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: Ranavirus cross-species infections have been documented, but the viral proteins involved in the interaction with cell receptors have not yet been identified. Here, viral cell-binding proteins and their cognate cellular receptors were investigated using two ranaviruses, Andrias davidianus ranavirus (ADRV) and Rana grylio virus (RGV), and two different cell lines, Chinese giant salamander thymus cells (GSTC) and Epithelioma papulosum cyprinid (EPC) cells. The heparan sulfate (HS) analog heparin inhibited plaque formation of ADRV and RGV in the two cell lines by more than 80% at a concentration of 5 g/mL. In addition, enzymatic removal of cell surface HS by heparinase I markedly reduced plaque formation by both viruses and competition with heparin reduced virus-cell binding. These results indicate that cell surface HS is involved in ADRV and RGV cell binding and infection. Furthermore, recombinant viral envelope proteins ADRV-58L and RGV-53R bound heparin-Sepharose beads implying the potential that cell surface HS is involved in the initial interaction between ranaviruses and susceptible host cells. To our knowledge, this is the first report identifying cell surface HS as ranavirus binding factor and furthers understanding of interactions between ranaviruses and host cells.
Example Output: Ranaviruses Bind Cells from Different Species through Interaction with Heparan Sulfate

Example Input: With sharp increasing in biological sequences, the traditional sequence alignment methods become unsuitable and infeasible. It motivates a surge of fast alignment-free techniques for sequence analysis. Among these methods, many sorts of feature vector methods are established and applied to reconstruction of species phylogeny. The vectors basically consist of some typical numerical features for certain biological problems. The features may come from the primary sequences, secondary or three dimensional structures of macromolecules. In this study, we propose a novel numerical vector based on only primary sequences of organism to build their phylogeny. Three chemical and physical properties of primary sequences: purine, pyrimidine and keto are also incorporated to the vector. Using each property, we convert the nucleotide sequence into a new sequence consisting of only two kinds of letters. Therefore, three sequences are constructed according to the three properties. For each letter of each sequence we calculate the number of the letter, the average position of the letter and the variation of the position of the letter appearing in the sequence. Tested on several datasets related to mammals, viruses and bacteria, this new tool is fast in speed and accurate for inferring the phylogeny of organisms. Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 2 Scientific RepoRts | 7: 12226 |
Example Output: A novel fast vector method for genetic sequence comparison OPEN

Example Input: A R T I C L E I N F O Geo-social interaction patterns Geo-social scale Location-based social network Agent-based epidemic models Social network analysis Infectious disease transmission and control High performance computing A B S T R A C T Cities play an important role in fostering and amplifying the transmission of airborne diseases (e.g., influenza) because of dense human contacts. Before an outbreak of airborne diseases within a city, how to determine an appropriate containment area for effective vaccination strategies is unknown. This research treats airborne disease spreads as geo-social interaction patterns, because viruses transmit among different groups of people over geographical locations through human interactions and population movement. Previous research argued that an appropriate scale identified through human geo-social interaction patterns can provide great potential for effective vaccination. However, little work has been done to examine the effectiveness of such vaccination at large scales (e.g., city) that are characterized by spatially heterogeneous population distribution and movement. This article therefore aims to understand the impact of geo-social interaction patterns on effective vaccination in the urbanized area of Portland, Oregon. To achieve this goal, we simulate influenza transmission on a large-scale location-based social network to 1) identify human geo-social interaction patterns for designing effective vaccination strategies, and 2) and evaluate the efficacy of different vaccination strategies according to the identified geo-social patterns. The simulation results illustrate the effectiveness of vaccination strategies based on geosocial interaction patterns in containing the epidemic outbreak at the source. This research can provide evidence to inform public health approaches to determine effective scales in the design of disease control strategies.
Example Output:
A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area