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
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
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
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
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
Clear aligner treatment is a novel technique in current orthodontic practice.Distinct from traditional fixed orthodontic appliances,clear aligners have different material features and biomechanical characteristics and...Clear aligner treatment is a novel technique in current orthodontic practice.Distinct from traditional fixed orthodontic appliances,clear aligners have different material features and biomechanical characteristics and treatment efficiencies,presenting new clinical challenges.Therefore,a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique.This expert consensus discusses case selection and grading of treatment difficulty,principle of clear aligner therapy,clinical procedures and potential complications,which are crucial to the clinical success of clear aligner treatment.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
This research aims to study the bio-adsorption process of two dyes,Cibacron Green H3G(CG-H3G)and Terasil Red(TR),in a single system and to bring them closer to the industrial textile discharge by a binary mixture of t...This research aims to study the bio-adsorption process of two dyes,Cibacron Green H3G(CG-H3G)and Terasil Red(TR),in a single system and to bring them closer to the industrial textile discharge by a binary mixture of two dyes(TR+CG-H3G).The Cockle Shell(CS)was used as a natural bio-adsorbent.The characterizations of CS were investigated by Fourier transform infrared(FTIR),X-ray diffraction(XRD),scanning electron microscopy(SEM),energy-dispersive X-ray spectroscopy(EDX)and Brunauer–Emmett–Teller(BET).The adsorption potential of Cockle Shells was tested in two cases(single and binary system)and determined by:contact time(0–60 min),bio-adsorption dose(3–15 g/L),initial concentration(10–300 mg/L),temperature(22–61°C)and pH solution(2–12).The study of bio-adsorption(equilibrium and kinetics)was conducted at 22°C.The kinetic studies demon-strated that a pseudo-second-order adsorption mechanism had a good correlation coefficient(R2≥0.999).The Langmuir isotherm modeling provided a well-defined description of TR and CG-H3G bio-adsorption on cockle shells,exhibiting maximum capacities of 29.41 and 3.69 mg/g respectively at 22°C.The thermodynamic study shows that the reaction between the TR,CG-H3G dyes molecules and the bio-adsorbent is exothermic,spontaneous in the range of 22–31°C with the aleatory character decrease at the solid-liquid interface.The study of selectivity in single and binary systems has been performed under optimal operating conditions using the industrial textile rejection pH(pH=6.04).CG-H3G dye is found to have a higher selectivity than TR in single(0–60 min)and binary systems with a range of 6–45 min,as shown by the selectivity measurement.It was discovered that CS has the capability to remove both CG-H3G and TR dyes in both simple and binary systems,making it a superior bio-adsorbent.展开更多
Nitrogen(N)enrichment has resulted in widespread alteration of grassland ecosystem processes and functions mainly through disturbance in soil enzyme activities.However,we lack a comprehensive understanding of how N de...Nitrogen(N)enrichment has resulted in widespread alteration of grassland ecosystem processes and functions mainly through disturbance in soil enzyme activities.However,we lack a comprehensive understanding of how N deposition affects specific key soil enzymes that mediate plant-soil feedback of grassland.Here,with a meta-analysis on 1446 cases from field observations in China,we show that N deposition differently affects soil enzymes associated with soil biochemical processes.Specifically,N-promoted C,N,and P-acquiring hydrolase activities significantly increased by 8.73%,7.67%,and 8.69%,respectively,related to an increase in microbial-specific enzyme secretion.The increased relative N availability and soil acidification were two potential mechanisms accounting for the changes in soil enzyme activities with N enrichment.The mixed N addition in combination of NH_(4)NO_(3) and urea showed greater stimulation effect on soil enzyme activities.However,the high rate and long-term N addition tended to weaken the positive responses of soil C-,Nand P-acquiring hydrolase activities to N enrichment.Spatially increased mean annual precipitation and temperature primarily promoted the positive effects of N enrichment on N-and P-acquiring hydrolase activities,and the stimulation of C-and N-acquiring hydrolase activities by N enrichment was intensified with the increase in soil depth.Finally,multimodal inference showed that grassland type was the most important regulator of responses of microbial C,N,and P-acquiring hydrolase activities to N enrichment.This meta-analysis provides a comprehensive insight into understanding the key role of N enrichment in shaping soil enzyme activities of grassland ecosystems.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
