Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).Ho...Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).展开更多
Chitosan(CS),a natural polymer derived from chitin found in the exoskeletons of crustaceans,has garnered significant interest in the pharmaceutical field due to its unique properties,including biocompatibility and bio...Chitosan(CS),a natural polymer derived from chitin found in the exoskeletons of crustaceans,has garnered significant interest in the pharmaceutical field due to its unique properties,including biocompatibility and biodegradability.In recent years,various studies have reported that CS can affect drug bioavailability,and interestingly,it works as an oral absorption enhancer and inhibitor.This review offers an in-depth analysis of the mechanisms underlying such a phenomenon and supports its application as a pharmaceutical excipient.CS enhances oral drug absorption through various mechanisms,such as interaction with the intestinal mucosa,tight junction modulation,inhibition of efflux transporters,enzyme inhibition,solubility and stability enhancement,and complexation.On the other side,CS exhibits the ability to inhibit the absorption of certain drugs by adsorbing to lipids and sterols,modulating bile acids and gut microbiota,altering drug-cell interaction at the polar interface,and mucus-mediated entrapment and interference.Future potential pharmaceutical research in this field includes elucidating the underneath absorption relevant mechanisms,rational use in formulations as excipient,exploring functional CS derivatives,and developing CS-based drug delivery systems.This comprehensive review highlights CS's versatile and significant role in enhancing and inhibiting oral drug absorption,providing insights into the complexities of drug delivery and the potential of CS to improve therapeutic outcomes.展开更多
Background:Long non-coding RNAs(lncRNAs)act as epigenetic regulators for tumor hallmarks.This investigation sought to probe the carcinogenic trait of PAN3-AS1 across pan-cancer comprehensively.Methods:We studied the d...Background:Long non-coding RNAs(lncRNAs)act as epigenetic regulators for tumor hallmarks.This investigation sought to probe the carcinogenic trait of PAN3-AS1 across pan-cancer comprehensively.Methods:We studied the diagnostic and prognostic features and the immune landscape of PAN3-AS1 across pan-cancer by bioinformatics approaches.The hierarchical regulatory networks governing PAN3-AS1 expression in colon cancer were explored via chromatin immunoprecipitation,luciferase activity assays,and RNA immunoprecipitation,etc.We screened drugs sensitive to WAP four-disulfide core domain 13(WFDC13)by virtual screening and molecular docking.Results:Single-cell transcriptomics demonstrated that a variety of immune populations abnormally expressed PAN3-AS1 beyond tumor cells.Integration of data from multiple databases revealed that PAN3-AS1 was highly expressed and associated with a bad prognosis in various malignancies.Notably,PAN3-AS1 expression was correlated with a suppressive immune microenvironment.Moreover,we observed poor immunotherapy efficacy when PAN3-AS1 was highly expressed in melanoma.In vitro assays and functional enrichment analysis revealed that PAN3-AS1 was associated with cell proliferation and the immune response in colon cancer.Our experiments confirmed that PAN3-AS1 facilitated WFDC13 expression through competitive binding to hsa-miR-423-5p in colon cancer.Moreover,the present paper illustrated that enhancer activity exerts an important modulatory ability for PAN3-AS1 expression.Conclusion:In short,PAN3-AS1 is a valuable biomarker for diagnosis and prognosis.PAN3-AS1 exhibits linkage to a cold tumor immune microenvironment(TME)and forecasts durable benefit from immunotherapy.Addressing the PAN3-AS1/miR-423-5p/WFDC13 axis might provide a novel option for improving immunotherapy efficacy in colon cancer.展开更多
Mango(Mangifera indica L.)is one of the main economic crops in Hainan,China,prized for its distinctive flavor and high nutritional value.It is also rich in health-promoting antioxidants such as vitamin C and flavonoid...Mango(Mangifera indica L.)is one of the main economic crops in Hainan,China,prized for its distinctive flavor and high nutritional value.It is also rich in health-promoting antioxidants such as vitamin C and flavonoids.Enhanced ultraviolet-B(UV—B)radiation,a growing global environmental concern,alters plant antioxidant systems,with increased flavonoid accumulation as a common adaptive response.However,its effects on mango fruit remain largely unexplored.To investigate the antioxidant responses of mango to enhanced UV-B radiation and identify key responsive flavonoid compounds and regulatory genes,we exposed‘Tainong 1’mango fruits growing under natural light to 96 kJ·m^(-2)·d^(-1)of UV-B radiation to simulate high UV-B conditions.Treated fruits were smaller in size and had a pulp of a more intense yellow colour.Further,malondialdehyde content in treated fruits was higher during the phase of rapid fruit enlargement.Additionally,treated fruits showed increased sugar-acid ratios,total phenol,total flavonoid,carotenoid,and ascorbic acid contents.Furthermore,they showed significantly enhanced antioxidant activity,as measured by the FRAP,ABTS,and DPPH assays.Extensive targeted metabolomic-analysis identified flavonoids as the largest category of compounds differentially expressed in treated and control groups.Quantitative metabolomics of flavonoids identified hyperoside,quercimeritrin,and(-)-catechin gallate as the key flavonoid metabolites responsive to UV-B treatment.Transcriptome analysis revealed an enrichment of the flavonoid biosynthesis pathway,with most associated differentially expressed genes showing upregulation.Furthermore,qRT-PCR analysis confirmed that the expression of the genes MiCHS7,MiCHI1,MiCHI2,MiFLS,MiF3H2,and MiF3H3 correlated with changes in key flavonoid metabolites.Indeed,correlation analysis indicated that MiCHS7,MiCHI1,MiFLS,and MiF3H3 are potential key genes involved in flavonoid accumulation under UV-B treatment.Thus,our study provides a theoretical basis for breeding for new resilient varieties and developing UV-B-resistant mango cultivation techniques.展开更多
It is difficult to recover chrysocolla from sulfidation flotation which is closely related to the mineral surface composition.In this study,the effects of fluoride roasting on the surface composition of chrysocolla we...It is difficult to recover chrysocolla from sulfidation flotation which is closely related to the mineral surface composition.In this study,the effects of fluoride roasting on the surface composition of chrysocolla were investigated,its impact on sulfidation flotation was explored,and the mechanisms involved in both fluoride roasting and sulfidation flotation were discussed.With CaF_(2)as the roasting reagent,Na_(2)S·9H_(2)O as the sulfidation reagent,and sodium butyl xanthate(NaBX)as the collector,the results of the flotation experiments showed that fluoride roasting improved the floatability of chrysocolla,and the recovery rate increased from 16.87%to 82.74%.X-ray diffraction analysis revealed that after fluoride roasting,approximately all the Cu on the chrysocolla surface was exposed in the form of CuO,which could provide a basis for subsequent sulfidation flotation.The microscopy and elemental analyses revealed that large quantities of"pagoda-like"grains were observed on the sulfidation surface of the fluoride-roasted chrysocolla,indicating high crystallinity particles of copper sulfide.This suggests that the effect of sulfide formation on the chrysocolla surface was more pronounced.X-ray photoelectron spectroscopy revealed that fluoride roasting increased the relative contents of sulfur and copper on the surface and that both the Cu~+and polysulfide fractions on the surface of the minerals increased.This enhances the effect of sulfidation,which is conducive to flotation recovery.Therefore,fluoride roasting improved the effect of copper species transformation and sulfidation on the surface of chysocolla,promoted the adsorption of collectors,and improved the recovery of chrysocolla from sulfidation flotation.展开更多
Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspectio...Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspection,which requires extensive experience,suffers from low efficiency,and is highly subjective.As the results are presented as radar images,image processing methods can be applied for fast and objective identification.Deep learning-based approaches now offer a robust solution for automated roadbed disease detection.This study proposes an enhanced Faster Region-based Convolutional Neural Networks(R-CNN)framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation(2D-DFT)for frequency-domain feature fusion.A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering,histogram equalization,and binarization.The proposed model segments defect regions,applies binary masking,and fuses frequency-domain features to improve small-target detection under noisy backgrounds.Experimental results show that the improved Faster R-CNN achieves a mean Average Precision(mAP)of 0.92,representing a 0.22 increase over the baseline.Precision improved by 26%while recall remained stable at 87%.The model was further validated on real urban road data,demonstrating robust detection capability even under interference.These findings highlight the potential of combining GPR with deep learning for efficient,non-destructive roadbed health monitoring.展开更多
Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional ...Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at https://github.com/CHEN-hJ516/RetinexWT(accessed on 14 October 2025).展开更多
Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods,this paper proposes the IPKE-MoE framework,which consists of three parts,namely,a sensitive w...Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods,this paper proposes the IPKE-MoE framework,which consists of three parts,namely,a sensitive word variant extraction framework,a sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer.First,sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models(LLMs).Next,the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models.Specifically,after locating variants via n-gram algorithms,variant types are mapped to embedding vectors and fused with original word vectors.Finally,a mixture-of-experts(MoE)classification layer is designed(sensitive word,sentiment,and semantic experts),which decouples the relationship between sensitiveword existence and text toxicity throughmultiple experts.This framework effectively combines the comprehension ability of Large Language Models(LLMs)with the discriminative ability of smaller models.Our two experiments demonstrate that the sensitive word variant extraction framework based on dynamically iterated prompt templates outperforms other baseline prompt templates.TheRoCBert models incorporating the sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer achieve superior classification performance compared to other baselines.展开更多
Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater imag...Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.展开更多
BACKGROUND Gastrointestinal(GI)tumors are among the most prevalent malignancies,and surgical intervention remains a primary treatment modality.However,the complexity of GI surgery often leads to prolonged recovery and...BACKGROUND Gastrointestinal(GI)tumors are among the most prevalent malignancies,and surgical intervention remains a primary treatment modality.However,the complexity of GI surgery often leads to prolonged recovery and high postoperative complication rates,which threaten patient safety and functional outcomes.Enhanced recovery after surgery(ERAS)principles have been shown to improve perioperative outcomes through evidence-based,multidisciplinary care pathways.Despite its widespread adoption,there is a paucity of research focusing specifically on optimizing ERAS-guided nursing processes in the post-anesthesia care unit(PACU)and evaluating its impact on perioperative safety in patients undergoing GI tumor surgery.This study aimed to investigate whether an ERASbased PACU nursing protocol could enhance recovery,reduce complications,and improve patient safety in this surgical population.AIM To explore the impact of optimizing the recovery room nursing process based on ERAS on the perioperative safety of patients with GI tumors.METHODS A total of 260 patients with GI tumors who underwent elective surgeries under general anesthesia in our hospital from August 2023 to August 2025 and were then observed in the recovery unit(PACU)were selected.They were randomly divided into the observation group(the PACU nursing process was optimized based on ERAS)and the control group(the conventional PACU nursing process was adopted)by the random number grouping method,with 130 cases in each group.The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,time of leaving the room after tube removal,retention time in the recovery room,occurrence of complications,satisfaction and readmission rate were compared between the two groups after entering the room.Compare the occurrence of adverse events in the PACU nursing process between the two groups.RESULTS The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,retention time in the recovery room,total incidence of complications and readmission rate in the observation group were significantly lower than those in the control group,and the satisfaction rate was higher than that in the control group(P<0.05).The occurrence of adverse events in the PACU nursing process in the observation group was lower than that in the control group(P<0.05).CONCLUSION Optimizing the PACU nursing process based on ERAS can effectively accelerate the recovery process of patients undergoing GI tumor surgery,reduce adverse events,improve nursing satisfaction,and at the same time,lower the incidence of adverse events in the PACU nursing process,providing a more refined management basis for clinical practice.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
This study integrates explicit input enhancement into comparative continuation writing,defined as a task in which learners produce a continuation by comparing their own expression with an input text,aligning with its ...This study integrates explicit input enhancement into comparative continuation writing,defined as a task in which learners produce a continuation by comparing their own expression with an input text,aligning with its discourse structure and linguistic features,while developing their own ideas.It aims to examine whether English as a Foreign Language(EFL)learners in China exhibit differences in discourse competence and writing performance when completing comparative continuation writing combined with different input enhancement techniques,and whether the alignment effect occurs at the discourse level.Sixty first-year Chinese senior middle school students were divided into four groups:three groups engaged in comparative continuation writing with varying input enhancement,achieved by combining different techniques,while a control group performed a designated-topic writing task.The results revealed that three comparative continuation writing groups outperformed the designated-topic writing group in discourse competence,particularly in the use of temporal connectives.However,differences and some inconsistencies were observed among the comparative continuation writing groups across individual indices.The study highlights effective ways to incorporate comparative continuation writing into English instruction and demonstrates how explicit input enhancement can complement the task,simultaneously activating the alignment effect proposed by the xu-argument and enhancing discourse competence in writing.展开更多
MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO...MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO_(x)-CeO_(2)catalyst that achieves enhanced NO conversion rate and stability under harsh conditions.The catalyst was synthesized by decorating MnOx crystals with amorphous CeO_(2),followed by loading hydrophobic silica on the external surfaces.The hydrophobic silica allowed the adsorption of NH_(3)and NO and diffusion of H,suppressed the adsorption of H_(2)O,and prevented SO_(2)interaction with the Mn active sites,achieving selective molecular discrimination at the catalyst surface.At 120℃,under H_(2)O and SO_(2)exposure,the optimal hydrophobic catalyst maintains 82%NO conversion rate compared with 69%for the unmodified catalyst.The average adsorption energies of NH_(3),H_(2)O,and SO_(2)decreased by 0.05,0.43,and 0.52 eV,respectively.The NO reduction pathway follows the Eley-Rideal mechanism,NH_(3)^(*)+*→NH_(2)^(*)+H^(*)followed by NH_(2)^(*)+NO^(*)→N_(2)^(*)+H_(2)O^(*),with NH_(3)dehydrogenation being the rate determining step.Hydrophobic modification increased the activation energy for H atom transfer,leading to a minor decrease in the NO conversion rate at 120℃.This work demonstrates a viable strategy for developing robust NH_(3)-S CR catalysts capable of efficient operation in water-and sulfur-rich environments.展开更多
To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM...To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.展开更多
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
Objectives:The eukaryotic initiation factor 4F(eIF4F)translation initiation complex inhibitors(eIF4Fi)were recently found to hyperactivate extracellular signal-regulated kinases 1/2(ERK1/2)signals,which contribute to ...Objectives:The eukaryotic initiation factor 4F(eIF4F)translation initiation complex inhibitors(eIF4Fi)were recently found to hyperactivate extracellular signal-regulated kinases 1/2(ERK1/2)signals,which contribute to acquired resistance to BRAF(B-Raf proto-oncogene,serine/threonine kinase)inhibitors in melanoma.This present study aims to elucidate how to overcome the resistance of the eIF4Fi in BRAFV600E mutant melanoma cells and explore the underlying mechanisms.Methods:Melanoma A375(vemurafenib[VEM]-sensitive)and A375R(VEM-resistant)cells were exposed to eIF4Fi RocA at varying doses and durations in vitro.We investigated the impact of RocA on the activity of ERK1/2,AKT serine/threonine kinase 1(AKT1),eIF4E,and enhancer of zeste homolog 2(EZH2).We then examined the impact of RocA on pro-apoptotic BH3-only proteins and proliferative proteins.We subsequently determined the effect of combined eIF4Fi,AKT1 inhibitor,EZH2 inhibitor or VEM on tumor growth in vitro and in vivo.Results:RocA inhibited proliferation and induced apoptosis in A375 cells,but inhibited proliferation in A375R cells.RocA rapidly reactivated ERK1/2 at 3 h and returned to baseline levels at 48 h.However,eIF4E and AKT1 activation began at 12 h and peaked at 48 h.ERK1/2 positively regulated EZH2 and EZH2-dependent expression of c-Fos and EGR1,while AKT1 negatively regulated c-Myc,c-Jun,and BMF,but positively regulated eIF4E.RocA downregulated ERK1/2(or EZH2,AKT1,and eIF4E)independent bcl-2 and Mcl-1 expression.AKT1i enhanced RocA-induced cell apoptosis,while EZH2i reduced RocA-induced cell proliferation.Combined CR-1-31-B,EZH2i,and AKT1i effectively overcame resistance to RocA and VEM resistance both in vitro and in vivo.Conclusion:The eIF4F complex inhibitor reactivates ERK1/2-EZH2 and AKT1 signaling pathways,resulting in resistance to both eIF4Fi and VEM.Combined administration of an eIF4Fi with EZH2 and AKT1 inhibitors effectively enhances sensitivity to both eIF4F complex and BRAF inhibitors.展开更多
Metal-organic framework materials exhibit considerable potential as molecularly selective surfaceenhanced Raman spectroscopy(SERS)substrates because of their microporous structures,which enrich small molecules while e...Metal-organic framework materials exhibit considerable potential as molecularly selective surfaceenhanced Raman spectroscopy(SERS)substrates because of their microporous structures,which enrich small molecules while excluding larger ones.In this study,we develop a template-assisted chemical-etching strategy to prepare layered tuneable SERS substrates based on hierarchical porous zeolitic imidazolate framework-67(HP-ZIF-67)with a rhombic dodecahedral structure.The synergistic SERS enhancement mechanisms of HP-ZIF-67,which combine electromagnetic(EM)and chemical(CM)effects,were systematically studied through numerical simulations and experiments.Calculations revealed that under 633-nm laser excitation,the contributions of the EM and CM effects to the total SERS enhancement factor of HP-ZIF-67 were 60%and 40%,respectively.The hierarchical porous structure enhanced the fluid-flow flux over the microporous ZIF-67 because the increased pore radius reduced the viscous resistance and facilitated rapid molecular transport through the interconnected macro-meso-channels.Precise modulation of the CM and EM effects,combined with enhanced mass transfer,facilitated the development of HP-ZIF-67and HP-ZIF-67@Au as efficient SERS sensors.An investigation of the relationship between pore-size distribution and EM effects revealed the pivotal role of light confinement by whispering-gallery-mode microcavities in enhancing the SERS performance.The optimised HPZIF-67@Au composites functioned as flexible and highly sensitive in situ SERS sensors for gases and liquids,including volatile organic-compound gas and liquid-pesticide residues.This study introduces a novel design concept and provides a robust theoretical foundation for the future development of exhaled-breath point-of-care diagnostic devices and sweat-based wearable biomedical sensors.展开更多
基金supported by the Jilin Science and Technology Development Program,China (20240602032RC)the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZD001)+1 种基金the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZY012)the Jilin Province Development and Reform Commission-Project for Improving the Independent Innovation Capacity of Major Grain Crops,China (2024C002)。
文摘Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).
