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Background: The risk of outbreaks escalating into pandemics has soared with globalization. Therefore, understanding transmission mechanisms of infectious diseases has become critical to formulating global public health policy. This systematic review assessed evidence in the medical and public health literature for the military as a disease vector., Methods: We searched 3 electronic databases without temporal restrictions. Two researchers independently extracted study data using a standardized form. Through team discussions, studies were grouped according to their type of transmission mechanism and direct quotes were extracted to generate themes and sub-themes. A content analysis was later performed and frequency distributions for each theme were generated., Results: Of 6477 studies, 210 met our inclusion criteria and provided evidence, spanning over two centuries (1810-2020), for the military as a pathogen transmitter, within itself or between it and civilians. Biological mechanisms driving transmission included person-to-person transmission, contaminated food and water, vector-borne, and airborne routes. Contaminated food and/or water were the most common biological transmission route. Social mechanisms facilitating transmission included crowded living spaces, unhygienic conditions, strenuous working, training conditions, absent or inadequate vaccination programs, pressure from military leadership, poor compliance with public health advice, contractor mismanagement, high-risk behaviours, and occupation-specific freedom of movement. Living conditions were the most common social transmission mechanism, with young, low ranking military personnel repeatedly reported as the most affected group. Selected social mechanisms, such as employment-related freedom of movement, were unique to the military as a social institution. While few studies explicitly studied civilian populations, considerably more contained information that implied that civilians were likely impacted by outbreaks described in the military., Conclusions: This study identified features of the military that pose a significant threat to global health, especially to civilian health in countries with substantial military presence or underdeveloped health systems. While biological transmission mechanisms are shared by other social groups, selected social transmission mechanisms are unique to the military. As an increasingly interconnected world faces the challenges of COVID-19 and future infectious diseases, the identified features of the military may exacerbate current and similar challenges and impair attempts to implement successful and equitable global public health policies., (© 2021. The Author(s).)

During a survey of fungal diversity associated with insects, mud, soil, and freshwater niches in different areas in Korea, nine interesting fungal strains were isolated. Based on their morphological characteristics and molecular phylogeny analyses, using a combined data set of β-tubulin ( BenA ), calmodulin ( CaM ), and second largest subunit of RNA polymerase ( RPB2 ) sequences, the strains CNUFC AM-44, CNUFC JCW3-4, CNUFC S708, CNUFC WT202, CNUFC AS1-29, CNUFC JCW3-5, CNUFC JDP37, and CNUFC JDP62 were identified as Aspergillus alabamensis , A. floridensis , A. subversicolor , Penicillium flavigenum , P . laevigatum , P. lenticrescens , Talaromyces adpressus , and T . beijingensis , respectively. The strain CNUFC JT1301 belongs to series Westlingiorum in section Citrina and is phylogenetically related to P . manginii . However, slow growth when cultivated on CYA, MEA, CREA is observed and the property can be used to easily distinguish the new species from these species. Additionally, P. manginii is known to produce sclerotia, while CNUFC JT1301 strain does not. Herein, the new fungal species is proposed as P . aquadulcis sp. nov. Eight species, A . alabamensis , A. floridensis , A. subversicolor , P . flavigenum , P . laevigatum , P. lenticrescens , T . adpressus , and T . beijingensis , have not been previously reported in Korea. The present study expands the known distribution of fungal species belonging to the families Aspergillaceae and Trichocomaceae in Korea., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Korean Society of Mycology.)

Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.

Objective: This study aimed to evaluate the following null hypothesis: there are no differences in the morphology of the temporomandibular joint (TMJ) structures in relation to vertical and sagittal cephalometric patterns., Methods: This retrospective study was performed with 131 participants showing no TMJ symptoms. The participants were divided into Class I, II, and III groups on the basis of their sagittal cephalometric relationships and into hyperdivergent, normodivergent, and hypodivergent groups on the basis of their vertical cephalometric relationships. The following measurements were performed using cone-beam computed tomography images and compared among the groups: condylar volume, condylar size (width, length, and height), fossa size (length and height), and condyle-to-fossa joint spaces at the anterior, superior, and posterior condylar poles., Results: The null hypothesis was rejected. The Class III group showed larger values for condylar width, condylar height, and fossa height than the Class II group ( p < 0.05). Condylar volume and superior joint space in the hyperdivergent group were significantly smaller than those in the other two vertical groups ( p < 0.001), whereas fossa length and height were significantly larger in the hyperdivergent group than in the other groups ( p < 0.01). The hypodivergent group showed a greater condylar width than the hyperdivergent group ( p < 0.01). The sagittal and vertical cephalometric patterns showed statistically significant interactions for fossa length and height., Conclusions: TMJ morphology differed across diverse skeletal cephalometric patterns. The fossa length and height were affected by the interactions of the vertical and sagittal skeletal patterns.

