Objectives:The eukaryotic initiation factor 4F(eIF4F)translation initiation complex inhibitors(eIF4Fi)were recently found to hyperactivate extracellular signal-regulated kinases 1/2(ERK1/2)signals,which contribute to ...Objectives:The eukaryotic initiation factor 4F(eIF4F)translation initiation complex inhibitors(eIF4Fi)were recently found to hyperactivate extracellular signal-regulated kinases 1/2(ERK1/2)signals,which contribute to acquired resistance to BRAF(B-Raf proto-oncogene,serine/threonine kinase)inhibitors in melanoma.This present study aims to elucidate how to overcome the resistance of the eIF4Fi in BRAFV600E mutant melanoma cells and explore the underlying mechanisms.Methods:Melanoma A375(vemurafenib[VEM]-sensitive)and A375R(VEM-resistant)cells were exposed to eIF4Fi RocA at varying doses and durations in vitro.We investigated the impact of RocA on the activity of ERK1/2,AKT serine/threonine kinase 1(AKT1),eIF4E,and enhancer of zeste homolog 2(EZH2).We then examined the impact of RocA on pro-apoptotic BH3-only proteins and proliferative proteins.We subsequently determined the effect of combined eIF4Fi,AKT1 inhibitor,EZH2 inhibitor or VEM on tumor growth in vitro and in vivo.Results:RocA inhibited proliferation and induced apoptosis in A375 cells,but inhibited proliferation in A375R cells.RocA rapidly reactivated ERK1/2 at 3 h and returned to baseline levels at 48 h.However,eIF4E and AKT1 activation began at 12 h and peaked at 48 h.ERK1/2 positively regulated EZH2 and EZH2-dependent expression of c-Fos and EGR1,while AKT1 negatively regulated c-Myc,c-Jun,and BMF,but positively regulated eIF4E.RocA downregulated ERK1/2(or EZH2,AKT1,and eIF4E)independent bcl-2 and Mcl-1 expression.AKT1i enhanced RocA-induced cell apoptosis,while EZH2i reduced RocA-induced cell proliferation.Combined CR-1-31-B,EZH2i,and AKT1i effectively overcame resistance to RocA and VEM resistance both in vitro and in vivo.Conclusion:The eIF4F complex inhibitor reactivates ERK1/2-EZH2 and AKT1 signaling pathways,resulting in resistance to both eIF4Fi and VEM.Combined administration of an eIF4Fi with EZH2 and AKT1 inhibitors effectively enhances sensitivity to both eIF4F complex and BRAF inhibitors.展开更多
To realize the practical application of anion exchange membrane water electrolysis(AEMWE),it is essential to develop highly active,durable,and cost-effective electrocatalyst for oxygen evolution reaction(OER).Herein,w...To realize the practical application of anion exchange membrane water electrolysis(AEMWE),it is essential to develop highly active,durable,and cost-effective electrocatalyst for oxygen evolution reaction(OER).Herein,we report a hollow-structured Ni_(x)Co_(1−x)O/Ni_(3)S_(2)/Co_(9)S_(8)heterostructure synthesized via sequential template-assisted growth,thermal oxidation,and controlled sulfidation process.The abundant bimetallic heterointerfaces not only provide additional active sites but also promote electronic modulation via charge redistribution.Additionally,the porous and hollow architecture enhances active surface area and mass transfer ability,thereby increasing the number of accessible active sites for alkaline OER.As a result,the prepared electrocatalyst achieves low overpotential of 310 mV at 10 mA cm^(−2)and small Tafel slope of 55.94 mV dec^(−1),demonstrating the exceptional electrocatalytic performance for alkaline OER.When integrated as the anode in an AEMWE cell,it delivers outstanding performance with only 1.657 V at 1.0 A cm^(−2)and reaches high current density of 5.0 A cm^(−2)at 1.989 V,surpassing those of commercial RuO_(2).The cell also shows excellent long-term durability over 100 h with minimal degradation.This study highlights the strong potential of rationally engineered oxide/sulfide heterostructures for next-generation alkaline water electrolysis.展开更多
Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This...Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an in...Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.展开更多
As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety o...As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles.In this work,we introduce a novel neural scene representation called Street Detection Gaussians(SDGs),which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection.At its core lies the dynamic Gaussian representation,where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution.The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radardata anddense visual features,resulting ina22%reduction inocclusionerrors compared tovisiononly systems.A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process,enabling the optimization of both geometric and semantic fidelity.Evaluated on the Waymo Open Dataset and the KITTI Dataset,the system achieves real-time performance(135 Frames Per Second(FPS)),photorealistic quality(Peak Signal-to-Noise Ratio(PSNR)34.9 dB),and state-of-the-art detection accuracy(78.1%Mean Average Precision(mAP)),demonstrating a 3.8×end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.展开更多
Pericytes are multifunctional mural cells that surround the abluminal wall of endothelial cells and are associated with vascular development,vascular permeability,and angiogenesis.Additionally,pericytes demonstrate st...Pericytes are multifunctional mural cells that surround the abluminal wall of endothelial cells and are associated with vascular development,vascular permeability,and angiogenesis.Additionally,pericytes demonstrate stem cell-like properties and contribute to neuroinflammatory processes.Pericytes have been extensively studied in the central nervous system.However,specific mechanisms underlying its involvement in various physiological and pathological conditions,especially in erectile dysfunction(ED),remain poorly understood.Advancements in in vitro and in vitro techniques,such as single-cell RNA sequencing,are expanding our understanding of pericytes.Recent studies have shown that pericyte dysfunction is considered an important factor in the pathogenesis of vascular and neurological ED.Therefore,this study aims to analyze the specific role of pericytes in ED,focusing on diabetic and neurogenic ED.This article provides a comprehensive review of research findings on PubMed from 2000 to 2023,concerning pericyte dysfunction in the process of ED,offering valuable insights,and suggesting directions for further research.展开更多
Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive...Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive thyroid-related cell lines cultured under simulated microgravity.Methods:Five thyroid-related cell lines—normal thyrocytes(Nthy-ori 3-1),papillary thyroid cancer(PTC)cells(SNU-790,TPC-1),poorly differentiated thyroid cancer cell(BCPAP),and anaplastic thyroid cancer cell(SNU-80)—were cultured under simulated microgravity(10-3 g)using a clinostat.Differentially expressed genes(DEGs)were analyzed using cDNA microarray,followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.Results:DEG analysis under simulated microgravity revealed distinct gene expression profiles by gravity condition,with 2980 DEGs in SNU-790,1033 in BCPAP,562 in TPC-1,477 in Nthy-ori 3-1,and 246 in SNU-80,as confirmed by hierarchical clustering.In PTC cell lines(SNU-790,TPC-1),G2–M phase–related genes were upregulated.In non-PTC cell lines(BCPAP,SNU-80),genes associated with innate immune response,Toll-like receptor signaling,were upregulated,whereas Hypoxia-Inducible Factor 1-alpha(HIF-1α)signaling-related genes were downregulated.Additionally,under simulated microgravity,significant migration was observed in SNU-790(3×104 cells)and BCPAP(2×104 and 3×104),while significant invasion occurred in SNU-790,Nthy-ori 3-1,and BCPAP at a seeding density of 2×104.Other conditions showed no significant differences.Conclusion:This study comprehensively evaluates the effects of simulated microgravity using a diverse panel of thyroid-related cell lines.Thesefindings provide valuable insight into how microgravity could influence cancer biology,emphasizing the importance of further research on cancer behavior in space environments and its implications for human health during long-term space missions.展开更多
Water electrolysis using renewable electricity is a promising strategy for high-purity hydrogen production.To realize the practical application of water electrolysis,an electrocatalyst with high redox properties and l...Water electrolysis using renewable electricity is a promising strategy for high-purity hydrogen production.To realize the practical application of water electrolysis,an electrocatalyst with high redox properties and low cost is essential for enhancing the sluggish oxygen evolution reaction.Herein,we fabricated Fe-doped nickel oxalate(Fe-NiC_(2)O_(4))directly grown on nickel(Ni)foam as an efficient electrocatalyst for the alkaline oxygen evolution reaction using a facile one-step hydrothermal method.Fe-NiC_(2)O_(4) served as a precursor for obtaining highly active Fe-doped Ni oxyhydroxide(Fe-NiOOH)via in situ electrochemical oxidation.Consequently,0.75Fe-NiOOH was demonstrated to be the optimal electrocatalyst,exhibiting outstanding oxygen evolution reaction activity with a low overpotential of 220 mV at a current density of 100 mA cm^(-2) and a Tafel slope of 20.5 mV dec^(-1).Furthermore,Fe-NiOOH maintained its oxygen evolution reaction activity without performance decay during long-term electrochemical measurements,owing to the phase transformation from nickel oxyhydroxide(NiOOH)toγ-NiOOH(gamma nickel oxyhydroxide).These performances significantly surpass those of recently reported transition-metal-based electrocatalysts.展开更多
BACKGROUND Despite advancements,endoscopic retrograde cholangiopancreatography(ERCP)poses challenges,including the risk of post-ERCP pancreatitis and difficulty of biliary cannulation.AIM To compare dome and tapered t...BACKGROUND Despite advancements,endoscopic retrograde cholangiopancreatography(ERCP)poses challenges,including the risk of post-ERCP pancreatitis and difficulty of biliary cannulation.AIM To compare dome and tapered tip sphincterotomes,focusing on their efficacy in achieving successful biliary cannulation and reducing the incidence of post-ERCP pancreatitis.METHODS In this prospective,single-blind,randomized pilot study conducted at Inha Uni-versity Hospital,85 patients undergoing ERCP were equally divided into dome and tapered tip sphincterotome groups.The co-primary outcomes were the success rate of selective biliary cannulation and incidence of post-ERCP pancre-atitis.The secondary outcomes included biliary cannulation time,number of unintended pancreatic duct access events,and total procedure time.RESULTS The success rates of selective biliary cannulation were 74.4%and 85.7%in the dome and tapered tip groups,respectively,with no significant difference(P=0.20).Similarly,the incidence of post-ERCP pancreatitis did not differ significantly between the groups(5 cases in the tapered tip group vs 6 in the dome tip group,P=0.72).However,difficult cannulation was significantly more common in the dome tip group than in the tapered tip group(P=0.05).Selective biliary cannula-tion time emerged as a significant predictor of post-ERCP pancreatitis(multivariate odds ratio=9.33,95%confi-dence interval:1.31-66.44,P=0.03).CONCLUSION This study indicated that the sphincterotome tip type does not markedly affect biliary cannulation success or post-ERCP pancreatitis rates.However,cannulation duration is a key risk factor for post-ERCP pancreatitis.These findings provide preliminary insights that highlight the importance of refining ERCP practices,including sphinc-terotome selection,while underscoring the need for larger multicenter studies to improve procedure time and patient safety.展开更多
