In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the...To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the alloy across different planes were investigated.The anisotropy of SLM-fabricated Ti-6Al-4V alloys was analyzed,and the electron backscatter diffraction technique was used to investigate the influence of different grain types and orientations on the stress-strain distribution at various scales.Results reveal that in room-temperature compression tests at a strain rate of 10^(-3) s^(-1),both the compressive yield strength and microhardness vary along the deposition direction,indicating a certain degree of mechanical property anisotropy.The alloy exhibits a columnar microstructure;along the deposition direction,the grains appear equiaxed,and they have internal hexagonal close-packed(hcp)α/α'martensitic structure.α'phase has a preferential orientation approximately along the<0001>direction.Anisotropy arises from the high aspect ratio of columnar grains,along with the weak texture of the microstructure and low symmetry of the hcp crystal structure.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their effi...BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their efficacy is limited.This study investigated whether combining SSRIs with traditional Chinese medicine(TCM)Free San could enhance their therapeutic effects.AIM To evaluate the clinical efficacy and safety of combining SSRIs with Free San in treating PSD,and to assess its impact on HPA axis function.METHODS Ninety-two patients with PSD were enrolled and randomly divided into control groups(n=46)and study groups(n=46).The control group received the SSRI paroxetine alone,whereas the study group received paroxetine combined with Free San for 4 weeks.Hamilton Depression Scale and TCM syndrome scores were assessed before and after treatment.Serum serotonin,norepinephrine,cortisol,cor-ticotropin-releasing hormone,and adrenocorticotropic hormone were measured.The treatment responses and adverse reactions were recorded.RESULTS After treatment,the Hamilton Depression Scale and TCM syndrome scores were significantly lower in the study group than in the control group(P<0.05).Serum serotonin and norepinephrine levels were significantly higher in the study group than in the control group,whereas cortisol,corticotropin-releasing hormone,and adrenocorticotropic hormone levels were significantly lower(P<0.05).The total efficacy rates were 84.78%and 65.22%in the study and control groups,respectively(P<0.05).No significant differences in adverse reactions were observed between the two groups(P>0.05).CONCLUSION Combining SSRIs with Free San can enhance therapeutic efficacy,improve depressive symptoms,and regulate HPA axis function in patients with PSD with good safety and clinical application value.展开更多
GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a ...GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a hub for protein–protein interaction, while a non-canonical RNA-binding site is placed towards the C-terminus. The singular organization of structural domains present in GEMIN5 enables this protein to perform multiple functions through its ability to interact with distinct partners, both RNAs and proteins. This protein exerts a different role in translation regulation depending on its physiological state, such that while GEMIN5 down-regulates global RNA translation, the C-terminal half of the protein promotes translation of its mRNA. Additionally, GEMIN5 is responsible for the preferential partitioning of mRNAs into polysomes. Besides selective translation, GEMIN5 forms part of distinct ribonucleoprotein complexes, reflecting the dynamic organization of macromolecular complexes in response to internal and external signals. In accordance with its contribution to fundamental cellular processes, recent reports described clinical loss of function mutants suggesting that GEMIN5 deficiency is detrimental to cell growth and survival. Remarkably, patients carrying GEMIN5 biallelic variants suffer from neurodevelopmental delay, hypotonia, and cerebellar ataxia. Molecular analyses of individual variants, which are defective in protein dimerization, display decreased levels of ribosome association, reinforcing the involvement of the protein in translation regulation. Importantly, the number of clinical variants and the phenotypic spectrum associated with GEMIN5 disorders is increasing as the knowledge of the protein functions and the pathways linked to its activity augments. Here we discuss relevant advances concerning the functional and structural features of GEMIN5 and its separate domains in RNA-binding, protein interactome, and translation regulation, and how these data can help to understand the involvement of protein malfunction in clinical variants found in patients developing neurodevelopmental disorders.展开更多
Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(...Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(HER)and the high energy barrier in the hydrogenation step of nitrogen-containing intermediates.Here,we report a selective etching strategy to construct Ru M nanoalloys(M=Fe,Co,Ni,Cu)uniformly dispersed on porous nitrogen-doped carbon substrates for efficient neutral NH3electrosynthesis.Density functional theory calculations confirm that the synergic effect between Ru and transition metal M modulates the electronic structure of the alloy,significantly lowering the energy barrier for the conversion of*NO_(2)to*HNO_(2).Experimentally,the optimized Ru Fe-NC catalyst achieves 100%Faraday efficiency with a high yield rate of 0.83 mg h^(-1)mg^(-1)catat a low potential of-0.1 V vs.RHE,outperforming most reported catalysts.In situ spectroscopic analyses further demonstrate that the Ru M-NC effectively promotes the hydrogenation of nitrogen intermediates while inhibiting the formation of hydrogen radicals,thereby reducing HER competition.The Ru FeNC assembled Zn-NO_(3)^(-)battery achieved a high open-circuit voltage and an outstanding power density and capacity,which drive selective NO_(3)^(-)conversion to NH3.This work provides a powerful synergistic design strategy for efficient NH3electrosynthesis and a general framework for the development of advanced multi-component catalysts for sustainable nitrogen conversion.展开更多
Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via lo...Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via low-temperature coprecipitation,exhibiting excellent performance for the selective hydrogenation of 5-hydroxymethylfurfural(HMF).A linear correlation is first observed between solvent polarity(E_(T)(30))and product selectivity within both polar aprotic and protic solvent classes,suggesting that solvent properties play a vital role in directing reaction pathways.Among these,1,4-dioxane(aprotic)favors the formation of 2,5-bis(hydroxymethyl)furan(BHMF)with 97.5%selectivity,while isopropanol(iPrOH,protic)promotes 2,5-dimethylfuran production with up to 99.5%selectivity.Mechanistic investigations further reveal that beyond polarity,proton-donating ability is critical in facilitating hydrodeoxygenation.iPrOH enables a hydrogen shuttle mechanism where protons assist in hydroxyl group removal,lowering the activation barrier.In contrast,1,4-dioxane,lacking hydrogen bond donors,stabilizes BHMF and hinders further conversion.Density functional theory calculations confirm a lower activation energy in iPrOH(0.60 eV)compared to 1,4-dioxane(1.07 eV).This work offers mechanistic insights and a practical strategy for solvent-mediated control of product selectivity in biomass hydrogenation,highlighting the decisive role of solvent-catalyst-substrate interactions.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Radiative cooling textiles with spectrally selective surfaces offer a promising energy-efficient approach for sub-ambient cooling of outdoor objects and individuals.However,the spectrally selective mid-infrared emissi...Radiative cooling textiles with spectrally selective surfaces offer a promising energy-efficient approach for sub-ambient cooling of outdoor objects and individuals.However,the spectrally selective mid-infrared emission of these textiles significantly hinders their efficient radiative heat exchange with self-heated objects,thereby posing a significant challenge to their versatile cooling applicability.Herein,we present a bicomponent blow spinning strategy for the production of scalable,ultra-flexible,and healable textiles featuring a tailored dual gradient in both chemical composition and fiber diameter.The gradient in the fiber diameter of this textile introduces a hierarchically porous structure across the sunlight incident area,thereby achieving a competitive solar reflectivity of 98.7%on its outer surface.Additionally,the gradient in the chemical composition of this textile contributes to the formation of Janus infrared-absorbing surfaces:The outer surface demonstrates a high mid-infrared emission,whereas the inner surface shows a broad infrared absorptivity,facilitating radiative heat exchange with underlying self-heated objects.Consequently,this textile demonstrates multi-scenario radiative cooling capabilities,enabling versatile outdoor cooling for unheated objects by 7.8℃ and self-heated objects by 13.6℃,compared to commercial sunshade fabrics.展开更多
BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initi...BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.展开更多
Lithium-oxygen(Li-O2)batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage,utilizing ambient air as an energy source,eliminating the need for costly cathode materials,and of...Lithium-oxygen(Li-O2)batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage,utilizing ambient air as an energy source,eliminating the need for costly cathode materials,and offering the highest theoretical energy density(~3.5 k Wh kg^(-1))among discussed candidates.Contributing to the poor cycle life of currently reported Li-O_(2)cells is singlet oxygen(1O_(2))formation,inducing parasitic reactions,degrading key components,and severely deteriorating cell performance.Here,we harness the chirality-induced spin selectivity effect of chiral cobalt oxide nanosheets(Co_(3)O_(4)NSs)as cathode materials to suppress 1O_(2)in Li-O_(2)batteries for the first time.Operando photoluminescence spectroscopy reveals a 3.7-fold and 3.23-fold reduction in 1O_(2)during discharge and charge,respectively,compared to conventional carbon paperbased cells,consistent with differential electrochemical mass spectrometry results,which indicate a near-theoretical charge-to-O_(2)ratio(2.04 e-/O_(2)).Density functional theory calculations demonstrate that chirality induces a peak shift near the Fermi level,enhancing Co 3d-O 2p hybridization,stabilizing reaction intermediates,and lowering activation barriers for Li_(2)O_(2)formation and decomposition.These findings establish a new strategy for improving the stability and energy efficiency of sustainable Li-O_(2)batteries,abridging the current gap to commercialization.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
Metal hydrides with high hydrogen density provide promising hydrogen storage paths for hydrogen transportation.However,the requirement of highly pure H_(2)for re-hydrogenation limits its wide application.Here,amorphou...Metal hydrides with high hydrogen density provide promising hydrogen storage paths for hydrogen transportation.However,the requirement of highly pure H_(2)for re-hydrogenation limits its wide application.Here,amorphous Al_(2)O_(3)shells(10 nm)were deposited on the surface of highly active hydrogen storage material particles(MgH_(2)-ZrTi)by atomic layer deposition to obtain MgH_(2)-ZrTi@Al_(2)O_(3),which have been demonstrated to be air stable with selective adsorption of H_(2)under a hydrogen atmosphere with different impurities(CH_(4),O_(2),N_(2),and CO_(2)).About 4.79 wt%H_(2)was adsorbed by MgH_(2)-ZrTi@10nmAl_(2)O_(3)at 75℃under 10%CH_(4)+90%H_(2)atmosphere within 3 h with no kinetic or density decay after 5 cycles(~100%capacity retention).Furthermore,about 4 wt%of H_(2)was absorbed by MgH_(2)-ZrTi@10nmAl_(2)O_(3)under 0.1%O_(2)+0.4%N_(2)+99.5%H_(2)and 0.1%CO_(2)+0.4%N_(2)+99.5%H_(2)atmospheres at 100℃within 0.5 h,respectively,demonstrating the selective hydrogen absorption of MgH_(2)-ZrTi@10nmAl_(2)O_(3)in both oxygen-containing and carbon dioxide-containing atmospheres hydrogen atmosphere.The absorption and desorption curves of MgH_(2)-ZrTi@10nmAl_(2)O_(3)with and without absorption in pure hydrogen and then in 21%O_(2)+79%N_(2)for 1 h were found to overlap,further confirming the successful shielding effect of Al_(2)O_(3)shells against O_(2)and N_(2).The MgH_(2)-ZrTi@10nmAl_(2)O_(3)has been demonstrated to be air stable and have excellent selective hydrogen absorption performance under the atmosphere with CH_(4),O_(2),N_(2),and CO_(2).展开更多
The endoplasmic reticulum,a key cellular organelle,regulates a wide variety of cellular activities.Endoplasmic reticulum autophagy,one of the quality control systems of the endoplasmic reticulum,plays a pivotal role i...The endoplasmic reticulum,a key cellular organelle,regulates a wide variety of cellular activities.Endoplasmic reticulum autophagy,one of the quality control systems of the endoplasmic reticulum,plays a pivotal role in maintaining endoplasmic reticulum homeostasis by controlling endoplasmic reticulum turnover,remodeling,and proteostasis.In this review,we briefly describe the endoplasmic reticulum quality control system,and subsequently focus on the role of endoplasmic reticulum autophagy,emphasizing the spatial and temporal mechanisms underlying the regulation of endoplasmic reticulum autophagy according to cellular requirements.We also summarize the evidence relating to how defective or abnormal endoplasmic reticulum autophagy contributes to the pathogenesis of neurodegenerative diseases.In summary,this review highlights the mechanisms associated with the regulation of endoplasmic reticulum autophagy and how they influence the pathophysiology of degenerative nerve disorders.This review would help researchers to understand the roles and regulatory mechanisms of endoplasmic reticulum-phagy in neurodegenerative disorders.展开更多
Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to prote...Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to proteotoxic stress response and in other processes such as inflammation,cell division,and migration.HSPB8 binds misfolded clients to prevent their aggregation by assisting protein refolding or degradation through chaperone-assisted selective autophagy.In line with this function,the pro-degradative activity of HSPB8 has been found protective in several neurodegenerative and neuromuscular diseases characterized by protein misfolding and aggregation.In cancer,HSPB8 has a dual role being capable of exerting either a pro-or an anti-tumoral activity depending on the pathways and factors expressed by the model of cancer under investigation.Moreover,HSPB8 exerts a protective function in different diseases by modulating the inflammatory response,which characterizes not only neurodegenerative diseases,but also other chronic or acute conditions affecting the nervous system,such as multiple sclerosis and intracerebellar hemorrhage.Of note,HSPB8 modulation may represent a therapeutic approach in other neurological conditions that develop as a secondary consequence of other diseases.This is the case of cognitive impairment related to diabetes mellitus,in which HSPB8 exerts a protective activity by assuring mitochondrial homeostasis.This review aims to summarize the diverse and multiple functions of HSPB8 in different pathological conditions,focusing on the beneficial effects of its modulation.Drug-based and alternative therapeutic approaches targeting HSPB8 and its regulated pathways will be discussed,emphasizing how new strategies for cell and tissue-specific delivery represent an avenue to advance in disease treatments.展开更多
Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金National Natural Science Foundation of China(51504138,51674118,52271177)Hunan Provincial Natural Science Foundation of China(2023JJ50181)Supported by State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology(P2024-022)。
文摘To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the alloy across different planes were investigated.The anisotropy of SLM-fabricated Ti-6Al-4V alloys was analyzed,and the electron backscatter diffraction technique was used to investigate the influence of different grain types and orientations on the stress-strain distribution at various scales.Results reveal that in room-temperature compression tests at a strain rate of 10^(-3) s^(-1),both the compressive yield strength and microhardness vary along the deposition direction,indicating a certain degree of mechanical property anisotropy.The alloy exhibits a columnar microstructure;along the deposition direction,the grains appear equiaxed,and they have internal hexagonal close-packed(hcp)α/α'martensitic structure.α'phase has a preferential orientation approximately along the<0001>direction.Anisotropy arises from the high aspect ratio of columnar grains,along with the weak texture of the microstructure and low symmetry of the hcp crystal structure.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金Supported by Open Project of Jiangsu Province Key Laboratory of Integrated Traditional Chinese and Western Medicine for the Prevention and Treatment of Geriatric Diseases,No.202232.
文摘BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their efficacy is limited.This study investigated whether combining SSRIs with traditional Chinese medicine(TCM)Free San could enhance their therapeutic effects.AIM To evaluate the clinical efficacy and safety of combining SSRIs with Free San in treating PSD,and to assess its impact on HPA axis function.METHODS Ninety-two patients with PSD were enrolled and randomly divided into control groups(n=46)and study groups(n=46).The control group received the SSRI paroxetine alone,whereas the study group received paroxetine combined with Free San for 4 weeks.Hamilton Depression Scale and TCM syndrome scores were assessed before and after treatment.Serum serotonin,norepinephrine,cortisol,cor-ticotropin-releasing hormone,and adrenocorticotropic hormone were measured.The treatment responses and adverse reactions were recorded.RESULTS After treatment,the Hamilton Depression Scale and TCM syndrome scores were significantly lower in the study group than in the control group(P<0.05).Serum serotonin and norepinephrine levels were significantly higher in the study group than in the control group,whereas cortisol,corticotropin-releasing hormone,and adrenocorticotropic hormone levels were significantly lower(P<0.05).The total efficacy rates were 84.78%and 65.22%in the study and control groups,respectively(P<0.05).No significant differences in adverse reactions were observed between the two groups(P>0.05).CONCLUSION Combining SSRIs with Free San can enhance therapeutic efficacy,improve depressive symptoms,and regulate HPA axis function in patients with PSD with good safety and clinical application value.
基金partially supported by grants PID2020-115096RB-I00 and PID2023-148273NB-I00 from Ministerio de Ciencia y Universidad (MICIU/AEI)(to EMS)。
文摘GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a hub for protein–protein interaction, while a non-canonical RNA-binding site is placed towards the C-terminus. The singular organization of structural domains present in GEMIN5 enables this protein to perform multiple functions through its ability to interact with distinct partners, both RNAs and proteins. This protein exerts a different role in translation regulation depending on its physiological state, such that while GEMIN5 down-regulates global RNA translation, the C-terminal half of the protein promotes translation of its mRNA. Additionally, GEMIN5 is responsible for the preferential partitioning of mRNAs into polysomes. Besides selective translation, GEMIN5 forms part of distinct ribonucleoprotein complexes, reflecting the dynamic organization of macromolecular complexes in response to internal and external signals. In accordance with its contribution to fundamental cellular processes, recent reports described clinical loss of function mutants suggesting that GEMIN5 deficiency is detrimental to cell growth and survival. Remarkably, patients carrying GEMIN5 biallelic variants suffer from neurodevelopmental delay, hypotonia, and cerebellar ataxia. Molecular analyses of individual variants, which are defective in protein dimerization, display decreased levels of ribosome association, reinforcing the involvement of the protein in translation regulation. Importantly, the number of clinical variants and the phenotypic spectrum associated with GEMIN5 disorders is increasing as the knowledge of the protein functions and the pathways linked to its activity augments. Here we discuss relevant advances concerning the functional and structural features of GEMIN5 and its separate domains in RNA-binding, protein interactome, and translation regulation, and how these data can help to understand the involvement of protein malfunction in clinical variants found in patients developing neurodevelopmental disorders.
基金financially supported by National Natural Science Foundation of China(22466010)Guizhou Provincial Basic Research Program(Natural Science)ZK[2023]47 and key program ZD[2025]075+6 种基金Innovation and Entrepreneurship Project for overseas Talents in Guizhou Province[2022]02Specific Natural Science Foundation of Guizhou University(X202207)the national undergraduate innovation and entrepreneurship training program(gzugc2023006gzusc2024012)SRT project of Guizhou university(2023SRT0292023SRT024)supported by Shanghai Technical Service Center of Science and Engineering Computing,Shanghai University。
文摘Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(HER)and the high energy barrier in the hydrogenation step of nitrogen-containing intermediates.Here,we report a selective etching strategy to construct Ru M nanoalloys(M=Fe,Co,Ni,Cu)uniformly dispersed on porous nitrogen-doped carbon substrates for efficient neutral NH3electrosynthesis.Density functional theory calculations confirm that the synergic effect between Ru and transition metal M modulates the electronic structure of the alloy,significantly lowering the energy barrier for the conversion of*NO_(2)to*HNO_(2).Experimentally,the optimized Ru Fe-NC catalyst achieves 100%Faraday efficiency with a high yield rate of 0.83 mg h^(-1)mg^(-1)catat a low potential of-0.1 V vs.RHE,outperforming most reported catalysts.In situ spectroscopic analyses further demonstrate that the Ru M-NC effectively promotes the hydrogenation of nitrogen intermediates while inhibiting the formation of hydrogen radicals,thereby reducing HER competition.The Ru FeNC assembled Zn-NO_(3)^(-)battery achieved a high open-circuit voltage and an outstanding power density and capacity,which drive selective NO_(3)^(-)conversion to NH3.This work provides a powerful synergistic design strategy for efficient NH3electrosynthesis and a general framework for the development of advanced multi-component catalysts for sustainable nitrogen conversion.
基金the National Nature Science Foundation of China for Excellent Young Scientists Fund(32222058)Fundamental Research Foundation of CAF(CAFYBB2022QB001).
文摘Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via low-temperature coprecipitation,exhibiting excellent performance for the selective hydrogenation of 5-hydroxymethylfurfural(HMF).A linear correlation is first observed between solvent polarity(E_(T)(30))and product selectivity within both polar aprotic and protic solvent classes,suggesting that solvent properties play a vital role in directing reaction pathways.Among these,1,4-dioxane(aprotic)favors the formation of 2,5-bis(hydroxymethyl)furan(BHMF)with 97.5%selectivity,while isopropanol(iPrOH,protic)promotes 2,5-dimethylfuran production with up to 99.5%selectivity.Mechanistic investigations further reveal that beyond polarity,proton-donating ability is critical in facilitating hydrodeoxygenation.iPrOH enables a hydrogen shuttle mechanism where protons assist in hydroxyl group removal,lowering the activation barrier.In contrast,1,4-dioxane,lacking hydrogen bond donors,stabilizes BHMF and hinders further conversion.Density functional theory calculations confirm a lower activation energy in iPrOH(0.60 eV)compared to 1,4-dioxane(1.07 eV).This work offers mechanistic insights and a practical strategy for solvent-mediated control of product selectivity in biomass hydrogenation,highlighting the decisive role of solvent-catalyst-substrate interactions.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金financial support from the National Natural Science Foundation of China(Grant No.52273067,52233006)the Fundamental Research Funds for the Central Universities(Grant No.2232023A-03)+3 种基金the Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(Grant No.23SG29)the Natural Science Foundation of Shanghai(Grant No.24ZR1402400)the Shanghai Scientific and Technological Innovation Project(Grant No.24520713000)Innovation Program of Shanghai Municipal Education Commission(Grant No.2021-01-07-00-03-E00108).
文摘Radiative cooling textiles with spectrally selective surfaces offer a promising energy-efficient approach for sub-ambient cooling of outdoor objects and individuals.However,the spectrally selective mid-infrared emission of these textiles significantly hinders their efficient radiative heat exchange with self-heated objects,thereby posing a significant challenge to their versatile cooling applicability.Herein,we present a bicomponent blow spinning strategy for the production of scalable,ultra-flexible,and healable textiles featuring a tailored dual gradient in both chemical composition and fiber diameter.The gradient in the fiber diameter of this textile introduces a hierarchically porous structure across the sunlight incident area,thereby achieving a competitive solar reflectivity of 98.7%on its outer surface.Additionally,the gradient in the chemical composition of this textile contributes to the formation of Janus infrared-absorbing surfaces:The outer surface demonstrates a high mid-infrared emission,whereas the inner surface shows a broad infrared absorptivity,facilitating radiative heat exchange with underlying self-heated objects.Consequently,this textile demonstrates multi-scenario radiative cooling capabilities,enabling versatile outdoor cooling for unheated objects by 7.8℃ and self-heated objects by 13.6℃,compared to commercial sunshade fabrics.
基金the Natural Science Foundation of Yongchuan District,No.2023yc-jckx20021.
文摘BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.
