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Polycyclic aromatic hydrocarbons (PAHs), notably benzo[a]pyrene (BaP), are environmental contaminants with multiple adverse ecological implications. Numerous studies have suggested the use of BaP biodegradation using various bacterial strains to remove BaP from the environment. This study investigates the BaP biodegradation capability of Pigmentiphaga kullae strain KIT-003, isolated from the Nak-dong River (South Korea) under specific environmental conditions. The optimum conditions of biodegradation were found to be pH 7.0, 35°C, and a salinity of 0 %. GC-MS analysis suggested alternative pathways by which KIT-003 produced catechol from BaP through several intermediate metabolites, including 4-formylchrysene-5-carboxylic acid, 5,6-dihydro-5,6-dihydroxychrysene-5-carboxylic acid (isomer: 3,4-dihydro-3,4-dihydroxychrysene-4-carboxylic acid), naphthalene-1,2-dicarboxylic acid, and 2-hydroxy-1-naphthoic acid. Proteomic profiles indicated upregulation of enzymes associated with aromatic compound degradation, such as nahAc and nahB, and of those integral to the tricarboxylic acid cycle, reflecting the strain's adaptability to and degradation of BaP. Lipidomic analysis of KIT-003 demonstrated that BaP exposure induced an accumulation of glycerolipids such as diacylglycerol and triacylglycerol, indicating their crucial role in bacterial adaptation mechanisms under BaP stress. This study provides significant scientific knowledge regarding the intricate mechanisms involved in BaP degradation by microorganisms.

Abstract Pesticides are indispensable tools in modern agriculture for enhancing crop productivity. However, the inherent toxicity of pesticides raises significant concerns regarding human exposure, particularly among agricultural workers. This study investigated the exposure and associated risks of two commonly used pesticides in open-field pepper cultivation, namely, chlorothalonil and flubendiamide, in the Republic of Korea. We used a comprehensive approach, encompassing dermal and inhalation exposure measurements in agricultural workers during two critical scenarios: mixing/loading and application. Results revealed that during mixing/loading, dermal exposure to chlorothalonil was 3.33 mg (0.0002% of the total active ingredient [a.i.]), while flubendiamide exposure amounted to 0.173 mg (0.0001% of the a.i.). Conversely, dermal exposure increased significantly during application to 648 mg (chlorothalonil) and 93.1 mg (flubendiamide), representing 0.037% and 0.065% of the total a.i., respectively. Inhalation exposure was also evident, with chlorothalonil and flubendiamide exposure levels varying across scenarios. Notably, the risk assessment using the Risk Index (RI) indicated acceptable risk of exposure during mixing/loading but raised concerns during application, where all RIs exceeded 1, signifying potential risk. We suggest implementing additional personal protective equipment (PPE) during pesticide application, such as gowns and lower-body PPE, to mitigate these risks.

Summary: To gain deeper insights into transcriptomes and epigenomes of organoids, liver organoids from two states (expandable and more differentiated) were subjected to single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) analyses. Mitochondrial gene expression was higher in differentiated than in non-differentiated hepatocytes, with ATAC-seq peaks increasing near the mitochondrial control region. Differentiation of liver organoids resulted in the expression of transcription factors that act as enhancers and repressors. In addition, epigenetic mechanisms regulating the expression of alpha-fetoprotein (AFP) and albumin (ALB) differed in liver organoids and adult liver. Knockdown of PDX1, an essential transcription factor for pancreas development, led to the hepatic maturation of liver organoids through regulation of AFP and ALB expression. This integrative analysis of the transcriptomes and epigenomes of liver organoids at the single-cell level may contribute to a better understanding of the regulatory networks during liver development and the further development of mature in vitro human liver models.

The main challenge of FSCIL is the trade-off between underfitting to a new session task and preventing forgetting the knowledge for earlier sessions. In this paper, we reveal that the angular space occupied by the features within the embedded area is relatively narrow. Consequently, after the base session training with the converged feature extractor, adding features of new classes easily overlaps with the previously occupied space of previous classes. Furthermore, in contrast to the base session classes, whose features are relatively well aggregated, the features of new session classes are dispersed over large regions. Thus, we propose the Incrementally Learned Angular Representation (ILAR) learning structure to address these issues. During the base session, ILAR attempts to increase the marginal spacing between the distributions of features for each class. In incremental sessions, modifications of features and classifiers corresponding to the previous session classes are limited to preserve the past knowledge while the features of new classes are readjusted. Furthermore, we generate additional features according to the statistics learned from the base session to reduce the variance caused by data shortage. Experiments proceeded on three popular benchmark datasets, including CIFAR100, miniImageNet, and CUB200. We demonstrate that the proposed method achieves state-of-the-art performances by effectively enhancing the new session classification ability while preserving the knowledge of the past sessions.

Time-series data with missing values are a common occurrence in various fields, including healthcare, meteorology, and robotics. The process of imputation aims to fill in the missing values with valid values. Most imputation methods implicitly train models due to the presence of missing values. In this paper, we propose Random Drop Imputation with Self-training (RDIS), a novel training method for time-series data imputation models. In RDIS, we generate extra missing values by applying a random drop to the observed values in incomplete data. We can explicitly train the imputation models by filling in the missing values. Moreover, we utilize self-training with pseudo values to exploit the original missing values. To enhance the quality of pseudo values, we set a threshold and filter them based on entropy calculation. To evaluate the effectiveness of RDIS for imputing time-series data, we test it across several imputation models and obtain competitive results on three real-world datasets.

In the conventional successive cancellation (SC) decoder for polar codes, all the future bits to be estimated later are treated as random variables. However, polar codes inevitably involve frozen bits, and their concatenated coding schemes also include parity bits (or dynamic frozen bits) causally generated from the past bits estimated earlier. We refer to the frozen and parity bits located behind a target decoding bit as its future constraints (FCs). Although the values of FCs are deterministic given the past estimates, they have not been exploited in the conventional SC-based decoders, not leading to optimality. In this paper, with a primary focus on the binary erasure channel (BEC), we propose SC-check (SCC) and belief propagation SCC (BP-SCC) decoding algorithms in order to leverage FCs in decoding. We further devise an improved tree search technique based on stack-based backjumping (SBJ) to solve dynamic constraint satisfaction problems (CSPs) formulated by FCs. Over the BEC, numerical results show that a combination of the BP-SCC algorithm and the SBJ tree search technique achieves the erasure recovery performance close to the dependence testing (DT) bound, a bound of achievable finite-length performance.

The industrial field faces the problem of process optimization by finding the factors affecting the yield of the process and controlling them appropriately. However, due to limited resources such as time and money, optimization is performed using a low evaluation budget. In addition, for process stability, the lower limit of the yield is set so that the yield must be maintained above this limit during optimization. Bayesian Optimization (BO) can be an effective solution in acquiring optimal samples that satisfy a safety constraint given a low evaluation budget. However, many existing BO algorithms have some limitations such as significant performance degradation due to model misspecification, and high computational load. Thus, we propose a practical safe BO algorithm, A-SafeBO, that effectively reduces performance degradation due to model misspecification using only a limited evaluation budget. Additionally, our algorithm performs computations for a large number of observations and high-dimensional input spaces by using Ensemble Gaussian Processes and Safe Particle Swarm Optimization. Here, we also propose a new acquisition function that leads to a wider exploration even under the constraint of safety. This will help deviate from the local optimum and achieve a better recommendation. Our algorithm empirically guarantees convergence and performance through evaluations on several synthetic benchmarks and a real-world optimization problem.

This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user’s walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.

Abstract Background Host tp53 mutations are frequently found during the early stages of colitis-associated colorectal cancer (CAC), but whether such mutations induce gut microbiota dysbiosis and chronic intestinal inflammation that contributes to the development of CAC, remains unknown. Results We found that zebrafish tp53 mutant larvae exhibited elevated intestinal inflammation, by monitoring the NFκB activity in the mid-distal intestines of zebrafish larvae using an NFκB:EGFP transgenic reporter line in vivo as well as neutrophil infiltration into the intestine. This inflammation was due to dysbiotic gut microbiota with reduced diversity, revealed using both 16S rRNA amplicon sequencing and a germfree larva model. In this dysbiosis, Aeromonas spp. were aberrantly enriched as major pathobionts and exhibited the capacity for aggressive colonization in tp53 mutants. Importantly, the ex-germfree experiments supported the causality of the host tp53 mutation for inducing the inflammation. Transcriptome and high-performance liquid chromatography analyses of the host gastrointestinal tracts identified dysregulated sialic acid (SA) metabolism concomitant with increased host Neu5Gc levels as the key determinant of aberrant inflammation, which was reversed by the sialidase inhibitors oseltamivir and Philippin A. Conclusions These results demonstrate a crucial role for host tp53 in maintaining symbiosis and immune homeostasis via SA metabolism. Disturbed SA metabolism via a tp53 mutation may be exploited by specific elements of the gut microbiome, eliciting both dysbiosis and inflammation. Manipulating sialometabolism may therefore provide an efficacious therapeutic strategy for tp53 mutation-induced dysbiosis, inflammation, and ultimately, related cancers. Video Abstract

Ground segmentation of outdoor LiDAR point clouds remains challenging due to its sparse and irregular nature. This paper presents SectorGSnet: a ground segmentation framework for outdoor LiDAR point clouds, aiming to accomplish this task efficiently and effectively. The framework consists of a sector encoder module and a segmentation module. The former module introduces a novel bird’s-eye-view (BEV) sector partition strategy that discretizes the point cloud into sectors of varying sizes to enhance the 2D representation ability of the point cloud. Then, the points within each sector are fed into a multimodal PointNet encoder to obtain the corresponding sector feature map. In the latter module, a lightweight segmentation network next learns binary classification from the sector feature map, and point labels are restored from the sector segmentation results. Our proposed framework is trained and evaluated on SemanticKITTI against state-of-the-art 2D projection-based approaches and achieves an excellent balance between performance and computational complexity. We conducted inference at 170.6 Hz on a desktop PC with a GTX2080Ti GPU, and also experimented on a resource-limited platform with only 10 watts of power.