Prostate cancer is the second most common malignancy and the sixth leading cause of cancer-related death in men worldwide.Radical prostatectomy(RP)is the standard treatment for localized prostate cancer,but the proced...Prostate cancer is the second most common malignancy and the sixth leading cause of cancer-related death in men worldwide.Radical prostatectomy(RP)is the standard treatment for localized prostate cancer,but the procedure often results in postoperative erectile dysfunction(ED).The poor efficacy of phosphodiesterase 5 inhibitors after surgery highlights the need to develop new therapies to enhance cavernous nerve regeneration and improve the erectile function of these patients.In the present study,we aimed to examine the potential of heparin-binding epidermal growth factor-like growth factor(HB-EGF)in preserving erectile function in cavernous nerve injury(CNI)mice.We found that HB-EGF expression was reduced significantly on the 1st day after CNI in penile tissue.Ex vivo and in vitro studies showed that HB-EGF promotes major pelvic ganglion neurite sprouting and neuro-2a(N2a)cell migration.In vivo studies showed that exogenous HB-EGF treatment significantly restored the erectile function of CNI mice to 86.9%of sham levels.Immunofluorescence staining showed that mural and neuronal cells were preserved by inducing cell proliferation and reducing apoptosis and reactive oxygen species production.Western blot analysis showed that HB-EGF upregulated protein kinase B and extracellular signal-regulated kinase activation and neurotrophic factor expression.Overall,HB-EGF is a major promising therapeutic agent for treating ED in postoperative RP.展开更多
Economic inequality is a persistent global issue with profound implications for mental health,particularly among older adults.Previous studies have largely focused on objective economic well-being indicators such as i...Economic inequality is a persistent global issue with profound implications for mental health,particularly among older adults.Previous studies have largely focused on objective economic well-being indicators such as income,employment status,and poverty.While these factors are crucial,emerging evidence suggests that welfare benefits and subjective economic well-being components of what can be deemed augmented economic well-being,may play equally significant roles in shaping psychological health among aging populations.This research explores how disparities in these aspects contribute to psychological distress in older adults.We highlight the role of financial security,government assistance pro-grams,and personal economic satisfaction.We propose policy interventions to mitigate adverse effects and emphasize the need for a holistic approach to econo-mic well-being to improve mental health outcomes in older adults.This study reveals that subjective financial security and access to welfare benefits often surpass income as predictors of mental health.These insights underscore the importance of broadening economic frameworks in aging research and deve-loping integrated policies to enhance financial and psychological well-being.展开更多
Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the...Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.展开更多
Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving dat...Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing.Despite its privacy-preserving advantages,FL remains vulnerable to backdoor attacks,where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process.While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL,they often incur a significant degradation in the performance of the aggregated models on benign tasks.To address this limitation,we propose a novel backdoor defense mechanism based on differential privacy.Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models.By filtering out models that are clearly backdoor-infected before applying differential privacy,our method reduces the required noise level for differential privacy,thereby enhancing model robustness while preserving performance.Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15%across various backdoor scenarios while maintaining high main task accuracy.展开更多
Objective:This study aimed to examine the developmental trajectories of internalizing behaviors among adolescents and to identify key personal and environmental factors associated with these developmental patterns ove...Objective:This study aimed to examine the developmental trajectories of internalizing behaviors among adolescents and to identify key personal and environmental factors associated with these developmental patterns over time.Methods:Data were collected from 2242 adolescents(49.6%girls,aged 13.9–18.9 years)in South Korea.Latent class growth analysis was used to identify distinct developmental patterns of internalizing behaviors.Multinomial logistic regression analyses were conducted to examine the associations between these developmental patterns and various factors including gender,self-esteem,abuse and neglect experiences,peer relationships,and media use.Results:The analysis revealed three latent classes of internalizing behavior trajectories among adolescents.The first group,the“mid decreasing group”,comprised 54.5%of the sample(1221 students),indicating a moderate level of internalizing behavior that declined over time.The second group,the“high decreasing group”,included 19.1%of the sample(429 students),characterized by initially high levels of internalizing behavior that decreased.The third group,the“low maintained group”,represented 26.4%of the sample(592 students),indicating consistently low levels of internalizing behavior.Factors such as gender,self-esteem,experiences of abuse and neglect,peer relationships(trust and alienation),smartphone dependency,and time spent watching TV/videos were significantly associated with these latent groups.Conclusion:Three distinct developmental patterns of internalizing behaviors were identified among adolescents:mid-decreasing(54.5%),high-decreasing(19.1%),and low-maintained(26.4%).Gender,self-esteem,abuse experiences,and peer relationships were significant predictors of these developmental patterns.展开更多
Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments.However,decreasing the number of parameters to enhance computational efficiency...Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments.However,decreasing the number of parameters to enhance computational efficiency can also lead to performance degradation.Moreover,these methods face challenges in balancing global and local features,increasing the risk of errors in multi-scale segmentation.This issue is particularly pronounced when segmenting small and complex structures within the human body.To address this problem,we propose a multi-stage hierarchical architecture composed of a detector and a segmentor.The detector extracts regions of interest(ROIs)in a 3D image,while the segmentor performs segmentation in the extracted ROI.Removing unnecessary areas in the detector allows the segmentation to be performed on a more compact input.The segmentor is designed with multiple stages,where each stage utilizes different input sizes.It implements a stage-skippingmechanism that deactivates certain stages using the initial input size.This approach minimizes unnecessary computations on segmenting the essential regions to reduce computational overhead.The proposed framework preserves segmentation performance while reducing resource consumption,enabling segmentation even in resource-constrained environments.展开更多
The efficiency of carbon dioxide(CO_(2))adsorption in carbonaceous materials is primarily influenced by their microporosity and thermodynamic affinity for CO_(2).However,achieving optimal heteroatom doping and precise...The efficiency of carbon dioxide(CO_(2))adsorption in carbonaceous materials is primarily influenced by their microporosity and thermodynamic affinity for CO_(2).However,achieving optimal heteroatom doping and precise micropore engineering through advanced activation techniques remains a significant challenge.We introduce a solvent-free one-pot method using polythiophene,melamine,and KOH to prepare highly microporous,heteroatom-co-doped carbons(NSC).This approach leverages sulfur from polythiophene,nitrogen from melamine,and the activation agent KOH to enhance CO_(2)capture performance.展开更多
Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions,which is a typical manifestation of this trend.With the increasing complexity of im...Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions,which is a typical manifestation of this trend.With the increasing complexity of image in tasks and the growth of data scale,existing deep learning methods still have some limitations.For example,they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal.To solve this problem,the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network.The encoder of the proposed model generator consists of a dual-branch parallel structure with stacked CNN blocks and Transformer blocks,aiming to extract global and local feature information from images.Furthermore,a dual-branch fusion module is adopted to combine the features obtained from both branches.Additionally,a gated full-scale skip connection module is proposed to further enhance the coherence of the inpainting results and alleviate information loss.Finally,experimental results from the three public datasets demonstrate the superior performance of the proposed method.展开更多
To emulate the functionality of the human retina and achieve a neuromorphic visual system,the development of a photonic synapse capable of multispectral color discrimination is of paramount importance.However,attainin...To emulate the functionality of the human retina and achieve a neuromorphic visual system,the development of a photonic synapse capable of multispectral color discrimination is of paramount importance.However,attaining robust color discrimination across a wide intensity range,even irrespective of medium limitations in the channel layer,poses a significant challenge.Here,we propose an approach that can bestow the color-discriminating synaptic functionality upon a three-terminal transistor flash memory even with enhanced discriminating capabilities.By incorporating the strong induced dipole moment effect at the excitation,modulated by the wavelength of the incident light,into the floating gate,we achieve outstanding RGB color-discriminating synaptic functionality within a remarkable intensity range spanning from 0.05 to 40 mW cm^(-2).This approach is not restricted to a specific medium in the channel layer,thereby enhancing its applicability.The effectiveness of this color-discriminating synaptic functionality is demonstrated through visual pre-processing of a photonic synapse array,involving the differentiation of RGB channels and the enhancement of image contrast with noise reduction.Consequently,a convolutional neural network can achieve an impressive inference accuracy of over 94%for Canadian-Institute-For-Advanced-Research-10 colorful image recognition task after the pre-processing.Our proposed approach offers a promising solution for achieving robust and versatile RGB color discrimination in photonic synapses,enabling significant advancements in artificial visual systems.展开更多
