The COVID-19 pandemic caused by the SARS-CoV-2 virus continually poses serious threats to global public health. The main protease (Mpro) of SARS-CoV-2 plays a central role in viral replication. We designed and synthesized 32 new bicycloproline-containing Mpro inhibitors derived from either Boceprevir or Telaprevir, both of which are approved antivirals. All compounds inhibited SARS-CoV-2 Mpro activity in vitro with IC50 values ranging from 7.6 to 748.5 nM. The co-crystal structure of Mpro in complex with MI-23, one of the most potent compounds, revealed its interaction mode. Two compounds (MI-09 and MI-30) showed excellent antiviral activity in cell-based assays. In a SARS-CoV-2 infection transgenic mouse model, oral or intraperitoneal treatment with MI-09 or MI-30 significantly reduced lung viral loads and lung lesions. Both also displayed good pharmacokinetic properties and safety in rats.

Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pretrained model. It is the first time copying words from the source context and fully pretraining a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin. The code and pre-trained models are released at https://github.com/zhawe01/fairseq-gec.

Migration has affected a large number of children in many settings. Despite growing attention to these children, important gaps remain in our understanding of their psychosocial development, as well as the factors that mediate and moderate the impact of migration on children. The present study examines the influences of migration on children’s psychosocial well-being in China using a new nationally representative survey. We compared different groups of children age 3-15, including migrant children, left-behind children, and rural and urban children in nonmigrant families. Results show that rural children left behind by both parents were significantly worse off in psychological and behavioral well-being than rural nonmigrant children. By contrast, rural children left behind by one parent and migrant children were no worse off. The disadvantage of left-behind children was mediated by their caregivers’ emotional well-being and parenting practices. Frequent contact with migrant parents, but not receipt of remittances, helped ameliorate the vulnerability of left-behind children. These results add to our understanding of how migration affects child development in general.

ZSM-5 zeolite nanoboxes with accessible mesomicro-pore architecture and strong acid sites are important in relevant heterogeneous catalysis suffering from mass transfer limitations and weak acidities.R ational design of parent zeolites with concentrated and non-protective coordination of Al species can facilitate post-synthetic treatment to produce mesoporous ZSM-5 nanoboxes.I nt his work, as imple and effective method was developed to convert parent MFI zeolites with tetrahedral extra-framework Al into Al-enriched mesoporous ZSM-5 nanoboxes with low silicon-to-aluminium ratios of % 16. The parent MFI zeolite was prepared by rapid ageing of the zeolite sol gel synthesis mixture.The accessibility to the meso-micro-porous intra-crystalline network was probed systematically by comparative pulsed field gradient nuclear magnetic resonance diffusion measurements,w hich, together with the strong acidity of nanoboxes,provided superb catalytic activity and longevity in hydrocarbon cracking for propylene production.

This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge (Ostermann et al., 2018b).We use Threeway Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/ intfloat/commonsense-rc.

This paper presents a new sequence-tosequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks. Unlike encoder-only (e.g., BERT) or decoder-only (e.g., OpenAI GPT) pre-training approaches, PoDA jointly pretrains both the encoder and decoder by denoising the noise-corrupted text, and it also has the advantage of keeping the network architecture unchanged in the subsequent fine-tuning stage. Meanwhile, we design a hybrid model of Transformer and pointer-generator networks as the backbone architecture for PoDA. We conduct experiments on two text generation tasks: abstractive summarization, and grammatical error correction. Results on four datasets show that PoDA can improve model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. 1

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