Real-time hand gesture recognition plays a vital role in human-computer interaction (HCI). Recent radar-based hand gesture recognition methods have focused on achieving high classification accuracy using deep neural network (DNN)-based classifiers. However, the hand gesture recognition system should not only classify the gestures accurately but also detect out-of-distribution (OOD) samples to be used in real-world HCI scenarios with high reliability. Recognition systems without OOD detection capability misclassify unintended gestures in silence, especially in real-time scenarios. To tackle this problem, we propose a real-time hand gesture recognition system that can simultaneously classify hand gestures and detect OOD samples by using a Frequency Modulated Continuous Wave (FMCW) radar sensor. First, we design radar data processing technique and Transformer encoder-based classifier to achieve high classification accuracy. Second, the relative Mahalanobis distance (RMD)-based OOD detection method is adopted to increase the reliability of the proposed system. Finally, one in-distribution dataset and two OOD datasets are collected to verify the proposed system. The proposed system achieves a classification accuracy of 93.95% on the in-distribution dataset. We conduct the OOD detection experiments with two OOD datasets for which the proposed system reports AUROC values of 92.96% and 92.84%, respectively. Furthermore, the feasibility of the proposed system is certified through a real-time experimental demonstration.

Optimization of equipment structure and process conditions is essential to obtain thin films with the required properties, such as film thickness, trapped charge density, leakage current, and memory characteristics, that ensure reliability of the corresponding device. In this study, we fabricated metal–insulator–semiconductor (MIS) structure capacitors using HfO2 thin films separately deposited by remote plasma (RP) atomic layer deposition (ALD) and direct-plasma (DP) ALD and determined the optimal process temperature by measuring the leakage current and breakdown strength as functions of process temperature. Additionally, we analyzed the effects of the plasma application method on the charge trapping properties of HfO2 thin films and properties of the interface between Si and HfO2. Subsequently, we synthesized charge-trapping memory (CTM) devices utilizing the deposited thin films as charge-trapping layers (CTLs) and evaluated their memory properties. The results indicated excellent memory window characteristics of the RP-HfO2 MIS capacitors compared to those of the DP-HfO2 MIS capacitors. Moreover, the memory characteristics of the RP-HfO2 CTM devices were outstanding as compared to those of the DP-HfO2 CTM devices. In conclusion, the methodology proposed herein can be useful for future implementations of multiple levels of charge-storage nonvolatile memories or synaptic devices that require many states.

The dissipation patterns of chlorfenapyr, cyenopyrafen, indoxacarb, and spirotetramat on strawberries and the effects of different household washing methods were investigated. A risk assessment was also conducted by monitoring the insecticide residues detected. The concentrations ranged from 0.011 to 0.27 mg/kg for chlorfenapyr, 0.064 to 0.99 mg/kg for cyenopyrafen, 0.042 to 0.53 mg/kg for indoxacarb, and from 0.25 to 1.3 mg/kg for spirotetramat, which were all below the maximum residue limits (MRLs) reported. Soaking the fruit in solution and then rinsing with running water (B) led to better residue removal (40.9 ± 23.7%) than only soaking in solution (A) (24.7 ± 22.5%). However, neither method decreased chlorfenapyr concentrations, suggesting that the physical–chemical properties of chlorfenapyr could also affect its removal on strawberries. Regarding the different washing solutions in method B, 3% vinegar (removal efficiency: 48.7%) and 3% salt (45.7%) were the most efficient, followed by 3% green tea (38.9%), and tap water only (24.6%). Additionally, the estimated risk quotients (RQs) for strawberry consumption for women were about 1.5 times higher than those observed for men, but both were lower than 1, suggesting minimal risk to humans.

Nowadays, the welding process is essential in various manufacturing industrial fields, such as aerospace, vehicle production, and shipbuilding. The welding defects caused in the process need to be monitored as they can cause serious accidents and losses. Traditional computer vision methods in an industrial application are inefficient when the detection targets have variations in shape, scale, and color because the detection performance depends on the hand-crafted features. To overcome this limitation, deep learning models, such as the convolutional neural network (CNN), are applied to industrial defect detection. These CNN-based models trained on static images, however, have a low performance that cannot meet the industrial requirements. To deal with the challenge, bidirectional Convolutional Recurrent Reconstructive Network (bi-CRRN) is proposed for welding defect detection and localization based on welding video. Spatio-temporal data, specifically the forward and backward sequences, are considered in our bi-CRRN to get high detection performance. Moreover, an automatic defect detection equipment is developed to weld a material and monitor the welding bead simultaneously. We demonstrate that the proposed bi-CRRN outperforms the other segmentation network models in welding defect detection.

Since the Convolutional Neural Network (CNN) has surfaced and fascinated the world, many researchers have exploited CNN for image classification, object detection, semantic segmentation, etc. However, the conventional CNNs have a pyramidal structure and were designed to process images which have the same size. Although some CNNs can accept images of various sizes, performance is degraded for images smaller than the size of images used for training. In this paper, we propose MarsNet, a CNN based end-to-end network for multi-label classification with an ability to accept various size inputs. In order to allow the network to accept such images, dilated residual network (DRN) is modified to get higher resolution feature maps, and horizontal vertical pooling (HVP) is newly designed to efficiently aggregate positional information from the feature maps. Furthermore, multi-label scoring module and threshold estimation module are employed to serve the purpose of multi-label classification. We verify the effectiveness of the proposed network through two distinctive experiments. We first verify our model by inspecting and classifying multiple types of defects occurred in PCB screen printer using solder paste inspection (SPI) datasets. Secondly, we verify our network using VOC 2007 dataset. Our network is pioneering in that no research has attempted to accomplish multi-label classification for defects in addition to being able to take input images of various sizes in SPI field.

