Worldwide selection footprints for drought and heat in bread wheat (Triticum aestivum L.)

Genome–environment Associations (GEA) or Environmental Genome-Wide Association scans (EnvGWAS) have been poorly applied for studying the genomics of adaptive traits in bread wheat landraces (Triticum aestivum L.). We analyzed 990 landraces and seven climatic variables (mean temperature, maximum temperature, precipitation, precipitation seasonality, heat index of mean temperature, heat index of maximum temperature, and drought index) in GEA using the FarmCPU approach with GAPIT. Historical temperature and precipitation values were obtained as monthly averages from 1970 to 2000. Based on 26,064 high-quality SNP loci, landraces were classified into ten subpopulations exhibiting high genetic differentiation. The GEA identified 59 SNPs and nearly 89 protein-encoding genes involved in the response processes to abiotic stress. Genes related to biosynthesis and signaling are mainly mediated by auxins, abscisic acid (ABA), ethylene (ET), salicylic acid (SA), and jasmonates (JA), which are known to operate together in modulation responses to heat stress and drought in plants. In addition, we identified some proteins associated with the response and tolerance to stress by high temperatures, water deficit, and cell wall functions. The results provide candidate regions for selection aimed to improve drought and heat tolerance in bread wheat and provide insights into the genetic mechanisms involved in adaptation to extreme environments

Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data

Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative

Tensiones residuales en uniones soldadas por FSW en aluminio 7075-T651

Las tensiones residuales pueden ser un aspecto de relevancia en la integridad estructural de componentes en servicio, pudiendo presentar una importante influencia sobre la vida a la fatiga, entre otros mecanismos de falla. El proceso de soldadura por fricción-agitación (FSW) ha revolucionado en los últimos años el campo de la tecnología de la soldadura. Su mayor aplicación se ha dado en las aleaciones de aluminio, aunque hoy en día se utiliza para prácticamente todos los materiales. Una de las ventajas enunciadas de este proceso es el menor nivel de tensiones residuales resultantes, debido a que se produce en estado sólido por lo que los gradientes térmicos durante la soldadura son menores. Entre las aleaciones de aluminio de uso estructural, las aleaciones termoenvejecibles de la serie 7XXX se utilizan habitualmente en la industria aeronáutica y aeroespacial debido a su alta resistencia mecánica. El objetivo del presente trabajo es analizar el efecto de la velocidad de avance en FSW sobre las tensiones residuales en juntas de aluminio AA7075-T651, mediante la técnica de seccionamiento. Se soldaron probetas de dicha aleación de 150x150x4mm mediante FSW, variando la velocidad de avance entre 51 y 206 mm.min-1. Durante la soldadura se adquirieron los ciclos térmicos. Posteriormente se midieron las tensiones residuales longitudinales a distintas distancias del cordón de soldadura, en cada caso. A partir de los ciclos térmicos adquiridos se obtuvieron los gradientes térmicos en la zona de medición. Las tensiones residuales máximas se encontraron entre 52 y 78 MPa, correspondiente entre 10 y 15 % del límite de fluencia del material. Las mismas aumentaron con la velocidad de avance, consistentemente con un aumento en el gradiente térmico observado. Dichos valores de tensiones residuales son menores que los obtenidos para la soldadura de estos materiales mediante procesos del tipo GMAW.Residual stresses could be a relevant issue in the structural integrity of a component in service and usually have a major influence on their fatigue life, among other failure mechanisms. In recent years Friction-Stir Welding (FSW) has revolutionized the welding technology. Its main application has been given in aluminum alloys, but nowadays can be applicable to different materials. One of the several advantages of this process is a lower level of residual stresses, because the thermal gradients introduced during welding are lower. Among aluminum alloys used for structural applications, 7XXX strain-aged series are usually employed in the aerospace industry, because of their high strength. The aim of this paper is to analyze the effect of welding speed of FSW on residual stresses in aluminum AA7075-T651 joints, using sectioning technique. Coupons were welded AA7075-T651 FSW of 150x150x4 mm by varying the welding speed between 51 and 206 mm.min-1. During the welding thermal cycles were acquired. Subsequently, longitudinal residual stresses measured at different distances from the weld, in each case. Based on the acquired thermal cycling thermal gradients were obtained in the measurement area. Maximum residual stresses ranged from 52 to 78 MPa, corresponding to a range of 10 and 15% of the yield strength. They increased with increasing welding speed, consistent with an increase in the thermal gradient observed. These residual stresses values are lower than those obtained for the welding of these materials by type GMAW processes

Respondent-Driven Sampling of Injection Drug Users in Two U.S.–Mexico Border Cities: Recruitment Dynamics and Impact on Estimates of HIV and Syphilis Prevalence

Respondent-driven sampling (RDS), a chain referral sampling approach, is increasingly used to recruit participants from hard-to-reach populations, such as injection drug users (IDUs). Using RDS, we recruited IDUs in Tijuana and Ciudad (Cd.) Juárez, two Mexican cities bordering San Diego, CA and El Paso, TX, respectively, and compared recruitment dynamics, reported network size, and estimates of HIV and syphilis prevalence. Between February and April 2005, we used RDS to recruit IDUs in Tijuana (15 seeds, 207 recruits) and Cd. Juárez (9 seeds, 197 recruits), Mexico for a cross-sectional study of behavioral and contextual factors associated with HIV, HCV and syphilis infections. All subjects provided informed consent, an anonymous interview, and a venous blood sample for serologic testing of HIV, HCV, HBV (Cd. Juárez only) and syphilis antibody. Log-linear models were used to analyze the association between the state of the recruiter and that of the recruitee in the referral chains, and population estimates of the presence of syphilis antibody were obtained, correcting for biased sampling using RDS-based estimators. Sampling of the targeted 200 recruits per city was achieved rapidly (2 months in Tijuana, 2 weeks in Cd. Juárez). After excluding seeds and missing data, the sample prevalence of HCV, HIV and syphilis were 96.6, 1.9 and 13.5% respectively in Tijuana, and 95.3, 4.1, and 2.7% respectively in Cd. Juárez (where HBV prevalence was 84.7%). Syphilis cases were clustered in recruitment trees. RDS-corrected estimates of syphilis antibody prevalence ranged from 12.8 to 26.8% in Tijuana and from 2.9 to 15.6% in Ciudad Juárez, depending on how recruitment patterns were modeled, and assumptions about how network size affected an individual’s probability of being included in the sample. RDS was an effective method to rapidly recruit IDUs in these cities. Although the frequency of HIV was low, syphilis prevalence was high, particularly in Tijuana. RDS-corrected estimates of syphilis prevalence were sensitive to model assumptions, suggesting that further validation of RDS is necessary

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

Genetic trends in CIMMYT’s tropical maize breeding pipelines

Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha−1 yr−1 in ESA, 118 kg ha−1 yr−1 South Asia and 143 kg ha−1 yr−1 in Latin America. Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT’s breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain