Error detection in motor behavior is a fundamental cognitive function heavily relying on local cortical information processing. Neural activity in the high-gamma frequency band (HGB) closely reflects such local cortical processing, but little is known about its role in error processing, particularly in the healthy human brain. Here we characterize the error-related response of the human brain based on data obtained with noninvasive EEG optimized for HGB mapping in 31 healthy subjects (15 females, 16 males), and additional intracranial EEG data from 9 epilepsy patients (4 females, 5 males). Our findings reveal a multiscale picture of the global and local dynamics of error-related HGB activity in the human brain. On the global level as reflected in the noninvasive EEG, the error-related response started with an early component dominated by anterior brain regions, followed by a shift to parietal regions, and a subsequent phase characterized by sustained parietal HGB activity. This phase lasted for more than 1 s after the error onset. On the local level reflected in the intracranial EEG, a cascade of both transient and sustained error-related responses involved an even more extended network, spanning beyond frontal and parietal regions to the insula and the hippocampus. HGB mapping appeared especially well suited to investigate late, sustained components of the error response, possibly linked to downstream functional stages such as error-related learning and behavioral adaptation. Our findings establish the basic spatio-temporal properties of HGB activity as a neural correlate of error processing, complementing traditional error-related potential studies.
- MeSH
- Adult MeSH
- Electroencephalography MeSH
- Electrocorticography MeSH
- Gamma Rhythm physiology MeSH
- Humans MeSH
- Brain Mapping methods MeSH
- Young Adult MeSH
- Brain physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Analytical devices that combine sensitive biological component with a physicochemical detector hold a great potential for various applications, e.g., environmental monitoring, food analysis or medical diagnostics. Continuous efforts to develop inexpensive sensitive biodevices for detecting target substances typically focus on the design of biorecognition elements and their physical implementation, while the methods for processing signals generated by such devices have received far less attention. Here, we present fundamental considerations related to signal processing in biosensor design and investigate how undemanding signal treatment facilitates calibration and operation of enzyme-based biodevices. Our signal treatment approach was thoroughly validated with two model systems: (i) a biodevice for detecting chemical warfare agents and environmental pollutants based on the activity of haloalkane dehalogenase, with the sensitive range for bis(2-chloroethyl) ether of 0.01-0.8 mM and (ii) a biodevice for detecting hazardous pesticides based on the activity of γ-hexachlorocyclohexane dehydrochlorinase with the sensitive range for γ-hexachlorocyclohexane of 0.01-0.3 mM. We demonstrate that the advanced signal processing based on curve fitting enables precise quantification of parameters important for sensitive operation of enzyme-based biodevices, including: (i) automated exclusion of signal regions with substantial noise, (ii) derivation of calibration curves with significantly reduced error, (iii) shortening of the detection time, and (iv) reliable extrapolation of the signal to the initial conditions. The presented simple signal curve fitting supports rational design of optimal system setup by explicit and flexible quantification of its properties and will find a broad use in the development of sensitive and robust biodevices.
- MeSH
- Biosensing Techniques methods MeSH
- Chemical Warfare Agents analysis MeSH
- Hydrocarbons, Chlorinated analysis MeSH
- Enzymes metabolism MeSH
- Ether analogs & derivatives analysis MeSH
- Hexanes analysis MeSH
- Hydrolases metabolism MeSH
- Calibration MeSH
- Environmental Pollutants analysis MeSH
- Lyases metabolism MeSH
- Signal Processing, Computer-Assisted * MeSH
- Sensitivity and Specificity MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.
Composite membranes containing molecular sieve particles embedded in a polyimide matrix are promising due to their increased permeability and high selectivity in gas separation processes. Determination of permeability of dense membranes is time-consuming and the resulting values are loaded with experimental errors. The impact of uncertainty in various quantities on the reliability of the permeability values measured by the constant volume/ variable pressure method was analyzed. The total uncertainty of the measurements on polyimide/Silicalite-1 membranes is 7–13 %, the errors in membrane thickness and permeation coefficient being the main contributing factors.
The monitoring of data from global positioning system (GPS) receivers and remote sensors of physiological and environmental data allow forming an information database for observed data processing. In this paper, we propose the use of such a database for the analysis of physical activities during cycling. The main idea of the proposed algorithm is to use cross-correlations between the heart rate and the altitude gradient to evaluate the delay between these variables and to study its time evolution. The data acquired during 22 identical cycling routes, each about 130 km long, include more than 6,700 segments of length 60 s recorded with varying sampling periods. General statistical and digital signal processing methods used include mathematical tools to reject gross errors, resampling using selected interpolation methods, digital filtering of noise signal components, and estimating cross-correlations between the position data and the physiological signals. The results of a regression between GPS and physiological data include the estimate of the time delay between the heart rate change and gradient altitude of about 7.5 s and its decrease during each training route.
- MeSH
- Algorithms MeSH
- Bicycling physiology MeSH
- Geographic Information Systems * MeSH
- Humans MeSH
- Signal Processing, Computer-Assisted * MeSH
- Regression Analysis MeSH
- Heart Rate physiology MeSH
- Telemetry methods MeSH
- Geography MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Organic acidurias are a large group of inherited metabolic disorders (IMDs), commonly diagnosed by GC-MS analysis of organic acids in urine after acidic extraction and trimethylsilylation. In this study, a GC×GC-ToF-MS method has been optimized for the analysis of pathological metabolites in urine. An automated data processing strategy based on the use of mass spectra and GC retention times for the target search and quantification of pathological metabolites has been developed. Using this procedure, each unknown sample is automatically examined for the presence of markers of several diseases at the same time. The method has been applied for the analysis of 6 challenging proficiency testing samples from patients with IMDs (thymidine phosphorylase deficiency, mevalonic aciduria, hawkinsinuria, aromatic l-amino acid decarboxylase deficiency, propionic acidemia and medium-chain acyl-CoA dehydrogenase deficiency). Using the GC×GC-ToF-MS method, we were able to determine complete sets of markers for all the IMDs. The quality of the mass spectral matches for the pathological markers was higher than 800 (out of 1000).
- MeSH
- Electronic Data Processing methods MeSH
- Humans MeSH
- Urine chemistry MeSH
- Gas Chromatography-Mass Spectrometry instrumentation methods MeSH
- Metabolism, Inborn Errors diagnosis urine MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
