The membrane lipids from fast-elongating wild-type cotton (Gossypium hirsutum) fibers at 10 days post-anthesis, wild-type ovules with fiber cells removed, and ovules from the fuzzless-lintless mutant harvested at the same age, were extracted, separated, and quantified. Fiber cells contained significantly higher amounts of phosphatidylinositol (PI) than both ovule samples with PI 34:3 being the most predominant species. The genes encoding fatty acid desaturases (Δ(15)GhFAD), PI synthase (PIS) and PI kinase (PIK) were expressed in a fiber-preferential manner. Further analysis of phosphatidylinositol monophosphate (PIP) indicated that elongating fibers contained four- to five-fold higher amounts of PIP 34:3 than the ovules. Exogenously applied linolenic acid (C18:3), soybean L-α-PI, and PIPs containing PIP 34:3 promoted significant fiber growth, whereas a liver PI lacking the C18:3 moiety, linoleic acid, and PIP 36:2 were completely ineffective. The growth inhibitory effects of carbenoxolone, 5-hydroxytryptamine, and wortmannin were reverted by C18:3, PI, or PIP, respectively, suggesting that PIP signaling is essential for fiber cell growth. Furthermore, cotton plants expressing virus-induced gene-silencing constructs that specifically suppressed GhΔ(15)FAD, GhPIS, or GhPIK expression, resulted in significantly short-fibered phenotypes. Our data provide the basis for in-depth studies on the roles of PI and PIP in mediating cotton fiber growth.

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Fault early warning of equipment in nuclear power plant can effectively reduce unplanned forced shutdown and avoid significant safety accidents. This paper presents a Bayesian Long Short-Term Memory (LSTM) neural network method for fault early warning method of nuclear power turbine. The Long Short-Term Memory neural network prediction model is developed to address data uncertainty while taking into account complicated situation of the equipment operation. Quantitative reliability validation method is established based on Bayesian inference. A wavelet packet multi-scale time-frequency analysis is employed for data denoising. A Probabilistic Principal Component Analysis (PPCA) method combined with key factor analysis is proposed for dimension reduction and dealing with the data uncertainty. The principal component inverse search method is developed to identify the critical factors mainly contributing to the turbine fault. Numerical results indicate that the proposed novel model is validated with Bayesian confidence of 92% by using the real-world steam turbine data and the model can provide accurate warning in the early creep stage of the fault. INDEX TERMS Bayesian inference, long short-term memory, discrete wavelet packet transform, nuclear power turbine, probabilistic principal component analysis.

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