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".

Input: Consider Input: Paragraph: We aimed to test the hypothesis that gene silencing of tumor necrosis factor alpha converting enzyme (TACE) may attenuate lesion inflammation and positive vascular remodeling and enhance plaque stability in a rabbit model of atherosclerosis. Lentivirus-mediated TACE shRNA was injected into the abdominal aortic plaques of rabbits which effectively down-regulated TACE expression and activities from week 8 to week 16. TACE gene silencing reduced remodeling index and plaque burden, and diminished the content of macrophages and lipids while increased that of smooth muscle cells and collagen in the aortic plaques. In addition, TACE gene silencing attenuated the local expression of P65, iNOS, ICAM-1, VEGF and Flt-1 and activities of MMP9 and MMP2 while increased the local expression of TGF-β1 together with reduced number of neovessels in the aorta. TACE shRNA treatment resulted in down-regulated expression of TACE in macrophages and blunted ERK-P38 phosphorylation and tube formation of co-cultured mouse vascular smooth muscle cells or human umbilical vein endothelial cells. In conclusion, gene silencing of TACE enhanced plaque stability and improved vascular positive remodeling. The mechanisms may involve attenuated local inflammation, neovascularization and MMP activation, as well as enhanced collagen production probably via down-regulated ERK-NF-κB and upregulated TGF-β1 signaling pathways. Tumor necrosis factor alpha converting enzyme (TACE), also known as ADAM17 (A disintegrin and A metalloproteinase 17), was initially discovered as a protease that cleaves the 26-kDa precursor of TNF-α and sheds transmembrane TNF-α to generate a soluble form of TNF-α that can bind to TNF-α receptors to induce inflammatory response 1 . Recently it has been recognized that TACE is a type I transmembrane protein and a member of a superfamily of Zn dependent metalloproteases. The major physiological role of TACE is to regulate the proteolytic release of a number of growth factors, cytokines, adhesion molecules and cleavage enzymes from cellular membrane 2,3 . The major pro-inflammatory cytokine processed by TACE is TNF-α which is produced by macrophages, monocytes and T-cells, and acts as a major player in the pathogenesis of inflammation. It has been increasingly recognized that TACE-mediated shedding is involved in a variety of diseases such as ischemia, heart failure, arthritis, atherosclerosis, diabetes, cancer, neurological and immune diseases 3-5 . Ashley EA et al. provided functional evidence for TACE as a candidate gene of atherosclerosis susceptibility and found that polymorphisms of the TACE genes had relation to cardiovascular mortality in humans 6 . Canault M et al. found that TACE expression was associated with lesion formation in atherosclerosis-prone sites of apolipoprotein E-knockout (ApoE -/-) mice 7 . Oksala N demonstrated that TACE was upregulated in advanced human atherosclerotic lesions in samples from carotid, aortic, and femoral arteries compared to samples from internal thoracic artery free of atherosclerotic plaques 8 . In addition, Stoehr R et al. showed that loss of TIMP3, an endogenous inhibitor of TACE, exacerbated atherosclerosis 1 
 Title: Gene silencing of TACE enhances plaque stability and improves vascular remodeling in a rabbit model of atherosclerosis

Output: False


Input: Consider Input: Paragraph: Background 3 In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics 4 algorithms. Sequenced datasets include background organisms, creating ambiguity in the true 5 source organism for each read. Bacterial and viral datasets were created with even and staggered 6 coverage to evaluate organism identification, read mapping, and gene identification capabilities of 7 available algorithms. These truth datasets are provided as a resource for the development and 8 refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform 9 decision makers on strengths and weaknesses of available algorithms and how the results may be best 10 leveraged for bacterial and viral organism identification and characterization. 11 12 Source organisms were selected to mirror communities described in the Human Microbiome Project as 13 well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The 14 six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: 15 MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. 16 Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species 19 levels, false positive organisms identified to genus and species level, read mapping, relative abundance 20 estimation, and gene calling. No algorithm out performed the others in all categories, and the 21 algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria 22 and LMAT for viruses. The algorithms were ranked by overall performance using a normalized 23 weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken 24 and LMAT. 25 Conclusions 27 Simulated FASTQ datasets with well--characterized truth data about microbial community composition 28 reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms 29 evaluated. The simulated datasets are available to download from the Sequence Read Archive 30 (SRP062063). 31 32 
 Title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim

Output: False


Input: Consider Input: Paragraph: Combinations of porcine respiratory coronavirus (PRCV) and either of two swine influenza viruses (H1N1 or H3N2) were administered intranasally and by aerosol to six-to eight-week-old specific pathogen:free pigs. The clinical responses, gross respiratory lesions and growth performances of these pigs were studied and compared with those of single (PRCV, H1N1 or H3N2) and mock-infected animals. PRCV infection caused fever, growth retardation and lung lesions, but no respiratory symptoms. Infection with swine influenza viruses caused rather similar, mild symptoms of disease, with H1N1 infection being the least severe. Combined infections with influenza viruses and PRCV did not appear to enhance the pathogenicity of these viruses. Furthermore, viruses were isolated more frequently from tissues and nasal swabs taken from 'single' than 'dual' infected animals, suggesting a possible in vivo interference between replication of PRCV and swine influenza virus. 
 Title: Pathogenicity of concurrent infection of pigs with porcine respiratory coronavirus and swine influenza virus


Output: True