基金financially supported by National Key Research and Development Program of China (No.2021YFD1800900)National Natural Science Foundation of China (No.82073790)+2 种基金Special Fund for Youth Team of Southwest University (No.SWUXJLJ202306)Chongqing Science and Technology Commission (Nos.CSTB2022TIAD-LUX0001,CSTB2023NSCQ-JQX0002)Innovation Research 2035 Pilot Plan of Southwest University (No.SWUXDPY22007)。
文摘Chitosan(CS),a natural polymer derived from chitin found in the exoskeletons of crustaceans,has garnered significant interest in the pharmaceutical field due to its unique properties,including biocompatibility and biodegradability.In recent years,various studies have reported that CS can affect drug bioavailability,and interestingly,it works as an oral absorption enhancer and inhibitor.This review offers an in-depth analysis of the mechanisms underlying such a phenomenon and supports its application as a pharmaceutical excipient.CS enhances oral drug absorption through various mechanisms,such as interaction with the intestinal mucosa,tight junction modulation,inhibition of efflux transporters,enzyme inhibition,solubility and stability enhancement,and complexation.On the other side,CS exhibits the ability to inhibit the absorption of certain drugs by adsorbing to lipids and sterols,modulating bile acids and gut microbiota,altering drug-cell interaction at the polar interface,and mucus-mediated entrapment and interference.Future potential pharmaceutical research in this field includes elucidating the underneath absorption relevant mechanisms,rational use in formulations as excipient,exploring functional CS derivatives,and developing CS-based drug delivery systems.This comprehensive review highlights CS's versatile and significant role in enhancing and inhibiting oral drug absorption,providing insights into the complexities of drug delivery and the potential of CS to improve therapeutic outcomes.
基金supported by the Shandong Province Medical and Health Technology Project(202303111442).
文摘Background:Long non-coding RNAs(lncRNAs)act as epigenetic regulators for tumor hallmarks.This investigation sought to probe the carcinogenic trait of PAN3-AS1 across pan-cancer comprehensively.Methods:We studied the diagnostic and prognostic features and the immune landscape of PAN3-AS1 across pan-cancer by bioinformatics approaches.The hierarchical regulatory networks governing PAN3-AS1 expression in colon cancer were explored via chromatin immunoprecipitation,luciferase activity assays,and RNA immunoprecipitation,etc.We screened drugs sensitive to WAP four-disulfide core domain 13(WFDC13)by virtual screening and molecular docking.Results:Single-cell transcriptomics demonstrated that a variety of immune populations abnormally expressed PAN3-AS1 beyond tumor cells.Integration of data from multiple databases revealed that PAN3-AS1 was highly expressed and associated with a bad prognosis in various malignancies.Notably,PAN3-AS1 expression was correlated with a suppressive immune microenvironment.Moreover,we observed poor immunotherapy efficacy when PAN3-AS1 was highly expressed in melanoma.In vitro assays and functional enrichment analysis revealed that PAN3-AS1 was associated with cell proliferation and the immune response in colon cancer.Our experiments confirmed that PAN3-AS1 facilitated WFDC13 expression through competitive binding to hsa-miR-423-5p in colon cancer.Moreover,the present paper illustrated that enhancer activity exerts an important modulatory ability for PAN3-AS1 expression.Conclusion:In short,PAN3-AS1 is a valuable biomarker for diagnosis and prognosis.PAN3-AS1 exhibits linkage to a cold tumor immune microenvironment(TME)and forecasts durable benefit from immunotherapy.Addressing the PAN3-AS1/miR-423-5p/WFDC13 axis might provide a novel option for improving immunotherapy efficacy in colon cancer.
基金financially supported by the National Natural Science Foundation of China(Grant No.32160677)the Hainan University Mango Research System.
文摘Mango(Mangifera indica L.)is one of the main economic crops in Hainan,China,prized for its distinctive flavor and high nutritional value.It is also rich in health-promoting antioxidants such as vitamin C and flavonoids.Enhanced ultraviolet-B(UV—B)radiation,a growing global environmental concern,alters plant antioxidant systems,with increased flavonoid accumulation as a common adaptive response.However,its effects on mango fruit remain largely unexplored.To investigate the antioxidant responses of mango to enhanced UV-B radiation and identify key responsive flavonoid compounds and regulatory genes,we exposed‘Tainong 1’mango fruits growing under natural light to 96 kJ·m^(-2)·d^(-1)of UV-B radiation to simulate high UV-B conditions.Treated fruits were smaller in size and had a pulp of a more intense yellow colour.Further,malondialdehyde content in treated fruits was higher during the phase of rapid fruit enlargement.Additionally,treated fruits showed increased sugar-acid ratios,total phenol,total flavonoid,carotenoid,and ascorbic acid contents.Furthermore,they showed significantly enhanced antioxidant activity,as measured by the FRAP,ABTS,and DPPH assays.Extensive targeted metabolomic-analysis identified flavonoids as the largest category of compounds differentially expressed in treated and control groups.Quantitative metabolomics of flavonoids identified hyperoside,quercimeritrin,and(-)-catechin gallate as the key flavonoid metabolites responsive to UV-B treatment.Transcriptome analysis revealed an enrichment of the flavonoid biosynthesis pathway,with most associated differentially expressed genes showing upregulation.Furthermore,qRT-PCR analysis confirmed that the expression of the genes MiCHS7,MiCHI1,MiCHI2,MiFLS,MiF3H2,and MiF3H3 correlated with changes in key flavonoid metabolites.Indeed,correlation analysis indicated that MiCHS7,MiCHI1,MiFLS,and MiF3H3 are potential key genes involved in flavonoid accumulation under UV-B treatment.Thus,our study provides a theoretical basis for breeding for new resilient varieties and developing UV-B-resistant mango cultivation techniques.
