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  • 1.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Šulčius, Sigitas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Open Access Centre for Nature Research, Nature Research Centre, Vilnius, Lithuania.
    Staniulis, Juozas
    Laboratory of Plant Viruses, Nature Research Centre, Institute of Botany, Vilnius, Lithuania.
    Paškauskas, Ričardas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Laboratory of Algology and Microbial Ecology, Nature Research Centre, Vilnius, Lithuania.
    Automatic detection and morphological delineation of bacteriophages in electron microscopy images2015In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 64, p. 101-116Article in journal (Refereed)
    Abstract [en]

    Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host. © 2015 Elsevier Ltd.

  • 2.
    Stasiunas, Antanas
    et al.
    a Department of Applied Electronics, Kaunas University of Technology, Lithuania .
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Miliauskas, Rimvydas
    Department of Physiology, Kaunas University of Medicine, Lithuania.
    Stasiuniene, Natalija
    Department of Biochemistry, Kaunas University of Medicine, Lithuania.
    An adaptive model simulating the somatic motility and the active hair bundle motion of the OHC2009In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 39, no 9, p. 800-809Article in journal (Refereed)
    Abstract [en]

    The outer hair cells (OHC) of the mammalian inner ear change the sensitivity and frequency selectivity of the filtering system of the cochlea using two kinds of mechanical activity: the somatic motility and the active hair bundle motion. We designed a non-linear adaptive model of the OHC employing both mechanisms of the mechanical activity. The modeling results show that the high sensitivity and frequency selectivity of the filtering system of the cochlea depend on the somatic motility of the OHC. However, both mechanisms of mechanical activity are involved in the adaptation to sound intensity and efferent-synaptic influence. The fast (alternating) component (AC) of the mechanical–electrical transduction signal controls the motor protein prestin and fast changes in axial length of the cell. The slow (direct) component (DC) appearing at high signal intensity affects the axial stiffness, the cell length and the position of the hair bundle. The efferent influence is realized by the same mechanism.

  • 3.
    Stasiunas, tanas
    et al.
    Kaunas University of Technology.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Kemesis, povilas
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Miliauskas, Rimvydas
    Kaunas University of Medicine.
    Stasiuniene, Natalija
    Kaunas University of Medicine.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system2005In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 35, no 6, p. 495-510Article in journal (Refereed)
    Abstract [en]

    An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.

  • 4.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Kaseta, Marius
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Using the patient's questionnaire data to screen laryngeal disorders2009In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 39, no 2, p. 148-155Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. By applying the genetic search, the most important questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the questionnaire data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as questionnaire statements is applied. When testing the developed tools on the set of data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.

1 - 4 of 4
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