The potential of 2-amino-1-propanol(AP)as a novel depressant in selectively floating ilmenite from titanaugite under weakly acidic conditions was investigated.Micro-flotation results show that AP significantly reduces...The potential of 2-amino-1-propanol(AP)as a novel depressant in selectively floating ilmenite from titanaugite under weakly acidic conditions was investigated.Micro-flotation results show that AP significantly reduces the recovery of titanaugite while having no evident impact on ilmenite flotation.Subsequent bench-scale flotation tests further confirm a remarkable improvement in separation efficiency upon the introduction of AP.Contact angle and adsorption tests reveal a stronger affinity of AP towards the titanaugite surface in comparison to ilmenite.Zeta potential measurements and X-ray photoelectron spectroscopy(XPS)analyses exhibit favorable adsorption characteristics of AP on titanaugite,resulting from a synergy of electrostatic attraction and chemical interaction.In contrast,electrostatic repulsion hinders any significant interaction between AP and the ilmenite surface.These findings highlight the potential of AP as a highly efficient depressant for ilmenite flotation,paving the way for reduced reliance on sulfuric acid in the industry.展开更多
Emerging new races of wheat stem rust(Puccinia graminis f.sp.tritici)are threatening global wheat(Triticum aestivum L.)production.Host resistance is the most effective and environmentally friendly method of controllin...Emerging new races of wheat stem rust(Puccinia graminis f.sp.tritici)are threatening global wheat(Triticum aestivum L.)production.Host resistance is the most effective and environmentally friendly method of controlling stem rust.The stem rust resistance gene Sr59 was previously identified within a T2DS 2RL wheat-rye whole arm translocation,providing broad-spectrum resistance to various stem rust races.Seedling evaluation,molecular marker analysis,and cytogenetic studies identified wheat-rye introgression line#284 containing a new translocation chromosome T2BL 2BS-2RL.This line has demonstrated broad-spectrum resistance to stem rust at the seedling stage.Seedling evaluation and cytogenetic analysis of three backcross populations between the line#284 and the adapted cultivars SLU-Elite,Navruz,and Linkert confirmed that Sr59 is located within the short distal 2RL translocation.This study aimed physical mapping of Sr59 in the 2RL introgression segment and develop a robust molecular marker for marker-assisted selection.Using genotyping-by-sequencing(GBS),GBS-derived SNPs were aligned with full-length annotated rye nucleotide-binding leucine-rich repeat(NLR)genes in the parental lines CS ph1b,SLU238,SLU-Elite,Navruz,and Linkert,as well as in 33 BC4F5progeny.Four NLR genes were identified on the 2R chromosome,with Chr2R_NLR_60 being tightly linked to the Sr59resistance gene.In-silico functional enrichment analysis of the translocated 2RL region(25,681,915 bp)identified 223 genes,with seven candidate genes associated with plant disease resistance and three linked to agronomic performance,contributing to oxidative stress response,protein kinase activity,and cellular homeostasis.These findings facilitate a better understanding of the genetic basis of stem rust resistance provided by Sr59.展开更多
Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the el...Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the electron distribution,thereby increasing the catalytic activity.However,this focus often overshadows an equally essential aspect of HAOPs:the adsorption effect.Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts.The efficacy of these interactions is influenced by a variety of physicochemical properties,including surface chemistry and pore sizes,which determine the affinities between contaminants and material surfaces.This dispar ity in affinity is pivotal because it underpins the selective removal of contaminants,especially in complex waste streams containing diverse contaminants and competing matrices.Consequently,understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving pro cess efficiency but also for enhancing the selectivity of contaminant removal.Herein,we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs.Such interactions dictate the overall performance of the treatment processes by balancing the adsorption,reaction,and desorption rates on the catalyst surfaces.Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practical ity in water treatment and wastewater decontamination.Overall,we propose that revisiting adsorption driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.展开更多
The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly re...The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.展开更多
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%.
