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Calystegia soldanella is a species broadly used in an edible source and traditional herb in South Korea and China. It has diverse bioactivities, such as dropsy, antipyretic, and diuretic effect. In this study, we investigated the main components with antioxidant activities from the 70% EtOH extract of C. soldanella. The structural determination of compounds was achieved by nuclear magnetic resonance and HR-ESI-MS spectroscopic data. The main components from C. soldanella were identified as flavonoids and quinic acid derivatives. Furthermore, the 70% EtOH extract, partitioned layers, and compounds (1–8) of C. soldanella were evaluated for their antioxidant effects. The most active compound 8 (isochlorogenic acid C) exhibited the IC50 values of 12.4 ± 0.1 (2,2’-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical) and 37.9 ± 0.5 μM (2,2-diphenyl-1-picrylhydrazyl radical), respectively. Two active compounds (7 and 8) displayed higher activities than ascorbic acid (positive control). In addition, we also confirmed the antioxidant effects on the intracellular reactive oxygen species production of compound 8. Therefore, the present study suggests extract of C. soldanella is a promising natural antioxidative candidate.

In our ongoing search for new secondary metabolites from fungi, a basidiomycete fungus Irpex consors was selected for mycochemical investigation, and three new zwitterionic alkaloids (1-3) and five known compounds (4-8) were isolated from the culture broth (16 l) of I. consors. The culture filtrate was fractionated by a series of column chromatography including Diaion HP-20, silica gel, and Sephadex LH-20, Sep-Pak C18 cartridge, medium pressure liquid chromatography (MPLC), and high pressure liquid chromatography (HPLC) to yield eight compounds (1-8). The structures of the isolated compounds were elucidated by the interpretation of nuclear magnetic resonance (NMR) spectra and high-resolution mass spectrometry (HR-MS). Their antioxidant and antibacterial activities were examined. The zwitterionic structures of three new sesquiterpene alkaloids (1-3) were determined together with five known compounds identified as stereumamide E (4), stereumamide G (5), stereumamide H (6), stereumamide D (7), and sterostrein H (8). This is the first report of the zwitterionic alkaloids in the culture broth of I. consors. Three new zwitterionic alkaloids were named as consoramides A–C (1-3).

In our ongoing search for new secondary metabolites from fungal strains, one novel compound (1) and nine known compounds (2-10) were isolated from the EtOAc-soluble layer of the culture broth of Panus rudis. The culture broth of P. rudis was extracted in acetone and fractionated by solvent partition; column chromatography using silica gel, Sephadex LH-20, and Sephadex G-10; MPLC; and HPLC. The structures of isolated compounds were elucidated by one- and two-dimensional NMR and LC-ESI-mass measurements. One new compound, panepoxydiol (1), and nine known compounds, (E)-3-(3-hydroxy-3-methylbut-1-en-1-yl)-7-oxabicyclo[4.1.0]hept-3-ene-2,5-diol (2), isopanepoxydone (3), neopanepoxydone (4), panepoxydone (5), panepophenanthrin (6), 4-hydroxy-2,2-dimethyl-6-methoxychromane (7), 6-hydroxy-2,2-dimethyl-3-chromen (8), 2,2-dimethyl-6-methoxychroman-4-one (9), 3,4-dihydroxy-2,2-dimethyl-6-methoxychromane (10), were isolated from the culture broth of P. rudis. This is the first report of isolation of a new compound panepoxydiol (1) and nine other chemical constituents (2-5, 7-10) from the culture broth of P. rudis.