The global mortality rate due to liver diseases,particularly liver fibrosis,is increasing.Among various treatment methods,stem cell therapy using placenta-derived mesenchymal stem cells(PDMSCs)offers distinct benefits...The global mortality rate due to liver diseases,particularly liver fibrosis,is increasing.Among various treatment methods,stem cell therapy using placenta-derived mesenchymal stem cells(PDMSCs)offers distinct benefits,including ease of isolation and superior proliferative potential.To enhance the therapeutic efficacy of PDMSCs,the WKYMVm peptide was selected for cell engineering.Immobilization of WKYMVm on PDMSC membranes facilitates effective peptide binding to the formyl peptide receptor 2 on adjacent PDMSCs and hepatocytes,thereby enhancing cell activation and achieving more efficient peptide utilization compared to bolus peptide treatment.Increased cell activation enhances the secretion of paracrine factors including growth factors and cytokines,which in turn improves liver function and vascular repair in both in vitro and in vivo models.This approach not only enhances the angiogenic and therapeutic capacities of stem cells,but also enables efficient peptide utilization,minimizing potential side effects and costs associated with high peptide dosages.Overall,our study demonstrates significant promise of stem cell therapy for treating liver fibrosis.Thus,stem cell therapy offers considerable prospects for clinical applications.展开更多
文摘Objectives:The eukaryotic initiation factor 4F(eIF4F)translation initiation complex inhibitors(eIF4Fi)were recently found to hyperactivate extracellular signal-regulated kinases 1/2(ERK1/2)signals,which contribute to acquired resistance to BRAF(B-Raf proto-oncogene,serine/threonine kinase)inhibitors in melanoma.This present study aims to elucidate how to overcome the resistance of the eIF4Fi in BRAFV600E mutant melanoma cells and explore the underlying mechanisms.Methods:Melanoma A375(vemurafenib[VEM]-sensitive)and A375R(VEM-resistant)cells were exposed to eIF4Fi RocA at varying doses and durations in vitro.We investigated the impact of RocA on the activity of ERK1/2,AKT serine/threonine kinase 1(AKT1),eIF4E,and enhancer of zeste homolog 2(EZH2).We then examined the impact of RocA on pro-apoptotic BH3-only proteins and proliferative proteins.We subsequently determined the effect of combined eIF4Fi,AKT1 inhibitor,EZH2 inhibitor or VEM on tumor growth in vitro and in vivo.Results:RocA inhibited proliferation and induced apoptosis in A375 cells,but inhibited proliferation in A375R cells.RocA rapidly reactivated ERK1/2 at 3 h and returned to baseline levels at 48 h.However,eIF4E and AKT1 activation began at 12 h and peaked at 48 h.ERK1/2 positively regulated EZH2 and EZH2-dependent expression of c-Fos and EGR1,while AKT1 negatively regulated c-Myc,c-Jun,and BMF,but positively regulated eIF4E.RocA downregulated ERK1/2(or EZH2,AKT1,and eIF4E)independent bcl-2 and Mcl-1 expression.AKT1i enhanced RocA-induced cell apoptosis,while EZH2i reduced RocA-induced cell proliferation.Combined CR-1-31-B,EZH2i,and AKT1i effectively overcame resistance to RocA and VEM resistance both in vitro and in vivo.Conclusion:The eIF4F complex inhibitor reactivates ERK1/2-EZH2 and AKT1 signaling pathways,resulting in resistance to both eIF4Fi and VEM.Combined administration of an eIF4Fi with EZH2 and AKT1 inhibitors effectively enhances sensitivity to both eIF4F complex and BRAF inhibitors.
基金supported by the Korea Institute for Advancement of Technology (KIAT)the Ministry of Trade,Industry&Energy (MOTIE) of the Republic of Korea (No. P0022130)by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government (MSIT)(IITP-2025-RS-2023-00259678)
文摘To realize the practical application of anion exchange membrane water electrolysis(AEMWE),it is essential to develop highly active,durable,and cost-effective electrocatalyst for oxygen evolution reaction(OER).Herein,we report a hollow-structured Ni_(x)Co_(1−x)O/Ni_(3)S_(2)/Co_(9)S_(8)heterostructure synthesized via sequential template-assisted growth,thermal oxidation,and controlled sulfidation process.The abundant bimetallic heterointerfaces not only provide additional active sites but also promote electronic modulation via charge redistribution.Additionally,the porous and hollow architecture enhances active surface area and mass transfer ability,thereby increasing the number of accessible active sites for alkaline OER.As a result,the prepared electrocatalyst achieves low overpotential of 310 mV at 10 mA cm^(−2)and small Tafel slope of 55.94 mV dec^(−1),demonstrating the exceptional electrocatalytic performance for alkaline OER.When integrated as the anode in an AEMWE cell,it delivers outstanding performance with only 1.657 V at 1.0 A cm^(−2)and reaches high current density of 5.0 A cm^(−2)at 1.989 V,surpassing those of commercial RuO_(2).The cell also shows excellent long-term durability over 100 h with minimal degradation.This study highlights the strong potential of rationally engineered oxide/sulfide heterostructures for next-generation alkaline water electrolysis.
文摘Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
文摘Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.
文摘As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles.In this work,we introduce a novel neural scene representation called Street Detection Gaussians(SDGs),which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection.At its core lies the dynamic Gaussian representation,where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution.The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radardata anddense visual features,resulting ina22%reduction inocclusionerrors compared tovisiononly systems.A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process,enabling the optimization of both geometric and semantic fidelity.Evaluated on the Waymo Open Dataset and the KITTI Dataset,the system achieves real-time performance(135 Frames Per Second(FPS)),photorealistic quality(Peak Signal-to-Noise Ratio(PSNR)34.9 dB),and state-of-the-art detection accuracy(78.1%Mean Average Precision(mAP)),demonstrating a 3.8×end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.
基金supported by the Inha University Research Grant(to JKR).
