No abstract

An ongoing outbreak of pneumonia associated with 2019 novel coronavirus was reported in China. It is unclear whether the virus is infective exists during the incubation period, although person-to-person transmission has been reported elsewhere. We report the epidemiological features of a familial cluster of 4 patients in Shanghai, including an 88-year-old man with limited mobility who was exposed only to asymptomatic family members whose symptoms developed later. The epidemiological evidence has shown possible transmission of 2019 novel coronavirus during the incubation period.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters.

Lung cancer is the leading cause of cancer-related deaths worldwide. To identify genetic factors that modify the risk of lung cancer in individuals of Chinese ancestry, we performed a genome-wide association scan in 5,408 subjects (2,331 individuals with lung cancer (cases) and 3,077 controls) followed by a two-stage validation among 12,722 subjects (6,313 cases and 6,409 controls). The combined analyses identified six well-replicated SNPs with independent effects and significant lung cancer associations (P < 5.0 × 10(-8)) located in TP63 (rs4488809 at 3q28, P = 7.2 × 10(-26)), TERT-CLPTM1L (rs465498 and rs2736100 at 5p15.33, P = 1.2 × 10(-20) and P = 1.0 × 10(-27), respectively), MIPEP-TNFRSF19 (rs753955 at 13q12.12, P = 1.5 × 10(-12)) and MTMR3-HORMAD2-LIF (rs17728461 and rs36600 at 22q12.2, P = 1.1 × 10(-11) and P = 6.2 × 10(-13), respectively). Two of these loci (13q12.12 and 22q12.2) were newly identified in the Chinese population. These results suggest that genetic variants in 3q28, 5p15.33, 13q12.12 and 22q12.2 may contribute to the susceptibility of lung cancer in Han Chinese.