基金financially supported by the National Natural Science Foundation of China(No.52374259)the Open Fund of the State Key Laboratory of Mineral Processing Science and Technology,China(No.BGRIMM-KJSKL-2023-11)the Major Science and Technology Projects in Yunnan Province,China(No.202302 AF080004)。
文摘It is difficult to recover chrysocolla from sulfidation flotation which is closely related to the mineral surface composition.In this study,the effects of fluoride roasting on the surface composition of chrysocolla were investigated,its impact on sulfidation flotation was explored,and the mechanisms involved in both fluoride roasting and sulfidation flotation were discussed.With CaF_(2)as the roasting reagent,Na_(2)S·9H_(2)O as the sulfidation reagent,and sodium butyl xanthate(NaBX)as the collector,the results of the flotation experiments showed that fluoride roasting improved the floatability of chrysocolla,and the recovery rate increased from 16.87%to 82.74%.X-ray diffraction analysis revealed that after fluoride roasting,approximately all the Cu on the chrysocolla surface was exposed in the form of CuO,which could provide a basis for subsequent sulfidation flotation.The microscopy and elemental analyses revealed that large quantities of"pagoda-like"grains were observed on the sulfidation surface of the fluoride-roasted chrysocolla,indicating high crystallinity particles of copper sulfide.This suggests that the effect of sulfide formation on the chrysocolla surface was more pronounced.X-ray photoelectron spectroscopy revealed that fluoride roasting increased the relative contents of sulfur and copper on the surface and that both the Cu~+and polysulfide fractions on the surface of the minerals increased.This enhances the effect of sulfidation,which is conducive to flotation recovery.Therefore,fluoride roasting improved the effect of copper species transformation and sulfidation on the surface of chysocolla,promoted the adsorption of collectors,and improved the recovery of chrysocolla from sulfidation flotation.
基金supported by the Second Batch of Key Textbook Construction Projects of“14th Five-Year Plan”of Zhejiang Vocational Colleges(SZDJC-2412).
文摘Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspection,which requires extensive experience,suffers from low efficiency,and is highly subjective.As the results are presented as radar images,image processing methods can be applied for fast and objective identification.Deep learning-based approaches now offer a robust solution for automated roadbed disease detection.This study proposes an enhanced Faster Region-based Convolutional Neural Networks(R-CNN)framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation(2D-DFT)for frequency-domain feature fusion.A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering,histogram equalization,and binarization.The proposed model segments defect regions,applies binary masking,and fuses frequency-domain features to improve small-target detection under noisy backgrounds.Experimental results show that the improved Faster R-CNN achieves a mean Average Precision(mAP)of 0.92,representing a 0.22 increase over the baseline.Precision improved by 26%while recall remained stable at 87%.The model was further validated on real urban road data,demonstrating robust detection capability even under interference.These findings highlight the potential of combining GPR with deep learning for efficient,non-destructive roadbed health monitoring.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at https://github.com/CHEN-hJ516/RetinexWT(accessed on 14 October 2025).
基金funded by the National Natural Science Foundation of China(Grant No.62441212)the Major Project of the Natural Science Foundation of Inner Mongolia(Grant No.2025ZD008).
文摘Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods,this paper proposes the IPKE-MoE framework,which consists of three parts,namely,a sensitive word variant extraction framework,a sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer.First,sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models(LLMs).Next,the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models.Specifically,after locating variants via n-gram algorithms,variant types are mapped to embedding vectors and fused with original word vectors.Finally,a mixture-of-experts(MoE)classification layer is designed(sensitive word,sentiment,and semantic experts),which decouples the relationship between sensitiveword existence and text toxicity throughmultiple experts.This framework effectively combines the comprehension ability of Large Language Models(LLMs)with the discriminative ability of smaller models.Our two experiments demonstrate that the sensitive word variant extraction framework based on dynamically iterated prompt templates outperforms other baseline prompt templates.TheRoCBert models incorporating the sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer achieve superior classification performance compared to other baselines.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.
