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1,2,3,4,6-penta-O-galloyl-β-D-glucose (PGG) is the main phenolic active ingredient in Paeoniae Radix Alba, which is commonly used for the treatment of osteoporosis (OP). PGG is a potent natural antioxidant, and its effects on OP remain unknown. This study aimed to investigate the effects of PGG on promoting bone formation and explore its estrogen receptor (ER)-related mechanisms. A hydrogen peroxide-induced osteoblast apoptosis model was established in MC3T3-E1 cells. The effects of PGG were assessed using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, flow cytometry, alkaline phosphatase (ALP) staining, RT-qPCR, and Western blot methods. Furthermore, a prednisolone-induced zebrafish OP model was employed to study the effects in vivo. ER inhibitors and molecular docking methods were used further to investigate the interactions between PGG and ERs. The results showed that PGG significantly enhanced cell viability and decreased cell apoptosis by restoring mitochondrial function, attenuating reactive oxygen species levels, decreasing the mitochondrial membrane potential, and enhancing ATP production. PGG enhanced ALP expression and activity and elevated osteogenic differentiation. PGG also promoted bone formation in the zebrafish model, and these effects were reversed by ICI182780. These results provide evidence that the effects of PGG in alleviating apoptosis and promoting bone formation may depend on ERs. As such, PGG is considered a valuable candidate for treating OP. [ABSTRACT FROM AUTHOR]

Hierarchical porous carbon derived from discarded biomass for energy storage materials has attracted increasing research attention due to its cost-effectiveness, ease of fabrication, environmental protection, and sustainability. Brewed tea leaves are rich in heteroatoms that are beneficial to capacitive energy storage behavior. Therefore, we synthesized high electrochemical performance carbon-based composites from Tie guan yin tea leaf waste using a facile procedure comprising hydrothermal, chemical activation, and calcination processes. In particular, potassium permanganate (KMnO4) was incorporated into the potassium hydroxide (KOH) activation agent; therefore, during the activation process, KOH continued to erode the biomass precursor, producing abundant pores, and KMnO4 synchronously underwent a redox reaction to form MnO nanoparticles and anchor on the porous carbon through chemical bonding. MnO nanoparticles provided additional pseudocapacitive charge storage capabilities through redox reactions. The results show that the amount of MnO produced is proportional to the amount of KMnO4 incorporated. However, the specific surface area of the composite material decreases with the incorporated amount of KMnO4 due to the accumulation and aggregation of MnO nanoparticles, thereby even blocking some micropores. Optimization of MnO nanocrystal loading can promote the crystallinity and graphitization degree of carbonaceous materials. The specimen prepared with a weight ratio of KMnO4 to hydrochar of 0.02 exhibited a high capacitance of 337 F/g, an increase of 70%, owing to the synergistic effect between the Tie guan yin tea leaf-derived activated carbon and MnO nanoparticles. With this facile preparation method and the resulting high electrochemical performance, the development of manganese oxide/carbon composites derived from tea leaf biomass is expected to become a promising candidate as an energy storage material for supercapacitors. [ABSTRACT FROM AUTHOR]

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets. [ABSTRACT FROM AUTHOR]

Speech audiometry is a vital tool in assessing an individual's ability to perceive and comprehend speech, traditionally requiring specialized testing that can be time-consuming and resource -intensive. This paper approaches a novel use of deep learning to predict speech audiometry using pure-tone audiometry (PTA) data. By utilizing PTA data, which measure hearing sensitivity at specific frequencies, we aim to develop a model that can bypass the need for direct speech testing. This study investigates two neural network architectures: a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1D-CNN). These models are trained to predict key speech audiometry outcomes, including speech recognition thresholds and speech discrimination scores. To evaluate the effectiveness of these models, we employed two key performance metrics: the coefficient of determination (R2) and mean absolute error (MAE). The MLP model demonstrated predictive solid power with an R2 score of 88.79% and an average MAE of 7.26, while the 1D-CNN model achieved a slightly higher level of accuracy with an MAE score of 88.35% and an MAE of 6.90. The superior performance of the 1D-CNN model suggests that it captures relevant features from PTA data more effectively than the MLP. These results show that both models hold promise for predicting speech audiometry, potentially simplifying the audiological evaluation process. This approach is applied in clinical settings for hearing loss assessment, the selection of hearing aids, and the development of personalized auditory rehabilitation programs. [ABSTRACT FROM AUTHOR]

Gossypol (GP), a polyphenolic compound in cottonseed, has notable effects on female reproduction and the respiratory system in pigs. This study aimed to discern the alterations in gene expression within swine granulosa cells (GCs) when treated with two concentrations of GP (6.25 and 12.5 µM) for 72 h, in vitro. The analysis revealed significant changes in the expression of numerous genes in the GP-treated groups. A Gene Ontology analysis highlighted that the differentially expressed genes (DEGs) primarily pertained to processes such as the mitotic cell cycle, chromosome organization, centromeric region, and protein binding. Pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated distinct impacts on various pathways in response to different GP concentrations. Specifically, in the GP6.25 group, pathways related to the cycle oocyte meiosis, progesterone-mediated oocyte maturation, and p53 signaling were prominently affected. Meanwhile, in the GP12.5 group, pathways associated with PI3K-Akt signaling, focal adhesion, HIF-1 signaling, cell cycle, and ECM–receptor interaction showed significant alterations. Notably, genes linked to female reproductive function (CDK1, CCNB1, CPEB1, MMP3), cellular component organization (BIRC5, CYP1A1, TGFB3, COL1A2), and oxidation–reduction processes (PRDX6, MGST1, SOD3) exhibited differential expression in GP-treated groups. These findings offer valuable insights into the changes in GC gene expression in pigs exposed to GP. [ABSTRACT FROM AUTHOR]

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail. [ABSTRACT FROM AUTHOR]

Background: The causal association of specific gut microbiota with dementia remains incompletely understood. We aimed to access the causal relationships in which one or more gut microbiota account for dementia. Method: Using data from the MiBioGen and FinnGen consortia, we employed multiple Mendelian randomization (MR) approaches including two-sample MR (TSMR), multivariable MR (MVMR), and Bayesian model averaging MR to comprehensively evaluate the causal associations between 119 genera and dementia, and to prioritize the predominant bacterium. Result: We identified 21 genera that had causal effects on dementia and suggested Barnesiella (OR = 0.827, 95%CI = 0.722–0.948, marginal inclusion probability [MIP] = 0.464; model-averaged causal estimate [MACE] = −0.068) and Allisonella (OR = 0.770, 95%CI = 0.693–0.855, MIP = 0.898, MACE = −0.204) as the predominant genera for AD and all-cause dementia. Conclusions: These findings confirm the causal relationships between specific gut microbiota and dementia, highlighting the necessity of multiple MR approaches in gut microbiota analysis, and provides promising genera as potential novel biomarkers for dementia risk. [ABSTRACT FROM AUTHOR]

Acetaminophen (APAP) overdose can induce hepatocyte necrosis and acute liver failure in experimental rodents and humans. APAP is mainly metabolized via hepatic cytochrome P450 enzymes to generate the highly reactive metabolite N-acetyl-p-benzoquinone imine (NAPQI), which forms acetaminophen protein adducts (APAP-adducts) and damages mitochondria, triggering necrosis. APAP-adducts and damaged mitochondria can be selectively removed by autophagy. Increasing evidence implies that the activation of autophagy may be beneficial for APAP-induced liver injury (AILI). In this minireview, we briefly summarize recent progress on autophagy, in particular, the pharmacological targeting of SQSTM1/p62 and TFEB in AILI. [ABSTRACT FROM AUTHOR]

The causal association of circulating metabolites with dementia remains uncertain. We assessed the causal association of circulating metabolites with dementia utilizing Mendelian randomization (MR) methods. We performed univariable MR analysis to evaluate the associations of 486 metabolites with dementia, Alzheimer's disease (AD), and vascular dementia (VaD) risk. For secondary validation, we replicated the analyses using an additional dataset with 123 metabolites. We observed 118 metabolites relevant to the risk of dementia, 59 of which were lipids, supporting the crucial role of lipids in dementia pathogenesis. After Bonferroni adjustment, we identified nine traits of HDL particles as potential causal mediators of dementia. Regarding dementia subtypes, protective effects were observed for epiandrosterone sulfate on AD (OR = 0.60, 95% CI: 0.48–0.75) and glycoproteins on VaD (OR = 0.89, 95% CI: 0.83–0.95). Bayesian model averaging MR (MR-BMA) analysis was further conducted to prioritize the predominant metabolites for dementia risk, which highlighted the mean diameter of HDL particles and the concentration of very large HDL particles as the predominant protective factors against dementia. Moreover, pathway analysis identified 17 significant and 2 shared metabolic pathways. These findings provide support for the identification of promising predictive biomarkers and therapeutic targets for dementia. [ABSTRACT FROM AUTHOR]

(1) Background: Polyetheretherketone (PEEK) has been used as an alternative to titanium in implant prosthetic systems, but its impact on stress distribution in implant systems needs to be investigated. This study aimed to compare the effect of polyetheretherketone (PEEK) and titanium abutments on implant prosthetic systems and the supporting bone using three-dimensional finite element analysis (FEA). (2) Methods: Three-dimensional finite element analysis was conducted using CATIA V5 and Abaqus V6.12 software to model mandibular first-molar implant systems with titanium and PEEK abutments. Under external loading conditions, finite element analysis was conducted for the stresses in the implant components and surrounding bones of each group. (3) Results: The implant fixture of the PEEK model exhibited the highest von Mises stress (VMS). The lowest VMS was observed in the abutment screw of the titanium model. Both implant systems demonstrated similar stress distributions and magnitudes in cortical and cancellous bones. (4) Conclusion: PEEK abutments show a similar stress distribution in the surrounding bone compared to titanium. However, PEEK absorbs the stresses within the implant system and exhibits the highest VMS values due to its low mechanical and physical properties. Therefore, PEEK abutments need improved mechanical properties for better clinical application. [ABSTRACT FROM AUTHOR]

Background: Vision-based pulmonary diagnostics present a unique approach for tracking and measuring natural breathing behaviors through remote imaging. While many existing methods correlate chest and diaphragm movements to respiratory behavior, we look at how the direct visualization of thermal CO2 exhale flow patterns can be tracked to directly measure expiratory flow. Methods: In this work, we present a novel method for isolating and extracting turbulent exhale flow signals from thermal image sequences through flow-field prediction and optical flow measurement. The objective of this work is to introduce a respiratory diagnostic tool that can be used to capture and quantify natural breathing, to identify and measure respiratory metrics such as breathing rate, flow, and volume. One of the primary contributions of this work is a method for capturing and measuring natural exhale behaviors that describe individualized pulmonary traits. By monitoring subtle individualized respiratory traits, we can perform secondary analysis to identify unique personalized signatures and abnormalities to gain insight into pulmonary function. In our study, we perform data acquisition within a clinical setting to train an inference model (FieldNet) that predicts flow-fields to quantify observed exhale behaviors over time. Results: Expiratory flow measurements capturing individualized flow signatures from our initial cohort demonstrate how the proposed flow field model can be used to isolate and analyze turbulent exhale behaviors and measure anomalous behavior. Conclusions: Our results illustrate that detailed spatial flow analysis can contribute to unique signatures for identifying patient specific natural breathing behaviors and abnormality detection. This provides the first-step towards a non-contact respiratory technology that directly captures effort-independent behaviors based on the direct measurement of imaged CO2 exhaled airflow patterns. [ABSTRACT FROM AUTHOR]

Video classification has achieved remarkable success in recent years, driven by advanced deep learning models that automatically categorize video content. This paper provides a comprehensive review of video classification techniques and the datasets used in this field. We summarize key findings from recent research, focusing on network architectures, model evaluation metrics, and parallel processing methods that enhance training speed. Our review includes an in-depth analysis of state-of-the-art deep learning models and hybrid architectures, comparing models to traditional approaches and highlighting their advantages and limitations. Critical challenges such as handling large-scale datasets, improving model robustness, and addressing computational constraints are explored. By evaluating performance metrics, we identify areas where current models excel and where improvements are needed. Additionally, we discuss data augmentation techniques designed to enhance dataset accuracy and address specific challenges in video classification tasks. This survey also examines the evolution of convolutional neural networks (CNNs) in image processing and their adaptation to video classification tasks. We propose future research directions and provide a detailed comparison of existing approaches using the UCF-101 dataset, highlighting progress and ongoing challenges in achieving robust video classification. [ABSTRACT FROM AUTHOR]

