scholarly journals Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin G. Frasch ◽  
Bernd Walter ◽  
Christophe L. Herry ◽  
Reinhard Bauer
Keyword(s):  
Brain Injuries ◽  
Experimental Conditions ◽  
Data Set ◽  
High Frequency Oscillations ◽  
Full Band ◽  
Technical Validation ◽  
Monitoring Technologies ◽  
Neurological Injuries ◽  
Electrocardiogram Ecg ◽  
Electrical Activities

AbstractIschemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive electroencephalogram (EEG) biomarkers of corticothalamic communication under normal, sedation, and hypoxic/ischemic conditions with ensuing recovery. We provide technical validation using EEGLAB. We also delineate the corresponding changes in the electrocardiogram (ECG)-derived heart rate variability (HRV) with the potential for future in-depth analyses of joint EEG-ECG dynamics. We review an open-source methodology to derive signatures of coupling between the ECoG and electrothalamogram (EThG) signals contained in the presented data set to better characterize the dynamics of thalamocortical communication during these clinically relevant states. The data set is presented in full band sampled at 2000 Hz, so the additional potential exists for insights from the full-band EEG and high-frequency oscillations under the bespoke experimental conditions. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

scholarly journals Update to the dataset of cerebral ischemia in juvenile pigs with evoked potentials

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin G. Frasch ◽  
Bernd Walter ◽  
Christoph Anders ◽  
Reinhard Bauer
Keyword(s):  
Evoked Potentials ◽  
Data Set ◽  
Maxillary Nerve ◽  
High Frequency Oscillations ◽  
Full Band ◽  
Technical Validation ◽  
Monitoring Technologies ◽  
Neurological Injuries ◽  
Electroencephalogram Eeg ◽  
Electrical Activities

AbstractWe expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data set includes three stimulation paradigms: auditory (AEP, 40 and 2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG) biomarkers of corticothalamic communication under these conditions. The data set is presented in full band sampled at 2000 Hz. We provide technical validation of the evoked responses for the states of sedation, ischemia and recovery. This extended data set now permits mutual inferences between spontaneous and evoked activities across the recorded modalities. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

scholarly journals Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

2020 ◽  
Author(s):  
Casey L. Trevino ◽  
Jack J. Lin ◽  
Indranil Sen-Gupta ◽  
Beth A. Lopour
Keyword(s):  
High Frequency ◽  
Classification Accuracy ◽  
Patient Specific ◽  
Seizure Onset ◽  
Data Set ◽  
Localization Accuracy ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Wide Range ◽  
The Impact

AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.

Seismic resolution enhancement in shale-oil reservoirs

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. B281-B287 ◽  
Author(s):  
Xiwu Liu ◽  
Fengxia Gao ◽  
Yuanyin Zhang ◽  
Ying Rao ◽  
Yanghua Wang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Oil Reservoirs ◽  
Inversion Method ◽  
Shale Oil ◽  
Data Set ◽  
Extension Method ◽  
Seismic Resolution ◽  
Full Band ◽  
Shale Oil Reservoirs

We developed a case study of seismic resolution enhancement for shale-oil reservoirs in the Q Depression, China, featured by rhythmic bedding. We proposed an innovative method for resolution enhancement, called the full-band extension method. We implemented this method in three consecutive steps: wavelet extraction, filter construction, and data filtering. First, we extracted a constant-phase wavelet from the entire seismic data set. Then, we constructed the full-band extension filter in the frequency domain using the least-squares inversion method. Finally, we applied the band extension filter to the entire seismic data set. We determined that this full-band extension method, with a stretched frequency band from 7–70 to 2–90 Hz, may significantly enhance 3D seismic resolution and distinguish reflection events of rhythmite groups in shale-oil reservoirs.

scholarly journals Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 967 ◽  
Author(s):  
Ting-Li Han ◽  
Yang Yang ◽  
Hua Zhang ◽  
Kai P. Law
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Epidemiological Study ◽  
Internal Standard ◽  
Analytical Techniques ◽  
Experimental Conditions ◽  
Data Set ◽  
Raw Data ◽  
Model Based ◽  
Critical Issues

Background: A challenge of metabolomics is data processing the enormous amount of information generated by sophisticated analytical techniques. The raw data of an untargeted metabolomic experiment are composited with unwanted biological and technical variations that confound the biological variations of interest. The art of data normalisation to offset these variations and/or eliminate experimental or biological biases has made significant progress recently. However, published comparative studies are often biased or have omissions. Methods: We investigated the issues with our own data set, using five different representative methods of internal standard-based, model-based, and pooled quality control-based approaches, and examined the performance of these methods against each other in an epidemiological study of gestational diabetes using plasma. Results: Our results demonstrated that the quality control-based approaches gave the highest data precision in all methods tested, and would be the method of choice for controlled experimental conditions. But for our epidemiological study, the model-based approaches were able to classify the clinical groups more effectively than the quality control-based approaches because of their ability to minimise not only technical variations, but also biological biases from the raw data. Conclusions: We suggest that metabolomic researchers should optimise and justify the method they have chosen for their experimental condition in order to obtain an optimal biological outcome.

scholarly journals OPTIMIZATION OF RABBIT VENTRICULAR ELECTROPHYSIOLOGICAL MODEL AND SIMULATION OF SYNTHETIC ELECTROCARDIOGRAM

2021 ◽  
pp. 2150001
Author(s):  
HONGLEI ZHU ◽  
LIAN JIN ◽  
JIAYU ZHANG ◽  
XIAOMEI WU
Keyword(s):  
Normal State ◽  
Simulation Method ◽  
The Real ◽  
Model And Simulation ◽  
Network Simulations ◽  
Qrs Duration ◽  
Right Ventricular Apical Pacing ◽  
Cardiac Excitation ◽  
Electrocardiogram Ecg ◽  
Electrical Activities

This study aimed to use computer simulation method to study the mechanism of cardiac electrical activities. We optimized an electrophysiological rabbit ventricular model, including myocardial segmentation, heterogeneity and a realistic His-Purkinje network. Simulations of normal state, several types of ventricular premature contractions (VPC), conduction system pacing and right ventricular apical pacing were performed and the detailed cardiac electrical activities were studied from cell level to electrocardiogram (ECG) level. A detailed multiscale optimized ventricular model was obtained. The model effectively simulated various types of electrical activities. The synthetic ECG results were very similar to the real clinical ECG. The duration of QRS of typical VPC is 58[Formula: see text]ms, 71% longer than that of a normal-state synthetic QRS and the amplitude of the QRS is 35% larger, while the QRS duration and amplitude of the real clinical ECG of typical VPC are 69% longer and 36% larger than those of the real normal QRS. The duration of QRS of ventricular fusion beat is 31[Formula: see text]ms, 91% of that of a normal-state synthetic QRS and the amplitude of the QRS is 36% larger, while the QRS duration of the real clinical ECG of a ventricular fusion beat is 92% of the real normal QRS and the amplitude is 37% larger. Therefore, the results indicate that this model is effective and reliable in studying the detailed process of cardiac excitation and pacing.

scholarly journals APPLICATION OF BAYESIAN TECHNIQUE FOR PARAMETER ESTIMATION IN A FERMENTATIVE PROCESS

2020 ◽  
Vol 19 (1) ◽  
pp. 105
Author(s):  
C. A. Azevedo ◽  
C. T. Falcón ◽  
D. C. Estumano
Keyword(s):  
Parameter Estimation ◽  
Experimental Tests ◽  
Parameters Estimation ◽  
Operating Conditions ◽  
Biofuel Production ◽  
Experimental Conditions ◽  
Data Set ◽  
Operating Strategies ◽  
Log Normal ◽  
Fermentative Process

In the current world scenario, there has been noted an increase of researches on biofuel production, more specifically bioethanol, produced from biomass, in order to obtain more information to analyze, understand and optimize this fermentative process. The modelling process, which include the determination of a kinetic model and its respective parameters, is a fundamental step in defining operating strategies and understand how the experimental conditions can affect the optimal system operating conditions. The present work employs a bayesian technique to estimate the parameters of a classical kinectic model used by Silva and collaborators (2016), because, unlike the classical techniques, it is possible to take into account the uncertainty of the measurements and the prior knowledge of the parameters can be accounted for in probabilistic terms. In this context, by using simulated measurements, for the parameters estimation it is propose a sensitivity analysis of the parameters model to define the most relevant ones to be estimate and the use of the Monte Carlo Markov Chain method through the Metropolis-Hastings algorithm, evaluating the influence of four types of priori probability distribution of data set: uniform, gaussian, log-normal and Rayleigh. The obtained results showed that the sensibility analysis is an important step on parameter estimation and algorithm used was satisfactory in estimating the parameters of the kinectic model used, demonstrating the possibility of using it as a tool for time and cost reduction in experimental tests.

scholarly journals A minimum data set for traumatic brain injuries in Iran

2021 ◽  
Author(s):  
Maryam Edalatfar ◽  
Mohsen Sadeghi-Naini ◽  
Hamid Reza Khayat Kashani ◽  
Mitra Movahed ◽  
Mahdi Sharif-Alhoseini
Keyword(s):  
Brain Injuries ◽  
Traumatic Brain Injuries ◽  
Minimum Data Set ◽  
Data Set ◽  
Minimum Data

scholarly journals Dissecting random and systematic differences between noisy composite data sets

