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".
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Question: Paragraph: Similarity in time series is an important feature of dynamical systems such as financial systems, with potential use for clustering of series in system. Here, we mainly introduce a novel method: the reconstructed phase space information clustering method to analyze the financial markets. The method is used to examine the similarity of different sequences by calculating the distances between them, which the main difference from previous method is the way to map the original time series to symbolic sequences. Here we make use of the state space reconstruction to construct the symbolic sequences and quantify the similarity of different stock markets and exchange rate markets considering the chaotic behavior between the complex time series. And we compare the results of similarity of artificial and real data using the modified method, information categorization method and system clustering method. We conclude that the reconstructed phase space information clustering method is effective to research the close relationship in time series and for short time series especially. Besides, we report the results of similarity of different exchange rate time series in different periods and find the effect of the exchange rate regime in 2008 on the time series. Also we acquire some characteristics of exchange rate time
Title: The similarity analysis of financial stocks based on information clustering

Answer: False

Question: Paragraph: So far, there has been scarce information about the status of immunoglobulins (Ig) and the gene expression of inflammatory cytokines in buffaloes showing digestive troubles. The purpose of the present study was to explore the modulation of gene expression of some immuneinflammatory markers in buffaloes suffered from various digestive disorders. For this reason, 50 native breed water buffaloes were studied. Forty of these buffaloes showed various symptoms of digestive disorders and were allocated into 4 groups of equal sizes (group 1: uncategorized stomatitis; group 2: acute traumatic reticuloperitonitis [TRR]; group 3: acute rumen impaction; and group 4: undifferentiated enteritis). Ten apparently healthy buffaloes were randomly selected and considered as a control group. RNA was firstly extracted from the whole blood then a reverse transcription kits was used to convert the RNA to cDNA. Real-time PCR was used to measure the expression of mRNAs of interleukin (IL)-1, IL-6, IL-10, and tumor necrosis factor (TNF)-, IgG, and IgA, while glyceraldehyde-3-phosphate dehydrogenase (GAPDH) used as an internal reference. The results of realtime PCR revealed a significant (P 0.05) upregulation of the gene expression of IL-1, IL-6, IL-10, and TNF- in blood of diseased buffaloes compared with those of controls. Animals showing acute TRP had peak values of both IL-6 and IL-10; while those exhibiting enteritis and rumen impaction had the highest values of IL-1 and TNF-, respectively. The results of qPCR also revealed a significant (P 0.05) downregulation of both IgG and IgA gene expression in blood of all diseased buffaloes compared with controls. The lowest values of both genes were recorded in buffaloes showing acute TRP. The results herein suggest that the tested genes could have a pivotal role in the pathophysiologic mechanism of the underlying diseases.
Title: Modulation of immunity and inflammatory gene expression in buffalo (Bubalus bubalis) with some digestive disorders

Answer: True

Question: Paragraph: Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data-and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.
Title: Spatial and temporal epidemiological analysis in the Big Data era

Answer:
False