In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between-brain coupling. In this study, we investigated this by "performing functional magnetic resonance imaging on two individuals simultaneously (dual-fMRI) while they competed or cooperated with one another in a turn-based or concurrent fashion." To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data-driven, model-free analytical techniques: group-independent component analysis and inter-subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co-operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo-parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuo-motor processing and social decision-making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn-based exchanges. These results demonstrate the utility of data-driven approaches applied to "dual-fMRI" data in elucidating the interpersonal neural processes that give rise to the two-in-one dynamic characterizing social interaction.

• MeSH
• dospělí MeSH
• kompetitivní chování * MeSH
• kooperační chování * MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mapování mozku metody   MeSH
• mladý dospělý MeSH
• mozeček diagnostické zobrazování   fyziologie   MeSH
• mozková kůra diagnostické zobrazování   fyziologie   MeSH
• sociální interakce * MeSH
• sociální kognice * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Examination of semen characteristics is routinely performed for fertility status investigation of the male partner of an infertile couple as well as for evaluation of the sperm donor candidate. A useful tool for preliminary assessment of semen characteristics might be an artificial neural network. Thus, the aim of the present study was to construct an artificial neural network, which could be used for predicting the result of semen analysis based on the basic questionnaire data. On the basis of eleven survey questions two models of artificial neural networks to predict semen parameters were developed. The first model aims to predict the overall performance and profile of semen. The second network was developed to predict the concentration of sperm. The network to evaluate sperm concentration proved to be the most efficient. 92.93% of the patients in the learning process were properly qualified for the group with a correct or incorrect result, while the result for the test set was 85.71%. This study suggests that an artificial neural network based on eleven survey questions might be a valuable tool for preliminary evaluation and prediction of the semen profile.

• MeSH
• analýza spermatu * metody   přístrojové vybavení   MeSH
• lidé MeSH
• motilita spermií MeSH
• mužská infertilita MeSH
• neuronové sítě * MeSH
• počet spermií metody   přístrojové vybavení   MeSH
• průzkumy a dotazníky MeSH
• sperma * MeSH
• spermabanky MeSH
• spermie * abnormality   růst a vývoj   ultrastruktura   MeSH
• Check Tag
• lidé MeSH

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.

• MeSH
• analýza hlavních komponent MeSH
• datové soubory jako téma MeSH
• imunoglobuliny krev   MeSH
• kostní dřeň metabolismus   patologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• metabolické sítě a dráhy MeSH
• metabolom * MeSH
• mnohočetný myelom krev   diagnóza   patologie   MeSH
• neuronové sítě * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice MeSH
• studie případů a kontrol MeSH
• umělá inteligence * statistika a číselné údaje   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.

• MeSH
• difuzní magnetická rezonance MeSH
• epilepsie diagnostické zobrazování   MeSH
• lidé MeSH
• mozek * fyziologie   MeSH
• nervová síť fyziologie   MeSH
• nervové vedení fyziologie   MeSH
• teoretické modely MeSH
• záchvaty diagnostické zobrazování   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH
• srovnávací studie MeSH

BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. RESULTS: Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. CONCLUSIONS: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

• MeSH
• genomika MeSH
• neuronové sítě * MeSH
• sekundární struktura proteinů MeSH
• strojové učení * MeSH
• Publikační typ
• časopisecké články MeSH

Essential hypertension is a multifactorial disorder which belongs to the main risk factors responsible for renal and cardiovascular complications. This review is focused on the experimental research of neural and vascular mechanisms involved in the high blood pressure control. The attention is paid to the abnormalities in the regulation of sympathetic nervous system activity and adrenoceptor alterations as well as the changes of membrane and intracellular processes in the vascular smooth muscle cells of spontaneously hypertensive rats. These abnormalities lead to increased vascular tone arising from altered regulation of calcium influx through L-VDCC channels, which has a crucial role for excitation-contraction coupling, as well as for so-called "calcium sensitization" mediated by the RhoA/Rho-kinase pathway. Regulation of both pathways is dependent on the complex interplay of various vasodilator and vasoconstrictor stimuli. Two major antagonistic players in the regulation of blood pressure, i.e. sympathetic nervous system (by stimulation of adrenoceptors coupled to stimulatory and inhibitory G proteins) and nitric oxide (by cGMP signaling pathway), elicit their actions via the control of calcium influx through L-VDCC. However, L-type calcium current can also be regulated by the changes in membrane potential elicited by the activation of potassium channels, the impaired function of which was detected in hypertensive animals. The dominant role of enhanced calcium influx in the pathogenesis of high blood pressure of genetically hypertensive animals is confirmed not only by therapeutic efficacy of calcium antagonists but especially by the absence of hypertension in animals in which L-type calcium current was diminished by pertussis toxin-induced inactivation of inhibitory G proteins. Although there is considerable information on the complex neural and vascular alterations in rats with established hypertension, the detailed description of their appearance during the induction of hypertension is still missing.

• MeSH
• adrenergní receptory fyziologie   MeSH
• cévní endotel patofyziologie   MeSH
• guanosinmonofosfát cyklický metabolismus   MeSH
• hypertenze metabolismus   patofyziologie   MeSH
• krevní tlak fyziologie   MeSH
• lidé MeSH
• membránové potenciály MeSH
• svaly hladké cévní fyziologie   MeSH
• sympatický nervový systém patofyziologie   MeSH
• vápník metabolismus   MeSH
• vápníkové kanály - typ L metabolismus   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• práce podpořená grantem MeSH
• přehledy MeSH

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.

• MeSH
• bolest diagnóza   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice metody   MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the clinical management. Despite extensive efforts, identifying effective biomarkers for neuropathic pain remains elusive. Here we propose a novel approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with artificial neural networks (ANNs) to discriminate between mass spectral profiles associated with chronic neuropathic pain induced by SCI in female mice. Functional evaluations revealed persistent chronic neuropathic pain following mild SCI as well as minor locomotor disruptions, confirming the value of collecting serum samples. Mass spectra analysis revealed distinct profiles between chronic SCI and sham controls. On applying ANNs, 100% success was achieved in distinguishing between the two groups through the intensities of m/z peaks. Additionally, the ANNs also successfully discriminated between chronic and acute SCI phases. When reflexive pain response data was integrated with mass spectra, there was no improvement in the classification. These findings offer insights into neuropathic pain pathophysiology and underscore the potential of MALDI-TOF MS coupled with ANNs as a diagnostic tool for chronic neuropathic pain, potentially guiding attempts to discover biomarkers and develop treatments.

• MeSH
• biologické markery krev   MeSH
• chronická bolest krev   diagnóza   etiologie   MeSH
• myši inbrední C57BL MeSH
• myši MeSH
• neuralgie * krev   diagnóza   etiologie   MeSH
• neuronové sítě * MeSH
• poranění míchy * komplikace   krev   MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice * metody   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep spindles of σ-band activity (12-15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and hyperpolarization-activated cation-nonselective currents, and for different parameter regimes of the cortical node. The latter are listed as follows: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically the strongest during the UP state, which follows a DOWN state "window of opportunity" for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.

• Publikační typ
• časopisecké články MeSH

Neural stem cells (NSC) capable of differentiating into neurons, astrocytes and oligodendrocytes are a promising source of cells for the treatment of central nervous system diseases. Access to signaling proteins present in such cells in low copies and with specific regulatory functions has been very restrictive until now as judged by classical proteomic approaches and limitations due to scarcity of stem cell populations. Hence, we utilized the Kinex Antibody Microarray analysis where profiles of the proliferating porcine NSC and differentiated counterparts were compared and selected changes were verified by immunoblotting. Differentiated neural cells exhibited an increased level of RafB proto-oncogene-encoded protein-serine kinase, MAP kinase protein-serine kinase 3, heme oxygenase 2 (HO2) and protein phosphatase 4 catalytical subunit. On the other hand, relatively high level of G protein-coupled receptor-serine kinase 2 and enhanced phosphorylations of alphaB-crystallin (S45), protein-serine kinase C gamma (T655), protein-serine kinase D (PKCmu; S738+S742) together with eukaryotic translation initiation factor 2 alpha (eIF2alpha) (S51) raised intriguing questions as regards their potential functionality within stem cells. In-depth study of HO2 and phospho-S45 alphaB-crystallin confirmed expression profiles and intense cytoplasmic localization of HO2 in neurons but a weaker signal in glial cells. Phospho-S45 alphaB-crystallin was localized in nuclei of differentiated neural cells. Computer simulation of possible interaction network connecting regulated proteins, exposed additional relationships including direct interactions of HO2 with amyloid precursor protein or huntingtin-associated protein 1.

• MeSH
• alfa-krystaliny - řetězec B metabolismus   MeSH
• biologické markery metabolismus   MeSH
• buněčná diferenciace MeSH
• buněčné jádro metabolismus   MeSH
• financování organizované MeSH
• fosforylace MeSH
• hemová oxygenasa (decyklizující) metabolismus   MeSH
• imunoblotting MeSH
• kmenové buňky fyziologie   MeSH
• kultivované buňky MeSH
• mapování interakce mezi proteiny MeSH
• neurony fyziologie   MeSH
• počítačová simulace MeSH
• prasata MeSH
• proteiny fyziologie   metabolismus   MeSH
• protilátky MeSH
• signální transdukce MeSH
• tretinoin farmakologie   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH