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: Several studies suggest that the open reading frame 3 (ORF3) gene of porcine epidemic diarrhea virus (PEDV) is related to viral infectivity and pathogenicity, but its function remains unknown. Here, we propose a structure model of the ORF3 protein consisting of four TM domains and forming a tetrameric assembly. ORF3 protein can be detected in PEDV-infected cells and it functions as an ion channel in both Xenopus laevis oocytes and yeast. Mutation analysis showed that Tyr170 in TM4 is important for potassium channel activity. Furthermore, viral production is reduced in infected Vero cells when ORF3 gene is silenced by siRNA. Interestingly, the ORF3 gene from an attenuated PEDV encodes a truncated protein with 49 nucleotide deletions, which lacks the ion channel activity.
Example Output: PEDV ORF3 encodes an ion channel protein and regulates virus production

Example Input: Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.
Example Output: In silico pharmacology for drug discovery: applications to targets and beyond

Example Input: Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the fi eld and establishing collaborative networks for alert verifi cation and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.
Example Output:
Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance