Genetic mapping of the rice ionome in leaves and grain:Identification of QTLs for 17 elements including arsenic, cadmium, iron and selenium

Research into the composition of cereal grains is motivated by increased interest in food quality. Here multi-element analysis is conducted on leaves and grain of the Bala x Azucena rice mapping population grown in the field. Quantitative trait loci (QTLs) for the concentration of 17 elements were detected, revealing 36 QTLs for leaves and 41 for grains. Epistasis was detected for most elements. There was very little correlation between leaf and grain element concentrations. For selenium, lead, phosphorus and magnesium QTLs were detected in the same location for both tissues. In general, there were no major QTL clusters, suggesting separate regulation of each element. QTLs for grain iron, zinc, molybdenum and selenium are potential targets for marker assisted selection to improve seed nutritional quality. An epistatic interaction for grain arsenic also looks promising to decrease the concentration of this carcinogenic element. © Springer Science+Business Media B.V. 2009.

Unsupervised Solution Post Identification from Discussion Forums

Discussion forums have evolved into a dependablesource of knowledge to solvecommon problems. However, only a minorityof the posts in discussion forumsare solution posts. Identifying solutionposts from discussion forums, hence, is animportant research problem. In this paper,we present a technique for unsupervisedsolution post identification leveraginga so far unexplored textual feature, thatof lexical correlations between problemsand solutions. We use translation modelsand language models to exploit lexicalcorrelations and solution post characterrespectively. Our technique is designedto not rely much on structural featuressuch as post metadata since suchfeatures are often not uniformly availableacross forums. Our clustering-based iterativesolution identification approach basedon the EM-formulation performs favorablyin an empirical evaluation, beatingthe only unsupervised solution identificationtechnique from literature by a verylarge margin. We also show that our unsupervisedtechnique is competitive againstmethods that require supervision, outperformingone such technique comfortably.