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- LAPS (4)
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Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.
Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives.
Data-driven prediction and prevention of extreme events in a spatially extended excitable system
(2015)
The stimulation and dominance of potentially harmful phytoplankton taxa at a given locale and time are determined by local environmental conditions as well as by transport to or from neighboring regions. The present study investigated the occurrence of common harmful algal bloom (HAB) taxa within the Southern California Bight, using cross-correlation functions to determine potential dependencies between HAB taxa and environmental factors, and potential links to algal transport via local hydrography and currents. A simulation study, in which Lagrangian particles were released, was used to assess travel times due to advection by prevailing ocean currents in the bight. Our results indicate that transport of some taxa may be an important mechanism for the expansion of their distributions into other regions, which was supported by mean travel times derived from our simulation study and other literature on ocean currents in the Southern California Bight. In other cases, however, phytoplankton dynamics were rather linked to local environmental conditions, including coastal upwelling events. Overall, our study shows that complex current patterns in the Southern California Bight may contribute significantly to the formation and expansion of HABs in addition to local environmental factors determining the spatiotemporal dynamics of phytoplankton blooms.
We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Prosthetic textile implants of different shapes, sizes and polymers are used to correct the apical prolapse after hysterectomy (removal of the uterus). The selection of the implant before or during minimally invasive surgery depends on the patient’s anatomical defect, intended function after reconstruction and most importantly the surgeon’s preference. Weakness or damage of the supporting tissues during childbirth, menopause or previous pelvic surgeries may put females in higher risk of prolapse. Numerical simulations of reconstructed pelvic floor with weakened tissues and organ supported by textile product models: DynaMesh®-PRS soft, DynaMesh®-PRP soft and DynaMesh®-CESA from FEG Textiletechnik mbH, Germany are compared.
The porosity of surgical meshes makes them flexible for large elastic deformation and establishes the healing conditions of good tissue in growth. The biomechanic modeling of orthotropic and compressible materials requires new materials models and simulstaneoaus fit of deformation in the load direction as well as trannsversely to to load. This nonlinear modeling can be achieved by an optical deformation measurement. At the same time the full field deformation measurement allows the dermination of the change of porosity with deformation. Also the socalled effective porosity, which has been defined to asses the tisssue interatcion with the mesh implants, can be determined from the global deformation of the surgical meshes.
In this work, a cell-based biosensor to evaluate the sterilization efficacy of hydrogen peroxide vapor sterilization processes is characterized. The transducer of the biosensor is based on interdigitated gold electrodes fabricated on an inert glass substrate. Impedance spectroscopy is applied to evaluate the sensor behavior and the alteration of test microorganisms due to the sterilization process. These alterations are related to changes in relative permittivity and electrical conductivity of the bacterial spores. Sensor measurements are conducted with and without bacterial spores (Bacillus atrophaeus), as well as after an industrial sterilization protocol. Equivalent two-dimensional numerical models based on finite element method of the periodic finger structures of the interdigitated gold electrodes are designed and validated using COMSOL® Multiphysics software by the application of known dielectric properties. The validated models are used to compute the electrical properties at different sensor states (blank, loaded with spores, and after sterilization). As a final result, we will derive and tabulate the frequency-dependent electrical parameters of the spore layer using a novel model that combines experimental data with numerical optimization techniques.
Field-effect-based electrolyte-insulator-semiconductor (EIS) sensors were modified with a bilayer of positively charged weak polyelectrolyte (poly(allylamine hydrochloride) (PAH)) and probe single-stranded DNA (ssDNA) and are used for the detection of complementary single-stranded target DNA (cDNA) in different test solutions. The sensing mechanism is based on the detection of the intrinsic molecular charge of target cDNA molecules after the hybridization event between cDNA and immobilized probe ssDNA. The test solutions contain synthetic cDNA oligonucleotides (with a sequence of tuberculosis mycobacteria genome) or PCR-amplified DNA (which origins from a template DNA strand that has been extracted from Mycobacterium avium paratuberculosis-spiked human sputum samples), respectively. Sensor responses up to 41 mV have been measured for the test solutions with DNA, while only small signals of ∼5 mV were detected for solutions without DNA. The lower detection limit of the EIS sensors was ∼0.3 nM, and the sensitivity was ∼7.2 mV/decade. Fluorescence experiments using SybrGreen I fluorescence dye support the electrochemical results.
