BSU NEW APPROACH FOR INTEGRATION OF PSYCHOLOGICAL KNOWLEDGE A.O. Karasevich

BSU NEW APPROACH FOR INTEGRATION OF PSYCHOLOGICAL KNOWLEDGE A.O. Karasevich Abstract. We first created Four-Dimensional (Space-Time) Model of Psyche. This model integrates all existing psychological paradigms. On the basis of the Model is possible to solve a wide range theoretical and application problems. This article represents the foundations of our new concept. Key words: Four-Dimensional Psychophysical Model of Reality, Dinamic Model of Psyche, modelling of psyche Four-Dimensional Psychophysical Model of Reality is a promising new approach for the integration and systematization of worldwide psychological knowledge that can be successfully used in general, social, medical fields of psychology. Using the Model we can integrate the basic psychological paradigms to unite their practical potential, and to find new areas of perspective research. Psychology has accumulated huge amount of scientific knowledge and skills. The problem is this knowledge is not systematized. We can do that with the use of Four-Dimensional Psychophysical Model of Reality. Visualization makes knowledge understandable and easily accessible. The computer program, which is now developed on the basis of the Model, can be used to model all diversity of psychic processes. The Model enables joint description of psyche, human organism and environment (Figure 1). Fig. 1. Components of the Model Desire-goal" matrix is the basis of Dynamic Model of Psyche. It includes the time period of mental activity between activator of mental activity ("desire") and "brake" of mental activity ("goal"). "Desire" and "goal" in The Model are identical with many different notions of psychological paradigms. Under our Model there are external (organismal) and internal (psychical) activities. Organismal activity includes the organism movement and work of organs. Psychical activity includes the processes of sensation, perception, internal speech, thought, imagination, memory and emotion. It corresponds to the dynamic block of psyche. Stationary block of psyche covers all inborn and accumulated psychical material of the human. At the instant only small part of the psyche is in the psychical processes (actualized part). Other biggest not actualized part of the psyche is a stationary block of psyche. Stationary block has many components: knowledge, needs, goals, motives, temperament, character, self-esteem, ways to respond, and many others.

The Model consists of three elements: dynamical and stationary blocks of the psyche, organism (organismal activity) (Figure 2). The one is three-dimensional and dynamic. It is graphically made with 3ds Max. The Model can demonstrate all psychological paradigms and social processes. Fig. 2 Structure of the Model: 1 external layer (denotes behavior and organismal activity), 2 "desire-goal" matrix, 3 dynamic block of psyche, 4 stationary block of psyche, 5 growth direction of the Model, 6 - environment Four-Dimensional Psychophysical Model can be applied for modeling of: human psyche, brain and behavior (Figure 3); psychological theories. For example, Polikarpov's theory (Theory of Temporal Feedback) that explains the possibility of the future prediction (Figure 4) [1]; physical theories. For example, Fontana's theory (The Four Space-times Model of Reality) and Shulman's theory (Spherical Expanding Universe Theory) that explain structure of the Universe (Figure 5) [2, 3]. We briefly have modeled only three point of time in the context of human activity, but for a detailed modeling we need to analyze the huge number of such moments. Necessary to create a computer program to demonstrate the dynamics of the whole of human activity. This program will take into account the full range of determinants of mental activity. This program will be able to describe in detail the structure of the psyche, human physiology, his behavior and the environment in general. The creation of this program is a priority direction in the development of the Model, because the simulation of single sections is time-consuming and complicated work. The integration paradigms must be accurate, compact, laconic, otherwise the integration will not give anything except a methodological confusion. Our Model is simple, but it includes everything needed for modeling a general psychological scientific knowledge. The Model especially designed maximum open. For example, the number of components of a stationary block of psyche and their contents are not strictly defined. This enables to find a lot of areas of intersection of scientific knowledge. A detailed version of the Model (with a demonstration of the dynamics of the nervous system) integrates psychological knowledge and neurobiology. Also, in the integration of scientific knowledge, we can describe diversity of its philosophical foundation. It reinforces the general methodological foundation of Model. Let us describe the Four-Dimensional Psychophysical Model of Reality using the figure (Figure 3). Let us take modeling of single point in time in the context of the overall activity of human. This is an example of a possible structure of the Model. In the figure indicated: a location of human in the space at the moment (this figure may include an human's environment), b sphere model (shows content of the human psyche at the moment ), c model of human brain (we can describe

physiological processes), d general model of psyche, e dinamical model of psyche, side-view, with the map of consciousness (shown only a process of thinking). Fig. 3. Structure of Four-Dimensional Psychophysical Model of Reality: 1 - stationary block of psyche, 2 - external layer of model (denotes behavior and organismal activity), 3 - dynamic block of psyche, 4 - growth direction of model, 5 - transition of the external world into the psyche, 6 - process of thinking, 7 - other psychological processes involved in map of consciousness, 8 - influence of stationary block of psyche on psychical activity, 9 - conscious part of the dynamic block of psyche, 10 - unconscious part of the dynamic block of psyche Perspectives of the Model application: Creating a computer program based on the Model; Integration and systematization of global psychological knowledge; Interdisciplinary dialogue with the use of the Model. Versions of computer program based on the Model: "Home Psychologist" version. Will be used for personal use. Everyone will be able to understand the own inner world and the causes of the all own actions, to get psychological counseling via Internet, and to create the own psychological map; Version for business. Will include testing of employees and monitor their mental state for optimization of working capacity;

