A Comprehensive Evaluation of Dioxins and Furans Occurrence in River Sediments from a Secondary Steel Recycling Craft Village in Northern Vietnam.

This first study investigated the presence of dioxins and furans in river sediments around a craft village in Vietnam, focusing on Secondary Steel Recycling. Sediment samples were collected from various locations along the riverbed near the Da Hoi Secondary Steel Recycling village in Bac Ninh province. The analysis was conducted using a HRGC/HRMS-DFS device, detecting a total of 17 dioxin/furan isomers in all samples, with an average total concentration of 288.86 ng/kg d.w. The concentrations of dioxin/furan congeners showed minimal variation among sediment samples, ranging from 253.9 to 344.2 ng/kg d.w. The predominant compounds in the dioxin group were OCDD, while in the furan group, they were 1,2,3,4,6,7,8-HpCDF and OCDF. The chlorine content in the molecule appeared to be closely related to the concentration of dioxins and their percentage distribution. However, the levels of furan isomers did not vary significantly. The distribution of these compounds was not dependent on the flow direction, as they were mainly found in solid waste and are not water-soluble. Although the hepta and octa congeners had high concentrations, when converted to TEQ values, the tetra and penta groups (for dioxins) and the penta and hexa groups (for furans) contributed more to toxicity. Furthermore, the source of dioxins in sediments at Da Hoi does not only originate from steel recycling production activities but also from other combustion sites. The average total toxicity was 10.92 ng TEQ/kg d.w, ranging from 4.99 to 17.88 ng TEQ/kg d.w, which did not exceed the threshold specified in QCVN 43:2017/BTNMT, the National Technical Regulation on Sediment Quality. Nonetheless, these levels are still concerning. The presence of these toxic substances not only impacts aquatic organisms in the sampled water environment but also poses potential health risks to residents living nearby.

Synthetic mRNA delivered to human cells leads to expression of Cpl-1 bacteriophage-endolysin with activity against Streptococcus pneumoniae.

Endolysins are bacteriophage-encoded hydrolases that show high antibacterial activity and a narrow substrate spectrum. We hypothesize that an mRNA-based approach to endolysin therapy can overcome some challenges of conventional endolysin therapy, namely organ targeting and bioavailability. We show that synthetic mRNA applied to three human cell lines (HEK293T, A549, HepG2 cells) leads to expression and cytosolic accumulation of the Cpl-1 endolysin with activity against Streptococcus pneumoniae. Addition of a human lysozyme signal peptide sequence translocates the Cpl-1 to the endoplasmic reticulum leading to secretion (hlySP-sCpl-1). The pneumococcal killing effect of hlySP-sCpl-1 was enhanced by introduction of a point mutation to avoid N-linked-glycosylation. hlySP-sCpl-1N215D, collected from the culture supernatant of A549 cells 6 h post-transfection showed a significant killing effect and was active against nine pneumococcal strains. mRNA-based cytosolic Cpl-1 and secretory hlySP-sCpl-1N215D show potential for innovative treatment strategies against pneumococcal disease and, to our best knowledge, represent the first approach to mRNA-based endolysin therapy. We assume that many other bacterial pathogens could be targeted with this novel approach.

The prevalence of self-reported anxiety, depression, and associated factors among Hanoi Medical University's students during the first wave of COVID-19 pandemic.

BACKGROUND: Medical students are known to have higher levels of these issues than the general population but in Vietnam the effects of the pandemic on medical student mental health was not documented. OBJECTIVES: To estimate the prevalence and identify factors associated with self-reported anxiety disorder, depression, and perception of worsening mental health among Vietnamese medical students during the COVID-19 pandemic. METHOD: A cross-sectional study was conducted from April 7th to 29th, 2020. All students in Doctor of General Medicine, Doctor of Preventive Medicine, and Bachelor of Nursing tracks at Hanoi Medical University (3672 students) were invited to participate. Data were collected using an online questionnaire including demographic characteristics, Generalized Anxiety Disorder 7 items, Patient Health Questionnaire 9 items, Fear of COVID-19 scale, and question about worsening mental health status. Robust Poisson regression was used to assess the association between mental health status and associated factors. RESULTS: Among 1583 students (43.1% response rate), the prevalence of students screened positive for anxiety disorder was 7.3%(95%C.I.:6.0-8.7), depression was 14.5%(95%C.I.:12.8-16.3), and perceiving worsening mental health was 6.9%(95%C.I.:5.7-8.3). In multivariable regression models, significant factors associated with self-reported anxiety disorder included being male (PR = 1.99,95%C.I.:1.35-2.92), difficulty in paying for healthcare services (PR = 2.05,95%C.I.:1.39-3.01), and high level of fear of COVID-19 (Q3:PR = 2.36,95%C.I.:1.38-4.02 and Q4:PR = 4.75,95%C.I.:2.65-8.49). Significant factors associated with self-reported depression were difficulty in paying for healthcare services (PR = 1.78,95%C.I.:1.37-2.30), and high level of fear of COVID-19 (Q3:PR = 1.41,95%C.I.:1.02-1.95 and Q4:PR = 2.23,95%C.I.:1.51-3.29). Significant factors associated with perceived worsening mental health status included having clinical experience (PR = 1.83,95%C.I.:1.17-2.88) and having atypical symptoms of COVID-19 (PR = 1.96,95%C.I.:1.31-2.94). CONCLUSION: The prevalence of self-reported depression, anxiety disorder, and worsening mental health among Vietnamese students during the first wave of COVID-19 was lower than in medical students in other countries. Further investigation is needed to confirm this finding.

