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: Background. One health is a concept that was officially adopted by international organizations and scholarly bodies in 1984. It is the notion of combining human, animal, and environmental components to address global health challenges that have an ecological interconnectedness. Methods. A cross-sectional study of the available literature cited was conducted from January 1984 when the one health concept was adopted till December 2012 to examine the role of the one health approach towards zoonoses. Inclusion criteria included publications, professional presentations, funding allocations, official documentation books, and book chapters, and exclusion criteria included those citations written outside the period of review. Results. A total of 737 resources met the inclusion criteria and were considered in this review. Resources showed a continuous upward trend for the years from 2006 to 2012. The predominant resources were journal publications with environmental health as the significant scope focus for one health. There was also an emphasis on the distribution of the work from developed countries. All categories of years, resources, scopes, and country locale differed from the means ( = 0.000). Year of initiative, scope, and country locale showed a dependent relationship ( = 0.022, = 0.003, and = 0.021, resp.). Conclusion. Our findings demonstrate the rapid growth in embracing the concept of one health, particularly in developed countries over the past six years. The advantages and benefits of this approach in tackling zoonoses are manifold, yet they are still not seemingly being embraced in developing countries where zoonoses have the greatest impact.
Example Output: Zoonoses and One Health: A Review of the Literature

Example Input: Synonymous codon usage bias (CUB) is a defined as the non-random usage of codons encoding the same amino acid across different genomes. This phenomenon is common to all organisms and the real weight of the many factors involved in its shaping still remains to be fully determined. So far, relatively little attention has been put in the analysis of CUB in bivalve mollusks due to the limited genomic data available. Taking advantage of the massive sequence data generated from next generation sequencing projects, we explored codon preferences in 64 different species pertaining to the six major evolutionary lineages in Bivalvia. We detected remarkable differences across species, which are only partially dependent on phylogeny. While the intensity of CUB is mild in most organisms, a heterogeneous group of species (including Arcida and Mytilida, among the others) display higher bias and a strong preference for AT-ending codons. We show that the relative strength and direction of mutational bias, selection for translational efficiency and for translational accuracy contribute to the establishment of synonymous codon usage in bivalves. Although many aspects underlying bivalve CUB still remain obscure, we provide for the first time an overview of this phenomenon in this large, commercially and environmentally important, class of marine invertebrates. Fields et al., 2014). Details about the data used for the different species are provided in Table S1 . Sequence data were processed as follows: predicted CDS from the fully sequenced genomes of C. gigas (release 9) and P. fucata were retrieved from http://oysterdb.cn and http://marinegenomics.oist.jp/pinctada fucata, respectively. De novo transcriptome assemblies were performed for all the other 62 bivalve species with the CLC Genomics Workbench (v.7.5, CLC Bio, Aarhus, Denmark) using the de novo assembly tool with "automatic word size" and "automatic bubble size" parameters selected, and setting the minimum allowed contig length to 300 bp. In all transcriptomes, ORFs (Open Reading Frames) longer than 100 codons were predicted with TransDecoder (http://transdecoder.sourceforge.net). We selected the predicted CDS of C. gigas, and of one representative species for the Imparidentia (R. decussatus), Protobranchia (S. velum) and Palaeoheterodonta (P. grandis) lineages to identify a subset of evolutionarily conserved protein-coding genes with a 1:1 orthology ratio across Bivalvia. This was achieved by performing reciprocal tBLASTx searches (the e-value threshold was set a 1 × 10 −10 and only hits displaying sequence identity >50% were considered). This procedure resulted in a selection of 2,846 conserved protein-coding genes, whose orthologous sequences were retrieved in the remaining 60 species. Due to the heterogeneous tissue and developmental stage origin, the different sequencing platforms and depth applied, several of these evolutionarily conserved sequences could not be identified or were fragmented in some transcriptomes. In order to ensure a minimum quality criteria, all the selected species had to display at least 25% of the sequences included in the dataset of evolutionarily conserved genes, with an average length >500 nucleotides.
Example Output: Distributed under Creative Commons CC-BY 4.0 Analysis of synonymous codon usage patterns in sixty-four different bivalve species

Example Input: Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems' perspective we need to construct complete and accurate host-pathogen protein-protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host-pathogen interactions. As an application example of the methods covered, we predict host-pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research.
Example Output:
Computational analysis of interactomes: Current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space