Functional study of the soybean stamen-preferentially expressed gene GmFLA22a in regulating male fertility.

China has a high dependence on soybean imports, yield increase at a faster rate is an urgent problem that need to be solved at present. The application of heterosis is one of the effective ways to significantly increase crop yield. In recent years, the development of an intelligent male sterility system based on recessive nuclear sterile genes has provided a potential solution for rapidly harnessing the heterosis in soybean. However, research on male sterility genes in soybean has been lagged behind. Based on transcriptome data of soybean floral organs in our research group, a soybean stamen-preferentially expressed gene GmFLA22a was identified. It encodes a fasciclin-like arabinogalactan protein with the FAS1 domain, and subcellular localization studies revealed that it may play roles in the endoplasmic reticulum. Take advantage of the gene editing technology, the Gmfla22a mutant was generated in this study. However, there was a significant reduction in the seed-setting rate in the mutant plants at the reproductive growth stage. The pollen viability and germination rate of Gmfla22a mutant plants showed no apparent abnormalities. Histological staining demonstrated that the release of pollen grains in the mutant plants was delayed and incomplete, which may due to the locule wall thickening in the anther development. This could be the reason of the reduced seed-setting rate in Gmfla22a mutants. In summary, our study has preliminarily revealed that GmFLA22a may be involved in regulating soybean male fertility. It provides crucial genetic materials for further uncovering its molecular function and gene resources and theoretical basis for the utilization of heterosis in soybean.

Estimation of respiration rate from three-dimensional acceleration data based on body sensor network.

Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.