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 cancer burden is a global problem, and oncology nurses should be accountable for delivering safe and effective cancer care and providing the best possible experience for patients. The development and application of evidence-based practice in cancer care is an effective strategy in achieving this goal; however, the journey in which such practice involves may encounter various challenges. In this article, the author discusses her own experience, successful and unsuccessful of such a journey. Both challenges and opportunities are identified, and suggestions put forward for making collaborative efforts. 
 Title: Evidence-Based Practice in Cancer Care Asia-Pacifi c
Example Output: True

Example Input: Paragraph: Background: BCoV is identified in both healthy and diarrheic calves, complicating its assessment as a primary pathogen. Objectives: To investigate the detection rates of bovine coronavirus (BCoV) in feces of healthy and diarrheic calves and to describe the usefulness of a pancoronavirus reverse transcriptase (RT) PCR (PanCoV-RT-PCR) assay to identify BCoV in samples of diarrheic calves. Animals: Two hundred and eighty-six calves <21 days. Calves with liquid or semiliquid feces, temperature >39.5°C, and inappetence were considered as cases, and those that had pasty or firm feces and normal physical examination were designated as controls. Methods: Prospective case-control study. A specific BCoV-RT-PCR assay was used to detect BCoV in fecal samples. Association between BCoV and health status was evaluated by exact and random effect logistic regression. Fecal (n = 28) and nasal (n = 8) samples from diarrheic calves were tested for the presence of BCoV by both the PanCoV-RT-PCR and a specific BCoV-RT-PCR assays. A Kappa coefficient test was used to assess the level of agreement of both assays. Results: BCoV was detected in 55% (157/286) of calves; 46% (66/143), and 64% (91/143) of healthy and diarrheic calves, respectively. Diarrheic calves had higher odds of BCoV presence than healthy calves (OR: 2.16, 95% CI: 1.26 to 3.83, P = 0.004). A good agreement between PanCoV-RT-PCR and BCoV-RT-PCR to detect BCoV was identified (j = 0.68, 95% CI: 0.392 to 0.967; P < 0.001). Conclusions and Clinical Importance: BCoV was more likely to be detected in diarrheic than healthy calves. The PanCoV-RT-PCR assay can be a useful tool to detect CoV samples from diarrheic calves. 
 Title: Detection of Bovine Coronavirus in Healthy and Diarrheic Dairy Calves
Example Output: True

Example Input: Paragraph: Unbiased metagenomic sequencing holds significant potential as a diagnostic tool for the simultaneous detection of any previously genetically described viral nucleic acids in clinical samples. Viral genome sequences can also inform on likely phenotypes including drug susceptibility or neutralization serotypes. In this study, different variables of the laboratory methods often used to generate viral metagenomics libraries on the efficiency of viral detection and virus genome coverage were compared. A biological reagent consisting of 25 different human RNA and DNA viral pathogens was used to estimate the effect of filtration and nuclease digestion, DNA/RNA extraction methods, pre-amplification and the use of different library preparation kits on the detection of viral nucleic acids. Filtration and nuclease treatment led to slight decreases in the percentage of viral sequence reads and number of viruses detected. For nucleic acid extractions silica spin columns improved viral sequence recovery relative to magnetic beads and Trizol extraction. Pre-amplification using random RT-PCR while generating more viral sequence reads resulted in detection of fewer viruses, more overlapping sequences, and lower genome coverage. The ScriptSeq library preparation method retrieved more viruses and a greater fraction of their genomes than the TruSeq and Nextera methods. Viral metagenomics sequencing was able to simultaneously detect up to 22 different viruses in the biological reagent analyzed including all those detected by qPCR. Further optimization will be required for the detection of viruses in biologically more complex samples such as tissues, blood, or feces. 
 Title: Comparing viral metagenomics methods using a highly multiplexed human viral pathogens reagent NIH Public Access
Example Output:
False