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We identified a new human voltage-gated potassium channel blocker, NnK-1, in the jellyfish Nemopilema nomurai based on its genomic information. The gene sequence encoding NnK-1 contains 5408 base pairs, with five introns and six exons. The coding sequence of the NnK-1 precursor is 894 nucleotides long and encodes 297 amino acids containing five presumptive ShK-like peptides. An electrophysiological assay demonstrated that the fifth peptide, NnK-1, which was chemically synthesized, is an effective blocker of hKv1.3, hKv1.4, and hKv1.5. Multiple-sequence alignment with cnidarian Shk-like peptides, which have Kv1.3-blocking activity, revealed that three residues (3Asp, 25Lys, and 34Thr) of NnK-1, together with six cysteine residues, were conserved. Therefore, we hypothesized that these three residues are crucial for the binding of the toxin to voltage-gated potassium channels. This notion was confirmed by an electrophysiological assay with a synthetic peptide (NnK-1 mu) where these three peptides were substituted with 3Glu, 25Arg, and 34Met. In conclusion, we successfully identified and characterized a new voltage-gated potassium channel blocker in jellyfish that interacts with three different voltage-gated potassium channels. A peptide that interacts with multiple voltage-gated potassium channels has many therapeutic applications in various physiological and pathophysiological contexts.

This scientific investigation emphasizes the essential integration of nature’s influence in crafting multifunctional surfaces with bio-inspired designs for enhanced functionality and environmental advantages. The study introduces an innovative approach, merging color decoration, humidity sensing, and antiviral properties into a unified surface using chitosan, an organo-biological polymer, to create cost-effective multilayered films through sol-gel deposition and UV photoinduced deposition of metal nanoparticles. The resulting chitosan films showcase diverse structural colors and demonstrate significant antiviral efficiency, with a 50% and 85% virus inhibition rate within a rapid 20 min reaction, validated through fluorescence cell expression and real-time qPCR (polymerase chain reaction) assays. Silver-deposited chitosan films further enhance antiviral activity, achieving remarkable 91% and 95% inhibition in independent assays. These films exhibit humidity-responsive color modifications across a 25–90% relative humidity range, enabling real-time monitoring validated through simulation studies. The proposed three-in-one functional surface can have versatile applications in surface decoration, medicine, air conditioning, and the food industry. It can serve as a real-time humidity sensor for indoor and outdoor surfaces, find use in biomedical devices for continuous humidity monitoring, and offer antiviral protection for frequently handled devices and tools. The customizable colors enhance visual appeal, making it a comprehensive solution for diverse applications.

Vascular diseases are the leading causes of death worldwide, and they are attributable to multiple pathologies, such as atherosclerosis, diabetes, and chronic obstructive pulmonary disease. Exposure to various environmental contaminants is associated with the development of various diseases, including vascular diseases. Among environmental contaminants, micro- and nanoplastics have gained attention as global environmental risk factors that threaten human health. Recently, extensive research has been conducted on the effects of micro- and nanoplastics on various human diseases, including vascular diseases. In this review, we highlight the effects of micro- and nanoplastics on vascular diseases.

Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become paramount. Monitoring systems are generally implemented based on multiple sensors, and the collected data are processed on a server to provide monitoring services to users. Due to the use of multiple sensors and a reliance on servers, there are limitations to economical maintenance and a risk of highly personal information being leaked. In this paper, we propose an intelligent monitoring system with privacy preservation based on edge AI. The proposed system achieves cost competitiveness and ensures high security by blocking communication between the camera module and the server with an edge AI module. Additionally, applying edge computing technology allows for the efficient processing of data traffic. The edge AI module was designed with Verilog HDL and was implemented on a field-programmable gate array (FPGA). Through experiments conducted on 6144 frames, we achieved 95.34% accuracy. Synthesis results in a 180 nm CMOS technology indicated a gate count of 1516 K and a power consumption of 344.44 mW.

Gene therapy is an innovative approach in the field of regenerative medicine. This therapy entails the transfer of genetic material into a patient’s cells to treat diseases. In particular, gene therapy for neurological diseases has recently achieved significant progress, with numerous studies investigating the use of adeno-associated viruses for the targeted delivery of therapeutic genetic fragments. This approach has potential applications for treating incurable diseases, including paralysis and motor impairment caused by spinal cord injury and Parkinson’s disease, and it is characterized by dopaminergic neuron degeneration. Recently, several studies have explored the potential of direct lineage reprogramming (DLR) for treating incurable diseases, and highlighted the advantages of DLR over conventional stem cell therapy. However, application of DLR technology in clinical practice is hindered by its low efficiency compared with cell therapy using stem cell differentiation. To overcome this limitation, researchers have explored various strategies such as the efficiency of DLR. In this study, we focused on innovative strategies, including the use of a nanoporous particle-based gene delivery system to improve the reprogramming efficiency of DLR-induced neurons. We believe that discussing these approaches can facilitate the development of more effective gene therapies for neurological disorders.

Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

Mitochondria are highly dynamic cellular organelles that perform crucial functions such as respiration, energy production, metabolism, and cell fate decisions. Mitochondrial damage and dysfunction critically lead to the pathogenesis of various diseases including cancer, diabetes, and neurodegenerative and cardiovascular disorders. Mitochondrial damage in response to environmental contaminant exposure and its association with the pathogenesis of diseases has also been reported. Recently, persistent pollutants, such as micro- and nanoplastics, have become growing global environmental threats with potential health risks. In this review, we discuss the impact of micro- and nanoplastics on mitochondria and review current knowledge in this field.

Most NTRK fusions occur at very low frequencies in various common cancers. Recent recommendations on NTRK testing recommend immunohistochemistry (IHC) as the initial test for tumor types with a low frequency of NTRK fusions. This study investigated the accuracy of an IHC assay to detect NTRK fusions and characterize the clinicopathological and molecular features of NTRK-rearranged tumors. This retrospective study was conducted on 1113 solid tumor samples known to harbor no oncogenic driver alterations, including 510 non-small cell lung cancers (NSCLC), 503 colorectal cancers (CRC), and 79 inflammatory myofibroblastic tumors (IMT). Additionally, 21 ALK expression-positive cases were included. TRK expression was evaluated using a pan-Trk IHC assay, and positive cases were validated using NGS. TRK expression was observed in three NSCLCs (0.6%), six CRCs (1.2%), and six IMTs (6%). NTRK fusions were finally detected in two NSCLCs (0.4%), six CRCs (1.2%), and one IMT (1%). In NSCLC and CRC, the majority of NTRK fusions were readily discernible due to diffuse moderate-to-strong cytoplasmic staining on pan-Trk IHC. In IMT, focal weak nuclear staining indicated the presence of NTRK fusion. Therefore, the utility of pan-Trk IHC should be assessed considering that the difference in performance depends on tumor type.

Diclofenac, a nonsteroidal anti-inflammatory drug (NSAID) used to treat inflammatory diseases induces cellular toxicity by increasing the production of reactive oxygen species (ROS) and impairing autophagic flux. In this study, we investigated whether diclofenac induces cancer cell death and the mechanism by which diclofenac causes cell death. We observed that diclofenac induces mitotic arrest with a half-maximal effective concentration of 170 μM and cell death with a half-maximal lethal dose of 200 µM during 18-h incubation in HeLa cells. Cellular microtubule imaging and in vitro tubulin polymerization assays demonstrated that treatment with diclofenac elicits microtubule destabilization. Autophagy relies on microtubule-mediated transport and the fusion of autophagic vesicles. We observed that diclofenac inhibits both phagophore movement, an early step of autophagy, and the fusion of autophagosomes and lysosomes, a late step of autophagy. Diclofenac also induces the fragmentation of mitochondria and the Golgi during cell death. We found that diclofenac induces cell death further in combination with 5-fuorouracil, a DNA replication inhibitor than in single treatment in cancer cells. Pancreatic cancer cells, which have high basal autophagy, are particularly sensitive to cell death by diclofenac. Our study suggests that microtubule destabilization by diclofenac induces cancer cell death via compromised spindle assembly checkpoints and increased ROS through impaired autophagy flux. Diclofenac may be a candidate therapeutic drug in certain type of cancers by inhibiting microtubule-mediated cellular events in combination with clinically utilized nucleoside metabolic inhibitors, including 5-fluorouracil, to block cancer cell proliferation.

Growing interest in intelligent vehicles is leading automotive systems to include numerous electronic control units (ECUs) inside. As a result, efficient implementation and management of automotive systems is gaining importance. Flexible updating and reconfiguration of ECUs is one appropriate strategy for these goals. Software updates to the ECUs are expected to improve performance and bug handling, but there are limitations due to the fixed hardware circuit. By applying hardware-reconfigurable ECUs to the automotive system, patches that are not able to be handled with only software updates are enabled. In this paper, a remotely hardware-reconfigurable ECU for automotive systems is proposed. The proposed ECU is implemented with a field programmable gate array (FPGA) and microcontroller unit (MCU) to support in-system reconfiguration (ISR). The communication interface between the FPGA and MCU employs Zipwire communication for high speed and resilient communication. For the Zipwire communication, a Zipwire controller is designed and implemented in the FPGA. The proposed hardware-reconfigurable ECU was successfully implemented, and feasibility was demonstrated.

Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.

Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.

Vascular diseases are major causes of death worldwide, causing pathologies including diabetes, atherosclerosis, and chronic obstructive pulmonary disease (COPD). Exposure of the vascular system to a variety of stressors and inducers has been implicated in the development of various human diseases, including chronic inflammatory diseases. In the vascular wall, antioxidant enzymes form the first line of defense against oxidative stress. Recently, extensive research into the beneficial effects of phytochemicals has been conducted; phytochemicals are found in commonly used spices, fruits, and herbs, and are used to prevent various pathologic conditions, including vascular diseases. The present review aims to highlight the effects of dietary phytochemicals role on antioxidant enzymes in vascular diseases.

The development of the mobile industry brings about the demand for high-performance embedded systems in order to meet the requirement of user-centered application. Because of the limitation of memory resource, employing compressed data is efficient for an embedded system. However, the workload for data decompression causes an extreme bottleneck to the embedded processor. One of the ways to alleviate the bottleneck is to integrate a hardware accelerator along with the processor, constructing a system-on-chip (SoC) for the embedded system. In this paper, we propose a lossless decompression accelerator for an embedded processor, which supports LZ77 decompression and static Huffman decoding for an inflate algorithm. The accelerator is implemented on a field programmable gate array (FPGA) to verify the functional suitability and fabricated in a Samsung 65 nm complementary metal oxide semiconductor (CMOS) process. The performance of the accelerator is evaluated by the Canterbury corpus benchmark and achieved throughput up to 20.7 MB/s at 50 MHz system clock frequency.

Crotonaldehyde is an extremely toxic α,β-unsaturated aldehyde found in cigarette smoke, and it causes inflammation and vascular dysfunction. Autophagy has been reported to play a key role in the pathogenesis of vascular diseases. However, the precise mechanism underlying the role of acute exposure crotonaldehyde in vascular disease development remains unclear. In the present study, we aimed to investigate the effect of crotonaldehyde-induced autophagy in endothelial cells. Acute exposure to crotonaldehyde decreased cell viability and induced autophagy followed by cell death. In addition, inhibiting the autophagic flux markedly promoted the viability of endothelial cells exposed to high concentrations of crotonaldehyde. Crotonaldehyde activated the AMP-activated protein kinase (AMPK) and p38 mitogen-activated protein kinase (MAPK) pathways, and pretreatment with inhibitors specific to these kinases showed autophagy inhibition and partial improvement in cell viability. These data show that acute exposure to high concentrations of crotonaldehyde induces autophagy-mediated cell death. These results might be helpful to elucidate the mechanisms underlying crotonaldehyde toxicity in the vascular system and contribute to environmental risk assessment.

The purpose of this study was to evaluate and validate methylene blue migration from printed time–temperature indicators (TTIs) into food. It also highlights the importance of establishing regulatory measures and safety standards for food packaging, suggesting that this can contribute to improving food packaging safety. Liquid chromatography–mass spectrometry (LC-MS/MS) was used to quantify methylene blue migration in various food simulant and food matrix samples. The results show that the level of methylene blue migration varies significantly depending on the chemical properties of the food mimetic and the composition of the food matrix. The established method demonstrated a high sensitivity, with limits of detection (LODs) of 0.0019–0.0706 μg/L (kg) and limits of quantification (LOQs) of 0.0057–0.2138 μg/L (kg). This study highlights the need for a regulatory framework to mitigate the health risks associated with methylene blue in intelligent packaging systems and argues that regulatory thresholds should be set to ensure food safety and quality.

Sedum kamtschaticum Fischer, of the Crassulaceae family, is a perennial and medicinal plant used in Asian folk medicine to alleviate inflammatory disease and improve blood circulation. As part of our ongoing exploration into natural products, seeking to identifying bioactive compounds, we characterized, identified, and isolated an unreported bioactive compound, prenylhydroquinone glycoside (1), which we named kirinchoside from S. kamtschaticum leaves. Using high-resolution (HR)-ESIMS, NMR spectroscopic data, and enzymatic hydrolysis, followed by LC–MS analysis, we determined the structure of this isolated compound. Despite a previous report on the planar structure of compound 1 (kirinchoside), the absolute configuration of 1 had not been verified. We investigated the effects of kirinchoside on hyperphosphorylated tau (p-tau) accumulation, a hallmark of Alzheimer’s disease (AD) progression. We observed that treatment with 5 μM kirinchoside suppressed p-tau levels by 16.9% in amyloid β (Aβ)1–42-treated SH-SY5Y cells, compared to the negative control. These findings indicate that kirinchoside, an unreported prenylhydroquinone glycoside found in S. kamtschaticum leaves, could be a candidate preventive agent against AD via inhibition of p-tau accumulation.