Version for science. Will be intended for professional psychologists. This version will be a big psychological guide. Conditions for research, discussion and interpretation of results will be created. Fig. 4. Polikarpov's Theory of Temporal Feedback: 1 - index of an event, 2 - external layer of the model (denotes behavior and organismal activity), 3 - stationary block of psyche, 4 - dynamic block of psyche, 5 an object related to the future event, 6 - an object into the human psyche, 7 - growth direction of the model, 8 - the first "desire-goal" matrix (sleep), 9 - temporal feedback, 10 - attractor, 11 - the second "desiregoal" matrix (thinking of sleep after waking up), 12 - the third "desire-goal" matrix (an event related to the object), 13 - transition of an object to the human psyche, 14 - organismal activity associated with the object, 15 - "space" of an event

Fig. 5. Fontana's theory and Shulman's theory: 1 - four space-times, 2 - motion our universe along -axis, 3 - continuum of -frames, 4 - - frame is the present, 5 - -frames are the future, 6 - growth direction of the model, 7 - -frame is the past, 8 - -frame is the present, 9 - -frame is the future, 10 - -frames are the past, 11 - -frame is the present, 12 - dynamic block of psyche, 13 - external layer of the model (denotes behavior and organismal activity), 14 - stationary block of psyche, 15 - inflow of matter, energy and information from white hole towards black hole, 16 - black holes, 17 - influx of energy to the internal supermassive black holes, 18 - our universe is a three-dimensional membrane (three-dimensional black hole) between outer a four-dimensional super-universe and a giant black hole, 19 - space of a giant black hole, 20 - super-universe (white hole), 21 - direction of sphere increasing Four-Dimensional Psychophysical Model of Reality may cooperate with physics, biology (in particular neurobiology), sociology, philosophy, and other sciences. The Model can demonstrate extensive scientific knowledge and explain human behavior. The Model can create new practical theory. References: 1. Polikarpov V.A. Theory of Temporal Feedback (in Russ.) / Library of Electronic Publications, Institute for Time Nature Explorations, Lomonosov Moscow State University. http://www.chronos.msu.ru/rreports/polikarpov_teoriatemporalnoi.pdf 2. Fontana G. The Four Space-times Model of Reality // AIP Conference Proceedings. 2005. Vol. 746 (1). P.1403. 3. Shulman M.H. Cosmology and metabolism (in Russ.) / Library of Electronic Publications, Institute for Time Nature Explorations, Lomonosov Moscow State University. http://www.chronos.msu.ru/rreports/shulman_metabolizm.pdf

BSU DIAGNOSTICS OF BIOMEDICAL AGENTS BY PHOTONIC - PLASMONIC MICRORESONATOR SENSORS A.V. SAETCHNIKOV Belarusian State University, Minsk, Belarus saetchnikov.anton@tut.by Fluid pumping cell for plasmonic photonic microcavity sensor for label-free bio-molecule detection and identification has been developed and tested on drugs, drugs with gold nanoparticle solutions and additional gold layer. Experimental data on optical resonance spectra of whispering gallery modes of dielectric microspheres in antibiotic solutions, gold nanoparticle solutions under varied in wide range of concentration are represented. Several antibiotics of different generations: Amoxicillin, Azithromycin, Cephazolin, Chloramphenicol, Levofloxacin, Lincomicin Benzylpenicillin, Riphampicon both in de-ionized water and physiological solution had been used for measurements. Drag identification had been performed by developed multilayer perceptron network. For a network training the method of the back propagation error in various modifications had been used. Input vectors corresponded to 6 classes of biological substances under investigation. The result of classification was considered as positive when each region, representing a certain substance in a space: relative spectral shift of an optical resonance maxima - relative efficiency of excitation of WGM, had a single border. Key words: Drug identification, WGM optical resonance, antibiotics, multilayer perceptron network 1. INTRODUCTION Label-free biomolecule detection in sensing systems based on evanescent wave optical sensors is recently under very intensive development [1. 2]. Recently a number of evanescent wave optical sensors have been developed and used for label-free biomolecule detection in sensing systems with extremely high sensitivity [3 8]. A novel emerging method for the label-free analysis of nanoparticles and biomolecules in liquid fluids using optical micro cavity resonance of whispering-gallerytype modes is being developed [9 12]. Several schemes of experimental realization of the method have been tested [14 17]. The sensitivity of the developed scheme has been tested by monitoring the whispering gallery mode spectral shift. Water solutions of ethanol, HCl, glucose, vitamin C and biotin have been used [14 17]. Particular efforts were made for an optimal geometry for micro resonance observation under extremely low power of tunable laser exciting resonance. It was demonstrated that optical resonance under optimal geometry could be detected under the laser power of less than 1 microwatt. Material of microsphere the most appropriate for microbial application was also under investigation. Resonance shifts of C reactive protein water solutions as well as albumin solutions in pure water and with HCI modeling blood have been investigated in developed experimental geometry [14 17]. Introducing controlled amount of nanoparticles (50 nm in diameter glass gel solution) into microsphere surrounding was accompanied also by correlative resonance shift. The most attention was concentrated on development of a technique for recognition and identification of nanoparticles and biomolecules in liquid fluids [8 13]. We demonstrated that the only spectral shift is not sufficient for identification of biological agents by developed approach. So classifier based on probabilistic neural network and perceptron neural network for biological agents and micro/nano particles classification has been developed [8 13]. It was tested on solutions of different biological agents. The aim is to create a new ultra-sensitive platform for automated monitoring and diagnosis of biological objects using a photonic-plasmonic resonators. To reach this aim requires: To create compact experimental setup Standardization and automation of experiment Fast and optimal processing of resolved information. To increase sensitivity of microresonator

