MiR-10b alleviates high glucose-induced human retinal endothelial cell injury by regulating TIAM1 signaling

Purpose: To investigate the effects of microRNA (miR)-10b on high glucose (HG)-induced human retinal endothelial cell (HREC) injury and the mechanisms involved.Methods: Levels of miR-10b were measured in HRECs using quantitative reverse transcriptasepolymerase chain reaction (qRT-PCR) after the addition of glucose (5.5 and 30 mM). Cell viability was measured using Cell Counting Kit-8 assay, while levels of reactive oxygen species (ROS) weredetermined using fluorimetry. An enzyme-linked immunosorbent assay (ELISA) was used to measure cellular apoptosis. Luciferase reporter assay was used to validate the miR-10b-binding sites of target genes. The levels of T-cell lymphoma invasion and metastasis (TIAM1) and NADPH oxidase-2 (NOX2) were determined using qRT-PCR. Ras-related C3 botulinum toxin substrate 1 (Rac1) activation was evaluated using a pull-down assay. The protein levels of TIAM1 and Rac1 were assayed by western blotting.Results: After HG stimulation, miR-10b expression was downregulated. Viability of HRECs decreased, whereas ROS production increased. However, the overexpression of miR-10b inhibited apoptosis and ROS production in HG-treated HRECs (p < 0.05), while luciferase reporter analysis revealed a possible binding site for miR-10b to target the 3'-untranslated region (UTR) of TIAM1. In addition, the overexpression of miR-10b distinctly reduced the expression levels of TIAM1 and NOX2, but decreased the activation of Rac1 in HG-treated HRECs (p < 0.05); these inhibitory effects of miR-10b were significantly reversed after TIAM1 application.Conclusion: MiR-10b alleviates HG-induced HREC injury by regulating TIAM1 signaling. MiR-10b therapy is a potential therapeutic strategy for patients suffering from diabetic retinopathy. Keywords: MicroRNA-10b, Human retinal endothelial cells, High glucose, TIAM1-Rac1 axi

Retracted: MiR-10b alleviates high glucose-induced human retinal endothelial cell injury by regulating TIAM1 signaling

This article previously published in Volume 19 Issue 8 of this journal in August 2020 has been retracted in line with the guidelines from the Committee on Publication Ethics (COPE, http://publication ethics.org/resources/guidelines).Retraction: Chen Y, Zhu Y, Zhao S. MiR-10b alleviates high glucose-induced human retinal endothelial cell injury by regulating TIAM1 signaling. Trop J Pharm Res, 2020, 19(8): 1577-1583.To the editor:I am retracting this article because some of the results we presented are irreproducible.Signed: Sheng Zha

Phosphorus recovery from sludge by pH enhanced anaerobic fermentation and vivianite crystallization

Phosphorus (P) shortage is a global issue. However, P recovery from waste activated sludge (WAS) has huge potential. In this study, an innovative method for the recovery of P from WAS via pH adjustment-enhanced anaerobic fermentation (AF) and vivianite crystallization was developed. The results indicate that P could be effectively released from WAS to the supernatant under an appropriate pH during AF. P release efficiency increased by 31.6 % at pH 5.0 and 26.1 % at pH 11.0 compared to the control. Over 99 % of the P in the liquid could be recovered by subsequent vivianite crystallization, and similar to 60 % total P recovery efficiency was obtained. The scanning electron microscopy and X-ray diffraction analyses showed that the co-precipitation of Ca2+ and Mg2+ affected vivianite purity. The recovered vivianite purity from the pH 11.0 supernatant (91.39 %) was higher than the pH 5.0 supernatant (85.44 %) because of lower Ca2+ and Mg2+ ions in the former. In addition, the heavy metals in the recovered vivianite were lower than their own risk thresholds. This study provides new insights into the recovery of P from WAS by pH adjustment-enhanced AF and vivianite crystallization

NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers

Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, and styles (e.g., singing). Current large TTS systems usually quantize speech into discrete tokens and use language models to generate these tokens one by one, which suffer from unstable prosody, word skipping/repeating issue, and poor voice quality. In this paper, we develop NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual vector quantizers to get the quantized latent vectors and uses a diffusion model to generate these latent vectors conditioned on text input. To enhance the zero-shot capability that is important to achieve diverse speech synthesis, we design a speech prompting mechanism to facilitate in-context learning in the diffusion model and the duration/pitch predictor. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, robustness, and voice quality in a zero-shot setting, and performs novel zero-shot singing synthesis with only a speech prompt. Audio samples are available at https://speechresearch.github.io/naturalspeech2.Comment: A large-scale text-to-speech and singing voice synthesis system with latent diffusion model

Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability

Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic speech recognition. In this paper, we describe our recent development of RNN-T models with reduced GPU memory consumption during training, better initialization strategy, and advanced encoder modeling with future lookahead. When trained with Microsoft's 65 thousand hours of anonymized training data, the developed RNN-T model surpasses a very well trained hybrid model with both better recognition accuracy and lower latency. We further study how to customize RNN-T models to a new domain, which is important for deploying E2E models to practical scenarios. By comparing several methods leveraging text-only data in the new domain, we found that updating RNN-T's prediction and joint networks using text-to-speech generated from domain-specific text is the most effective.Comment: Accepted by Interspeech 202