Předmětem článkuje nelineární robustní 3D filtrace MR obrazu, která umožňuje realizaci vysokého prostorového rozlišení při zachování únosné míry šumu. Důraz je kladen na rozdílné vlastnosti 2D a 3D filtrů a na rozdíl mezi lineárními a nelineániími robustními technikami filtrace. Základní pojmy jsou vysvětleny v matematicky odlehčené podobě a postupy nelineární filtrace jsou dokumentovány na číselných příkladech. Rozdíly mezi jednotlivými metodami jsou zobrazeny v grafické formě. Software pro nelineární filtraci 3D MR obrazů je realizován v prostředí Matlab a zahrnuje čtení souborů podle normy Interfile, vlastní numerické výpočty, grafickou prezentaci a archivaci výsledků.

Nonlinear robust 3D filtering of MR image is a subject of the paper. It enables to realize high space resolution with guaranteed acceptable level of noise. Different properties of 2D and 3D filters plus the difference between linear and nonlinear robust techniques are pointed. Basic terms are explained in simple and „light" mathematical form and the procedures of nonlinear filtering are explained on numerical examples. The method differences are depicted using graphical form. The software for nonlinear filtering of 3D MR images is realized in Matlab environment including Interfile reading, numeric calculations, graphical presentation and result archivation.

• MeSH
• Filtration MeSH
• Research Support as Topic MeSH
• Image Interpretation, Computer-Assisted MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Nonlinear Dynamics MeSH
• Check Tag
• Humans MeSH
• Publication type
• Comparative Study MeSH

A full-wave model for nonlinear ultrasound propagation through a heterogeneous and absorbing medium in an axisymmetric coordinate system is developed. The model equations are solved using a nonstandard or k-space pseudospectral time domain method. Spatial gradients in the axial direction are calculated using the Fourier collocation spectral method, and spatial gradients in the radial direction are calculated using discrete trigonometric transforms. Time integration is performed using a k-space corrected finite difference scheme. This scheme is exact for plane waves propagating linearly in the axial direction in a homogeneous and lossless medium and significantly reduces numerical dispersion in the more general case. The implementation of the model is described, and performance benchmarks are given for a range of grid sizes. The model is validated by comparison with several analytical solutions. This includes one-dimensional absorption and nonlinearity, the pressure field generated by plane-piston and bowl transducers, and the scattering of a plane wave by a sphere. The general utility of the model is then demonstrated by simulating nonlinear transcranial ultrasound using a simplified head model.

• Publication type
• Journal Article MeSH

Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets.

• MeSH
• Algorithms * MeSH
• Least-Squares Analysis MeSH
• Artificial Intelligence * MeSH
• Publication type
• Journal Article MeSH

Recent evidence suggests that energy metabolism contributes to molecular mechanisms controlling stem cell identity. For example, human embryonic stem cells (hESCs) receive their metabolic energy mostly via glycolysis rather than mitochondrial oxidative phosphorylation. This suggests a connection of metabolic homeostasis to stemness. Nicotinamide adenine dinucleotide (NAD) is an important cellular redox carrier and a cofactor for various metabolic pathways, including glycolysis. Therefore, accurate determination of NAD cellular levels and dynamics is of growing importance for understanding the physiology of stem cells. Conventional analytic methods for the determination of metabolite levels rely on linear calibration curves. However, in actual practice many two-enzyme cycling assays, such as the assay systems used in this work, display prominently nonlinear behavior. Here we present a diaphorase/lactate dehydrogenase NAD cycling assay optimized for hESCs, together with a mechanism-based, nonlinear regression models for the determination of NAD(+), NADH, and total NAD. We also present experimental data on metabolic homeostasis of hESC under various physiological conditions. We show that NAD(+)/NADH ratio varies considerably with time in culture after routine change of medium, while the total NAD content undergoes relatively minor changes. In addition, we show that the NAD(+)/NADH ratio, as well as the total NAD levels, vary between stem cells and their differentiated counterparts. Importantly, the NAD(+)/NADH ratio was found to be substantially higher in hESC-derived fibroblasts versus hESCs. Overall, our nonlinear mathematical model is applicable to other enzymatic amplification systems.

