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".

Example Input: Paragraph: Introduction: Outbreaks of infectious diseases trigger an increase in scientific research and output. Early in outbreaks, evidence is scarce, but it accumulates rapidly. We are continuously facing new disease outbreaks, including the new coronavirus (SARS-nCoV-2) in December 2019.The objective of this study was to describe the accumulation of evidence during the 2013-2016 Zika virus (ZIKV) outbreak in the Pacific and the Americas related to aetiological causal questions about congenital abnormalities and Guillain-Barre syndrome. Methods: We hypothesised that the temporal sequence would follow a pre-specified order, according to study design. We assessed 1) how long it takes before findings from a specific study design appear, 2) how publication of preprints could reduce the time to publication and 3) how time to publication evolves over time. Results: We included 346 publications published between March 6, 2014 and January 1, 2019. In the 2013--2016 ZIKV outbreak, case reports, case series and basic research studies were published first. Case-control and cohort studies appeared between 400-700 days after ZIKV was first detected in the region of the study origin. Delay due to the publication process were lowest at the beginning of the outbreak. Only 4.6% of the publications was available as preprints. Discussion: The accumulation of evidence over time in new causal problems generally followed a hierarchy. Preprints reduced the delay to initial publication. Our methods can be applied to new emerging infectious diseases.
Title: Emergence of evidence during disease outbreaks: lessons learnt from the Zika virus outbreak
Example Output: False

Example Input: Paragraph: Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect virulencerelated genes in novel agents, as we show here for the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020.
Title: Interpretable detection of novel human viruses from genome sequencing data
Example Output: True

Example Input: Paragraph: Objectives. To assess the beneficial and adverse effects of Xiaoyaosan for depression. Search Strategy. Electronic databases were searched until December 2009. Inclusion Criteria. We included randomized clinical trials testing Xiaoyaosan against placebo, antidepressants, or combined with antidepressants against antidepressants alone. Data Extraction and Analyses. Study selection, data extraction, quality assessment, and data analyses were conducted according to the Cochrane standards. Results. 26 randomized trials (involving 1837 patients) were included and the methodological quality was evaluated as generally low. The pooled results showed that Xiaoyaosan combined with antidepressants was more effective in comprehensive effect, the score of HAMD and the score of SDS compared with antidepressants alone. Xiaoyaosan was superior to antidepressants for the score of HAMD. However, Xiaoyaosan was not different from placebo for the score of SDS. There was no adverse effects reported in the trials from Xiaoyaosan. Conclusions. Xiaoyaosan appears to be effective on improving symptoms in patients with depression. However, due to poor methodological quality in the majority of included trials, the potential benefit from Xiaoyaosan need to be confirmed in rigorous trials and the design and reporting of trials should follow international standards.
Title: Chinese Herbal Formula Xiao Yao San for Treatment of Depression: A Systematic Review of Randomized Controlled Trials
Example Output:
False