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.

[EX Q]: Bio-aerosols originate from different sources and their potentially pathogenic nature may form a hazard to healthcare workers and patients. So far no extensive review on existing evidence regarding bio-aerosols is available. This study aimed to review evidence on bio-aerosols in healthcare and the dental setting. The objectives were 1) What are the sources that generate bio-aerosols?; 2) What is the microbial load and composition of bio-aerosols and how were they measured?; and 3) What is the hazard posed by pathogenic micro-organisms transported via the aerosol route of transmission? Systematic scoping review design. Searched in PubMed and EMBASE from inception to 09-03-2016. References were screened and selected based on abstract and full text according to eligibility criteria. Full text articles were assessed for inclusion and summarized. The results are presented in three separate objectives and summarized for an overview of evidence. The search yielded 5,823 studies, of which 62 were included. Dental hand pieces were found to generate aerosols in the dental settings. Another 30 sources from human activities, interventions and daily cleaning performances in the hospital also generate aerosols. Fiftyfive bacterial species, 45 fungi genera and ten viruses were identified in a hospital setting and 16 bacterial and 23 fungal species in the dental environment. Patients with certain risk factors had a higher chance to acquire Legionella in hospitals. Such infections can lead to irreversible septic shock and death. Only a few studies found that bio-aerosol generating procedures resulted in transmission of infectious diseases or allergic reactions.
[EX A]: A scoping review on bio-aerosols in healthcare and the dental environment

[EX 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.
[EX A]: China through a drug-target interaction deep learning model

[EX Q]: The structure of the 5' GCGAUUUCUGACCGCUUUUUUGUCAG 3' RNA oligonucleotide was investigated using biochemical and chemical probes and nuclear magnetic resonance spectroscopy. Formation of a pseudoknot is indicated by the imino proton spectrum. Imino protons are observed consistent with formation of two helical stem regions; nuclear Overhauser enhancements between imino protons show that the two stem regions stack to form a continuous helix. In the stem regions, nucleotide conformations (3'-endo, anti) and internucleotide distances, derived from two-dimensional correlated, spectroscopy and two-dimensional nuclear Overhauser effect spectra, are characteristic of A-form geometry. The data suggest minor distortion in helical stacking at the junctions of stems and loops. The model of the pseudoknot is consistent with the structure originally proposed by Pleij et al. 1981; Heerschap, 1985; Hall et aZ., 1989) and Escherichia coli 5 S rRNA (122 nucleotides) Leontis 437
[EX A]:
Conformation of an RNA Pseudoknot