Changes in Power and Information Flow in Resting-state EEG by Working Memory Process

Many studies have analyzed working memory (WM) from electroencephalogram (EEG). However, little is known about changes in the brain neurodynamics among resting-state (RS) according to the WM process. Here, we identified frequency-specific power and information flow patterns among three RS EEG before and after WM encoding and WM retrieval. Our results demonstrated the difference in power and information flow among RS EEG in delta (1-3.5 Hz), alpha (8-13.5 Hz), and beta (14-29.5 Hz) bands. In particular, there was a marked increase in the alpha band after WM retrieval. In addition, we calculated the association between significant characteristics of RS EEG and WM performance, and interestingly, correlations were found only in the alpha band. These results suggest that RS EEG according to the WM process has a significant impact on the variability and WM performance of brain mechanisms in relation to cognitive function.Comment: Submitted to 2023 11th IEEE International Winter Conference on Brain-Computer Interfac

Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling

In this paper, we propose a Siamese sleep transformer (SST) that effectively extracts features from single-channel raw electroencephalogram signals for robust sleep stage scoring. Despite the significant advances in sleep stage scoring in the last few years, most of them mainly focused on the increment of model performance. However, other problems still exist: the bias of labels in datasets and the instability of model performance by repetitive training. To alleviate these problems, we propose the SST, a novel sleep stage scoring model with a selective batch sampling strategy and self-knowledge distillation. To evaluate how robust the model was to the bias of labels, we used different datasets for training and testing: the sleep heart health study and the Sleep-EDF datasets. In this condition, the SST showed competitive performance in sleep stage scoring. In addition, we demonstrated the effectiveness of the selective batch sampling strategy with a reduction of the standard deviation of performance by repetitive training. These results could show that SST extracted effective learning features against the bias of labels in datasets, and the selective batch sampling strategy worked for the model robustness in training.Comment: Submitted to 2023 11th IEEE International Winter Conference on Brain-Computer Interfac

Interfacial architecture for extra Li+ storage in all-solid-state lithium batteries

The performance of nanocomposite electrodes prepared by controlled ball-milling of TiS2 and a Li2S-P2S5 solid electrolyte (SE) for all-solid-state lithium batteries is investigated, focusing on the evolution of the microstructure. Compared to the manually mixed electrodes, the ball-milled electrodes exhibit abnormally increased first-charge capacities of 416 mA h g-1and 837 mA h g-1 in the voltage ranges 1.5-3.0 V and 1.0-3.0 V, respectively, at 50 mA g-1 and 30??C. The ball-milled electrodes also show excellent capacity retention of 95% in the 1.5-3.0 V range after 60 cycles as compared to the manually mixed electrodes. More importantly, a variety of characterization techniques show that the origin of the extra Li+ storage is associated with an amorphous Li-Ti-P-S phase formed during the controlled ball-milling process.open1

Reinforcing effects of methamphetamine in an animal model of Attention-Deficit/Hyperactivity Disorder-the Spontaneously Hypertensive Rat

Substrains of the Spontaneously Hypertensive rat (SHR), a putative animal model of Attention-Deficit/Hyperactivity Disorder (ADHD), have demonstrated increased sensitivity to many drugs of abuse, including psychostimulants. Therefore, it was suggested that studies in SHR may help elucidate ADHD and comorbidity with substance use disorder (SUD). However, the drug intake profile of the SHR in the most relevant animal model of drug addiction, the self-administration (SA) test, and its response on the conditioned place preference (CPP) paradigm are not yet determined. In the present study, we employed SA and CPP tests to investigate the reinforcing effects of the psychostimulant methamphetamine in an SHR substrain obtained from Charles River, Japan (SHR/NCrlCrlj). Concurrent tests were also performed in Wistar rats, the strain representing "normal" heterogeneous population. To address if the presence of ADHD behaviors further increases sensitivity to the rewarding effect of methamphetamine during adolescence, a critical period for the onset of drug abuse, CPP tests were especially conducted in adolescent Wistar and SHR/NCrlCrlj. We found that the SHR/NCrlCrlj also acquired methamphetamine SA and CPP, indicating reinforcing effects of methamphetamine in this ADHD animal model. However, we did not observe increased responsiveness of the SHR/NCrlCrlj to methamphetamine in both SA and CPP assays. This indicates that the reinforcing effects of methamphetamine may be similar in strains and that the SHR/NCrlCrlj may not adequately model ADHD and increased sensitivity to methamphetamine

Enhanced magnetic and thermoelectric properties in epitaxial polycrystalline SrRuO3 thin film

Transition metal oxide thin films show versatile electrical, magnetic, and thermal properties which can be tailored by deliberately introducing macroscopic grain boundaries via polycrystalline solids. In this study, we focus on the modification of the magnetic and thermal transport properties by fabricating single- and polycrystalline epitaxial SrRuO3 thin films using pulsed laser epitaxy. Using epitaxial stabilization technique with atomically flat polycrystalline SrTiO3 substrate, epitaxial polycrystalline SrRuO3 thin film with crystalline quality of each grain comparable to that of single-crystalline counterpart is realized. In particular, alleviated compressive strain near the grain boundaries due to coalescence is evidenced structurally, which induced enhancement of ferromagnetic ordering of the polycrystalline epitaxial thin film. The structural variations associated with the grain boundaries further reduce the thermal conductivity without deteriorating the electronic transport, and lead to enhanced thermoelectric efficiency in the epitaxial polycrystalline thin films, compared with their single-crystalline counterpart.Comment: 24 pages, 5 figure

Identification of protein functions using a machine-learning approach based on sequence-derived properties

<p>Abstract</p> <p>Background</p> <p>Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.</p> <p>Results</p> <p>A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.</p> <p>Conclusion</p> <p>We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new <it>PNPRD </it>features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.</p