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: A novel porcine deltacoronavirus (PdCV) was first discovered in Ohio and Indiana in February 2014, rapidly spread to other states in the United States and Canada, and caused significant economic loss in the swine industry. The origin and virulence of this novel porcine coronavirus are not known. Here, we characterized U.S. PdCV isolates and determined their virulence in gnotobiotic and conventional piglets. Genome analyses revealed that U.S. PdCV isolates possess unique genetic characteristics and share a close relationship with Hong Kong and South Korean PdCV strains and coronaviruses (CoVs) of Asian leopard cats and Chinese ferret-badgers. The PdCV-positive intestinal content (Ohio CVM1) and the cell culture-adapted PdCV Michigan (MI) strain were orally inoculated into gnotobiotic and/or conventional piglets. Within 1 to 3 days postinfection, profuse watery diarrhea, vomiting, and dehydration were observed. Clinical signs were associated with epithelial necrosis in the gastric pits and small intestine, the latter resulting in severe villous atrophy. Mild interstitial pneumonia was identified in the lungs of PdCVinfected piglets. High levels of viral RNA (8 to 11 log RNA copies/g) were detected in intestinal tissues/luminal contents and feces of infected piglets, whereas moderate RNA levels (2 to 5 log RNA copies/g) were detected in blood, lung, liver, and kidney, indicating multisystemic dissemination of the virus. Polyclonal immune serum against PdCV but not immune serum against porcine epidemic diarrhea virus (PEDV) reacted with PdCV-infected small-intestinal epithelial cells, indicating that PdCV is antigenically distinct from PEDV. Collectively, we demonstrate for the first time that PdCV caused severe gastrointestinal diseases in swine. Indiana and subsequently spread rapidly across the United States and Canada, causing significant economic losses. Here, we report the detailed genetic characterization, phylogeny, and virulence of emergent PdCV strains in the United States. We found that PdCV caused severe diarrhea, vomiting, and dehydration in gnotobiotic and conventional piglets, signs that were clinically indistinguishable from those caused by PEDV and TGEV. In addition to extensive intestinal lesions, PdCV caused significant lesions in the stomach and mild pulmonary lesions that have not been reported for TGEV and PEDV. The finding that PdCV is a significant enteric disease of swine highlights the need to develop effective measures to control this disease. . 2015. Origin, evolution, and virulence of porcine deltacoronaviruses in the United States. mBio 6(2): e00064-15. 
 Title: Origin, Evolution, and Virulence of Porcine Deltacoronaviruses in the United States
False

Paragraph: Proteinchip profiling is a powerful and innovative proteomic technology for the discovery of biomarkers and the development of diagnostic/prognostic assays. On the basis of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen's proteinchip system offers a single, unified, and high throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell. The study of proteomics helps to better understand the mechanism of a disease. Remarkable findings in disease biomarkers have shed light on the early diagnosis, monitoring, and prognosis of various diseases, especially for cancer. In this paper, the development and technology of SELDI-TOF MS are introduced. The research progress and encouraging research results in malignancies, infectious diseases, neurological diseases, and diabetes mellitus using SELDI-TOF MS are reviewed. This paper concludes by evaluating the pros and cons, and the future perspectives are also expounded. 
 Title: Research Progress in SELDI-TOF MS and Its Clinical Applications
True

Paragraph: The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition probability of the semi-Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi-Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. Multi-state stochastic processes provide a convenient framework for the analysis of event history data (Andersen et al., 1993; Commenges, 1999; Andersen and Keiding, 2002) . The semi-Markov model is a generalization of the classical homogeneous Markov model. It is preferred in many practical applications because it accommodates the dependence of transitions between states on the state durations (Andersen, Esbjerg, and Sorensen, 2000; Kang and Lagakos, 2007) . Suppose {S(t):t ≥ 0} is a finite-state stochastic process with state space ℰ = {1,…, r}. Denote its sequence of consecutive states by {J m :m = 0, 1, 2,…}, and let the corresponding transition times be {T m :m = 0, 1,…}. Without loss of generality, we assume T 0 = 0. Let X m = T m − T m−1 be the sojourn time of the mth transition. Then {S(t):t ≥ 0} is a homogeneous 
 Title: Estimation with Right-Censored Observations Under A Semi- Markov Model

False