Ultrasonic testing (UT) is one of the most popular non-destructive evaluation (NDE) techniques used in many industries to evaluate structural integrity. The commonly used NDE techniques are basic inspection techniques, such as visual testing (VT), penetration testing (PT), and magnetic testing (MT), and advanced inspection techniques, such as UT, radiography testing (RT), eddy current testing (ECT), and phased array ultrasonic testing (PAUT). Among the numerous advanced techniques, ultrasonic testing (UT) is usually used for the inspection of welds in various industries. However, the application of UT still has some shortcomings to overcome. One major shortcoming that reduces the precision of UT is the extra signals from the geometrical interface of a specimen. UT uses the reflection indications of the ultrasonic beam. However, the reflection signals from the welding interface and geometry along with the target flaw signal produce mixed signals. The inspectors use a 1-D reflection outcome called the ultrasonic A-scan to evaluate the welding integrity. The mixed ultrasonic A-scan signals are often very difficult to analyze because inspectors must distinguish the target flaw signal of welding from the mixed ultrasonic A-scan signal, which includes the flaw indication as well as the background signal. Therefore, a method to distinguish between the flaw signal and the background signal must be developed for the efficiency of UT. Autoencoder is an artificial neural network that is made for feature extraction from the input. Denoising autoencoder (DAE) is one of the derivative models of the autoencoder which adds or eliminates random noise signals to extract the prominent features. DAE is already widely used in the denoising of images and sound data. The characteristics of DAE are used in this research to distinguish the ultrasonic flaw signal from the mixed ultrasonic A-scan signal. For the training, 2463 mixed A-scan signals were obtained from 45 different standard blocks in which 5 different types of flaws were embedded. For testing, we used 1000 mixed A-scan signals. The performance of the network was evaluated using a point-by-point comparison method. The autoencoder was trained to denoise the background signal from the mixed ultrasonic A-scan, and the target flaw signal was extracted from the original A-scan signal.

The technology of successful cryopreservation is a very important factor in research and commercial applications. However, the survival and development of the vitrified-thawed (VT) oocytes are lower than those of non-vitrified-thawed (non-VT) oocytes. This study investigated the effect of the addition of hydroxypropyl cellulose (HPC) to a vitrification solution of bovine oocytes. For the vitrification, bovine metaphase II oocytes were pretreated with a solution containing 10% ethylene glycol supplemented with 0, 10, 50, or 100 µg/mL HPC for 5 min, then exposed to a solution containing 30% ethylene glycol supplemented with 0, 10, 50, or 100 µg/mL HPC for 30 sec, and then directly plunged into liquid nitrogen. Oocytes exposed to 0, 10, 50, and 100 µg/mL HPC were named the 0, 10, 50, and 100 HPC groups, respectively. Samples were thawed via sequential incubation in Dulbecco’s phosphate-buffered saline (D-BPS) supplemented with 10% fetal bovine serum and decreasing concentrations of sucrose (1, 0.5, 0.25, and 0.125 M) for 1 min each time. After thawing, VT oocytes were treated at 0.05% hyaluronidase, and cumulus cells were removed by mechanical pipetting. The oocytes were washed with HEPES-buffered Tyrode’s medium and incubated in a droplet of previously cultured in vitro maturation medium for 1 h to recover. The survival rate of the oocytes was significantly higher in the 50 HPC group (84.2%) than in the 0 (75.4%), 10 (80.4%), and 100 (75.5%) HPC groups. The reactive oxygen species (ROS) levels of the non-VT and 50 HPC groups were lower than the 0, 10, and 100 HPC groups. The mRNA levels of proapoptotic genes (Bax) were lower in the non-VT, 0, and 50 HPC groups than in the other groups. The mRNA expression levels of antiapoptotic genes (BCl2) was higher in the non-VT than in the other groups. The mRNA level of a stress-related gene (Hsp70) was lower in the 50 HPC than in the other groups. At day 8, the developmental capacity of embryos obtained via parthenogenetic activation (PA) was determined in the non-VT, 0 HPC, and 50 HPC groups. The cleavage rate of the non-VT group was significantly higher, but the blastocyst development rate and total cell number per blastocyst did not significantly differ between the non-VT and 50 HPC groups. The mRNA levels of proapoptotic genes (Bax and Caspase-3) and a stress-related gene (Hsp70) were higher in the 0 HPC group than in the non-VT and 50 HPC groups. In conclusion, supplementation of vitrification solution with HPC improves the survival rate of VT bovine oocytes and the development capacity of embryos derived from these oocytes via PA.

