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".
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Question: Paragraph: The infection of the avian coronavirus infectious bronchitis virus (IBV) is initiated by the binding of the spike glycoprotein S to sialic acids on the chicken host cell. In this study we identified the receptorbinding domain (RBD) of the spike of the prototype IBV strain M41. By analyzing the ability of recombinantly expressed chimeric and truncated spike proteins to bind to chicken tissues, we demonstrate that the N-terminal 253 amino acids of the spike are both required and sufficient for binding to chicken respiratory tract in an α-2,3-sialic acid-dependent manner. Critical amino acids for attachment of M41 spike are present within the N-terminal residues 19-69, which overlap with a hypervariable region in the S1 gene. Our results may help to understand the differences between IBV S1 genotypes and the ultimate pathogenesis of IBV in chickens. 
 Title: Mapping of the receptor-binding domain and amino acids critical for attachment in the spike protein of avian coronavirus infectious bronchitis virus

Answer: True


Question: Paragraph: A novel porcine deltacoronavirus (PdCV) was first discovered in Ohio and Indiana in February 2014, rapidly spread to other states in the United States and Canada, and caused significant economic loss in the swine industry. The origin and virulence of this novel porcine coronavirus are not known. Here, we characterized U.S. PdCV isolates and determined their virulence in gnotobiotic and conventional piglets. Genome analyses revealed that U.S. PdCV isolates possess unique genetic characteristics and share a close relationship with Hong Kong and South Korean PdCV strains and coronaviruses (CoVs) of Asian leopard cats and Chinese ferret-badgers. The PdCV-positive intestinal content (Ohio CVM1) and the cell culture-adapted PdCV Michigan (MI) strain were orally inoculated into gnotobiotic and/or conventional piglets. Within 1 to 3 days postinfection, profuse watery diarrhea, vomiting, and dehydration were observed. Clinical signs were associated with epithelial necrosis in the gastric pits and small intestine, the latter resulting in severe villous atrophy. Mild interstitial pneumonia was identified in the lungs of PdCVinfected piglets. High levels of viral RNA (8 to 11 log RNA copies/g) were detected in intestinal tissues/luminal contents and feces of infected piglets, whereas moderate RNA levels (2 to 5 log RNA copies/g) were detected in blood, lung, liver, and kidney, indicating multisystemic dissemination of the virus. Polyclonal immune serum against PdCV but not immune serum against porcine epidemic diarrhea virus (PEDV) reacted with PdCV-infected small-intestinal epithelial cells, indicating that PdCV is antigenically distinct from PEDV. Collectively, we demonstrate for the first time that PdCV caused severe gastrointestinal diseases in swine. Indiana and subsequently spread rapidly across the United States and Canada, causing significant economic losses. Here, we report the detailed genetic characterization, phylogeny, and virulence of emergent PdCV strains in the United States. We found that PdCV caused severe diarrhea, vomiting, and dehydration in gnotobiotic and conventional piglets, signs that were clinically indistinguishable from those caused by PEDV and TGEV. In addition to extensive intestinal lesions, PdCV caused significant lesions in the stomach and mild pulmonary lesions that have not been reported for TGEV and PEDV. The finding that PdCV is a significant enteric disease of swine highlights the need to develop effective measures to control this disease. . 2015. Origin, evolution, and virulence of porcine deltacoronaviruses in the United States. mBio 6(2): e00064-15. 
 Title: Origin, Evolution, and Virulence of Porcine Deltacoronaviruses in the United States

