The correlation between resting EEG power and nonattachment scale

E-Poster: no. 4453INTRODUCTION: Psychological activity is supported by the brain electronic activity, to some extent, recorded by electroencephalography (EEG) (Davidson, et al., 2000). Therefore, it is plausible that some psychological measurements could be correlated with EEG measurements. For example, previous studies have shown that patterns of the frontal and posterior alpha-wave can predict basic dimensions of personality, extraversion and neuroticism (Schmidtke and Heller, 2004). In this paper, we aimed to study the correlations between resting-state EEG and four popular psychological self-reported ...postprin

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical 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 is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Four conjectures in Nonlinear Analysis

In this chapter, I formulate four challenging conjectures in Nonlinear Analysis. More precisely: a conjecture on the Monge-Amp\`ere equation; a conjecture on an eigenvalue problem; a conjecture on a non-local problem; a conjecture on disconnectedness versus infinitely many solutions.Comment: arXiv admin note: text overlap with arXiv:1504.01010, arXiv:1409.5919, arXiv:1612.0819

Optimization of organics and nitrogen removal in intermittently aerated vertical flow constructed wetlands: Effects of aeration time and aeration rate

© 2016 Elsevier Ltd In this study, to optimize aeration for the enhancement of organics and nitrogen removal in intermittently aerated vertical flow constructed wetlands (VF CWs) for treating domestic wastewater, the experimental VF CWs were operated at different aeration time (1 h d−1, 2 h d−1, 4 h d−1, 6 h d−1, 8 h d−1 and 10 h d−1) and aeration rate (0.1 L min−1, 0.2 L min−1, 0.5 L min−1, 1.0 L min−1 and 2.0 L min−1) to investigate the effect of artificial aeration on the removal efficiency of organics and nitrogen. The results showed that the optimal aeration time and aeration rate were 4 h d−1 and 1.0 L min−1, which could create the appropriate aerobic and anoxic regions in CWs with the greater removal of COD (97.2%), NH4+-N (98.4%) and TN (90.6%) achieved simultaneously during the experiment. The results demonstrate that the optimized intermittent aeration is reliable option to enhance the treatment performance of organics and nitrogen at a lower operating cost

Intensified organics and nitrogen removal in the intermittent-aerated constructed wetland using a novel sludge-ceramsite as substrate

© 2016 Elsevier Ltd. In this study, a novel sludge-ceramsite was applied as main substrate in intermittent-aerated subsurface flow constructed wetlands (SSF CWs) for treating decentralized domestic wastewater, and intensified organics and nitrogen removal in different SSF CWs (with and without intermittent aeration, with and without sludge-ceramsite substrate) were evaluated. High removal of 97.2% COD, 98.9% NH4+-N and 85.8% TN were obtained simultaneously in the intermittent-aerated CW system using sludge-ceramsite substrate compared with non-aerated CWs. Moreover, results from fluorescence in situ hybridization (FISH) analysis revealed that the growth of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) in the intermittent-aerated CW system with sludge-ceramsite substrate was enhanced, thus indicating that the application of intermittent aeration and sludge-ceramsite plays an important role in nitrogen transformations. These results suggest that a combination of intermittent aeration and sludge-ceramsite substrate is reliable to enhance the treatment performance in SSF CWs

Strategies and techniques to enhance constructed wetland performance for sustainable wastewater treatment

© 2015, Springer-Verlag Berlin Heidelberg. Constructed wetlands (CWs) have been used as an alternative to conventional technologies for wastewater treatment for more than five decades. Recently, the use of various modified CWs to improve treatment performance has also been reported in the literature. However, the available knowledge on various CW technologies considering the intensified and reliable removal of pollutants is still limited. Hence, this paper aims to provide an overview of the current development of CW strategies and techniques for enhanced wastewater treatment. Basic information on configurations and characteristics of different innovations was summarized. Then, overall treatment performance of those systems and their shortcomings were further discussed. Lastly, future perspectives were also identified for specialists to design more effective and sustainable CWs. This information is used to inspire some novel intensifying methodologies, and benefit the successful applications of potential CW technologies

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

You turn me cold: evidence for temperature contagion

Introduction During social interactions, our own physiological responses influence those of others. Synchronization of physiological (and behavioural) responses can facilitate emotional understanding and group coherence through inter-subjectivity. Here we investigate if observing cues indicating a change in another's body temperature results in a corresponding temperature change in the observer. Methods Thirty-six healthy participants (age; 22.9±3.1 yrs) each observed, then rated, eight purpose-made videos (3 min duration) that depicted actors with either their right or left hand in visibly warm (warm videos) or cold water (cold videos). Four control videos with the actors' hand in front of the water were also shown. Temperature of participant observers' right and left hands was concurrently measured using a thermistor within a Wheatstone bridge with a theoretical temperature sensitivity of <0.0001°C. Temperature data were analysed in a repeated measures ANOVA (temperature × actor's hand × observer's hand). Results Participants rated the videos showing hands immersed in cold water as being significantly cooler than hands immersed in warm water, F(1,34) = 256.67, p0.1). There was however no evidence of left-right mirroring of these temperature effects p>0.1). Sensitivity to temperature contagion was also predicted by inter-individual differences in self-report empathy. Conclusions We illustrate physiological contagion of temperature in healthy individuals, suggesting that empathetic understanding for primary low-level physiological challenges (as well as more complex emotions) are grounded in somatic simulation

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

An improved parameter estimation and comparison for soft tissue constitutive models containing an exponential function

Motivated by the well-known result that stiffness of soft tissue is proportional to the stress, many of the constitutive laws for soft tissues contain an exponential function. In this work, we analyze properties of the exponential function and how it affects the estimation and comparison of elastic parameters for soft tissues. In particular, we find that as a consequence of the exponential function there are lines of high covariance in the elastic parameter space. As a result, one can have widely varying mechanical parameters defining the tissue stiffness but similar effective stress–strain responses. Drawing from elementary algebra, we propose simple changes in the norm and the parameter space, which significantly improve the convergence of parameter estimation and robustness in the presence of noise. More importantly, we demonstrate that these changes improve the conditioning of the problem and provide a more robust solution in the case of heterogeneous material by reducing the chances of getting trapped in a local minima. Based upon the new insight, we also propose a transformed parameter space which will allow for rational parameter comparison and avoid misleading conclusions regarding soft tissue mechanics