The influence of human genetic variation on early transcriptional responses and protective immunity following immunization with Rotarix vaccine in infants in Ho Chi Minh City in Vietnam : a study protocol for an open single-arm interventional trial [awaiting peer review]

Background: Rotavirus (RoV) remains the leading cause of acute gastroenteritis in infants and children aged under five years in both high- and low-middle-income countries (LMICs). In LMICs, RoV infections are associated with substantial mortality. Two RoV vaccines (Rotarix and Rotateq) are widely available for use in infants, both of which have been shown to be highly efficacious in Europe and North America. However, for unknown reasons, these RoV vaccines have markedly lower efficacy in LMICs. We hypothesize that poor RoV vaccine efficacy across in certain regions may be associated with genetic heritability or gene expression in the human host. Methods/design: We designed an open-label single-arm interventional trial with the Rotarix RoV vaccine to identify genetic and transcriptomic markers associated with generating a protective immune response against RoV. Overall, 1,000 infants will be recruited prior to Expanded Program on Immunization (EPI) vaccinations at two months of age and vaccinated with oral Rotarix vaccine at two and three months, after which the infants will be followed-up for diarrheal disease until 18 months of age. Blood sampling for genetics, transcriptomics, and immunological analysis will be conducted before each Rotarix vaccination, 2-3 days post-vaccination, and at each follow-up visit (i.e. 6, 12 and 18 months of age). Stool samples will be collected during each diarrheal episode to identify RoV infection. The primary outcome will be Rotarix vaccine failure events (i.e. symptomatic RoV infection despite vaccination), secondary outcomes will be antibody responses and genotypic characterization of the infection virus in Rotarix failure events. Discussion: This study will be the largest and best powered study of its kind to be conducted to date in infants, and will be critical for our understanding of RoV immunity, human genetics in the Vietnam population, and mechanisms determining RoV vaccine-mediated protection. Registration: ClinicalTrials.gov, ID: NCT03587389. Registered on 16 July 2018

Circularly Polarized MIMO Antenna Based on Microstrip Patch and Metasurface Structures

This paper shows a two-element multiple-input–multiple-output (MIMO) circularly polarized antenna. The proposed design achieves polarization diversity by using two conventional truncated corner square patches. Since the operating bandwidth of the conventional design is extremely narrow, a metasurface is utilized for bandwidth enhancement. In the open literature, several MS-based MIMO antennas have been reported. However, these designs can achieve high isolation with wide spacing between the MIMO elements. For the proposed design, the configuration of the MS is modified so that high isolation can be obtained with smaller element spacing. The design concept is verified by measurements on a fabricated prototype. The measured operating bandwidth (BW), which is an overlap between −10 dB impedance and 3-dB axial ration BWs, is from 5.0 to 5.6 GHz (11.3%). Across this band, the isolation is always higher than 20 dB and the realized gain is higher than 4.4 dBi

Childhood overweight and obesity amongst primary school children in Hai Phong City, Vietnam

BACKGROUND AND OBJECTIVES: Childhood obesity is a rising health concern in Vietnam, however, research in this area is not extensive. The aim of this study was to determine the prevalence of childhood overweight and obesity, and to study associations between weight status and selected lifestyle factors, such as diet and physical activity levels, among children aged 6-10 years in Hai Phong City, Vietnam.METHODS AND STUDY DESIGN: Two hundred and seventy-six children from an urban and a rural primary school participated in this cross-sectional study. Data on weight, height and waist circumference were used to calculate BMI, and waist-height ratio to determine the proportion of children who were overweight, obese and had high central adiposity. Information on diet, physical activity and socioeconomic status of families was collected using questionnaires.RESULTS: Prevalences of overweight, obesity and high abdominal adiposity were 11.2%, 10.1% and 19.9%, respectively. Children who completed >=60 minutes of physical activity daily were 50% and 80% less likely to be overweight and have high abdominal adiposity, respectively. Computer usage increased the odds of overweight and high abdominal adiposity by 4.5 and 3.9 times, respectively. Mothers with higher education and income levels increased the risk of their children being overweight (pCONCLUSIONS: Physical inactivity and high maternal education and income levels increased the risk of childhood overweight and obesity in this cohort. Future interventions should target parents and their children by providing both with educational modules centred on healthy eating habits, parental feeding practices and strategies for increasing physical activity.</p

Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors

Automatic sorting of banknotes in payment facilities, such as automated payment machines or vending machines, consists of many tasks such as recognition of banknote type, classification of fitness for recirculation, and counterfeit detection. Previous studies addressing these problems have mostly reported separately on each of these classification tasks and for a specific type of currency only. In other words, there has been little research conducted considering a combination of these multiple tasks, such as classification of banknote denomination and fitness of banknotes, as well as considering a multinational currency condition of the method. To overcome this issue, we propose a multinational banknote type and fitness classification method that both recognizes the denomination and input direction of banknotes and determines whether the banknote is suitable for reuse or should be replaced by a new one. We also propose a method for estimating the fitness value of banknotes and the consistency of the estimation results among input trials of a banknote. Our method is based on a combination of infrared-light transmission and visible-light reflection images of the input banknote and uses deep-learning techniques with a convolutional neural network. The experimental results on a dataset composed of Indian rupee (INR), Korean won (KRW), and United States dollar (USD) banknote images with mixture of two and three fitness levels showed that the proposed method gives good performance in the combination condition of currency types and classification tasks

Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals

The automatic handling of banknotes can be conducted not only by specialized facilities, such as vending machines, teller machines, and banknote counters, but also by handheld devices, such as smartphones, with the utilization of built-in cameras and detection algorithms. As smartphones are becoming increasingly popular, they can be used to assist visually impaired individuals in daily tasks, including banknote handling. Although previous studies regarding banknote detection by smartphone cameras for visually impaired individuals have been conducted, these studies are limited, even when conducted in a cross-dataset environment. Therefore, we propose a deep learning-based method for detecting fake multinational banknotes using smartphone cameras in a cross-dataset environment. Experimental results of the self-collected genuine and fake multinational datasets for US dollar, Euro, Korean won, and Jordanian dinar banknotes confirm that our method demonstrates a higher detection accuracy than conventional &ldquo;you only look once, version 3&rdquo; (YOLOv3) methods and the combined method of YOLOv3 and the state-of-the-art convolutional neural network (CNN)

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases

Human Age Estimation Method Robust to Camera Sensor and/or Face Movement

Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method

Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information

Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods

Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor

Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use of artificial iris patterns and lead to a reduction in its security level. The accuracies of previous presentation attack detection research are limited because they used only features extracted from global iris region image. To overcome this problem, we propose a new presentation attack detection method for iris recognition by combining features extracted from both local and global iris regions, using convolutional neural networks and support vector machines based on a near-infrared (NIR) light camera sensor. The detection results using each kind of image features are fused, based on two fusion methods of feature level and score level to enhance the detection ability of each kind of image features. Through extensive experiments using two popular public datasets (LivDet-Iris-2017 Warsaw and Notre Dame Contact Lens Detection 2015) and their fusion, we validate the efficiency of our proposed method by providing smaller detection errors than those produced by previous studies

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods