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: To examine the effect of provision of information about the infection control in the specific infection disease treatment unit in a city hospital on the outpatient's intention of outpatient service use, respondents who underwent outpatient medical care at the hospital (N = 821) were asked whether or not they intended to continue the outpatient visit at the hospital if a severe acute respiratory syndrome (SARS) patient was admitted to the unit. Although 56% of respondents replied that they could continue to visit the department if a SARS patient was admitted to the unit in the hospital before they read the information, the proportion of those who intended to continue outpatient care significantly increased by 15% after they read it. The logistic regression analyses revealed that respondents who had frequently visited the outpatient department (P < 0.001), those who felt relieved by reading the information about the unit (P < 0.001), and those who did not worry about nosocomial SARS infection inside the hospital (P < 0.001) were significantly more likely to reply that they would continue outpatient visits. We estimated that admission of a SARS patient to the unit would result in a 20% decrease in the cumulative total number of outpatients in the hospital during a 180-day interval after admission of a SARS patient to the unit, and the cumulative total number of outpatients increased by 7% after they read the information. This study suggests that providing outpatients with appropriate information about SARS infection control in the hospital had a statistically significant and substantial impact on the outpatients' intention to continue outpatient visits at the hospital.

A: Estimation of the impact of providing outpatients with information about SARS infection control on their intention of outpatient visit
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Q: This is a study on the Avian coronavirus IBV and chicken host-relationship from the codon usage point of view based on fifty-nine non-redundant IBV S1 sequences (nt 1-507) from strains detected worldwide and chicken tissuespecific protein genes sequences from IBV-replicating sites. The effective number of codons (ENC) values ranged from 36 to 47.8, indicating a high-to-moderate codon usage bias. The highest IBV codon adaptation index (CAI) value was 0.7, indicating a distant virus versus host synonymous codons usage. The ENC 9 GC3 % curve indicates that both mutational pressure and natural selection are the driving forces on codon usage pattern in S1. The low CAI values agree with a low S protein expression and considering that S protein is a determinant for attachment and neutralization, this could be a further mechanism besides mRNA transcription attenuation for a low expression of this protein leading to an immune camouflage.

A: Avian coronavirus Spike Glycoprotein Ectodomain Shows a Low Codon Adaptation to Gallus gallus with Virus-Exclusive Codons in Strategic Amino Acids Positions
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Q: The adhesion of microbial pathogens to host cells is mediated by adhesins. Experimental methods used for characterizing adhesins are time-consuming and demand large resources. The availability of specialized software can rapidly aid experimenters in simplifying this problem. We have employed 105 compositional properties and artificial neural networks to develop SPAAN, which predicts the probability of a protein being an adhesin (P ad ). Results: SPAAN had optimal sensitivity of 89% and specificity of 100% on a defined test set and could identify 97.4% of known adhesins at high P ad value from a wide range of bacteria. Furthermore, SPAAN facilitated improved annotation of several proteins as adhesins. Novel adhesins were identified in 17 pathogenic organisms causing diseases in humans and plants. In the severe acute respiratory syndrome (SARS) associated human corona virus, the spike glycoprotein and nsps (nsp2, nsp5, nsp6 and nsp7) were identified as having adhesin-like characteristics. These results offer new lead for rapid experimental testing.

A:
SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks
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