2. EXPERIMENTAL SETUP Several schemes of sensor cell for reliable detection have been developed and tested. The optimized technique is the following. Standard biocompatible polymer or glass microspheres are used as sensitive elements. They are fixed in the solution flow by thin adhesive layer on the surface being in the field of evanescence wave. Compact spin-coater system (Spincoater P670) with digital dosage was used to put and dry previously a thin film of adhesive on the surface of substrate or directly on the coupling element (Menzel - Glaeser 200 Deckglaeser 20x20) (Fig.1). After that, microspheres were superimposed on the surface of adhesive layer and final drying procedure by free solvent evaporation during 12 hours followed. Under optimized experimentally parameters of the process microspheres were reliably fixed as it was tested (Fig.2). Nevertheless, we test each covering glass with microspheres by use microscope to find some broken microspheres and to check film quality. The most part of their surface appeared in contact with tested solution and so can react to solution. To combine optical with plasmon resonance gold nanoparticles injected directly into solution or thin film gold layers deposited on the substrate before adhesive have been used. The spheres used in these experiments were 50 120 micron in diameter. The light from a tunable diode laser (New Focus 6308, 680 nm) is coupled into the microsphere through a prism (Fig.3). Laser beam was sharply focused on the single microsphere to increase the contrast and intensity of the resonance scattering signal and decrease a power. Due to these only few microwatt of CW laser power was enough to register resonance spectra. Rectangular or equilateral prisms with refractive index 1.51 and 1.72 have been used as coupling element. Micromechanical system for adjustment of laser excitation to meet requirements of both optical whispering gallery mode (WGM) and plasmon resonances has been developed. This micromechanical system have been used to search for the microsphere on which the laser beam is focused. It is easy to find needed microsphere, when laseroutput synchronization-mode were used. The microsphere is submerged into a fluidic cell and brought into contact with the prism. Fig.1. Compact spin-coating system

a) b) Fig.2.Adhesive layer with fixed microspheres (a) and the same - 1 with optical coupling element - 2 (b) The cell contains initially de-ionized (DI) water or physiological solution. To vary the refractive index, a solution of ethanol and water is incrementally added to the fluidic cell with a digital syringe. Solutions of antibiotics of several generation or gold NP gels have been incrementally added to the fluidic cell also with a digital syringe. Following each injection, the WGM modes are monitored until equilibrium is reached, and then, the subsequent injection is made. a) b) Fig. 3. Scheme of experimental geometry of a sensitive sell with a detector (a) and a complete set-up (b). 1 microsphere, 2 adhesive layer, 3 coupling element (prism), 4 CMOS camera, 5 fluid pumping chamber, 6 sensitive sell/detector unit, 7- tuneable laser, 8 laser power supply, 9 digital dosage system, 10 computer To observe the WGM, the laser repeatedly scans across a spectral range of approximately 3 nm at a frequency of about 0.1 nm/s (this parameters could be changed). Light scattered by the micro-

sphere is collected through a microscope lens by a CMOS camera (VRmagic VRmC-3+ PRO) and monitored with a data acquisition card and computer (Fig.3). When the wavelength of the tuneable laser corresponds to a resonance of the sphere, the power of the light scattered by the micro sphere increases, and a spectral maximum indicating the WGM spectral position is recorded. The width of such a resonance after filtering is used to estimate the resonance quality. The sensitivity of the scheme was tested to determine refractive index variation by monitoring the magnitude of the whispering gallery modes (WGM) spectral shift as in [16 19]. General overview of the experimental set up is represented on the Fig. 4. 3. EXPERIMENT AUTOMATION Fig. 4. General overview of the experimental set up Software package was designed to automate the experimental process, as well as for the processing of the obtained results. Software environment Matlab was selected, as the basis of the development environment and the implementation of the package, Library Image Acquisition Toolboox which were connected to the library VRmUsbCamSDK to capture video from the camera. The GUIDE package MATLAB 8.0.0.783 (R2012b) for Microsoft Windows is used to build a graphical interface. A significant advantage of MATLAB is optimization of core, implemented in the programming language C + +, for mathematical calculations with matrices, which rapidly increases the speed of data analysis. The software package includes the following functional elements to perform an experiment: Connection to the laser controller (via selected Com-port at a specific port configuration); Control parameters of the laser beam (change of power of the laser, tuning range of the laser, speed of adjustment, setting the current wavelength mode, on / off external synchronization, start / stop the scan mode.); Connect to the camera and capture data (searches for cameras on all available usb-ports); Control the camera settings (exposure, gain, clock); Display real time video; saving obtained results (saved in a video file format *. avi, and information about lost frames). The software package allows to perform complete experiment by one click (synchronously starts the capture of data from the camera and the adjustment of the laser beam). The software package includes the following functional elements for processing obtained results:

Loading data (load *. avi-file as with no information about the lost frames, and with the information about the lost frames); Calculation of the intensity over the selected area(s) with a microcavity(s) with subsequent normalization (selects the resonance area, for which is calculated energy. Normalization performs after processing the entire video.); Construction of the resonance spectrum and the allocation of free spectral range (FSR) for each video sequence; calculation of parameters (relative spectral shift, relative efficiency); neural network classification (perceptron); Fig. 5. The main window of the software package (in the window is the current video stream) Most part of the parameters of the analysis are entered by the user via a standard windowing operating systems Windows (Fig. 5.). Output of intermediate and final results performed in the form of graphs and external objects (databases to allow analysis of the results, summary tables). It is possible to save the results of the analysis of the data as image files. Intermediate and final results of the analysis can be saved in a special format. It is possible to open and explore final data. 4. CLASSIFIER BASED ON NEURAL NETWORKS Here is represented an improved version of classifier based on multilayer perceptron and results of data processing for antibiotics of different generation in various solutions. A constructing a qualifier on the basis of a neural network has been passed following stages: data preprocessing network designing and training diagnostics of a network performance We will explain below in more details listed stages, with reference to the decision of our problem. Data preprocessing: The data were obtained in the form of the video file form camera in a format *.avi. All sequence was broken into frames where the area of a resonance was allocated in each frame. Example of one frame is given on Fig. 6 a), on the images left side resonance area was allocated. The image was filtered for noise reduction and integrated on two coordinates for evaluation of integrated energy of a measured signal (Fig. 6 b)).