Neuraminidase (NA) cleaves the glycosidic bond linkages of sialic acids to release the mature virions from infected cells and has been an attractive therapeutic target for anti-influenza agents. In our ongoing investigation of NA inhibitors in mushroom extracts, we found that the extract the fruiting body of Glaziella splendens potently inhibited neuraminidase. The fruiting bodies of G. splendens were extracted and partitioned successively with hexane, ethyl acetate, and butanol. The ethyl acetate soluble-layer was subjected to silica gel and Sephadex LH-20 column chromatographies, and MPLC to obtain five compounds (1–5). Their structures were determined by spectroscopic methods. NA inhibitory activity of these compounds was evaluated using NAs from recombinant rvH1N1, H3N2, and H5N1 influenza A viruses. One compound (1) was elucidated as a new azaphilone derivative, and four compounds (2–5) were identified as entonaemin A, comazaphilone D, rubiginosin A, and entonaemin B, respectively. Compounds 3 and 4 showed considerable inhibitory activity against three types of neuraminidases with the IC50 values of 30.9, 41.8, and 35.7 µM for 3 and 46.5, 50.4, and 29.9 µM for 4, respectively. This study reveals that the fruiting bodies of G. splendens possess azaphilone derivatives with the NA inhibitory activity. This is the first report on the isolation of neuraminidase inhibitors from the fruiting bodies of G. splendens.

Abstract With the growing complexity of data structures, which include high-dimensional and multilabel datasets, the significance of feature selection has become more emphasized. Multilabel feature selection endeavors to identify a subset of features that concurrently exhibit relevance across multiple labels. Owing to the impracticality of performing exhaustive searches to obtain the optimal feature subset, conventional approaches in multilabel feature selection often resort to a heuristic search process. In this context, memetic multilabel feature selection has received considerable attention because of its superior search capability; the fitness of the feature subset created by the stochastic search is further enhanced through a refinement process predicated on the employed multilabel feature filter. Thus, it is imperative to employ an effective refinement process that frequently succeeds in improving the target feature subset to maximize the benefits of hybridization. However, the refinement process in conventional memetic multilabel feature selection often overlooks potential biases in feature scores and compatibility issues between the multilabel feature filter and the subsequent learner. Consequently, conventional methods may not effectively identify the optimal feature subset in complex multilabel datasets. In this study, we propose a new memetic multilabel feature selection method that addresses these limitations by incorporating the pruning of features and labels into the refinement process. The effectiveness of the proposed method was demonstrated through experiments on 14 multilabel datasets.

Abstract Body mass index (BMI), as an important risk factor related to metabolic disease. However, in some studies higher BMI was emphasized as a beneficial factor in the clinical course of patients after acute myocardial infarction (AMI) in a concept known as the “BMI paradox.” The purpose of this study was to investigate how clinical outcomes of patients treated for AMI differed according to BMI levels. A total of 10,566 patients in the Korea Acute Myocardial Infarction Registry-National Institutes of Health (KAMIR-NIH) from May 2010 to June 2015 were divided into three BMI groups (group 1: BMI

Continual learning in the online class-incremental setting aims to learn new classes continuously from a consistent data stream while retaining the knowledge of old classes to prevent catastrophic forgetting. Traditional replay-based methods store and use old-class data to achieve this. However, they often overlook the representation shift caused by the incoming data streams, which leads to suboptimal classification accuracy. In this study, we propose a solution for mitigating representation shifts by incorporating asymmetric mixup training into the replay method. Our approach is based on the concept that mixup-based training enhances the stability of model predictions and gradient norms between training samples. Our method differs from typical mixup augmentation, which is uniformly applied to all data. Instead, it selectively targets the old data stored in the memory buffer, deliberately excluding the classes from the newly incoming data. This approach enables the model to learn new data while preserving the representation of the old data. Moreover, our experiments demonstrate the effectiveness of the proposed method, which not only enhances the performance of replay-based methods but can also be seamlessly integrated as an additional compatible module into various replay-based techniques. Evaluation on the CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets demonstrates that our approach surpasses existing replay-based methods. It addresses the limitations of conventional replay techniques and offers a potential solution for continual learning scenarios. Our source code is publicly available at https://github.com/laymond1/MixER.