In recent years, in the field of education technology, artificial intelligence tutors have come to be expected to provide individualized educational services to help learners achieve high levels of academic success. To this end, AI tutors need to be able to understand the current status and preferences of a learner and then suggest appropriate learning contents accordingly. However, it is challenging to monitor learner status and preferences continually and to recommend appropriate educational services. In this paper, we propose an individualized AI tutor as an integrated system of three developmental learning networks (DLNs) by extending a deep adaptive resonance theory (Deep ART) network, a neural network capable of incremental learning. Specifically, the learner status DLN is able to easily add new input channels about learner status without disrupting existing classifiers. The learner preference DLN is to categorize learner preferences based on frequency as well as sequence of events. The learner experience DLN is updated to immediately reflect alteration of the educational effectiveness in the current classification. Our AI tutor is currently embedded in a commercialized mobile application for teaching the Korean language to children. Experimental results show that the AI tutor application efficiently helps children learn the Korean language.

In the field of surface mount technology (SMT), early detection of defects in production machines is crucial to prevent yield reduction. In order to detect defects in the production machine without attaching additional costly sensors, attempts have been made to classify defects in solder paste printers using defective solder paste pattern (DSPP) images automatically obtained through solder paste inspection (SPI). However, since the DSPP images are sparse, have various sizes, and are hardly collected, existing CNN-based classifiers tend to fail to generalize and over-fitted to the train set. Besides, existing studies employing only multi-label classifiers are less helpful since when two or more defects are observed in the DSPP image, the location of each defect can not be specified. To solve these problems, we propose a dual-level defect detection PointNet (D3PointNet), which extracts point cloud features from DSPP images and then performs the defect detection in two semantic levels: a micro-level and a macro-level. In the micro-level, a type of printer defect per point is identified through segmentation. In the macro-level, all types of printer defects appearing in a DSPP image are identified by multi-label classification. Experimental results show that the proposed D3PointNet is robust to the sparsity and size changes of the DSPP image, and its exact match score was 10.2% higher than that of the existing CNN-based state-of-the-art multi-label classification model in the DSPP image dataset.

Early and accurate detection of scalp hair loss is imperative to provide timely and effective treatment plans to halt further progression and save medical costs. Many techniques have been developed leveraging deep learning to automate the hair loss detection process. However, the accuracy and robustness of assessing hair loss severity still remain a challenge and barrier for transitioning such a technique into practice. The presented work proposes an efficient and accurate algorithm to classify hair follicles and estimate hair loss severity, which was implemented and validated using a multitask deep learning method via a Mask R-CNN framework. A microscopic image of the scalp was resized, augmented, then processed through pre-trained ResNet models for feature extraction. The key features considered in this study concerning hair loss severity include the number of hair follicles, the thickness of the hair, and the number of hairs in each hair follicle. Based on these key features, labeling of hair follicles (healthy, normal, and severe) were performed on the images collected from 10 men in varying stages of hair loss. More specifically, Mask R-CNN was applied for instance segmentation of the hair follicle region and to classify the hair follicle state into three categories, following the labeling convention (healthy, normal and severe). Based on the state of each hair follicle captured from a single image, an estimation of hair loss severity was determined for that particular region of the scalp, namely local hair loss severity index (P), and by combining P of multiple images taken and processed from different parts of the scalp, we constructed the hair loss severity estimation (Pavg) and visualized in a heatmap to illustrate the overall hair loss type and condition. The proposed hair follicle classification and hair loss severity estimation using Mask R-CNN demonstrated a more efficient and accurate algorithm compared to other methods previously used, enhancing the classification accuracy by 4 to 15%. This performance supports its potential for use in clinical settings to enhance the accuracy and efficiency of current hair loss diagnosis and prognosis techniques.

A crankcase explosion relief valve (CERV) is installed to minimize the effects of explosions occurring in the crankcase of marine engines. According to the regulations of the International Association of Classification Societies (IACS), installation of CERV is mandatory and it must be designed and manufactured based on the engine size. As there are various types of engines, the CERV must be developed accordingly. A high cost is required for the explosion test in the development process, so the test is performed for verification purposes in the final development stage. However, if the developed CERV causes an inappropriate result that is different from the expected performance in the explosion test, it needs to return to the development process so conducting the explosion test in the final stage may be unreasonable. In this study, to predict CERV performance, the chamber and CERV were modeled in 2D, and numerical analysis was performed assuming that the DISC of the CERV was opened by causing an explosion inside the chamber. The results of the numerical analysis were verified by comparing the results with the pressure rising during the explosion. In addition, the reasonable results were obtained for temperature, pressure, and combustion products through comparison to the theoretical calculation results. If the numerical analysis method used in this study is applied, it is expected to be able to predict the performance and reactants at the stage before the explosion test when developing the crankcase explosion relief valve.