a) b) c) Fig. 6. Example of frame for analysis. a) original frame; b) frame with selected resonance area; c) calculated energy of signal The technique to determine parameters of solutions of the biological agents, based on WGM optical resonance is described in detail in [13, 14]. As the entrance data following signal parameters were used: relative (to a free spectral range) spectral shift ( R ) of frequency of WGM optical resonance in microsphere and relative efficiency of WGM excitation ( I R ) obtained within a free spectral range which depended on both type concentration of investigated agents. The data before submitting on a network input ware pre-processed (normalized and standardized). They were calculated as: R ( max - 0 max )/ FSR,(1) IR I( )/ Imaxd,(2) FSR where I( ) intensity of the radiation disseminated by microsphere on the frequency, FSR free spectral range, I max the maximum intensity of the radiation disseminated by microsphere within a free spectral interval, max frequency with maximum intensity of the radiation, 0max frequency with maximum intensity of the radiation in pure water or physiological solution. Then we broke the data set into two subsets training and tested (randomly). Spectrum structure were fairly complicated due to relatively high Q factor (~ 10 5 ), but identification of resonance maxima and their spectral shifts have been surely evaluated. Multiple store of spectrum with statistical preprocessing have been used. All the experiments were performed using the same microsphere as a sensitive sell. Network designing and training: The network topology was designed: a number of the hidden layers of multi-layered perceptron, a number of neurons in each of layers, a method of training of a neural network, activation functions of layers, type and size of a deviation of the received values from required values. For a network training the method of the back propagation error in various

modifications has been used. Input vectors correspond to number of biological substances under investigation. Diagnostics of a network performance: The result of classification was considered as positive when each of the region, representing a certain substance in a space: relative spectral shift of an optical resonance maxima - relative efficiency of excitation of WGM, was singly connected. To obtain three-dimensional classification diagram with maximum accuracy all experimental data were used. It is possible to increase the accuracy of an assessment by increasing experimental sampling with a smaller step of a concentration variation. 5. RESULTS 5.1 WGM resonance spectra of biological agents The sensitivity to refractive index changes of the microsphere sensor system is determined by monitoring the magnitude of the WGM spectral shift when a known quantity of ethanol is added to the solution (Fig. 7). The change of the refractive index for each injection of ethanol is calculated from reference data. The mixing process is completed as soon as the frequency shift is stabilized (steady-state value). The steady-state spectral shift of WGM s correlates very well linearly with variation in concentration. The expected WGM spectral shift can be calculated theoretically from the asymptotic relation between wavelength and refractive index. Estimated sensitivity is approximately 30 nm/riu. The detection limit of our system can be calculated from this sensitivity. Due to extremely narrow (300 khz) laser line width, it is expected that we can resolve 1/20th to 1/50th of the WGM line width. For a high Q-factor (25000-30000), this results in a refractive index detection limit of the order of 10-7 RIU, which is comparable to or even better than surface plasmon resonances or waveguide-based sensors. The change in refractive index per 1% (w/w) variation of ethanol is 0.0002 RIUs. It should be pointed out that due to the relatively low Q factor in the geometry under consideration and the possibility to affect the coupling between the prism and the sphere by moving the sphere over the surface of the prism it was possible to record resonance spectra with only a few modes (even single). a)

b) c) Fig. 7. Resonance spectra (a) for 0, 6.5 and 12.3 % of ethanol in water; spectral shift (b) and RE (c) of WGM resonances for water/ethanol solutions a) b) c) Figure 8. Resonance spectra (a) for 0, 0.8, 1.4, 2.2 and 2.5 % of glucose solution; spectral shift (b) and RE (c) of WGM resonances for water/ glucose solutions Resonance spectra of WGM, spectral shift and RE for water solution of glucose are represented in Fig. 8. Obtained experimental data demonstrate excellent capability of a microsphere resonator to measure the refractive index of liquids. Spectral shift depends linearly on glucose concentration and RE increases monotonically on glucose concentration.the microsphere can achieve similar or even better results than typical waveguide-based sensors while requiring only a sample volume of pico-

liters. The high sensitivity and low sample consumption are excellent characteristics for lab-on-achip systems. 5.2 Antibiotic solutions and mixtures The next step was the investigation of antibiotic solutions. In this part of paper results for penicillin solution (concentration 0.6 g pro 10 ml) and cefatoxin solution (concentration 0.5 mg pro 10 ul water) are represented. Other types of antibiotic solutions are also analyzed. Resonance spectra, spectral shift and RE for water solution of penicillin are represented in the Fig. 9. Resonance spectra, spectral shift and RE for water solution of cefatoxin are represented in the Fig. 10. Some features can be observed. It can be seen, that variation of penicillin concentration on the level of around 5,4 g/l and cefatoxin concentration on the level of around 2,27 g/l can be surely detected. The power of the excitation beam can be extremely low (a few microwatt). This is good for biological agents in situ. On the other hand, the resonance spectra are relatively complicated due to the high Q-factor and the sharp focusing. Spectral shift depends linearly on the penicillin solution concentration, and relative efficiency does not depend significantly on the concentration of glucose. Spectral shift depends exponentially on the cefatoxin concentration but RE decreases while cefatoxin concentration increases. This means that it is impossible to determine the difference between the concentrations of cefatoxin while working with high concentrations of cefatoxin. One can see maxima of several modes, which depends on the type of microresonator. Such a resonance spectrum is rather complicated for data evaluation, but most of the maxima could be recognised and the shift of the peaks caused by variation of antibiotic concentration can be determined. a) b) c) Figure 9. Resonance spectra (a) for 0, 9, 16 and 28 % of penicillin solution in water; spectral shift (b) and RE (c) of WGM resonances for water/penicillin solutions

