybrids with respect to root and sugar yield traits.

Zinc and iron are essential micronutrients for the growth, development, and maintenance of the immune system at high levels. Analysis of genetic control of grain Zn and Fe through identification of QTLs helps to identify linked markers for use in marker- assisted breeding and biofortification of wheat grain in breeding programs. Meta-QTL analysis (MQTL analysis) is a robust method to integrate information on QTLs in independent populations, which is useful for understanding the genetic architecture of quantitative traits. In this meta-analysis, information on QTLs for iron and zinc contents in seven independent segregating populations was used to develop a reference consensus map. The consensus map consisting of 558 DNA markers covered 1028 cM of the wheat genome and seven MQTLs were identified on six wheat chromosomes. The MQTLs identified possessed a narrower (7.03 cM) confidence interval (CI) than the original QTLs with a mean CI of 17.60 cM. Of the MQTLs identified, three MQTLs covering at least four initial QTLs from independent studies were the most important hot spots for the genetic control of grain Zn and Fe traits over different genetic backgrounds and environments. The identified MQTLs were predominantly distributed in the non-telomeric and sub-telomeric chromosomal regions. Functional analysis of the identified MQTLs suggested that the genomic regions of the most identified MQTLs were associated with the position of candidate genes (CGs) for Zn and Fe homeostasis. Overall, the results of the meta-analysis showed that QTLs for Zn and Fe contents were highly co-located (57.1%), suggesting the simultaneous improvement of both micronutrients and biofortification of wheat.

Camelthorn, Alhagi maurorum Medik. (Fabaceae, Leguminosae), a native component of the Asian flora, is invasive in Australia, South Africa and the USA where it is considered a noxious weed in several states. To date there is no biological control program against this weed; however, initial investigations into potential biocontrol agents revealed an eriophyid mite, Aceria alhagi Vidovic & Kamali, causing considerable damage in the native range. The mite attacks the growing tips as well as the flowers of the plants, not only reducing height and plant vigor but also reducing seed set. To assess the host range and impact of this potential biological control agent, on the target weed, no-choice tests as well as an open-field impact experiment were conducted at the research farm of the School of Agriculture, Shiraz University, Shiraz, Iran, over 2 years (2018 and 2019). Results from the no-choice tests suggest that A. alhagi poses negligible risk to the non-target plants tested in this study. In the first year of the open field impact test, plants did not flower; however, plant height was significantly reduced by mite attack. In the second year, significant reductions in plant biomass (26%), seed production (95%) and photosynthesis (53%) were observed in response to mite attack that would potentially limit the competitiveness of camelthorn as well as long-distance dispersal through seed in the invaded range. These results suggest that A. alhagi is a promising candidate for the biological control of camelthorn and should be prioritized for any future studies, expanding on the host range testing and safety.

Analysis of genetic diversity in medicinal plants assists germplasm conservation and selection for use in breeding schemes. The aims of the present study were to assess genetic diversity and differentiation of severalPlantagospecies using Inter Simple Sequence Repeats (ISSR) markers and identify marker-trait associations (MTAs). Thirty-onePlantagoaccessions belonging to eight species with various mating system and chromosome number were collected from geographical regions of Iran environments. Polymorphism in the DNA ofPlantagoaccessions were analyzed using polymerase chain reaction (PCR) of 25 ISSR primers. The data for number of polymorphic bands were analyzed on the basis of several genetic diversity parameters. The results of gel analysis indicated that the ISSR primers amplified 5 to 21 polymorphic bands with 100 to 3000 bp size. The mean polymorphism was 83.83% and five primers showed 100% polymorphism amongPlantagoaccessions. The Polymorphism Information Content (PIC) for ISSR as a dominant marker ranged from 0.1103 to 0.3829 with the mean 0.2727 in the species tested. Accessions inP. amplexicaulisandP. pysillumspecies represented the highest Nei’s and Shannon’s genetic diversity whilst the lowest obtained forP. lagopus. Analysis of phylogenetic network generated by the Neighbor-Net Algorithm showed moderate split of the eight species tested and the network depicted moderate conflict. The principal coordinate analysis (PCoA) results showed lower conflict in separation of accessions of the eight species. Fifty-six significant MTAs were detected for the traits tested inPlantagoaccessions, of which six were shared between three seed and mucilage traits and 24 were common between two traits. The coefficient of determination (R2) for the identified MTAs varied between 32 and 73%. In conclusion, the results of genetic diversity analysis suggested that ISSR marker could efficiently differentiatePlantagospecies and the information of genetic diversity might assistPlantagoimprovement and conservation.

