EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342)

Stabilized conforming nodal integration: Exactness and variational justification

In most Galerkin mesh-free methods, background integration cells partitioning the problem domain are required to evaluate the weak form. It is therefore worthwhile to consider these methods using the notions of domain decomposition with the integration cells being the subdomains. Presuming that the analytical solution is admissible in the trial solution, domain and boundary integration exactness, which depend on the orders of the employed trial solution and the required solution exactness, are identified for the strict satisfaction of traction reciprocity and natural boundary condition in the weak form. Unfortunately, trial solutions constructed by many mesh-free approximants contain non-polynomial terms which cannot be exactly integrated by Gaussian quadratures. Recently, stabilized conforming (SC) nodal integration for Galerkin mesh-free methods was proposed and illustrated to be linearly exact. This paper will discuss how linear exactness is ensured and how spurious oscillation encountered by direct nodal integration is suppressed in SC nodal integration from a domain decomposition point of view. Moreover, it will be shown that SC nodal integration can be formulated by the Hellinger-Reissner Principle and thus justified in the classical variational sense. Applications of the method to straight beam, plate and curved beam problems are presented. © 2004 Elsevier B.V. All rights reserved.postprin

Quinoidization of regioregular oligo(THIENO[3,4-b]THIOPHENE)s

Caracterización de oligotiofenosUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity.

Background:Neuroimaging studies have identified obesity-related differences in the brain's resting state activity. An imbalance between homeostatic and reward aspects of ingestive behaviour may contribute to obesity and food addiction. The interactions between early life adversity (ELA), the reward network and food addiction were investigated to identify obesity and sex-related differences, which may drive obesity and food addiction. Methods:Functional resting state magnetic resonance imaging was acquired in 186 participants (high body mass index [BMI]: ≥25: 53 women and 54 men; normal BMI: 18.50-24.99: 49 women and 30 men). Participants completed questionnaires to assess ELA (Early Traumatic Inventory) and food addiction (Yale Food Addiction Scale). A tripartite network analysis based on graph theory was used to investigate the interaction between ELA, brain connectivity and food addiction. Interactions were determined by computing Spearman rank correlations, thresholded at q < 0.05 corrected for multiple comparisons. Results:Participants with high BMI demonstrate an association between ELA and food addiction, with reward regions playing a role in this interaction. Among women with high BMI, increased ELA was associated with increased centrality of reward and emotion regulation regions. Men with high BMI showed associations between ELA and food addiction with somatosensory regions playing a role in this interaction. Conclusions:The findings suggest that ELA may alter brain networks, leading to increased vulnerability for food addiction and obesity later in life. These alterations are sex specific and involve brain regions influenced by dopaminergic or serotonergic signalling

Influence of cultural factors in dynamic trust in automation

The use of autonomous systems has been rapidly increasing in recent decades. To improve human-automation interaction, trust has been closely studied. Research shows trust is critical in the development of appropriate reliance on automation. To examine how trust mediates the human-automation relationships across cultures, the present study investigated the influences of cultural factors on trust in automation. Theoretically guided empirical studies were conducted in the U.S., Taiwan and Turkey to examine how cultural dynamics affect various aspects of trust in automation. The results found significant cultural differences in human trust attitude in automation

Epidemiology of acute primary angle-closure glaucoma in the Hong Kong Chinese population: prospective study.

OBJECTIVES: To determine the incidence of acute primary angle-closure glaucoma in the Hong Kong Chinese population, and to identify risk factors for this condition. DESIGN: Prospective study. SETTING: University teaching hospital, Hong Kong. PARTICIPANTS: Patients with acute primary angle-closure glaucoma presenting between 1 March 1998 and 29 February 2000. MAIN OUTCOME MEASURES: Demographic data, presenting symptoms and signs, temporal details of the presentation, and precipitating factors. The crude regional incidence was calculated according to the Hong Kong population census of 1991 and the age-specific incidence was calculated. RESULTS: Seventy-two cases (72 eyes of 72 patients) of acute primary angle-closure glaucoma were recruited. The crude incidence was 10.4 per 100,000 per year in the population aged 30 years and older. Patients at higher risk of attacks were those aged 70 years or older (age-specific incidence, 58.7 per 100,000 per year) and females, who had a relative risk of 3.8 compared with males (95% confidence interval, 1.7-8.4). Only four (5.6%) patients had a positive family history of acute primary angle-closure glaucoma. Seventeen (23.6%) patients were noted to have an upper respiratory tract infection before the attack, and 25 (34.7%) patients had taken antitussive agents. There was a statistically significant inverse correlation between the monthly attack rate and the monthly rate of influenza (Spearman's rank correlation coefficient = -0.388; P=0.031). CONCLUSION: There is a high incidence of acute primary angle-closure glaucoma among Chinese residents of Hong Kong, with elderly females at highest risk. A significant proportion of patients reported upper respiratory tract infection or the use of antitussive medication prior to attacks.published_or_final_versio