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A Newcastle disease virus (NDV)-vectored vaccine expressing clade 2.3.4.4b H5 Hemagglutinin was developed and assessed for efficacy against H5N1 highly pathogenic avian influenza (HPAI) in specific pathogen-free (SPF) chickens, broilers, and domestic ducks. In SPF chickens, the live recombinant NDV-vectored vaccine, rK148/22-H5, achieved complete survival against HPAI and NDV challenges and significantly reduced viral shedding. Notably, the live rK148/22-H5 vaccine conferred good clinical protection in broilers despite the presence of maternally derived antibodies. Good clinical protection was observed in domestic ducks, with decreased viral shedding. It demonstrated complete survival and reduced cloacal viral shedding when used as an inactivated vaccine from SPF chickens. The rK148/22-H5 vaccine is potentially a viable and supportive option for biosecurity measure, effectively protecting in chickens against the deadly clade 2.3.4.4b H5 HPAI and NDV infections. Furthermore, it aligns with the strategy of Differentiating Infected from Vaccinated Animals (DIVA). [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (Covid-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) became a pandemic, causing significant burden on public health worldwide. Although the timely development and production of mRNA and adenoviral vector vaccines against SARS-CoV-2 have been successful, issues still exist in vaccine platforms for wide use and production. With the potential for proliferative capability and heat stability, the Newcastle disease virus (NDV)-vectored vaccine is a highly economical and conceivable candidate for treating emerging diseases. In this study, a recombinant NDV-vectored vaccine expressing the spike (S) protein of SARS-CoV-2, rK148/beta-S, was developed and evaluated for its efficacy against SARS-CoV-2 in K18-hACE-2 transgenic mice. Intramuscular vaccination with low dose (106.0 EID 50) conferred a survival rate of 76 % after lethal challenge of a SARS-CoV-2 beta (B.1.351) variant. When administered with a high dose (107.0 EID 50), vaccinated mice exhibited 100 % survival rate and reduced lung viral load against both beta and delta variants (B.1.617.2). Together with the protective immunity, rK148/beta-S is an accessible and cost-effective SARS-CoV-2 vaccine. [ABSTRACT FROM AUTHOR]

This study presents a gait subphase recognition method using an electromyogram (EMG) with a signal graph matching (ESGM) algorithm. Existing pattern recognition and machine learning using EMG signals has several innate problems in gait subphase detection. With respect to time domain features, their feature values may be analogous because two different gait steps may have similar muscle activation. In addition, the current gait subphase might not be recognized until the next gait subphase passes because the window size needed for feature extraction is larger than the period of the gait subphase. The ESGM algorithm is a new approach that compares reference EMG signals and input EMG signals according to time variance to solve these problems and considers variations of physiological muscle activity. We also determined all the elements of the ESGM algorithm using kinematic gait analysis and optimized the algorithm using experiments. Therefore, the ESGM algorithm reflects better timing characteristics of EMG signals than the time domain feature extraction algorithm. In addition, it can provide real-time and user-adaptive recognition of the gait subphase by using only EMG signals. Experimental results show that the average accuracy of the proposed method is 13% better than existing methods and the average detection latency of the proposed method was 5.5 times lower than existing methods. [ABSTRACT FROM AUTHOR]

Abstract: Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP riu4) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP riu4 and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM). [ABSTRACT FROM AUTHOR]

Abstract: The prioritization of engineering characteristics (ECs) provides an important basis for decision-making in QFD. However, the prioritization results in the conventional QFD may be misleading since it does not consider the uncertainty of input information. This paper develops two robustness indices and proposes the notion of robust prioritization that ensures the EC prioritization to be robust against the uncertainty. The robustness indices consider robustness from two perspectives, namely, the absolute ranking of ECs and the priority relationship among ECs. Based on the two indices, robust prioritization seeks to identify a set of ECs or a priority relationship among ECs in such a way that the result of robust prioritization is stable despite the uncertainty. Finally, the proposed robustness indices and robust prioritization are demonstrated in a case study conducted on the ADSL-based high-speed internet service. [Copyright &y& Elsevier]

In this study, we propose a novel deep-learning architecture with sparse learning for human activity recognition. The proposed model contains 1D CNNs and LSTM layers with a self-attention mechanism to enhance a substantial number of time points in time-series data for human activity recognition systems. Based on the recent success of squeeze-and-excite (SE) networks, the proposed deep learning model utilizes the SE module to enhance channel-wise interdependencies, which in turn leads to a boost in performance. In addition, we utilized sparse learning to retrain only weak nodes and freeze stronger nodes in a fully connected layer prior to classification layer. Furthermore, we utilized an entropy-inspired formula to find sparsely located weaker nodes and validated our model on various datasets, including Opportunity, UCI-HAR, and WISDM. Herein, we present an extensive analysis and survey of state-of-the-art studies, in addition to our proposed research. For a fair comparison, we evaluated our deep learning architecture using various performance metrics and achieved better results; the proposed model outperformed state-of-the-art algorithms for human activity recognition. [ABSTRACT FROM AUTHOR]