The mechanical properties of beta titanium alloys can be improved by precipitating the α phase in the β-phase matrix and controlling the microstructure via appropriate aging treatments. In this study, heat treatment in the range of 400 to 550 °C is performed to optimize the aging of Ti-3Al-8V-6Cr-4Mo-4Zr alloys. The increase in the aging temperature and holding time increases the hardness and compressive yield strength owing to the precipitation of the secondary α phase in the β matrix. The precipitation driving force at 400 °C is low because of the slow diffusion rate, and therefore the improvements in the hardness and strength are small. At temperatures above 500 °C, phase separation occurs rapidly (β → β + β′), and the β′ phase acts as a nucleation site for the secondary α phase. The phase transformation from the β′ to the secondary α phase is promoted at 500 °C, resulting in the highest hardness (406.3 HV) and compressive yield strength (1433.8 MPa) at 24 h. At 550 °C, the secondary α phase grows and the hardness and compressive yield strength degrade. These results can be effectively applied to manufacture springs with excellent formability and mechanical properties. [ABSTRACT FROM AUTHOR]

This study evaluates the efficacy of several Convolutional Neural Network (CNN) models for the classification of hearing loss in patients using preprocessed auditory brainstem response (ABR) image data. Specifically, we employed six CNN architectures—VGG16, VGG19, DenseNet121, DenseNet-201, AlexNet, and InceptionV3—to differentiate between patients with hearing loss and those with normal hearing. A dataset comprising 7990 preprocessed ABR images was utilized to assess the performance and accuracy of these models. Each model was systematically tested to determine its capability to accurately classify hearing loss. A comparative analysis of the models focused on metrics of accuracy and computational efficiency. The results indicated that the AlexNet model exhibited superior performance, achieving an accuracy of 95.93%. The findings from this research suggest that deep learning models, particularly AlexNet in this instance, hold significant potential for automating the diagnosis of hearing loss using ABR graph data. Future work will aim to refine these models to enhance their diagnostic accuracy and efficiency, fostering their practical application in clinical settings. [ABSTRACT FROM AUTHOR]

Surface treatment of implants facilitates osseointegration, with nanostructured surfaces exhibiting accelerated peri-implant bone regeneration. This study compared bone-to-implant contact (BIC) in implants with hydroxyapatite (HA), sand-blasted and acid-etched (SLA), and SLA with calcium (Ca)-coated (XPEED®) surfaces. Seventy-five disk-shaped grade 4 Ti specimens divided into three groups were prepared, with 16 implants per group tested in New Zealand white rabbits. Surface characterization was performed using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), digital microscopy, and a contact angle analyzer. Cell viability, proliferation, and adhesion were assessed using MC3T3-E1 cells. Apatite formation was evaluated using modified simulated body fluid (m-SBF) incubation. After 4 weeks of healing, the outcomes reviewed were BIC, bone area (BA), removal torque tests, and histomorphometric evaluation. A microstructure analysis revealed irregular pores across all groups, with the XPEED group exhibiting a nanostructured Ca-coated surface. Surface characterization showed a crystalline CaTiO3 layer on XPEED surfaces, with evenly distributed Ca penetrating the implants. All surfaces provided excellent environments for cell growth. The XPEED and SLA groups showed significantly higher cell density and viability with superior osseointegration than HA (p < 0.05); XPEED exhibited the highest absorbance values. Thus, XPEED surface treatment improved implant performance, biocompatibility, stability, and osseointegration. [ABSTRACT FROM AUTHOR]

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances. [ABSTRACT FROM AUTHOR]

This study conducted phenotypic evaluations on a wheat F3 population derived from 155 F2 plants. Traits related to seed color, including chlorophyll a, chlorophyll b, carotenoid, anthocyanin, L*, a*, and b*, were assessed, revealing highly significant correlations among various traits. Genotyping using 81,587 SNP markers resulted in 3969 high-quality markers, revealing a genome-wide distribution with varying densities across chromosomes. A genome-wide association study using fixed and random model circulating probability unification (FarmCPU) and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) identified 11 significant marker–trait associations (MTAs) associated with L*, a*, and b*, and chromosomal distribution patterns revealed predominant locations on chromosomes 2A, 2B, and 4B. A comprehensive annotation uncovered 69 genes within the genomic vicinity of each MTA, providing potential functional insights. Gene expression analysis during seed development identified greater than 2-fold increases or decreases in expression in colored wheat for 16 of 69 genes. Among these, eight genes, including transcription factors and genes related to flavonoid and ubiquitination pathways, exhibited distinct expression patterns during seed development, providing further approaches for exploring seed coloration. This comprehensive exploration expands our understanding of the genetic basis of seed color and paves the way for informed discussions on the molecular intricacies contributing to this phenotypic trait. [ABSTRACT FROM AUTHOR]

An electrochemical floatation system (EFS)-based sewage-treatment process that applied a dimensionally stable anode (DSA) was developed to secure treated sewage water from sewage-treatment plants for reuse. The DSA was fabricated at a temperature of 673–923 K by dispersing a low quantity of activated carbon on a titanium plate coated with mixed metal oxides (Pt, Re, Pd, Re, and Ir) to a thickness of 5 μm. The average size of the bubbles generated through the DSA of the EFS was 20–40 μm, thus confirming microbubble generation. An efficiency assessment of a titanium-based DSA confirmed metal oxide activity through the removal of Escherichia coli (100%) and total organic carbon (TOC; 48.83%) at a reaction time shorter than 10 min and a low current density (19.6 A/m2). During the long-term operation of the EFS with the separation membrane process, the average removal efficiency was 94.7%, 90.0%, 96.1%, 90.9%, and 98.0% for suspended solids, biochemical oxygen demand, TOC, total nitrogen, and total phosphorous, respectively. Coliforms were not detected. Overall, our EFS coupled with the separation membrane process produced high-quality recycled water that could be used for various purposes according to the water-quality standards for different applications. [ABSTRACT FROM AUTHOR]

In virtual reality, augmented reality, or animation, the goal is to represent the movement of deformable objects in the real world as similar as possible in the virtual world. Therefore, this paper proposed a method to automatically extract cloth stiffness values from video scenes, and then they are applied as material properties for virtual cloth simulation. We propose the use of deep learning (DL) models to tackle this issue. The Transformer model, in combination with pre-trained architectures like DenseNet121, ResNet50, VGG16, and VGG19, stands as a leading choice for video classification tasks. Position-Based Dynamics (PBD) is a computational framework widely used in computer graphics and physics-based simulations for deformable entities, notably cloth. It provides an inherently stable and efficient way to replicate complex dynamic behaviors, such as folding, stretching, and collision interactions. Our proposed model characterizes virtual cloth based on softness-to-stiffness labels and accurately categorizes videos using this labeling. The cloth movement dataset utilized in this research is derived from a meticulously designed stiffness-oriented cloth simulation. Our experimental assessment encompasses an extensive dataset of 3840 videos, contributing to a multi-label video classification dataset. Our results demonstrate that our proposed model achieves an impressive average accuracy of 99.50%. These accuracies significantly outperform alternative models such as RNN, GRU, LSTM, and Transformer. [ABSTRACT FROM AUTHOR]