Drought is one of the most important abiotic stresses affecting wheat growth. Evaluation of a structured population over seasons and moisture conditions allow for the detection of genes involved in drought tolerance. The aims of the present study were to analyze root and agronomic characters under two moisture conditions and identify marker-trait associations in 220 doubled haploid (DH) lines developed from a cross between Kukri and RAC875. The DH lines were evaluated for grain yield (GY) and several root and agronomic traits in four trials included drought stress experiments (DrExp) at heading stage and well-watered conditions (NExp) throughout the growth season in 2015 and 2016. A linkage map comprised of 1333 SSR, DArT and SNP markers were used for the analysis of quantitative traits loci (QTL). The results of QTL analysis showed that four and seven QTL were identified for root traits in the DrExp 15 and DrExp 16 trials, respectively. The results of QTL analysis suggested that the contribution of the three genomes to drought tolerance was not similar. The Q.Rdrw-4A QTL explained 7.05 and 10.43% of root dry weight variances in the DrExp15 and DrExp 16 trials, respectively. A QTL mapped on the D_GA8KES401CIKOJ–160-BS00067285_51 marker interval for root length was co-localized with a QTL for grain weight per spike under drought conditions. A year-specific main effect QTL and several stable epistatic QTL were identified for days to heading in the DrExp and NExp experiments, respectively. Search for gene annotation in the wheat genome showed that the position of this QTL was overlapped with the tRNA, sRNA and protein-encoding genes. Three QTL were identified for GY of which the Q.Gy-2D QTL identified in NExp 16 whilst Q.Gy-3D and an epistatic effect detected in drought conditions. The common QTL identified allows for increasing selection efficiency and the fine-tuning of root and agronomic traits for sustainable wheat production under water deficit conditions.

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-2019) has caused a global health emergency. Identification of regions with high risk for COVID-19 outbreak is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak and identify areas with a high risk of human infection with virus in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran. The daily observations of infected cases was tested in the third-degree polynomial and the autoregressive and moving average (ARMA) models to examine the patterns of virus infestation in the province and in Iran. The results of disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors including minimum temperature of coldest month (MTCM), maximum temperature of warmest month (MTWM), precipitation in wettest month (PWM), precipitation of driest month (PDM), distance from roads, distance from mosques, distance from hospitals, distance from fuel stations, human footprint, density of cities, distance from bus stations, distance from banks, distance from bakeries, distance from attraction sites, distance from automated teller machines (ATMs), and density of villages – were selected for spatial modelling. The predictive ability of an SVM model was assessed using the receiver operator characteristic – area under the curve (ROC-AUC) validation technique. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) a good prediction of change detection. The growth rate (GR) average for active cases in Fars for a period of 41 days was 1.26, whilst it was 1.13 in country and the world. The results of the third-degree polynomial and ARMA models revealed an increasing trend for GR with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although an explosive growth of the infected cases is expected in the country. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

