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".

Input: Consider Input: Paragraph: Background: Based on clinical representation of disease symptoms multiple sclerosis (MScl) patients can be divided into two major subtypes; relapsing remitting (RR) MScl (85-90%) and primary progressive (PP) MScl (10-15%). Proteomics analysis of cerebrospinal fluid (CSF) has detected a number of proteins that were elevated in MScl patients. Here we specifically aimed to differentiate between the PP and RR subtypes of MScl by comparing CSF proteins. Methodology/Principal Findings: CSF samples (n = 31) were handled according to the same protocol for quantitative mass spectrometry measurements we reported previously. In the comparison of PP MScl versus RR MScl we observed a number of differentially abundant proteins, such as protein jagged-1 and vitamin D-binding protein. Protein jagged-1 was over three times less abundant in PP MScl compared to RR MScl. Vitamin D-binding protein was only detected in the RR MScl samples. These two proteins were validated by independent techniques (western blot and ELISA) as differentially abundant in the comparison between both MScl types. 
 Title: Proteomics Comparison of Cerebrospinal Fluid of Relapsing Remitting and Primary Progressive Multiple Sclerosis

Output: True


Input: Consider Input: Paragraph: Pandemic 2009 H1N1 is normally susceptible to oseltamivir, but variants harboring the H275Y (CAC → TAC) mutation exhibit resistance. We describe the use of a combined reverse-transcription polymerase chain reaction (RT-PCR)/pyrosequencing approach to identify the H275 residue. A total of 223 specimens were tested with this method: 216 randomly selected clinical specimens positive for 2009 H1N1 and 7 cell-culture supernatants from the Centers for Disease Control and Prevention (CDC; 4 resistant, 3 susceptible 2009 H1N1 strains). The assay detected H275Y in 1 clinical respiratory sample (0.5%) and all 4 oseltamivir-resistant strains from the CDC; the remaining 215 clinical and 3 susceptible CDC specimens were wild-type. Sanger sequencing confirmed the results for 50 of 50 selected isolates. The RT-PCR/pyrosequencing method was highly specific, producing no amplicons or valid sequences from samples containing non-H1N1 viruses or bacteria. Our findings suggest that this method provides a rapid tool for H275Y detection, with high sensitivity and potential benefit for patient care. 
 Title: Reverse-transcription polymerase chain reaction/pyrosequencing to characterize neuraminidase H275 residue of influenza A 2009 H1N1 virus for rapid and specific detection of the viral oseltamivir resistance marker in a clinical laboratory

Output: False


Input: Consider Input: Paragraph: Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society. 
 Title: Predicting Infectious Disease Using Deep Learning and Big Data


Output: False