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.

Q: The coronavirus (CoV) responsible for severe acute respiratory syndrome (SARS), SARS-CoV, encodes two large polyproteins (pp1a and pp1ab) that are processed by two viral proteases to yield mature non-structural proteins (nsps). Many of these nsps have essential roles in viral replication, but several have no assigned function and possess amino acid sequences that are unique to the CoV family. One such protein is SARS-CoV nsp1, which is processed from the N-terminus of both pp1a and pp1ab. The mature SARS-CoV protein is present in cells several hours post-infection and co-localizes to the viral replication complex, but its function in the viral life cycle remains unknown. Furthermore, nsp1 sequences are highly divergent across the CoV family, and it has been suggested that this is due to nsp1 possessing a function speciWc to viral interactions with its host cell or acting as a host speciWc virulence factor. In order to initiate structural and biophysical studies of SARS-CoV nsp1, a recombinant expression system and a puriWcation protocol have been developed, yielding milligram quantities of highly puriWed SARS-CoV nsp1. The puriWed protein was characterized using circular dichroism, size exclusion chromatography, and multi-angle light scattering.

A: Expression, puriWcation and characterization of recombinant severe acute respiratory syndrome coronavirus non-structural protein 1
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Q: 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.

A: China through a drug-target interaction deep learning model
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Q: Objective: To explore socio-demographic data of the population as proxies for risk factors in disease transmission modeling at different geographic scales. Methods: Patient records of confirmed H1N1 influenza were analyzed at three geographic aggregation levels together with population census statistics. Results: The study confirmed that four population factors were related in different degrees to disease incidence, but the results varied according to spatial resolution. The degree of association actually decreased when data of a higher spatial resolution were used. Conclusions: We concluded that variables at suitable spatial resolution may be useful in improving the predictive powers of models for disease outbreaks. HOSTED BY

A:
Effects of geographic scale on population factors in acute disease diffusion analysis
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