基金Supported by 2025 Henan Medical Education Research Project,No.WJLX2025038.
文摘BACKGROUND Gastrointestinal(GI)tumors are among the most prevalent malignancies,and surgical intervention remains a primary treatment modality.However,the complexity of GI surgery often leads to prolonged recovery and high postoperative complication rates,which threaten patient safety and functional outcomes.Enhanced recovery after surgery(ERAS)principles have been shown to improve perioperative outcomes through evidence-based,multidisciplinary care pathways.Despite its widespread adoption,there is a paucity of research focusing specifically on optimizing ERAS-guided nursing processes in the post-anesthesia care unit(PACU)and evaluating its impact on perioperative safety in patients undergoing GI tumor surgery.This study aimed to investigate whether an ERASbased PACU nursing protocol could enhance recovery,reduce complications,and improve patient safety in this surgical population.AIM To explore the impact of optimizing the recovery room nursing process based on ERAS on the perioperative safety of patients with GI tumors.METHODS A total of 260 patients with GI tumors who underwent elective surgeries under general anesthesia in our hospital from August 2023 to August 2025 and were then observed in the recovery unit(PACU)were selected.They were randomly divided into the observation group(the PACU nursing process was optimized based on ERAS)and the control group(the conventional PACU nursing process was adopted)by the random number grouping method,with 130 cases in each group.The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,time of leaving the room after tube removal,retention time in the recovery room,occurrence of complications,satisfaction and readmission rate were compared between the two groups after entering the room.Compare the occurrence of adverse events in the PACU nursing process between the two groups.RESULTS The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,retention time in the recovery room,total incidence of complications and readmission rate in the observation group were significantly lower than those in the control group,and the satisfaction rate was higher than that in the control group(P<0.05).The occurrence of adverse events in the PACU nursing process in the observation group was lower than that in the control group(P<0.05).CONCLUSION Optimizing the PACU nursing process based on ERAS can effectively accelerate the recovery process of patients undergoing GI tumor surgery,reduce adverse events,improve nursing satisfaction,and at the same time,lower the incidence of adverse events in the PACU nursing process,providing a more refined management basis for clinical practice.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
文摘This study integrates explicit input enhancement into comparative continuation writing,defined as a task in which learners produce a continuation by comparing their own expression with an input text,aligning with its discourse structure and linguistic features,while developing their own ideas.It aims to examine whether English as a Foreign Language(EFL)learners in China exhibit differences in discourse competence and writing performance when completing comparative continuation writing combined with different input enhancement techniques,and whether the alignment effect occurs at the discourse level.Sixty first-year Chinese senior middle school students were divided into four groups:three groups engaged in comparative continuation writing with varying input enhancement,achieved by combining different techniques,while a control group performed a designated-topic writing task.The results revealed that three comparative continuation writing groups outperformed the designated-topic writing group in discourse competence,particularly in the use of temporal connectives.However,differences and some inconsistencies were observed among the comparative continuation writing groups across individual indices.The study highlights effective ways to incorporate comparative continuation writing into English instruction and demonstrates how explicit input enhancement can complement the task,simultaneously activating the alignment effect proposed by the xu-argument and enhancing discourse competence in writing.
基金financially sponsored by the National Natural Science Foundation of China(No.52204414)the National Energy-Saving and Low-Carbon Materials Production and Application Demonstration Platform Program,China(No.TC220H06N)+1 种基金the National Key R&D Program of China(No.2021YFC1910504)the Fundamental Research Funds for the Central Universities,China(No.FRFTP-20-097A1Z)。
文摘MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO_(x)-CeO_(2)catalyst that achieves enhanced NO conversion rate and stability under harsh conditions.The catalyst was synthesized by decorating MnOx crystals with amorphous CeO_(2),followed by loading hydrophobic silica on the external surfaces.The hydrophobic silica allowed the adsorption of NH_(3)and NO and diffusion of H,suppressed the adsorption of H_(2)O,and prevented SO_(2)interaction with the Mn active sites,achieving selective molecular discrimination at the catalyst surface.At 120℃,under H_(2)O and SO_(2)exposure,the optimal hydrophobic catalyst maintains 82%NO conversion rate compared with 69%for the unmodified catalyst.The average adsorption energies of NH_(3),H_(2)O,and SO_(2)decreased by 0.05,0.43,and 0.52 eV,respectively.The NO reduction pathway follows the Eley-Rideal mechanism,NH_(3)^(*)+*→NH_(2)^(*)+H^(*)followed by NH_(2)^(*)+NO^(*)→N_(2)^(*)+H_(2)O^(*),with NH_(3)dehydrogenation being the rate determining step.Hydrophobic modification increased the activation energy for H atom transfer,leading to a minor decrease in the NO conversion rate at 120℃.This work demonstrates a viable strategy for developing robust NH_(3)-S CR catalysts capable of efficient operation in water-and sulfur-rich environments.