Methoxyfenozide and pymetrozine are used for pest control in the cultivation of Chinese cabbage. This has raised concerns in recent years due to health risks. Therefore, this study aimed to determine the residual concentrations of pesticides in the target crop and associated health risks. The dynamics and influence of environmental factors on the dissipation of methoxyfenozide and pymetrozine residues in Chinese cabbage were investigated. Analyses were performed using a modified QuEchERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) and an optimized high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The observed half-lives of methoxyfenozide and pymetrozine in cabbage samples ranged between two sampling seasons: in May–June, half-lives of methoxyfenozide and pymetrozine were 1.20 days and 1.89 days, respectively; during October–November, half-lives of methoxyfenozide and pymetrozine were 11.8 and 2.80 days, respectively. Meanwhile, a negative Spearman correlation was found between the residual concentrations and temperature (p < 0.01). This indicates that higher temperatures resulted in higher dissipation rates for methoxyfenozide and pymetrozine, suggesting that these pesticides degraded faster at higher temperatures. Additionally, higher pesticide residues in Chinese cabbage during low-temperature seasons resulted in higher risk quotients (RQ) (RQ > 1) for both analyzed compounds, which suggests that the effect of temperature on pesticide degradation needs to be considered as an essential factor while setting up the maximum residue limits (MRL).

Due to the development of short-range radar with high-resolution, the radar sensor has a high potential to be used in real human-computer interaction (HCI) applications. The radar sensor has advantages over optical cameras in that it is unaffected by illumination and it is able to detect the objects in an occluded environment. This paper proposes a hand gesture recognition system for a real-time application of HCI using 60 GHz frequency-modulated continuous wave (FMCW) radar, Soli, developed by Google. The overall system includes signal processing part that generates range-Doppler map (RDM) sequences without clutter and machine learning part including a long short-term memory (LSTM) encoder to learn the temporal characteristics of the RDM sequences. A set of data is collected from 10 participants for the experiment. The proposed hand gesture recognition system successfully distinguishes 10 gestures with a high classification accuracy of 99.10%. It also recognizes the gestures of a new participant with an accuracy of 98.48%.

This paper presents the ballistic armor performance examination and thickness estimation for the latest naval ship structure materials in the Republic of Korea. Up to date, research regarding methods of ballistic experiments establishing database on the latest hull structure materials as well as a precise method of estimating required thickness of armor against specific projectiles have been rarely researched. In order to build a database and estimate proper thicknesses of structure materials, this study used four structure materials that have been widely applied in naval ships such as AH36 steel, AL5083, AL5086, and Fiber Reinforced Plastics (FRP). A 7.62 × 39 mm mild steel core bullet normally fired by AK-47 gun was considered as a threat due to its representativeness. Tate and Alekseevskii's penetration algorithm was also used to calculate a correction factor (α) and then estimate the armor thickness of naval ship hull structure materials with a given impact velocity. Through live fire experiments, the proposed method performance difference was measured to be 0.6% in AH36, 0.4% in AL5083, 0.0% in AL5086, and 8.0% in FRP compared with the experiment results. Keywords: Ballistic protection, Ballistic analysis, Armor design for ship structure, Ship survivability, Vulnerability, Tate equation

An analytical method was developed to simultaneously determine pyridate, quizalofop-ethyl, and cyhalofop-butyl in brown rice, soybean, potato, pepper, and mandarin using LC-MS/MS. Purification was optimized using various sorbents: primary–secondary amine, octadecyl (C18) silica gel, graphitized carbon black, zirconium dioxide-modified silica particles, zirconium dioxide-modified silica particles (Z-SEP), and multi-walled carbon nanotubes (MWCNTs). Three versions of QuECHERS methods were then tested using the optimal purification agent. Finally, samples were extracted using acetonitrile and QuEChERS EN salts and purified using the Z-SEP sorbent. A six-point matrix-matched external calibration curve was constructed for the analytes. Good linearity was achieved with a determination coefficient ≥0.999. The limits of detection and quantification were 0.0075 mg/kg and 0.01 mg/kg, respectively. The method was validated after fortifying the target standards to the blank matrices at three concentration levels with five replicates for each concentration. The average recovery was within an acceptable range (70–120%), with a relative standard deviation

ABSTRACT To understand toxin-stimulated host-pathogen interactions, we performed dual-transcriptome sequencing experiments using human epithelial (HT-29) and differentiated THP-1 (dTHP-1) immune cells infected with the sepsis-causing pathogen Vibrio vulnificus (either the wild-type [WT] pathogen or a multifunctional-autoprocessing repeats-in-toxin [MARTX] toxin-deficient strain). Gene set enrichment analyses revealed MARTX toxin-dependent responses, including negative regulation of extracellular related kinase 1 (ERK1) and ERK2 (ERK1/2) signaling and cell cycle regulation in HT-29 and dTHP-1 cells, respectively. Further analysis of the expression of immune-related genes suggested that the MARTX toxin dampens immune responses in gut epithelial cells but accelerates inflammation and nuclear factor κB (NF-κB) signaling in immune cells. With respect to the pathogen, siderophore biosynthesis genes were significantly more highly expressed in WT V. vulnificus than in the MARTX toxin-deficient mutant upon infection of dTHP-1 cells. Consistent with these results, iron homeostasis genes that limit iron levels for invading pathogens were overexpressed in WT V. vulnificus-infected dTHP-1 cells. Taken together, these results suggest that MARTX toxin regulates host inflammatory responses during V. vulnificus infection while also countering host defense mechanisms such as iron limitation. IMPORTANCE V. vulnificus is an opportunistic human pathogen that can cause life-threatening sepsis in immunocompromised patients via seafood poisoning or wound infection. Among the toxic substances produced by this pathogen, the MARTX toxin greatly contributes to disease progression by promoting the dysfunction and death of host cells, which allows the bacteria to disseminate and colonize the host. In response to this, host cells mount a counterattack against the invaders by upregulating various defense genes. In this study, the gene expression profiles of both host cells and V. vulnificus were analyzed by RNA sequencing to gain a comprehensive understanding of host-pathogen interactions. Our results suggest that V. vulnificus uses the MARTX toxin to subvert host cell immune responses as well as to oppose host counterattacks such as iron limitation.