基金supported by Basic Science Research Program(Priority Research Institute)through the NRF of Korea funded by the Ministry of Education(2021R1A6A1A10039823)by the Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education(2020R1A6C101B194)。
文摘Lithium-oxygen(Li-O2)batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage,utilizing ambient air as an energy source,eliminating the need for costly cathode materials,and offering the highest theoretical energy density(~3.5 k Wh kg^(-1))among discussed candidates.Contributing to the poor cycle life of currently reported Li-O_(2)cells is singlet oxygen(1O_(2))formation,inducing parasitic reactions,degrading key components,and severely deteriorating cell performance.Here,we harness the chirality-induced spin selectivity effect of chiral cobalt oxide nanosheets(Co_(3)O_(4)NSs)as cathode materials to suppress 1O_(2)in Li-O_(2)batteries for the first time.Operando photoluminescence spectroscopy reveals a 3.7-fold and 3.23-fold reduction in 1O_(2)during discharge and charge,respectively,compared to conventional carbon paperbased cells,consistent with differential electrochemical mass spectrometry results,which indicate a near-theoretical charge-to-O_(2)ratio(2.04 e-/O_(2)).Density functional theory calculations demonstrate that chirality induces a peak shift near the Fermi level,enhancing Co 3d-O 2p hybridization,stabilizing reaction intermediates,and lowering activation barriers for Li_(2)O_(2)formation and decomposition.These findings establish a new strategy for improving the stability and energy efficiency of sustainable Li-O_(2)batteries,abridging the current gap to commercialization.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by the National Natural Science Foundation of China(22175136)the State Key Laboratory of Electrical Insulation and Power Equipment(EIPE23127)the Fundamental Research Funds for the Central Universities(xtr052024009).
文摘Metal hydrides with high hydrogen density provide promising hydrogen storage paths for hydrogen transportation.However,the requirement of highly pure H_(2)for re-hydrogenation limits its wide application.Here,amorphous Al_(2)O_(3)shells(10 nm)were deposited on the surface of highly active hydrogen storage material particles(MgH_(2)-ZrTi)by atomic layer deposition to obtain MgH_(2)-ZrTi@Al_(2)O_(3),which have been demonstrated to be air stable with selective adsorption of H_(2)under a hydrogen atmosphere with different impurities(CH_(4),O_(2),N_(2),and CO_(2)).About 4.79 wt%H_(2)was adsorbed by MgH_(2)-ZrTi@10nmAl_(2)O_(3)at 75℃under 10%CH_(4)+90%H_(2)atmosphere within 3 h with no kinetic or density decay after 5 cycles(~100%capacity retention).Furthermore,about 4 wt%of H_(2)was absorbed by MgH_(2)-ZrTi@10nmAl_(2)O_(3)under 0.1%O_(2)+0.4%N_(2)+99.5%H_(2)and 0.1%CO_(2)+0.4%N_(2)+99.5%H_(2)atmospheres at 100℃within 0.5 h,respectively,demonstrating the selective hydrogen absorption of MgH_(2)-ZrTi@10nmAl_(2)O_(3)in both oxygen-containing and carbon dioxide-containing atmospheres hydrogen atmosphere.The absorption and desorption curves of MgH_(2)-ZrTi@10nmAl_(2)O_(3)with and without absorption in pure hydrogen and then in 21%O_(2)+79%N_(2)for 1 h were found to overlap,further confirming the successful shielding effect of Al_(2)O_(3)shells against O_(2)and N_(2).The MgH_(2)-ZrTi@10nmAl_(2)O_(3)has been demonstrated to be air stable and have excellent selective hydrogen absorption performance under the atmosphere with CH_(4),O_(2),N_(2),and CO_(2).
