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".

Paragraph: Theoretically, homogeneous environments favor the evolution of specialists whereas heterogeneous environments favor generalists. Canine distemper is a multi-host carnivore disease caused by canine distemper virus (CDV). The described cell receptor of CDV is SLAM (CD150). Attachment of CDV hemagglutinin protein (CDV-H) to this receptor facilitates fusion and virus entry in cooperation with the fusion protein (CDV-F). We investigated whether CDV strains co-evolved in the large, homogeneous domestic dog population exhibited specialist traits, and strains adapted to the heterogeneous environment of smaller populations of different carnivores exhibited generalist traits. Comparison of amino acid sequences of the SLAM binding region revealed higher similarity between sequences from Canidae species than to sequences from other carnivore families. Using an in vitro assay, we quantified syncytia formation mediated by CDV-H proteins from dog and non-dog CDV strains in cells expressing dog, lion or cat SLAM. CDV-H proteins from dog strains produced significantly higher values with cells expressing dog SLAM than with cells expressing lion or cat SLAM. CDV-H proteins from strains of non-dog species produced similar values in all three cell types, but lower values in cells expressing dog SLAM than the values obtained for CDV-H proteins from dog strains. By experimentally changing one amino acid (Y549H) in the CDV-H protein of one dog strain we decreased expression of specialist traits and increased expression of generalist traits, thereby confirming its functional importance. A virus titer assay demonstrated that dog strains produced higher titers in cells expressing dog SLAM than cells expressing SLAM of non-dog hosts, which suggested possible fitness benefits of specialization post-cell entry. We provide in vitro evidence for the expression of specialist and generalist traits by CDV strains, and fitness trade-offs across carnivore host environments caused by antagonistic pleiotropy. These findings extend knowledge on CDV molecular epidemiology of particular relevance to wild carnivores.
Title: Antagonistic Pleiotropy and Fitness Trade-Offs Reveal Specialist and Generalist Traits in Strains of Canine Distemper Virus
False

Paragraph: Rift Valley fever virus (RVFV; genus Phlebovirus, family Bunyaviridae) is a mosquito-borne zoonotic pathogen which can cause hemorrhagic fever, neurological disorders or blindness in humans, and a high rate of abortion in ruminants. MP-12 strain, a live-attenuated candidate vaccine, is attenuated in the M-and L-segments, but the S-segment retains the virulent phenotype. MP-12 was manufactured as an Investigational New Drug vaccine by using MRC-5 cells and encodes a functional NSs gene, the major virulence factor of RVFV which 1) induces a shutoff of the host transcription, 2) inhibits interferon (IFN)b promoter activation, and 3) promotes the degradation of dsRNA-dependent protein kinase (PKR). MP-12 lacks a marker for differentiation of infected from vaccinated animals (DIVA). Although MP-12 lacking NSs works for DIVA, it does not replicate efficiently in type-I IFN-competent MRC-5 cells, while the use of type-I IFN-incompetent cells may negatively affect its genetic stability. To generate modified MP-12 vaccine candidates encoding a DIVA marker, while still replicating efficiently in MRC-5 cells, we generated recombinant MP-12 encoding Punta Toro virus Adames strain NSs (rMP12-PTNSs) or Sandfly fever Sicilian virus NSs (rMP12-SFSNSs) in place of MP-12 NSs. We have demonstrated that those recombinant MP-12 viruses inhibit IFN-b mRNA synthesis, yet do not promote the degradation of PKR. The rMP12-PTNSs, but not rMP12-SFSNSs, replicated more efficiently than recombinant MP-12 lacking NSs in MRC-5 cells. Mice vaccinated with rMP12-PTNSs or rMP12-SFSNSs induced neutralizing antibodies at a level equivalent to those vaccinated with MP-12, and were efficiently protected from wild-type RVFV challenge. The rMP12-PTNSs and rMP12-SFSNSs did not induce antibodies cross-reactive to anti-RVFV NSs antibody and are therefore applicable to DIVA. Thus, rMP12-PTNSs is highly efficacious, replicates efficiently in MRC-5 cells, and encodes a DIVA marker, all of which are important for vaccine development for Rift Valley fever.
Title: Characterization of Rift Valley Fever Virus MP-12 Strain Encoding NSs of Punta Toro Virus or Sandfly Fever Sicilian Virus
True

Paragraph: Background: Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections. Method: The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute. Results: The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%. We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.
Title: Building predictive models for MERS-CoV infections using data mining techniques Building predictive models for MERS-CoV infections using data mining techniques 745
True