instruction:
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".
question:
Paragraph: Sprague-Dawley rats were injected intraperitoneally with a suspension of Trypanosoma brucei brucei. An early induction of major histocompatibility complex (MHC) class I antigens as well as an infiltration of macrophage-like cells and cytotoxic T-cells was detected with immunohistochemical techniques in circumventricular organs, such as the median eminence, neurohypophysis, subfornical organ, pineal gland and area postrema. These areas, which lack a blood-brain barrier, correspond to those showing early invasion of trypanosomes. In addition, there was a marked induction of MHC class I in neurons in two hypothalamic nuclei, the paraventricular and supraoptic nuclei. Neurons in these two nuclei are located behind the blood-brain barrier, but project to the neurohypophysis and to the median eminence, thereby exposing their axon terminals to factors circulating in the blood or released locally from invading trypanosomes or from macrophages or cytotoxic T-cells. It is suggested that the alteration in the nerve cell bodies in the hypothalamic nuclei is caused by retrograde axonal signals from these target areas. 
 Title: Early major histocompatibility complex (MHC) class I antigen induction in hypothalamic supraoptic and paraventricular nuclei in trypanosome-infected rats
answer:
True


question:
Paragraph: Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic. Pandemic growth rates from sequences E. de Silva et al. 1799 Pandemic growth rates from sequences E. de Silva et al. 1807  
 Title: Inferring pandemic growth rates from sequence data
answer:
True


question:
Paragraph: Background: Pseudomonas aeruginosa (PA) is an opportunistic Gram-negative bacterium that causes serious life threatening and nosocomial infections including pneumonia. PA has the ability to alter host genome to facilitate its invasion, thus increasing the virulence of the organism. Sphingosine-1-phosphate (S1P), a bioactive lipid, is known to play a key role in facilitating infection. Sphingosine kinases (SPHK) 1&2 phosphorylate sphingosine to generate S1P in mammalian cells. We reported earlier that Sphk2 −/− mice offered significant protection against lung inflammation, compared to wild type (WT) animals. Therefore, we profiled the differential expression of genes between the protected group of Sphk2 −/− and the wild type controls to better understand the underlying protective mechanisms related to the Sphk2 deletion in lung inflammatory injury. Whole transcriptome shotgun sequencing (RNA-Seq) was performed on mouse lung tissue using NextSeq 500 sequencing system. Results: Two-way analysis of variance (ANOVA) analysis was performed and differentially expressed genes following PA infection were identified using whole transcriptome of Sphk2 −/− mice and their WT counterparts. Pathway (PW) enrichment analyses of the RNA seq data identified several signaling pathways that are likely to play a crucial role in pneumonia caused by PA such as those involved in: 1. Immune response to PA infection and NF-κB signal transduction; 2. PKC signal transduction; 3. Impact on epigenetic regulation; 4. Epithelial sodium channel pathway; 5. Mucin expression; and 6. Bacterial infection related pathways. Our genomic data suggests a potential role for SPHK2 in PA-induced pneumonia through elevated expression of inflammatory genes in lung tissue. Further, validation by RT-PCR on 10 differentially expressed genes showed 100% concordance in terms of vectoral changes as well as significant fold change. (Continued on next page) 
 Title: Genetic deletion of Sphk2 confers protection against Pseudomonas aeruginosa mediated differential expression of genes related to virulent infection and inflammation in mouse lung
answer:

False