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Lipoteichoic acid (LTA) and peptidoglycan (PGN) are considered as key virulence factors of Staphylococcus aureus, which is a representative sepsis-causing Gram-positive pathogen. However, cooperative effect of S. aureus LTA and PGN on nitric oxide (NO) production is still unclear despite the pivotal roles of NO in initiation and progression of sepsis. We here evaluated the cooperative effects of S. aureus LTA (SaLTA) and muramyl dipeptide (MDP), the minimal structure of PGN, on NO production in both a mouse macrophage-like cell line, RAW 264.7 and mouse bone marrow-derived macrophages (BMMs). Although MDP alone did not affect NO production, MDP potently enhanced SaLTA-induced NO production via the expression of inducible NO synthases. The enhanced NO production was ameliorated in BMMs from TLR2-, CD14-, MyD88-, and NOD2-deficient mice. Moreover, the augmented SaLTA-induced NO production by MDP was attenuated by inhibitors specific for PAFR and MAP kinases. Furthermore, MDP also potently increased SaLTA-induced activities of STAT1, NF-κB, and AP-1 transcription factors, and specific inhibitors for these transcription factors suppressed the elevated NO production. Collectively, these results demonstrated that MDP potentiates SaLTA-induced NO production via TLR2/NOD2/PAFR, MAP kinases signaling axis, resulting in the activation of NF-κB, AP-1 and STAT1 transcription factors.

Abstract Planar object tracking (POT) is crucial in vision‐based robotic applications. Despite significant advancements, tracking planar objects under real‐world conditions remains a challenge owing to various factors. The method proposed by the authors leverages SIFT features and generalized Hough transform, which is enhanced by spatial locality, to mitigate background clutter and false matching. The experimental results demonstrate that this method significantly outperforms existing approaches, achieving higher precision across various alignment error thresholds.

The global prevalence of obesity is rising year by year, which has become a public health problem worldwide. Many animal and clinical studies have shown that Lactiplantibacillus plantarum is considered an ideal probiotic and potential supplement for the treatment of obesity. In this study, we aimed to complete the genome sequence of L. plantarum HOM2217, which was isolated from human milk, and study its physiological characteristics and anti-obesity effects in 3T3-L1 cells and rats fed a high-fat diet (HFD) to determine its potential as a starter for functional food products. Whole-genome analysis demonstrated that HOM2217 contained a single circular chromosome of 3,267,529 bp with a GC content of 44.5% and one plasmid (62,350 bp) with a GC content of 38.5%. Compared to the reference strains, HOM2217 demonstrated superior tolerance to gastrointestinal conditions, higher adhesion to intestinal epithelial cell lines, potent antimicrobial activity against Enterobacter cloacae ATCC 13047, and effective cholesterol removal ability in vitro. Treatment with heat-killed HOM2217 significantly reduced lipid accumulation and intracellular triglyceride production in 3T3-L1 adipocytes. Daily treatment of HFD-fed rats with HOM2217 for 7 weeks decreased body weight, body weight gain, and body fat without changes in food intake. HOM2217 also significantly increased the serum high-density lipoprotein cholesterol (HDL-C) level, decreased the serum tumor necrosis factor (TNF-α) and increased short-chain fatty acid (SCFA) (formic acid, acetic acid, and butyric acid) levels in the cecum. Thus, HOM2217 could potentially prevent obesity in rats by inhibiting inflammatory responses and regulating lipid metabolism and SCFAs expression. Therefore, HOM2217 has potential as an alternative treatment for obesity.

The nano-precipitates with minimal misfit strengthen Al–Zn–Mg alloys by impeding dislocation motion. However, grain boundary precipitates (GBPs) are known to have a completely incoherent relationship with the matrix, making them less effective in strengthening the alloys. Instead, GBPs are recognized for preoccupying elements such as Zn or Mg, thereby diminishing the quantity of nano-strengthening phases. GBPs significantly influence the deformation and fracture behaviors of Al–Zn–Mg alloys by accelerating stress localization and crack formation. In this study, we investigated how the accumulation of dislocations in the GBP areas leads to stress localization and subsequent cracking, using micropillar tests and crystal plasticity finite element method (CP-FEM) analysis. The dislocation pile-up in the GBPs causes stress localization around the grain boundaries, inducing crack initiation and leading to intergranular fracture. These findings enhance our understanding of the role of GBPs in the deformation and cracking of Al–Zn–Mg alloys and may pave the way for new concepts in managing GBPs.

Abstract Water electrolyzers powered by renewable energy are emerging as clean and sustainable technology for producing hydrogen without carbon emissions. Specifically, anion exchange membrane (AEM) electrolyzers utilizing non‐platinum group metal (non‐PGM) catalysts have garnered attention as a cost‐effective method for hydrogen production, especially when integrated with solar cells. Nonetheless, the progress of such integrated systems is hindered by inadequate water electrolysis efficiency, primarily caused by poor oxygen evolution reaction (OER) electrodes. To address this issue, a NiFeCo─OOH has developed as an OER electrocatalyst and successfully demonstrated its efficacy in an AEM electrolyzer, which is powered by renewable electricity and integrated with a silicon solar cell.

Abstract Osteoarthritis (OA) is a progressive and irreversible degenerative joint disease that is characterized by cartilage destruction, osteophyte formation, subchondral bone remodeling, and synovitis. Despite affecting millions of patients, effective and safe disease-modifying osteoarthritis drugs are lacking. Here we reveal an unexpected role for the small molecule 5-aminosalicylic acid (5-ASA), which is used as an anti-inflammatory drug in ulcerative colitis. We show that 5-ASA competes with extracellular-matrix collagen-II to bind to osteoclast-associated receptor (OSCAR) on chondrocytes. Intra-articular 5-ASA injections ameliorate OA generated by surgery-induced medial-meniscus destabilization in male mice. Significantly, this effect is also observed when 5-ASA was administered well after OA onset. Moreover, mice with DMM-induced OA that are treated with 5-ASA at weeks 8–11 and sacrificed at week 12 have thicker cartilage than untreated mice that were sacrificed at week 8. Mechanistically, 5-ASA reverses OSCAR-mediated transcriptional repression of PPARγ in articular chondrocytes, thereby suppressing COX-2-related inflammation. It also improves chondrogenesis, strongly downregulates ECM catabolism, and promotes ECM anabolism. Our results suggest that 5-ASA could serve as a DMOAD.

Weizmannia coagulans (formerly Bacillus coagulans) is a spore-forming and lactic acid-producing bacterium. It has recently attracted much attention from researchers and food manufacturers due to its probiotic functions and stability in processing and storage. W. coagulans is capable of improving gut health through the regulation of gut microbiota, modulation of immunity, and improving digestibility and metabolism. Spores, germinated cells and metabolites of W. coagulans modulate the gut micro-environment and further affect other organs. W. coagulans is an environment-friendly probiotic since it can contribute to the host by reconstructing the balance of gut microbiota and only temporarily resides in the intestine after administration. W. coagulans has been generally recognized as safe (GRAS) by the US Food and Drug Administration (FDA), thus it is an ideal probiotic for improving gut health. The merit of its stability in processing and storage provides W. coagulans spores many possibilities for its use in various types of functional foods. This review presents an overview of the characteristics of W. coagulans that make it an ideal probiotic candidate and highlights the proposed health benefits with scientific evidence conferred by the administration of W. coagulans.

In today’s society, beauty is more than just aesthetics, it has a profound impact on many aspects of life, including social interactions, self-confidence, and job opportunities. To quantify this beauty, the field of Facial Beauty Prediction (FBP) is gaining traction. In the field of FBP, traditional methods often fall short due to their reliance on absolute beauty scores, which do not fully capture the subjective nature of human aesthetic perception. This study presents a novel approach to address this gap through the development of Anchor-Net, a self-supervised learning model that predicts differences in relative beauty scores by comparing images. The objective of this research is to offer a more nuanced understanding of facial beauty by employing a reference image (anchor) alongside a prediction image, thereby aligning closer with how humans perceive aesthetic differences. To construct Anchor-Net, we first developed a Base model that predicts beauty scores using a model pre-trained with VGGFace2. This Base model was then adapted into Anchor-Net, which is designed to train on the difference in beauty scores between a reference image and a prediction image. Our methodology involved two transfer learning steps to leverage the strengths of pre existing models while tailoring them to our specific research problem. The experimental validation of Anchor-Net was conducted on the SCUT-FBP5500 benchmark dataset, utilizing a 6:4 training-testing split and 5-fold cross-validation to ensure robust testing of the model’s predictive capabilities. The results demonstrate that Anchor-Net outperforms other state-of-the-art deep learning algorithms on all metrics: Pearson Correlation (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Anchor-Net outperformed other models with a PC of 0.0021, MAE of 0.0055, and RMSE of 0.0065 on a 6:4 training-test split. On average, it achieved a PC of 0.0034, MAE of 0.0155, and RMSE of 0.0135 on 5-fold cross-validation. This research proposes a novel approach to FBP and suggests a broader application of relative comparison methodologies in fields where absolute measurements fall short.

Sequential recommendation systems aim to predict users’ subsequent interactions by analyzing their historical behavior. Significant advancements have been achieved in this field through the utilization of models such as RNN, GRU, and models incorporating the self-attention mechanism. Most state-of-the-art methods enhance recommendation effectiveness by incorporating side information, such as category or brand, within the attention layer to enrich item representations. However, in our pilot experiments, we found that increasing the amount of side information could negatively impact the recommendation performance when integrated directly into the attention layer due to the learning of interdependency among them. To alleviate this problem, we propose Independent Representation of side Information on each item for Sequential recommendation (IRIS). IRIS learns pairs of both item and side information independently, without any fusion among side information in attention layer. In contrast to existing approaches, IRIS avoids direct fusion among side information in the attention layer, opting instead for a novel strategy of learning independent pairs of both item and side information. Our proposed method constructs an attention layer specifically designed to learn these pairs, thus avoiding potential distortions to item representations caused by intermixing side information. Experiments on four public datasets show that IRIS outperforms state-of-the-art sequential recommendation models. Ablation study also indicates robust scalability of side information within IRIS.

Deep learning models have significantly addressed the challenges of multivariate time series forecasting. Recently, Transformer-based models which have primarily focused on either temporal or inter-variate (spatial) dependencies have demonstrated exceptional performance. These models decide whether to embed multivariate time series data temporally or spatially. Hence, we propose the dual embedded transformer (DEformer) which simultaneously considers both temporal and spatial dependencies. Our model enables capturing these dependencies independently through two distinct encoders. The temporal encoder, which processes the entire time series of each variate, is designed to learn temporal dependency. Conversely, the spatial encoder, which processes the multivariate data at each time step, is optimized to capture spatial dependency. Both encoders share an identical architecture, comprising an attention mechanism to model correlations and a feed-forward network to enhance feature representation. Through empirical studies on challenging real-world datasets, we not only demonstrate that our method can outperforms state-of-the-art approaches, but also prove the performance of independently embedding both dependencies through ablation study.

