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: CD13/Aminopeptidase N (CD13) is known to play an important role in tumour cell invasion. We examined whether basic fibroblast growth factor (bFGF) is involved in the regulation of CD13 expression in human melanoma cells. 1F6 human melanoma cells were stably transfected with constructs encoding either the 18 kDa (18kD) or all (ALL) bFGF isoform proteins. We observed highly increased CD13 mRNA and protein expression in the 1F6 clones regardless of the overexpression of either the 18kD or all isoform proteins. Neutral aminopeptidase activity was increased five-fold and could be inhibited by bestatin and the CD13-neutralising antibody WM15. The enhanced invasion through Matrigel, but not migration in a wound assay, was efficiently abrogated by both bestatin and WM15. Upregulation of CD13 expression was the result of increased epithelial and myeloid promoter activity up to 4.5fold in 1F6-18kD and 1F6-ALL clones. Interestingly, in a panel of human melanoma cell lines, a significant correlation (r 2 0.883, Po0.05) between bFGF and CD13 mRNA and protein expression was detected. High bFGF and CD13 expression were clearly related with an aggressive phenotype. Taken together, our data indicate that high bFGF expression upregulates CD13 expression in human melanoma cells by activating both the myeloid and the epithelial CD13 promoter. In addition, we show that high bFGF and CD13 expression results in enhanced invasive capacity and metastatic behaviour of human melanoma cells.
Title: CD13/Aminopeptidase N overexpression by basic fibroblast growth factor mediates enhanced invasiveness of 1F6 human melanoma cells
[EX A]: False

[EX Q]: Paragraph: Coronaviruses (CoVs), enveloped positive-sense RNA viruses, are characterized by club-like spikes that project from their surface, an unusually large RNA genome, and a unique replication strategy. Coronaviruses cause a variety of diseases in mammals and birds ranging from enteritis in cows and pigs and upper respiratory disease chickens to potentially lethal human respiratory infections. Here we provide a brief introduction to coronaviruses discussing their replication and pathogenicity, and current prevention and treatment strategies. We will also discuss the outbreaks of the highly pathogenic Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the recently identified Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV). Coronaviruses (CoVs) are the largest group of viruses belonging to the Nidovirales order, which includes Coronaviridae, Arteriviridae, and Roniviridae families. The Coronavirinae comprise one of two subfamilies in the Coronaviridae family, with the other being the Torovirinae. The Coronavirinae are further subdivided into four groups, the alpha, beta, gamma and delta coronaviruses. The viruses were initially sorted into these groups based on serology but are now divided by phylogenetic clustering. All viruses in the Nidovirales order are enveloped, non-segmented positive-sense RNA viruses. They all contain very large genomes for RNA viruses, with Coronavirinae having the largest identified RNA genomes, containing approximately 30 kilobase (kb) genomes. Other common features within the Nidovirales order include: i) a highly conserved genomic organization, with a large replicase gene preceding structural and accessory genes; ii) expression of many nonstructural genes by ribosomal frameshifting; iii) several unique or unusual enzymatic activities encoded within the large replicase-transcriptase polyprotein; and iv) expression of downstream genes by synthesis of 3 nested sub-genomic mRNAs. In fact, the Nidovirales order name is derived from these nested 3 mRNAs as nido is Latin for "nest". The major differences within the Nidovirus families are in the number, type, and Coronavirus virions are spherical with diameters of approximately 125 nm as depicted in recent studies by cryo-electron tomography and cryo-electron microscopy [2,3]. The most prominent feature of coronaviruses is the club-shape spike projections emanating from the surface of the virion. These spikes are a defining feature of the virion and give them the appearance of a solar corona, prompting the name, coronaviruses. Within the envelope of the virion is the nucleocapsid. Coronaviruses have helically symmetrical nucleocapsids, which is uncommon among positive-sense RNA viruses, but far more common for negativesense RNA viruses. Coronavirus virus particles contain four main structural proteins. These are the spike (S), membrane (M), envelope (E), and nucleocapsid (N) proteins, all of which are encoded within the 3 end of the viral genome. The S protein (150 kDa), utilizes an N-terminal signal sequence to gain access to the ER, and is heavily N-linked glycosylated. Homotrimers of the virus encoded S protein make up the distinctive spike structure on the surface of the virus [4, 5] . The trimeric S glycoprotein is a class I fusion protein [6] and mediates attachment to the host receptor [7] . In most, but not all, coronaviruses, S is cleaved by a host cell furin-like protease into two separate polypeptides noted S1 and S2 [8, 9] . S1 makes up the large receptor-binding domain of the S protein while S2 forms the stalk of the spike molecule [10] . The M protein is the most abundant structural protein in the virion. It is a small (25-30 kDa) protein with 3 transmembrane domains [11] and is thought to give the virion its shape. It has a small N-terminal glycosylated ectodomain and a much larger C-terminal Fehr and Perlman The initial attachment of the virion to the host cell is initiated by interactions between the S protein and its receptor. The sites of receptor binding domains (RBD) within the S1 region of a coronavirus S protein vary depending on the virus, with some having the RBD at the Nterminus of S1 (MHV) while others (SARS-CoV) have the RBD at the C-terminus of S1 [29, 30] . The S-protein/receptor interaction is the primary determinant for a coronavirus to Fehr and Perlman
Title: Coronaviruses: An Overview of Their Replication and Pathogenesis HHS Public Access
[EX A]: False

[EX Q]: Paragraph: Health planners use forecasts of key metrics associated with influenza-like illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts; however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the mediumterm in parallel with the systematic consideration of more sophisticated ensemble approaches. It is estimated that there are between 3 and 5 million worldwide annual seasonal cases of severe influenza illness, and between 290 000 and 650 000 respiratory deaths. Influenzalike illness (ILI) describes a set of symptoms and is a practical way for health-care workers PLOS Computational Biology | https://doi.to easily estimate likely influenza cases. The Centers for Disease Control and Prevention (CDC) collects and disseminates ILI information, and has, for the last several years, run a forecasting challenge (the CDC Flu Challenge) for modelers to predict near-term weekly incidence, week of season onset, week of peak, and intensity of peak. We have developed a modeling framework that accounts for a range of mechanisms thought to be important for influenza transmission, such as climatic conditions, school vacations, and coupling between different regions. In this study we describe our forecast procedure for the 2016-17 season and highlight which features of our models resulted in better or worse forecasts. Most notably, we found that when the dynamics of different regions are coupled together, the forecast accuracy improves. We also found that the most accurate forecasts required some level of forecaster interaction, that is, the procedure could not be completely automated without a reduction in accuracy. Influenza forecasting PLOS Computational Biology | https://doi.
Title: Forecasting national and regional influenza- like illness for the USA
[EX A]:
True