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".

[Q]: Paragraph: Dendritic cells (DCs) link the sensing of the environment by the innate immune system to the initiation of adaptive immune responses. Accordingly, DCs are considered to be a major target in the development of immunomodulating compounds. In this study, the effect of niclosamide, a Food and Drug Administrationapproved antihelminthic drug, on the activation of lipopolysaccharide (LPS)-stimulated murine bone marrow-derived DCs was examined. Our experimental results show that niclosamide reduced the pro-inflammatory cytokine and chemokine expression of LPS-activated DCs. In addition, niclosamide also affected the expression of MHC and costimulatory molecules and influenced the ability of the cells to take up antigens. Therefore, in mixed cell cultures composed of syngeneic OVA-specific T cells and DCs, niclosamide-treated DCs showed a decreased ability to stimulate T cell proliferation and IFN-c production. Furthermore, intravenous injection of niclosamide also attenuated contact hypersensitivity (CHS) in mice during sensitization with 2,4-dinitro-1-fluorobenzene. Blocking the LPS-induced activation of MAPK-ERK, JNK and NF-jB may contribute to the inhibitory effect of niclosamide on DC activation. Collectively, our findings suggest that niclosamide can manipulate the function of DCs. These results provide new insight into the immunopharmacological role of niclosamide and suggest that it may be useful for the treatment of chronic inflammatory disorders or DC-mediated autoimmune diseases.
Title: Antihelminthic niclosamide modulates dendritic cells activation and function
[A]: True

[Q]: Paragraph: We describe the association of caspase-dependent and caspase-independent mechanisms in macrophage apoptosis induced by LpqH, a 19 kDa Mycobacterium tuberculosis lipoprotein. LpqH triggered TLR2 activation, with upregulation of death receptors and ligands, which was followed by a death receptor signaling cascade with activation of initiator caspase 8 and executioner caspase 3. In this caspase-mediated phase, mitochondrial factors were involved in loss of mitochondrial transmembrane potential (m), release of cytochrome c, and caspase 9 activation. Interestingly, a caspase-independent pathway was also identified; by immunoblot, the mitochondrial apoptosis inducing factor (AIF) was demonstrated in nuclei and cytosol of LpqH-treated macrophages. Confocal microscopy revealed translocation of AIF to the nuclei of the majority of apoptotic cells. These findings emphasize the complex and redundant nature of the macrophage death response to mycobacteria.
Title: Mycobacterium tuberculosis Lipoprotein (LpqH) Induces Macrophage Apoptosis through Extrinsic and Intrinsic Pathways: A Role for the Mitochondrial Apoptosis-Inducing Factor
[A]: True

[Q]: Paragraph: There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains. PLOS Computational Biology | https://doi.
Title: Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data
[A]:
True