2017 ◽  
Vol 73 (4) ◽  
pp. 286-293 ◽  
Author(s):  
Kay Diederichs
Keyword(s):  
Correlation Coefficient ◽  
Unit Sphere ◽  
Dimensional Space ◽  
Scaling Analysis ◽  
Data Sets ◽  
Random Errors ◽  
Experimental Conditions ◽  
Data Set ◽  
Pairwise Correlation ◽  
Composite Data

Composite data sets measured on different objects are usually affected by random errors, but may also be influenced by systematic (genuine) differences in the objects themselves, or the experimental conditions. If the individual measurements forming each data set are quantitative and approximately normally distributed, a correlation coefficient is often used to compare data sets. However, the relations between data sets are not obvious from the matrix of pairwise correlations since the numerical value of the correlation coefficient is lowered by both random and systematic differences between the data sets. This work presents a multidimensional scaling analysis of the pairwise correlation coefficients which places data sets into a unit sphere within low-dimensional space, at a position given by their CC* values [as defined by Karplus & Diederichs (2012),Science,336, 1030–1033] in the radial direction and by their systematic differences in one or more angular directions. This dimensionality reduction can not only be used for classification purposes, but also to derive data-set relations on a continuous scale. Projecting the arrangement of data sets onto the subspace spanned by systematic differences (the surface of a unit sphere) allows, irrespective of the random-error levels, the identification of clusters of closely related data sets. The method gains power with increasing numbers of data sets. It is illustrated with an example from low signal-to-noise ratio image processing, and an application in macromolecular crystallography is shown, but the approach is completely general and thus should be widely applicable.

scholarly journals An in vitro toolbox to accelerate anti-malarial drug discovery and development

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Susan A. Charman ◽  
Alice Andreu ◽  
Helena Barker ◽  
Scott Blundell ◽  
Anna Campbell ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Human Liver ◽  
Liver Microsomes ◽  
Intrinsic Clearance ◽  
Target Product ◽  
Experimental Conditions ◽  
Data Set ◽  
Vitro Data ◽  
In Vitro Data

Abstract Background Modelling and simulation are being increasingly utilized to support the discovery and development of new anti-malarial drugs. These approaches require reliable in vitro data for physicochemical properties, permeability, binding, intrinsic clearance and cytochrome P450 inhibition. This work was conducted to generate an in vitro data toolbox using standardized methods for a set of 45 anti-malarial drugs and to assess changes in physicochemical properties in relation to changing target product and candidate profiles. Methods Ionization constants were determined by potentiometric titration and partition coefficients were measured using a shake-flask method. Solubility was assessed in biorelevant media and permeability coefficients and efflux ratios were determined using Caco-2 cell monolayers. Binding to plasma and media proteins was measured using either ultracentrifugation or rapid equilibrium dialysis. Metabolic stability and cytochrome P450 inhibition were assessed using human liver microsomes. Sample analysis was conducted by LC–MS/MS. Results Both solubility and fraction unbound decreased, and permeability and unbound intrinsic clearance increased, with increasing Log D7.4. In general, development compounds were somewhat more lipophilic than legacy drugs. For many compounds, permeability and protein binding were challenging to assess and both required the use of experimental conditions that minimized the impact of non-specific binding. Intrinsic clearance in human liver microsomes was varied across the data set and several compounds exhibited no measurable substrate loss under the conditions used. Inhibition of cytochrome P450 enzymes was minimal for most compounds. Conclusions This is the first data set to describe in vitro properties for 45 legacy and development anti-malarial drugs. The studies identified several practical methodological issues common to many of the more lipophilic compounds and highlighted areas which require more work to customize experimental conditions for compounds being designed to meet the new target product profiles. The dataset will be a valuable tool for malaria researchers aiming to develop PBPK models for the prediction of human PK properties and/or drug–drug interactions. Furthermore, generation of this comprehensive data set within a single laboratory allows direct comparison of properties across a large dataset and evaluation of changing property trends that have occurred over time with changing target product and candidate profiles.

Embedded System for Heart Disease Recognition using Fuzzy Clustering and Correlation

2012 ◽  
pp. 327-350
Author(s):  
Helton Hugo de Carvalho Júnior ◽  
Robson Luiz Moreno ◽  
Tales Cleber Pimenta
Keyword(s):  
Heart Disease ◽  
Embedded System ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Heart Diseases ◽  
Significant Loss ◽  
Data Set ◽  
Viability Analysis ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

This chapter presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram – ECG signal processing by reducing the amount of data samples without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson-Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicates common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database – EDB as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan®-3A FPGA. The FPGA implemented a Xilinx Microblaze® Soft-Core Processor running at a 50 MHz clock rate.

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