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In this article, a new low‐profile broadband circularly polarized antenna with a single‐layer metasurface is designed. The metasurface is composed of 4 × 4 rotated rectangle‐loops. Compared to single rotated rectangle, introducing inner‐cut rectangle slot can increase the design flexibilities by changing this slot size for wider circularly polarized operating bandwidth and reduce the size of the antenna in same frequency. The proposed antenna has the advantages of a wide 3‐dB axial ratio bandwidth from 5.4 to 6.05 GHz and an excellent 10‐dB impedance bandwidth from 5 to 6.05 GHz.

bDuring development, proneural transcription factors of the basic helix-loop-helix (bHLH) family are required to commit cells to a neural fate. In Drosophila neurogenesis, a key mechanism promoting sense organ precursor (SOP) fate is the synergy between proneural factors and their coactivator Senseless in transcriptional activation of target genes. Here we present evidence that posttranslational modification by SUMO enhances this synergy via an effect on Senseless protein. We show that Senseless is a direct target for SUMO modification and that mutagenesis of a predicted SUMOylation motif in Senseless reduces Senseless/ proneural synergy both in vivo and in cell culture. We propose that SUMOylation of Senseless via lysine 509 promotes its synergy with proneural proteins during transcriptional activation and hence regulates an important step in neurogenesis leading to the formation and maturation of the SOPs.

Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%. INDEX TERMS Deep learning, C-LSTM, epileptic seizure, high-dimension electroencephalogram (EEG).