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Abstract Recent advancements in next-generation sequencing (NGS) technologies have created new opportunities for comprehensive screening of multiple parasite species. In this study, we cloned the 18 S rDNA V9 region of 11 species of intestinal parasites into plasmids. Equal amounts and concentrations of these 11 plasmids were pooled, and amplicon NGS targeting the 18 S rDNA V9 region was performed using the Illumina iSeq 100 platform. A total of 434,849 reads were identified, and all 11 parasite species were detected, although the number of output reads for each parasite varied. The read count ratio, in descending order, was as follows: Clonorchis sinensis, 17.2%; Entamoeba histolytica, 16.7%; Dibothriocephalus latus, 14.4%; Trichuris trichiura, 10.8%; Fasciola hepatica, 8.7%; Necator americanus, 8.5%; Paragonimus westermani, 8.5%; Taenia saginata, 7.1%; Giardia intestinalis, 5.0%; Ascaris lumbricoides, 1.7%; and Enterobius vermicularis, 0.9%. We found that the DNA secondary structures showed a negative association with the number of output reads. Additionally, variations in the amplicon PCR annealing temperature affected the relative abundance of output reads for each parasite. These findings can be applied to improve parasite detection methodologies and ultimately enhance efforts to control and prevent intestinal parasitic infections.

BackgroundA significant portion of South Korea's population, approximately a quarter, owns pets, with dogs being the most popular choice among them. However, studies analyzing the fecal organism communities of dogs in South Korea are lacking, and limited efforts have been exerted to identify pathogens with potential zoonotic implications. Therefore, this study aimed to investigate potential pathogens using metabarcoding analysis and evaluate the risk of zoonotic diseases in dog feces in Seoul, South Korea.MethodologyFecal samples were collected from both pet and stray dogs in the Mapo district of Seoul. Next-generation sequencing (NGS) was utilized, employing 16S rRNA amplicon sequencing to identify prokaryotic pathogens, and 18S rRNA amplicon sequencing for eukaryotic pathogens. The data obtained from the QIIME2 pipeline were subjected to various statistical analyses to identify different putative pathogens and their compositions.Principal findingsSignificant variations in microbiota composition were found between stray and pet dogs, and putative prokaryotic and eukaryotic pathogens were identified. The most prevalent putative bacterial pathogens were Fusobacterium, Helicobacter, and Campylobacter. The most prevalent putative eukaryotic pathogens were Giardia, Pentatrichomonas, and Cystoisospora. Interestingly, Campylobacter, Giardia, and Pentatrichomonas were found to be significantly more prevalent in stray dogs than in pet dogs. The variation in the prevalence of potential pathogens in dog feces could be attributed to environmental factors, including dietary variances and interactions with wildlife, particularly in stray dogs. These factors likely contributed to the observed differences in pathogen occurrence between stray and pet dogs.Conclusions/significanceThis study offers valuable insights into the zoonotic risks associated with dog populations residing in diverse environments. By identifying and characterizing putative pathogens in dog feces, this research provides essential information on the impact of habitat on dog-associated pathogens, highlighting the importance of public health planning and zoonotic risk management.

