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".

Paragraph: Experimental coronavirus retinopathy (ECOR) is a virally triggered model of retinal degeneration composed of both genetic and autoimmune components. Since TNF-a plays a role in immune-mediated processes we evaluated the levels of TNF-a/TNF-a receptors and the downstream signaling molecule nitric oxide (NO) during disease in both retinal degeneration susceptible BALB/c and degeneration resistant CD-1 mice. Following coronavirus injection, TNF-a mRNA was detected at higher levels within the retinas, and concentrations of TNF-a ( p < 0.005) and sTNFR1 ( p < 0.0005) proteins were increased within the sera of BALB/c but not CD-1 mice. While concentrations of sTNFR2 proteins were elevated in both BALB/c ( p < 0.00005) and CD-1 ( p < 0.005) mice compared to controls, concentrations were higher in BALB/c mice ( p < 0.0005). Gene expression of iNOS while initially high in BALB/c mice decreased during the acute phase of infection, while it increased in CD-1 mice. These trends are attributable to differences in monocyte TNFR2 release ( p < 0.0005) between the strains since sTNFR2 decreased ( p < 0.01) levels of NO production. These studies demonstrate that retinal degeneration following viral infection is associated with increased release of TNF-a/TNF receptors combined with a down-regulation of NO. Furthermore they suggest that these molecules are involved in alterations in immune response leading to autoimmune reactivity. Published by Elsevier B.V. 
 Title: Retinal degeneration in experimental coronavirus retinopathy (ECOR) is associated with increased TNF-a, soluble TNFR2 and altered TNF-a signaling
False

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
False

Paragraph: The exposure-dose-response characterization of an inhalation hazard established in an animal species needs to be translated to an equivalent characterization in humans relative to comparable doses or exposure scenarios. Here, the first geometry model of the conducting airways for rhesus monkeys is developed based upon CT images of the conducting airways of a 6-month-old male, rhesus monkey. An algorithm was developed for adding the alveolar region airways using published rhesus morphometric data. The resultant lung geometry model can be used in mechanistic particle or gaseous dosimetry models. Such dosimetry models require estimates of the upper respiratory tract volume of the animal and the functional residual capacity, as well as of the tidal volume and breathing frequency of the animal. The relationship of these variables to rhesus monkeys of differing body weights was established by synthesizing and modeling published data as well as modeling pulmonary function measurements on 121 rhesus control animals. Deposition patterns of particles up to 10 μm in size were examined for endotracheal and and up to 5 μm for spontaneous breathing in infant and young adult monkeys and compared to those for humans. Deposition fraction of respirable size particles was found to be higher in the conducting airways of infant and young adult rhesus monkeys compared to humans. Due to the filtering effect of the conducting airways, pulmonary deposition in rhesus monkeys was lower than that in humans. Future research areas are identified that would either allow replacing assumptions or improving the newly developed lung model. 
 Title: Development of a rhesus monkey lung geometry model and application to particle deposition in comparison to humans HHS Public Access

False