Abstract Aims Although various non‐invasive cardiac examinations are known to be predictive of long‐term outcomes in patients with heart failure (HF), combining them properly would provide synergism. We aimed to show that non‐invasive cardiac assessments targeting left ventricular filling pressure (LVFP), left atrial remodelling, and exercise capacity would provide better prognostication in combination. Methods and results This prospective observational study included consecutive hospitalized stage A–C HF patients evaluated with N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP), echocardiography including two‐dimensional speckle tracking, and cardiopulmonary exercise testing. According to NT‐proBNP and echocardiographic semi‐quantitative LVFP grading (Echo‐LVFP), patients were classified into three LVFP groups: normal range of both Echo‐LVFP and NT‐proBNP (Group 1), normal range of Echo‐LVFP but elevated NT‐proBNP (Group 2), and elevated Echo‐LVFP and NT‐proBNP (Group 3). The adverse outcome was defined as a composite of cardiovascular death, non‐fatal acute coronary syndrome, acute stroke, or HF‐related hospitalization. Among 224 HF patients (mean age of 63.8 ± 11.6 years, 158 men) analysed, 160 (71.4%) had ischaemic aetiology. During the follow‐up of 18.6 ± 9.8 months, event‐free survival in Group 2 (n = 56, age of 65.4 ± 12.4) was better than that in Group 3 (n = 45, age of 68.5 ± 11.5) but worse than that in Group 1 (n = 123, mean age of 61.4 ± 10.5) (log‐rank P

Abstract WDR11, a gene associated with Kallmann syndrome, is important in reproductive system development but molecular understanding of its action remains incomplete. We previously reported that Wdr11-deficient embryos exhibit defective ciliogenesis and developmental defects associated with Hedgehog (HH) signalling. Here we demonstrate that WDR11 is required for primordial germ cell (PGC) development, regulating canonical and noncanonical HH signalling in parallel. Loss of WDR11 disrupts PGC motility and proliferation driven by the cilia-independent, PTCH2/GAS1-dependent noncanonical HH pathway. WDR11 modulates the growth of somatic cells surrounding PGCs by regulating the cilia-dependent, PTCH1/BOC-dependent canonical HH pathway. We reveal that PTCH1/BOC or PTCH2/GAS1 receptor context dictates SMO localisation inside or outside of cilia, respectively, and loss of WDR11 affects the signalling responses of SMO in both situations. We show that GAS1 is induced by PTCH2-specific HH signalling, which is lost in the absence of WDR11. We also provide evidence supporting a role for WDR11 in ciliogenesis through regulation of anterograde intraflagellar transport potentially via its interaction with IFT20. Since WDR11 is a target of noncanonical SMO signalling, WDR11 represents a novel mechanism by which noncanonical and canonical HH signals communicate and cooperate.

Abstract Statin therapy is essential for secondary prevention in patients with atherosclerotic cardiovascular disease (ASCVD). However, the effects of statin therapy in patients receiving chronic dialysis remain uncertain. We aimed to evaluate the effect of statin therapy on long-term mortality in patients on dialysis after a first-time ASCVD. Patients receiving maintenance dialysis aged ≥ 18 years with a first-time ASCVD event between 2013 and 2018 were included in the Korean National Health Insurance Service database. Associations of statin use with long-term mortality were examined using Cox proportional hazards regression models adjusted for demographics and comorbidities. Among 17,242 patients on dialysis, 9611 (55.7%) were prescribed statins after a first-time ASCVD event. Among statin users, 7376 (76.7%) used moderate-intensity statins. During a mean follow-up of 32.6 ± 20.9 months, statin use was associated with a lower risk of all-cause mortality than statin nonuse after adjusting for confounding factors (hazard ratio [HR]: 0.92; 95% confidence interval [CI] 0.88–0.97; p = 0.0009). Despite a lack of evidence, more than half of patients on dialysis were prescribed statins after an ASCVD event. In patients on dialysis after ASCVD, statin therapy significantly reduced the risk of long-term all-cause mortality.