ABSTRACT Lovebugs appeared in large numbers across a wide area in Seoul, South Korea, in June 2023. The sudden appearance of exotic insects not only discomforts people but also fosters anxiety, as their potential for pathogen transmission would be unknown. In this study, targeted next-generation sequencing (NGS) of the 16S rRNA gene V4 region was performed using iSeq 100 to screen for bacteria in lovebugs. Forty-one lovebugs (20 females and 21 males) collected in Seoul, Korea, were identified as Plecia longiforceps based on mitochondrial cytochrome oxidase subunit 1 sequencing data using PCR. We analyzed the microbiome of the lovebugs and detected 453 species of bacteria. Among all bacteria screened based on NGS, Rickettsia was detected in all samples with an average relative abundance of 80.40%, followed by Pandoraea and Ewingella. Diversity (alpha and beta) between females and males did not differ; however, only Tumebacillus showed a higher relative abundance in females. Sequencing analysis of Rickettsia using a gltA gene-specific primer by PCR showed that it had higher sequence similarity to the Rickettsia symbiont of arthropods than to the spotted fever group rickettsiae. Eleven samples in which Pandoraea was detected by iSeq 100 were confirmed by PCR and exhibited 100% sequence identity to Pandoraea oxalativorans strain DSM 23570. Consequently, the likelihood of pathogen transmission to humans is low. The applied method may play a crucial role in swiftly identifying bacterial species in the event of future outbreaks of exotic insects that may be harmful to humans.IMPORTANCELovebugs have recently emerged in large numbers in Seoul, causing major concern regarding potential health risks. By performing the next-generation sequencing of the 16S rRNA gene V4 region, we comprehensively examined the microbiome of these insects. We identified the presence of numerous bacteria, including Rickettsia and Pandoraea. Reassuringly, subsequent tests confirmed that these detected bacteria were not pathogenic. The present study addresses health concerns related to lovebugs and shows the accuracy and efficiency of our detection technique. Such methods prove invaluable for rapidly identifying bacterial species during potential outbreaks of unfamiliar insects, thereby ensuring public safety.

Four species of dominant wild animals, namely, Prionailurus bengalensis euptilurus, Nyctereutes procyonoides koreensis, Hydropotes inermis argyropus, and Sus scrofa coreanus, are hosts of potential infectious agents, including helminths and protozoa. Therefore, it is necessary to analyze the infectious agents present in these wild animals to monitor and control the spread of pathogens. In the present study, fecal samples from 51 wild animals were collected from the mountains of Yangpyeong, Hoengseong, and Cheongyang in South Korea and metabarcoding of the V9 region of the 18S rRNA gene was performed to identify various parasite species that infect these wild animals. Genes from nematodes, such as Metastrongylus sp., Strongyloides spp., Ancylostoma sp., and Toxocara sp., were detected in the fecal samples from wild animals. In addition, platyhelminthes, including Spirometra sp., Echinostomatidae gen. sp., Alaria sp., Neodiplostomum sp., and Clonorchis sp., and protozoa, including Entamoeba sp., Blastocystis sp., Isospora sp., Tritrichomonas sp., Pentatrichomonas sp., and Cryptosporidium sp., were detected. In the present study, various parasites infecting wild animals were successfully identified using metabarcoding. Our technique may play a crucial role in monitoring parasites within wild animals, especially those causing zoonoses.

The objective of this study was to perform fault diagnosis (FD) specific to various faults that can occur in the thrusters of remotely operated vehicles (ROVs) during hovering control. Underwater thrusters are predominantly utilized as propulsion systems in the majority of ROVs and are essential components for implementing motions such as trajectory tracking and hovering. Faults in the underwater thrusters can limit the operational capabilities of ROVs, leading to permanent damage. Therefore, this study focused on the FD for faults frequently caused by external factors such as entanglement with floating debris and propeller breakage. For diagnosing faults, a data-based technique that identifies patterns according to data characteristics was utilized. In imitation of the fault situations, data for normal, breakage and entangled conditions were acquired, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to differentiate between these fault conditions. The proposed methodology was validated by configuring an ROV and conducting experiments in an engineering water tank to verify the performance of the FD.

