Effect of nitrogen fertilizer on the resistance of rice near-isogenic lines with BPH resistance genes.

BACKGROUND: Nitrogen is an essential macronutrient for plant growth and development. Crops with a high nitrogen input usually have high yields. However, outbreaks of brown planthoppers (Nilaparvata lugens; BPH) frequently occur on rice farms with excessive nitrogen inputs. Rice plants carrying BPH resistance genes are used for integrated pest management. Thus, the impact of nitrogen on the resistance of rice near-isogenic lines (NILs) with BPH resistance genes was investigated. RESULTS: We tested these NILs using a standard seedbox screening test and a modified bulk seedling test under different nitrogen treatments. The amount of nitrogen applied had an impact on the resistance of some lines with BPH resistance genes. In addition, three NILs (NIL-BPH9, NIL-BPH17, and NIL-BPH32) were further examined for antibiosis and antixenosis under varying nitrogen regimes. The N. lugens nymph population growth rate, honeydew excretion, female fecundity, and nymph survival rate on the three NILs were not affected by different nitrogen treatments except the nymph survival rate on NIL-BPH9 and the nymph population growth rate on NIL-BPH17. Furthermore, in the settlement preference test, the preference of N. lugens nymphs for IR24 over NIL-BPH9 or NIL-BPH17 increased under the high-nitrogen regime, whereas the preference of N. lugens nymphs for IR24 over NIL-BPH32 was not affected by the nitrogen treatments. CONCLUSIONS: Our results indicated that the resistance of three tested NILs did not respond to different nitrogen regimes and that NIL-BPH17 exerted the most substantial inhibitory effect on N. lugens growth and development.

The Impact of Climate Change on the Resistance of Rice Near-Isogenic Lines with Resistance Genes Against Brown Planthopper.

BACKGROUND: The impact of climate change on insect resistance genes is elusive. Hence, we investigated the responses of rice near-isogenic lines (NILs) that carry resistance genes against brown planthopper (BPH) under different environmental conditions. RESULTS: We tested these NILs under three environmental settings (the atmospheric temperature with corresponding carbon dioxide at the ambient, year 2050 and year 2100) based on the Intergovernmental Panel on Climate Change prediction. Comparing between different environments, two of nine NILs that carried a single BPH-resistant gene maintained their resistance under the environmental changes, whereas two of three NILs showed gene pyramiding with two maintained BPH resistance genes despite the environmental changes. In addition, two NILs (NIL-BPH17 and NIL-BPH20) were examined in their antibiosis and antixenosis effects under these environmental changes. BPH showed different responses to these two NILs, where the inhibitory effect of NIL-BPH17 on the BPH growth and development was unaffected, while NIL-BPH20 may have lost its resistance during the environmental changes. CONCLUSION: Our results indicate that BPH resistance genes could be affected by climate change. NIL-BPH17 has a strong inhibitory effect on BPH feeding on phloem and would be unaffected by environmental changes, while NIL-BPH20 would lose its ability during the environmental changes.

Confidence limits for conformance proportions in normal mixture models.

Conformance proportions are important numerical indices for quality assessments. When the population is characterized by a normal mixture model, estimating conformance proportions can be a practical issue. To account for the inherent structure of normal mixture models, universal and individual conformance proportions are first defined for the purpose of evaluating the overall population and specific subpopulations of interest, respectively. On the basis of generalized fiducial quantities, a systematic method is then proposed in this paper to obtain confidence limits for the two classes of conformance proportions. The simulation results demonstrate that the proposed method can maintain the empirical coverage rate sufficiently close to the nominal level. In addition, two examples are given to illustrate the proposed method.

The Role of Plant Abiotic Factors on the Interactions Between Cnaphalocrocis medinalis (Lepidoptera: Crambidae) and its Host Plant.

Atmospheric temperature increases along with increasing atmospheric CO2 concentration. This is a major concern for agroecosystems. Although the impact of an elevated temperature or increased CO2 has been widely reported, there are few studies investigating the combined effect of these two environmental factors on plant-insect interactions. In this study, plant responses (phenological traits, defensive enzyme activity, secondary compounds, defense-related gene expression and phytohormone) of Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Pyralidae) -susceptible and resistant rice under various conditions (environment, soil type, variety, C. medinalis infestation) were used to examine the rice-C. medinalis interaction. The results showed that leaf chlorophyll content and trichome density in rice were variety-dependent. Plant defensive enzyme activities were affected environment, variety, or C. medinalis infestation. In addition, total phenolic content of rice leaves was decreased by elevated CO2 and temperature and C. medinalis infestation. Defense-related gene expression patterns were affected by environment, soil type, or C. medinalis infestation. Abscisic acid and salicylic acid content were decreased by C. medinalis infestation. However, jasmonic acid content was increased by C. medinalis infestation. Furthermore, under elevated CO2 and temperature, rice plants had higher abscisic acid content than plants under ambient conditions. The adult morphological traits of C. medinalis also were affected by environment. Under elevated CO2 and temperature, C. medinalis adults had greater body length in the second and third generations. Taken together these results indicated that elevated CO2 and temperature not only affects plants but also the specialized insects that feed on them.

An Exhaustive Scan Method for SNP Main Effects and SNP × SNP Interactions Over Highly Homozygous Genomes.

Genome-wide association studies (GWAS) have been a powerful tool for exploring potential relationships between single-nucleotide polymorphisms (SNPs) and biological traits. For screening out important genetic variants, it is desired to perform an exhaustive scan over a whole genome. However, this is usually a challenging and daunting task in computation, due mainly to the large number of SNPs in GWAS. In this article, we propose a computationally effective algorithm for highly homozygous genomes. Pseudo standard error (PSE) is known to be a highly efficient and robust estimator for the standard deviation of a quantitative trait. We thus develop a statistical testing procedure for determining significant SNP main effects and SNP × SNP interactions associated with a quantitative trait based on PSE. A simulation study is first conducted to evaluate its empirical size and power. It is shown that the proposed PSE-based method can generally maintain the empirical size sufficiently close to the nominal significance level. However, the power investigation indicates that the PSE-based method might lack power in identifying significant effects for low-frequency variants if their true effect sizes are not large enough. A software is provided for implementing the proposed algorithm and its computational efficiency is evaluated through another simulation study. An exhaustive scan is usually done within a very reasonable runtime and a rice genome data set is analyzed by the software.

Statistical designs for two-color spotted microarray experiments.

Two-color cDNA or oligonucleotide-based spotted microarrays have been commonly used in measuring the expression levels of thousands of genes simultaneously. To realize the immense potential of this powerful new technology, budgeted within limited resources or other constraints, practical designs with high efficiencies are in demand. In this study, we address the design issue concerning the arrangement of the mRNA samples labeled with fluorescent dyes and hybridized on the slides. A normalization model is proposed to characterize major sources of systematic variation in a two-color microarray experiment. This normalization model establishes a connection between designs for two-color microarray experiments with a particular class of classical row-column designs. A heuristic algorithm for constructing A-optimal or highly efficient designs is provided. Statistical optimality results are found for some of the designs generated from the algorithm. It is believed that the constructed designs are the best or very close to the best possible for estimating the relative gene expression levels among the mRNA samples of interest.