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: 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: Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets.
Example Output: Concept learning via granular computing: A cognitive viewpoint

Example Input: Because of its potentially important role in the pathogenesis of sepsis, the expression of soluble decoy receptor 3 (DcR3) was investigated in sera of sepsis patients. The serum levels of DcR3 and its tumor necrosis factor-like ligand TL1A and homologous decoy receptor OPG were quantified by ELISA. The values of DcR3 to diagnose sepsis were analyzed by receiver-operating characteristic (ROC) curves. The results showed that DcR3 was significantly elevated in sepsis compared to systemic inflammatory response syndrome (SIRS), a condition similar to sepsis but resulting from noninfectious insults. DcR3 showed superior area under the ROC curve (AUC, 0.958) compared to poor AUCs of TL1A and OPG. At a cut-off of 3.24 ng/mL, DcR3 predicted sepsis from SIRS with 96% sensitivity and 82.6% specificity. DcR3 also predicted sepsis from cancer and inflammatory bowel disease with equally excellent values. Therefore, DcR3 serum level has the potential to serve as a reliable biomarker of sepsis.
Example Output:
Specific elevation of DcR3 in sera of sepsis patients and its potential role as a clinically important biomarker of sepsis