文摘Pericytes are multifunctional mural cells that surround the abluminal wall of endothelial cells and are associated with vascular development,vascular permeability,and angiogenesis.Additionally,pericytes demonstrate stem cell-like properties and contribute to neuroinflammatory processes.Pericytes have been extensively studied in the central nervous system.However,specific mechanisms underlying its involvement in various physiological and pathological conditions,especially in erectile dysfunction(ED),remain poorly understood.Advancements in in vitro and in vitro techniques,such as single-cell RNA sequencing,are expanding our understanding of pericytes.Recent studies have shown that pericyte dysfunction is considered an important factor in the pathogenesis of vascular and neurological ED.Therefore,this study aims to analyze the specific role of pericytes in ED,focusing on diabetic and neurogenic ED.This article provides a comprehensive review of research findings on PubMed from 2000 to 2023,concerning pericyte dysfunction in the process of ED,offering valuable insights,and suggesting directions for further research.
文摘Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive thyroid-related cell lines cultured under simulated microgravity.Methods:Five thyroid-related cell lines—normal thyrocytes(Nthy-ori 3-1),papillary thyroid cancer(PTC)cells(SNU-790,TPC-1),poorly differentiated thyroid cancer cell(BCPAP),and anaplastic thyroid cancer cell(SNU-80)—were cultured under simulated microgravity(10-3 g)using a clinostat.Differentially expressed genes(DEGs)were analyzed using cDNA microarray,followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.Results:DEG analysis under simulated microgravity revealed distinct gene expression profiles by gravity condition,with 2980 DEGs in SNU-790,1033 in BCPAP,562 in TPC-1,477 in Nthy-ori 3-1,and 246 in SNU-80,as confirmed by hierarchical clustering.In PTC cell lines(SNU-790,TPC-1),G2–M phase–related genes were upregulated.In non-PTC cell lines(BCPAP,SNU-80),genes associated with innate immune response,Toll-like receptor signaling,were upregulated,whereas Hypoxia-Inducible Factor 1-alpha(HIF-1α)signaling-related genes were downregulated.Additionally,under simulated microgravity,significant migration was observed in SNU-790(3×104 cells)and BCPAP(2×104 and 3×104),while significant invasion occurred in SNU-790,Nthy-ori 3-1,and BCPAP at a seeding density of 2×104.Other conditions showed no significant differences.Conclusion:This study comprehensively evaluates the effects of simulated microgravity using a diverse panel of thyroid-related cell lines.Thesefindings provide valuable insight into how microgravity could influence cancer biology,emphasizing the importance of further research on cancer behavior in space environments and its implications for human health during long-term space missions.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)(No.20213030040590)the Korea Institute for Advancement of Technology(KIAT)and the Ministry of Trade,Industry&Energy(MOTIE),Republic of Korea(No.P0022130)Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education(2021R1A6C101A404)。
文摘Water electrolysis using renewable electricity is a promising strategy for high-purity hydrogen production.To realize the practical application of water electrolysis,an electrocatalyst with high redox properties and low cost is essential for enhancing the sluggish oxygen evolution reaction.Herein,we fabricated Fe-doped nickel oxalate(Fe-NiC_(2)O_(4))directly grown on nickel(Ni)foam as an efficient electrocatalyst for the alkaline oxygen evolution reaction using a facile one-step hydrothermal method.Fe-NiC_(2)O_(4) served as a precursor for obtaining highly active Fe-doped Ni oxyhydroxide(Fe-NiOOH)via in situ electrochemical oxidation.Consequently,0.75Fe-NiOOH was demonstrated to be the optimal electrocatalyst,exhibiting outstanding oxygen evolution reaction activity with a low overpotential of 220 mV at a current density of 100 mA cm^(-2) and a Tafel slope of 20.5 mV dec^(-1).Furthermore,Fe-NiOOH maintained its oxygen evolution reaction activity without performance decay during long-term electrochemical measurements,owing to the phase transformation from nickel oxyhydroxide(NiOOH)toγ-NiOOH(gamma nickel oxyhydroxide).These performances significantly surpass those of recently reported transition-metal-based electrocatalysts.
基金Supported by the Shihwa Medical Center Research Fund.
文摘BACKGROUND Despite advancements,endoscopic retrograde cholangiopancreatography(ERCP)poses challenges,including the risk of post-ERCP pancreatitis and difficulty of biliary cannulation.AIM To compare dome and tapered tip sphincterotomes,focusing on their efficacy in achieving successful biliary cannulation and reducing the incidence of post-ERCP pancreatitis.METHODS In this prospective,single-blind,randomized pilot study conducted at Inha Uni-versity Hospital,85 patients undergoing ERCP were equally divided into dome and tapered tip sphincterotome groups.The co-primary outcomes were the success rate of selective biliary cannulation and incidence of post-ERCP pancre-atitis.The secondary outcomes included biliary cannulation time,number of unintended pancreatic duct access events,and total procedure time.RESULTS The success rates of selective biliary cannulation were 74.4%and 85.7%in the dome and tapered tip groups,respectively,with no significant difference(P=0.20).Similarly,the incidence of post-ERCP pancreatitis did not differ significantly between the groups(5 cases in the tapered tip group vs 6 in the dome tip group,P=0.72).However,difficult cannulation was significantly more common in the dome tip group than in the tapered tip group(P=0.05).Selective biliary cannula-tion time emerged as a significant predictor of post-ERCP pancreatitis(multivariate odds ratio=9.33,95%confi-dence interval:1.31-66.44,P=0.03).CONCLUSION This study indicated that the sphincterotome tip type does not markedly affect biliary cannulation success or post-ERCP pancreatitis rates.However,cannulation duration is a key risk factor for post-ERCP pancreatitis.These findings provide preliminary insights that highlight the importance of refining ERCP practices,including sphinc-terotome selection,while underscoring the need for larger multicenter studies to improve procedure time and patient safety.
