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: Chemical denaturant sensitivity of the dimeric main protease from severe acute respiratory syndrome (SARS) coronavirus to guanidinium chloride was examined in terms of fluorescence spectroscopy, circular dichroism, analytical ultracentrifuge, and enzyme activity change. The dimeric enzyme dissociated at guanidinium chloride concentration of ,0.4 M, at which the enzymatic activity loss showed close correlation with the subunit dissociation. Further increase in guanidinium chloride induced a reversible biphasic unfolding of the enzyme. The unfolding of the C-terminal domain-truncated enzyme, on the other hand, followed a monophasic unfolding curve. Different mutants of the full-length protease (W31 and W207/W218), with tryptophanyl residue(s) mutated to phenylalanine at the C-terminal or N-terminal domain, respectively, were constructed. Unfolding curves of these mutants were monophasic but corresponded to the first and second phases of the protease, respectively. The unfolding intermediate of the protease thus represented a folded C-terminal domain but an unfolded N-terminal domain, which is enzymatically inactive due to loss of regulatory properties. The various enzyme forms were characterized in terms of hydrophobicity and size-and-shape distributions. We provide direct evidence for the functional role of C-terminal domain in stabilization of the catalytic N-terminal domain of SARS coronavirus main protease.

A: Reversible Unfolding of the Severe Acute Respiratory Syndrome Coronavirus Main Protease in Guanidinium Chloride
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Q: In this study, the performance of 10 serological assays for the diagnosis of Mycoplasma pneumoniae infection was evaluated. A total of 145 sera from 120 patients were tested. They were obtained from patients who were serologically positive for M. pneumoniae infection as well as from patients who were infected with microorganisms that may cause interstitial pneumonia. The following assays were utilized: SeroMP IgM and IgG, SeroMP recombinant IgM, IgA and IgG, Liaison M. pneumoniae IgM and IgG and M. pneumoniae IgM, IgA and IgG ELISA Medac. The SeroMP Recombinant and Liaison assays both showed low IgM specificity, and crossreactivity was mainly observed in groups of patients with acute cytomegalovirus and Epstein-Barr virus infections. For IgA, the Medac assay was less specific than the SeroMP Recombinant assay. Discrepancies between the four tests were observed in IgG analyses, and due to the lack of a gold standard, 22 results were removed prior to determining the sensitivity and specificity. Therefore, the overall performance of IgG assays may be overstated; nevertheless, the SeroMP assay demonstrated a lack of sensitivity. The seroprevalence of IgG appears to be very low, raising concerns regarding whether the serological techniques can detect IgG levels over time. Serology remains a biological tool of choice for diagnosing M. pneumoniae infection, but improvement and standardization of the assays are needed, particularly for the determination of IgG.

A: Evaluation of 10 serological assays for diagnosing Mycoplasma pneumoniae infection ☆
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Q: Since accurate forecasting of tourist arrivals is very important for planning for potential tourism demand and improving the tourism infrastructure, various tourist arrivals forecasting methods have been developed. The purpose of this study is to apply the adaptive network-based fuzzy inference system (ANFIS) model to forecast the tourist arrivals to Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the fuzzy time series model, grey forecasting model and Markov residual modified model. Thus, the ANFIS model is a promising alternative for forecasting the tourist arrivals. We also use the ANFIS model to forecast the monthly tourist arrivals to Taiwan from Japan, Hong Kong and Macao, and the United States.

A:
Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system
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