• This paper proposes a new DOSbFC algorithm to remove baseline drift of EOG Signals. • This paper presents a new electrode positioning scheme based on eyeglasses. • This paper provides a long-term eye movement detection function with high accuracy. This paper presents a new method to remove baseline drift and noise by using a differential electrooculography (EOG) signal based on a fixation curve (DOSbFC) and a new electrode positioning scheme based on eyeglasses for user convenience. In addition, a desktop application and mobile applications to control the human–computer interface were implemented. Finally, we created experimental EOG eyeglasses and a new detection protocol using the proposed method for long-term step-by-step detection of eye movements and user comfort. The proposed DOSbFC calculates the difference values of accumulated EOG signals between the initial eye movement and fixation time. It allows long-term detection of eye movements with high accuracy and only requires a single calibration. The vertical and ground electrodes of the standard electrode positioning scheme caused discomfort of subjects; the proposed electrode positioning scheme solves these problems and enables the use of existing eyeglasses without design modification. The experimental results demonstrated that the average accuracy of the long-term eye movement detection was 94%, whereas those of the band pass filter and wavelet transform were 61% and 64%, respectively. This was because baseline drift and noise were removed by averaging the signal variations. Further experimental results demonstrated that the average information transfer rate of the proposed method was 6.0, whereas those of the band pass filter and wavelet transform were 1.1 and 0.9, respectively. [ABSTRACT FROM AUTHOR]

This study aims to propose a novel approach for gender recognition using best feature subset based on recursive feature elimination (RFE) in normal walking. This study has focused on the analysis of gait characteristics by distinguishing the gait phases as initial contact (IC), Mid-stance (MS), Pre-swing, and swing (SW), and collected the large number of gait to improve the reliability of quantitative assessment of natural variability associated with muscle activity during free walking. The gait system was designed using pressure and a tri-axis accelerometer sensor, and a 9-channel electromyography sensor for measuring the data. Gender recognition method was proposed using support vector machine (SVM) and random forest (RF) based on RFE to determine best feature subset. Statistical results show that effects of gender-based differences on gait characteristic including temporal, kinematics, and muscle activity were investigated. The temporal parameters of stride time and gait cycle (%) in the gait phases of IC, MS, and SW were significantly different between females and males (p < 0.01). The females exhibited both a lower angle and a root mean square acceleration of the knee joint as compared to the males, and there was a clear gender-based difference with respect to knee angle movement. In addition, most muscle activation measurements in the females were larger than those of the males with respect to the gait phases. Gender classification result shows that SVM-RFE was 99.11% (SVM classifier) and RF-RFE was 98.89% (SVM and RF classifier), having powerful performance. • The paper investigates the statistical effect of gender-based differences on gait. • The paper has focused to analysis gait characteristics in gait sub-phases. • A novel approach for gender classification is proposed using RFE. • The paper has the powerful performance for gender classification using SVMRFE. [ABSTRACT FROM AUTHOR]

Abstract: To evaluate the cardiac hypertrophy from chest radiograph images, radiologists usually examine the cardiothoracic ratio (frequently called CTR) which is a standard diagnostic index. The CTR is computed by the maximum transverse diameter of the heart shadow divided by the maximum transverse diameter of right and left lung boundaries. In this paper, we present a method to evaluate the cardiac hypertrophy by comparing the area of heart with that of lung, instead of the cardiothoracic ratio to get more desirable diagnostic results. We introduce a new index, a cardiothoracic area ratio (CTAR), which is computed by dividing the area of heart region by the area of lung region of specific interest. We first segment a chest region of interest in a radiograph image and then automatically compute the traditional CTR and the CTAR to evaluate the cardiac hypertrophy. And finally, we provide the visual presentation of those ratios on the chest radiograph image. The experimental results using a set of radiograph images show that the proposed method can be used effectively for determining the cardiac hypertrophy in a real-time diagnostic environment. It provides the higher discrimination power than the CTR to identify hypertrophied hearts by recognizing the heart enlargement. It also can be used together with the traditional CTR as a complementary measure when it is difficult to determine abnormalities by the CTR, reducing the rate of wrong diagnosis. [Copyright &y& Elsevier]

Abstract: The gradient descent algorithms like backpropagation (BP) or its variations on multi-layered feed-forward networks are widely used in many applications. However, the most serious problem associated with the BP is local minima problem. Especially, an exceeding number of hidden nodes make the corresponding network deepen the local minima problem. We propose an algorithm which shows stable performance on training despite of the large number of hidden nodes. This algorithm is called separate learning algorithm in which hidden-to-output and input-to-hidden separately trained. Simulations on some benchmark problems have been performed to demonstrate the validity of the proposed method. [Copyright &y& Elsevier]

• The proposed methods present a gait sub-phase detection using a sEMG and knee angle. • The proposed method try to solve the gait phase detecting and prediction problems that occur in the real-time process. • The method provides real-time detection of the gait subphase using EMG signals. This paper presents a gait sub-phase detection and prediction approach using surface electromyogram (sEMG) signals, pressure sensors, and the knee angle for a lower-limb power-assist robot. Pattern recognition and machine learning models using sEMG signals have several inherent problems for gait sub-phase detection. These problems are due to recognition delay, lack of consideration for the unique characteristics of sEMG signals based on the subject, and meaningless features. To solve these problems, we propose a new labeling technique based on the heel and toe, a muscle and feature selection, a user-adaptive classifier using a weighted voting technique to achieve gait sub-phase detection, and a gait sub-phase prediction technique using interpolation. Experimental results show that the average accuracies of the proposed labeling, the muscle and feature selection, and the user-adaptive classifier using weighted voting are 7%, 12%, and 17% better, respectively, than the existing methods using physical sensors. Results also show that the average prediction time of the proposed method is 80% faster than the existing methods. [ABSTRACT FROM AUTHOR]