文摘Prostate cancer is the second most common malignancy and the sixth leading cause of cancer-related death in men worldwide.Radical prostatectomy(RP)is the standard treatment for localized prostate cancer,but the procedure often results in postoperative erectile dysfunction(ED).The poor efficacy of phosphodiesterase 5 inhibitors after surgery highlights the need to develop new therapies to enhance cavernous nerve regeneration and improve the erectile function of these patients.In the present study,we aimed to examine the potential of heparin-binding epidermal growth factor-like growth factor(HB-EGF)in preserving erectile function in cavernous nerve injury(CNI)mice.We found that HB-EGF expression was reduced significantly on the 1st day after CNI in penile tissue.Ex vivo and in vitro studies showed that HB-EGF promotes major pelvic ganglion neurite sprouting and neuro-2a(N2a)cell migration.In vivo studies showed that exogenous HB-EGF treatment significantly restored the erectile function of CNI mice to 86.9%of sham levels.Immunofluorescence staining showed that mural and neuronal cells were preserved by inducing cell proliferation and reducing apoptosis and reactive oxygen species production.Western blot analysis showed that HB-EGF upregulated protein kinase B and extracellular signal-regulated kinase activation and neurotrophic factor expression.Overall,HB-EGF is a major promising therapeutic agent for treating ED in postoperative RP.
文摘Economic inequality is a persistent global issue with profound implications for mental health,particularly among older adults.Previous studies have largely focused on objective economic well-being indicators such as income,employment status,and poverty.While these factors are crucial,emerging evidence suggests that welfare benefits and subjective economic well-being components of what can be deemed augmented economic well-being,may play equally significant roles in shaping psychological health among aging populations.This research explores how disparities in these aspects contribute to psychological distress in older adults.We highlight the role of financial security,government assistance pro-grams,and personal economic satisfaction.We propose policy interventions to mitigate adverse effects and emphasize the need for a holistic approach to econo-mic well-being to improve mental health outcomes in older adults.This study reveals that subjective financial security and access to welfare benefits often surpass income as predictors of mental health.These insights underscore the importance of broadening economic frameworks in aging research and deve-loping integrated policies to enhance financial and psychological well-being.
文摘Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.
文摘Federated Learning(FL),a practical solution that leverages distributed data across devices without the need for centralized data storage,which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing.Despite its privacy-preserving advantages,FL remains vulnerable to backdoor attacks,where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process.While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL,they often incur a significant degradation in the performance of the aggregated models on benign tasks.To address this limitation,we propose a novel backdoor defense mechanism based on differential privacy.Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models.By filtering out models that are clearly backdoor-infected before applying differential privacy,our method reduces the required noise level for differential privacy,thereby enhancing model robustness while preserving performance.Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15%across various backdoor scenarios while maintaining high main task accuracy.
文摘Objective:This study aimed to examine the developmental trajectories of internalizing behaviors among adolescents and to identify key personal and environmental factors associated with these developmental patterns over time.Methods:Data were collected from 2242 adolescents(49.6%girls,aged 13.9–18.9 years)in South Korea.Latent class growth analysis was used to identify distinct developmental patterns of internalizing behaviors.Multinomial logistic regression analyses were conducted to examine the associations between these developmental patterns and various factors including gender,self-esteem,abuse and neglect experiences,peer relationships,and media use.Results:The analysis revealed three latent classes of internalizing behavior trajectories among adolescents.The first group,the“mid decreasing group”,comprised 54.5%of the sample(1221 students),indicating a moderate level of internalizing behavior that declined over time.The second group,the“high decreasing group”,included 19.1%of the sample(429 students),characterized by initially high levels of internalizing behavior that decreased.The third group,the“low maintained group”,represented 26.4%of the sample(592 students),indicating consistently low levels of internalizing behavior.Factors such as gender,self-esteem,experiences of abuse and neglect,peer relationships(trust and alienation),smartphone dependency,and time spent watching TV/videos were significantly associated with these latent groups.Conclusion:Three distinct developmental patterns of internalizing behaviors were identified among adolescents:mid-decreasing(54.5%),high-decreasing(19.1%),and low-maintained(26.4%).Gender,self-esteem,abuse experiences,and peer relationships were significant predictors of these developmental patterns.