基金supported by the National Natural Science Foundation of China under Grant No.12204062the Natural Science Foundation of Shandong Province under Grant No.ZR2022MF330。
文摘To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.
基金supported by the Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
文摘Objectives:The eukaryotic initiation factor 4F(eIF4F)translation initiation complex inhibitors(eIF4Fi)were recently found to hyperactivate extracellular signal-regulated kinases 1/2(ERK1/2)signals,which contribute to acquired resistance to BRAF(B-Raf proto-oncogene,serine/threonine kinase)inhibitors in melanoma.This present study aims to elucidate how to overcome the resistance of the eIF4Fi in BRAFV600E mutant melanoma cells and explore the underlying mechanisms.Methods:Melanoma A375(vemurafenib[VEM]-sensitive)and A375R(VEM-resistant)cells were exposed to eIF4Fi RocA at varying doses and durations in vitro.We investigated the impact of RocA on the activity of ERK1/2,AKT serine/threonine kinase 1(AKT1),eIF4E,and enhancer of zeste homolog 2(EZH2).We then examined the impact of RocA on pro-apoptotic BH3-only proteins and proliferative proteins.We subsequently determined the effect of combined eIF4Fi,AKT1 inhibitor,EZH2 inhibitor or VEM on tumor growth in vitro and in vivo.Results:RocA inhibited proliferation and induced apoptosis in A375 cells,but inhibited proliferation in A375R cells.RocA rapidly reactivated ERK1/2 at 3 h and returned to baseline levels at 48 h.However,eIF4E and AKT1 activation began at 12 h and peaked at 48 h.ERK1/2 positively regulated EZH2 and EZH2-dependent expression of c-Fos and EGR1,while AKT1 negatively regulated c-Myc,c-Jun,and BMF,but positively regulated eIF4E.RocA downregulated ERK1/2(or EZH2,AKT1,and eIF4E)independent bcl-2 and Mcl-1 expression.AKT1i enhanced RocA-induced cell apoptosis,while EZH2i reduced RocA-induced cell proliferation.Combined CR-1-31-B,EZH2i,and AKT1i effectively overcame resistance to RocA and VEM resistance both in vitro and in vivo.Conclusion:The eIF4F complex inhibitor reactivates ERK1/2-EZH2 and AKT1 signaling pathways,resulting in resistance to both eIF4Fi and VEM.Combined administration of an eIF4Fi with EZH2 and AKT1 inhibitors effectively enhances sensitivity to both eIF4F complex and BRAF inhibitors.
基金supported by the National Natural Science Foundation of China(Nos.62475128 and 12274055)Youth Innovation Team Program of Shandong Higher Education Institution(No.2024KJN016)+1 种基金Research Grants Council of Hong Kong through an ANR/RGC Joint Research Scheme grant(No.A-CityUl01/20)Centre for Functional Photonics of City University of Hong Kong,and Hong Kong Branch of National Precious Metals Material Engineering Research Center(ITC Fund)
文摘Metal-organic framework materials exhibit considerable potential as molecularly selective surfaceenhanced Raman spectroscopy(SERS)substrates because of their microporous structures,which enrich small molecules while excluding larger ones.In this study,we develop a template-assisted chemical-etching strategy to prepare layered tuneable SERS substrates based on hierarchical porous zeolitic imidazolate framework-67(HP-ZIF-67)with a rhombic dodecahedral structure.The synergistic SERS enhancement mechanisms of HP-ZIF-67,which combine electromagnetic(EM)and chemical(CM)effects,were systematically studied through numerical simulations and experiments.Calculations revealed that under 633-nm laser excitation,the contributions of the EM and CM effects to the total SERS enhancement factor of HP-ZIF-67 were 60%and 40%,respectively.The hierarchical porous structure enhanced the fluid-flow flux over the microporous ZIF-67 because the increased pore radius reduced the viscous resistance and facilitated rapid molecular transport through the interconnected macro-meso-channels.Precise modulation of the CM and EM effects,combined with enhanced mass transfer,facilitated the development of HP-ZIF-67and HP-ZIF-67@Au as efficient SERS sensors.An investigation of the relationship between pore-size distribution and EM effects revealed the pivotal role of light confinement by whispering-gallery-mode microcavities in enhancing the SERS performance.The optimised HPZIF-67@Au composites functioned as flexible and highly sensitive in situ SERS sensors for gases and liquids,including volatile organic-compound gas and liquid-pesticide residues.This study introduces a novel design concept and provides a robust theoretical foundation for the future development of exhaled-breath point-of-care diagnostic devices and sweat-based wearable biomedical sensors.
