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".

Q: Paragraph: Italy is currently experiencing the largest COVID-19 outbreak in Europe so far, with more than 100,000 confirmed cases. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. Since March 12, the whole country is under lockdown. Here we provide the first quantitative assessment of the impact of such measures on the mobility and the spatial proximity of Italians, through the analysis of a large-scale dataset on deidentified, geo-located smartphone users. With respect to pre-outbreak averages, we estimate a reduction of 50% of the total trips between Italian provinces, following the lockdown. In the same week, the average users' radius of gyration has declined by about 50% and the average degree of the users' proximity network has dropped by 47% at national level.
Title: COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown

A: True
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Q: Paragraph: Identification of the full complement of genes and other functional elements in any virus is crucial to fully understand its molecular biology and guide the development of effective control strategies. RNA viruses have compact multifunctional genomes that frequently contain overlapping genes and non-coding functional elements embedded within protein-coding sequences. Overlapping features often escape detection because it can be difficult to disentangle the multiple roles of the constituent nucleotides via mutational analyses, while high-throughput experimental techniques are often unable to distinguish functional elements from incidental features. However, RNA viruses evolve very rapidly so that, even within a single species, substitutions rapidly accumulate at neutral or near-neutral sites providing great potential for comparative genomics to distinguish the signature of purifying selection. Computationally identified features can then be efficiently targeted for experimental analysis. Here we analyze alignments of protein-coding virus sequences to identify regions where there is a statistically significant reduction in the degree of variability at synonymous sites, a characteristic signature of overlapping functional elements. Having previously tested this technique by experimental verification of discoveries in selected viruses, we now analyze sequence alignments for 700 RNA virus species to identify hundreds of such regions, many of which have not been previously described.
Title: Mapping overlapping functional elements embedded within the protein-coding regions of RNA viruses

A: False
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Q: Paragraph: Background: In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm.
Title: From sequence to enzyme mechanism using multi-label machine learning

A:
True
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