Abstract Background The striped field mouse Apodemus agrarius is a wild rodent commonly found in fields in Korea. It is a known carrier of various pathogens. Amplicon-based next-generation sequencing (NGS) targeting the 16S ribosomal RNA (rRNA) gene is the most common technique used to analyze the bacterial microbiome. Although many bacterial microbiome analyses have been attempted using feces of wild animals, only a few studies have used NGS to screen for parasites. This study aimed to rapidly detect bacterial, fungal and parasitic pathogens in the guts of A. agrarius using NGS-based metabarcoding analysis. Methods We conducted 18S/16S rDNA-targeted high-throughput sequencing on cecal samples collected from A. agrarius (n = 48) trapped in May and October 2017. Taxa of protozoa, fungi, helminths and bacteria in the cecal content were then identified. Results Among the protozoa identified, the most prevalent was Tritrichomonas sp., found in all of the cecal samples, followed by Monocercomonas sp. (95.8% prevalence; in 46/48 samples) and Giardia sp. (75% prevalence; in 36/48 samples). For helminths, Heligmosomoides sp. was the most common, found in 85.4% (41/48) of samples, followed by Hymenolepis sp. (10.4%; 5/48) and Syphacia sp. (25%; 12/48). The 16S rRNA gene analysis showed that the microbial composition of the cecal samples changed by season (P = 0.005), with the linear discriminant analysis effect size showing that in the spring Escherichia coli and Lactobacillus murinus were more abundant and Helicobacter rodentium was less abundant. Helicobacter japonicus was more abundant and Prevotella_uc was less abundant in males. The microbial composition changed based on the Heligmosomoides sp. infection status (P = 0.019); specifically, Lactobacillus gasseri and Lactobacillus intestinalis were more abundant in the Heligmosomoides sp.-positive group than in the Heligmosomoides sp.-negative group. Conclusions This study demonstrated that bacterial abundance changed based on the season and specific parasitic infection status of the trapped mice. These results highlight the advantages of NGS technology in monitoring zoonotic disease reservoirs. Graphical Abstract

Understanding the house dust mites (HDMs) microbiome is crucial due to its potential effects on the development of allergic diseases. In 1998, our laboratory collected Dermatophagoides farinae and D. pteronyssinus from beds in a Korean household and began cultivating these HDMs. Our laboratory has been actively investigating several topics about HDMs in recent years, including the bacterial and fungal microbiome and their interactions, as well as the impact of the HDM microbiome on airway inflammation. To study the D. farinae microbiome, we employed high-throughput sequencing of the 16S rDNA amplicons. The results revealed that the two most abundant bacteria were Enterococcus faecalis and Bartonella spp. In contrast, we found almost no bacteria in D. pteronyssinus. By inoculating bacteria to HDMs, we found that D. farinae is more susceptible to bacteria than D. pteronyssinus. This susceptibility was associated with the presence of certain fungal species in D. pteronyssinus. Additionally, we have recently made efforts to produce HDMs with reduced levels of symbiotic bacteria. We believe that standardizing and controlling the microbiome in HDMs are crucial steps for the future development and improvement of allergic immunotherapies.

Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective vibration-based methodology that does not require altering existing systems is employed. However, the vibration data collected within the hull is influenced by propeller-fluid interactions, hull damping, and structural resonant frequencies, resulting in noise and unpredictability. Furthermore, to differentiate faults not only at fixed rotational speeds but also over the entire range of a thruster’s rotational speeds, traditional frequency analysis based on the Fourier transform cannot be utilized. Hence, Continuous Wavelet Transform (CWT), known for attributions encapsulating physical characteristics in both time-frequency domain nuances, was applied to address these complications and transform vibration data into a scalogram. CWT results are diagnosed using a Vision Transformer (ViT) classifier known for its global context awareness in image processing. The effectiveness of this diagnosis approach was verified through experiments using a USV designed for field experiments. Seven cases with different fault types and severity were diagnosed and yielded average accuracy of 0.9855 and 0.9908 at different vibration points, respectively.

In Korea, feral pigeons pose significant public health risks because they carry various zoonotic pathogens. Human population density is a significant factor in zoonotic disease events. Seoul is one of the largest cities by population density among developed countries and where most of the homeless population in Korea exists. We designed this study to compare the microbiota of pigeon feces by regional characteristics and the presence of homeless individuals. Therefore, this study used 16S rRNA amplicon sequencing to detect possible pathogenic microbes and assess the current risk of zoonosis in Seoul, South Korea. Pigeon fecal samples (n = 144) obtained from 19 public sites (86 and 58 fecal samples from regions in and outside Seoul, respectively) were examined. Potentially pathogenic bacteria were also detected in the fecal samples; Campylobacter spp. was found in 19 samples from 13 regions, Listeriaceae was found in seven samples, and Chlamydia spp. was found in three samples from two regions. Principal coordinates analysis and permutational multivariate analysis of variance revealed a significant difference in bacterial composition between the regions in Seoul (n = 86) and outside Seoul (n = 58) and between the regions with (n = 81) and without (n = 63) homeless individuals. Overall, this study identified various potentially pathogenic microorganisms in pigeon feces at public sites in South Korea. Moreover, this study demonstrates that the microbial composition was influenced by regional characteristics and homelessness. Taken together, this study provides important information for public health strategic planning and disease control.

