We have isolated a starch mutant that was deficient in starch-branching enzyme I (BEI) from the endosperm mutant stocks of rice (Oryza sativa) induced by the treatment of fertilized egg cells with N-methyl-N-nitrosourea. The deficiency of BEI in this mutant was controlled by a single recessive gene, tentatively designated as starch-branching enzyme mutant 1 (sbe1). The mutant endosperm exhibited the normal phenotype and contained the same amount of starch as the wild type. However, the mutation apparently altered the fine structure of amylopectin. The mutant amylopectin was characterized by significant decrease in both long chains with degree of polymerization (DP) Ն 37 and short chains with DP 12 to 21, marked increase in short chains with DP Յ 10 (A chains), and slight increase in intermediate chains with DP 24 to 34, suggesting that BEI specifically synthesizes B 1 and B 2-3 chains. The endosperm starch from the sbe1 mutant had a lower onset concentration for urea gelatinization and a lower onset temperature for thermo-gelatinization compared with the wild type, indicating that the genetic modification of amylopectin fine structure is responsible for changes in physicochemical properties of sbe1 starch.
Botrytis cinerea field isolates collected in Japan were screened for resistance to Qo inhibitor fungicides (QoIs). Of the 198 isolates screened, six grew well on a medium containing azoxystrobin, a QoI, when salicylhydroxamic acid, an alternative oxidase inhibitor, was present. The resistance mutation in the cytochrome b gene ( cytb ) was characterized. All QoI-resistant isolates had the same mutation (GGT to GCT) in cytb that led to the substitution of glycine by alanine at position 143 of cytochrome b , which is known to confer QoI resistance in plant pathogens. To detect this mutation, a hybridization probe assay based on real-time PCR amplification and melting curve analysis was developed. Using DNA samples prepared from aubergines coinfected with QoI-resistant and QoI-sensitive B. cinerea isolates, two similar peak profiles with their corresponding melting temperatures were obtained. This result suggests that QoI-resistant and QoI-sensitive isolates may compete equally in terms of pathogenicity, and the assay may be used to assess the population ratio of mutant and wild-type isolates. However, the hybridization probe did not anneal to PCR products derived from the DNA samples of some QoI-sensitive isolates. Structural analysis of cytb revealed that B. cinerea field isolates could be classified into two groups: one with three introns and the other with an additional intron (Bcbi-143/144 intron) inserted between the 143rd and 144th codons. All 88 isolates possessing the Bcbi-143/144 intron were azoxystrobinsensitive, suggesting that the QoI-resistant mutation at codon 143 in cytb prevents self-splicing of the Bcbi-143/144 intron, as proposed in some other plant pathogens.
Prior research suggests that predicting defect-inducing changes, i.e., Just-In-Time (JIT) defect prediction is a more practical alternative to traditional defect prediction techniques, providing immediate feedback while design decisions are still fresh in the minds of developers. Unfortunately, similar to traditional defect prediction models, JIT models require a large amount of training data, which is not available when projects are in initial development phases. To address this flaw in traditional defect prediction, prior work has proposed cross-project models, i.e., models learned from older projects with su cient history. However, cross-project models have not yet been explored in the context of JIT prediction. Therefore, in this study, we empirically evaluate the performance of JIT cross-project models. Through a case study on 11 open source projects, we find that in a JIT cross-project context: (1) high performance within-project models rarely perform well; (2) models trained on projects that have similar correlations between predictor and dependent variables often perform well; and (3) ensemble learning techniques that leverage historical data from several other projects (e.g., voting experts) often perform well. Our findings empirically confirm that JIT cross-project models learned using other projects are a viable solution for projects with little historical data. However, JIT cross-project models perform best when the data used to learn them is carefully selected.
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