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Acetylcholine has long been suggested to play a role in controlling physiological processes in plants, but no mechanism has been shown for its action. We show here that a chloride channel in the tonoplast (vacuolar membrane) of Chara corallina responds to acetylcholine. The channel has a conductance of 45 pS. The effect of acetylcholine is enhanced by nicotine, with the open probability increasing from 0.05 in the presence of 4 mM acetylcholine to 0.3 in the presence of 4 mM acetylcholine + 6 mM nicotine. Some effects of acetylcholine were seen at concentrations as low as 20 microM, with a maximum effect between 1 and 10 mM. In the intact cell, acetylcholine prolongs the depolarized phase of the action potential. We propose that this acetylcholine-gated channel has evolved separately from the mammalian acetylcholine-gated channel, and suggest that this represents a third form of acetylcholine signal transduction, after the nicotinic and muscarinic pathways in animal systems.

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Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This paper reviews recent advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.

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Owing to the constraints of time and space complexity, network intrusion detection systems (NIDSs) based on support vector machines (SVMs) face the “curse of dimensionality” in a large-scale, high-dimensional feature space. This study proposes a joint training model that combines a stacked autoencoder (SAE) with an SVM and the kernel approximation technique. The training model uses the SAE to perform feature dimension reduction, uses random Fourier features to perform kernel approximation, and then random Fourier mapping is explicitly applied to the sub-sample to generate the random feature space, making it possible to apply a linear SVM to uniformly approximate to the Gaussian kernel SVM. Finally, the SAE performs joint training with the efficient linear SVM. We studied the effects of an SAE structure and a random Fourier feature on classification performance, and compared that performance with that of other training models, including some without kernel approximation. At the same time, we compare the accuracy of the proposed model with that of other models, which include basic machine learning models and the state-of-the-art models in other literatures. The experimental results demonstrate that the proposed model outperforms the previously proposed methods in terms of classification performance and also reduces the training time. Our model is feasible and works efficiently on large-scale datasets.