TASK DEFINITION: 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".
PROBLEM: Paragraph: Bernard, S., Bottreau, E., Aynaud, J.M., Have, P. and Szymansky, J., 1989. Natural infection with the porcine respiratory coronavirus induces protective lactogenic immunity against transmissible gastroenteritis. Vet. Microbiol., 21: 1-8. Our objective was to evaluate the level of passive protection against transmissible gastroenteritis (TGE) among 57 newborn piglets nursing from seven seropositive sows previously naturally infected with porcine respiratory coronavirus (PRCV). After challenge exposure we observed mortality rates of 44% for litters of seven PRCV-infected sows, 40% for litters of four sows orally immunized with the attenuated TGEV strain Nouzilly, and 91% for litters of seven seronegative susceptible sows. A blocking ELISA with two appropriate monoclonal antibodies distinguished serological responses of PRCV-infected sows from those of TGEV-immunized sows. The results suggest that natural infection of the sow with PRCV may induce a degree of protective lactogenic immunity against TGE. 
 Title: Natural Infection with the Porcine Respiratory Coronavirus Induces Protective Lactogenic Immunity against Transmissible Gastroenteritis

SOLUTION: True

PROBLEM: Paragraph: During viral entry, enveloped viruses require the fusion of their lipid envelope with host cell membranes. For coronaviruses, this critical step is governed by the virally-encoded spike (S) protein, a class I viral fusion protein that has several unique features. Coronavirus entry is unusual in that it is often biphasic in nature, and can occur at or near the cell surface or in late endosomes. Recent advances in structural, biochemical and molecular biology of the coronavirus S protein has shed light on the intricacies of coronavirus entry, in particular the molecular triggers of coronavirus S-mediated membrane fusion. Furthermore, characterization of the coronavirus fusion peptide (FP), the segment of the fusion protein that inserts to a target lipid bilayer during membrane fusion, has revealed its particular attributes which imparts some of the unusual properties of the S protein, such as Ca 2+ -dependency. These unusual characteristics can explain at least in part the biphasic nature of coronavirus entry. In this review, using severe acute respiratory syndrome coronavirus (SARS-CoV) as model virus, we give an overview of advances in research on the coronavirus fusion peptide with an emphasis on its role and properties within the biological context of host cell entry. 
 Title: Physiological and molecular triggers for SARS-CoV membrane fusion and entry into host cells

SOLUTION: True

PROBLEM: Paragraph: In this paper, we outline the theory of epidemic percolation networks and their use in the analysis of stochastic SIR epidemic models on undirected contact networks. We then show how the same theory can be used to analyze stochastic SIR models with random and proportionate mixing. The epidemic percolation networks for these models are purely directed because undirected edges disappear in the limit of a large population. In a series of simulations, we show that epidemic percolation networks accurately predict the mean outbreak size and probability and final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populations. Finally, we show that epidemic percolation networks can be used to re-derive classical results from several different areas of infectious disease epidemiology. In an appendix, we show that an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model in a closed population and prove that the distribution of outbreak sizes given the infection of any given node in the SIR model is identical to the distribution of its out-component sizes in the corresponding probability space of epidemic percolation networks. We conclude that the theory of percolation on semi-directed networks provides a very general framework for the analysis of stochastic SIR models in closed populations. 
 Title: Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing

SOLUTION:
True