Twenty-eight genotypes including triticale, wheat and rye were grown in a greenhouse under control, 14 and 21 dS/m salinity (NaCl and CaCl2, 1:1 ratio) levels to assess variations in agronomic traits, and to assess association of dehydration responsive elements (DREB genes) and five microsatellite (SSR) markers with salt tolerance. The results of grain yield variation and principal component (PC) analysis for statistical indices revealed that the first PC under 14 and 21 dS/m salinity levels was associated with salt tolerance. Five microsatellite (Xgwm291, Xgwm312, Xgwm410, Xgwm2181 and Xgwm126) and two DREB (DREB1 and DREB2) markers were used for detection of polymorphism. In total, 35 alleles ranging from 130 to 850 bp in size were identified of which, 25 alleles were found for SSR markers and 10 belonged to DREB genes. The number of alleles ranged from 3 to 9 with an average of 5 per primer. The polymorphic information content (PIC) value ranged from 0.51 (Xgwm291) to 0.77 (DREB1) with an average of 0.68. The Xgwm291 primer amplified 3 bands (130, 160 and 185 bp) in triticale and wheat but neither were detected in rye. Remarkably, the 130 bp band was amplified in TRT826 which was categorized as salt tolerant on the basis of K+/Na+ ratio and grain yield variations. Accordingly, this band could be associated with salt tolerance and its isolation and sequencing could clarify its characteristics for possible use in marker assisted breeding (MAB) for salt tolerance in triticale and wheat.

Plantago ovata and P. psyllium are of the most abundant and widely distributed Plantago species in the world. World-wide, it is an important and growing industrial and medicinal crop for dry land agriculture. In spite of this, basic characterization for economically important traits of these species is lacking for many accessions. In particular, there are gaps in our knowledge about accession identity and ploidy levels and the effects of agronomic practices of yield. This study is aimed at determining mitotic chromosome number, karyotype characteristics, morphological and mucilage character variation of wild accessions of Plantago species collected from a wide geographic range in Iran. Chromosome numbers were determined by a modification of conventional root tip squash method and karyotypes were digitally analysed. Morphological characters were evaluated under experimental field condition. Three replications of morphological and mucilage characters were also measured in the wild in situ natural habitats. The analysis of the karyotype indices showed that all P. ovata accessions were diploid (2n = 8) with small, homogeneous chromosomes. Likewise, P. psyllium accessions were diploid (2n = 12) and had symmetrical karyotype demonstrating P. psyllium as a primitive species. Results showed significant differences in accessions and environments with respect to seed yield, thousand grain weight, mucilage yield, mucilage content, days to flowering and plant height. P. ovata accumulated higher mucilage than P. psyllium. Mucilage content was higher in samples collected from natural habitats compared with those grown under experimental field conditions whereas mucilage yield was lower under field evaluation in natural habitats. Genotype by environment analysis revealed that the most stable accessions were G2 (P. ovata) and G6, G7 and G8 (P. psyllium) for seed yield, and G2 and G4 (P. ovata) and G8 (P. psyllium) for mucilage content, G4 (P. ovata) and all P. psyllium for grain weight. All accessions tested showed stability for mucilage yield, flowering stalk height and days to flowering. The outcome of karyotype analysis and field evaluations in this study provides information important to Plantago breeders focused on crop improvement.

A better understanding of metabolites assists breeding industrial-friendly medicinal plants. The main objectives of the present study were to compare and screen several fenugreek accessions and estimate genetic variability with respect to phenolic and non-pheneolic metabolites. The leaf extracts of accessions in field experiment being established in two growing seasons (2014-15, 2015-16) were subjected to high- performance liquid chromatography (HPLC). No one accession was found to have high levels of all metabolite compounds but several accessions accumulated more than one metabolite at high level. T.f.g[H-37527] had the highest catechin (65.33 mg L−1) and rutin (26.46 mg L−1) whereas p-coumaric acid was not identified in several accessions. Catechin (36%), trans-ferulic acid (15%) and hesperetin (13%) with the highest share in the total metabolite content were the predominant compounds identified. Coumarin with the highest genotypic variation (GCV = 128.05%) was identified at 280 nm, whereas hesperetin which was identified at the same wavelength had the lowest GCV (39.97%). All metabolites were identified at 280 nm wavelength except chlorogenic acid which was identified at 320 nm. Estimation of response to selection (RS) showed that catechin (RS = 35.87%) was more responsive to selection whereas quercetin (RS = 3.82%) was relatively less responsive. Assessment of interrelationships between metabolites demonstrated the strong association of catechin, trans-ferulic acid and rutin accumulation in accessions tested. Overall, knowledge of genetic variation identified for metabolites tested significantly contributes to designing intra-specific crosses with potential interest to metabolite breeding programs and creates a solid basis for genotype selection aimed at better food quality, higher pharmaceutical uses and further improvement of fenugreek.

