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".
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Question: Paragraph: BACKGROUND: Study of human coronavirus and other virus-associated respiratory illnesses is needed to describe their clinical effects on chronically ill, older adults. METHODS: A prospective study during 2009 to 2013 clinically assessed acute respiratory illnesses soon after onset and 3 to 4 weeks later in patients aged !60 years with chronic lung and heart diseases (group 1, 100 subjects) and healthy adults aged 18 to 40 years (group 2, 101 subjects). Respiratory secretions were tested for nucleic acids of a panel of respiratory viruses. An increase in antibody titer was assessed for 4 coronavirus strains. : Virus-associated illnesses (29 [39.1%] of 74 illnesses in group 1 and 59 [48.7%] of 121 illnesses in group 2) occurred in all calendar quarters, most commonly in the first and fourth quarters. Coronaviruses (group 1: 14 [18.9%] illnesses; group 2: 26 [21.5%] illnesses) and enteroviruses/rhinoviruses (group 1: 14 [18.9%] illnesses; group 2: 37 [30.6%] illnesses) were most common. Virus co-infections occurred in 10 illnesses. Illnesses with 9 to 11 symptoms were more common in group 1 (17 [23.0%]) than in group 2 (15 [12.4%]) (P < .05). Compared with group 2, more group 1 subjects reported dyspnea, more severe disease of longer duration, and treatment for acute illness with prednisone and antibiotics. Coronavirusassociated illnesses (percent of illnesses, group 1 vs group 2) were characterized by myalgias (21% vs 68%, P < .01), chills (50% vs 52%), dyspnea (71% vs 24%, P < .01), headache (64% vs 72%), malaise (64% vs 84%), cough (86% vs 68%), sputum production (86% vs 60%), sore throat (64% vs 80%), and nasal congestion (93% vs 96%). CONCLUSIONS: Respiratory illnesses were commonly associated with coronaviruses and enteroviruses/ rhinoviruses affecting chronically ill, older patients more than healthy, young adults. Published by Elsevier Inc. 
 Title: Coronavirus and Other Respiratory Illnesses Comparing Older with Young Adults

Answer: True


Question: Paragraph: Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences. 
 Title: Generalized lattice graphs for 2D-visualization of biological information

Answer: True


Question: Paragraph: Journal Pre-proof 1 such as ventilators in limited supply, preparations should be made ahead of time on how to allocate these finite resources. More information about COVID-19 can be found in [2], [3], and [7] . The best course of action to "flatten the curve" is to follow WHO guidelines. The best way to keep hospitals under capacity is social distancing: limiting or cancelling large gatherings, only travelling when necessary, and keeping a distance from others all help to prevent the spread. The presented heuristic is based on the exponential growth of the data collected Journal Pre-proof J o u r n a l P r e -p r o o f 4 WWK, MAM, WP, AW-D, PFZ, DS, TA, AHZ, MD, AND JM by WHO situation reports for days 31 to 57. As pointed out in [4] the predictability could be improved by pairwise comparisons based on abductive reasoning [5]. Abduction is frequently used in diagnostic expert systems. The abductive reasoning (or inference) process was used for this study. It is a type of logical inference which starts with a set of observations and then searches for Journal Pre-proof J o u r n a l P r e -p r o o f 1,000,000 COVID-19 CASES X Journal Pre-proof J o u r n a l P r e -p r o o f 
 Title: Journal Pre-proof 1000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic 1,000,000 CASES OF COVID-19 OUTSIDE OF CHINA: THE DATE PREDICTED BY A SIMPLE HEURISTIC

Answer:
True