• MeSH
• Cell Extracts MeSH
• Electrophoresis, Capillary MeSH
• Embryonic Stem Cells metabolism   MeSH
• Calibration MeSH
• Humans MeSH
• NAD metabolism   MeSH
• Nonlinear Dynamics * MeSH
• Oxazines metabolism   MeSH
• Regression Analysis MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: Mood regulation is a complex and poorly understood process. In this study, we aimed to analyze the underlying dynamics of mood regulation in unaffected first degree relatives of patients diagnosed with bipolar disorder using time-series analysis. METHODS: We recruited 30 unaffected first-degree relatives of bipolar disorder patients. Participants rated their mood, anxiety and energy levels using a paper-based visual analog scale; they recorded their sleep and life events as well. Participants provided information on these variables over a three month period, twice per day. We compared their data using Box-Jenkins time series analysis with data from 30 healthy controls (HC) and 30 euthymic bipolar patients (BD) to obtain information on the autocorrelation and cross-correlation of the series, and calculated entropy for mood, anxiety and energy series. RESULTS: We analyzed 14,980 data points: 5200 in the healthy control group; 4970 in the bipolar group and 4810 in the unaffected relatives group. There were no significant differences between groups in terms of age, sex or education levels. Using Kolmogorov-Smirnov test, we found that individual measures were normally distributed in the whole sample (D = 0.23, p > 0.1). Autocorrelation functions for mood in all groups are governed by the ARIMA (1,1,0) model, which means that current values in the series are related to one previous point only. In terms of entropy for the mood series, unaffected relatives and bipolar patients showed lower values [mean (SD) : 1.028 ± 0.679; 1.042 ± 0.680], respectively, compared to healthy controls [(1.476 ± 0.33); F (2,74) = 4.39, p < 0.01]. The same case was seen in the energy series, with lower values in the unaffected relatives and bipolar patient groups [mean (SD) : 1.644 ± 0.566; 1.511 ± 0.879], respectively, compared to healthy controls [2.230 ± 0.531; F(2, 75) = 7.89, p < 0.001]. LIMITATIONS: Low resolution for the visual analog scale. CONCLUSIONS: Using nonlinear analyses, we found that the underlying structure of mood regulation in unaffected relatives is undistinguishable from the one found in bipolar patients. Compared to healthy controls, both bipolar patients and their unaffected relatives showed lower entropy levels, which is in keeping with a more rigid system, not as flexible to cope with the demands of a changing environment.

• MeSH
• Affect * MeSH
• Bipolar Disorder diagnosis   psychology   MeSH
• Cyclothymic Disorder psychology   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Nonlinear Dynamics MeSH
• Self-Control psychology   MeSH
• Case-Control Studies MeSH
• Anxiety psychology   MeSH
• Visual Analog Scale MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Oscillatory phenomena in the brain activity and their synchronization are frequently studied using mathematical models and analytic tools derived from nonlinear dynamics. In many experimental situations, however, neural signals have a broadband character and if oscillatory activity is present, its dynamical origin is unknown. To cope with these problems, a framework for detecting nonlinear oscillatory activity in broadband time series is presented. First, a narrow-band oscillatory mode is extracted from a broadband background. Second, it is tested whether the extracted mode is significantly different from linearly filtered noise, modelled as a linear stochastic process possibly passed through a static nonlinear transformation. If a nonlinear oscillatory mode is positively detected, further analysis using nonlinear approaches such as the phase synchronization analysis can potentially bring new information. For linear processes, however, standard approaches such as the coherence analysis are more appropriate and provide sufficient description of underlying interactions with smaller computational effort. The method is illustrated in a numerical example and applied to analyze experimentally obtained human EEG time series from a sleeping subject.