Abstract Background Porcine circovirus, a non‐enveloped single‐stranded DNA virus belonging to the genus Circovirus of the family Circoviridae, is a major pathogen of porcine circovirus‐associated disease. Porcine circovirus 3, a novel porcine circovirus, has been identified in individuals with clinical symptoms. Objectives The prevalence of porcine circovirus 2 and porcine circovirus 3 and the confirmation of diagnosis of this emerging viral disease have not been fully studied yet. Therefore, the objective of the present study was to investigate the prevalence of porcine circovirus 2 and porcine circovirus 3 in slaughtered pigs and wild boars in Korea between 2018 and 2019. Methods Lungs and hilar lymph nodes of healthy pigs slaughtered in slaughterhouses and captured wild pigs were collected, and viruses were detected by multiplex quantitative polymerase chain reaction and two staining methods (in situ hybridization and immunohistochemistry) to confirm the presence of porcine circovirus 2 and porcine circovirus 3. Results Positive rates of porcine circovirus 2 in lungs and hilar lymph nodes were 78.1% (75/96) and 89.5% (86/96) in slaughtered pigs, respectively. They were 18.0% (30/167) and 46.3% (24/55) in wild boars, respectively. Positive rates of porcine circovirus 3 in lungs and hilar lymph nodes were 30.2% (29/96) and 13.5% (13/96) in slaughtered pigs, respectively. They were 4.2% (7/167) and 5.5% (3/55) in wild boars, respectively. At the farm level, positive rates of porcine circovirus 2 and porcine circovirus 3 were 97.9% (47/48) and 54.2% (26/48), respectively. Positive rates of porcine circovirus 2 and porcine circovirus 3 decreased in spring. Immunohistochemistry and in situ hybridization confirmed the presence of porcine circovirus 2 and porcine circovirus 3 in lungs, but not porcine circovirus 3 in the hilar lymph nodes. Conclusion These results suggest that the prevalence of porcine circovirus 2 and porcine circovirus 3 might vary depending on the season and the type of sample. Wild boars might play a role in the epidemiology of porcine circovirus 2 and porcine circovirus 3 in South Korea. Continuous surveillance and further study are needed for this emerging disease.

Deficiencies in the ING4 tumor suppressor are associated with advanced stage tumors and poor patient survival in cancer. ING4 was shown to inhibit NF-kB in several cancers. As NF-kB is a key mediator of immune response, the ING4/NF-kB axis is likely to manifest in tumor-immune modulation but has not been investigated. To characterize the tumor immune microenvironment associated with ING4-deficient tumors, three approaches were employed in this study: First, tissue microarrays composed of 246 primary breast tumors including 97 ING4-deficient tumors were evaluated for the presence of selective immune markers, CD68, CD4, CD8, and PD-1, using immunohistochemical staining. Second, an immune-competent mouse model of ING4-deficient breast cancer was devised utilizing CRISPR-mediated deletion of Ing4 in a Tp53 deletion-derived mammary tumor cell line; mammary tumors were evaluated for immune markers using flow cytometry. Lastly, the METABRIC gene expression dataset was evaluated for patient survival related to the immune markers associated with Ing4-deleted tumors. The results showed that CD68, CD4, CD8, or PD-1, was not significantly associated with ING4-deficient breast tumors, indicating no enrichment of macrophages, T cells, or exhausted T cell types. In mice, Ing4-deleted mammary tumors had a growth rate comparable to Ing4-intact tumors but showed increased tumor penetrance and metastasis. Immune marker analyses of Ing4-deleted tumors revealed a significant increase in tumor-associated macrophages (Gr-1loCD11b+F4/80+) and a decrease in granzyme B-positive (GzmB+) CD4+ T cells, indicating a suppressive and/or less tumoricidal immune microenvironment. The METABRIC data analyses showed that low expression of GZMB was significantly associated with poor patient survival, as was ING4-low expression, in the basal subtype of breast cancer. Patients with GZMB-low/ING4-low tumors had the worst survival outcomes (HR = 2.80, 95% CI 1.36-5.75, p = 0.0004), supportive of the idea that the GZMB-low immune environment contributes to ING4-deficient tumor progression. Collectively, the study results demonstrate that ING4-deficient tumors harbor a microenvironment that contributes to immune evasion and metastasis.

This study proposes a novel framework for evaluating the stability of data-driven controllers and the concept of statistical stability. The proposed framework can be used when it is challenging to show stability through conventional control theory. The novelty of this paper lies in that it provides a method for scientifically analyzing the stability of data-driven controllers, thereby improving the quality of data-driven controllers. The proposed framework consists of three parts: the generative model, controller optimizer, and verification model. A variational autoencoder is used to classify and randomly generate data, and the generated data are used to train the controller. A support vector machine is used to classify areas where the controller is statistically stable. The statistical stability of an optimal controller designed using a deep neural network structure is analyzed using the proposed framework.

ABSTRACTThis research is to simulate hydrogen leakage at bunker station with various wind directions and velocities using Computational Fluid Dynamics (CFD) model to understand hydrogen dispersion behaviour and provide general guidelines to establish risk prevent measures and mitigations at early design phase. This case study examines hydrogen plume behavior in various wind conditions, focusing on horizontal and vertical dispersion as well as mean travel distance over time. Regardless of wind direction, hydrogen disperses in alignment with the wind. As time progresses post-leakage, the plume elongates in the wind's longitudinal direction and contracts vertically, maintaining a consistent shape. Wind direction and the direction of hydrogen release notably influence dispersion patterns. When the wind aligns vertically with the release point, hydrogen plume distance increases with higher wind speeds. Conversely, when wind opposes the release direction, plume length tends to decrease at high speeds. Intriguingly, the maximum distance of 27.85 m occurs when wind and leak directions are orthogonal at 180°. For wind speeds up to 5 m/s, all wind directions show a similar increase in plume distance. However, at 7 m/s, scenarios with horizontal, perpendicular wind directions exhibit a distinct change. Analyzing hydrogen dispersion aids in establishing safety criteria and risk mitigation distances for hydrogen leakages in bunker stations during the early design phase.

