Obtaining interspecific hybrids, and molecular analysis by microsatellite markers in grapevine

The objective of this work was to assess the potential of interspecific hybridization of Vitis labruscana and Muscadinia rotundifolia by using artificial cross-pollinations. Microsatellite markers were used to confirm interspecific hybridizations and the identity of the parental genotypes. In crosses in which M. rotundifolia was used as the female parent, no true hybrids were obtained. In the reciprocal crosses, 114 seedlings were identified as true V. labruscana x M. rotundifolia hybrids. Self pollination occurred in direct and in reciprocal crosses. The crossings between 'Bordo' x 'Carlos', 'Magnolia', 'Regale' and' Roanoke', and between' Isabel' x 'Bountiful', 'Carlos', 'Magnolia', 'Regale' and 'Roanoke' were confirmed. The 15 markers evaluated showed that two M. rotundifolia parental genotypes had the same fingerprint profile, indicating a like lyplanting error. The success of hybridization depends mainly on the species and on the cultivar used as the female parent. Microsatellite markers are efficient to confirm the paternity of interspecific F1 hybrids and to determine the correct identity of M. rotundifolia cultivars

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans

Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms. Copyright © 2021 Steiner, Giles, Patterson, Feng, El Rouby, Claudio, Marcatto, Tavares, Galvez, Calderon-Ospina, Sun, Hutz, Scott, Cavallari, Fonseca-Mendoza, Duconge, Botton, Santos and Karnes.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Giving and receiving rewards

Awards in the form of orders, medals, decorations, prizes, and titles are ubiquitous in monarchies and republics, private organizations, and not-for-profit and profit-oriented firms. Nevertheless, this kind of nonmaterial extrinsic incentive has been given little attention in the social sciences, including psychology. The demand for awards relies on an individual's desire for distinction, and the supply of awards is governed by the desire to motivate. The technique of analytic narratives is used to show that a number of empirically testable propositions about awards are consistent with observable data