Combined Plasma DHA-Containing Phosphatidylcholine PCaa C38:6 and Tetradecanoyl-Carnitine as an Early Biomarker for Assessing the Mortality Risk among Sarcopenic Patients.

The coming of the hyper-aged society in Taiwan prompts us to investigate the relationship between the metabolic status of sarcopenic patients and their most adverse outcome-death. We studied the association between any plasma metabolites and the risk for mortality among older Taiwanese sarcopenic patients. We applied a targeted metabolomic approach to study the plasma metabolites of adults aged ≥65 years, and identified the metabolic signature predictive of the mortality of sarcopenic patients who died within a 5.5-year follow-up period. Thirty-five sarcopenic patients who died within the follow-up period (Dead cohort) had shown a specific plasma metabolic signature, as compared with 54 patients who were alive (Alive cohort). Only 10 of 116 non-sarcopenic individuals died during the same period. After multivariable adjustment, we found that sex, hypertension, tetradecanoyl-carnitine (C14-carnitine), and docosahexaenoic acid (DHA)-containing phosphatidylcholine diacyl (PCaa) C38:6 and C40:6 were important risk factors for the mortality of sarcopenic patients. Low PCaa C38:6 levels and high C14-carnitine levels correlated with an increased mortality risk; this was even the same for those patients with hypertension (HTN). Our findings suggest that plasma PCaa C38:6 and acylcarnitine C14-carnitine, when combined, can be a better early biomarker for evaluating the mortality risk of sarcopenia patients.

A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network.

The heart is one of the human body's vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to design and realize an artificial intelligence (AI)-based abnormal heart beat detection with applications for early detection and timely treatment for heart diseases. A convolutional neural network (CNN) was employed to achieve a fast and accurate identification. In order to meet the requirements of the modularity and scalability of the circuit, modular and efficient processing element (PE) units and activation function modules were designed. The proposed CNN was implemented using a TSMC 0.18 µm CMOS technology and had an operating frequency of 60 MHz with chip area of 1.42 mm2 and maximum power dissipation of 4.4 mW. Furthermore, six types of ECG signals drawn from the MIT-BIH arrhythmia database were used for performance evaluation. Results produced by the proposed hardware showed that the discrimination rate was 96.3% with high efficiency in calculation, suggesting that it may be suitable for wearable devices in healthcare.

A VLSI Implementation of Independent Component Analysis for Biomedical Signal Separation Using CORDIC Engine.

This study aims to design and implement a very large scale integration (VLSI) chip of the extend InfoMax independent component analysis (ICA) algorithm which can separate the super-Gaussian source signals. In order to substantially reduce the circuit area, the proposed circuit utilizes the time sharing matrix multiplication array (MMA) to realize a series of matrix multiplication operations and employs the coordinate rotation digital computer (CORDIC) algorithm to calculate the hyperbolic functions sinh(θ) and cosh(θ) with the rotation of the hyperbolic coordinate system. Also, the rotation of the linear coordinate system of the CORDIC is adopted for the design of a divider used for obtaining the required function value of tanh(θ) simply by evaluating sinh(θ)/cosh(θ). Implemented in a TSMC 90-nm CMOS technology, the proposed ICA has an operation frequency of 100 MHz with 90.8 K gate counts. Furthermore, the measurement results show the ICA core can be successfully applied to separating mixed medical signals into independent sources.

An optimal effective working height for hammering tasks.

BACKGROUND: Hammering is a functional task in which the wrist generally follows a path of motion, however, previous studies paid relatively little attention to it. OBJECTIVE: The main objective of this study is to determine the optimal working height while using a straight-handled hammer to perform the hammering task. METHODS: Ten participants performed the hammering tasks on three different working heights (64, 80, and 96 cm) using three subjective hammering forces (light, medium, and heavy). RESULTS: The results revealed that there were linear increasing trends of shoulder abduction, which increased with the increased working height when hammering and the trunk flexion revealed a contrary result. However, the ulnar deviation and trunk lateral bending were insensitive to the working heights. The hammering forces merely resulted in trunk flexion and lateral bending and no posture differences at upper extremities. CONCLUSION: The trade-off between acceptable trunk flexion and shoulder abduction was considered to determine a reasonable effective working height (by measuring downward from worker's elbow height) in the range from 25 cm to 35 cm with a mean 30 cm. This range will be valuable for hammering job design.

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation.

Compressed sensing (CS) is a promising approach to the compression and reconstruction of electrocardiogram (ECG) signals. It has been shown that following reconstruction, most of the changes between the original and reconstructed signals are distributed in the Q, R, and S waves (QRS) region. Furthermore, any increase in the compression ratio tends to increase the magnitude of the change. This paper presents a novel approach integrating the near-precise compressed (NPC) and CS algorithms. The simulation results presented notable improvements in signal-to-noise ratio (SNR) and compression ratio (CR). The efficacy of this approach was verified by fabricating a highly efficient low-cost chip using the Taiwan Semiconductor Manufacturing Company's (TSMC) 0.18-µm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The proposed core has an operating frequency of 60 MHz and gate counts of 2.69 K.

Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

Target locations in visual field and character recognition by students of Chinese.

The potential influence of target location in a visual field on search should be considered in layouts of control panels and advertisements. This investigation was done to verify the assumption that the upper-left portion of a page or its equivalent naturally attracts the attention of the viewer. Exp. 1 used a tachistoscope to test which of eight Chinese characters first attracted the attention of viewers. The eight Chinese characters are arranged in a square and a circular configuration. In the square layout, a large square (18 cm x 18 cm) was first conceptually subdivided into nine equal parts (6 cm x 6 cm). Then, the eight Chinese characters were put in the center of each part, leaving the central part blank. In the circular layout, the same Chinese characters were symmetrically placed on the conceptual circumference (r = 6 cm) of a circle within a large square. Exp. 2 was a paper-and-pencil test. An embedded-fault-character-search was used to examine the location of the first faulty character discovered by the subjects. 60 college students and 36 schoolchildren were selected as subjects for the tachistoscopic experiment and paper-and-pencil test. Finally, five graduate students participated in Exp. 3 in which an eye camera registered subjects' eye movements to measured distribution of durations of looking over eight locations. The measurements indicated a slight predominance of the upper-left portion for college students and graduate students, and a slight predominance of the upper-right portion for schoolchildren.