Genome-wide association analysis identifies resistance loci for bacterial blight in a diverse collection of indica rice germplasm

Bacterial blight, which is caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most devastating rice diseases worldwide. The development and use of disease-resistant cultivars have been the most effective strategy to control bacterial blight. Identifying the genes mediating bacterial blight resistance is a prerequisite for breeding cultivars with broad-spectrum and durable resistance. We herein describe a genome-wide association study involving 172 diverse Oryza sativa ssp. indica accessions to identify loci influencing the resistance to representative strains of six Xoo races. Twelve resistance loci containing 121 significantly associated signals were identified using 317,894 single nucleotide polymorphisms, which explained 13.3–59.9% of the variability in lesion length caused by Xoo races P1, P6, and P9a. Two hotspot regions (L11 and L12) were located within or nearby two cloned R genes (xa25 and Xa26) and one fine-mapped R gene (Xa4). Our results confirmed the relatively high resolution of genome-wide association studies. Moreover, we detected novel significant associations on chromosomes 2, 3, and 6–10. Haplotype analyses of xa25, the Xa26 paralog (MRKc; LOC_Os11g47290), and a Xa4 candidate gene (LOC_11g46870) revealed differences in bacterial blight resistance among indica subgroups. These differences were responsible for the observed variations in lesion lengths resulting from infections by Xoo races P1 and P9a. Our findings may be relevant for future studies involving bacterial blight resistance gene cloning, and provide insights into the genetic basis for bacterial blight resistance in indica rice, which may be useful for knowledge-based crop improvement. (Résumé d'auteur

Temperature control strategies for radiant floor heating systems

A dynamic model of a radiant floor heating (RFH) system useful for control analysis is developed. The overall model consists of a boiler, an embedded tube floor slab and building enclosure. The overall model was described by nonlinear differential equations, which were solved using finite numerical methods. The predicted responses from the model were compared with published experimental data. The comparisons were made covering a wide range of weather and operating conditions under several different control strategies. The model predictions compare well with the experimental data. The effective thermal capacity of the floor slab was found being an important parameter in calibrating the model results with the experimental data. Three different control strategies for improving the temperature regulation in RFH systems are proposed. These are: a multistage on-off control, an augmented constant gain control (ACGC) and a variable gain control (VGC). Simulation results show that the multistage control maintains zone air temperature close to the setpoint better than the existing on-off control scheme does. Likewise, ACGC gives good zone temperature control compared to the classical proportional control. A model based approach for updating the controller gains of the VGC is proposed. Both ACGC and VGC are shown to be robust to changes in weather conditions and internal heat gains. The advantage of the control strategies proposed in this thesis is that they eliminate the use of outdoor temperature sensor required in some existing control schemes. Being simple and robust, the multistage control scheme with two stages and the ACGC are good candidate controls for RFH systems

Fast micro-differential evolution for topological active net optimization

This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a ``best improvement local search'' (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm

Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

Interaction of XRCC1

Background. To explore the correlation between the Arg399Gln polymorphism and susceptibility to esophageal cancer in Korean and Han Chinese individuals in Harbin, China, and its potential interaction with alcohol consumption. Methods. This prospective study included 203 patients with esophageal squamous cell carcinoma; 88 were of Korean descent and 115 were of Han Chinese descent. A group of healthy controls included 105 participants of Korean descent and 105 of Han Chinese descent. Genotyping of the Arg399Gln locus of XRCC1 was performed by PCR-RFLP. Results. The allelic and genotypic frequencies were not significantly different between individuals with esophageal cancer and controls or between individuals of Korean and Han Chinese descent (P>0.05). However, when individuals with the wild-type Arg/Arg genotype also consumed alcohol, the risk of esophageal cancer was lower (OR = 3.539; 95% CI = 2.039–6.142; P<0.05). Conclusions. The XRCC1 Arg399Gln polymorphism does not appear to be associated with esophageal cancer in individuals of Korean or Han Chinese descent in Harbin, China. However, alcohol consumption may decrease the risk of esophageal cancer in persons with the wild-type genotype

{μ-6,6′-Dimeth­oxy-2,2′-[propane-1,3-diylbis(nitrilo­methyl­idyne)]diphenolato}trinitratocopper(II)erbium(III) acetone solvate

In the title complex, [CuEr(C19H20N2O4)(NO3)3]·CH3COCH3, the CuII ion is coordinated in a square-planar environment by two O atoms and two N atoms of a Schiff base ligand. The ErIII ion is bis-chelated by three nitrate ligands and coordinated by four O atoms of the Schiff base ligand in a slightly distorted bicapped square-anti­prismatic environment