Simple Summary: Alterations in the composition and function of the intestinal microbiota are linked to chronic enteropathy; however, the temporal variability of the intestinal microbiota is under-explored. This study aimed to evaluate the temporal variability of the feline dysbiosis index and the core bacterial taxa in healthy adult cats. A total of 142 fecal samples collected from 17 adult pet cats were included. The qPCR-based feline dysbiosis index was used to assess the fecal microbiota. The results showed temporal stability in the feline dysbiosis index in all healthy adult cats throughout the study. The feline dysbiosis index was consistently within the reference interval over two months in individual cats, and most targeted bacteria remained within their respective reference intervals. While individual variation was observed, the magnitude of impact was minimal compared to disease status and antibiotic use. In conclusion, our findings show the temporal stability of the feline dysbiosis index in healthy adult cats in the absence of perturbations. While shifts in gut microbiota have been studied in diseased states, the temporal variability of the microbiome in cats has not been widely studied. This study investigated the temporal variability of the feline dysbiosis index (DI) and the abundance of core bacterial groups in healthy adult cats. The secondary aim was to evaluate the relationship between the fecal abundance of Clostridium hiranonis and the fecal concentrations of unconjugated bile acids. A total of 142 fecal samples collected from 17 healthy cats were prospectively included: nine cats with weekly collection over 3 weeks (at least four time points), five cats with monthly collection over 2 months (three time points), and three cats with additional collections for up to 10 months. The DI remained stable within the reference intervals over two months for all cats (Friedman test, p > 0.2), and 100% of the DI values (n = 142) collected throughout the study period remained within the RI. While some temporal individual variation was observed for individual taxa, the magnitude was minimal compared to cats with chronic enteropathy and antibiotic exposure. Additionally, the abundance of Clostridium hiranonis was significantly correlated with the percentage of fecal primary bile acids, supporting its role as a bile acid converter in cats. [ABSTRACT FROM AUTHOR]

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones. [ABSTRACT FROM AUTHOR]

Cannabichromene (CBC), a non-psychoactive cannabinoid found in Cannabis sativa, has recently been shown to possess several medicinal properties. However, how CBC produces anti-inflammatory effects and the mechanisms of this remain poorly studied. Therefore, we extracted and purified the CBC from the Cannabis sativa cv. pink pepper (hemp cultivar). The efficacy of CBC in reducing inflammation in RAW 264.7 macrophages and a λ-carrageenan-induced mouse model was then evaluated. CBC had no cytotoxicity up to a concentration of 20 μM and inhibited nitric oxide production by approximately 50% at a concentration of 20 μM. In addition, CBC treatment significantly inhibited causes of inflammation such as inducible nitric oxide synthase (iNOS), interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α) at both the mRNA and protein levels. Moreover, CBC suppressed LPS-stimulated inflammation in RAW 264.7 cells by downregulating the nuclear factor kappa B (NF-kB) and mitogen-activated protein kinase pathways (MAPK). Furthermore, our in vivo experiments confirmed that the λ-carrageenan-induced increase in the levels of the cytokines iNOS, IL-1β, and IL-6 was abrogated following treatment with CBC. Therefore, CBC has potential anti-inflammatory effects and may be useful for preventing or treating inflammation. [ABSTRACT FROM AUTHOR]

Fluorosilicone rubber, essential in automotive and aerospace owing to its excellent chemical resistance, plays a pivotal role in sealing technology, addressing the industry's evolving demands. This study explores the preparation and properties of fibrillated cellulose-reinforced fluorosilicone rubber composites to enhance their stiffness and oil resistance. Fibrillated cellulose sourced as a wet cake and subjected to processing and modification is incorporated into a fluorosilicone rubber matrix. The resulting composites are analysed by tensile and compression tests, along with compressive stress-relaxation testing in air and in an oil-immersed environment. The findings demonstrate significant improvements in the mechanical properties, including an increased Young's modulus and elongation at break, whereas the tensile strength remained uncompromised throughout the testing procedures. Morphological analysis of the fracture surfaces revealed a remarkable interfacial affinity between the fibrillated cellulose and rubber matrix, which was attributed in part to the modified fatty acids and inorganic nanoparticles. The presence of fibrillated cellulose enhanced the stress-relaxation characteristics under oil-immersion conditions. These results contribute to the domain of advanced elastomer materials, with potential for applications requiring enhanced mechanical properties and superior oil resistance. [ABSTRACT FROM AUTHOR]

Auditory brainstem response (ABR) is the response of the brain stem through the auditory nerve. The ABR test is a method of testing for loss of hearing through electrical signals. Basically, the test is conducted on patients such as the elderly, the disabled, and infants who have difficulty in communication. This test has the advantage of being able to determine the presence or absence of objective hearing loss by brain stem reactions only, without any communication. This paper proposes the image preprocessing process required to construct an efficient graph image data set for deep learning models using auditory brainstem response data. To improve the performance of the deep learning model, we standardized the ABR image data measured on various devices with different forms. In addition, we applied the VGG16 model, a CNN-based deep learning network model developed by a research team at the University of Oxford, using preprocessed ABR data to classify the presence or absence of hearing loss and analyzed the accuracy of the proposed method. This experimental test was performed using 10,000 preprocessed data, and the model was tested with various weights to verify classification learning. Based on the learning results, we believe it is possible to help set the criteria for preprocessing and the learning process in medical graph data, including ABR graph data. [ABSTRACT FROM AUTHOR]

This paper proposes a design method to reduce cogging torque and torque ripple in the concentrated winding of IPMSMs (Interior Permanent Magnet Synchronous Motors) used in motors for the semiconductor ETCH process. IPMSMs can utilize reluctance torque through the difference in inductance between the d axis and q axis, but they are at a disadvantage in terms of reducing cogging torque while tapering the rotor and stator to reduce torque ripple. In addition, the existing single tapering can push the permanent magnets into the rotor. If the rotor's permanent magnets are embedded, the magnetic reluctance will increase, and the overall performance of the motor will decrease. However, an optimum design method was derived in which the magnets do not move during rotor tapering. This geometric design is an optimum design method that reduces cogging torque and torque ripple. This paper compares and analyzes four models, the concentrated winding model, distributed winding model, conventional tapering model, and separated tapering model, using 2D and 3D finite element analysis (FEA). [ABSTRACT FROM AUTHOR]

Herpes simplex virus 1 (HSV-1) is double-stranded DNA virus that belongs to the Orthoherpesviridae family. It causes serious neurological diseases of the central nervous system, such as encephalitis. The current U.S. Food and Drug Administration (FDA)-approved drugs for preventing HSV-1 infection include acyclovir (ACV) and valacyclovir; however, their long-term use causes severe side effects and often results in the emergence of drug-resistant strains. Therefore, it is important to discover new antiviral agents that are safe and effective against HSV-1 infection. Korean chestnut honey (KCH) has various pharmacological activities, such as antioxidant, antibacterial, and anti-inflammation effects; however, antiviral effects against HSV-1 have not yet been reported. Therefore, we determined the antiviral activity and mechanism of action of KCH after HSV-1 infection on the cellular level. KCH inhibited the HSV-1 infection of host cells through binding and virucidal steps. KCH decreased the production of reactive oxygen species (ROS) and calcium (Ca2+) following HSV-1 infection and suppressed the production of inflammatory cytokines by inhibiting nuclear factor kappa-light-chain-enhancer of activated B cells (NF-кB) activity. Furthermore, we found that KCH inhibited the expression of the nod-like receptor protein 3 (NLRP3) inflammasome during HSV-1 infection. Taken together, the antiviral effects of KCH occur through multiple targets, including the inhibition of viral replication and the ROS-mediated NLRP3 inflammasome pathway. Our findings suggest that KCH has potential for the treatment of HSV-1 infection and related diseases. [ABSTRACT FROM AUTHOR]

Policosanols (PCs) are long-chain linear aliphatic alcohols that are present in the primary leaves of cereal crops, such as barley and wheat, sugar cane wax, and beeswax. PCs have been used as a nutraceutical for improving hyperlipidemia and hypercholesterolemia. However, the PC content in mutant wheat lines has not been investigated. To select highly functional wheat sprouts with a high content of PCs in wheat mutant lines developed via gamma-irradiated mutation breeding, we cultivated the sprouts of wheat mutant lines in a growth chamber with white LED light (6000 K) and analyzed the PC content in these samples using GC-MS. We studied the PC content in 91 wheat sprout samples: the original variety (Woori-mil × D-7; WS01), commercially available cv. Geumgang (WS87) and cv. Cheongwoo (WS91), and mutant lines (WS02–WS86 and WS88–WS90) developed from WS01 and WS87. Compared to WS01, 18 mutant lines exhibited a high total PC content (506.08–873.24 mg/100 g dry weight). Among them, the top 10 mutant lines were evaluated for their PC production after cultivating under blue (440 nm), green (520 nm), and red (660 nm) LED light irradiation; however, these colored LED lights reduced the total PC production by 35.8–49.7%, suggesting that the cultivation with white LED lights was more efficient in promoting PCs' yield, compared to different LED lights. Therefore, our findings show the potential of radiation-bred wheat varieties as functional foods against hyperlipidemia and obesity and the optimal light conditions for high PC production. [ABSTRACT FROM AUTHOR]

Cannabis sativa L. contains more than 80 cannabinoids, among which cannabidiol (CBD) is the main neuroactive component. We aimed to investigate the anti-inflammatory efficacy of CBD in vitro and in vivo isolated from "Pink pepper", a novel hemp cultivar, by repeating the method of selecting and cultivating individuals with the highest CBD content. We investigated the effects of CBD on inflammatory markers elevated by lipopolysaccharide (LPS) treatment in RAW 264.7 mouse macrophage cells through Western blot and RT-PCR. In addition, we confirmed these effects through the ELISA of inflamed paw tissue of a λ-carrageenan-induced mouse edema model that received an oral administration of CBD. CBD inhibited the LPS-induced phosphorylation of NF-κB and MAPK in RAW 264.7 and exhibited anti-inflammatory effects by participating in these pathways. In our in vivo study, we confirmed that CBD also inhibited the inflammatory mediators of proteins extracted from edematous mouse paw tissue. These results show that CBD isolated from "Pink pepper" exhibits potent anti-inflammatory effects. These anti-inflammatory effects of CBD have pharmacological and physiological significance, highlighting the industrial value of this novel cultivar. [ABSTRACT FROM AUTHOR]

Simple Summary: Polo-like kinase 4 (PLK4) is associated with tumorigenesis and prognosis in various types of cancer. Prognostic analysis of PLK4 expression and data analysis of its association with the somatic mutation and drug resistance through clustering were performed using the Cancer Genome Atlas-lung adenocarcinoma (TCGA-LUAD) dataset. According to these results, PLK4 expression is associated with poor prognosis, TP53 mutations, and drug resistance in patients with LUAD. (1) Background: This study investigated whether polo-like kinase 4 (PLK4) is a suitable therapeutic target or biomarker for lung adenocarcinoma (LUAD). (2) Methods: We acquired LUAD data from The Cancer Genome Atlas (TCGA) database through the UCSC Xena data portal. Gene expression, clinical, survival, and mutation data from multiple samples were analyzed. Gene enrichment analysis, unsupervised clustering of PLK4-related pathways, and differential gene expression analyses were performed. Additionally, correlations, t-tests, survival analyses, and statistical analyses were performed. (3) Results: PLK4 expression was higher in LUAD tissues than in normal tissues and was associated with poor prognosis for both overall and progression-free survival in LUAD. PLK4 was highly correlated with cell-proliferation-related pathways using Gene Ontology (GO) biological process terms. PLK4 expression and pathways that were highly correlated with PLK4 expression levels were upregulated in patients with LUAD with the TP53 mutation. (4) Conclusions: PLK4 expression affects the survival of patients with LUAD and is a potential therapeutic target for LUAD with TP53 mutations. [ABSTRACT FROM AUTHOR]