It is difficult to detect multiple myeloma (MM) infiltration of the bone marrow on computed tomography (CT) scans of patients with osteopenia. Our aim is to determine the feasibility of using radiomics analysis to detect MM infiltration of the bone marrow on CT scans of patients with osteopenia. The contrast-enhanced thoracic CT scans of 104 patients with MM and 104 age- and sex-matched controls were retrospectively evaluated. All individuals had decreased bone density on radiography. The study group was divided into development (n = 160) and temporal validation sets (n = 48). The radiomics model was developed using 805 texture features extracted from the bone marrow for a development set, using a Random Forest algorithm. The developed models were applied to evaluate a temporal validation set. For comparison, three radiologists evaluated the CTs for the possibility of MM infiltration in the bone marrow. The diagnostic performances were assessed and compared using an area under the receiver operating characteristic curve (AUC) analysis. The AUC of the radiomics model was not significantly different from those of the radiologists (p = 0.056–0.821). The radiomics analysis results showed potential for detecting MM infiltration in the bone marrow on CT scans of patients with osteopenia.

To optimize the reproduction of Jeju black cattle (JBC), freezing conditions for sperm were established and sperm motility, vitality, morphology, and fertility were evaluated to select the optimal bull for breeding. Semen samples from five JBC bulls were individually mixed with freezing medium at a final concentration of 1 × 108 sperm/mL and frozen in liquid nitrogen vapor at a height of 3 or 7 cm (referred to as 3 cm sperm and 7 cm sperm, respectively). When the freezing conditions were compared, the motility of 7 cm sperm was significantly higher than that of 3 cm sperm for the JBC-A bull. The motility, curvilinear velocity, straight-line velocity, and average path velocity of fresh and frozen–thawed sperm were the highest for the JBC-A bull. The vitalities of fresh and frozen–thawed sperm were the highest for the JBC-A/E and JBC-A bulls, respectively. The percentage of normal cells in fresh sperm was the highest for the JBC-D bull. The rates of the normal formation of two pronuclei and total sperm penetration were the highest in zygotes fertilized with sperm from the JBC-A bull. The sperm from the JBC-A bull had superior qualities and are thus the most appropriate choice for the preservation and reproduction of these endangered cattle.

The industrial use of ultrasonic flaw classification using neural networks in weldments must overcome many challenges. A major constraint is the use of numerous systems, including a combination of transducers and equipment. This causes high complexity in the datasets used in the training of neural networks, which decreases performance. In this study, the performance of a neural network was enhanced using signal processing on an ultrasonic weldment flaw dataset to achieve system invariance. The dataset contained 5839 ultrasonic flaw signals collected by various types of transducers connected to KrautKramer USN60. Every signal in the dataset was from 45 FlawTech/Sonaspection weldment specimens with five types of flaw: crack, lack of fusion, slag inclusion, porosity, and incomplete penetration. The neural network used in this study is a residual neural network with 19 layers. The performance evaluation of the same network structure showed that the original database can achieve 62.17% ± 4.13% accuracy, and that the invariant database using the system invariant method can achieve 91.45% ± 1.77% accuracy. The results demonstrate that using a system invariant method for ultrasonic flaw classification in weldments can improve the performance of a neural network with a highly complex dataset.

A new cycloartane-type triterpene glycoside, agroastragaloside V (1) was isolated from the roots of Astragalus membranaceus. The structure was identified as 3-O-β-(2'-O-acetyl)-D-xylopyranosyl-6-O-β-D-glucopyranosyl-(24S)-3β,6α,24α,25-tetrahydroxy- 9,19-cyclolanostane, by means of spectroscopic methods, including HR-FAB/MS, 1D NMR (1H, 13C, DEPT), 2D NMR (gCOSY, gHSQC, gHMBC, NOESY), and IR spectroscopy. Four known cycloartane glycosides, namely, agroastragaloside I (2), agroastragaloside II (3), isoastragaloside II (4) and astragaloside IV (5) were also isolated. All isolated compounds were tested for the ability to inhibit LPS-induced nitric oxide production in RAW264.7 macrophages.