Abstract Epigenetic dysregulation is a major driver of tumorigenesis. To identify tumor‐suppressive microRNAs repressed by DNA methylation in gastric cancer (GC), we analyzed the genome‐wide DNA methylation and microRNA expression profiles of EpCAM+/CD44+ GC cells. Among the set of microRNAs screened, miR‐1271 was identified as a microRNA repressed by DNA methylation in GC. Forced miR‐1271 expression substantially suppressed the growth, migration, and invasion of GC cells. To identify candidate target genes and signaling pathways regulated by miR‐1271, we performed RNA sequencing. Among the genes down‐regulated by miR‐1271, MAP2K1 (MEK1) was significantly repressed by miR‐1271, and the associated ERK/MAPK signaling pathway was also inhibited. TEAD4 was also repressed by miR‐1271, and the associated YAP1 signatures within genes regulated by miR‐1271 were significantly enriched. These findings uncovered MEK1 and TEAD4 as novel miR‐1271 targets and suggest that the epigenetic silencing of miR‐1271 is crucial for GC development.

Colon cancer: A delicate balance to regulate growth Insights into a signaling protein’s role in cell growth could inform new therapeutic strategies for treating colon cancer. SOCS-2 acts as an ‘off switch’ for cell signaling pathways. It has been identified as possibly protective against many cancers, although some cancers are associated with elevated SOCS-2 levels. Researchers led by Daeghon Kim at Chonbuk National University Hospital in South Korea have now shown that the effects of SOCS-2 are apparently dependent on how much of it is present. Moderate levels of SOCS-2 can suppress growth in colon cancer cells, but Kim’s team showed that excessive SOCS-2 has the opposite effect, promoting proliferation. The researchers also identified a gene commonly mutated in cancer cells that can drive overproduction of SOCS-2. Drugs that inhibit SOCS-2 or block its production may therefore offer useful treatments for colorectal cancer.

In this paper, we address a number of weaknesses of the windowed decoding of spatially coupled low-density parity-check (SC LDPC) codes and propose three modifications that simultaneously improve its performance, complexity, and latency. An effective termination method of the windowed decoding and the reuse of edge messages of previous target symbols provide a good performance-latency tradeoff when compared with the conventional windowed decoder. Also, we propose a scheme that lowers the error floor, in which the amplified edge messages of the previous window are used in the present window. The proposed windowed decoding, consisting of the three schemes, provides a significant performance gain with smaller latency. The validity of the new windowed decoding is verified by the evaluation with codes from different SC LDPC ensembles.

Abstract Thread-embedding therapy has been widely applied for cosmetic purposes such as wrinkle reduction and skin tightening. Particularly, gold thread was reported to support connective tissue regeneration, but, its role in hair biology remains largely unknown due to lack of investigation. When we implanted gold thread and Happy Lift™ in human patient for facial lifting, we unexpectedly found an increase of hair regrowth in spite of no use of hair growth medications. When embedded into the depilated dorsal skin of mice, gold thread or polyglycolic acid (PGA) thread, similarly to 5% minoxidil, significantly increased the number of hair follicles on day 14 after implantation. And, hair re-growth promotion in the gold threadimplanted mice were significantly higher than that in PGA thread group on day 11 after depilation. In particular, the skin tissue of gold thread-implanted mice showed stronger PCNA staining and higher collagen density compared with control mice. These results indicate that gold thread implantation can be an effective way to promote hair re-growth although further confirmatory study is needed for more information on therapeutic mechanisms and long-term safety.

Self-powered wireless sensor systems have emerged as an important topic for condition monitoring in nuclear power plants. However, commercial wireless sensor systems still cannot be fully self-sustainable due to the high power consumption caused by excessive signal processing in a mini-electronic computing system. In this sense, it is essential not only to integrate the sensor system with energy-harvesting devices but also to develop simple data processing methods for low power schemes. In this paper, we report a patch-type vibration visualization (PVV) sensor system based on the triboelectric effect and a visualization technique for self-sustainable operation. The PVV sensor system composed of a polyethylene terephthalate (PET)/Al/LCD screen directly converts the triboelectric signal into an informative black pattern on the LCD screen without excessive signal processing, enabling extremely low power operation. In addition, a proposed image processing method reconverts the black patterns to frequency and acceleration values through a remote-control camera. With these simple signal-to-pattern conversion and pattern-to-data reconversion techniques, a vibration visualization sensor network has successfully been demonstrated.

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. ‘dada2’ performs trimming of the high-throughput sequencing data. ‘QuasR’ and ‘mosaics’ perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, ‘ChIPseeker’ performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.

Currently, Illumina sequencers are the globally leading sequencing platform in the next-generation sequencing market. Recently, MGI Tech launched a series of new sequencers, including the MGISEQ-2000, which promise to deliver high-quality sequencing data faster and at lower prices than Illumina’s sequencers. In this study, we compared the performance of two major sequencers (MGISEQ-2000 and HiSeq 4000) to test whether the MGISEQ-2000 sequencer delivers high-quality sequence data as suggested. We performed RNA sequencing of four human colon cancer samples with the two platforms, and compared the sequencing quality and expression values. The data produced from the MGISEQ-2000 and HiSeq 4000 showed high concordance, with Pearson correlation coefficients ranging from 0.98 to 0.99. Various quality control (QC) analyses showed that the MGISEQ-2000 data fulfilled the required QC measures. Our study suggests that the performance of the MGISEQ-2000 is comparable to that of the HiSeq 4000 and that the MGISEQ-2000 can be a useful platform for sequencing.

