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.

Ex 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.

Ex Output:
Distributed under Creative Commons CC-BY 4.0 Analysis of synonymous codon usage patterns in sixty-four different bivalve species


Ex Input:
Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

Ex Output:
Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol


Ex Input:
Several tiny organisms of various size ranges present in air are called airborne particles or bioaerosol which mainly includes live or dead fungi and bacteria, their secondary metabolites, viruses, pollens, etc. which have been related to health issues of human beings and other life stocks. Bio-terror attacks in 2001 as well as pandemic outbreak of flue due to influenza A H1N1 virus in 2009 have alarmed us about the importance of bioaerosol research. Hence characterization i.e. identification and quantification of different airborne microorganisms in various indoor environments is necessary to identify the associated risks and to establish exposure threshold. Along with the bioaerosol sampling and their analytical techniques, various literatures revealing the concentration levels of bioaerosol have been mentioned in this review thereby contributing to the knowledge of identification and quantification of bioaerosols and their different constituents in various indoor environments (both occupational and non-occupational sections). Apart from recognition of bioaerosol, developments of their control mechanisms also play an important role. Hence several control methods have also been briefly reviewed. However, several individual levels of efforts such as periodic cleaning operations, maintenance activities and proper ventilation system also serve in their best way to improve indoor air quality.

Ex Output:

Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms