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Magnetoencephalography (MEG) and electroencephalography (EEG) are contemporary methods to investigate the function and organization of the brain. Simultaneously acquired MEG-EEG data are inherently multi-dimensional and exhibit coupling. This study uses a coupled tensor decomposition to extract the signal sources from MEG-EEG during intermittent photic stimulation (IPS). We employ the Coupled Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (C-SECSI). After comparing its performance with alternative methods using simulated benchmark data, we apply it to MEG-EEG recordings of 12 participants during IPS with fractions of the individual alpha frequency between 0.4 and 1.3. In the benchmark tests, C-SECSI is more accurate than SECSI and alternative methods, especially in ill-conditioned scenarios, e.g., involving collinear factors or noise sources with different variances. The component field-maps allow us to separate physiologically meaningful oscillations of visually evoked brain activity from background signals. The frequency signatures of the components identify either an entrainment to the respective stimulation frequency or its first harmonic, or an oscillation in the individual alpha band or theta band. In the group analysis of both, MEG and EEG data, we observe a reciprocal relationship between alpha and theta band oscillations. The coupled tensor decomposition using C-SECSI is a robust, powerful method for the extraction of physiologically meaningful sources from multidimensional biomedical data. Unsupervised signal source extraction is an essential solution for rendering advanced multi-modal signal acquisition technology accessible to clinical diagnostics, pre-surgical planning, and brain computer interface applications. [https://www.selleckchem.com/products/shr0302.html https://www.selleckchem.com/products/shr0302.html] Copyright © 2020 Naskovska, Lau, Korobkov, Haueisen and Haardt.Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (n = 20) and healthy controls (n = 20) collected during the Flanker Task, with 2800 samples for each group. We treat the data as in audio or image classification approaches, where deep networks have proven successful by exploiting invariances and compositional features in the data. The model reaches a classification accuracy of 88% ± 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network used for comparison, aon.Monozygotic twins are genetically identical but rarely phenotypically identical. Epigenetic and transcriptional variation could influence this phenotypic discordance. Investigation of intra-pair differences in molecular markers and a given phenotype in monozygotic twins controls most of the genetic contribution, enabling studies of the molecular features of the phenotype. This study aimed to identify genes associated with cognition in later life using integrated enrichment analyses of the results of blood-derived intra-pair epigenome-wide and transcriptome-wide association analyses of cognition in 452 middle-aged and old-aged monozygotic twins (56-80 years). Integrated analyses were performed with an unsupervised approach using KeyPathwayMiner, and a supervised approach using the KEGG and Reactome databases. The supervised approach identified several enriched gene sets, including "neuroactive ligand receptor interaction" (p-value = 1.62∗10-2), "Neurotrophin signaling" (p-value = 2.52∗10-3), "Alzheimer's disease" (p-value = 1.20∗10-2), and "long-term depression" (p-value = 1.62∗10-2). The unsupervised approach resulted in a 238 gene network, including the Alzheimer's disease gene APP (Amyloid Beta Precursor Protein) as an exception node, and several novel candidate genes. The strength of the unsupervised method is that it can reveal previously uncharacterized sub-pathways and detect interplay between biological processes, which remain undetected by the current supervised methods. In conclusion, this study identified several previously reported cognition genes and pathways and, additionally, puts forward novel candidates for further verification and validation. Copyright © 2020 Soerensen, Hozakowska-Roszkowska, Nygaard, Larsen, Schwämmle, Christensen, Christiansen and Tan.Hereditary hemochromatosis (HH) is an autosomal-recessive disorder of the iron metabolism. Patients are typically affected by dysregulated iron levels, which can lead to iron accumulation within essential organs, such as liver, heart and pancreas. Furthermore, many HH patients are also afflicted by several immune defects and increased occurrence of autoimmune diseases that are linked to human homeostatic iron regulator protein (HFE) in the immune response. Here we examined immune cell phenotype and function in 21 HH patients compared to 21 healthy controls with a focus on Natural Killer (NK) cells. We observed increased basal and stimulated production of pro-inflammatory cytokines such as IL-1β or IL-18 in HH patients compared to healthy controls. However, we did not find major changes in the phenotype, the amount or the cytotoxic function of NK cells in HH patients. Instead, our data show a general decrease in the total number of granulocytes in HH patients (2774 ± 958 per μl versus 3457 ± 1122 per μl in healthy controls). These data demonstrate that NK cells of HH patients are not significantly affected and that the patients' treatment by regular phlebotomy is sufficient to avoid systemic iron overload and its consequences to the immune system. Copyright © 2020 Bönnemann et al.This study was undertaken to determine whether exposure of operating room personnel to inhalation anesthetics, including nitrous oxide, isoflurane, and sevoflurane was associated with any hepatotoxic or nephrotoxic changes. Fifty-two operating room personnel and 52 non-exposed subjects were studied. A questionnaire pertaining to demographic characteristics and medical history of participants was completed. Fasting blood samples were taken from all subjects to measure the functional parameters of kidneys and liver. Biological monitoring was also performed to detect the urinary concentration of IAs. Urinary concentrations of nitrous oxide, isoflurane, and sevoflurane were found to be 175.8 ± 77.52, 4.95 ± 3.43, and 15.0 3± 16.06 ppm, respectively. The mean levels of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyltransferase, Alpha-glutathione-S-transferase, as well as the serum levels of kidney injury molecule-1, creatinine and calcium were significantly higher in the exposed group.