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases-Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

Background: Although ophthalmic devices have made remarkable progress and are widely used, most lack standardization of both image review and results reporting systems, making interoperability unachievable. We developed and validated new software for extracting, transforming, and storing information from report images produced by ophthalmic examination devices to generate standardized, structured, and interoperable information to assist ophthalmologists in eye clinics., Results: We selected report images derived from optical coherence tomography (OCT). The new software consists of three parts: (1) The Area Explorer, which determines whether the designated area in the configuration file contains numeric values or tomographic images; (2) The Value Reader, which converts images to text according to ophthalmic measurements; and (3) The Finding Classifier, which classifies pathologic findings from tomographic images included in the report. After assessment of Value Reader accuracy by human experts, all report images were converted and stored in a database. We applied the Value Reader, which achieved 99.67% accuracy, to a total of 433,175 OCT report images acquired in a single tertiary hospital from 07/04/2006 to 08/31/2019. The Finding Classifier provided pathologic findings (e.g., macular edema and subretinal fluid) and disease activity. Patient longitudinal data could be easily reviewed to document changes in measurements over time. The final results were loaded into a common data model (CDM), and the cropped tomographic images were loaded into the Picture Archive Communication System., Conclusions: The newly developed software extracts valuable information from OCT images and may be extended to other types of report image files produced by medical devices. Furthermore, powerful databases such as the CDM may be implemented or augmented by adding the information captured through our program.

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., Competing Interests: The authors declare no conflict of interest.

Retinal fundus images are used to detect organ damage from vascular diseases (e.g. diabetes mellitus and hypertension) and screen ocular diseases. We aimed to assess convolutional neural network (CNN) models that predict age and sex from retinal fundus images in normal participants and in participants with underlying systemic vascular-altered status. In addition, we also tried to investigate clues regarding differences between normal ageing and vascular pathologic changes using the CNN models. In this study, we developed CNN age and sex prediction models using 219,302 fundus images from normal participants without hypertension, diabetes mellitus (DM), and any smoking history. The trained models were assessed in four test-sets with 24,366 images from normal participants, 40,659 images from hypertension participants, 14,189 images from DM participants, and 113,510 images from smokers. The CNN model accurately predicted age in normal participants; the correlation between predicted age and chronologic age was R 2  = 0.92, and the mean absolute error (MAE) was 3.06 years. MAEs in test-sets with hypertension (3.46 years), DM (3.55 years), and smoking (2.65 years) were similar to that of normal participants; however, R 2 values were relatively low (hypertension, R 2  = 0.74; DM, R 2  = 0.75; smoking, R 2  = 0.86). In subgroups with participants over 60 years, the MAEs increased to above 4.0 years and the accuracies declined for all test-sets. Fundus-predicted sex demonstrated acceptable accuracy (area under curve > 0.96) in all test-sets. Retinal fundus images from participants with underlying vascular-altered conditions (hypertension, DM, or smoking) indicated similar MAEs and low coefficients of determination (R 2 ) between the predicted age and chronologic age, thus suggesting that the ageing process and pathologic vascular changes exhibit different features. Our models demonstrate the most improved performance yet and provided clues to the relationship and difference between ageing and pathologic changes from underlying systemic vascular conditions. In the process of fundus change, systemic vascular diseases are thought to have a different effect from ageing. Research in context. Evidence before this study. The human retina and optic disc continuously change with ageing, and they share physiologic or pathologic characteristics with brain and systemic vascular status. As retinal fundus images provide high-resolution in-vivo images of retinal vessels and parenchyma without any invasive procedure, it has been used to screen ocular diseases and has attracted significant attention as a predictive biomarker for cerebral and systemic vascular diseases. Recently, deep neural networks have revolutionised the field of medical image analysis including retinal fundus images and shown reliable results in predicting age, sex, and presence of cardiovascular diseases. Added value of this study. This is the first study demonstrating how a convolutional neural network (CNN) trained using retinal fundus images from normal participants measures the age of participants with underlying vascular conditions such as hypertension, diabetes mellitus (DM), or history of smoking using a large database, SBRIA, which contains 412,026 retinal fundus images from 155,449 participants. Our results indicated that the model accurately predicted age in normal participants, while correlations (coefficient of determination, R 2 ) in test-sets with hypertension, DM, and smoking were relatively low. Additionally, a subgroup analysis indicated that mean absolute errors (MAEs) increased and accuracies declined significantly in subgroups with participants over 60 years of age in both normal participants and participants with vascular-altered conditions. These results suggest that pathologic retinal vascular changes occurring in systemic vascular diseases are different form the changes in spontaneous ageing process, and the ageing process observed in retinal fundus images may saturate at age about 60 years. Implications of all available evidence. Based on this study and previous reports, the CNN could accurately and reliably predict age and sex using retinal fundus images. The fact that retinal changes caused by ageing and systemic vascular diseases occur differently motivates one to understand the retina deeper. Deep learning-based fundus image reading may be a more useful and beneficial tool for screening and diagnosing systemic and ocular diseases after further development.

