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.

Example Input: Contact networks are convenient models to investigate epidemics, with nodes and links representing potential hosts and infection pathways, respectively. The outcomes of outbreak simulations on networks are driven both by the underlying epidemic model, and by the networks' structural properties, so that the same pathogen can generate different epidemic dynamics on different networks. Here we ask whether there are general properties that make a contact network intrinsically vulnerable to epidemics (that is, regardless of specific epidemiological parameters). By conducting simulations on a large set of modelled networks, we show that, when a broad range of network topologies is taken into account, the effect of specific network properties on outbreak magnitude is stronger than that of fundamental pathogen features such as transmission rate, infection duration, and immunization ability. Then, by focusing on a large set of real world networks of the same type (potential contacts between field voles, Microtus agrestis), we showed how network structure can be used to accurately assess the relative, intrinsic vulnerability of networks towards a specific pathogen, even when those have limited topological variability. These results have profound implications for how we prevent disease outbreaks; in many real world situations, the topology of host contact networks can be described and used to infer intrinsic vulnerability. Such an approach can increase preparedness and inform preventive measures against emerging diseases for which limited epidemiological information is available, enabling the identification of priority targets before an epidemic event.
Example Output: The intrinsic vulnerability of networks to epidemics

Example Input: Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http:// elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances. Figure 5 . Schema of the ELM resource and data life cycle. Annotated ELM classes, and instances thereof, can be searched by database query. Via sequence search by the motif detection pipeline, annotated ELM classes yield putative instances in query sequences. By adding experimental evidence and references, these putative instances become candidate instances for annotation, and, with further curation, ultimately become fully annotated instances.
Example Output: ELM-the database of eukaryotic linear motifs

Example Input: Middle East respiratory syndrome coronavirus (MERS-CoV) was first identified in 2012 as a novel etiological agent of severe respiratory disease in humans. As during infection by other viruses, host sensing of viral double-stranded RNA (dsRNA) induces several antiviral pathways. These include interferon (IFN), oligoadenylate synthetase (OAS)-RNase L, and protein kinase R (PKR). Coronaviruses, including MERS-CoV, potently suppress the activation of these pathways, inducing only modest host responses. Our study describes the functions of two accessory proteins unique to MERS-CoV and related viruses, NS4a and NS4b, during infection in human airway epithelium-derived A549 cells. NS4a has been previously characterized as a dsRNA binding protein, while NS4b is a 2=,5=-phosphodiesterase with structural and enzymatic similarity to NS2 encoded by mouse hepatitis virus (MHV). We found that deletion of NS4a results in increased interferon lambda (IFNL1) expression, as does mutation of either the catalytic site or nuclear localization sequence of NS4b. All of the mutant viruses we tested exhibited slight decreases in replication. We previously reported that, like MHV NS2, NS4b antagonizes OAS-RNase L, but suppression of IFN is a previously unidentified function for viral phosphodiesterases. Unexpectedly, deletion of NS4a does not result in robust activation of the PKR or OAS-RNase L pathways. Therefore, MERS-CoV likely encodes other proteins that contribute to suppression or evasion of these antiviral innate immune pathways that should be an important focus of future work. This study provides additional insight into the complex interactions between MERS-CoV and the host immune response. IMPORTANCE Middle East respiratory syndrome coronavirus (MERS-CoV) is the second novel zoonotic coronavirus to emerge in the 21st century and cause outbreaks of severe respiratory disease. More than 2,200 cases and 800 deaths have been reported to date, yet there are no licensed vaccines or treatments. Coronaviruses encode unique accessory proteins that are not required for replication but most likely play roles in immune antagonism and/or pathogenesis. Our study describes the functions of MERS-CoV accessory proteins NS4a and NS4b during infection of a human airway-derived cell line. Loss of these accessory proteins during MERS-CoV infection leads to host antiviral activation and modestly attenuates replication. In the case of both NS4a and NS4b, we have identified roles during infection not previously described, yet the lack of robust activation suggests much remains to be learned about the interactions between MERS-CoV and the infected host. of infection to uncover the full range of their interactions with the innate immune response. RESULTS In order to study the effects of NS4a and NS4b on MERS-CoV interactions with the host innate immune system, we used a panel of recombinant MERS-CoV mutants. Deletion mutants MERS-ΔNS4a and MERS-ΔNS4ab were generated from the MERS-CoV infectious clone derived from the MERS-EMC2012 strain (27) as follows and are described in detail in Materials and Methods and diagrammed in Fig. 1A and B. Briefly, MERS-ΔNS4a was generated by altering the start codon (ATG¡ATT) and adding an in-frame stop codon 10 codons downstream (TGG¡TGA) to ablate synthesis of the FIG 1 MERS-CoV NS4a and NS4b recombinant mutants. (A) MERS-CoV genome RNA with open reading frames shown. (B) NS4a and NS4b proteins expressed by wild-type and mutant MERS-CoVs. The catalytic His residues of the PDE are shown, and the vertical black bar indicates the NLS of NS4b; the red lettering indicates amino acid substitutions of the catalytic His residue and within the NLS. (C) Expression of viral proteins from recombinant MERS-CoV viruses. A549 DPP4 cells were infected at an MOI of 10 with WT MERS-CoV, MERS-ΔNS4a, MERS-ΔNS4ab, MERS-NS4b H182R , or MERS-NS4b NLSmut or mock infected. Cell lysates were prepared at 24 and 48 h postinfection, analyzed by SDS-PAGE, and probed by Western blotting with rabbit antiserum against NS4a and NS4b or mouse monoclonal antibodies against MERS nucleocapsid protein (N) and GAPDH. The Western blot data are from one representative of three independent infections.
Example Output:
Antagonism of dsRNA-Induced Innate Immune Pathways by NS4a and NS4b Accessory Proteins during MERS Coronavirus Infection