基金supported by the National Natural Science Foundation of China,Nos.92049120 and 81870897STI2030-Major Projects,No.2021ZD0204001+6 种基金Guangdong Key Project for Development of New Tools for the Diagnosis and Treatment of Autism,No.2018B030335001the Natural Science Foundation of Jiangsu Province,No.BK20181436the National Major Scientific and Technological Special Project for Significant New Drug Development,No.2019ZX09301102the Discipline Construction Program of the Second Affiliated Hospital of Soochow University,No.XKTJ-TD202003Sino-German Cooperation Mobility Programme,No.M-0679the Science and Technology Project of Suzhou,No.SKY2022161Research Project of Neurological Diseases of the Second Affiliated Hospital of Soochow University Medical Center,No.ND2023A01(all to QHM)。
文摘The endoplasmic reticulum,a key cellular organelle,regulates a wide variety of cellular activities.Endoplasmic reticulum autophagy,one of the quality control systems of the endoplasmic reticulum,plays a pivotal role in maintaining endoplasmic reticulum homeostasis by controlling endoplasmic reticulum turnover,remodeling,and proteostasis.In this review,we briefly describe the endoplasmic reticulum quality control system,and subsequently focus on the role of endoplasmic reticulum autophagy,emphasizing the spatial and temporal mechanisms underlying the regulation of endoplasmic reticulum autophagy according to cellular requirements.We also summarize the evidence relating to how defective or abnormal endoplasmic reticulum autophagy contributes to the pathogenesis of neurodegenerative diseases.In summary,this review highlights the mechanisms associated with the regulation of endoplasmic reticulum autophagy and how they influence the pathophysiology of degenerative nerve disorders.This review would help researchers to understand the roles and regulatory mechanisms of endoplasmic reticulum-phagy in neurodegenerative disorders.
基金supported by:Fondazione Telethon-Italy(No.GGP19128 to AP)Fondazione Cariplo-Italy(No.2021-1544 to RC)+14 种基金Fondazione Italiana di Ricerca per la Sclerosi Laterale Amiotrofica(AriSLA)-Italy(No.MLOpathy to APTarget-RAN to AP)Association Française contre les Myopathies-France(AFM Telethon No.23236 to AP)Kennedy’s Disease Association-USA(2018 grant to RC2020 grant to MG)Ministero dell’Universitàe della Ricerca(MIUR)-Italy(PRIN-Progetti di ricerca di interesse nazionale(No.2017F2A2C5 to APNo.2022EFLFL8 to APNo.2020PBS5MJ to VCNo.2022KSJZF5 to VC)PRIN-Progetti di ricerca di interesse nazionale-bando 2022,PNRR finanziato dall’Unione europea-Next Generation EU,componente M4C2,investimento 1.1(No.P2022B5J32 to RC and No.P20225R4Y5 to VC)CN3:RNA-Codice Proposta:CN_00000041Tematica Sviluppo di terapia genica e farmaci con tecnologia a RNA(Centro Nazionale di Ricerca-CN3 National Center for Gene Therapy and Drugs based on RNA Technology to AP)Progetto Dipartimenti di Eccellenza(to DiSFeB)Ministero della Salute,Agenzia Italiana del Farmaco(AIFA)-Italy(Co_ALS to AP)Universitàdegli Studi di Milano(piano di sviluppo della ricerca(PSR)UNIMI-linea B(to RC and BT).
文摘Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to proteotoxic stress response and in other processes such as inflammation,cell division,and migration.HSPB8 binds misfolded clients to prevent their aggregation by assisting protein refolding or degradation through chaperone-assisted selective autophagy.In line with this function,the pro-degradative activity of HSPB8 has been found protective in several neurodegenerative and neuromuscular diseases characterized by protein misfolding and aggregation.In cancer,HSPB8 has a dual role being capable of exerting either a pro-or an anti-tumoral activity depending on the pathways and factors expressed by the model of cancer under investigation.Moreover,HSPB8 exerts a protective function in different diseases by modulating the inflammatory response,which characterizes not only neurodegenerative diseases,but also other chronic or acute conditions affecting the nervous system,such as multiple sclerosis and intracerebellar hemorrhage.Of note,HSPB8 modulation may represent a therapeutic approach in other neurological conditions that develop as a secondary consequence of other diseases.This is the case of cognitive impairment related to diabetes mellitus,in which HSPB8 exerts a protective activity by assuring mitochondrial homeostasis.This review aims to summarize the diverse and multiple functions of HSPB8 in different pathological conditions,focusing on the beneficial effects of its modulation.Drug-based and alternative therapeutic approaches targeting HSPB8 and its regulated pathways will be discussed,emphasizing how new strategies for cell and tissue-specific delivery represent an avenue to advance in disease treatments.
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