In lab-on-chip systems, electrodes are important for the manipulation (e.g., cell stimulation, electrolysis) within such systems. An alternative to commonly used electrode structures can be a light-addressable electrode. Here, due to the photoelectric effect, the conducting area can be adjusted by modification of the illumination area which enables a flexible control of the electrode. In this work, titanium dioxide based light-addressable electrodes are fabricated by a sol–gel technique and a spin-coating process, to deposit a thin film on a fluorine-doped tin oxide glass. To characterize the fabricated electrodes, the thickness, and morphological structure are measured by a profilometer and a scanning electron microscope. For the electrochemical behavior, the dark current and the photocurrent are determined for various film thicknesses. For the spatial resolution behavior, the dependency of the photocurrent while changing the area of the illuminated area is studied. Furthermore, the addressing of single fluid compartments in a three-chamber system, which is added to the electrode, is demonstrated.
A chip-based amperometric biosensor referring on using the bioelectrocatalytical amplification principle for the detection of low adrenaline concentrations is presented. The adrenaline biosensor has been prepared by modification of a platinum thin-film electrode with an enzyme membrane containing the pyrroloquinoline quinone-dependent glucose dehydrogenase and glutaraldehyde. Measuring conditions such as temperature, pH value, and glucose concentration have been optimized to achieve a high sensitivity and a low detection limit of about 1 nM adrenaline measured in phosphate buffer at neutral pH value. The response of the biosensor to different catecholamines has also been proven. Long-term stability of the adrenaline biosensor has been studied over 10 days. In addition, the biosensor has been successfully applied for adrenaline detection in human blood plasma for future biomedical applications. Furthermore, preliminary experiments have been carried to detect the adrenaline-concentration difference measured in peripheral blood and adrenal venous blood, representing the adrenal vein sampling procedure of a physician.
A light-addressable potentiometric sensor (LAPS) is a field-effect-based potentiometric device, which detects concentration changes of an analyte solution on the sensor surface in a spatially resolved way. It uses a light source to generate electron–hole pairs inside the semiconductor, which are separated in the depletion region due to an applied bias voltage across the sensor structure and hence, a surface-potential-dependent photocurrent can be read out. However, depending on the beam angle of the light source, scattering effects can occur, which influence the recorded signal in LAPS-based differential measurements. To solve this problem, a novel illumination unit based on a field programmable gate array (FPGA) consisting of 16 small-sized tunable infrared laser-diode modules (LDMs) is developed. Due to the improved focus of the LDMs with a beam angle of only 2 mrad, undesirable scattering effects are minimized. Escherichia coli (E. coli) K12 bacteria are used as a test microorganism to study the extracellular acidification on the sensor surface. Furthermore, a salt bridge chamber is built up and integrated with the LAPS system enabling multi-chamber differential measurements with a single Ag/AgCl reference electrode.
Multi-analyte biosensors may offer the opportunity to perform cost-effective and rapid analysis with reduced sample volume, as compared to electrochemical biosensing of each analyte individually. This work describes the development of an enzyme-based biosensor system for multi-parametric determination of four different organic acids. The biosensor array comprises five working electrodes for simultaneous sensing of ethanol, formate, d-lactate, and l-lactate, and an integrated counter electrode. Storage stability of the biosensor was evaluated under different conditions (stored at +4 °C in buffer solution and dry at −21 °C, +4 °C, and room temperature) over a period of 140 days. After repeated and regular application, the individual sensing electrodes exhibited the best stability when stored at −21 °C. Furthermore, measurements in silage samples (maize and sugarcane silage) were conducted with the portable biosensor system. Comparison with a conventional photometric technique demonstrated successful employment for rapid monitoring of complex media.
A capacitive electrolyte-insulator-semiconductor (EIS) field-effect biosensor for acetoin detection has been presented for the first time. The EIS sensor consists of a layer structure of Al/p-Si/SiO₂/Ta₂O₅/enzyme acetoin reductase. The enzyme, also referred to as butane-2,3-diol dehydrogenase from B. clausii DSM 8716T, has been recently characterized. The enzyme catalyzes the (R)-specific reduction of racemic acetoin to (R,R)- and meso-butane-2,3-diol, respectively. Two different enzyme immobilization strategies (cross-linking by using glutaraldehyde and adsorption) have been studied. Typical biosensor parameters such as optimal pH working range, sensitivity, hysteresis, linear concentration range and long-term stability have been examined by means of constant-capacitance (ConCap) mode measurements. Furthermore, preliminary experiments have been successfully carried out for the detection of acetoin in diluted white wine samples.