Mobile devices play a crucial role in human activity recognition as they enable real-time sensing of user interaction for learning algorithms like neural networks. To facilitate human activity recognition on mobile devices, it is important to deploy efficient neural network architectures due to the limited computational capacity of these devices. However, conventional neural architecture search methods often generate less effective architectures because they neglect the specific requirements of target devices on which the neural network would operate in real-time. Moreover, these methods are impractical in the mobile device environment due to their high computational cost for architecture search. To address these challenges, we propose an efficient neural architecture search method based on a latency lookup table. Our proposed method efficiently performs the network search process based on differentiable NAS while considering the actual latency of mobile devices, which is stored in a lookup table. The experimental results on public datasets provide evidence that the proposed method outperforms conventional methods in terms of speed. We achieved a search time of under 1.5 hours on each dataset, which is more than seven times faster on average compared to conventional methods. Furthermore, our in-depth analysis shows that the optimal architecture can vary depending on the target mobile devices, such as Galaxy A31 and S10. By tailoring the models to each device, optimized models achieved an additional 4-5% improvement in inference time for each respective device.

In the East China Sea (ECS), the sea surface salinity (SSS) changes as the Changjiang Diluted Water (CDW) propagates toward the Korean Peninsula via the ocean current and winds every summer annually. Although the vertical stratifications resulting from the CDW volume changes are important, it has not been analyzed yet. Therefore, in this study, we aimed to estimate the salinity at a depth of 10 m (S10m) using convolutional neural network (CNN) model based on multi-satellite measurements and analyze CDW volume variations. The main CDW mass in the ECS reaches approximately 10 m in depth; thus, the CNN model was developed using sea surface physical factors as input and in situ S10m obtained from the National Institute of Fisheries Science (NIFS) as ground truth data from 2015 to 2021. The CNN tests result showed a determination coefficient (R2) of 0.81, root mean square error (RMSE) of 0.63 psu, and relative RMSE (RRMSE) of 2.00%. Unlike the sea surface distribution, the spatial distribution of S10m showed that the CDW was predominantly present in the center of the ECS. From SHapley Additive exPlanations (SHAP) analysis, SSS exhibited a strong positive relationship with S10m, and the sea level anomaly showed a strong negative relationship. After calculating the volume of the CDW from the surface to a depth of 10 m, the maximum (3.01×1012 m3) and minimum volumes (1.31×1012 m3) were represented in 2016 and 2018, respectively. Finally, the warming effect induced by the CDW volume changes was analyzed in two different years: 2016 and 2018. Specifically, in 2016, the sea surface temperature increased by more than 4.79 °C in the Ieodo location, while in 2018, it increased by 2.19 °C. Thus, our findings can obtain information about the volume variation of the CDW and its effect on the ECS in summer.

Background The benefits of long‐term maintenance beta‐blocker (BB) therapy in patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) have not been well established. Methods and Results Using the Korean nationwide registry, a total of 7159 patients with AMI treated with PCI who received BBs at discharge and were free from death or cardiovascular events for 3 months after PCI were included in the analysis. Patients were divided into 4 groups according to BB maintenance duration: 36 months, was associated with better clinical outcomes. These findings might imply that a better prognosis can be expected if patients with AMI maintain BB therapy for ≥36 months after PCI. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02806102.

Background The secondary prevention with pharmacologic therapy is essential for preventing recurrent cardiovascular events in patients experiencing acute myocardial infarction. Guideline‐based optimal medical therapy (OMT) for patients with acute myocardial infarction consists of antiplatelet therapy, angiotensin‐converting enzyme inhibitors/angiotensin II receptor blockers, β‐blockers, and statins. We aimed to determine the prescription rate of OMT use at discharge and to evaluate the impact of OMT on long‐term clinical outcomes in patients with acute myocardial infarction who underwent percutaneous coronary intervention in the drug‐eluting stent era using nationwide cohort data. Methods and Results Using the National Health Insurance claims data in South Korea, patients with acute myocardial infarction who had undergone percutaneous coronary intervention with a drug‐eluting stent between July 2013 and June 2017 were enrolled. A total of 35 972 patients were classified into the OMT and non‐OMT groups according to the post–percutaneous coronary intervention discharge medication. The primary end point was all‐cause death, and the 2 groups were compared using a propensity‐score matching analysis. Fifty‐seven percent of patients were prescribed OMT at discharge. During the follow‐up period (median, 2.0 years [interquartile range, 1.1–3.2 years]), OMT was associated with a significant reduction in the all‐cause mortality (adjusted hazard ratio [aHR], 0.82 [95% CI, 0.76–0.90]; P

