Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases 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 carries out 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 modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled 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 the

Wheat crops frequently experience a combination of abiotic stresses in the field, but most quantitative trait loci (QTL) studies have focused on the identification of QTLs for traits under single stress field conditions. A recombinant inbred line (RIL) population derived from SeriM82 × Babax was used to map QTLs under well-irrigated, heat, drought, and a combination of heat and drought stress conditions in two years. A total of 477 DNA markers were used to construct linkage groups that covered 1619.6 cM of the genome, with an average distance of 3.39 cM between adjacent markers. Moderate to relatively high heritability estimates (0.60-0.70) were observed for plant height (PHE), grain yield (YLD), and grain per square meter (GM2). The most important QTLs for days to heading (DHE), thousand grain weight (TGW), and YLD were detected on chromosomes 1B, 1D-a, and 7D-b. The prominent QTLs related to canopy temperature were on 3B. Results showed that common QTLs for DHE, YLD, and TGW on 7D-b were validated in heat and drought trials. Three QTLs for chlorophyll content in SPAD unit (on 1A/6B), leaf rolling (ROL) (on 3B/4A), and GM2 (on 1B/7D-b) showed significant epistasis × environment interaction. Six heat- or drought-specific QTLs (linked to 7D-acc/cat-10, 1B-agc/cta-9, 1A-aag/cta-8, 4A-acg/cta-3, 1B-aca/caa-3, and 1B-agc/cta-9 for day to maturity (DMA), SPAD, spikelet compactness (SCOM), TGW, GM2, and GM2, respectively) were stable and validated over two years. The major DHE QTL linked to 7D-acc/cat-10, with no QTL × environment (QE) interaction increased TGW and YLD. This QTL (5.68 ≤ LOD ≤ 10.5) explained up to 19.6% variation in YLD in drought, heat, and combined stress trials. This marker as a candidate could be used for verification in other populations and identifying superior allelic variations in wheat cultivars or its wild progenitors to increase the efficiency of selection of high yielding lines adapted to end-season heat and drought stress conditions.

A doubled haploid (DH) population derived from a cross between the Japanese cultivar 'Fukuho-kumogi' and the Israeli wheat line 'Oligoculm' was used to map genome regions involved in the expression of grain yield, yield components, and spike features in wheat (Triticum aestivum L). A total of 371 markers (RAPD, SSR, RFLP, AFLP, and two morphological traits) were used to construct the linkage map that covered 4190 cM of wheat genome including 28 linkage groups. The results of composite interval mapping for all studied traits showed that some of the quantitative trait loci (QTL) were stable over experiments conducted in 2004 and 2005. The major QTL located in the Hair-Xpsp2999 interval on chromosome 1A controlled the expression of grains/spike (R(2) = 12.9% in 2004 and 22.4% in 2005), grain weight/spike (R(2) = 21.4% in 2004 and 15.8% in 2005), and spike number (R(2) = 15.6% in 2004 and 5.4% in 2005). The QTL for grain yield located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of total variation in this trait in 2004 and 2005, respectively. Alleles inherited from 'Oligoculm' increased the length of spikes and had decreasing effects on spike number. According to the data obtained in 2005, locus Xgwm261 was associated with a highly significant spike length QTL (R(2) = 42.33%) and also the major QTL for spikelet compactness (R(2) = 26.1%).

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Genotype × environment interaction (GEI) is an important aspect of both plant breeding and the successful introduction of new cultivars. In the present study, additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects and genotype (G) × environment (E) interaction (GGE) biplot analyses were used to identify stable genotypes and to dissect GEI in Plantago. In total, 10 managed field trials were considered as environments to analyze GEI in thirty genotypes belonging to eight Plantago species. Genotypes were evaluated in a drought stress treatment and in normal irrigation conditions at two locations in Shiraz (Bajgah) for three years (2013-2014- 2015) and Kooshkak (Marvdasht, Fars, Iran) for two years (2014–2015). Three traits, seed yield and mucilage yield and content, were measured at each experimental site and in natural Plantago habitats. AMMI2 biplot analyses identified genotypes from several species with higher stability for seed yield and other genotypes with stable mucilage content and yield. P. lanceolata (G26), P. officinalis (G10), P. ovata (G14), P. ampleexcaulis (G11) and P. major (G4) had higher stability for seed yield. For mucilage yield, G21, G18 and G20 (P. psyllium), G1, G2 and G4 (P. major), G9 and G10 (P. officinalis) and P. lanceolata were identified as stable. G13 (P. ovata), G5 and G6 (P. major) and G30 (P. lagopus) had higher stability for mucilage content. No one genotype was found to have high levels of stability for more than one trait but some species had more than one genotype exhibiting stable trait performance. Based on trait variation, GGE biplot analysis identified two representative environments, one for seed yield and one for mucilage yield and content, with good discriminating ability. The identification of stable genotypes and representative environments should assist the breeding of new Plantago cultivars.

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection 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 for identifying the areas having high risk of infection and to evaluate the behaviour of infection 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 whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend 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 a more volatile growth of the infected cases is expected in the province. 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.

Drought stress limits crop production in the world. Therefore, employing high-yielding cultivars tolerant to drought is an effective approach to reduce its detrimental effects. To identify drought tolerant genotypes, 36 wheat genotypes were evaluated during the 2010-2011 and 2011-2012 growth seasons. A field experiment was conducted in a split plot design with two irrigation treatments (100% field capacity until harvest and no irrigation after anthesis) as main plots in three replications and genotypes as subplots. Grain yield, its components and drought tolerance indices were measured. Results showed a significant reduction in yield and its components under drought conditions. Grain yield had significant positive correlations with stress tolerance index (STI), mean productivity index (MP) and geometric mean productivity (GMP) while it was negatively correlated with stress susceptibility index (SSI) and tolerance index (TOL) under stress condition. These results indicated that superior genotypes could be selected based on high values of STI, MP and A c c e p t e d M a n u s c r i p t Downloaded by [University of Arizona] at 04:19 05 July 2014 GMP and low value of SSI. The results were validated by principal component analysis (PCA) as it showed genotypes with high PC1 and low PC2 were more desirable. Based on the results, genotypes number 8, 11, 17, 30, 34 and 35 were recognized as suitable for both conditions.

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