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".

[EX Q]: Paragraph: The mucus-dwelling parasite Ostertagia ostertagi is one of the most important gastrointestinal nematodes in cattle. Our group has previously demonstrated the protective capacity of a vaccine against this parasite based on a native activation-associated secreted protein ASP1 (nASP) in combination with the saponin adjuvant QuilA. The aim of the current study was to analyse the effect of both antigen and adjuvant on the cellular and humoral vaccine-induced immune responses by comparing the native ASP to a recombinant version expressed in Pichia pastoris (pASP) and replacing QuilA by Al(OH) 3 . Immunization of cattle with the protective nASP+QuilA vaccine was associated with antigen-induced proliferation of natural killer (NK) cells combined with IFN- secretion and the induction of a mixed IgG1/IgG2 antibody response. ASP-specific activation and proliferation of NK cells was also observed in mice following the same vaccination regime. Replacing QuilA by Al(OH) 3 or nASP by pASP significantly decreased the capacity of the vaccines to trigger both NK cell activation and antibody responses and failed to induce protection against a challenge infection. Reduction of the structurally anchoring disulphide bonds of the nASP completely abolished its ability to induce NK cell activation and antibody responses, highlighting the importance of protein conformation for the immunostimulatory activity.
Title: Host protective ASP-based vaccine against the parasitic nematode Ostertagia ostertagi triggers NK cell activation and mixed IgG1-IgG2 response
[EX A]: True

[EX Q]: Paragraph: Determining the fitness of viral genotypes has become a standard practice in virology as it is essential to evaluate their evolutionary potential. Darwinian fitness, defined as the advantage of a given genotype with respect to a reference one, is a complex property that captures, in a single figure, differences in performance at every stage of viral infection. To what extent does viral fitness result from specific molecular interactions with host factors and regulatory networks during infection? Can we identify host genes in functional classes whose expression depends on viral fitness? Here, we compared the transcriptomes of tobacco plants infected with seven genotypes of tobacco etch potyvirus that differ in fitness. We found that the larger the fitness differences among genotypes, the more dissimilar the transcriptomic profiles are. Consistently, two different mutations, one in the viral RNA polymerase and another in the viral suppressor of RNA silencing, resulted in significantly similar gene expression profiles. Moreover, we identified host genes whose expression showed a significant correlation, positive or negative, with the virus' fitness. Differentially expressed genes which were positively correlated with viral fitness activate hormone-and RNA silencing-mediated pathways of plant defense. In contrast, those that were negatively correlated with fitness affect metabolism, reducing growth, and development. Overall, these results reveal the high information content of viral fitness and suggest its potential use to predict differences in genomic profiles of infected hosts.
Title: Viral Fitness Correlates with the Magnitude and Direction of the Perturbation Induced in the Host's Transcriptome: The Tobacco Etch Potyvirus-Tobacco Case Study
[EX A]: False

[EX Q]: Paragraph: The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6 A and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set. A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau-Broto indices, Balaban distance connectivity index, Kier-Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type. In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis.
Title: Natural/random protein classification models based on star network topological indices
[EX A]:
False