a) b) c) Figure 10. Resonance spectra (a) for 0, 0.4, 1.1, 1.8, 2.5 and 3 % of cefatoxim solution in water; spectral shift (b) and RE (c) of WGM resonances for water/cefatoxim solutions 5.3 Results for neural network data processing Spectral data have been processed by procedures explained above and the results of classification by neural network have been represented as three dimensional diagram of a certain drug concentration, identified by neural network and depend on two WGM optical resonance parameters. Estimated sensitivity of the method was about 6 mkg/l. Optimized parameters for classification of agents under investigation were the following: the maximum number of training cycles 50000; training criterion of 0.00001, activation function for all layers nonlinear sigmoidal logistic type (logsig). For training of a network the method of gradient descent with adaptation of speed of training parameter (GDA), number of the hidden layers 3 with 17 neurons in each was used. In the input and output layers the number of neurons corresponded to dimension of input and output signals (2 for input and 1 for output). 11212 epochs of training were required for a training of a network. Results of classification Benzylpenicillin, Cefazolin, Amoxicillin, Azithromycin, Chloramphenicol, Levofloxacin in de-ionized water and physiological solution are represented on the Figures 11, 12.

Fig. 11. Results of classification for water solution of Amoxicillin (1), Azithromycin (2), Cefazolin (3), Chloramphenicol (4), Levofloxacin (5), Lincomicin (6), Benzylpenicillin (7), Riphampicon (8) Fig. 12. Results of classification for physiological solution of Amoxicillin (1), Azithromycin (2), Cefazolin (3), Chloramphenicol (4), Levofloxacin (5), Benzylpenicillin (6) Using specially developed procedure of data classification two input parameters were coupled to concentration of solutions. Similar procedures described above can be applied to get a general instrumentation for antibiotic classification in both solvents. As a result the neural network constructed by developed algorithm, classified antibiotics of different generations with accuracy till 99 %. 5.4 Influence of plasmonic nanoparticles and layers on WGM resonance spectra of biological agents New opportunity to improve a sensitivity of a label - free biomolecule detection in sensing systems based on microcavity evanescent wave optical sensors has been recently found and is being under intensive development. Novel technique based on combination of optical resonance on microring structures with plasmon resonance. Developed technique used standard glass polymer microspheres as sensitive elements. They are fixed in the solution flow by adhesive layer on the surface being in the field of evanescence wave. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecules and NP injection was obtained caused WGM spectra modification. But after NP treatment spectral shift and intensity of WGM resonances in biomolecule solutions increased. WGM resonances in microspheres fixed on substrate with gold layer with optimized layer thickness