文摘Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments.However,decreasing the number of parameters to enhance computational efficiency can also lead to performance degradation.Moreover,these methods face challenges in balancing global and local features,increasing the risk of errors in multi-scale segmentation.This issue is particularly pronounced when segmenting small and complex structures within the human body.To address this problem,we propose a multi-stage hierarchical architecture composed of a detector and a segmentor.The detector extracts regions of interest(ROIs)in a 3D image,while the segmentor performs segmentation in the extracted ROI.Removing unnecessary areas in the detector allows the segmentation to be performed on a more compact input.The segmentor is designed with multiple stages,where each stage utilizes different input sizes.It implements a stage-skippingmechanism that deactivates certain stages using the initial input size.This approach minimizes unnecessary computations on segmenting the essential regions to reduce computational overhead.The proposed framework preserves segmentation performance while reducing resource consumption,enabling segmentation even in resource-constrained environments.
基金supported by the National Research Foundation of Korea(NRF-2023R1A2C1004109)supported by Korea Energy(No.2024Research and Development in Field Technology,Yeongheung-01)。
文摘The efficiency of carbon dioxide(CO_(2))adsorption in carbonaceous materials is primarily influenced by their microporosity and thermodynamic affinity for CO_(2).However,achieving optimal heteroatom doping and precise micropore engineering through advanced activation techniques remains a significant challenge.We introduce a solvent-free one-pot method using polythiophene,melamine,and KOH to prepare highly microporous,heteroatom-co-doped carbons(NSC).This approach leverages sulfur from polythiophene,nitrogen from melamine,and the activation agent KOH to enhance CO_(2)capture performance.
基金supported by Scientific Research Fund of Hunan Provincial Natural Science Foundation under Grant 20231J60257Hunan Provincial Engineering Research Center for Intelligent Rehabilitation Robotics and Assistive Equipment under Grant 2025SH501Inha University and Design of a Conflict Detection and Validation Tool under Grant HX2024123.
文摘Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions,which is a typical manifestation of this trend.With the increasing complexity of image in tasks and the growth of data scale,existing deep learning methods still have some limitations.For example,they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal.To solve this problem,the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network.The encoder of the proposed model generator consists of a dual-branch parallel structure with stacked CNN blocks and Transformer blocks,aiming to extract global and local feature information from images.Furthermore,a dual-branch fusion module is adopted to combine the features obtained from both branches.Additionally,a gated full-scale skip connection module is proposed to further enhance the coherence of the inpainting results and alleviate information loss.Finally,experimental results from the three public datasets demonstrate the superior performance of the proposed method.
基金supported by National Research Foundation of Korea(NRF)[RS-2024-00350701 and RS-2023-00207828].
文摘To emulate the functionality of the human retina and achieve a neuromorphic visual system,the development of a photonic synapse capable of multispectral color discrimination is of paramount importance.However,attaining robust color discrimination across a wide intensity range,even irrespective of medium limitations in the channel layer,poses a significant challenge.Here,we propose an approach that can bestow the color-discriminating synaptic functionality upon a three-terminal transistor flash memory even with enhanced discriminating capabilities.By incorporating the strong induced dipole moment effect at the excitation,modulated by the wavelength of the incident light,into the floating gate,we achieve outstanding RGB color-discriminating synaptic functionality within a remarkable intensity range spanning from 0.05 to 40 mW cm^(-2).This approach is not restricted to a specific medium in the channel layer,thereby enhancing its applicability.The effectiveness of this color-discriminating synaptic functionality is demonstrated through visual pre-processing of a photonic synapse array,involving the differentiation of RGB channels and the enhancement of image contrast with noise reduction.Consequently,a convolutional neural network can achieve an impressive inference accuracy of over 94%for Canadian-Institute-For-Advanced-Research-10 colorful image recognition task after the pre-processing.Our proposed approach offers a promising solution for achieving robust and versatile RGB color discrimination in photonic synapses,enabling significant advancements in artificial visual systems.
基金supported by grants from the Korean Fund for Regenerative Medicine(KFRM)grant funded by the Korean government(the Ministry of Science and ICT,the Ministry of Health and Welfare)(RS-2022-00070304)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2024-00398030).
文摘The global mortality rate due to liver diseases,particularly liver fibrosis,is increasing.Among various treatment methods,stem cell therapy using placenta-derived mesenchymal stem cells(PDMSCs)offers distinct benefits,including ease of isolation and superior proliferative potential.To enhance the therapeutic efficacy of PDMSCs,the WKYMVm peptide was selected for cell engineering.Immobilization of WKYMVm on PDMSC membranes facilitates effective peptide binding to the formyl peptide receptor 2 on adjacent PDMSCs and hepatocytes,thereby enhancing cell activation and achieving more efficient peptide utilization compared to bolus peptide treatment.Increased cell activation enhances the secretion of paracrine factors including growth factors and cytokines,which in turn improves liver function and vascular repair in both in vitro and in vivo models.This approach not only enhances the angiogenic and therapeutic capacities of stem cells,but also enables efficient peptide utilization,minimizing potential side effects and costs associated with high peptide dosages.Overall,our study demonstrates significant promise of stem cell therapy for treating liver fibrosis.Thus,stem cell therapy offers considerable prospects for clinical applications.