• MeSH
• Biological Clocks physiology   MeSH
• Time Factors MeSH
• Electroencephalography methods   MeSH
• Humans MeSH
• Models, Neurological MeSH
• Brain physiology   MeSH
• Nonlinear Dynamics MeSH
• Signal Processing, Computer-Assisted MeSH
• Sleep physiology   MeSH
• Spectrum Analysis MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• Conspectus
• Přírodní vědy. Matematické vědy
• NML Fields
• přírodní vědy

Applications of causal techniques to neural time series have increased extensively over last decades, including a wide and diverse family of methods focusing on electroencephalogram (EEG) analysis. Besides connectivity inferred in defined frequency bands, there is a growing interest in the analysis of cross-frequency interactions, in particular phase and amplitude coupling and directionality. Some studies show contradicting results of coupling directionality from high frequency to low frequency signal components, in spite of generally considered modulation of a high-frequency amplitude by a low-frequency phase. We have compared two widely used methods to estimate the directionality in cross frequency coupling: conditional mutual information (CMI) and phase slope index (PSI). The latter, applied to infer cross-frequency phase-amplitude directionality from animal intracranial recordings, gives opposite results when comparing to CMI. Both metrics were tested in a numerically simulated example of unidirectionally coupled Rössler systems, which helped to find the explanation of the contradictory results: PSI correctly estimates the lead/lag relationship which, however, is not generally equivalent to causality in the sense of directionality of coupling in nonlinear systems, correctly inferred by using CMI with surrogate data testing.

• MeSH
• Electroencephalography * methods   MeSH
• Humans MeSH
• Models, Neurological MeSH
• Brain physiology   MeSH
• Nonlinear Dynamics * MeSH
• Computer Simulation MeSH
• Signal Processing, Computer-Assisted MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH

• MeSH
• Autonomic Nervous System physiopathology   MeSH
• Models, Biological * MeSH
• Early Diagnosis MeSH
• Cardiovascular Diseases * diagnosis   MeSH
• Humans MeSH
• Models, Cardiovascular MeSH
• Nonlinear Dynamics * MeSH
• Heart Rate physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

OBJECTIVES: Autism is a severe neurodevelopmental disorder with a high rate of epilepsy and subclinical epileptiform activity. High physical connectivity on a microcolumnar level leading to epileptiform activity and low functional informational connectivity are assumed in autism. The aim of this study was to investigate nonlinear EEG brain dynamics in terms of synchronization in a group of children with autism spectrum disorders compared to a control group. We expected a lower degree of synchronization in autistic subjects. METHODS: The autistic group consisted of 27 patients with autism spectrum disorders diagnosed according to ICD-10. The mean age of the sample was 7.1 (SD 3.6) years, 14 of them were mentally retarded. Normal EEG was found in 9 patients, epileptiform EEG in 18 autistic patients. Four patients had a history of epileptic seizures, fully compensated in long term. The control group consisted of 20 children (mean age of 8.4, SD 2.3 years) with normal intelligence, without an epileptic history, investigated within the frame of the research program for cochlear implantation. They had normal neurological examination and suffered from perceptive deafness. Normal EEG was found in 17 of the control subjects, epileptiform EEG was in 3 control subjects. We analyzed night sleep EEG recordings from 10 channels (F3, F4, F7, F8, C3, C4, T3, T4, P3 and P4) with the inclusion of sleep stages NREM 2, 3 and 4 in the subsequent analyses. Coarse-grained entropy information rates between neighbouring electrodes were computed, expressing the synchronization between 11 selected electrode couples. RESULTS: Synchronization was significantly lower in the autistic group in all three examined NREM stages even when age and intelligence were taken into account as covariates. CONCLUSIONS: The results of the study confirmed the validity of the underconnectivity model in autism.

• MeSH
• Autistic Disorder physiopathology   MeSH
• Child MeSH
• Electroencephalography MeSH
• Financing, Organized MeSH
• Humans MeSH
• Intellectual Disability physiopathology   MeSH
• Polysomnography MeSH
• Child, Preschool MeSH
• Sleep physiology   MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Child, Preschool MeSH