instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
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The infection of a novel coronavirus found in Wuhan of China (2019-nCoV) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for 2019-nCoV, various strategies are being tested in China, including drug repurposing. In this study, we used our pretrained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of 2019-nCoV. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing a inhibitory potency with Kd of 94.94 nM against the 2019-nCoV 3C-like proteinase, followed by efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of 2019-nCoV with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra, could be used for the treatment of 2019-nCoV, although there is no real-world evidence supporting the prediction. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for 2019-nCoV.
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China through a drug-target interaction deep learning model

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This study uses panel regression tests to examine the response of hotel performance to international tourism development and crisis events in Taiwan. Hotel performance measures are revenue (revenue per available room and occupancy rate), profitability (return on assets and return on equity) and stock performance. The crises were the earthquake on September 21, 1999 (the 9/21 earthquake), the terrorist attacks of September 11, 2001 in the US (the 9/11 terrorist attacks) and the outbreak of Severe Acute Respiratory Syndrome on April 22, 2003 (the SARS outbreak). This study makes four major contributions. First, test results confirm that international tourism development (ITD), proxied by the growth of total inbound tourist arrivals, has a more direct influence on hotel sales and profitability than it does on hotel stock performance. Second, this study identifies that the absence of a strong tie between ITD and hotel stock returns that was found in previous studies is due to the time-varying discount rate caused by investors' changing expectations for the prospect of future cash flows from holding hotel stocks. Third, this study finds new evidence that while the poor performance of hotel stocks caused by the 9/21 earthquake and the 9/11 terrorist attacks was attributed to the loss of hotel sales revenue, the adverse effect of the SARS outbreak on hotel stock returns is attributed not only to decreased hotel sales revenue but also to the increased discount rate. Lastly, this study is the first to investigate whether the response of hotel stock returns to ITD depends on the state of economy and concludes that the response of hotel stock performance to ITD in business cycle contraction is statistically different from that in business cycle expansion. Further, although the influence of ITD on hotel stock performance is still irrelevant during expansion periods, ITD can significantly enhance hotel stock returns during contraction periods.
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The response of hotel performance to international tourism development and crisis events

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Introduction: N95 or higher filtering respirators have been recommended in healthcare settings, although there is still a risk of infection due to the improper selection and wearing of respirators. We aimed to assess the effects of training with N95 or higher filter respirators on the protection performance of respirators among healthcare providers in the emergency medical center (EMC). Methods: This randomized crossover study evaluated 23 healthcare providers. Quantitative fit tests (QNFTs) were performed before and after training using three types of N95 or higher filter respirators (cup-type, fold-type, valve-type). Training was performed by lecture, real-time feedback, and fit check. The primary outcome was the fit factor, and the secondary outcomes were overall fit factor, adequate protection rate, and respiratory preference. Results: Fit factors, overall fit factor, and adequate protection rate were higher after training than before training for the 3 types of respirators (all P < .05). For normal breathing, fit factors before and after training were 121 (10-185) vs 192 (161-200) for cup-type, 200 (39-200) vs 200 (200-200) for fold-type, and 85 (18-157) vs 173 (117-200) for valve-type. For normal breathing, the adequate protection rates before and after training were 62 (0-100) vs 100 (90-100) for cup-type, 100 (0-100) vs 100 (100-100) for fold-type, and 19 (0-100) vs 100 (44-100) for valve-type (all P < .05). The most preferred respirator type was the valve-type (10 persons, 45.5%). Conclusions: Training on wearing an N95 or higher respirator improved the protection performance of respirators among healthcare providers working in the EMC. The selection of proper respirators and training would be beneficial to the safety of healthcare providers. Abbreviations: CDC = American Centers for Disease Control and Prevention, EMC = emergency medical center, FFP = filtering facepiece respirator, MERS = Middle East respiratory syndrome, NIOSH = National Institute for Occupational Safety and Health, OSHA = Occupational Safety and Health Administration, PPE = personal protective equipment, QLFT = qualitative fit tests, QNFT = quantitative fit tests.
answer:
Comparison of fit factors among healthcare providers working in the Emergency Department Center before and after training with three types of N95 and higher filter respirators