As one of the most devastating diseases of rice, sheath blight causes severe rice yield loss. However, little progress has been made in rice breeding for sheath blight resistance. It has been reported that polygalacturonase inhibiting proteins can inhibit the degradation of the plant cell wall by polygalacturonases from pathogens. Here, we prokaryotically expressed and purified OsPGIP1 protein, which was verified by Western blot analysis. Activity assay confirmed the inhibitory activity of OsPGIP1 against the PGase from Rhizoctonia solani. In addition, the location of OsPGIP1 was determined by subcellular localization. Subsequently, we overexpressed OsPGIP1 in Zhonghua 11 (Oryza sativa L. ssp. japonica), and applied PCR and Southern blot analysis to identify the positive T0 transgenic plants with single-copy insertions. Germination assay of the seeds from T1 transgenic plants was carried out to select homozygous OsPGIP1 transgenic lines, and the expression levels of OsPGIP1 in these lines were analyzed by quantitative real-time PCR. Field testing of R. solani inoculation showed that the sheath blight resistance of the transgenic rice was significantly improved. Furthermore, the levels of sheath blight resistance were in accordance with the expression levels of OsPGIP1 in the transgenic lines. Our results reveal the functions of OsPGIP1 and its resistance mechanism to rice sheath blight, which will facilitate rice breeding for sheath blight resistance.

Background Meta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis. Methods In this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs). To further advance the literature, we also introduce a new method of the continuity correction for estimating the relative risk. Results From the simulation studies, the new method performs well in terms of mean squared error when there are few studies. In contrast, the generalized linear mixed model performs the best when the number of studies is large. In addition, by reanalyzing recent coronavirus disease 2019 (COVID-19) data, it is evident that the double-zero-event studies impact the estimate of the mean effect size. Conclusions We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis, or instead use the GLMM when the number of studies is large. The double-zero-event studies may be informative, and so we suggest not excluding them.

We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling's tests, our proposed test statistics display some interesting characteristics. In particular, they are a summation of the log-transformed squared -statistics rather than a direct summation of those components. More importantly, to derive the asymptotic normality of our test statistics under the null and local alternative hypotheses, we do not need the requirement that the covariance matrices follow a diagonal matrix structure. As a consequence, our proposed test methods are very flexible and readily applicable in practice. Simulation studies and a real data analysis are also carried out to demonstrate the advantages of our likelihood ratio test methods.

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The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.