Background. Symbiotic bacteria in house dust mites pose a risk of immunological side effects in the clinical use of immunotherapeutic agents. In this study, we investigated the duration for which the bacterial concentration in Dermatophagoides farinae could be kept low with antibiotic treatment, and whether the allergenic properties of the mite changed under ampicillin treatment. Methods. D. farinae was cultivated in the presence of ampicillin powder in an autoclaved medium for 6 weeks. After subsequent subcultures without ampicillin, the mites were harvested, and the extract was prepared. The amounts of bacteria, lipopolysaccharides (LPS), and two major allergens (Der f 1 and Der f 2) were measured. Human bronchial epithelial cells and mice were treated with the D. farinae extract to assess the allergic airway inflammation. Results. The number of bacteria and level of LPS were reduced by 150-fold and 33-fold, respectively, at least 18 weeks after ampicillin treatment. The concentration of Der f 1 and Der f 2 remained unchanged by ampicillin treatment. The secretion of interleukin (IL)-6 and IL-8 from the human airway epithelial cells decreased when treated with the extract of ampicillin-treated D. farinae compared with that of ampicillin-untreated D. farinae. A mouse asthma model was developed using ampicillin-treated D. farinae. We observed that the level of lung function, airway inflammation, and serum-specific immunoglobulin were not different for the mouse asthma model developed using ampicillin-treated D. farinae than the model developed using ampicillin-untreated D. farinae. Conclusions. We showed that bacterial content in D. farinae was reduced by ampicillin treatment, which was sufficient to induce allergic sensitization and an immune response. This method will be used to develop more controlled allergy immunotherapeutic agents.

Abstract Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.

Variations across cells, modules, packs, and vehicles can cause significant errors in the state estimation of LIBs using machine learning algorithms, especially when trained with small datasets. Training with large datasets that account for all variations is often impractical due to resource and time constraints at initial product release. To address this issue, we proposed a novel architecture that leverages electronic control units, edge computers, and the cloud to detect unrevealed variations and abnormal degradations in LIBs. The architecture comprised a generalized deep neural network (DNN) for generalizability, a personalized DNN for accuracy within a vehicle, and a detector. We emphasized that a generalized DNN trained with small datasets must show reasonable estimation accuracy during cross validation, which is critical for real applications before online training. We demonstrated the feasibility of the architecture by conducting experiments on 65 DNN models, where we found distinct hyperparameter configurations. The results showed that the personalized DNN achieves a root mean square error (RMSE) of 0.33%, while the generalized DNN achieves an RMSE of 4.6%. Finally, the Mahalanobis distance was used to consider the SOH differences between the generalized DNN and personalized DNN to detect abnormal degradations.

This paper proposes a Subsampled Sum-Modified-Laplacian (SSML) operator for the block classification of the Adaptive Loop Filter (ALF) in Versatile Video Coding (VVC). The VVC Test Model (VTM)-2.0 includes Geometry transformation-based ALF (GALF) with 4 × 4 block classification, a single 7 × 7 Luma diamond-shaped filter, and spatial adaptation at the Coding Tree Block (CTB) level to improve the coding efficiency of VVC. However, in the 4 × 4 block classification, 1-D (1-Dimensional) ModifiedLaplacian (ML) values for various directions are calculated at all sample positions within an 8 × 8 window, and these are summed to derive the gradients for the corresponding directions. For a CTB where the Wiener filter is applied, every 4 × 4 Luma block within the CTB must calculate Sum-Modified-Laplacian (SML) operations, which results in increased computational complexity of the decoder. Therefore, four different subsampling patterns based on 4 × 4 block classification for the SSML operator that calculate the 1-D ML values at subsampled sample positions within the 8 × 8 window are proposed as attempts to reduce the computational complexity of the block classification process in the ALF of VTM-2.0. As all of the proposed subsampling patterns reduce the number of sample positions needed to calculate the 1-D ML operations by half, the experimental results demonstrate that the proposed method achieves total decoding time reductions in the range of 2% to 3%, while the coding gain of the ALF is maintained.

