**4. Acknowledgment**

This work was supported by State Fond of Fundamental Investigations of Ukraine, grant F28.7/020. Author thanks Dr. V.M. Starodub for assistance in the preparation of this article.

#### **5. References**


It was demonstrated that the proposed LPhPC is very suitable for the enzymatic biosensor creation. The process of the biological material immobilization on the surface of transducer can be done anyway phasic process and may be served as basis for technology of the biosensor production. Enzymes are long (up to 6 months) remaining active in staying as a part of the developed compositions, capable of photo polymerization and in the polymer membrane obtained from this composition. It was chosen the conditions (temperature, filtration of UV irradiation, the presence of competitive inhibitors) that increase the residual activity of immobilized enzymes. Extensively it was studied the properties of the developed electrochemical biosensors based on the IsFETs for the determination of glucose and urea as well it was show that they have the characteristics needed for use in laboratory, clinical,

This work was supported by State Fond of Fundamental Investigations of Ukraine, grant F28.7/020. Author thanks Dr. V.M. Starodub for assistance in the preparation of this article.

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**3. Conclusion** 

food and biotech practice.

**4. Acknowledgment** 

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**19** 

 *Sweden* 

**Visual Detection of Change Points and Trends Using Animated Bubble Charts** 

The rapid growth of automatic data collection systems has increased the need for algorithms that can efficiently reveal important features of large or complex datasets. For example, it is often of great interest to examine the occurrence of abrupt changes in long bi- or multivariate time series of data. Several numerical algorithms and statistical tests have been developed to detect abrupt shifts in the mean or other parameters of uni- or multivariate distributions (Caussinus & Mestre, 2004; Hawkins, 1977, 2001; Srivastava & Worsley, 1986; Stephens, 1994). However, there is also a need for visualization techniques that can help the user identify any type of abrupt changes or trends in the collected data. More generally, techniques are needed that can simultaneously highlight important features of the data and filter out irrelevant information (Bederson & Boltman, 1999; Bundesen, 1990; Cleveland & McGill, 1984; Healey, 2000; Ware, 2004). In this chapter, we present flexible and user-friendly animations of bubble charts in which subsets of the collected data are sequentially highlighted on a static background representing all data

The basic ideas of interactive visualization of quantitative data were presented before computer technologies were sufficiently developed to enable widespread use of such methods. In 1978, Newton introduced a form of linked brushing that allowed the user to select a subset of observations in one display and simultaneously highlight the same subset in another display. About a decade later, several ground-breaking articles were published. Asimov (1985) introduced the concept of helicopter tours for viewing highdimensional datasets via a structured progression of 2D projections, and Becker and coworkers (1987a, b) provided a systematic framework for brushing, linking, and other forms of interactive statistical graphics. Moreover, Unwin and colleagues (1988) demonstrated how zooming, rescaling, and overlaying can facilitate visual analysis of

More recently, improvements in computing power, display resolution, and numerical algorithms have brought interactive visualization of quantitative data to higher levels and stimulated the development of new applications. The software XGobi and its descendant GGobi set a new standard for interactive modification of linked plotting windows, and an application programming interface made such methods available to the rapidly growing group of R users (Cook & Swayne, 2007; Swayne et al., 2003; the GGobi website, 2011). Zooming and rescaling were established as standard tools in software packages for time

**1. Introduction** 

points.

multivariate time series data.

Sackmone Sirisack and Anders Grimvall

*Linköping University,* 