in biomolecule solutions also had higher intensity and spectra modification then without gold layer. So combining advantages of plasmon enhancing optical microcavity resonance with identification tools can give a new platform for ultra sensitive label-free biomedical sensor. CONCLUSION Thus, a techniques of detection and identification of drugs on the basis of spectroscopy of an optical resonance of WGM has been developed. Reasonability of the use of multilayered perceptron for processing and interpretation of the data obtained by a biosensor based on WGM optical resonance has been confirmed. In the last decade surface plasmon resonance (SPR) biosensors have made great strides both in terms of technology and its applications. SPR biosensors have become a central tool for study of molecular interactions and have been widely used for detection of chemical and biological analytes. Food analysis belongs to major areas of potential applications of SPR biosensors. Therefore, numerous SPR biosensors for detection of analyses implicated in food safety (e.g. pathogens, toxins, drug residues, vitamins, hormones, chemical contaminants, and allergens) have been developed. Influences of biomolecules and NP injection on WGM shift looked competitive, but further studies are needed to make a final conclusion. References 1. F. Vollmer and S. Arnold, "Whispering-gallery-mode biosensing: label-free detection down to single molecules," Nat. Meth., vol. 5, pp. 591 596, 2008. 2. X. D. Fan, I. M. White, S. I. Shopoua, H. Y. Zhu, J. D. Suter, and Y. Z. Sun, Sensitive optical biosensors for unlabeled targets: A review, Anal. Chim. Acta, vol. 620, pp. 8 26, 2008. 3. A. B. Matsko and V. S. Ilchenko, Optical resonators with whispering-gallery modes Part I: Basics, IIEEE J. Sel. Top. Quantum Electron., vol. 12, no 1, pp. 3 14, 2006. 4. V. S. Ilchenko and A. B. Matsko, Optical resonators with whispering-gallery modes Part II: Applications, IEEE J. Sel. Top. Quantum Electron., vol. 12, no 1, pp. 15 32, 2006. 5. M. A. Santiago-Cordoba, M. Cetinkaya, S. V. Boriskina, F. Vollmer, M. C. Demirel, Ultrasensitive detection of a protein by optical trapping in a photonic-plasmonic microcavity, J. Biophotonics, vol. 5, no 8 9, pp. 629 638, 2012. 6. S. Arnold, V. R. Dantham, C. Barbre, B. A. Garetz, X. Fan, Periodic plasmonic enhancing epitopes on a whispering gallery mode biosensor Optics express, vol. 20, no 24, pp. 26147-26160. 2012. 7. V. R. Dantham, S. Holler, V. Kolchenko, Z. Wan and S. Arnold, Taking whispering gallery-mode single virus detection and sizing to the limit, Appl. Phys. Lett., vol. 101, pp. 043704-1 043704-4, 2012. 8. M. Baaske and F. Vollmer, Optical Resonator Biosensors: Molecular Diagnostic and Nanoparticle Detection on an Integrated Platform, Chem. Phys. Chem., vol. 13, pp. 427 436, 2012. 9. V.A. Saetchnikov, E.A. Tcherniavskaia, G. Schweiger and A. Ostendorf, Classification of the micro and nanoparticles and biological agents by neural network analysis of the parameters of optical resonance of whispering gallery mode in dielectric microspheres,, Proceeding of the SPIE, vol. 8090, - pp. 80900R1-80900R11, 2011. 10. E.A. Tcherniavskaia, V.A. Saetchnikov, Application of neural networks for classification of biological compounds from the characteristics of whispering-gallery-mode optical resonance, Journal of Applied Spectroscopy, vol. 78, no 3, pp. 457-460, 2011. 11. V.A. Saetchnikov, E.A. Tcherniavskaia, G. Schweiger, A. Ostendorf and A.V. Saetchnikov, Neural Network analysis of the resonance whispering gallery mode characteristics of biological agents, Nonlinear Phenomena in Complex Systems, vol. 14, no 3, pp. 253 263, 2011. 12. V. A. Saetchnikov, E. A. Tcherniavskaia, G. Schweiger and A. Ostendorf, Classification of antibiotics by neural network analysis of optical resonance data of whispering gallery modes in dielectric microsphere, Nanophotonics IV, Proceeding of the SPIE, vol. 8424, pp. 345-356, 2012. 13. Vladimir A. Saetchnikov ; Elina A. Tcherniavskaia ; Anton V. Saetchnikov ; Gustav Schweiger and Andreas Ostendorf " Drag detection and identification by whispering gallery mode optical resonance based sensor ", Proc. SPIE 8801, Novel Biophotonic Techniques and Applications II, 880108 (June 18, 2013); 14. E.A. Tcherniavskaia, V.A. Saetchnikov, Detection and identification of microparticles/nanoparticles and blood components using optical resonance of whispering-gallery modes in microspheres, Journal of Applied Spectroscopy, vol. 77, no 5, pp. 692-699, 2010. 15. V.A. Saetchnikov, E.A. Tcherniavskaia, Using optical resonance of whispering gallery modes in microspheres for real-time detection and identification of biological compounds, Journal of Applied Spectroscopy, vol. 77, no 5, pp. 714-721, 2010. 16. V.A. Saetchnikov, E.A. Tcherniavskaia, Analysis of the biochemical composition of biological tissue in vivo by the diffuse light scattering method, Journal of Applied Spectroscopy, vol. 77, no 6, pp. 878-886, 2011. 17. V.A. Saetchnikov, E.A. Tcherniavskaia, G. Schweiger and A. Ostendorf, Optical micro resonance based sensor schemes for detection and identification of nano particles and biological agents in situ, Nanophotonics III, Proceeding of the SPIE, vol. pp. 7712, 771221-771232, 2010.

BSU FENUGREEK CELL CULTURE IN VITRO AS A POTENTIAL SOURCE OF PHENOLIC ANTIOXIDANTS H. O. LOHVINA, V. M. YURIN Abstract. In this study potential of fenugreek cell lines as alternative source of phenolic antioxidants was evaluated. Callus lines were initiated from leaves and stems of Ovari 4 and PSZ.G.SZ fenugreek varieties. Their growth was enhanced by optimisation of nutrient medium composition. The study of dynamic of cell growth and production of phenolic compounds in optimal nutrient media allow us to determine that the maxima of biomass accumulation in the calli coincide with high-level synthesis of phenolics. The maximal total phenolics and antioxidant activity of fenugreek callus lines were compared to these characteristics of leaves, stems and seeds of both fenugreek varieties. It was found that leaf callus of fenugreek variety Ovari 4 is most productive cell line with nearly the same amount of phenolics as fenugreek organs and seeds. Strong positive correlation between total phenolics and antioxidant properties of the calli indicates a key role of phenolic compounds as antioxidants in free radical scavenging. Therefore, the high producing cell line has potent to be used for phenolic antioxidant production. Key words: fenugreek, callus, total phenolics, antioxidant activity, positive correlation 1. INTRODUCTION Phenolics constitute a large group of plant secondary metabolites that includes compounds from simple phenol molecules to polymeric structures. Phenolic phytochemicals are actively involved in redox processes, boosting immunity and growth regulation [1; 2]. They also protect plants against the negative effects of stress being powerful antioxidants, which prevent free-radical oxidation of the DNA molecules, proteins and lipids [3; 4]. Because of strong antioxidant activity they act as anticancer, cardioprotective, anti-bacterial, anti-inflammatory, immunomodulatory and neuroprotective agents [5; 6; 7; 8; 9]. Phenolics are widely distributed in the plant kingdom. However, their amount, qualitative composition, quantitative ratio and, consequently, the pharmacological effects exhibited by extracts containing phenolics are species-specific [2]. Fenugreek (Trigonella foenumgraecum L.) is one of the oldest cultivated food, condiment and medicinal herbs. This is an annual plant belonging to the Fabaceae family. Fenugreek is native to the Mediterranean region, but now it is widely cultivated in warm temperate and tropical regions such as west and South Asia, North Africa, parts of Central and South Europe, Australia, North and South America [10; 11, c. 260-262]. It demonstrates pronounced antidiabetic [12], anticarcinogenic [13; 14], antihypertensive [15], immunomodulating [16], and other therapeutic effects. Its pharmacological activity is related to a number of biologically active substances, including phenolic compounds [11, p. 260-262; 17]. It was found positive correlation between polyphenolic content in fenugreek seeds and strong free radical scavenging ability [18; 19]. Fenugreek seed polyphenols protect DNA molecules against UV-C radiation. It also protects lipid complex of mitochondria and antioxidant enzyme superoxide dismutase from destruction initiated by hydrogen peroxide [20]. They prevent oxidative damage to red blood cells [21; 22] and play a significant role in maintaining both content and properties of collagen in alcohol-damaged liver cell [23]. Premanath et al. (2011) suggested that fenugreek leaves are also rich in phenolics, and can be a rich source of these metabolites, along with the seeds [24]. They also found that the ethanol extract of T. foenum-graecum leaves exhibits antioxidant properties and strong antibacterial activity against Escherichia coli, Proteus mirabilis, Klebsiella spp., Staphylococcus aureus, Pseudomonas aeruginosa and Enterobacter. Due to high concentration of phenolics and intensity of antioxidant effects this herb has potential to be used as a raw material for production of natural phenolic antioxidant complex. An alternative approach, which can provide industrialists with cheap and abundant fenugreek-derived substances, is the development and optimisation of procedures for the cell cultures. This technique not only provide means for getting high value natural plant products from cell culture in commercial scale, but also can overcome many problems associated with industrial production of these phytochemicals by extraction from field grown plants (mass cultivated or natural populations). In cultures, factory-type production of natural compounds can be carried out throughout the year, unaffected by the season. The risk of crop failure due to natural hazards and the danger of extinction of