Virtual reality (VR) is used in many fields, including entertainment, education, training, and healthcare, because it allows users to experience challenging and dangerous situations that may be impossible in real life. Advances in head-mounted display technology have enhanced visual immersion, offering content that closely resembles reality. However, several factors can reduce VR immersion, particularly issues with the interactions in the virtual world, such as locomotion. Additionally, the development of locomotion technology is occurring at a moderate pace. Continuous research is being conducted using hardware such as treadmills, and motion tracking using depth cameras, but they are costly and space-intensive. This paper presents a walk-in-place (WIP) algorithm that uses Mocopi, a low-cost motion-capture device, to track user movements in real time. Additionally, its feasibility for VR applications was evaluated by comparing its performance with that of a treadmill using the absolute trajectory error metric and survey data collected from human participants. The proposed WIP algorithm with low-cost Mocopi exhibited performance similar to that of the high-cost treadmill, with significantly positive results for spatial awareness. This study is expected to contribute to solving the issue of spatial constraints when experiencing infinite virtual spaces.

Self-assembly of block-copolymers yields nanoscale structures in a facile way, but the diversity of structures is limited. Here, the authors demonstrate how block copolymer layering can be used to access new non-equilibrium morphologies.

Abstract In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI (partial correlation coefficient: 0.420–0.875), and 13.2% were correlated with the SCDR (0.406–0.460). The mean subindex of Index 2 $$\left( {\frac{{P_{Fibrosis\, in\, Affected} - P_{Fibrosis\, in\, Unaffected} }}{{P_{Limb\, in\, Unaffected} }}} \right)$$ P F i b r o s i s i n A f f e c t e d - P F i b r o s i s i n U n a f f e c t e d P L i m b i n U n a f f e c t e d presented the highest correlation. DL has potential applications in CT image-based lymphedema-induced fibrosis recognition. The subtraction-type formula might be the most promising estimation method.

Weizmannia coagulans is considered an ideal probiotic owing to its function and stability during production and storage. This study aims to evaluate the efficacy and safety of W. coagulans HOM5301 as an immunoregulatory probiotic. Whole genome analysis demonstrated that W. coagulans HOM5301 had no antibiotic resistance or virulence genes, but contained four gene clusters of secondary metabolites related to antimicrobial activities. W. coagulans HOM5301 spores tolerated gastrointestinal tract conditions with a survival rate of 86.33%. The spent culture broth of W. coagulans HOM5301 showed strong antimicrobial activity against foodborne pathogens. W. coagulans HOM5301 stimulated macrophages to produce more TNF-α but less IL-10 than that of the controls. Daily treatment with W. coagulans HOM5301 spores for 31 days positively modulated the gut microbiota and boosted both innate and adaptive immunity in a dose-dependent manner in mice. In conclusion, W. coagulans HOM5301 can be safely consumed by humans to induce robust immunity.

Abstract A novel detachment method is presented for flexible substrates from glass carrier wafers using a CO2 point laser. In a conventional laser lift‐off process, laser‐induced illumination and thermal stress degrade device performance. In this approach, a point laser is applied on the edge of the flexible substrate, where electrical components do not exist, and thus, no performance degradation occurs during the laser process. In addition, the carrier surface properties are modified to control the adhesion force between the flexible substrate and the glass carrier. A spin‐on‐dielectric layer (SOD) is utilized, and Ar ion bombardment is performed on SOD. The point laser detachment method is demonstrated in flexible thin‐film transistors, and no change is observed in the electrical characteristics before and after detachment from the carrier wafer.

Abstract This work presents an analog neuromorphic synapse device consisting of two oxide semiconductor transistors for high‐precision neural networks. One of the two transistors controls the synaptic weight by charging or discharging the storage node, which leads to a conductance change in the other transistor. The programmed weight maintains for more than 300 s as electrons in the storage node are well preserved due to the extremely low off current of the oxide transistor. Ideal synaptic behaviors are achieved by utilizing superior properties of oxide transistors such as a high on/off ratio, low off current, and large‐area uniformity. To further improve the synaptic performance, self‐assembled monolayer treatment is applied for reducing the transistor conductance. The reduction of on current reduces the power consumption, and the reduced off current improves the retention characteristics. There is no noticeable decrease in simulated neural network accuracy even when the measured device‐to‐device variation is intentionally increased by 200%, indicating the possibility of large‐array operation with the synapse device.

Numerous wearable biomedical devices are developed for the continuous monitoring of personal health or condition. Biosignals acquisition with high sensitivity is important for designing wearable biomedical devices. A sensing electrode between the human body and wearable electronics significantly affects the sensitivity of the sensors. In this study, we fabricated hierarchically structured flexible electrodes on polyimide substrate (HSFE-PI) using micro-casting technique and gold nanoparticles electrodeposition. Polyimides provides robust and outstanding electrical characteristics, and the reliability of HSFE-PI was verified with a cyclic bending test. The integration of hierarchical structures significantly increased the surface area of the electrode by 2.06 times. We applied the HSFE-PI for electromyogram (EMG) and glucose sensing applications and achieved high sensitivity enhancement in both applications. The signal-to-noise ratio (SNR) of measured EMG signals was increased by 2.48 times, and the sensitivity of the glucose detection was increased by 1.42 times compared to the planar counterpart.

Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems.

