AFLP and AFLPderived SCAR markers associated with Striga gesnerioides resistance in cowpea

Cowpea, Vigna unguiculata (L.) Walp., is an important grain legume grown in tropical and subtropical regions, primarily Africa. The parasitic weed Striga gesnerioides (Willd.) Vatke is one of the most important constraints to cowpea production. Host plant resistance is the only practical control method. Five virulence genotypes (races) of S. gesnerioides have been identified in different regions of Africa. Several host resistance genes have also been identified that are effective against specific races of S. gesnerioides The rapid spread of this parasitic weed creates an urgent need for cowpea varieties with multiple resistance genes. A recently identified cowpea breeding line, IT93K-693-2, has resistance to all known races. The objective of this research was to develop DNA markers that are useful for marker-assisted selection (MAS) in breeding cowpea for resistance to S. gesnerioides An F2 population developed from the cross between IT93K-693-2 and the susceptible cultivar IAR1696 was characterized for resistance against race 3 of S. gesnerioides for genetic analysis and molecular mapping. IT93K-693-2 was found to have a single dominant gene for resistance. Four amplified fragment length polymorphism (AFLP) markers, designated E-ACT/M-CTC115, E-ACT/M-CAC115, E-ACA/M-CAG108 and E-AAG/E-CTA190, were identified and mapped 3.2, 4.8, 13.5 and 23.0 cM, respectively, from Rsg1, a gene in IT93K-693-2 that gives resistance to race 3 (or Nigerian strain) of S. gesnerioides The first two markers were validated in a second F2 population developed from crossing the same resistant parent with ‘Kamboinse local’, a different susceptible cultivar. The AFLP fragment from marker combination E-ACT/M-CAC, which is linked in coupling with Rsg1 was cloned, sequenced, and converted into a sequence characterized amplified region (SCAR) marker named SEACTMCAC83/85, which is codominant and useful in breeding programs

OrBEAGLE: Integrating Orthography into a Holographic Model of the Lexicon

Abstract. Many measures of human verbal behavior deal primarily with semantics (e.g., associative priming, semantic priming). Other measures are tied more closely to orthography (e.g., lexical decision time, visual word-form priming). Semantics and orthography are thus often studied and modeled separately. However, given that concepts must be built upon a foundation of percepts, it seems desirable that models of the human lexicon should mirror this structure. Using a holographic, distributed representation of visual word-forms in BEAGLE [12], a corpustrained model of semantics and word order, we show that free association data is better explained with the addition of orthographic information. However, we find that orthography plays a minor role in accounting for cue-target strengths in free association data. Thus, it seems that free association is primarily conceptual, relying more on semantic context and word order than word form information

Result disambiguation in web people search

We study the problem of disambiguating the results of a web people search engine: given a query consisting of a person name plus the result pages for this query, find correct referents for all mentions by clustering the pages according to the different people sharing the name. While the problem has been studied extensively, we discover that the increasing availability of results retrieved from social media platforms causes state-of-the-art methods to break down. We analyze the problem and propose a dual strategy where we distinguish between results obtained from social media platforms and those obtained from other sources. In our dual strategy, the two types of documents are disambiguated separately, using different strategies, and their results are then merged. We study several instantiations for the different stages in our proposed strategy and manage to achieve state-of-the-art performance