Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering

Graphene on Au-coated SiOx substrate: Its core-level photoelectron micro-spectroscopy study

The core-level electronic structures of the exfoliated graphene sheets on a Au-coated SiOx substrate have been studied by synchrotron radiation photoelectron spectroscopy (SR-PES) on a micron-scale. The graphene was firstly demonstrated its visibility on the Au-coated SiOx substrate by micro-optical characterization, and then conducted into SR-PES study. Because of the elimination of charging effect, precise C 1s core-level characterization clearly shows graphitic and contaminated carbon states of graphene. Different levels of Au-coating-induced p-type doping on single- and double-layer graphene sheets were also examined in the C 1s core-level shift. The Au-coated SiOx substrate can be treated as a simple but high-throughput platform for in situ studying graphene under further hybridization by PES

Intestinal Stricture in Crohn's Disease

Crohn's disease (CD) is a disease with chronic inflammation of unknown etiology involving any part of the gastrointestinal tract. The incidence and prevalence of CD are increasing recently in Asia. Half of the CD patients will have intestinal complications, such as strictures or fistulas, within 20 years after diagnosis. Twenty-five percentage of CD patients have had at least one small bowel stricture and 10% have had at least one colonic stricture and lead to significant complications. Most of these patients will require at least one surgery during their lifetime. Early diagnosis and evaluation with adequate managements for the patients can prevent disability and mortality of these patient. Here, we reviewed the current incidence of CD with stricture, the etiology of stricture, and how to diagnose and manage the stricture

Reduction of Monocyte Chemoattractant Protein-1 and Interleukin-8 Levels by Ticlopidine in TNF-α Stimulated Human Umbilical Vein Endothelial Cells

Atherosclerosis and its associated complications represent major causes of morbidity and mortality in the industrialized or Western countries. Monocyte chemoattractant protein-1 (MCP-1) is critical for the initiating and developing of atherosclerotic lesions. Interleukin-8 (IL-8), a CXC chemokine, stimulates neutrophil chemotaxis. Ticlopidine is one of the antiplatelet drugs used to prevent thrombus formation relevant to the pathophysiology of atherothrombosis. In this study, we found that ticlopidine dose-dependently decreased the mRNA and protein levels of TNF-α-stimulated MCP-1, IL-8, and vascular cell adhesion molecule-1 (VCAM-1) in human umbilical vein endothelial cells (HUVECs). Ticlopidine declined U937 cells adhesion and chemotaxis as compared to TNF-α stimulated alone. Furthermore, the inhibitory effects were neither due to decreased HUVEC viability, nor through NF-kB inhibition. These results suggest that ticlopidine decreased TNF-α induced MCP-1, IL-8, and VCAM-1 levels in HUVECs, and monocyte adhesion. Therefore, the data provide additional therapeutic machinery of ticlopidine in treatment and prevention of atherosclerosis

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0

System Verification and Runtime Monitoring with Multiple Weakly-Hard Constraints

A weakly-hard fault model can be captured by an (m,k) constraint, where 0≤ m ≤ k , meaning that there are at most m bad events (faults) among any k consecutive events. In this article, we use a weakly-hard fault model to constrain the occurrences of faults in system inputs. We develop approaches to verify properties for all possible values of (m,k) , where k is smaller than or equal to a given  K , in an exact and efficient manner. By verifying all possible values of (m,k) , we define weakly-hard requirements for the system environment and design a runtime monitor based on counting the number of faults in system inputs. If the system environment satisfies the weakly-hard requirements, then the satisfaction of desired properties is guaranteed; otherwise, the runtime monitor can notify the system to switch to a safe mode. This is especially essential for cyber-physical systems that need to provide guarantees with limited resources and the existence of faults. Experimental results with discrete second-order control, network routing, vehicle following, and lane changing demonstrate the generality and the efficiency of the proposed approaches. </jats:p

An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs)

In this work, an asynchronous multi-sensor micro control unit (MCU) core is proposed for wireless body sensor networks (WBSNs). It consists of asynchronous interfaces, a power management unit, a multi-sensor controller, a data encoder (DE), and an error correct coder (ECC). To improve the system performance and expansion abilities, the asynchronous interface is created for handshaking different clock domains between ADC and RF with MCU. To increase the use time of the WBSN system, a power management technique is developed for reducing power consumption. In addition, the multi-sensor controller is designed for detecting various biomedical signals. To prevent loss error from wireless transmission, use of an error correct coding technique is important in biomedical applications. The data encoder is added for lossless compression of various biomedical signals with a compression ratio of almost three. This design is successfully tested on a FPGA board. The VLSI architecture of this work contains 2.68-K gate counts and consumes power 496-μW at 133-MHz processing rate by using TSMC 0.13-μm CMOS process. Compared with the previous techniques, this work offers higher performance, more functions, and lower hardware cost than other micro controller designs