Physically based cloth simulation requires a model that represents cloth as a collection of nodes connected by different types of constraints. In this paper, we present a coefficient prediction framework using a Deep Learning (DL) technique to enhance video summarization for such simulations. Our proposed model represents virtual cloth as interconnected nodes that are subject to various constraints. To ensure temporal consistency, we train the video coefficient prediction using Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM), and Transformer models. Our lightweight video coefficient network combines Convolutional Neural Networks (CNN) and a Transformer to capture both local and global contexts, thus enabling highly efficient prediction of keyframe importance scores for short-length videos. We evaluated our proposed model and found that it achieved an average accuracy of 99.01%. Specifically, the accuracy for the coefficient prediction of GRU was 20%, while LSTM achieved an accuracy of 59%. Our methodology leverages various cloth simulations that utilize a mass-spring model to generate datasets representing cloth movement, thus allowing for the accurate prediction of the coefficients for virtual cloth within physically based simulations. By taking specific material parameters as input, our model successfully outputs a comprehensive set of geometric and physical properties for each cloth instance. This innovative approach seamlessly integrates DL techniques with physically based simulations, and it therefore has a high potential for use in modeling complex systems. [ABSTRACT FROM AUTHOR]

The genetic diversity of coronaviruses (CoVs) is high, and their infection in animals has not yet been fully revealed. By RT-PCR detection of the partial RNA-dependent RNA polymerase (RdRp) gene of CoVs, we screened a total of 502 small mammals in the Dali and Nujiang prefectures of Western Yunnan Province, China. The number of overall CoV positives was 20, including β-CoV (n = 13) and α-CoV (n = 7), with a 3.98% prevalence in rectal tissue samples. The identity of the partial RdRp genes obtained for 13 strains of β-CoV was 83.42–99.23% at the nucleotide level, and it is worth noting that the two strains from Kachin red-backed voles showed high identity to BOV-36/IND/2015 from Indian bovines and DcCoV-HKU23 from dromedary camels (Camelus dromedarius) in Morocco; the nucleotide identity was between 97.86 and 98.33%. Similarly, the identity of the seven strains of α-CoV among the partial RdRp sequences was 94.00–99.18% at nucleotide levels. The viral load in different tissues was measured by quantitative RT-PCR (qRT-PCR). The average CoV viral load in small mammalian rectal tissue was 1.35 × 106 copies/g; differently, the mean CoV viral load in liver, heart, lung, spleen, and kidney tissue was from 0.97 × 103 to 3.95 × 103 copies/g, which revealed that CoV has extensive tropism in rectal tissue in small mammals (p < 0.0001). These results revealed the genetic diversity, epidemiology, and infective tropism of α-CoV and β-CoV in small mammals from Dali and Nujiang, which deepens the comprehension of the retention and infection of coronavirus in natural hosts. [ABSTRACT FROM AUTHOR]

A correction notice was issued for a study on the detection of Alpha- and Betacoronaviruses in small mammals in Western Yunnan Province, China. The correction involved updating the species name "Short-tailed shrew" to "Chinese mole shrew" in Table 2 of the publication. The scientific conclusions of the study were deemed unaffected by this correction, which was approved by the Academic Editor. [Extracted from the article]

Titanium (Ti) and Ti-based alloys are commonly used in dental implants, and surface modifications of dental implants are important for achieving osseointegration (i.e., direct connection between the implant surface and bone). This study investigated the effect of an eco-friendly etching solution—a hydrogen peroxide–sodium bicarbonate mixture—on the surface properties and contact angles of osteoblast adhesion and proliferation on Ti surfaces. Disk-shaped Ti specimens were prepared using different surface treatments (machining, sandblasting, and sandblasting/acid-etching), and they were immersed in the etching solution and ultrasonically cleaned. Surface characterization was performed using scanning electron microscopy, digital microscopy, contact angle analysis, and X-ray photoelectron spectroscopy. MG-63 osteoblasts were cultured on the specimens, and their adhesion to the specimen surface and proliferation were examined using staining and the MTT assay, respectively. Additional etching with the etching solution caused the formation of nano/micro hierarchical structures, increased surface roughness, and enhanced hydrophilicity. Osteoblast adhesion and proliferation were found to improve on the modified surfaces. The eco-friendly etching method has the potential to enhance the biological properties of Ti implant surfaces and thereby improve dental implant performance. [ABSTRACT FROM AUTHOR]

The biological factors of dental implants have a significant impact on long-term prognosis. In the cement-retained type or screw cement-retained type prosthesis (SCRP) implants, dental luting cement is used between the abutment and the implant fixture. Residual excess cement remaining around the implant and gingiva is one of the major causes of peri-implantitis, which is the most common cause of late implant failure. The TDP implant prosthetic system is a new cementless screw-retained type prosthetic system, which overcomes the limitations of the conventional implant prosthetic systems. Using this system, not only serves to prevent peri-implantitis caused by residual excess cement, but also the risk of screw loosening and fracture is reduced due to the stress distribution through the substructure (link), which is connected to the zirconia crown via frictional force. In this case report, two patients with tooth defects visited our dental hospital for implant treatment. Both patients were treated using the TDP implant prosthetic system. As a result, functional and esthetically excellent implant prostheses were fabricated and delivered to the patients. On the 3-year follow-up examination, both patients showed successful results, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient approach for simulating volumetric deformable objects using the Position-Based Dynamics (PBD) method. Volumetric bodies generated by TetGen are used to represent three-dimensional objects, which accurately capture complex shapes and volumes. However, when a large number of constraints are applied to the system to solve using serialized algorithms on central processing units (CPU), the computational cost can become a bottleneck of the simulation. To address this issue, the proposed implementation algorithm takes advantage of graphic processing unit (GPU) acceleration and parallel processing to improve the efficiency of the simulation. We propose two specific contributions: firstly, the use of the PBD method with volume constraint for tetrahedral elements to simulate volumetric deformable objects realistically; secondly, an efficient GPU-accelerated algorithm for implementing the PBD method that significantly improves computational efficiency. We also applied the node-centric and constraint-centric algorithms to solve the stretch constraint in the GPU-based algorithm. The implementation was performed using Unity3D. The compute shader feature of Unity3D was utilized to perform thousands of parallel computations in a single pass, making it possible to simulate large and complex objects in real-time. The performance of the simulation can be accelerated by using GPU-based methods with stretch and bending constraints, which provides significant speedup factors compared to using only the CPU for deformable objects such as Bunny, Armadillo, and Dragon. The constraint-centric and node-centric GPU approaches provide speedup factors of up to 8.9x and 8x, respectively, while the GPU-based methods with all types of constraints exhibit a slight decrease but still operate at real-time speeds. Overall, this approach enables the simulation of complex and irregular shapes with plausible and realistic results, while also achieving speed, robustness, and flexibility. Additionally, the proposed approach can be applied to general simulation and other game engines that support GPU-based acceleration. [ABSTRACT FROM AUTHOR]

