instruction:
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".
question:
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 m 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 m 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 m) 23 , and Mycobacterium tuberculosis (1-3 m) 24 . Indeed, recent work by Yan et al. has confirmed that significant amounts of influenza viral RNA are present in small particles (<5 m) 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
answer:
False

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Paragraph: Background 3 In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics 4 algorithms. Sequenced datasets include background organisms, creating ambiguity in the true 5 source organism for each read. Bacterial and viral datasets were created with even and staggered 6 coverage to evaluate organism identification, read mapping, and gene identification capabilities of 7 available algorithms. These truth datasets are provided as a resource for the development and 8 refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform 9 decision makers on strengths and weaknesses of available algorithms and how the results may be best 10 leveraged for bacterial and viral organism identification and characterization. 11 12 Source organisms were selected to mirror communities described in the Human Microbiome Project as 13 well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The 14 six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: 15 MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. 16 Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species 19 levels, false positive organisms identified to genus and species level, read mapping, relative abundance 20 estimation, and gene calling. No algorithm out performed the others in all categories, and the 21 algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria 22 and LMAT for viruses. The algorithms were ranked by overall performance using a normalized 23 weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken 24 and LMAT. 25 Conclusions 27 Simulated FASTQ datasets with well--characterized truth data about microbial community composition 28 reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms 29 evaluated. The simulated datasets are available to download from the Sequence Read Archive 30 (SRP062063). 31 32
Title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim
answer:
False

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Paragraph: In 2013, PED emerged for the first time in the United States (US). The porcine epidemic diarrhea virus (PEDV) spread quickly throughout North America. Infection with PEDV causes watery diarrhea and up to 100% mortality in piglets, particularly for highly pathogenic non-InDel strains circulating in the US. PEDV is mainly transmitted by the fecal-oral route. Transmission via the venereal route has been suspected but not previously investigated. The aim of the study was to determine if PEDV could be detected in semen from infected specific pathogen-free (SPF) boars inoculated with a PEDV US non-InDel strain suggesting venereal transmission may occur. Two boars orally inoculated with PEDV showed clinical signs and virus shedding in feces. Transient presence of the PEDV genome was detected by RT-qPCR in the seminal (5.06 10 2 to 2.44 10 3 genomic copies/mL) and sperm-rich fraction of semen (5.64 10 2 to 3.40 10 4 genomic copies/mL) and a longer duration of viral shedding was observed in the sperm-rich fraction. The evidence of PEDV shedding in semen raises new questions in term of disease spread within the pig population with the use of potentially contaminated semen.
Title: Evidence of porcine epidemic diarrhea virus (PEDV) shedding in semen from infected specific pathogen-free boars
answer:
False