Answer: False


Question: Paragraph: Since we focused on the distribution of outbreaks rather than the total number of cases, we analyzed only the first positive report associated with each outbreak or the first negative report (if all suspected cases from that farm were rabies negative) when multiple animals were reported sick or dead. Information for each outbreak included species infected, date of disease onset, the district where the outbreak occurred (Peru is divided into regions, provinces, districts and towns), and GPS data when available from the farm or community where the outbreak occurred (N = 914 outbreaks). We excluded data from two regions on the coast of Peru (Lima and Ancash) that reported small numbers of unconfirmed outbreaks and 23 outbreaks from the region of Puno, where dog rabies causes most livestock infections [1]. Using a standardized questionnaire, we asked farmers whether their animals had been bitten by vampire bats in the last year; whether they knew that vampire bats can transmit rabies; whether they knew that SENASA was the relevant authority for reporting rabies; and if they vaccinated their animals against rabies. Questionnaires were performed in Quechua or Spanish by a local assistant. Procedures were approved by the Ethics Committee of the College of Veterinary Science, University of Glasgow. Written and signed informed consent approval was obtained from each farmer who participated in the study. Least-cost distances were calculated using the gdistance package of R, setting resistance values to 1 and 10000 below and above 3,600m, respectively. The transition function was used to create a least-cost distance across a conductance map, with 8 possible path directions. Distances were corrected to account for geographical projection using the geoCorrection function. The costDistance function was used to calculate the distance from the origin of each wave to the center of each hexbin. To assess the sensitivity of wavefront speeds and R 2 to the inferred location of index outbreaks, we performed a bootstrap analysis using the total number of outbreaks N [2]. This analysis consisted in four consecutive steps: (1) randomly sampling N outbreaks, allowing the same outbreak to be sampled multiple times (i.e. bootstrapping with replacement) using the sample function of R; (2) randomly sampling 5000 different plausible origins for each wave; (3) for each of those 5000 origins, calculating the slope and R 2 of the regression between the least-cost distance to the origin and the time of disease arrival; (4) selecting the slope of the regression with the highest R 2 for each origin. This procedure was repeated 200 times for each valley, and the range of both the speed of the wave (1/slope of the regression) and its associated R 2 were estimated. The distributions of the speed of the wave and R 2 obtained are shown in figure S1 . This analysis was the same as the one described above, except for step (1). This step was replaced by randomly selecting an elevation threshold from a uniform distribution ranging from 3500 to 4500 meters using the runif function in R. We focus our sensitivity analysis on elevations above 3600 meters because clear evidence of bat presence and viral circulation below this limit which implies the absence of any impassable threshold (figure 3, figure 5 ). In both valleys, the estimated speed of the wave and R 2 declined slightly with the elevation threshold ( figure S2 ). However, all estimated values remained within the 95% CI of the estimation using 3600 meters, the assumed value in our main analysis. Thus, the estimation of the wave speed was largely insensitive to the elevation threshold. The negative correlation between wavefront speeds and R 2 and the elevation threshold is explained by longer least-cost distances when using a smaller elevation threshold because there are more elevation obstacles in the landscape. We validated the predictions of outbreak arrival dates to a particular location ahead of the wave using the following procedure. First, we estimated the most likely origin of the wave by maximising the R 2 of the linear regression using the first 75% of outbreak locations. We then predicted the month of arrival for the remaining outbreak locations in the wave front (5 and 7 hexagonal cells to predict in Chalhuanca and Rio Apurimac, respectively) using the predict function in R. Finally, we calculated the averaged difference between the predicted and arrival date. All true arrival dates from the test dataset occurred within the 95% CI of the predicted arrival dates (figure S1). Similar results were obtained when using half of the data to predict the other half (50% training, 50% test set); all arrival dates of the test set occurred within the 95% CI of the predicted arrival dates. Figure S1 . Sensitivity of wavefront speeds to the inferred wave origins. The distribution of the estimated wave speeds (left) and their associated R 2 (right) from the bootstrap procedure are shown for Chalhuanca (top plots) and Rio Apurimac (bottom plots). Supplementary Figures and Tables Amazonian regions are colored in blue and red, respectively. 
 Title: Electronic Supplementary Materials Spatial Expansions and Travelling Waves of Rabies in Vampire Bats

Answer:
False