Polybrominated diphenyl ethers in indoor dusts from industrial factories, offices, and houses in northern Vietnam: Contamination characteristics and human exposure.

Information about the occurrence of polybrominated diphenyl ethers (PBDEs) in indoor dusts from various industrial sectors in Southeast Asia is still scarce. In this study, concentrations and congener-specific profiles of PBDEs were determined in indoor dusts from industrial factories, offices, and houses in northern Vietnam. Levels of Σ8PBDEs were higher in the office dusts (median 270; range 230-300 ng/g) and factory dusts (170; 89-510 ng/g) than in the house dusts (61; 25-140 ng/g). BDE-209 was the most dominant congener, accounting for 27-98% (average 62%) of Σ8PBDEs, suggesting the abundance of products treated with deca-BDE mixtures. Residential, commercial, and industrial activities in the studied locations of this survey were not significant sources of PBDEs as compared to those of informal waste processing activities in Vietnam. Relatively low PBDE concentrations detected in our dust samples partially reflect effectiveness of the global PBDE phase-out. Human exposure and health risk associated with dust-bound PBDEs were estimated, indicating acceptable levels of risk (i.e., neurobehavioral effects). The contributions of workplace dusts in total daily intake doses of PBDEs via dust ingestion were more important for local workers in informal recycling areas than factory workers and general population, raising the need of appropriate labor protection measures.

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models.

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing.

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network.

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence.

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains.

Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.

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.

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.

Antimicrobial and Cytotoxic Properties of Bioactive Metabolites Produced by Streptomyces cavourensis YBQ59 Isolated from Cinnamomum cassia Prels in Yen Bai Province of Vietnam.

The endophytic actinomycete strain YBQ59 was isolated from Cinnamomum cassia Prels in Yen Bai province (21°53'14″N; 104°35'9″E) of northern Vietnam. Based on analysis of morphological, physiological characteristics and 16S rRNA gene sequence (GenBank Acc. No. MF950891), the strain YBQ59 possessed high similarity to Streptomyces cavourensis subsp. cavourensis strain NRRL 2740, therefore assigned as S. cavourensis YBQ59. The ethyl acetate extract of the YBQ59 culture broth isolated eight pure secondary metabolites, identified as 1-monolinolein (1), bafilomycin D (2), nonactic acid (3), daidzein (4), 3'-hydroxydaidzein (5), 5,11-epoxy-10-cadinanol (6), prelactone B (7), and daucosterol (8). Compounds 1, 3-8 were reported for the first time from S. cavourensis. Compounds 1-5 exhibited antimicrobial activities against both methicillin-resistant Staphylococcus aureus ATCC 33591 (MRSA) and methicillin-resistant Staphylococcus epidermidis ATCC 35984 (MRSE) among which the compound 1 revealed the strongest effects with minimum inhibitory concentrations of 8.5 and 14.6 µg/mL, respectively. The compound 2 showed high potential effect against MRSA (MIC of 11.1 µg/mL) but less effect against MRSE (MIC of 30.3 µg/mL). The cytotoxicity of the compounds 1-7 was investigated against human lung adenocarcinoma EGFR-TKI-resistant cells, among which compounds 1, 2, and 5 exhibited the strong effect against A549 cells with IC50 values of 3.6, 6.7, and 7.8 µM, respectively. Taken together, the experimental findings in this study suggested that the compounds 1 and 2 could be reproducible metabolites applicable for inhibition of both drug-resistant bacteria and cancer cell lines.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

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.

Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor.

In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods.

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.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.