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,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Clear aligner treatment is a novel technique in current orthodontic practice.Distinct from traditional fixed orthodontic appliances,clear aligners have different material features and biomechanical characteristics and treatment efficiencies,presenting new clinical challenges.Therefore,a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique.This expert consensus discusses case selection and grading of treatment difficulty,principle of clear aligner therapy,clinical procedures and potential complications,which are crucial to the clinical success of clear aligner treatment.
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.
基金supported by the University Salah Boubnider-Constantine 3 (Algeria).
文摘This research aims to study the bio-adsorption process of two dyes,Cibacron Green H3G(CG-H3G)and Terasil Red(TR),in a single system and to bring them closer to the industrial textile discharge by a binary mixture of two dyes(TR+CG-H3G).The Cockle Shell(CS)was used as a natural bio-adsorbent.The characterizations of CS were investigated by Fourier transform infrared(FTIR),X-ray diffraction(XRD),scanning electron microscopy(SEM),energy-dispersive X-ray spectroscopy(EDX)and Brunauer–Emmett–Teller(BET).The adsorption potential of Cockle Shells was tested in two cases(single and binary system)and determined by:contact time(0–60 min),bio-adsorption dose(3–15 g/L),initial concentration(10–300 mg/L),temperature(22–61°C)and pH solution(2–12).The study of bio-adsorption(equilibrium and kinetics)was conducted at 22°C.The kinetic studies demon-strated that a pseudo-second-order adsorption mechanism had a good correlation coefficient(R2≥0.999).The Langmuir isotherm modeling provided a well-defined description of TR and CG-H3G bio-adsorption on cockle shells,exhibiting maximum capacities of 29.41 and 3.69 mg/g respectively at 22°C.The thermodynamic study shows that the reaction between the TR,CG-H3G dyes molecules and the bio-adsorbent is exothermic,spontaneous in the range of 22–31°C with the aleatory character decrease at the solid-liquid interface.The study of selectivity in single and binary systems has been performed under optimal operating conditions using the industrial textile rejection pH(pH=6.04).CG-H3G dye is found to have a higher selectivity than TR in single(0–60 min)and binary systems with a range of 6–45 min,as shown by the selectivity measurement.It was discovered that CS has the capability to remove both CG-H3G and TR dyes in both simple and binary systems,making it a superior bio-adsorbent.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28110300)National Natural Science Foundation of China(No.U23A2004)+3 种基金Natural Science Foundation of Jilin Province,China(No.YDZJ202201ZYTS522)Science and Technology Cooperation Program between Jilin Province and Chinese Academy of Sciences(No.2023SYHZ0053)Innovation Team Program of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.2023CXTD02)the European Commission under Marie Sk?odowska-Curie(No.101034371)。
文摘Nitrogen(N)enrichment has resulted in widespread alteration of grassland ecosystem processes and functions mainly through disturbance in soil enzyme activities.However,we lack a comprehensive understanding of how N deposition affects specific key soil enzymes that mediate plant-soil feedback of grassland.Here,with a meta-analysis on 1446 cases from field observations in China,we show that N deposition differently affects soil enzymes associated with soil biochemical processes.Specifically,N-promoted C,N,and P-acquiring hydrolase activities significantly increased by 8.73%,7.67%,and 8.69%,respectively,related to an increase in microbial-specific enzyme secretion.The increased relative N availability and soil acidification were two potential mechanisms accounting for the changes in soil enzyme activities with N enrichment.The mixed N addition in combination of NH_(4)NO_(3) and urea showed greater stimulation effect on soil enzyme activities.However,the high rate and long-term N addition tended to weaken the positive responses of soil C-,Nand P-acquiring hydrolase activities to N enrichment.Spatially increased mean annual precipitation and temperature primarily promoted the positive effects of N enrichment on N-and P-acquiring hydrolase activities,and the stimulation of C-and N-acquiring hydrolase activities by N enrichment was intensified with the increase in soil depth.Finally,multimodal inference showed that grassland type was the most important regulator of responses of microbial C,N,and P-acquiring hydrolase activities to N enrichment.This meta-analysis provides a comprehensive insight into understanding the key role of N enrichment in shaping soil enzyme activities of grassland ecosystems.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