some species due to their mass extraction from natural populations are eliminated. As a result of optimisation and standardisation of cultivation conditions a plant cell culture may produce higher amounts of the products than the intact plants from which they are derived [25; 26]. Thus, fenugreek cell culture is of great interest as a perspective source of phenolic compounds for natural antioxidants production. For the potential use of plant cell culture technology in pharmaceutical manufacturing a highproducing suspension culture is needed. Such suspension cultures are normally obtained from calli with high biomass productivity as well as significant biosynthetic activity. These characteristics depend on the origin of cultures in vitro, in particular, the type of primary explants and plant variety, and cultivation conditions [25]. Therefore, the main objective of this study was to achieve good growth of four fenugreek callus cell lines (leaf and stem originated of T. foenum-graecum variety Ovari 4 and PSZ.G.SZ) and select most productive cell line with higher phenolic content and antioxidant activity. With regards to the purpose, we investigated effect of phytohormones and sucrose concentrations on growth of fenugreek calli to optimise media composition. Then, cell growth in the optimal nutrient media was characterised and total phenolic content in the calli was evaluated during growth cycle to determine maximum accumulation of biomass and phenolic compounds. In order to identify best cell line of fenugreek that has potential to be used for suspension culture initiation we determined antioxidant activity of the calli in the maxima of growth and phenolic synthesis, and compared total phenolics and free radical scavenging ability in the calli and fenugreek plants. 2. MATERIALS AND METHODS 2.1 Callus cultures Seeds of T. foenum-graecum tax. conc. spring-summer variety Ovari 4 and tax. conc. winter variety PSZ.G.SZ were from Professor Shandor Makai s collection (Department of Medicinal and Aromatic Plants of the University of West Hungary). Winter and spring fenugreek varieties have been aseptically cultivated during 4-5 weeks. Leaf and stem fragments have been excised from these plants and cultivated in medium with phytohormones (auxin and cytokinin) to initiate callus formation [25; 27]. As a result, we obtained four primary callus cell lines: leaf originated of variety Ovari 4 (FO-L callus), stem originated of variety Ovari 4 (FO-S callus), leaf originated of variety PSZ.G.SZ (FP-L callus), and stem originated of variety PSZ.G.SZ (FP-S callus) [28]. 2.2 Cultivation conditions Cultivation of the calli has been carried out in the dark in thermostat at 24.5 C [25]. 2.3 Basal medium The basal medium contained full strength Murashige and Skoog medium [29], 3% sucrose and 0.08% microbiology grade agar (ph 5.7-5.8 adjusted with NaOH/HCl) [30]. 2.4 Phytohormone combinations Cytokinin kinetin and auxin 2,4-dichlorophenoxyacetic acid (1 and 2 mg/l) have been applied in four combinations for examination of phytohormone concentration influence on callus growth: 1 mg/l 2,4-D and kinetin, 1 mg/l 2,4-D and 2 mg/l kinetin, 2 mg/l 2,4-D and 1 mg/l kinetin, 2 mg/l 2,4-D and kinetin. 2.5 Sucrose concentrations Media, containing optimal combinations of phytohormones, were used to find out the effect of sucrose concentration on growth activity. Here four concentrations of sucrose were tested: 2, 3, 4 and 5%. 2.6 Growth characteristics Specific growth rate has been determined to examine growth activity of calli under influence of different concentrations of phytohormones and sucrose in nutrient media. Growth index has been

