Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface

Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary characteristics of EEG signals pose challenges for training neural networks to acquire appropriate knowledge. Inconsistent EEG signals resulting from these non-stationary characteristics can lead to poor performance. Therefore, it is crucial to investigate and address sample inconsistency to ensure robust performance in spontaneous BCIs. In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training. We present a two-stage dominance score estimation technique that compensates for performance degradation caused by sample inconsistencies. Our proposed method utilizes non-parametric estimation to infer sample inconsistency and assigns each sample a dominance score. This score is then aggregated with the loss function during training to modulate the impact of sample inconsistency. Furthermore, we design a curriculum learning approach that gradually increases the influence of inconsistent signals during training to improve overall performance. We evaluate our proposed method using public spontaneous BCI dataset. The experimental results confirm that our findings highlight the importance of addressing sample dominance for achieving robust performance in spontaneous BCIs.Comment: 5 pages, 2 figure

Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment

An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual imagery data for controlling the BCI-based robotic arm. Visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performs the task. We proposed a deep learning architecture to decode the visual imagery data using only two channels and also we investigated the combination of two EEG channels that has significant classification performance. When using the proposed method, the highest classification performance using two channels in the offline experiment was 0.661. Also, the highest success rate in the online experiment using two channels (AF3-Oz) was 0.78. Our results provide the possibility of controlling the BCI-based robotic arm using visual imagery data.Comment: 4 pages, 2 figures, 3 table

Cerebrospinal Fluid Biomarkers for the Diagnosis of Prodromal Alzheimer’s Disease in Amnestic Mild Cognitive Impairment

Background/Aims: Disease-modifying therapy for Alzheimer’s disease (AD) has led to a need for biomarkers to identify prodromal AD and very early stage of AD dementia. We aimed to identify the cutoff values of cerebrospinal fluid (CSF) biomarkers for detecting prodromal AD. Methods: We assessed 56 patients with amnestic mild cognitive impairment (aMCI) who underwent lumbar puncture. Additionally, 87 healthy elderly individuals and 34 patients with AD dementia served as controls. Positron emission tomography was performed using florbetaben as a probe. We analyzed the concentration of Aβ1–42, total tau protein (t-Tau), and tau protein phosphorylated at threonine 181 (p-Tau181) in CSF with INNOTEST enzyme-linked immunosorbent assay. Results: For the detection of prodromal AD in patients with aMCI, the cutoff values of CSF Aβ1–42, t-Tau, and p-Tau181 were 749.5 pg/mL, 225.6 pg/mL, and 43.5 pg/mL, respectively. To discriminate prodromal AD in patients with aMCI, the t-Tau/Aβ1–42 and ­p-Tau181/Aβ1–42 ratios defined cutoff values at 0.298 and 0.059, respectively. Conclusions: CSF biomarkers are very useful tools for the differential diagnosis of prodromal AD in aMCI patients. The concentration of CSF biomarkers is well correlated with the stages of the AD spectrum

Hydrolyzed Yeast Supplementation in Calf Starter Promotes Innate Immune Responses in Holstein Calves under Weaning Stress Condition

Weaned calves are susceptible to infectious diseases because of the stress and malnutrition that occurs during weaning. Therefore, the dairy industry requires effective feed additives to ameliorate stress responses and promote immunity. This study aimed to investigate the effects of hydrolyzed yeast (HY) supplementation on the growth performance, immune and stress parameters, and health status of calves after weaning. Eighteen Holstein calves were randomly assigned to two groups, either receiving a control calf starter or 0.2% HY calf starter from one week of age. All calves were weaned at six weeks of age as a stress challenge. The HY-fed calves had a significantly-higher body weight gain during the post-weaning period (kg/week) compared to the control. Cortisol levels at three days post-weaning (DPW) were significantly lower in the HY group than the control group. Calves fed HY had significantly-higher serum levels of tumor necrosis factor-α and interleukin-1β at one DPW. The HY-fed calves also had higher concentrations of the acute-phase proteins, haptoglobin, serum amyloid A, and transferrin at one DPW. In addition, the diarrhea severity in HY-fed calves was milder after weaning compared to the control group. Our results indicate that HY supplementation reduces stress responses and may promote innate immunity in newly-weaned calves

Sulforaphane controls TPA-induced MMP-9 expression through the NF-κB signaling pathway, but not AP-1, in MCF-7 breast cancer cells

Sulforaphane [1-isothiocyanato-4-(methylsulfinyl)-butane] is anisothiocyanate found in some cruciferous vegetables, especiallybroccoli. Sulforaphane has been shown to displayanti-cancer properties against various cancer cell lines. Matrixmetalloproteinase-9 (MMP-9), which degrades the extracellularmatrix (ECM), plays an important role in cancer cell invasion.In this study, we investigated the effect of sulforaphane on12-O-tetradecanoyl phorbol-13-acetate (TPA)-induced MMP-9expression and cell invasion in MCF-7 cells. TPA-inducedMMP-9 expression and cell invasion were decreased bysulforaphane treatment. TPA substantially increased NF-κB andAP-1 DNA binding activity. Pre-treatment with sulforaphaneinhibited TPA-stimulated NF-κB binding activity, but not AP-1binding activity. In addition, we found that sulforaphanesuppressed NF-κB activation, by inhibiting phosphorylation ofIκB in TPA-treated MCF-7 cells. In this study, we demonstratedthat the inhibition of TPA-induced MMP-9 expression and cellinvasion by sulforaphane was mediated by the suppression ofthe NF-κB pathway in MCF-7 cells. [BMB Reports 2013; 46(4):201-206

Expressed Sequence Tags Analysis and Design of Simple Sequence Repeats Markers from a Full-Length cDNA Library in Perilla frutescens (L.)

Perilla frutescens is valuable as a medicinal plant as well as a natural medicine and functional food. However, comparative genomics analyses of P. frutescens are limited due to a lack of gene annotations and characterization. A full-length cDNA library from P. frutescens leaves was constructed to identify functional gene clusters and probable EST-SSR markers via analysis of 1,056 expressed sequence tags. Unigene assembly was performed using basic local alignment search tool (BLAST) homology searches and annotated Gene Ontology (GO). A total of 18 simple sequence repeats (SSRs) were designed as primer pairs. This study is the first to report comparative genomics and EST-SSR markers from P. frutescens will help gene discovery and provide an important source for functional genomics and molecular genetic research in this interesting medicinal plant

Weave-pattern-dependent fabric piezoelectric pressure sensors based on polyvinylidene fluoride nanofibers electrospun with 50 nozzles

Wearable pressure sensors having versatile device structures have been extensively investigated to achieve high sensitivity under mechanical stimuli. Here, we introduce piezoelectric pressure sensors based on fabrics woven using polyvinylidene fluoride (PVDF) weft and polyethylene terephthalate (PET) warp yarns with different weave structures: 1/1 (plain), 2/2, and 3/3 weft rib patterns. The dependence of the pressure-sensing performance on the weave pattern is demonstrated with an actual large-scale fabric up to the similar to 2 m scale. An optimized pressure sensor with a 2/2 weft rib pattern produced a high sensitivity of 83 mV N-1, which was 245% higher than that of the 1/1 pattern. The detection performance of the optimal fabric was extensively evaluated with a variety of ambient input sources, such as pressing, bending, twisting, and crumpling, as well as various human motions. Further, a large all-fabric pressure sensor with arrayed touch pixel units demonstrated highly sensitive and stable sensing performance.N