DYRK1A is a major causative gene in Down syndrome (DS). Reduced incidence of solid tumors such as neuroblastoma in DS patients and increased vascular anomalies in DS fetuses suggest a potential role of DYRK1A in angiogenic processes, but in vivo evidence is still scarce. Here, we used zebrafish dyrk1aa mutant embryos to understand DYRK1A function in cerebral vasculature formation. Zebrafish dyrk1aa mutants exhibited cerebral hemorrhage and defects in angiogenesis of central arteries in the developing hindbrain. Such phenotypes were rescued by wild-type dyrk1aa mRNA, but not by a kinase-dead form, indicating the importance of DYRK1A kinase activity. Chemical screening using a bioactive small molecule library identified a calcium chelator, EGTA, as one of the hits that most robustly rescued the hemorrhage. Vascular defects of mutants were also rescued by independent modulation of calcium signaling by FK506. Furthermore, the transcriptomic analyses supported the alterations of calcium signaling networks in dyrk1aa mutants. Together, our results suggest that DYRK1A plays an essential role in angiogenesis and in maintenance of the developing cerebral vasculature via regulation of calcium signaling, which may have therapeutic potential for DYRK1A-related vascular diseases.

This article proposes a framework for competency adjustment and workload balancing in multirobot task allocation. Competency represents the ability of a robot to execute a task in terms of quality and cost, and workload balancing denotes distribution of workload among robots. This framework considers the quality and cost of a robot for a task and adjusts them in accordance with environmental changes. For workload balancing, the framework utilizes the concept of subsidy to encourage participation from the less active members of the robot team. The proposed framework is implemented in a simulated cleaning mission. Simulation results demonstrate that this framework can adjust the competency in accordance with environmental changes and distribute workload among robots in a balanced manner.

Gram-negative bacteria have an outer membrane inhibiting the entry of antibiotics. Porins, found within the outer membrane, are involved in regulating the permeability of β-lactam antibiotics. β-lactamases are enzymes that are able to inactivate the antibacterial properties of β-lactam antibiotics. Interestingly, porins and β-lactamase are found in outer membrane vesicles (OMVs) of β-lactam-resistant Escherichia coli and may be involved in the survival of susceptible strains of E. coli in the presence of antibiotics, through the hydrolysis of the β-lactam antibiotic. In this study, OMVs isolated from β-lactam-resistant E. coli and from mutants, lacking porin or β-lactamase, were evaluated to establish if the porins or β-lactamase in OMVs were involved in the degradation of β-lactam antibiotics. OMVs isolated from E. coli deficient in β-lactamase did not show any degradation ability against β-lactam antibiotics, while OMVs lacking OmpC or OmpF showed significantly lower levels of hydrolyzing activity than OMVs from parent E. coli. These data reveal an important role of OMVs in bacterial defense mechanisms demonstrating that the OmpC and OmpF proteins allow permeation of β-lactam antibiotics into the lumen of OMVs, and antibiotics that enter the OMVs can be degraded by β-lactamase.

The dorsal and ventral regions of the hippocampus are important in cognitive and emotional processing, respectively. Various approaches have revealed the differential molecular and structural characteristics, and functional roles of the hippocampus. Recent RNA sequencing (RNA-seq) technology has enriched our understanding of the hippocampus by elucidating more detailed information on gene expression patterns. However, no RNA-seq–based study on gene profiles in the developing hippocampus has been reported. Using RNA-seq–based bioinformatic analysis in conjunction with quantitative real-time polymerase chain reaction analysis and a comparison of in situ hybridization data obtained from the Allen Brain Atlas, we provide a thorough analysis of differentially expressed genes in the dorsal and ventral hippocampus at specific developmental ages representing the postnatally maturing hippocampus. Genes associated with particular functional pathways and marker genes for particular neurological diseases were found to be distinctively segregated within either the dorsal or ventral hippocampus at specific or at all developmental ages examined. We also report novel molecular markers enriched in the dorsal or ventral hippocampus. Taken together, this study provides insights into the molecular mechanisms underlying physiological functions linked to the dorsal or ventral hippocampus. The information provided in the study also contributes to a better understanding of brain functions and serves as a resource for future studies on the pathophysiology of dorsal and ventral hippocampal functions.

Cyclin Y (CCNY), which is a cyclin protein known to play a role in cell division, is unexpectedly and thus interestingly expressed in non-proliferating neuronal cells. There have been only a few studies reporting the neuronal functions of CCNY in synapse remodeling and hippocampal long-term potentiation. Therefore, we here provide global and comprehensive information on the putative functions of CCNY in biological and functional pathways in neuronal systems. We adopted high-throughput RNA-sequencing technology for analyzing transcriptomes regulated by CCNY and utilized bioinformatics for identifying putative molecules, biological processes, and functional pathways that are possibly connected to CCNY functions in hippocampal neuronal cells of rats. We revealed that several enriched annotation terms and pathways associated with CCNY expression within neurons, including apoptosis, learning or memory, synaptic plasticity, actin cytoskeleton, focal adhesion, extracellular matrix-receptor interaction and chemokine signaling pathway are targeted by CCNY. In addition, the mRNA levels of some genes enriched for those annotation terms and pathways or genes reported to be altered in Alzheimer's disease mouse model were further validated by quantitative real-time PCR in hippocampal neuronal cells. The present study provides an excellent resource for future investigations of CCNY functions in neuronal systems.

