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.

Input: Consider Input: Despite the recent emergence of enterovirus D68 (EV-D68), its clinical impact on adult population is less well defined. To better define the epidemiology of EV-D68, 6,800 nasopharyngeal aspirates (NPAs) from 2010-2014 were subject to EV-D68 detection by RT-PCR and sequencing of 5′UTR and partial VP1. EV-D68 was detected in 30 (0.44%) NPAs from 22 children and 8 adults/elderlies. Sixteen patients (including five elderly) (53%) had pneumonia and 13 (43%) patients were complicated by small airway disease exacerbation. Phylogenetic analysis of VP1, 2C and 3D regions showed four distinct lineages of EV-D68, clade A1, A2, B1 and B3, with adults/elderlies exclusively infected by clade A2. The potentially new clade, B3, has emerged in 2014, while strains closely related to recently emerged B1 strains in the United States were also detected as early as 2011 in Hong Kong. The four lineages possessed distinct aa sequence patterns in BC and DE loops. Amino acid residues 97 and 140, within BC and DE-surface loops of VP1 respectively, were under potential positive selection. EV-D68 infections in Hong Kong usually peak in spring/summer, though with a delayed autumn/winter peak in 2011. This report suggests that EV-D68 may cause severe respiratory illness in adults/elderlies with underlying co-morbidities.

Output: Enterovirus D68 Infections Associated with Severe Respiratory Illness in Elderly Patients and Emergence of a Novel Clade in Hong Kong


Input: Consider Input: Fowl adenovirus serotype 4 (FAdV-4) is the causative agent of hydropericardium syndrome. To clarify the effects of FAdV-4 on immune organs in birds, we conducted a detailed examination of dynamic morphology and damage mechanisms in chickens randomly divided into 4 groups (FAdV-4, vaccination, FAdV-4 plus vaccination, and control). FAdV-4 caused the depletion of lymphocytes and subsequent growth impairment in the thymus and bursa. Chickens infected with FAdV-4 and subjected to vaccination experienced greater inhibition of antibody responses to inactivated vaccines against Newcastle disease and avian influenza virus subtype H9 than uninfected and vaccinated chickens. The mechanisms underlying adenovirus-mediated lymphoid organ damage were further investigated via transferase-mediated dUTP nickend labeling and apoptotic genes transcription analyses. Notably, lymphocytes apoptosis in lymphoid organs and expression of specific gene transcripts was significantly upregulated after infection (P < 0.05). Furthermore, increased expression of interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α mRNA was observed (P < 0.05), compared to the control group. Our collective findings suggested that FAdV-4 caused structural and functional damage of immune organs via apoptosis along with induction of a severe inflammatory response.

Output: Immunosuppressive potential of fowl adenovirus serotype 4


Input: Consider Input: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to outperform several prominent methods in identifying strongly binding peptides. As a method with demonstrated performance in the quantitative modeling of MHCpeptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.


Output: Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models