In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Input: Consider Input: Quinone-methide triterpenes, celastrol (1), pristimerin (2), tingenone (3), and iguesterin (4) were isolated from Triterygium regelii and dihydrocelastrol (5) was synthesized by hydrogenation under palladium catalyst. Isolated quinone-methide triterpenes (1-4) and 5 were evaluated for SARS-CoV 3CL pro inhibitory activities and showed potent inhibitory activities with IC 50 values of 10.3, 5.5, 9.9, and 2.6 lM, respectively, whereas the corresponding 5 having phenol moiety was observed in low activity (IC 50 = 21.7 lM). As a result, quinone-methide moiety in A-ring and more hydrophobic E-ring assist to exhibit potent activity. Also, all quinone-methide triterpenes 1-4 have proven to be competitive by the kinetic analysis.

Output: SARS-CoV 3CL pro inhibitory effects of quinone-methide triterpenes from Tripterygium regelii


Input: Consider Input: Background: In recent years, new neonatal porcine diarrhoea (NNPD) of unknown aetiology has emerged in Denmark. NNPD affects piglets during the first week of life and results in impaired welfare, decreased weight gain, and in the worst-case scenario death. Commonly used preventative interventions such as vaccination or treatment with antibiotics, have a limited effect on NNPD. Previous studies have investigated the clinical manifestations, histopathology, and to some extent, microbiological findings; however, these studies were either inconclusive or suggested that Enterococci, possibly in interaction with Escherichia coli, contribute to the aetiology of NNPD. This study examined ileal and colonic luminal contents of 50 control piglets and 52 NNPD piglets by means of the qPCR-based Gut Microbiotassay and 16 samples by 454 sequencing to study the composition of the bacterial gut microbiota in relation to NNPD. Results: NNPD was associated with a diminished quantity of bacteria from the phyla Actinobacteria and Firmicutes while genus Enterococcus was more than 24 times more abundant in diarrhoeic piglets. The number of bacteria from the phylum Fusobacteria was also doubled in piglets suffering from diarrhoea. With increasing age, the gut microbiota of NNPD affected piglet and control piglets became more diverse. Independent of diarrhoeic status, piglets from first parity sows (gilts) possessed significantly more bacteria from family Enterobacteriaceae and species E. coli, and fewer bacteria from phylum Firmicutes. Piglets born to gilts had 25 times higher odds of having NNPD compared with piglets born to multiparous sows. Finally, the co-occurrence of genus Enterococcus and species E. coli contributed to the risk of having NNPD. The results of this study support previous findings that points towards genus Enterococcus and species E. coli to be involved in the pathogenesis of NNPD. Moreover, the results indicate that NNPD is associated with a disturbed bacterial composition and larger variation between the diarrhoeic piglets.

Output: Characterization of the bacterial gut microbiota of piglets suffering from new neonatal porcine diarrhoea


Input: Consider Input: 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 Hyvä 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. ß


Output: The impact of infectious diseases on hotel occupancy rate based on independent component analysis