High-capacity and structural stable cathode materials are challenges for sodium-ion batteries. Here, the authors report a layered P2-Na0.612K0.056MnO2 with large-sized K+ riveted in the Na-layers to enable 0.9 Na+ (de)insertion with a reversible phase transition of P2-P’2.

Abstract Activation of oxygen redox during the first cycle has been reported as the main trigger of voltage hysteresis during further cycles in high‐energy‐density Li‐rich 3d‐transition‐metal layered oxides. However, it remains unclear whether hysteresis only occurs due to oxygen redox. Here, it is identified that the voltage hysteresis can highly correlate to cationic reduction during discharge in the Li‐rich layered oxide, Li1.2Ni0.4Mn0.4O2. In this material, the potential region of discharge accompanied by hysteresis is apparently separated from that of discharge unrelated to hysteresis. The quantitative analysis of soft/hard X‐ray absorption spectroscopies discloses that hysteresis is associated with an incomplete cationic reduction of Ni during discharge. The galvanostatic intermittent titration technique shows that the inevitable energy consumption caused by hysteresis corresponds to an overpotential of 0.3 V. The results unveil that hysteresis can also be affected by cationic redox in Li‐rich layered cathodes, implying that oxygen redox cannot be the only reason for the evolution of voltage hysteresis. Therefore, appropriate control of both cationic and anionic redox of Li‐rich layered oxides will allow them to reach their maximum energy density and efficiency.

Cell culture systems have been widely used to address fundamental questions in biology without sacrificing animals. Three-dimensional (3D) organoids provide more information on in vivo conditions than traditional culture systems because multiple cell types in organoids interact with each other in 3D structures. Despite extensive research and advances in the organoid field, some important limitations remain and need further consideration. In this review, we summarize how organoids are generated from pluripotent stem cells and describe the recent technical progress that has made organoids more similar to in vivo tissues for the application of organoids to modeling cancer. Lastly, we briefly discuss some limitations that have been raised in this field.

This paper proposes a new design strategy for hybrid precoding and combining in the downlink of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) channels. When channel state information is available at the transmitter, the proposed scheme designs the analog precoder and combiners by iteratively factorizing the matrices for fully digital precoding and combining, respectively, using the alternating optimization technique. Then, the digital precoder and combiners are obtained through block diagonalization of effective MU-MIMO channels composed of the analog precoder, MU-MIMO channels, and analog combiners, in order to eliminate inter-user interferences. Moreover, focusing on rate balancing among users, we derive a new power allocation algorithm that exploits a modified gradient descent method. The proposed method iteratively adjusts the power allocated to each user in terms of maximizing the minimum user rate. Through numerical simulations, we verify the convergence of the proposed design procedure for hybrid precoding and combining. Moreover, it is shown that the proposed method outperforms the conventional hybrid precoding and combining methods for rate balancing as well as achieves the minimum user rate close to the performance upper bound.

A target tracking system with a laser rangefinder is developed, and the performance of the developed system is verified by a numerical simulation and field tests. To develop the target tracking system, appropriate coordinate systems, motion models, and estimation methods are selected, and a range filter is designed to compensate for the measurement errors of the laser rangefinder. The range filter independently processes the range measurements so that the target filter continuously produces the target information. The entire target tracking system development process is described in detail to facilitate future work of a similar nature. A scenario-based Monte Carlo simulation is performed to compare the performances of various state estimation methods. Experimental tests involving a low-speed fishing boat and a drone are performed to verify the performance and effectiveness of the proposed scheme. A separate performance analysis program with a graphical user interface is developed to analyze the simulation and field test results.

A data-driven path replanning algorithm based on Pythagorean-hodograph (PH) curve is proposed for unpowered gliding vehicles considering the terminal velocity constraint in uncertain windy environments. Based on the characteristics of the PH curve, the terminal velocity constraint is satisfied by adjusting path parameters including the arc length in path replanning. For the path replanning, a tailored PH curve regeneration algorithm is proposed to resolve the inconsistent path replanning issue of the existing PH curve generation method. An artificial-neural-network-based path replanner is trained to provide the path parameters corresponding to the terminal velocity constraint. Unlike most model-based methods, the proposed method can deal with the external disturbance by replanning the flight path. Compared with other data-driven studies, the proposed method does not require a computationally expensive trajectory optimization process to collect training data. Performance evaluation and comprehensive comparison are performed via numerical simulation considering various types of modeled and unmodeled wind uncertainties using several network configurations.

d-Glycero-β-d-manno-heptose-1-phosphate adenylyltransferase from Burkholderia pseudomallei (BpHldC) is the fourth enzyme in the ADP‐l‐glycero‐β‐d‐manno‐heptose biosynthesis pathway producing a lipopolysaccharide core. Therefore, BpHldC is an anti-melioidosis target. Three ChemBridge compounds purchased from ChemBridge Corporation (San Diego, CA) were found to have an effective inhibitory activity on BpHldC. Interestingly, ChemBridge 7929959 was the most effective compound due to the presence of the terminal benzyl group. The enzyme kinetic study revealed that most of them show mixed type inhibitory modes against ATP and βG1P. The induced-fit docking indicated that the medium affinity of ChemBridge 7929959 is originated from its benzyl group occupying the substrate-binding pocket of BpHldC. The inhibitory role of terminal aromatic groups was proven with ChemBridge 7570508. Combined with the previous study, ChemBridge 7929959 is found to work as a dual inhibitor against both HldC and HddC. Therefore, three ChemBridge compounds can be developed as a potent anti-melioidosis agent with a novel inhibitory concept.