Long-span steel structure trusses are widely used in factory buildings. However, with the increase in service time and dynamic load fatigue, transverse cracks at the bottom of the middle span and oblique deformation of the abdomen during the operation process may appear in a considerable part of long-span trusses with dynamic load. The U-shaped cracks at the bottom and belly, as well as the mid-span down deflection of the main truss, can also reduce the functionality of the factory building truss structure and limit the original crane load, thus affecting the normal safety and durability of the structure. Therefore, the principle of variable axial force cable system in the long-span factory building truss structure and 3D3S software modelling were applied. Analysing and studying the reinforcement method of large-span powerhouse trusses can provide practical experience for subsequent similar projects. In view of the above phenomenon, the large-span powerhouse trusses of Hongcheng Powerhouse No. 1 and No. 2, located in Tonglu, Zhejiang Province, were used as the research objects, and the variable axial force cable method was proposed to strengthen and lift the load. Considering the span of the powerhouse truss, a cable system with 22 m and a controlling force of 400 kN was proposed for Powerhouse 1, and a cable system with a variable axial force of 24 m was proposed for Powerhouse 2. The force model of large-span trusses was established by using the finite element method, which is commonly used to analyse the force of the truss. The influence of the reinforcement effect was analysed under two working conditions and compared from three aspects: stiffness, bearing capacity and stability. Furthermore, the phenomenon of uneven stress distribution was analysed. The stress distribution characteristics of each node were understood by simulating the most disadvantageous node plates with the greatest internal force before and after reinforcement. [ABSTRACT FROM AUTHOR]

The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to identify the role of radiotherapy (RT) in the treatment of thymic carcinoma as well as the optimal RT target volume. A total of 116 patients who received multimodal treatment including RT with or without surgery or chemotherapy were included. The RT target volume was defined as the tumor bed or gross tumor with margin, and selective irradiation of the nodal area was performed when involved. The 5-year local recurrence-free survival rate was 94.7%. Overall, 53 recurrences were observed, of which distant metastasis was the most common. No isolated infield or marginal failures were observed. No infield nodal failure occurred among the patients who had initial node metastases and received RT including the nodal areas. A high local control rate was achieved with the addition of RT. A target volume confined to the tumor bed or gross tumor plus margin with the involved lymph node stations seems reasonable. Introduction: We aimed to identify the role of radiotherapy (RT) in the treatment of thymic carcinoma as well as the optimal RT target volume. Materials and Methods: This single-institution retrospective study included 116 patients diagnosed with thymic carcinoma between November 2006 and December 2021 who received multimodal treatment including RT with or without surgery or chemotherapy. Seventy-nine patients (68.1%) were treated with postoperative RT, 17 patients (14.7%) with preoperative RT, 11 patients (9.5%) with definitive RT, and nine patients (7.8%) with palliative RT. The target volume was defined as the tumor bed or gross tumor with margin, and selective irradiation of the regional nodal area was performed when involved. Results: With a median follow-up of 37.0 (range, 6.7–174.3) months, the 5-year overall survival (OS), progression-free survival, and local recurrence-free survival rates were 75.2%, 47.7% and 94.7%, respectively. The 5-year OS was 51.9% in patients with unresectable disease. Overall, 53 recurrences were observed, of which distant metastasis was the most common pattern of failure (n = 32, 60.4%) after RT. No isolated infield or marginal failures were observed. Thirty patients (25.8%) who had lymph node metastases at the initial diagnosis had regional nodal areas irradiated. There was no lymph node failure inside the RT field. A tumor dimension of ≥5.7 cm (hazard ratio [HR] 3.01; 95% confidence interval [CI] 1.25–7.26; p = 0.030) and postoperative RT (HR 0.20; 95% CI 0.08–0.52; p = 0.001) were independently associated with OS. Intensity-modulated-RT-treated patients developed less overall toxicity (p < 0.001) and esophagitis (p < 0.021) than three-dimensional-conformal-RT-treated patients. Conclusions: A high local control rate was achieved with RT in the primary tumor sites and involved lymph node area in the treatment of thymic carcinoma. A target volume confined to the tumor bed or gross tumor plus margin with the involved lymph node stations seems reasonable. The advanced RT techniques with intensity-modulated RT have led to reduced RT-related toxicity. [ABSTRACT FROM AUTHOR]

When a magnetic bearing is used in the design of a high-speed motor, no friction and wear occur because of the principle of magnetic levitation; however, the size of the entire system increases. An integrated magnetic bearing motor is a motor with a magnetic bearing inserted inside the rotor that can minimize the increase in the size of the entire system. In this study, a method to reduce the bearing force ripple and torque ripple of an integrated magnetic bearing motor through parameters for a Halbach array and permanent magnet tapering is proposed. When designing an integrated magnetic bearing motor, because the magnetic bearing is located inside the rotor, the influence of the magnetic flux of the rotor and stator on the magnetic flux of the magnetic bearing should be minimized. By combining the magnetic fluxes of the magnetic bearing, rotor, and stator at the rotor back yoke, magnetic saturation occurs, and the performance of the bearing force and torque ripples decreases. The bearing force and torque according to the Halbach array and permanent magnet tapering were analyzed using finite element analysis. The average bearing force and torque were maximized, and the ripple was minimized through the rotor parameters. Thus, the validity of the main design variables selected to improve the output characteristics was confirmed. [ABSTRACT FROM AUTHOR]

Metformin, an antidiabetic drug, and Glycyrrhiza uralensis Fischer (GU), an oriental medicinal herb, have been reported to exert anti-obesity effects. This study investigated the synergistic action of metformin and GU in improving diet-induced obesity. Mice were fed a normal diet, a high-fat diet (HFD), or HFD + 0.015% GU water extract for 8 weeks. The HFD and GU groups were then randomly divided into two groups and fed the following diets for the next 8 weeks: HFD with 50 mg/kg metformin (HFDM) and GU with 50 mg/kg metformin (GUM). GUM prevented hepatic steatosis and adiposity by suppressing expression of mRNAs and enzyme activities related to lipogenesis in the liver and upregulating the expression of adipocyte mRNAs associated with fatty acid oxidation and lipolysis, and as a result, improved dyslipidemia. Moreover, GUM improved glucose homeostasis by inducing glucose uptake in tissues and upregulating mRNA expressions associated with glycolysis in the liver and muscle through AMP-activated protein kinase activation. GUM also improved inflammation by increasing antioxidant activity in the liver and erythrocytes and decreasing inflammatory cytokine productions. Here, we demonstrate that GU and metformin exert synergistic action in the prevention of obesity and its complications. [ABSTRACT FROM AUTHOR]

