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: Objective: Health literacy has become a global issue, and it is important that patients and individuals are able to use information technology to access health information and educational services. The research objective is to develop a Saudi e-health literacy scale (SeHL) for measuring e-health literacy among Saudis suffering from non-communicable diseases (NCD). Methods: Overall, 14 relevant papers in related interdisciplinary fields were reviewed to select the most useful literacy dimensions. From these articles, we extracted the most common dimensions used to measure e-health literacy across the disciplines. Multiple workshops with multidisciplinary team members reviewed and evaluated items for SeHL. Results: Four key aspects of e-health literacy-use of technology/media, information-seeking, usefulness and confidence-were identified and integrated as e-health literacy dimensions. These will be used to measure e-health literacy among Saudi patients with NCDs. A translation from Arabic to English was performed in order to ensure that translation process was accurate. A SeHL scale was developed to measure e-health literacy among Saudi patients. By understanding e-health literacy levels, we will be able to create a patient-education system to be used by patients in Saudi Arabia. Conclusions: As information technology is increasingly used by people of all ages all over the world, e-health literacy has been identified as a key factor in determining health outcomes. To date, no comprehensive scale exists to assess e-health literacy levels among speakers of Arabic, particularly among people with NCD such as diabetes, cardiovascular diseases and hypertension.
Example Output: Saudi Arabia, 5 Prince Sultan Military Medical City, AlSulimaniah, Riyadh 11159, Saudi Arabia, 6 King Faisal Specialist and Research Center

Example Input: This is a study on the Avian coronavirus IBV and chicken host-relationship from the codon usage point of view based on fifty-nine non-redundant IBV S1 sequences (nt 1-507) from strains detected worldwide and chicken tissuespecific protein genes sequences from IBV-replicating sites. The effective number of codons (ENC) values ranged from 36 to 47.8, indicating a high-to-moderate codon usage bias. The highest IBV codon adaptation index (CAI) value was 0.7, indicating a distant virus versus host synonymous codons usage. The ENC 9 GC3 % curve indicates that both mutational pressure and natural selection are the driving forces on codon usage pattern in S1. The low CAI values agree with a low S protein expression and considering that S protein is a determinant for attachment and neutralization, this could be a further mechanism besides mRNA transcription attenuation for a low expression of this protein leading to an immune camouflage.
Example Output: Avian coronavirus Spike Glycoprotein Ectodomain Shows a Low Codon Adaptation to Gallus gallus with Virus-Exclusive Codons in Strategic Amino Acids Positions

Example Input: Objective: Finding the longest common subsequence (LCS) among sequences is NP-hard. This is an important problem in bioinformatics for DNA sequence alignment and pattern discovery. In this research, we propose new CPU-based parallel implementations that can provide significant advantages in terms of execution times, monetary cost, and pervasiveness in finding LCS of DNA sequences in an environment where Graphics Processing Units are not available. For general purpose use, we also make the OpenMP-based tool publicly available to end users. In this study, we develop three novel parallel versions of the LCS algorithm on: (i) distributed memory machine using message passing interface (MPI); (ii) shared memory machine using OpenMP, and (iii) hybrid platform that utilizes both distributed and shared memory using MPI-OpenMP. The experimental results with both simulated and real DNA sequence data show that the shared memory OpenMP implementation provides at least two-times absolute speedup than the best sequential version of the algorithm and a relative speedup of almost 7. We provide a detailed comparison of the execution times among the implementations on different platforms with different versions of the algorithm. We also show that removing branch conditions negatively affects the performance of the CPU-based parallel algorithm on OpenMP platform.
Example Output:
An OpenMP-based tool for finding longest common subsequence in bioinformatics