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: For the purpose of this work, insect biotechnology, which is also known as yellow biotechnology, is the use of insects as well as insect-derived cells or molecules in medical (red biotechnology), agricultural (green biotechnology), and industrial (white) biotechnology. It is based on the application of biotechnological techniques on insects or their cells to develop products or services for human use. Such products are then applied in agriculture, medicine, and industrial biotechnology. Insect biotechnology has proven to be a useful resource in diverse industries, especially for the production of industrial enzymes including chitinases and cellulases, pharmaceuticals, microbial insecticides, insect genes, and many other substances. Insect cells (ICs), and particularly lepidopteran cells, constitute a competitive strategy to mammalian cells for the manufacturing of biotechnology products. Among the wide range of methods and expression hosts available for the production of biotech products, ICs are ideal for the production of complex proteins requiring extensive posttranslational modifi cation. The progress so far made in insect biotechnology essentially derives from scientifi c breakthroughs in molecular biology, especially with the advances in techniques that allow genetic manipulation of organisms and cells. Insect biotechnology has grown tremendously in the last 30 years.
Title: Insect Biotechnology
False

Paragraph: Ebola virus is the causative agent of Ebola virus disease in humans. The lethality of Ebola virus infection is about 50%, supporting the urgent need to develop anti-Ebola drugs. Glycoprotein (GP) is the only surface protein of the Ebola virus, which is functionally critical for the virus to attach and enter the host cells, and is a promising target for anti-Ebola virus drug development. In this study, using the recombinant HIV-1/Ebola pseudovirus platform we previously established, we evaluated a small molecule library containing various quinoline compounds for anti-Ebola virus entry inhibitors. Some of the quinoline compounds specifically inhibited the entry of the Ebola virus. Among them, compound SYL1712 was the most potent Ebola virus entry inhibitor with an IC 50 of~1 M. The binding of SYL1712 to the vial glycoprotein was computationally modeled and was predicted to interact with specific residues of GP. We used the time of the addition assay to show that compound SYL1712 blocks Ebola GP-mediated entry. Finally, consistent with being an Ebola virus entry inhibitor, compound SYL1712 inhibited infectious Ebola virus replication in tissue culture under biosafety level 4 containment, with an IC 50 of 2 M. In conclusion, we identified several related molecules with a diaryl-quinoline scaffold as potential anti-EBOV entry inhibitors, which can be further optimized for anti-Ebola drug development.
Title: Identification of Diaryl-Quinoline Compounds as Entry Inhibitors of Ebola Virus
True

Paragraph: The rapid growth of the tourism and hospitality industries serves as the major driving force for academic researchers to investigate the accurate forecasting of lodging demand in tourist destinations. For instance, Tsai et al. (2006) studied the relationship between hotel room demand/supply functions and room rate using a simultaneous equations model for determinants of room demand/supply in Las Vegas. In terms of optimal hotel rates, Pan (2007) proposed an optimal room rate model to set pricing strategies at peak or low seasons. Recently, Slattery (2009) suggested employing the Otus theory to study the relationships between economic structure and demand/ supply of hotel rooms. In this paper, a novel time series model is presented which models multivariate time series data. The advantage of this model is that the discovered factors are mutually independent and a univariate time series model can be applied to these factors without considering the factors' interdependence. The next section introduces the methodologies of independent component analysis. On the basis of this model, empirical studies on Hong Kong hotel occupancy rates are analyzed. The most dominant factor of infectious diseases is subsequently detected. At last, managerial implications and concluding remarks are provided. Independent component analysis (ICA) is a statistical technique that aims to capture observed data in terms of a linear combination of underlying latent variables. These latent variables are assumed to be non-Gaussian and mutually independent. A typical ICA model for an N-dimensional multivariate time series {x 1 =(x it , . . ., x Nt ) 0 }: t = 1, . . ., T) is where s t is a vector of statistically independent latent variables called independent components (ICs). A is an unknown constant mixing matrix, and T is the number of observations of the time series. The task of ICA is to identify both the ICs and the matrix A. Various algorithms for parameter estimation have been developed for ICA. Among them, a widely used one is the FastICA algorithm, which was proposed by Hyva rinen et al. (2001). Since traditional ICA algorithms lack the ranking of ICs, Wu et al. (2006) proposed an ordering algorithm for independent components based on the minimizing mean squared errors (MSE) criterion. Based on this ordering method, ICs are sorted in a way such that modeling errors are minimized and dominant ICs are identified. Modifying the approach of Wu et al. (2006) , the major steps of modeling hotel occupancy rate using ICA are presented as follows: 1. Import the multivariate dataset (X) with number of N time series, each time series has T observations. 2. Use the FastICA algorithm to find the independent components (S) and the mixing matrix (A). 3. Use the independent component (IC) ordering method to order the ICs to find 1st IC, 2nd IC, 3rd IC, and so on. The recent global outbreak of Influenza A (H1N1), or the more commonly known as swine flu, has negatively affected the tourism and hospitality industries in many countries. This article reports a study that applied independent component analysis, a novel statistical technique, to separate the dominant factors which determine the levels of hotel occupancy rates in Hong Kong. Empirical findings would provide useful insights on how the dynamic lodging demand reacts to epidemics based on the severity and duration of the events.
Title: The impact of infectious diseases on hotel occupancy rate based on independent component analysis
True