Dataset Information

Endosperm cell size reduction caused by osmotic adjustment during nighttime warming in rice.

ABSTRACT: High night temperature (HNT) often reduces yield in field crops. In rice, HNT during the ripening stage diminishes endosperm cell size, resulting in a considerable reduction in final kernel weight; however, little is known about the underlying mechanisms at cell level. In this study, we performed picolitre pressure-probe-electrospray-ionization mass spectrometry to directly determine metabolites in growing inner endosperm cells of intact seeds produced under HNT conditions, combining with ¹³C feeding and water status measurements including in situ turgor assay. Microscopic observation in the inner zone suggested that approximately 24.2% of decrease in cell expansion rate occurred under HNT at early ripening stage, leading to a reduction in cell volume. It has been shown that HNT-treated plants were subjected to mild shoot water deficit at night and endosperm cell turgor was sustained by a decline in osmotic potential. Cell metabolomics also suggests that active solute accumulation was caused by a partial inhibition of wall and starch biosynthesis under HNT conditions. Because metabolites were detected in the single cells, it is concluded that a partial arrest of cell expansion observed in the inner endosperms was caused by osmotic adjustment at mild water deficit during HNT conditions.

SUBMITTER: Wada H

PROVIDER: S-EPMC7904791 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Endosperm cell size reduction caused by osmotic adjustment during nighttime warming in rice.

Wada Hiroshi H Chang Fang-Yu FY Hatakeyama Yuto Y Erra-Balsells Rosa R Araki Takuya T Nakano Hiroshi H Nonami Hiroshi H

Scientific reports 20210224 1

High night temperature (HNT) often reduces yield in field crops. In rice, HNT during the ripening stage diminishes endosperm cell size, resulting in a considerable reduction in final kernel weight; however, little is known about the underlying mechanisms at cell level. In this study, we performed picolitre pressure-probe-electrospray-ionization mass spectrometry to directly determine metabolites in growing inner endosperm cells of intact seeds produced under HNT conditions, combining with <sup>1 ...[more]

PMID: 33627723

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Project description:BACKGROUND: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm that the classifier is robust with good generalization performance to new examples, or at least that it performs better than random guessing. A suggested alternative is to obtain a confidence interval of the error rate using repeated design and test sets selected from available examples. However, it is known that even in the ideal situation of repeated designs and tests with completely novel samples in each cycle, a small test set size leads to a large bias in the estimate of the true variance between design sets. Therefore different methods for small sample performance estimation such as a recently proposed procedure called Repeated Random Sampling (RSS) is also expected to result in heavily biased estimates, which in turn translates into biased confidence intervals. Here we explore such biases and develop a refined algorithm called Repeated Independent Design and Test (RIDT). RESULTS: Our simulations reveal that repeated designs and tests based on resampling in a fixed bag of samples yield a biased variance estimate. We also demonstrate that it is possible to obtain an improved variance estimate by means of a procedure that explicitly models how this bias depends on the number of samples used for testing. For the special case of repeated designs and tests using new samples for each design and test, we present an exact analytical expression for how the expected value of the bias decreases with the size of the test set. CONCLUSION: We show that via modeling and subsequent reduction of the small sample bias, it is possible to obtain an improved estimate of the variance of classifier performance between design sets. However, the uncertainty of the variance estimate is large in the simulations performed indicating that the method in its present form cannot be directly applied to small data sets.

Dataset Information

Endosperm cell size reduction caused by osmotic adjustment during nighttime warming in rice.

Publications

Endosperm cell size reduction caused by osmotic adjustment during nighttime warming in rice.

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