In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

[EX Q]: Paragraph: Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of 2 of 55 high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub. 
 Title: metabolites H OH OH The metaRbolomics Toolbox in Bioconductor and beyond
[EX A]: True

[EX Q]: Paragraph: Saffold virus (SAFV) is classified into the Cardiovirus genus of the Picornaviridae family. Up to now, eleven genotypes have been identified however, their clinical significance remains unclear. Here, we investigated the presence of SAFV in asymptomatic patients admitted for adenoidectomy. A total of 70 adenoid tissue samples were collected from children with clinical symptoms caused by hypertrophy of adenoids but without symptoms of airway infection. Samples were investigated for SAFV by RT-nested PCR and sequence analysis. Eleven of 70 (15.7%) samples were positive for SAFV. Nasopharyngeal swabs were available from 45 children just before surgery. SAFV was rarely found and only in children with SAFV-positive adenoids 2/8. Our findings indicate that the presence of SAFV seems to be more frequent in adenoid tissue than expected. This could support the notion of a longer than previously anticipated persistence of SAFV nucleic acids in the respiratory tract and possibly a chronic infection. Further investigations are necessary to establish the role of SAFV infection in humans. 
 Title: Frequent detection of Saffold cardiovirus in adenoids
[EX A]: True

[EX Q]: Paragraph: 1. Data collection. Epidemiological data were collected from a variety of online sources. If we were able to find reports listing numbers of confirmed and suspected cases, we used that information. If the epidemiological curve was available online as a graph, which is the case for data obtained from the CDC (Centers for Disease Control and Prevention) and Eurosurveillance sites, we estimated numbers from the graph and matched the plot of the estimated data with the online source. This can conveniently be done in Excel by plotting our estimated numbers on a graph with a transparent background and superimposing the resulting plot on a copy of the epidemiological curve posted online. The data we used are available online at github.com/ JocelineLega/EpiGro. • Data for the 2014-15 chikungunya outbreak in the PAHO countries and territories were extracted from weekly reports published on the PAHO website (1), for the 71-week period from January 3, 2014 to May 8, 2015. Confirmed (autochthonous + imported) and suspected cases are included in this analysis. Not all countries provided weekly revisions of the total number of reported cases, leading to groups of consecutive data points with equal value. In the calculation of the outbreak growth rate, we use a time unit of one week. We do not compensate for the fact that the 53 rd week of 2014 has only 4 days; since the data are first smoothed and then interpolated, such a variation leads to a change in the instantaneous growth rate that is comparable to the size of uncertainties due to fluctuations in the number of reported cases, and is therefore negligible. Excel files with these data are provided for the Dominican Republic (CHIK Dom Rep.xlsx), Guadeloupe (CHIK Guadeloupe.xlsx), and Dominica (CHIK Dominica.xlsx). 
 Title: DATA-DRIVEN OUTBREAK FORECASTING WITH A SIMPLE NONLINEAR GROWTH MODEL TECHNICAL APPENDICES
[EX A]:
False