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: New assays for the diagnosis of infectious diseases-particularly those that use molecular technologies-will revolutionize infectious diseases practices, but the fulfillment of the promise is several years away. Problems with currently available molecular assays include a lack of knowledge about the extent of microbial nucleic acid in "normal" hosts, concentration of agent material in small volume samples, lack of microbiologist expertise, lack of adequate reimbursement, and difficulty with validation based on conventional methods. Clinicians must appreciate the shortcomings of new technology to use it effectively and appropriately. However, we are realizing tangible progress in our ability to detect new etiological agents; the availability of rapid, accurate diagnostic tests for previously difficult infections; and advances into new, human responsebased paradigms for diagnostic testing. 
 Title: Implications of New Technology for Infectious Diseases Practice
Example Output: True

Example Input: Paragraph: Porcine epidemic diarrhea virus (PEDV), an enteropathogenic Alphacoronavirus, has caused enormous economic losses in the swine industry. p53 protein exists in a wide variety of animal cells, which is involved in cell cycle regulation, apoptosis, cell differentiation and other biological functions. In this study, we investigated the effects of PEDV infection on the cell cycle of Vero cells and p53 activation. The results demonstrated that PEDV infection induces cell cycle arrest at G0/G1 phase in Vero cells, while UV-inactivated PEDV does not cause cell cycle arrest. PEDV infection up-regulates the levels of p21, cdc2, cdk2, cdk4, Cyclin A protein and down-regulates Cyclin E protein. Further research results showed that inhibition of p53 signaling pathway can reverse the cell cycle arrest in G0/G1 phase induced by PEDV infection and cancel out the up-regulation of p21 and corresponding Cyclin/cdk mentioned above. In addition, PEDV infection of the cells synchronized in various stages of cell cycle showed that viral subgenomic RNA and virus titer were higher in the cells released from G0/G1 phase synchronized cells than that in the cells released from the G1/S phase and G2/M phase synchronized or asynchronous cells after 18 h p.i.. This is the first report to demonstrate that the p53-dependent pathway plays an important role in PEDV induced cell cycle arrest and beneficially contributes to viral infection. 
 Title: Porcine epidemic diarrhea virus through p53-dependent pathway causes cell cycle arrest in the G0/G1 phase
Example Output: False

Example Input: Paragraph: Background: The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact. Methods: In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks. Results: We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. Conclusions: These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance. 
 Title: ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics
Example Output:
True