University of Guadalajara

About: University of Guadalajara is a education organization based out in Guadalajara, Mexico. It is known for research contribution in the topics: Population & Context (language use). The organization has 13040 authors who have published 17399 publications receiving 168085 citations. The organization is also known as: UdeG & UdG.

The genetic architecture of teosinte catalyzed and constrained maize domestication

Abstract: The process of evolution under domestication has been studied using phylogenetics, population genetics–genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance–covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.

Spectral Optical Monitoring of the Narrow Line Seyfert 1 galaxy Ark 564

Abstract: We present the results of a long-term (1999--2010) spectral optical monitoring campaign of the active galactic nucleus (AGN) Ark 564, which shows a strong Fe II line emission in the optical. This AGN is a narrow line Seyfert 1 (NLS1) galaxies, a group of AGNs with specific spectral characteristics. We analyze the light curves of the permitted Ha, Hb, optical Fe II line fluxes, and the continuum flux in order to search for a time lag between them. Additionally, in order to estimate the contribution of iron lines from different multiplets, we fit the Hb and Fe II lines with a sum of Gaussian components. We found that during the monitoring period the spectral variation (F_max/F_min) of Ark 564 was between 1.5 for Ha to 1.8 for the Fe II lines. The correlation between the Fe II and Hb flux variations is of higher significance than that of Ha and Hb (whose correlation is almost absent). The permitted-line profiles are Lorentzian-like, and did not change shape during the monitoring period. We investigated, in detail, the optical Fe II emission and found different degrees of correlation between the Fe II emission arising from different spectral multiplets and the continuum flux. The relatively weak and different degrees of correlations between permitted lines and continuum fluxes indicate a rather complex source of ionization of the broad line emission region.

An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation

Abstract: A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency and convergence. A comprehensive set of experiments are performed to evaluate the MPA-OBL and prove the impact influence of merging OBL strategy with the original MPA in enhancing the quality of the solutions and the acceleration of the convergence speed, using IEEE CEC’2020 benchmark problems as recently complex optimization benchmark. In order to evaluate the performance of the proposed MPA-OBL, the effectiveness of conjunction of OBL with the original MPA and the other counterparts are calculated and compared with LSHADE with semi-parameter adaptation hybrid with CMA-ES (LSHADE_SPACMA-OBL), Restart covariance matrix adaptation ES (CMA_ES-OBL), Differential evolution (DE-OBL), Harris hawk optimization (HHO-OBL), Sine cosine algorithm (SCA-OBL), Salp swarm algorithm (SSA-OBL), and the original MPA. The extensive results and comparisons in terms of optimization metrics have revealed the superiority of the proposed MPA-OBL in solving the IEEE CEC’2020 benchmark problems and improving the convergence speed. Moreover, as a sequel to the proposed MPA-OBL, also, we have conducted experiments using two objective functions of Otsu and Kapur’s methods over a variety of benchmark images at different level of thresholds based on three commonly evaluation matrices namely Peak signal-to-noise ratio (PSNR), Structural similarity (SSIM), and Feature similarity (FSIM) indices are analyzed qualitatively and quantitatively. Eventually, the statistical post-hoc analysis reveal that the MPA-OBL obtains highly efficient and reliable results in comparison with the other competitor algorithms.