determined at day 5, 10, 15, 20, 25, 29, 32, 35, 40 of cultivation to construct callus growth curve [31; 32]. 2.7 Plant material Seeds, leaves and stems of fenugreek variety Ovari 4 (FO) and variety PSZ.G.SZ (FP) were used to estimate biosynthetic and antioxidant potential of the calli. To obtain above-ground biomass fenugreek seeds were couched and plat out. Plant were cultivated in phytostat conditions (14 h light/10 h dark) at room temperature and light intensity of 3000 lux till stage of seed formation. Then top part of plants was collected. Leaves and stems of both fenugreek varieties were washed in distilled water, dried at 60 C and powdered. Fenugreek seeds were also washed in distilled water, dried at room temperature and powdered. 2.8 Extraction procedure In order to conduct biochemical analysis fenugreek callus tissues were sampled 5, 10, 15, 20, 25, 29, 32, 35, 40 days after subculture, dried at 60 C and powdered. Plant material (0.1 g) was mixed with 70% ethanol, placed in a rotary shaker at 120 rpm at room temperature for 24 hours and then heated at 70 C for 1 h on a water bath to increase effectiveness of the extraction process. The samples were filtered through whatman filter paper. Extracts were centrifuged at 3500 g for 15 min. Supernatant was used in further experiments. For extract preparation from fenugreek organs (leaves and stems) and seeds, the same procedure was applied. 2.9 Determination of the total phenolics Total phenolic content was determined spectrophotometrically using Folin-Ciocalteu reagent (FCR) method [33]. This method is based on the reduction of FCR by phenolics with formation of blue colored reaction products showing maximum absorption at about 720-765 nm that mainly depends on type of phenolic compounds presented in solution and their ratio. 0.2 ml of the extract and 0.1 ml of FCR were added to 1.6 ml of distilled water. After 5 minutes 0.3 ml of 20% Na 2 CO 3 was added to the solution. Sample then was mixed vigorously and kept in the dark for 2 hours. After incubation the solution was mixed with 2 ml of distilled water. The absorbance was immediately read at 750 nm against reagent blank with the use of UV-visible spectrophotometer. This wavelength corresponds to absorption maximum demonstrated by gallic acid that was used as a standard for calibration curve. The total phenolic content was expressed as milligrams of gallic acid equivalents per gram of dry weight (mg GAE/g DW). 2.10 Free radical scavenging activity The hydrogen atom or electron donation abilities of the extracts were measured from the bleaching of a purple-colored ethanol solution of the stable radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) [34]. To start the reaction 1 ml of 0.3 mm freshly DPPH prepared ethanol solution was added to 2.5 ml of the same extract that firstly was used for determination of the total phenolics. The sample was kept in the dark at room temperature for 30 min. The optical density of the solution was measured by spectrophotometer at experimentally found wavelength of 520 nm. To get a blank 1 ml of 96% ethanol was mixed with 2.5 ml of 70% ethanol. A negative control includes 1 ml of 0.3 mm DPPH solution and 2.5 ml of 70% ethanol. As positive control gallic acid was used which scavenges more 95% radicals DPPH in concentration of 0.004 mg/ml. Antioxidant potential of the extracts was expressed by percentage of inhibition activity (%I). 2.11 Statistical analysis In all experiments, 3-4 independent replicates have been tested and statistically analysed (Student s t test). P-values less than 0.05 have been considered to be statistically significant. Correlation between total phenolics and antioxidant activity was established by correlation analysis (with Pearson s correlation coefficient).

3. RESULTS AND DISCUSSION Phytohormones auxins and cytokinins are essential for maintenance of cell culture growth [26; 35]. However optimal auxin and cytokinin concentrations are different for different cell lines. The optimal ratio of these hormones should be determined empirically [36]. Tests included examination of the effect of phytohormones (different concentrations of 2,4-D and kinetin). It was shown that the callus growth and biomass accumulation (measured after induction) varied in calli from different origins and different hormone mixtures. FO-L, FO-S, FP-L and FP-S calli demonstrated the most pronounced biomass increase in media containing 2 mg/l 2,4-D and kinetin, 1 mg/l 2,4-D and kinetin, 1 mg/l 2,4-D and 2 mg/l kinetin, 2 mg/l 2,4-D and kinetin, respectively (figure 1). The minimal and maximal specific growth rates have been found for FO-S cell line (0.014 day -1 ) and FP-L cell line (0.066 day -1 ), respectively. 0.08 Specific growth rate, day -1 0.07 0.06 0.05 0.04 0.03 0.02 0.01 1 2 3 4 0 FP-L callus FP-S callus FO-L callus FO-S callus Varients of nutrient medium Fig. 1. Effect of different combinations of 2,4-D and kinetin in the medium on growth of T. foenum-graecum calli: 1 1 mg/l 2,4-D + 1 mg/l kinetin; 2 2 mg/l 2,4-D + 1 mg/l kinetin; 3 1 mg/l 2,4-D + 2 mg/l kinetin; 4 2 mg/l 2,4-D + 2 mg/l kinetin The next stage of our work was to examine the effect of carbohydrates on the growth rate of calli (carbohydrate optimisation stage). Sucrose is a key component of most commercial growth media that are used for cultivation of isolated plant cells and tissues [25; 36]. Here, the effects of several sucrose concentrations (2, 3, 4 and 5% sucrose) have been examined in media that were preliminary optimised for hormone combination (see above; figure 1). 4% sucrose has been found to be optimal for cultivation in all calli (figure 2). In some cases, addition of 5% sucrose resulted in similar or lower rate of biomass increase that was probably related to osmotic stress caused by this sucrose level [37; 38]. Overall, media supplied with 4 and 5% sucrose promoted 1.5-2.4-fold increase in growth rate of calli as compared to 2 and 3% sucrose-supplemented media. According to published data, the stimulatory effect of sucrose on plant cell culture growth is associated with increased availability of highly energetic organic substrate that can be directly used for anabolic reactions [39]. Sucrose has also been shown to increase the duration of the stationary phase of growth cycle, auxin production and enzyme activities of the pentose phosphate pathway [26]. It should also be noted that for FO-L, FP-L and FP-S cell lines specific growth rate was nearly 0.14 day -1, with the exception of FO-S callus which in spite of cultivation in optimal nutrient medium showed two times lower growth activity.