prof. Ing. Valentine Provazník, Ph.D.

2. round (applications submitted from 01.07.2018 to 31.07.2018)

1. Advanced algorithms for monitoring human activity using mobile sensors

   The theme of this dissertation is aimed on monitoring and evaluation of activities performed by individuals using sensors commonly available in mobile devices (accelerometer, GPS, microphone, heartbeat sensor) and it can be divided into two parts. The goal of the first part is to analyze possibilities of mobile devices, become familiar with the types of sensor data and assess their potential. The goal of the second part is to design advanced algorithms for processing of captured data to identify different types of performed activities (rest, walk, run). Applicants are expected to be familiar with Matlab programming and have an overview in the area of processing and analysis of 1D signals.

   Tutor: Vítek Martin, Ing., Ph.D.

2. Advanced methods for ECG signal quality estimation

   The topic of dissertation thesis is focused on continuous quality monitoring in long-term ECG records. The first part is to evaluate the quality of ECG signal recorded from different locations on the body using mobile recorder and possibilities of simultaneously recorded physical activity by gyroscope. The second part is the design of advanced algorithms for continuous and real-time estimation of the ECG quality and subsequent identification of the section of the same quality. Applicants are expected to programming skills in Matlab and base knowledge of the processing and analysis of 1D signal.

   Tutor: Smital Lukáš, Ing., Ph.D.

3. Analysis of gene expression in cardiomyopathy by bioinformatics methods

   Cardiomyopathy is a common cause of heart failure and cardiac transplantation. This study is aimed to explore potential cardiomyopathy-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles from Gene Expression Omnibus database will be used. The differentially expressed genes will be searched between normal and cardiomyopathy-related samples using new bioinformatics methods. Further, potential transcription factors and microRNAs of these cardiomyopathy-related genes will be predicted based on their binding sequences. In addition, cardiomyopathy-related genes will be used to find potential small molecule drugs as potential therapeutic drugs for cardiomyopathy.

   Tutor: Provazník Valentine, prof. Ing., Ph.D.

4. Analysis of medical image data from spectral computer tomography

   Spectral computed tomography (SCT) is a new medical imaging modality, acquisiting the data with different photon energies of the used X-rays, so that it can provide more detailed information on the imaged tissues, to suppress or enhance imaging of some substances etc. The goal of the dissertation is studying of these possibilities and designing of the respective analytical algorithms, implementing them and testing on specific medical data in cooperation with medical institutions that were recently equipped with a SCT system. The research is run in cooperation with the international firm Philips Nederlands. Advisor specialist: MUDr. Petr Ouředníček, Ph.D.

   Tutor: Jan Jiří, prof. Ing., CSc.

5. Analysis of movement stereotypes in sports performance

   Motion analysis in sports is irreplaceable. A detailed analysis of movement stereotypes leads to improved quality of training plans and improve sports results of training individuals. Analysis can also be used for diagnostic purposes - monitoring faulty movement patterns after injuries in order to clarify procedures for rehabilitation treatments. This work will be focused on monitoring specific movement stereotypes, selection of appropriate parameters and subsequent analysis of data, which will be performed in order to describe the motion stereotypes in sports performance. The study will be carried out in cooperation with the Centre of Sports Activities of BUT.

   Tutor: Kolářová Jana, doc. Ing., Ph.D.

6. Detection of mental states using biosignals and video monitoring

   This topic is focused on analysis of biological signals in order to detect different mental states (especially fatigue and mental stress) mainly among drivers. The processing of different types of biosignals is expecte, e.g. ECG, EMG, EDA, SpO2 and others, but also analyzes of videosequences wil be considerd (such as eye movement detection). Applied methods will include both basic pre-processing methods and machine learning methods using modern deep learning approaches. The publicly available data will be used (PhysioNet) as well as own data acquired at DBME laboratories and in the frame of research projects solution.

   Tutor: Kolář Radim, doc. Ing., Ph.D.

7. Genome-wide scale approach study of gene expression in Clostridia and reconstruction of their gene regulatory networks

   The research is aimed to bioinformatics study of butanol producing bacteria Clostridia. The bacteria will be studied in dynamic experiment including possibilities to mutate selected genes to suppress their negative characteristics and increase production of butanol. Genomic data will be acquired using RNA-Seq technology. Gene ontology and relationship of selected genes to metabolism will be analyzed using advanced methods from standard and custom-made R/Bioconductor packages. Further, signaling pathways responsible for efflux efficiency and butanol tolerance of the strain will be modelled. Large RNA-Seq datasets will used to reconstruct Clostridia gene regulatory network using various statistical techniques (regression, mutual information, correlation, Bayesian theorem).

   Tutor: Provazník Valentine, prof. Ing., Ph.D.

