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 Input:
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

Ex Output:
True


Ex Input:
Paragraph: microRNAs (miRNAs) represent a newly identified class of nonprotein-coding *22 nt small RNA that plays important roles in multiple biological processes by degrading targeted mRNA or repressing mRNA translation. This study observed the morphology of swine testicular tissue at different developmental stages (including 1-day old, 1-7 month old) by Hematoxylin-eosin staining. We also examined the expression of miR-499 and its target genes (CYLC1, DMRT1, QKI, XRN2, ZNF313) in samples of tissue slices using quantitative reversetranscription polymerase chain reaction, which showed that miR-499 had a significant negative correlation with QKI gene. Then, the proteins of QKI gene expression were determined by western blot, which were consistent with results of quantitative polymerase chain reaction (qPCR) detection. Therefore, the luciferase reporter gene system was used to verify correlation between miR-499 and QKI gene. Activity of luciferase was significantly lower in miR-499 co-transfected with pmiR-RB-REPORT-QKI-WT group than the miR-499 co-transfected with pmiR-RB-REPORT-QKI-mut/si groups, indicating that target sequence of miR-499 existed in 3¢UTR of QKI gene. Furthermore, the expressions of miR-499 and QKI were detected in testicular cells that were transfected with miR-499, miR-499 negative control and untreated. The results showed that the diameter of convoluted seminiferous tubule growth increased with age. Significantly different expressions of miR-499 and its target genes were found in swine testicular tissue at different developmental stages ( p < 0.05), overexpressing miR-499 analysis, suggesting that miR-499 was negatively correlated to the expression of QKI ( p < 0.05). In conclusion, QKI is a target gene of miR-499. 
 Title: Differential Expression of miR-499 and Validation of Predicted Target Genes in the Testicular Tissue of Swine at Different Developmental Stages

Ex Output:
False


Ex Input:
Paragraph: Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the COVID-19 pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider both the age composition of local and national contexts as well as the social connectedness of older and younger generations. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted nowcasting. Governments are rapidly mobilizing to minimize transmission of COVID-19 through social distancing and travel restrictions to reduce fatalities and the outstripping of healthcare capacity. It is becoming clear that the pandemic's progression and impact may be strongly related to the demographic composition of the population, specifically population age structure. Demographic science can provide new insights into how the pandemic may unfold and the intensity and type of measures needed to slow it down. Currently, COVID-19 mortality risk is highly concentrated at older ages, particularly those aged 80+. In China, case-fatality rate (CFR) estimates range from 0.4% (40-49 years), jumping to 14.8% (80+ years).(1) This is consistent with the data from Italy as of March 16, where the reported CFR is 11.8% for those 70-79, 18.8% for 80-89, and 21.6% for those >90. Thus far in Italy, only 1.5% of deaths have occurred in those under aged 60.(2) As testing becomes more widespread, these case fatality rates may come down due to an increase in the true denominator. Nonetheless, in South Korea where testing was broader and the health system not overtaxed, the current CFR for those 80+ is still an alarming 9.26%. (7) The Importance of Age Structure Population age structure may explain the remarkable variation in fatalities across countries and why countries such as Italy are especially vulnerable. The deluge of critical and fatal COVID-19 cases in Italy was unexpected given the health and wealth of the affected region. Italy is one of the oldest populations in the world with 23.3% its population over age 65, compared to 12% in China (3). Italy is also a country characterized by extensive intergenerational contacts which are supported by a high degree of residential proximity between adult children and their parents (4). Even when inter-generational families do not live together, daily contacts among non-co-resident parent-child pairs are frequent. Many Italians also often prefer to live close to their extended family and commute to work daily. 
 Title: Demographic science aids in understanding the spread and fatality rates of COVID-19

Ex Output:

True