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: Diarrhea outbreaks in pig farms have raised major concerns in Europe and USA, as they can lead to dramatic pig losses. During a suspected outbreak in Belgium of porcine epidemic diarrhea virus (PEDV), we performed viral metagenomics to assess other potential viral pathogens. Although PEDV was detected, its low abundance indicated that other viruses were involved in the outbreak. Interestingly, a porcine bocavirus and several enteroviruses were most abundant in the sample. We also observed the presence of a porcine enterovirus genome with a gene insertion, resembling a C28 peptidase gene found in toroviruses, which was confirmed using re-sequencing, bioinformatics, and proteomics approaches. Moreover, the predicted cleavage sites for the insertion suggest that this gene was being expressed as a single protein, rather than a fused protein. Recombination in enteroviruses has been reported as a major mechanism to generate genetic diversity, but gene insertions across viral families are rather uncommon. Although such inter-family recombinations are rare, our finding suggests that these events may significantly contribute to viral evolution.
Example Output: Identification of an enterovirus recombinant with a torovirus-like gene insertion during a diarrhea outbreak in fattening pigs

Example Input: With the emergence of highly pathogenic variant strains, porcine epidemic diarrhea virus (PEDV) has led to significant economic loss in the global swine industry. Many studies have described how coronaviruses enter cells, but information on PEDV invasion strategies remains insufficient. Given that the differences in gene sequences and pathogenicity between classical and mutant strains of PEDV may lead to diverse invasion mechanisms, this study focused on the cellular entry pathways and cellular transport of the PEDV GI and GII subtype strains in Vero cells and IPEC-J2 cells. We first characterized the kinetics of PEDV entry into cells and found that the highest invasion rate of PEDV was approximately 33% in the IPEC-J2 cells and approximately 100% in the Vero cells. To clarify the specific endocytic pathways, systematic research methods were used and showed that PEDV enters cells via the clathrin-and caveolae-mediated endocytosis pathways, in which dynamin II, clathrin heavy chain, Eps15, cholesterol, and caveolin-1 were indispensably involved. In addition, lipid raft extraction assay showed that PEDV can also enter cells through lipid raft-mediated endocytosis. To investigate the trafficking of internalized PEDV, we found that PEDV entry into cells relied on low pH and internalized virions reached lysosomes through the early endosome-late endosome-lysosome pathway. The results concretely revealed the entry mechanisms of PEDV and provided an insightful theoretical basis for the further understanding of PEDV pathogenesis and guidance for new targets of antiviral drugs. ©️ The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'
Example Output: PEDV enters cells through clathrin-, caveolae-, and lipid raft-mediated endocytosis and traffics via the endo-/lysosome pathway

Example Input: This is a preprint version, not peer-reviewed yet, and readers should treat it accordingly. 2 Summary Background: The COVID-19 disease has turned into a global pandemic with unprecedented challenges for the global community. Understanding the state of the disease and planning for future trajectories relies heavily on data on the spread and mortality. Yet official data coming from various countries are highly unreliable: symptoms similar to common cold in majority of cases and limited screening resources and delayed testing procedures may contribute to underestimation of the burden of disease. Anecdotal and more limited data are available, but few have systematically combined those with official statistics into a coherent view of the epidemic. This study is a modeling-in-real-time of the emerging outbreak for understanding the state of the disease. Our focus is on the case of the spread of disease in Iran, as one of the epicenters of the disease in the first months of 2020. We develop a simple dynamic model of the epidemic to provide a more reliable picture of the state of the disease based on existing data. Building on the generic SEIR (Susceptible, Exposed, Infected, and Recovered) framework we incorporate two behavioral and logistical considerations. First we capture the endogenous changes in contact rate (average contact per person) as more death are reported. As a result the reproduction number changes endogenously in the model. Second we differentiate reported and true cases by including simple formulations for how only a fraction of cases might be diagnosed, and how that fraction changes in response to epidemic's progression. In estimating the model we use both the official data as well as the discovered infected travelers and unofficial medical community estimates and triangulate these sources to build a more complete picture. Calibration is completed by forming a likelihood function for observing the actual time series data conditional on model parameters, and conducting a Markov Chain Monte Carlo simulations. The model is used to estimate current "true" cases of infection and death. We analyze the future trajectory of the disease under six conditions related to the seasonal effects and policy measures targeting social distancing. The model closely replicates the past data but also shows the true number of cases is likely far larger. We estimate about 493,000 current infected cases (90% CI: 271K-810K) as 1 A Persian summary of the article is provided as an appendix.
Example Output:
Simulation-based Estimation of the Spread of COVID-19 in Iran