Abstract Background 4-Hexylresorcinol (4HR) is a food additive and class I histone deacetylase inhibitor. In this study, we examined the effects of 4HR administration on the submandibular gland in a growing rat model. Methods Four-week-old rats were used in this study. The experimental group (nine males and eight females) received 12.8 mg/kg of 4HR weekly for 12 weeks. Ten rats (five males and five females) were used as controls. The submandibular glands of rats were collected 12 weeks after the first administration of 4HR. The weight of the glands was measured. Histological analysis, immunoprecipitation-high-performance liquid chromatography (IP-HPLC), and western blotting were performed. Results The weights of the rat submandibular glands were higher in the experimental groups than in the control group, especially in male rats (P

Human activity recognition, which recognizes human activities from time-series signals collected by sensors, is an important task in human-centered intelligent systems such as in healthcare and smart vehicles. In these applications, rapid response of the system is necessary because critical events such as an elderly person falling or drowsy driving require immediate action. A straightforward approach to achieving this requirement is to reduce the amount of information the model must handle. To this end, traditional studies have attempted to abstract the original signal by sampling it with a pre-defined interval. However, it is difficult to achieve the best efficiency because the ideal sampling interval is unknown in advance. In this study, we propose a multi-temporal sampling module that allows the neural networks to consider multiple sampling intervals simultaneously. Experiments on four benchmark datasets showed that the proposed model achieved the best F1 score over seven conventional models under the computation budget of 10M multiply-accumulate operations. Especially, an experiment on PAMAP2 dataset demonstrated that the proposed model can achieve the best trade-off between efficiency and accuracy when the input signal is oversampled at a high sampling frequency. In addition, the proposed model achieved $\sim 1,000 \times $ improvement with respect to model size compared to the conventional methods.

BackgroundAlthough statins are an effective strategy for the secondary prevention of atherosclerotic cardiovascular disease (ASCVD) in the general population, the benefits for dialysis patients are controversial. We sought to assess trends of statin use and evaluate outcomes of statin therapy in dialysis patients with different types of ASCVD.MethodsThis nationwide retrospective population-based cohort study using data from the Korean National Health Insurance Service included adult patients (aged ≥ 18 years) undergoing chronic dialysis who had an initial ASCVD event in the time period of 2013 to 2018. Annual trends of statin use according to age, sex, and ASCVD types were analyzed. The association between 1-year mortality and statin use was examined using multivariable Cox proportional hazards regression analyses.ResultsAmong 17,242 subjects, 9,611(55.7%) patients were statin users. The overall prevalence of statin use increased from 52.9% in 2013 to 57.7% in 2018; the majority (77%) of dialysis patients were prescribed moderate-intensity statins. The proportions of low- or moderate-intensity statin use were similar, but high-intensity statin use increased from 5.7% in 2013 to 10.5% in 2018. The use of the statin/ezetimibe combination has gradually increased since 2016. Statin use was independently associated with the reduced 1-year all-cause mortality after adjusting for confounding factors (hazard ratio [HR] 0.89, 95% confidence interval [CI] 0.80-0.96, P = 0.004).ConclusionThe prevalence of statin prescriptions in dialysis patients after ASCVD event increased from 2013 to 2018. Most patients received moderate-intensity statin. However, high-intensity statin and statin/ezetimibe combination therapy has remarkably increased. Statin use was associated with decreased 1-year all-cause mortality in dialysis patients with ASCVD.