Background New-onset altered level of consciousness (ALC) is a common condition in patients visiting the emergency department (ED). We evaluated the clinical characteristics, causes, and prognosis of adult patients presenting with ALC at an ED of a university hospital to determine the etiologies, outcomes, and destinations of these patients. Methods The medical records of patients with ALC who visited the ED of a university hospital from February 2019 to January 2020 were reviewed. The cause of ALC and its classification were carefully decided by agreement through a discussion among board-certified clinicians in emergency medicine, neurology, and internal medicine. Results Of the 731 patients with ALC in the ED, whose mean age was 68.81±16.40 years, most were in their 80s (198, 27.09%). The most common etiology of ALC was systemic infection (30.78%), followed by metabolic causes (21.07%) and stroke (18.19%). Extracerebral and intracerebral etiologies of ALC in the ED accounted for 51.85% and 26.68% of the cases, respectively. The overall mortality of ALC in the ED was 17.67%. The largest number of ALC cases of 443 patients, i.e., 60.6% of all ALC patients in the ED, visited within the time zone of 07:00–19:00. Conclusion This study identified the extra-cerebral etiologies as the major causes of ALC in the ED.

With the development of the Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide users with preferred contents by filtering abundant information, along with providing means of exposing search results to users more effectively. These recommendation systems operate based on the user reactions to items or on the various user or item features. It is known that recommendation results based on sparse datasets are less reliable because recommender systems operate according to user responses. Thus, we propose a method to improve the dataset sparsity and increase the accuracy of the prediction results by using item features with user responses. A method based on the content-based filtering concept is proposed to extract category rates from the user–item matrix according to the user preferences and to organize these into vectors. Thereafter, we present a method to filter the user–item matrix using the extracted vectors and to regenerate the input matrix for collaborative filtering (CF). We compare the prediction results of our approach and conventional CF using the mean absolute error and root mean square error. Moreover, we calculate the sparsity of the regenerated matrix and the existing input matrix, and demonstrate that the regenerated matrix is more dense than the existing one. By computing the Jaccard similarity between the item sets in the regenerated and existing matrices, we verify the matrix distinctions. The results of the proposed methods confirm that if the regenerated matrix is used as the CF input, a denser matrix with higher predictive accuracy can be constructed than when using conventional methods. The validity of the proposed method was verified by analyzing the effect of the input matrix composed of high average ratings on the CF prediction performance. The low sparsity and high prediction accuracy of the proposed method are verified by comparisons with the results by conventional methods. Improvements of approximately 16% based on K-nearest neighbor and 15% based on singular value decomposition, and a three times improvement in the sparsity based on regenerated and original matrices are obtained. We propose a matrix reconstruction method that can improve the performance of recommendations.

Frequent occurrences of multi-drug resistance of pathogenic Gram-negative bacteria threaten human beings. The CMP-2-keto-3-deoxy-d-manno-octulosonic acid biosynthesis pathway is one of the new targets for antibiotic design. 2-Keto-3-deoxy-d-manno-octulosonate cytidylyltransferase (KdsB) is the key enzyme in this pathway. KdsB proteins from Burkholderia pseudomallei (Bp), B. thailandensis (Bt), Pseudomonas aeruginosa (Pa), and Chlamydia psittaci (Cp) have been assayed to find inhibitors. Interestingly, Rose Bengal (4,5,6,7-tetrachloro-2′,4′,5′,7′-tetraiodofluorescein) was turned out to be an inhibitor of three KdsBs (BpKdsB, BtKdsB, and PaKdsB) with promising IC50 values and increased thermostability. The inhibitory enzyme kinetics of Rose Bengal revealed that it is competitive with 2-keto-3-deoxy-manno-octulosonic acid (KDO) but non-competitive against cytidine 5′-triphosphate (CTP). Induced-fit docking analysis of PaKdsB revealed that Arg160 and Arg185 together with other interactions in the substrate binding site seemed to play an important role in binding with Rose Bengal. We suggest that Rose Bengal can be used as the scaffold to develop potential antibiotics.

There were severe panics caused by Severe Acute Respiratory Syndrome (SARS) and Middle-East Respiratory Syndrome-Coronavirus. Therefore, researches targeting these viruses have been required. Coronaviruses (CoVs) have been rising targets of some flavonoids. The antiviral activity of some flavonoids against CoVs is presumed directly caused by inhibiting 3C-like protease (3CLpro). Here, we applied a flavonoid library to systematically probe inhibitory compounds against SARS-CoV 3CLpro. Herbacetin, rhoifolin and pectolinarin were found to efficiently block the enzymatic activity of SARS-CoV 3CLpro. The interaction of the three flavonoids was confirmed using a tryptophan-based fluorescence method, too. An induced-fit docking analysis indicated that S1, S2 and S3′ sites are involved in binding with flavonoids. The comparison with previous studies showed that Triton X-100 played a critical role in objecting false positive or overestimated inhibitory activity of flavonoids. With the systematic analysis, the three flavonoids are suggested to be templates to design functionally improved inhibitors.

African swine fever (ASF) caused by the ASF virus (ASFV) is the most hazardous swine disease. Since a huge number of pigs have been slaughtered to avoid a pandemic spread, intense studies on the disease should be followed quickly. Recent studies reported that flavonoids have various antiviral activity including ASFV. In this report, ASFV protease was selected as an antiviral target protein to cope with ASF. With a FRET (Fluorescence resonance energy transfer) method, ASFV protease was assayed with a flavonoid library which was composed of sixty-five derivatives classified based on ten different scaffolds. Of these, the flavonols scaffold contains a potential anti-ASFV protease activity. The most prominent flavonol was myricetin with IC50 of 8.4 μM. Its derivative, myricitrin, with the rhamnoside moiety was also showed the profound inhibitory effect on ASFV protease. These two flavonols apparently provide a way to develop anti-ASFV agents based on their scaffold.