Background:The development of genome microarrays of the model plant;Arabidopsis thaliana, with increasing repositories of publicly available data and high-throughput data analysis tools, has opened new avenues to genome-wide systemic analysis of plant responses to environmental stresses.Objective:To identify differentially expressed genes and their regulatory networks inArabidopsis thalianaunder harsh environmental condition.Methods:Two replications of eight microarray data sets were derived from two different tissues (root and shoot) and two different time courses (control and 24 hours after the beginning of stress occurrence) for comparative data analysis through various bioinformatics tools.Results:Under drought stress, 2558 gene accessions in root and 3691 in shoot tissues had significantly differential expression with respect to control condition. Likewise, under salinity stress 9078 gene accessions in root and 5785 in shoot tissues were discriminated between stressed and non-stressed conditions. Furthermore, the transcription regulatory activity of differentially expressed genes was mainly due to hormone, light, circadian and stress responsivecis-acting regulatory elements among which ABRE, ERE, P-box, TATC-box, CGTCA-motif, GARE-motif, TGACG-motif, GAG-motif, GA-motif, GATA- motif, TCT-motif, GT1-motif, Box 4, G-Box, I-box, LAMP-element, Sp1, MBS, TC-rich repeats, TCA-element and HSE were the most important elements in the identified up-regulated genes.Conclusion:The results of the high-throughput comparative analyses in this study provide more options for plant breeders and give an insight into genes andcis-acting regulatory elements involved in plant response to drought and salinity stresses in strategic crops such as cereals.

In modern agriculture, precise integrated management of farmlands requires natural, sustainable and environmentally friendly inputs replacing chemical fertilizers application. A two-year (2015-2016) field experiment was conducted to determine the influence of integrated application of wheat (Triticum aestivum L.) residues and compost accompanied by nitrogen (N) on growth, seed yield and yield components of sunflower (Helianthus annuus L.) at School of Agriculture, Shiraz University, Shiraz, Iran. The experiment was conducted at split plots arranged in randomized complete blocks design with three replications. Treatments included four N (0, 60, 120, and 180 kg N ha-1) two wheat residues (1.5 and 3 t ha-1), two municipal compost (50 and 70 t ha-1) rates and control (no additives). The results showed that higher N rates increased seed yield, whereas it reduced its oil content. Application of wheat residues and compost increased growth, seed yield, and seed oil content compared to control. The effect of compost application on seed yield increase was more pronounced than crop residues. Therefore, for an environmentally andagronomically sound management strategy, high compost application (70 t ha-1) combined with proper N fertilization is recommended for sunflower production in the region.

Aggregate selection helps making selection decisions for improved profitability and permits to exploit information on correlated traits to improve accuracy. In the present study, the efficiency of trait selection was assessed by the Smith-Hazel (SHI) and the Pesek-Baker (PBI) models in 35 wheat genotypes under drought stress at heading and well-irrigated conditions at the Research Farm of Shiraz University, Iran in 2011 and 2012 years. The analysis of correlated response (CR) revealed that thousand grain weight (TGW) with CR = 24.05 (in 2011) and 9.15 (in 2012) and harvest index (HI) with CR = 18.37 (in 2011) and 9.08 (in 2012) had higher indirect effects on grain yield (GY) increase under drought stress. In both years, biological yield (BY), grain number/spike (GN), TGW, and GY had the highest genetic gain (ΔG) in SHI model. The top ten landraces in PBI for the trials in 2011 were also placed in the top rankings for grain yield under drought stress conditions. These results and estimation of the efficiency of selection (ΔH) revealed that PBI (ΔH = 19.95 and 16.5 in the first and 11.15 and 11.06 in the second year) was more efficient than SHI (ΔH = 9.58 and 8.97 in the first and 9.74 and 8.59 in the second year) in view of identifying superior genotypes based on aggregate trait selection. Overall, repeatability estimates for grain yield (33%-57.8%) was relatively low showing that individual plant selection has low efficiency for wheat improvement whilst aggregate trait selections leads to relatively high genetic gain (1.63-2.75) for grain yield.