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.

The effect of TiO2 layer applied to the conventional Fe2O3/FTO photoanode to improve the photoelectrochemical performance was assessed from the viewpoint of the microstructure and energy band structure. Regardless of the location of the TiO2 layer in the photoanodes, that is, Fe2O3/TiO2/FTO or TiO2/Fe2O3/FTO, high performance was obtained when α-Fe2O3 and H-TiNT/anatase-TiO2 phases existed in the constituent Fe2O3 and TiO2 layers after optimized heat treatments. The presence of the Fe2O3 nanoparticles with high uniformity in the each layer of the Fe2O3/TiO2/FTO photoanode achieved by a simple dipping process seemed to positively affect the performance improvement by modifying the energy band structure to a more favorable one for efficient electrons transfer. Our current study suggests that the application of the TiO2 interlayer, together with α -Fe2O3 nanoparticles present in the each constituent layers, could significantly contribute to the performance improvement of the conventional Fe2O3 photoanode.

Severe acute respiratory syndrome (SARS) is a life-threatening disease for which accurate diagnosis is essential. Although many tools have been developed for the diagnosis of SARS, false-positive reactions in negative sera may occur because of cross-reactivity with other coronaviruses. We have raised polyclonal and monoclonal antibodies (Abs) using a recombinant form of the SARS virus nucleocapsid protein. Cross-reactivity of these anti-SARS Abs against human coronavirus (HCoV) 229E and HCoV OC43 were determined by Western blotting. The Abs produced reacted with recombinant SARS virus nucleocapsid protein, but not with HCoV 229E or HCoV OC43.

Although rodents have previously been used in ecotoxicological studies, they are expensive, time-consuming, and are limited by strict legal restrictions. The present study used a zebrafish (Danio rerio) model and generated data that was useful for extrapolating toxicant effects in this system to that of humans. Here we treated embryos of the naive-type as well as a transiently transfected zebrafish liver cell line carrying a plasmid (phAhREEGFP), for comparing toxicity levels with the well-known aryl hydrocarbon receptor (AhR)-binding toxicants: 3,3',4,4',5-pentachlorobiphenyl (PCB126), 2,3,7,8-tetrachlorodibenzo-p-dioxin, and 3-methylcholanthrene. These toxicants induced a concentration-dependent increase in morphological disruption, indicating toxicity at early life-stages. The transient transgenic zebrafish liver cell line was sensitive enough to these toxicants to express the CYP1A1 regulated enhanced green fluorescent protein. The findings of this study demonstrated that the zebrafish in vivo model might allow for extremely rapid and reproducible toxicological profiling of early life-stage embryo development. We have also shown that the transient transgenic zebrafish liver cell line can be used for research on AhR mechanism studies.

Severe acute respiratory syndrome (SARS) is a lifethreatening emerging respiratory disease caused by the coronavirus, SARS-CoV. The nucleocapsid (N) protein of SARS-CoV is highly antigenic and may be a suitable candidate for diagnostic applications. We constructed truncated recombinant N proteins (N1 [1-422 aa], N2 [1- 109 aa], and N3 [110-422 aa]) and determined their antigenicity by Western blotting using convalescent SARS serum. The recombinants containing N1 and N3 reacted with convalescent SARS serum in Western blotting. However, the recombinant with N2 did not. In ELISA using N1 or N3 as the antigens, positive results were observed in 10 of 10 (100%) SARS-CoV-positive human sera. None of 50 healthy sera gave positive results in either assay. These data indicate that the ELISA using N1 or N3 has high sensitivity and specificity. These results suggest that the middle or C-terminal region of the SARS N protein is important for eliciting antibodies against SARS-CoV during the immune response, and ELISA reactions using N1 or N3 may be a valuable tool for SARS diagnosis.