Sphingosine 1-phosphate (S1P) lyase is an intracellular enzyme that catalyzes the irreversible degradation of S1P and has been suggested as a therapeutic target for the treatment of psoriasis vulgaris. Because S1P induces differentiation of keratinocytes, we examined whether modulation of S1P lyase and altered intracellular S1P levels regulate proliferation and differentiation of human neonatal epidermal keratinocyte (HEKn) cells. To identify the physiological functions of S1P lyase in skin, we inhibited S1P lyase in HEKn cells with an S1P lyase-specific inhibitor (SLI) and with S1P lyase 1 (SGPL1)-specific siRNA (siSGPL1). In HEKn cells, pharmacological treatment with the SLI caused G1 arrest by upregulation of p21 and p27 and induced keratin 1, an early differentiation marker. Similarly, genetic suppression by siSGPL1 arrested the cell cycle at the G1 phase and activated differentiation. In addition, enzyme suppression by siSGPL1 upregulated keratin 1 and differentiation markers including involucrin and loricrin. When hyperproliferation of HEKn cells was induced by interleukin (IL)-17 and IL-22, pharmacologic inhibition of S1P lyase by SLI decreased proliferation and activated differentiation of HEKn cells simultaneously. In addition, SLI administration ameliorated imiquimod-induced psoriatic symptoms including erythema, scaling, and epidermal thickness in vivo. We thus demonstrated that S1P lyase inhibition reduces cell proliferation and induces keratinocyte differentiation, and that inhibition may attenuate psoriasiform changes. Collectively, these findings suggest that S1P lyase is a modulating factor for proliferation and differentiation, and support its potential as a therapeutic target for psoriasis in human keratinocytes.

Coronavirus disease 2019 (COVID-19) has been a pandemic disease of which the termination is not yet predictable. Currently, researches to develop vaccines and treatments is going on globally to cope with this disastrous disease. Main protease (3CLpro) from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the good targets to find antiviral agents before vaccines are available. Some flavonoids are known to inhibit 3CLpro from SARS-CoV which causes SARS. Since their sequence identity is 96%, a similar approach was performed with a flavonoid library. Baicalin, herbacetin, and pectolinarin have been discovered to block the proteolytic activity of SARS-CoV-2 3CLpro. An in silico docking study showed that the binding modes of herbacetin and pectolinarin are similar to those obtained from the catalytic domain of SARS-CoV 3CLpro. However, their binding affinities are different due to the usage of whole SARS-CoV-2 3CLpro in this study. Baicalin showed an effective inhibitory activity against SARS-CoV-2 3CLpro and its docking mode is different from those of herbacetin and pectolinarin. This study suggests important scaffolds to design 3CLpro inhibitors to develop antiviral agents or health-foods and dietary supplements to cope with SARS-CoV-2.

The present study investigates the Compatibility Condition (CC) for multiple expressive elements in Vietnamese. We identify Vietnamese kinship terms, pronouns, and racial slurs as expressives, i.e. conventional implicature (Potts 2005), where different expressive items interact. We find that there are co-occurrences of expressives with different attitudes (e.g. weak/strong negative) and with expressive elements that have honorific and antihonorific properties. Under controlled occurrences, we examine what CC is and how it is measured. We propose the CC model and the CC index for occurrences of Vietnamese emotive-expressives and honorific-expressives. Furthermore, the CC may be intentionally flouted as a repair strategy. Finally, we show that emotion and honorific dimensions operate interdependently or autonomously and provide support for autonomy. The implication found is that interaction exists among various Vietnamese expressives, necessitating the compatibility constraint, while supporting multidimensionality (Potts 2005 et seq.), with at least two expressive dimensions.

In this study, iron oxide nanoparticles (FeOx NPs) were synthesized by using Fe solution recycled from NdFeB permanent magnet scrap. Furthermore, the effect of polyvinylpolypyrrolidone (PVP) as a surfactant on the characteristics of the FeOx NPs was investigated. Firstly, Fe solution was prepared by using 10% H2SO4 solution and Na2SO4 salt. In addition, three reducing agent solutions were prepared by dissolving PVP in 0.5 M NH4OH solution in distilled (D.I.) water with concentrations of 0 wt%, 1 wt%, and 2 wt%, respectively. Each reducing agent solution was added dropwise into the Fe solution to precipitate three precursors of FeOx NPs, and they were heat-treated at 400 °C to prepare three FeOx NPs samples, P0, P1, and P2. In X-ray diffractometer (XRD) analysis, diffraction peaks of P0 sample are consistent with the Fe3O4 with (311) preferred orientation. The XRD peak shifted from Fe3O4 to Fe2O3 structure as PVP concentration increased, and the crystal structure of P2 sample was transformed to Fe2O3 with (104) preferred orientation. Brunauer, Emmett, and Teller (BET) specific surface area increased in proportional to PVP concentration. HRTEM observation also supported the tendency; the particle size of the P0 sample was less than 40 nm, and particle size decreased as PVP concentration increased, leading to the particle size of the P2 sample being less than 20 nm in width. In addition, particle morphology started to be transformed from particle to rod shape as PVP concentration increased and, in the P2 sample, all the morphology of particles was transformed to a rod shape. Magnetic properties analysis revealed that the P0 sample exhibited the highest value of magnetic moment, 65.6 emu/g, and the magnetic moment was lowered in the P1 sample, and the P2 sample exhibited the lowest value of magnetic moment, 2.4 emu/g.

