BackgroundCold stress can cause serious abiotic damage that limits the growth, development and yield of rice. Cold tolerance during the booting stage of rice is a key factor that can guarantee a high and stable yield under cold stress. The cold tolerance of rice is controlled by quantitative trait loci (QTLs). Based on the complex genetic basis of cold tolerance in rice, additional efforts are needed to detect reliable QTLs and identify candidate genes. In this study, recombinant inbred lines (RILs) derived from a cross between a cold sensitive variety, Dongnong422, and strongly cold-tolerant variety, Kongyu131, were used to screen for cold-tolerant loci at the booting stage of rice.ResultsA novel major QTL, qPSST6, controlling the percent seed set under cold water treatment (PSST) under the field conditions of 17 °C cold water irrigation was located on the 28.4 cM interval on chromosome 6. Using the combination of bulked-segregant analysis (BSA) and next-generation sequencing (NGS) technology (Seq-BSA), a 1.81 Mb region that contains 269 predicted genes on chromosome 6 was identified as the candidate region of qPSST6. Two genes, LOC_Os06g39740 and LOC_Os06g39750, were annotated as “response to cold” by gene ontology (GO) analysis. qRT-PCR analysis revealed that LOC_Os06g39750 was strongly induced by cold stress. Haplotype analysis also demonstrate a key role of LOC_Os06g39750 in regulating the PSST of rice, suggesting that it was the candidate gene of qPSST6.ConclusionsThe information obtained in this study is useful for gene cloning of qPSST6 and for breeding cold-tolerant varieties of rice using marker assisted selection (MAS).Electronic supplementary materialThe online version of this article (10.1186/s12284-018-0218-1) contains supplementary material, which is available to authorized users.

Heavy metal exposure is a serious environmental stress in plants. However, plants have evolved several strategies to improve their heavy metal tolerance. Heavy metal-associated proteins (HMPs) participate in heavy metal detoxification. Here, we identified 46 and 55 HMPs in rice and Arabidopsis, respectively, and named them OsHMP 1-46 and AtHMP 1-55 according to their chromosomal locations. The HMPs from both plants were divided into six clades based on the characteristics of their heavy metal-associated domains (HMA). The HMP gene structures and motifs varied greatly among the different classifications. The HMPs had high collinearity and were segmentally duplicated. A cis-element analysis revealed that the HMPs may be regulated by different transcription factors. An expression profile analysis disclosed that only eight OsHMPs were constitutive in rice tissues. Of these, the expression of OsHMP37 was far higher than that of the other seven genes while OsHMP28 was expressed exclusively in the roots. For Arabidopsis, nine AtHMPs presented with very high transcript levels in all organs. Most of the selected OsHMPs were differentially expressed in various tissues under different heavy metal stresses. Only OsHMP09, OsHMP18, and OsHMP22 showed higher expression levels in all tissues under different heavy metal stresses. In contrast, most of the selected AtHMPs had nearly constant expression levels in different tissues under various heavy metal stresses. The AtHMP20, AtHMP23, AtHMP25, AtHMP31, AtHMP35, AtHMP46 expression levels under different heavy metal stresses were higher in the leaves and roots. The foregoing discoveries elucidated HMP evolution in monocotyledonous and dicotyledonous plants and may helpful functionally characterize HMPs in the future.

To eliminate the limitations of the peak‐over‐threshold (POT) extrapolation method, the paper proposes an improved POT extrapolation method to reconstruct the load sequence and frequency of the part. A threshold model is established based on a generalized Pareto distribution (GPD). Assuming a road surface's irregularity obeys a normal distribution, the load between the upper and lower thresholds is reconstructed to obtain more dynamic road load data. Rain‐flow counting method is used to calculate the peak‐to‐valley value and frequency of the load signal, and the characteristics of the load mean and amplitude before and after the improvement of the POT extrapolation method were discussed. The effectiveness and conservativeness of the enhanced method are verified by predicting the fatigue life of a swing support rod.

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The bootstrap method is mostly used to estimate statistical characteristics of small sample data. However, the limitations of the bootstrap method itself lead to a reduction in the reliability of small-sample estimates. In this article, an improved bootstrap method is developed to address this problem. In the statistically significant error range (the sample average error and the limit error of sampling) of the original single sample data, expanding the virtual test data that obey two distributions to overcome the limitations of the bootstrap method itself. This article compares and analyses these two methods through the case; the result indicates that the improved bootstrap method can enhance the reliability of the estimation results without changing its probability distribution. We also discussed how to reduce the fluctuation of the improved bootstrap method. And the effectiveness and feasibility of this improved method are discussed in the analysis of fatigue life test data.