Revealing the Electrophysiological Correlates of Working Memory-Load Effects in Symmetry Span Task With HHT Method

Complex span task is one of the commonly used cognitive tasks to evaluate an individual’s working memory capacity (WMC). It is a dual task consisting of a distractor subtask and a memory subtask. Though multiple studies have utilized complex span tasks, the electrophysiological correlates underlying the encoding and retrieval processes in working memory span task remain uninvestigated. One previous study that assessed electroencephalographic (EEG) measures utilizing complex span task found no significant difference between its working memory loads, a typical index observed in other working memory tasks (e.g., n-back task and digital span task). The following design constructs of the paradigm might have been the reason. (1) The fixed-time limit of the distractor subtask may have hindered the assessment of individual WMC precisely. (2) Employing a linear-system-favoring EEG data analysis method for a non-linear system such as the human brain. In the current study, the participants perform the Raven Advanced Progressive Matrices (RAMP) task on 1 day and the symmetry span (Sspan) task on the other. Prior to the formal Sspan task, the participants were instructed to judge 15 simple symmetry questions as quickly as possible. A participant-specific time-limit is chartered from these symmetry questions. The current study utilizes the Sspan task sequential to a distractor subtask. Instead of the fixed time-limit exercised in the previous study, the distractor subtask of the current study was equipped with the participant-specific time-limit obtained from the symmetry questions. This could provide a precise measure of individual WMC. This study investigates if the complex span task resonates EEG patterns similar to the other working memory tasks in terms of working memory-load by utilizing ensemble empirical mode decomposition (EEMD) of Hilbert-Huang transform (HHT). Prior expectations were to observe a decrement in the P300 component of event-related mode (ERM) and a decrement in the power of alpha and beta band frequency with increasing working memory-load. We observed a significantly higher P300 amplitude for the low-load condition compared to the high-load condition over the circumscribed brain network across F4 and C4 electrodes. Time–frequency analysis revealed a significant difference between the high- and low-load conditions at alpha and beta band over the frontal, central, and parietal channels. The results from our study demonstrate precise differences in EEG data pertaining to varied memory-load differences in the complex span task. Thus, assessing complex span tasks with the HHT-based analysis may aid in achieving a better signal to noise ratio and effect size for the results in working memory EEG studies

Lasing on nonlinear localized waves in curved geometry

The use of geometrical constraints opens many new perspectives in photonics and in fundamental studies of nonlinear waves. By implementing surface structures in vertical cavity surface emitting lasers as manifolds for curved space, we experimentally study the impacts of geometrical constraints on nonlinear wave localization. We observe localized waves pinned to the maximal curvature in an elliptical-ring, and confirm the reduction in the localization length of waves by measuring near and far field patterns, as well as the corresponding dispersion relation. Theoretically, analyses based on a dissipative model with a parabola curve give good agreement remarkably to experimental measurement on the transition from delocalized to localized waves. The introduction of curved geometry allows to control and design lasing modes in the nonlinear regime.Comment: 6 pages, 6 figure

On the Droplet Formation Process in Electrohydrodynamic Atomization

A novel method of using a secondary electrical field source to control both the spray mode and the droplet size in the electrohydrodynamic atomization process is presented. Size of particles fabricated using the electrohydrodynamic atomization process can also be controlled using the same method. To further understand the electrohydrodynamic atomization process and the effect of a secondary electrical field source, a Front Tracking/Finite Difference method was employed for the Computational Fluid Dynamic Simulation of the Electrohydrodynamic Atomization process. To take into account of the electrical stresses, the Maxwell Stress tensor was included in the Navier-Stokes equation. Special care was taken to accurately include a secondary electrical field source. The formation of the Taylor Cone, jet and liquid droplets was successfully simulated. The simulated results were compared to the experimental results and the comparison was found to be reasonable when empirically determined charge density on the surface of the liquid was used as a simulation input.Singapore-MIT Alliance (SMA

Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

<p>Abstract</p> <p>Background</p> <p>The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.</p> <p>Results</p> <p>Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified <it>DDX5 </it>as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, <it>DDX5 </it>and <it>GAPDH</it>. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using <it>DDX5 </it>and <it>GAPDH </it>as internal controls, respectively.</p> <p>Conclusion</p> <p>Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.</p

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer’s disease

AimsOur aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.MethodsA total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE &gt; 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE &lt; 25), AD2 (n = 35, CDR = 2, MMSE &lt; 16), and AD3 (n = 16, CDR = 3, MMSE &lt; 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.Results(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.ConclusionIntegrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage

A delta-doped quantum well system with additional modulation doping

A delta-doped quantum well with additional modulation doping may have potential applications. Utilizing such a hybrid system, it is possible to experimentally realize an extremely high two-dimensional electron gas (2DEG) density without suffering inter-electronic-subband scattering. In this article, the authors report on transport measurements on a delta-doped quantum well system with extra modulation doping. We have observed a 0-10 direct insulator-quantum Hall (I-QH) transition where the numbers 0 and 10 correspond to the insulator and Landau level filling factor ν = 10 QH state, respectively. In situ titled-magnetic field measurements reveal that the observed direct I-QH transition depends on the magnetic component perpendicular to the quantum well, and the electron system within this structure is 2D in nature. Furthermore, transport measurements on the 2DEG of this study show that carrier density, resistance and mobility are approximately temperature (T)-independent over a wide range of T. Such results could be an advantage for applications in T-insensitive devices