基金supported by the National Key Research and Development Program of China(No.2019YFC1803501)the National Natural Science Foundation of China(No.52074357)+2 种基金the Natural Science Foundation of Hunan Province,China(No.2022JJ30713)the Vanadium Titanium Union Foundationthe Project of Technology Innovation Center for Comprehensive Utilization of Strategic Mineral Resources,Ministry of Natural Resources,China。
文摘The potential of 2-amino-1-propanol(AP)as a novel depressant in selectively floating ilmenite from titanaugite under weakly acidic conditions was investigated.Micro-flotation results show that AP significantly reduces the recovery of titanaugite while having no evident impact on ilmenite flotation.Subsequent bench-scale flotation tests further confirm a remarkable improvement in separation efficiency upon the introduction of AP.Contact angle and adsorption tests reveal a stronger affinity of AP towards the titanaugite surface in comparison to ilmenite.Zeta potential measurements and X-ray photoelectron spectroscopy(XPS)analyses exhibit favorable adsorption characteristics of AP on titanaugite,resulting from a synergy of electrostatic attraction and chemical interaction.In contrast,electrostatic repulsion hinders any significant interaction between AP and the ilmenite surface.These findings highlight the potential of AP as a highly efficient depressant for ilmenite flotation,paving the way for reduced reliance on sulfuric acid in the industry.
基金the financial support from FORMAS(2018-01029)the Swedish Institute(01132-2022)for supporting Ivan Motsnyi’s visit and research at Swedish University of Agricultural Sciences。
文摘Emerging new races of wheat stem rust(Puccinia graminis f.sp.tritici)are threatening global wheat(Triticum aestivum L.)production.Host resistance is the most effective and environmentally friendly method of controlling stem rust.The stem rust resistance gene Sr59 was previously identified within a T2DS 2RL wheat-rye whole arm translocation,providing broad-spectrum resistance to various stem rust races.Seedling evaluation,molecular marker analysis,and cytogenetic studies identified wheat-rye introgression line#284 containing a new translocation chromosome T2BL 2BS-2RL.This line has demonstrated broad-spectrum resistance to stem rust at the seedling stage.Seedling evaluation and cytogenetic analysis of three backcross populations between the line#284 and the adapted cultivars SLU-Elite,Navruz,and Linkert confirmed that Sr59 is located within the short distal 2RL translocation.This study aimed physical mapping of Sr59 in the 2RL introgression segment and develop a robust molecular marker for marker-assisted selection.Using genotyping-by-sequencing(GBS),GBS-derived SNPs were aligned with full-length annotated rye nucleotide-binding leucine-rich repeat(NLR)genes in the parental lines CS ph1b,SLU238,SLU-Elite,Navruz,and Linkert,as well as in 33 BC4F5progeny.Four NLR genes were identified on the 2R chromosome,with Chr2R_NLR_60 being tightly linked to the Sr59resistance gene.In-silico functional enrichment analysis of the translocated 2RL region(25,681,915 bp)identified 223 genes,with seven candidate genes associated with plant disease resistance and three linked to agronomic performance,contributing to oxidative stress response,protein kinase activity,and cellular homeostasis.These findings facilitate a better understanding of the genetic basis of stem rust resistance provided by Sr59.
基金supported by the National Key Research and Development Program of China(2022YFC3205300)the National Natural Science Foundation of China(22176124).
文摘Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the electron distribution,thereby increasing the catalytic activity.However,this focus often overshadows an equally essential aspect of HAOPs:the adsorption effect.Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts.The efficacy of these interactions is influenced by a variety of physicochemical properties,including surface chemistry and pore sizes,which determine the affinities between contaminants and material surfaces.This dispar ity in affinity is pivotal because it underpins the selective removal of contaminants,especially in complex waste streams containing diverse contaminants and competing matrices.Consequently,understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving pro cess efficiency but also for enhancing the selectivity of contaminant removal.Herein,we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs.Such interactions dictate the overall performance of the treatment processes by balancing the adsorption,reaction,and desorption rates on the catalyst surfaces.Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practical ity in water treatment and wastewater decontamination.Overall,we propose that revisiting adsorption driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.
基金supported by the National Natural Science Foundation of China(32160782 and 32060737).
文摘The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.
