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: The development of intestinal lesions after inoculation with Brachyspira hyodysenteriae was followed by repeated endoscopy and biopsy sampling through a caecal cannula. Seven eight-week-old pigs were cannulated and inoculated, two were cannulated but not inoculated, and two pigs were inoculated but not cannulated. Endoscopy, biopsy, and blood sampling to determine SAA (serum amyloid A), haptoglobin, cortisol, and WBC counts were performed at scheduled time-points. At the third day of disease, endoscopy showed a hyperaemic, perturbed mucosa and excessive amount of mucus. Histologically, crypt hyperplasia, depletion of goblet cell mucus, and erosions were noted. Simultaneously, elevated acute phase proteins and circulating monocytes, and decreased number of intraepithelial CD3 + cells were observed. After five days the pigs recovered. Intestinal lesions were demarcated and interspersed among apparently normal mucosa and blood parameters returned to initial values. Endoscopy through an intestinal cannula made it possible to follow the development of intestinal alterations in vivo and describe the sequential events during the course of swine dysentery. The number of animals used in a study could thus be minimised and the precision of the experiment increased. 
 Title: Consecutive pathological and immunological alterations during experimentally induced swine dysentery - A study performed by repeated endoscopy and biopsy samplings through an intestinal cannula
Example Output: True

Example 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
Example Output: True

Example Input: Paragraph: The outbreak of the pandemic flu, Influenza A H1N1 (Swine Flu) in early 2009, provided a major challenge to health services around the world. Previous pandemics have led to stockpiling of goods, the victimisation of particular population groups, and the cancellation of travel and the boycotting of particular foods (e.g. pork). We examined initial behavioural and attitudinal responses towards Influenza A, H1N1 ("Swine flu") in the six days following the WHO pandemic alert level 5, and regional differences in these responses. Methods: 328 respondents completed a cross-sectional Internet or paper-based questionnaire study in Malaysia (N = 180) or Europe (N = 148). Measures assessed changes in transport usage, purchase of preparatory goods for a pandemic, perceived risk groups, indicators of anxiety, assessed estimated mortality rates for seasonal flu, effectiveness of seasonal flu vaccination, and changes in pork consumption Results: 26% of the respondents were 'very concerned' about being a flu victim (42% Malaysians, 5% Europeans, p < .001). 36% reported reduced public transport use (48% Malaysia, 22% Europe, p < .001), 39% flight cancellations (56% Malaysia, 17% Europe, p < .001). 8% had purchased preparatory materials (e.g. face masks: 8% Malaysia, 7% Europe), 41% Malaysia (15% Europe) intended to do so (p < .001). 63% of Europeans, 19% of Malaysians had discussed the pandemic with friends (p < .001). Groups seen as at 'high risk' of infection included the immune compromised (mentioned by 87% respondents), pig farmers (70%), elderly (57%), prostitutes/highly sexually active (53%), and the homeless (53%). In data collected only in Europe, 64% greatly underestimated the mortality rates of seasonal flu, 26% believed seasonal flu vaccination gave protection against swine flu. 7% had reduced/stopped eating pork. 3% had purchased anti-viral drugs for use at home, while 32% intended to do so if the pandemic worsened. Conclusion: Initial responses to Influenza A show large regional differences in anxiety, with Malaysians more anxious and more likely to reduce travel and to buy masks and food. Discussions with family and friends may reinforce existing anxiety levels. Particular groups (homosexuals, prostitutes, the homeless) are perceived as at greater risk, potentially leading to increased prejudice during a pandemic. Europeans underestimated mortality of seasonal flu, and require more information about the protection given by seasonal flu inoculation. 
 Title: BMC Infectious Diseases Initial psychological responses to Influenza A, H1N1 ("Swine flu")
Example Output:
False