This study was conducted to examine the effect of plant growth regulators on the rooting and growth of Rhododendron micranthum Turcz., a rare plant in South Korea. Indole-3-butyric acid (IBA) 150, 250, 350 mg·L−1 and α-naphthaleneacetic acid (NAA) 50, 150, 250 mg·L−1 treatment groups were established and cutting was performed in a commercial growth medium in March 2021 with 3 replications of 10 plants per group. After 70 days, the longest initial root growth was observed under the NAA 150 and 250 mg·L−1 treatments. Moreover, the number of roots and percentage of rooting were slightly higher in NAA 150 and 250 mg·L−1, indicating that these treatments were more effective in promoting plant growth. The above-ground part of plants treated with NAA 150 and 250 mg·L−1 had a taller plant height, longer and wider leaves, and a greater number of leaves compared to other groups. Accordingly, the survival rate of plants treated with NAA 150 and 250 mg·L−1 was higher than 80%, suggesting well-established roots and excellent above-ground growth. Therefore, treatment with growth regulator NAA at 150 and 250 mg·L−1 is considered beneficial for the cutting of R. micranthum Turcz. [ABSTRACT FROM AUTHOR]

Sudden cardiac death among hemodialysis patients is related to the hemodialysis schedule. Mortality is highest within 12 h before and after the first hemodialysis sessions of a week. We investigated the association of arrhythmia occurrence and heart rate variability (HRV) using an electrocardiogram (ECG) monitoring patch during the long interdialytic interval in hemodialysis patients. This was a prospective observational study with 55 participants on maintenance hemodialysis for at least six months. A patch-type ECG monitoring device was applied to record arrhythmia events and HRV during 72 h of a long interdialytic period. Forty-nine participants with sufficient ECG data out of 55 participants were suitable for the analysis. The incidence of supraventricular tachycardia and ventricular tachycardia did not significantly change over time. The square root of the mean squared differences of successive NN intervals (RMSSD), the proportion of adjacent NN intervals differing by >50 ms (pNN50), and high-frequency (HF) increased during the long interdialytic interval. The gap in RMSSD, pNN50, HF, and the low-frequency/high-frequency (LF/HF) ratio between patients with and without significant arrhythmias increased significantly over time during the long interdialytic interval. The daily changes in RMSSD, pNN50, HF, and the LF/HF ratio were more prominent in patients without significant arrhythmias than in those with significant arrhythmias. The electrolyte fluctuation between post-hemodialysis and subsequent pre-hemodialysis was not considered in this study. The study results suggest that the decreased autonomic response during interdialytic periods in dialysis patients is associated with poor cardiac arrhythmia events. [ABSTRACT FROM AUTHOR]

Proanthocyanidins (PAs) are major phytochemicals in rice, and accumulate abundantly in red pericarp (RP) rice. Consumers and rice breeders are increasingly showing interest in PAs because of their beneficial health effects; however, PA biosynthesis in rice is not well-understood. Therefore, to gain insights into this process, we performed comparative transcriptome analysis of grains harvested at 14 days after flowering (DAF; i.e., the stage at which active PA biosynthesis occurs without pericarp color change) and 28 DAF (the stage of late seed development with pericarp color change) from RP and white pericarp rice. In RP rice at 14 DAF, the expression levels of six structural genes (OsCHS, OsF3H, OsF3′H, OsDFR, OsANS, and OsLAR), one modification gene (OsUGT), and one transport gene (OsGSTU34) were significantly upregulated, along with the activation of Rc, the key regulator of PA accumulation in the pericarp. Functional enrichment analysis of 56 differentially expressed genes specifically upregulated in RP rice at 28 DAF revealed the presence of three laccase genes known to cause the browning reaction through oxidation. These results expand our understanding of PA biosynthesis in rice, and provide a genetic basis that will lead to further studies on the genes and underlying molecular mechanisms associated with this process. [ABSTRACT FROM AUTHOR]

Owing to the shortage of rare-earth magnetic materials, various methods are being examined to reduce the use of magnets. One of these is a consequent pole. The consequent pole model can reduce the use of magnets by 50% using only one pole of the magnet and replacing the other pole with iron. However, the consequent pole has the disadvantage of generating back EMF asymmetry and a high cogging torque. In this study, an intersect magnet consequent pole structure is proposed to overcome the disadvantages of the existing consequent pole. Two methods have been proposed to improve axial leakage magnetic flux caused by the intersect magnet consequent pole structure. Finally, we propose a method to reduce the cogging torque and minimize the use of magnets with the same performance standard. For motor design, two-dimensional and three-dimensional finite element analysis was used, and comparative analysis was performed via simulations for several models. The existing model and the final model were compared and verified. [ABSTRACT FROM AUTHOR]

Glutathione peroxidase 4 (GPX4) plays an important effect on ferroptosis. Down-regulating the expression of GPX4 mRNA can decrease the content of GPX4. In this work, a gold nanoflare (AuNF) probe loaded with anti-sense sequences targeting for GPX4 mRNA was designed to monitor and down-regulate intracellular GPX4 mRNA using fluorescence imaging in situ and using anti-sense technology. The results revealed that there was a marked difference for the expression of GPX4 mRNA in different cell lines, and the survival rate of cancer cells was not significantly effected when the relative mRNA and protein expression levels of GPX4 was down-regulated by AuNF probes. However, when co-treated with AuNF probes, the low expression of GPX4 strengthened erastin-induced ferroptosis, and this synergy showed a better effect on inhibiting the proliferation of cancer cells. [ABSTRACT FROM AUTHOR]

This study introduces a low-light image enhancement method using a hybrid deep-learning network and mixed-norm loss functions, in which the network consists of a decomposition-net, illuminance enhance-net, and chroma-net. To consider the correlation between R, G, and B channels, YCbCr channels converted from the RGB channels are used for training and restoration processes. With the luminance, the decomposition-net aims to decouple the reflectance and illuminance and to train the reflectance, leading to a more accurate feature map with noise reduction. The illumination enhance-net connected to the decomposition-net is used to enhance the illumination such that the illuminance is improved with reduced halo artifacts. In addition, the chroma-net is independently used to reduce color distortion. Moreover, a mixed-norm loss function used in the training process of each network is described to increase the stability and remove blurring in the reconstructed image by reflecting the properties of reflectance, illuminance, and chroma. The experimental results demonstrate that the proposed method leads to promising subjective and objective improvements over state-of-the-art deep-learning methods. [ABSTRACT FROM AUTHOR]