Linkage mapping of the Phg-1 and Co-14 genes for resistance to angular leaf spot and anthracnose in the common bean cultivar AND 277

The Andean common bean AND 277 has the Co-14 and the Phg-1 alleles that confer resistance to 21 and eight races, respectively, of the anthracnose (ANT) and angular leaf spot (ALS) pathogens. Because of its broad resistance spectrum, Co-14 is one of the main genes used in ANT resistance breeding. Additionally, Phg-1 is used for resistance to ALS. In this study, we elucidate the inheritance of the resistance of AND 277 to both pathogens using F2 populations from the AND 277 × Rudá and AND 277 × Ouro Negro crosses and F2:3 families from the AND 277 × Ouro Negro cross. Rudá and Ouro Negro are susceptible to all of the above races of both pathogens. Co-segregation analysis revealed that a single dominant gene in AND 277 confers resistance to races 65, 73, and 2047 of the ANT and to race 63-23 of the ALS pathogens. Co-14 and Phg-1 are tightly linked (0.0 cM) on linkage group Pv01. Through synteny mapping between common bean and soybean we also identified two new molecular markers, CV542014450 and TGA1.1570, tagging the Co-14 and Phg-1 loci. These markers are linked at 0.7 and 1.3 cM, respectively, from the Co-14/Phg-1 locus in coupling phase. The analysis of allele segregation in the BAT 93/Jalo EEP558 and California Dark Red Kidney/Yolano recombinant populations revealed that CV542014450 and TGA1.1570 segregated in the expected 1:1 ratio. Due to the physical linkage in cis configuration, Co-14 and Phg-1 are inherited together and can be monitored indirectly with the CV542014450 and TGA1.1570 markers. These results illustrate the rapid discovery of new markers through synteny mapping. These markers will reduce the time and costs associated with the pyramiding of these two disease resistance genes

Associations Between Discrimination and Cardiovascular Health Among Asian Indians in the United States

Asian Indians (AI) have a high risk of atherosclerotic cardiovascular disease. The study investigated associations between discrimination and (1) cardiovascular risk and (2) self-rated health among AI. Higher discrimination scores were hypothesized to relate to a higher cardiovascular risk score (CRS) and poorer self-rated health. Asian Indians (n = 757) recruited between 2010 and 2013 answered discrimination and self-reported health questions. The CRS (0–8 points) included body-mass index, systolic blood pressure, total cholesterol, and fasting blood glucose levels of AI. Multiple linear regression analyses were conducted to evaluate relationships between discrimination and the CRS and discrimination and self-rated health, adjusting for psychosocial and clinical factors. There were no significant relationships between discrimination and the CRS (p ≥ .05). Discrimination was related to poorer self-reported health, B = −.41 (SE = .17), p = .02. Findings suggest perhaps there are important levels at which discrimination may harm health

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier Science B.V. All rights reserved.48325126

An efficient model of neural networks for dynamic programming

Systems based on artificial neural networks have high computational rates owing to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. This paper presents a novel approach for solving dynamic programming problems using artificial neural networks. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. Simulated examples are presented and compared with other neural networks. The results demonstrate that the proposed method gives a significant improvement.32671572