Email Interview with Professor Jianhua Bai and his wife, owner of Hunan Gardens, Jean Wang

Professor Bai and Jean Wang talk about moving to Knox County as well as Chinese food and culture.https://digital.kenyon.edu/elfs_interviews/1074/thumbnail.jp

Spatial clustering and common regulatory elements correlate with coordinated gene expression

Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes. In prokaryotic cells, a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression. In eukaryotic cells, transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene. To examine how local gene environment and transcription factor regulation are coupled, we performed a combined analysis of time-course RNA-seq data of TGF-\b{eta} treated MCF10A cells and related epigenomic and Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered differentially expressed genes based on gene expression profiles and associated transcription factors. Genes in each class have similar temporal gene expression patterns and share common transcription factors. Next, we defined a set of linear and radial distribution functions, as used in statistical physics, to measure the distributions of genes within a class both spatially and linearly along the genomic sequence. Remarkably, genes within the same class despite sometimes being separated by tens of million bases (Mb) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do. Analyses extended to the process of mouse nervous system development arrived at similar conclusions. Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression.Comment: 30 pages, 9 figures, accepted in PLoS Computational Biolog

Myricetin promotes peripheral nerve regeneration in rat model of sciatic nerve injury via regulation of BDNFAkt/ GSK-3β/mTOR signalling pathway

Purpose: To investigate the effects of myricetin on peripheral nerve regeneration in sciatic nerve crush injury model.Methods: Separate groups of rats were administered myricetin at 25, 50 or 100 mg/kg body weight/day for 2 weeks. Functional recovery following sciatic nerve injury was assessed by foot position and walking track analyses, measurement of mechanical hyperalgesia, and withdrawal reflex latency (WRL).Results: Myricetin treatment resulted in significantly enhanced recovery of sensorimotor functions as evidenced by increased scores in functional analysis tests. Myricetin treatment remarkably elevated brain derived neurotrophic factor (BDNF) expression, and also enhanced activation of Akt and mTORc1, reflecting up-regulation of PI3K/Akt/mTORC1 signalling involved in nerve regeneration.Conclusion: Myricetin enhances functional recovery and nerve regeneration in rats. These findings suggest that myricetin is a potent neuroprotective agent with potential for the management of peripheral nerve injury.Keywords: Glycogen synthase kinase 3β, Mammalian target of rapamycin (mTOR), Myricetin, Nerve regeneratio

Self-Attention Transducers for End-to-End Speech Recognition

Recurrent neural network transducers (RNN-T) have been successfully applied in end-to-end speech recognition. However, the recurrent structure makes it difficult for parallelization . In this paper, we propose a self-attention transducer (SA-T) for speech recognition. RNNs are replaced with self-attention blocks, which are powerful to model long-term dependencies inside sequences and able to be efficiently parallelized. Furthermore, a path-aware regularization is proposed to assist SA-T to learn alignments and improve the performance. Additionally, a chunk-flow mechanism is utilized to achieve online decoding. All experiments are conducted on a Mandarin Chinese dataset AISHELL-1. The results demonstrate that our proposed approach achieves a 21.3% relative reduction in character error rate compared with the baseline RNN-T. In addition, the SA-T with chunk-flow mechanism can perform online decoding with only a little degradation of the performance