Abstract Background Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

Abstract High‐throughput sequencing (HTS) of 16S rRNA gene amplicons provides compositional information regarding the microbial community, but not the absolute abundance of the bacteria. We aimed to develop a standardized method for quantifying the absolute abundance of bacteria in microbiome studies. To demonstrate the utility of our approach, we quantified the number of bacteria from the compositional data of the fecal and cecal microbiomes. The 16S rRNA gene of a hyperthermophile, Thermus aquaticus, was cloned into Pichia pastoris (yeast) genome, and an equivalent amount of the yeast was added to the stool and cecal samples of mice before DNA extraction. 16S rRNA gene library construction and HTS were performed after DNA extraction. The absolute abundances of bacteria were calculated using T. aquaticus reads. The average relative abundances of T. aquaticus in the five stool and five cecal samples were 0.95% and 0.33%, respectively, indicating that the number of bacteria in a cecum sample is 2.9 times higher than that in a stool sample. The method proposed for quantifying the absolute abundance of the bacterial population in this study is expected to overcome the limitation of showing only compositional data in most microbiome studies.

Carbon/carbon (C/C) composite materials are widely used in aerospace structures operating in high temperature environments based on their high performance thermal and mechanical properties. The C/C composite material has a yarn architecture in which fiber bundles in different directions cross each other, and it is also divided into architecture types, such as 3-D orthogonal, 4-D in-plane, and 4-D diagonal, according to the arrangement of the fiber bundles. The thermo-mechanical performance of the carbon/carbon composite material may vary depending on the yarn architecture, and the material properties are also tailored according to constituent materials, such as fiber and matrix, and manufacturing parameters, such as yarn size, yarn spacing, and fiber volume fraction. In this paper, three types of geometric models are defined for repeating unit cells (RUCs), according to the yarn architecture of the C/C composite material, and the effective stiffness was predicted by applying the iso-strain assumption and stress averaging technique. In addition, the thermo-mechanical characteristics according to the yarn architecture and fiber volume fraction of RUC were compared and evaluated.

Abstract Background Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. Methods To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. Results The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. Conclusion This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.

MOTIVATION:Disease progression originates from the concept that an individual disease may go through different changes as it evolves, and such changes can cause new diseases. It is important to find a progression between diseases since knowing the prior-posterior relationship beforehand can prevent further complications or evolutions to other diseases. Furthermore, the series of progressions can be represented in the form of a chain, which enables us to readily infer successive influences from one disease to another after many passages through other diseases. METHODS:In this paper, we propose a systematic approach for finding a disease progression chain from a source disease to a target one via exploring a disease network. The network is constructed based on various sets of biomedical data. To find the most influential progression chains, the k-shortest path search algorithm is employed. The most representative algorithms such as A*, Dijkstra, and Yen's are incorporated into the proposed method. RESULTS:A disease network consisting of 3,302 diseases was constructed based on four sources of biomedical data: disease-protein relations, biological pathways, clinical history, and biomedical literature information. The last three sets of data contain prior-posterior information, and they endow directionality on the edges of the network. The results were interesting and informative: for example, when colitis and respiratory insufficiency were set as a source disease and a target one, respectively, five progression chains were found within several seconds (when k = 5). Each chain was provided with a progression score, which indicates the strength of plausibility relative to others. Similarly, the proposed method can be expanded to any pair of source-target diseases in the network. This can be utilized as a preliminary tool for inferring complications or progressions between diseases.