Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-theart performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves training efficiency. Meanwhile, any two inputs at different times are connected directly by a self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).
The last 40 years have witnessed how p53 rose from a viral binding protein to a central factor in both stress responses and tumor suppression. The exquisite regulation of p53 functions is of vital importance for cell fate decisions. Among the multiple layers of mechanisms controlling p53 function, posttranslational modifications (PTMs) represent an efficient and precise way. Major p53 PTMs include phosphorylation, ubiquitination, acetylation, and methylation. Meanwhile, other PTMs like sumoylation, neddylation, O-GlcNAcylation, adenosine diphosphate (ADP)-ribosylation, hydroxylation, and β-hydroxybutyrylation are also shown to play various roles in p53 regulation. By independent action or interaction, PTMs affect p53 stability, conformation, localization, and binding partners. Deregulation of the PTM-related pathway is among the major causes of p53-associated developmental disorders or diseases, especially in cancers. This review focuses on the roles of different p53 modification types and shows how these modifications are orchestrated to produce various outcomes by modulating p53 activities or targeted to treat different diseases caused by p53 dysregulation.
In Gram-negative bacteria, the assembly of β-barrel outer-membrane proteins (OMPs) requires the β-barrel-assembly machinery (BAM) complex. We determined the crystal structure of the 200-kDa BAM complex from Escherichia coli at 3.55-Å resolution. The structure revealed that the BAM complex assembles into a hat-like shape, in which the BamA β-barrel domain forms the hat's crown embedded in the outer membrane, and its five polypeptide transport-associated (POTRA) domains interact with the four lipoproteins BamB, BamC, BamD and BamE, thus forming the hat's brim in the periplasm. The assembly of the BAM complex creates a ring-like apparatus beneath the BamA β-barrel in the periplasm and a potential substrate-exit pore located at the outer membrane-periplasm interface. The complex structure suggests that the chaperone-bound OMP substrates may feed into the chamber of the ring-like apparatus and insert into the outer membrane via the potential substrate-exit pore in an energy-independent manner.
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