Kuru

Kompoliti, Katie

doi:10.1016/b978-0-12-374105-9.00185-4

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…1 Electrophysiological signals can be produced from different body organs and tissues, for instance, electroencephalograms (EEG) produced from the brain, 2 electrocardiograms (ECG) produced from the heart, 3,4 and electromyograms (EMG) produced from muscle tissues. 5 The physiological status of these body organs and tissues can be reflected by the electrophysiological signals, 6,7 therefore, human biopotentials play a vital role in pathological disease diagnosis and human health monitoring. 8 Among them, ambulatory ECG directly controls myocardial conduction of the heart, consequently it can diagnose abnormal heart rhythms 9 and several cardiovascular diseases (CVDs) such as coronary artery disease (CAD), 10 which is the leading cause of human death around the world according to world health organization (WHO).…”

Section: Introductionmentioning

confidence: 99%

Robust, self-adhesive and anti-bacterial silk-based LIG electrodes for electrophysiological monitoring

Abd-Elbaki,

Ragab,

Ismael

et al. 2023

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Robust, self-adhesive and anti-bacterial silk-based LIG electrodes for electrophysiological monitoring

Abd-Elbaki,

Ragab,

Ismael

et al. 2023

RSC Adv.

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“…The development of a new numerical method consisting of a multilayer perceptron neural network, aiming to predict the electromagnetic absorption index at an identified frequency, is designed to surpass the above issues. This method has been chosen due to the large and different scale of input data as it is presented in Table 1 in materials and data collection section; MLP has been shown to approximate virtually any function to any desired accuracy; however, this is validated only if the number of training data in the series is sufficiently large, which acquire a good understanding for the MLP network to learn the required input-output relationship accurately [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Optimization of Electromagnetic Absorption of Polycarbonate/CNT Composites Using Machine Learning

et al. 2020

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Electronic devices that transmit, distribute, or utilize electrical energy create electromagnetic interference (EMI) that can lead to malfunctioning and degradation of electronic devices. EMI shielding materials block the unwanted electromagnetic waves from reaching the target material. EMI issues can be solved by using a new family of building blocks constituted of polymer and nanofillers. The electromagnetic absorption index of this material is calculated by measuring the “S-parameters”. In this article, we investigated the use of artificial intelligence (AI) in the EMI shielding field by developing a new system based on a multilayer perceptron neural network designed to predict the electromagnetic absorption of polycarbonate-carbon nanotubes composites films. The proposed system included 15 different multilayer perception (MLP) networks; each network was specialized to predict the absorption value of a specific category sample. The selection of appropriate networks was done automatically, using an independent block. Optimization of the hyper-parameters using hold-out validation was required to ensure the best results. To evaluate the performance of our system, we calculated the similarity error, precision accuracy, and calculation time. The results obtained over our database showed clearly that the system provided a very good result with an average accuracy of 99.7997%, with an overall average calculation time of 0.01295 s. The composite based on polycarbonate−5 wt.% carbon nanotube was found to be the ultimate absorber over microwave range according to Rozanov formalism.

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“…MDs are classified as monogenic (Mendelian) or multifactorial or polygenic (Complex). However, mutations in known MD genes account for only a small percentage of cases, indicating that more genes and higher-risk alleles await discovery (Kompoliti and Verhagen, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

The Use of Next-Generation Sequencing in Movement Disorders

Krebs¹,

Paisán-Ruı́z²

2012

Front. Gene.

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New advances in genomic technology are being introduced at a greater speed and are revolutionizing the field of genetics for both complex and Mendelian diseases. For instance, during the past few years, genome-wide association studies (GWAS) have identified a large number of significant associations between genomic loci and movement disorders such as Parkinson’s disease and progressive supranuclear palsy. GWAS are carried out through the use of high-throughput SNP genotyping arrays, which are also used to perform linkage analyses in families previously considered statistically underpowered for genetic analyses. In inherited movement disorders, using this latter technology, it has repeatedly been shown that mutations in a single gene can lead to different phenotypes, while the same clinical entity can be caused by mutations in different genes. This is being highlighted with the use of next-generation sequencing technologies and leads to the search for genes or genetic modifiers that contribute to the phenotypic expression of movement disorders. Establishing an accurate genome–epigenome–phenotype relationship is becoming a major challenge in the post-genomic research that should be facilitated through the implementation of both functional and cellular analyses. In this review, we summarize the latest genetic discoveries made by the use of NGS technologies and purpose future directions and challenges to truly understand the pathophysiology of MDs.

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Kuru

Cited by 4 publications

References 7 publications

Robust, self-adhesive and anti-bacterial silk-based LIG electrodes for electrophysiological monitoring

Robust, self-adhesive and anti-bacterial silk-based LIG electrodes for electrophysiological monitoring

Simulation and Optimization of Electromagnetic Absorption of Polycarbonate/CNT Composites Using Machine Learning

The Use of Next-Generation Sequencing in Movement Disorders

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