The aim of the study was to elucidate the protective action of roasted Lycium chinense (rLF) enhanced kaempferol glycosides as bioactive components from obese-induced type 2 diabetes mice by investigating the histopathological, biochemical, and transcriptional analyses in blood, liver, and adipose tissue. The rLF extract significantly decreased the hypertrophy of fat deposition, pancreatic hyperplasia, hyperinsulinemia, hepatocellular steatosis, and nephropathy in a dose dependent manner (p

In product design, the initial design stage is being increasingly emphasized because it significantly influences the successive product development and production stages. However, for larger and more complex products, it is very difficult to accurately predict product reliability in the initial design stage. Various design methodologies have been proposed to resolve this issue, but maintaining reliability while exploring design alternatives is yet to be achieved. Therefore, this paper proposes a methodology for conceptual design considering reliability issues that may arise in the successive detailed design stages. The methodology integrates the independency of axiomatic design and the hierarchical structure of failure mode, effects, and criticality analysis (FMECA), which is a technique widely used to analyze product reliability. We applied the proposed methodology to a liquefied natural gas fuel gas supply system to verify its effectiveness in the reliability improvement of the design process. Keywords: Axiomatic design, Failure mode, effects, and criticality analysis (FMECA), Reliability, LNG fuel gas supply system

Polysaccharides from marine algae exhibit beneficial biological activities. In this study, we examined the effect of Codium fragile extract (CFE) on prebiotic and anti-obesity activity through in vitro experiments. CFE increases the growth of specific beneficial microbial populations with concomitant decrease in pathogenic microbes. Further, total phenolic content (TPC), total flavonoid content (TFC), and DPPH radical scavenging activity (DPPH activity) after fermentation with CFE as the carbon source were higher than for glucose as the control. Moreover, CFE inhibited adipocyte differentiation by inducing differentiation-related factors when the induction of 3T3-L1 preadipocytes into adipocytes was induced. Therefore, we suggest that CFE can be used as a prebiotic material with an anti-obesity effect for human health.

Laser transmission welding offers significant benefits over conventional welding techniques enabling single-stage rapid plastic joining. The quality of laser transmission welded products is commonly assessed by measuring the weld penetration depth, hardened weld boundary, and inspecting the formation of porosity. However, the existing methods of verification are inevitably accompanied by destruction of the specimen. Thus, non-destructive quality assessment methods for laser transmission welding have gained attention recently. Here, we demonstrated an extended industrial application of 860 nm wavelength-based spectral domain optical coherence tomography for the non-destructive inspection of the aforementioned quality parameters of laser transmission welded industrial plastic materials, i.e., polycarbonate and acrylonitrile butadiene styrene copolymer. The acquired cross-sectional resolution and volumetric and intensity profiles sufficiently contributed to the quality assessment procedure, revealing the weld boundary and porosity formation, and thus confirming the potential applicability of optical coherence tomography for the quality inspection of laser transmission welded products.

In this study, 3D-printable flexible piezoresistive composites containing various amounts of cilia-like hybrid fillers were developed. In the hybrid fillers, micro-scale Cu particles with a 0D structure may allow them to easily disperse into the flexible TPU matrix. Furthermore, nanoscale multi-walled carbon nanotubes (MWCNTs) with a high aspect ratio, present on the surface of the Cu particles, form an electrical network when the polymer matrix is strained, thus providing good piezoresistive performance as well as good flowability of the composite materials. With an optimal hybrid filler content (17.5 vol.%), the 3D-printed piezoresistive composite exhibits a gauge factor of 6.04, strain range of over 20%, and durability of over 100 cycles. These results highlight the potential applications of piezoresistive pressure sensors for health monitoring, touch sensors, and electronic skin.

Netrin-G ligand-1 (NGL-1), encoded by Lrrc4c, is a post-synaptic adhesion molecule implicated in various brain disorders, including bipolar disorder, autism spectrum disorder, and developmental delay. Although previous studies have explored the roles of NGL-1 in the regulation of synapse development and function, the importance of NGL-1 for specific behaviors and the nature of related neural circuits in mice remain unclear. Here, we report that mice lacking NGL-1 (Lrrc4c–/–) show strong hyperactivity and anxiolytic-like behavior. They also display impaired spatial and working memory, but normal object-recognition memory and social interaction. c-Fos staining under baseline and anxiety-inducing conditions revealed suppressed baseline neuronal activity as well as limited neuronal activation in widespread brain regions, including the anterior cingulate cortex (ACC), motor cortex, endopiriform nucleus, bed nuclei of the stria terminalis, and dentate gyrus. Neurons in the ACC, motor cortex, and dentate gyrus exhibit distinct alterations in excitatory synaptic transmission and intrinsic neuronal excitability. These results suggest that NGL-1 is important for normal locomotor activity, anxiety-like behavior, and learning and memory, as well as synapse properties and excitability of neurons in widespread brain regions under baseline and anxiety-inducing conditions.

Underground cavities can develop below structures, leading to ground settlement and hindering the development of urban infrastructure. Soil flow protectors (SFPs) have been developed to prevent and alleviate problems due to the formation of such cavities. In this study, we performed scaled model experiments to develop a design method for an SFP with an adequate safety factor under different installation lengths of its upper and lower parts in sandy ground. The installation of the SFP reduced the average surface settlement ratio to the range of 0.44–0.72, thus demonstrating its effectiveness in reducing ground settlement. In addition, we proposed a relational equation for determining the optimal length ratio of the SFP and the settlement ratio. An analysis of the influencing factors showed that the lower part of the SFP influenced the settlement reduction, whereas the upper part influenced the stability of the SFP depending on the ground settlement ratio. Finally, we have proposed an optimal length equation for the SFP and presented a flowchart for the design method. The results of this study can serve as a design basis for the efficient construction of infrastructure.