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".
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Question: Paragraph: Dendritic cells (DCs) are recognized as highly potent antigen-presenting cells that are able to stimulate cytotoxic T lymphocyte (CTL) responses with antitumor activity. Consequently, DCs have been explored as cellular vaccines in cancer immunotherapy. To that end, DCs are modified with tumor antigens to enable presentation of antigen-derived peptides to CTLs. In this review we discuss the use of viral vectors for in situ modification of DCs, focusing on their clinical applications as anticancer vaccines. Among the viral vectors discussed are those derived from viruses belonging to the families of the Poxviridae, Adenoviridae, Retroviridae, Togaviridae, Paramyxoviridae, and Rhabdoviridae. We will further shed light on how the combination of viral vector-based vaccination with T-cell supporting strategies will bring this strategy to the next level. Since their discovery in 1973, it was clear that dendritic cells (DCs) stood out above the immune cell pack (1, 2). They are morphologically distinct from all other immune cell types and are gifted with an unparalleled capacity to take up, process and present self and foreign antigens to both CD4 + and CD8 + T cells. DCs are critical intermediaries between the innate and adaptive immune systems, as they stimulate, regulate, and shape both immunity and tolerance in all its disguises. Ralph Steinmann, who discovered these cells, was awarded the Nobel Prize for Medicine in 2011, because the discovery of DCs changed medicine (3). Dendritic cells in both humans and mice represent a population of at least four different subtypes with distinct phenotypical and functional characteristics (4-7). These subsets are: plasmacytoid DCs (pDCs), two subsets of conventional DCs (cDC1 and cDC2), and inflammatory DCs. The latter represent a monocyte-derived subset that appears during inflammatory responses (Table 1) . Recently, additional types of human blood DCs, monocytes, and progenitors were revealed using single cell RNA-sequencing. The group of Prof. Nir Hacohen identified pDCs next to cDC progenitor-derived cDC1 (Clec9A + ) and two types of CD1c + cDC2, of which one can also be derived from CD14 + DCs. Furthermore they found a CD141 − CD1c − CD11c + DC subset derived from CD16 + monocytes and an AXL + Siglec6 + subset (8). Future research will have to unravel a possible murine representative for the human cDC2 and AXL + Siglec6 + DC subset. Also, Langerhans cells have been considered an important DC subset for vaccination as they are localized in the epidermis (HLA-DR + CD11c + CD1a + CD207 + ). However, recent evidence suggests that they are related to macrophages, another antigen-presenting cell (APC) type with potential antitumor activity (9). 
 Title: The Journey of in vivo Virus Engineered Dendritic Cells From Bench to Bedside: A Bumpy Road

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: . (A) Fourier shell correlation plots for the reconstructions reported in this paper. The FSC=0.5 criterion was used for all maps in determining resolution (Graphs prepared using draw_graph (J.F.F. and R.J.C.G., manuscript in preparation). (B) Left A view of the top side of the 80S ribosome large subunit (both subunits in a smaller view above, for orientation) to show fitted rRNA helices (red) and their agreement with the appearance of grooves on the isosurface of the reconstruction (cyan). The RNA model fitted is from the refined model described in the Supporting Information text. Right Two views of the 80S PK-tRNA-EF-2 structure sliced through in which the cut planes are coloured by density (red high density, blue low density) to show the detailed, 15Å resolution density reconstructed, in which individual rRNA helices are resolved. (C) Guinier analysis, after Rosenthal and Henderson (Rosenthal and Henderson, 2003) of the amplitude profiles of the reconstructions reported in this paper. In each plot the uncorrected amplitude profile is shown in grey; it has been fitted with a straight line (red) to give a slope which is the B factor describing in each case. These B factors were applied, negatively, to the reconstructions in order to sharpen them appropriately, yielding the amplitude profiles shown in green (Graphs prepared using draw_guinier (J.F.F. and R.J.C.G., manuscript in preparation). See Supplemental Experimental Procedures for more information. Four views related by 90° rotations, as indicated, of the 80S SL reconstruction fitted with coordinates for the mammalian ribosome as modeled by Chandramouli et al. (2008) into a canine ribosome reconstruction (PDB IDs 2ZKQ and 2ZKR). The 80S SL map is shown as a grey semitransparent surface, the fitted RNA and proteins of the 40S subunit in yellow (backbone for rRNA, ribbon representation for proteins) and of the 60S subunit in blue. The regions for which we cannot identify density and which were left out of the models presented in the main paper are coloured red. They are restricted to the periphery and consist of RNA expansion segments ES4, ES5, ES20, ES24, ES27, ES30 and ES31 of the large subunit and ES3 of the small, and proteins L7A, L18, L35 and the 60S acidic protein. (A) Atomic structure of the 70S ribosome from Thermus thermophilus bound with tRNAs and mRNA (Selmer et al., 2006). The RNA and proteins of both subunits are depicted as yellow ribbons, the P-site tRNA as a grey atomic surface. An eight-residue peptide has been inserted into the structure (taken from an eight-residue stretch of  sheet in EF-2), positioned such that it could be attached to the P-site tRNA and points down the tunnel through the large subunit (red atomic surface). (B) Electron density calculated for the structure shown in a. The calculation was done twice -with and without the eight-residue peptide. Both maps were filtered to 15Å resolution prior to comparison. Yellow: density for the atomic structure shown in a excepting the P-site tRNA which was not included in the density calculation and the eight residue peptide; grey: density calculated for the P-site tRNA separately and also filtered to 15Å resolution; red: difference density computed by subtracting the peptide-less structure from an exactly equivalent structure containing the peptide. It appears that, at 15Å resolution, an eight residue peptide can be identified by difference imaging within a ribosome. (C) The same calculation shown in B but all maps were smeared with a B factor of 5,000 Å 2 ; the difference density for the eight residues is still visible. (D) A close-up of the tRNA and peptide density shown in b, with the ribosomal RNA and proteins shown as ribbons We note that the tRNA density is much clearer than in our experimental maps, but this is because we have not accounted for the solvent or a scattering envelope in calculating the electron density. In addition, EM data is convoluted by the microscope envelope function. 
 Title: Supplemental Information Direct Observation of Distinct A/P Hybrid-State tRNAs in Translocating Ribosomes

Answer:
False