Abstract Background 4-hexylresorcinol (4HR) has been shown to have anti-oxidant activity similar to that of resveratrol. As resveratrol increases sirtuin (SIRT) activity, 4HR might behave similarly to resveratrol. Method In this study, the expression levels of SIRT1, SIRT3, and SIRT6 were evaluated after 4HR administration (1–100 μM). As NAD+ is a substrate for SIRTs, its levels with SIRT activity were also studied. Results In the results, SIRT3 (100 μM at 24 h) and SIRT6 (1–100 μM at 24 h and 10 μM at 8 h) were shown to have significantly higher expression levels compared to untreated control (p < 0.05). Pan-SIRT activity and the NAD+ level was significantly increased compared to that of the untreated control (p < 0.05; 10 and 100 μM at 24 h). Conclusion 4HR administration increased SIRT activity and the NAD+ level in Saos-2 cells.

Reduced amounts of aerosols blowing into the Yellow Sea (YS), owing to the temporary lockdown of factories in China during COVID-19, resulted in a 15% decrease in spring chlorophyll-a concentration (CHL) in March 2020 compared to its mean March values from 2003 to 2021. Particularly, the effect of land-based AOD is insignificant compared with that of atmospheric aerosols flowing into the YS, as indicated by the currents and wind directions. Hence, the main objective of this study was to understand the relationship between atmospheric aerosols and CHL by quantitatively considering relevant environmental changes using a Random Forest (RF) algorithm. Various input physical forcing variables to RF were employed, including aerosol optical depth (AOD), sea surface temperature (SST), mixed layer depth (MLD), wind divergence (WD), and total precipitation (TP). From the RF-based analysis, we estimated the relative contribution of each physical forcing variable to the difference in CHL during and after the COVID-19 lockdown period. The sensitivity of the RF model to changes in aerosol levels indicated positive effects of increased amounts of aerosols during spring blooms. Additionally, we calculated the quantitative contribution of aerosols to CHL changes. When SST was warmer and TP was lower than their climatology in March 2020, CHL increased by 0.22 mg m-3 and 0.02 mg m-3, respectively. Conversely, when MLD became shallower and AOD was lower than their climatology, CHL decreased as much as 0.01 mg m-3 and 0.20 mg m-3. Variations in WD caused no significant change in CHL. Overall, the specific estimations for reduced spring blooms were caused by a reduction in aerosols during the COVID-19 lockdown period. Furthermore, the RF developed in this study can be used to examine CHL changes and the relative role of significant environmental changes in biological blooms in the ocean for any normal year.

Reinforcement learning is one of the artificial intelligence methods that enable robots to judge and operate situations on their own by learning to perform tasks. Previous reinforcement learning research has mainly focused on tasks performed by individual robots; however, everyday tasks, such as balancing tables, often require cooperation between two individuals to avoid injury when moving. In this research, we propose a deep reinforcement learning-based technique for robots to perform a table-balancing task in cooperation with a human. The cooperative robot proposed in this paper recognizes human behavior to balance the table. This recognition is achieved by utilizing the robot’s camera to take an image of the state of the table, then the table-balance action is performed afterward. Deep Q-network (DQN) is a deep reinforcement learning technology applied to cooperative robots. As a result of learning table balancing, on average, the cooperative robot showed a 90% optimal policy convergence rate in 20 runs of training with optimal hyperparameters applied to DQN-based techniques. In the H/W experiment, the trained DQN-based robot achieved an operation precision of 90%, thus verifying its excellent performance.

Accurate music tag classification of music clips has been attracting great attention recently, because it allows one to provide various music excerpts, including unpopular ones, to users based on the clips’ acoustic similarities. Given a user’s preferred music, acoustic features are extracted and then fed into the classifier, which outputs the related tag to recommend new music. Furthermore, the accuracy of the tag classifiers can be improved by selecting the best feature subset based on the domain to which the tag belongs. However, recent studies have struggled to evaluate the superiority of various classifiers because they utilize different feature extractors. In this study, to conduct a direct comparison of existing methods of classification, we create 20 music datasets with the same acoustic feature structure. In addition, we propose an effective evolutionary feature selection algorithm to evaluate the effectiveness of feature selection for tag classification. Our experiments demonstrate that the proposed method improves the accuracy of tag classification, and the analysis with multiple datasets provides valuable insights, such as the important features for general music tag classification in target domains.