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".

Q: Paragraph: Mechanistic hypotheses about airborne infectious disease transmission have traditionally emphasized the role of coughing and sneezing, which are dramatic expiratory events that yield both easily visible droplets and large quantities of particles too small to see by eye. Nonetheless, it has long been known that normal speech also yields large quantities of particles that are too small to see by eye, but are large enough to carry a variety of communicable respiratory pathogens. Here we show that the rate of particle emission during normal human speech is positively correlated with the loudness (amplitude) of vocalization, ranging from approximately 1 to 50 particles per second (0.06 to 3 particles per cm 3 ) for low to high amplitudes, regardless of the language spoken (english, spanish, Mandarin, or Arabic). Furthermore, a small fraction of individuals behaves as "speech superemitters," consistently releasing an order of magnitude more particles than their peers. our data demonstrate that the phenomenon of speech superemission cannot be fully explained either by the phonic structures or the amplitude of the speech. these results suggest that other unknown physiological factors, varying dramatically among individuals, could affect the probability of respiratory infectious disease transmission, and also help explain the existence of superspreaders who are disproportionately responsible for outbreaks of airborne infectious disease. It has long been recognized that particles expelled during human expiratory events, such as sneezing, coughing, talking, and breathing, serve as vehicles for respiratory pathogen transmission 1-6 . The relative contribution of each expiratory activity in transmitting infectious microorganisms, however, remains unclear 4 . Much previous research has focused on coughing 7-12 and sneezing 11,13,14 activities that yield relatively large droplets (approximately 50 μm or larger) easily visible to the naked eye. Less noticeable, but arguably more infectious for some diseases, are the smaller particles emitted during sneezing and coughing as well as during breathing 15-17 and talking 16, 18, 19 . These small particles are believed to be generated during breathing and talking from the mucosal layers coating the respiratory tract via a combination of a "fluid-film burst" mechanism within the bronchioles and from vocal folds adduction and vibration within the larynx 6,20,21 . The particles emitted during breathing and typical speech predominantly average only 1 μm in diameter 15-17 and are thus too small to see without specialized equipment; most people outside of the community of bioaerosol researchers are less aware of them. Despite their small size, however, these micron-scale particles are sufficiently large to carry a variety of respiratory pathogens such as measles virus (50-500 nm) 22 , influenza virus (100 nm-1 µm) 23 , and Mycobacterium tuberculosis (1-3 µm) 24 . Indeed, recent work by Yan et al. has confirmed that significant amounts of influenza viral RNA are present in small particles (<5 μm) emitted by influenza-infected individuals during natural breathing, without coughing or sneezing 25 . These small particles are potentially more infectious than larger sneeze-or 
 Title: Aerosol emission and superemission during human speech increase with voice loudness

A: False
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Q: Paragraph: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time. 
 Title: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

A: True
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Q: Paragraph: Interferon-stimulated gene 15 (ISG15) is a member of the ubiquitin-like modifiers (ULM) family, which adopts a βgrasp fold domain(s) similar to ubiquitin (Ub) with only minor sequence homology. ISG15 consists of two Ub-like domains and aids the immune system in neutralizing infections by numerous pathogens and plays an important role in defending cells against many viruses including influenza A. Recently, Ub was found to be a substrate for ISG15, which can be ISGylated on Lys29 and Lys48, while the former is more dominant. The discovery of such hybrid ISG15-Ub chains brought forward various fundamental questions regarding the nature and effect of this conjugation. To further investigate the role of hybrid ISG15-Ub chains, the pure homogeneous material of these chains is needed in workable quantities. By applying advanced chemical strategies for protein synthesis, we report the total chemical synthesis of a 231-residue ISG15-Lys29-Ub hybrid chain. During the synthesis we encountered insoluble peptide fragments, and therefore we developed a new reversible Acm based solubilizing tag to efficiently tackle this hurdle. This new Acm tag was compared with the known Arg based Acm solubilizing tag and was found to be more reliable in terms of incorporation and efficiency as demonstrated in the synthesis of the native ISG15-Ub hybrid chain. 
 Title: Total Chemical Synthesis of ISGylated-Ubiquitin Hybrid Chain Assisted by Acetamidomethyl Derivatives with Dual Functions

A:
True
****