8. Pharmacophore based dynamics studies for multi-targeted receptors against natural compounds as potential inhibitors for Psoriasis

   In recent decades, understanding of the pathophysiology of psoriasis has changed from that of an intrinsic epidermal keratinocyte disease to a T-cell-mediated disease. It is now being considered a systemic inflammatory disease with an evident impact on the immune system. This has been reflected in changes in treatment modalities over the years, starting with non-selective treatments such as corticosteroids, methotrexate, and acitretin, and moving on to more selective treatments such as cyclosporine and the highly selective biological therapies. The introduction of tumor necrosis factor (TNF)-α inhibitors resulted in a breakthrough in the management of moderate-to-severe psoriasis. The current study will be focused on searching common inhibitors screened against mapped shared features from protein targets that include phosphodiesterase, Janus Kinase and A3 adenosine receptors. The study will be extended to simulation studies for deriving its stability in terms of thermodynamic properties. The selected molecules will be tested using BALB/c mice model for psoriasis. In-vivo testing in mice model shall be performed in order to estimate PASI score, Th-1 (IL-1β, IL-6, TNF-α, IFN-γ) and Th-17 (IL-22 and IL-17) cell expressing cytokines along with histopathology of the skin samples of the mice.

   Tutor: Provazník Valentine, prof. Ing., Ph.D.

1. round (applications submitted from 01.04.2018 to 15.05.2018)

1. Advance signal processing methods for gas chromatography – mass spectrometry data processing

   The dissertation focuses on development of methods for preprocessing and analysis of data achieved by modern technology which combines gas chromatography with mass spectrometry (GC-MS) and produces complex 3D signals. The aim of the work is to develop a computational tool for identification of unknown compounds within samples and comparison their concentrations across and within samples. The core of the work is to create a new methodology for deconvolution of spectra which are assembled by co-elution of signals representing chemical compounds of samples. The key for successful deconvolution process is the development of a of new signal preprocessing technique, where it is necessary to implement in particular the correction of retention times shifts and overlaps and background subtraction step for increasing the Signal to Noise Ratio (SNR). The created tool should allow at least the semi-automatic analysis of mutual similarity of metabolic compounds in toxicology. The applicant is expected to be mastering the basic analysis methods in biochemistry and should also have an overview in the field of processing and analysis of multidimensional signals. The programming in appropriate environment is expected. The topic will be solved in cooperation with the Research Centre for Toxic Compounds in the Environment (Recetox MUNI).

   Tutor: Vítková Helena, Ing., Ph.D.

2. Deep learning methods for image data processing in biometry and biomedicine

   This topic deals with current variants of artificial neural networks in the areas of processing and analysis of still images and videosequences. It is expected that deeper study will be needed in the following areas: convolution neural networks, transfer learning for application in other tasks, progressive learning to solve new and complex problems and application of recurrent neural networks for segmentation and tracking objects in the image data. Specific applications will primarily focus on segmentation and tracking of people and objects in general scenes, including face detection with the use in biometrics and biomedicine.

   Tutor: Kolář Radim, doc. Ing., Ph.D.

3. Laser speckle contrast imaging for blood flow rate evaluation

   The theme of this thesis is aimed on optical measurement of blood flow rate using image processing of speckles which are created using coherent light source. The main aim is the study and extension of this method using non-ideal optical medium and increasing the robustness of blood flow rate estimation. Laser Speckle Contrast Imaging methods will be used for estimation of parameters of tissue perfusion, especially in skin. Advanced methods of image processing, including segmentation, texture analysis and image acquisition will be used for the solution of this thesis. Overall, this project will extend diagnosis potential of Laser Speckle Contrast Imaging methods. The aim of the work is also to design and develop measurement technology to obtain relevant data.

   Tutor: Harabiš Vratislav, Ing., Ph.D.

4. Utilization of genomic signal processing techniques for bacterial genotyping

   The aim of the dissertation is to develop an effective method for detection of new genetic markers for fast laboratory genotyping of unknown bacterial strains. A connection of genomic signal processing methods with machine learning techniques will contribute to describe known genetic markers discovered in laboratory. This description will serve to localize new markers computationally instead of using an expensive and lengthy laboratory approach. The utilization of genomic signal processing methods is more effective than current computational methods, because it uses a short features vector for description of known genetic markers instead of a complete long DNA fragments which represent them. The developed method will make extensive epidemiological studies available, eq. localization of origin and distribution mapping of multi-resistant bacterial strains within health facilities. The applicant is expected to be mastering basic methodology of processing and analysis of genomic data and should also have an overview in the field of processing and analysis of 1D signals. The programming in appropriate environment is expected. The topic will be solved in cooperation with the Children's Hospital - University Hospital Brno.

   Tutor: Vítková Helena, Ing., Ph.D.