The fabrication technology for metallic fuel has been developed to produce the driver fuel in a PGSFR in Korea since 2007. In order to evaluate the irradiation integrity and validate the in-reactor of the starting metallic fuel with FMS cladding for the loading of the metallic fuel, U-10 wt.%Zr fuel rodlets were fabricated and evaluated for a verification of the starting driver fuel through an irradiation test in the BOR-60 fast reactor. The injection casting method was applied to U-10 wt.%Zr fuel slugs with a diameter of 5.5 mm. Consequently, fuel slugs per melting batch without casting defects were fabricated through the development of advanced casting technology and evaluation tests. The optimal GTAW welding conditions were also established through a number of experiments. In addition, a qualification test was carried out to prove the weld quality of the end plug welding of the metallic fuel rodlets. The wire wrapping of metallic fuel rodlets was successfully accomplished for the irradiation test. Thus, PGSFR fuel rodlets have been soundly fabricated for the irradiation test in a BOR-60 fast reactor.

U-10wt%Zr spherical particles for use as particulate fuel were prepared by centrifugal atomization and subjected to pressureless sintering, which is one of the simplest powder processing techniques. At sintering temperature of 1100°C for 30 or 60 min, all samples ranging from +50 to −325 mesh showed no apparent bonding between the particles. However, at 1150°C (80 min), all samples formed a bulk body and the microstructures showed apparent sintering stages. Particularly, sample B (50–70 mesh) and sample C (70–100 mesh) showed pore characteristics suitable for a particulate fuel. The results suggest that pressureless sinterability for U-10Zr particulate fuel can be improved by adding small-size (–325 mesh) particles.

For constructing marine liquefied natural gas (LNG) fuel/storage tanks, high manganese steel is being recognized as an alternative to stainless steel, nickel alloy, and aluminum alloy. In this study, the nonlinear tensile behavior of high manganese steel was investigated and numerically simulated at cryogenic temperatures at which natural gas exists as a liquid. Physical experimental tensile tests were carried out for a flat test specimen at 293 K and 110 K. In particular, the tensile behavior of a flat hole-notched high manganese steel specimen was experimentally obtained. A specimen with a hole was readily fractured compared to one without a hole. Tensile behavior of high manganese steel at the two cryogenic temperatures was compared to that of stainless steel, nickel, and aluminum alloy. In addition, numerical tests were performed for flat tensile specimens under identical experimental conditions. The elastoplastic damage model was derived and implemented using an Abaqus user-defined subroutine to appropriately simulate material behavior and degradation. The influence of some parameters on tensile behavior was investigated. The simulation results satisfactorily replicated the nonlinear tensile behavior of high manganese steel. The proposed numerical method, which is based on the damage-coupled material constitutive model, can be applied to structural analysis on the finite element analysis platform considering mechanical nonlinearities induced by severe conditions such as cryogenic temperature.

Metallic fuel slugs of U–10Zr–5Mn (wt%), a surrogate alloy for the U–TRU–Zr (TRU: a transuranic element) alloys proposed for sodium-cooled fast reactors, were prepared by injection casting in a laboratory-scale furnace, and their characteristics were evaluated. As-cast U–Zr–Mn fuel rods were generally sound, without cracks or thin sections. Approximately 68% of the original Mn content was lost under dynamic vacuum and the resulting slug was denser than those prepared under Ar pressure. The concentration of volatile Mn was as per the target composition along the entire length of the rods prepared under 400 and 600 Torr. Impurities, namely, oxygen, carbon, silicon, and nitrogen, totaled less than 2,000 ppm, satisfying fuel criteria.

Polar codes are the first family of error correcting codes that provably achieve the capacity of symmetric binary-input discrete memoryless channels with low complexity. Since the development of polar codes, there have been many studies to improve their finite-length performance. As a result, polar codes are now adopted as a channel code for the control channel of 5G new radio of the 3rd generation partnership project. However, the decoder implementation is one of the big practical problems and low complexity decoding has been studied. This paper addresses a low complexity successive cancellation list decoding for polar codes utilizing multiple cyclic redundancy check (CRC) codes. While some research uses multiple CRC codes to reduce memory and time complexity, we consider the operational complexity of decoding, and reduce it by optimizing CRC positions in combination with a modified decoding operation. Resultingly, the proposed scheme obtains not only complexity reduction from early stopping of decoding, but also additional reduction from the reduced number of decoding paths.

A newly reported species of Poaceae, Eragrostis aquatica, is reported from South Korea, which was collected from Naejang and Sucheong reservoir, Jeongeup-si, Jeollabuk-do. E. aquatica is known to be an endemic plant in Japan, but its distribution in South Korea was confirmed in the present study. It can be distinguished from Eragrostis pilosa and Eragrostis multicaulis by its habit and morphological characteristics, with perennial and spikelets 1.5-2.0 mm wide.