The multi-scale expression of enormously complicated laneway data requires differentiation of both contents and the way the contents are expressed. To accomplish multi-scale expression laneway data must support multi-...The multi-scale expression of enormously complicated laneway data requires differentiation of both contents and the way the contents are expressed. To accomplish multi-scale expression laneway data must support multi-scale transformation and have consistent topological relationships. Although the laneway data generated by traverse survey-ing is non-scale data it is still impossible to construct a multi-scale spatial database directly from it. In this paper an al-gorithm is presented to first calculate the laneway mid-line to support multi-scale transformation; then to express topo-logical relationships arising from the data structure; and,finally,a laneway spatial database is built and multi-scale ex-pression is achieved using components GIS-SuperMap Objects. The research result is of great significance for improv-ing the efficiency of laneway data storage and updating,for ensuring consistency of laneway data expression and for extending the potential value of a mine spatial database.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
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos...The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
[Objectives]The present study was conducted to investigate the change rule ofβ-fructofuranosidase gene expression and its enzyme activity in the midgut of 5 th instar silkworm(Bombyx mori),in order to provide a refer...[Objectives]The present study was conducted to investigate the change rule ofβ-fructofuranosidase gene expression and its enzyme activity in the midgut of 5 th instar silkworm(Bombyx mori),in order to provide a reference for illustrating the enzymatic mechanism of usingβ-fructofuranosidase to absorb sucrose nutrition from mulberry leaves.[Methods]Real-time fluorescent quantitative PCR was applied to analyze the expression of BmSuc1 and BmSuc2 in midgut of 5 th-instar silkworm larvae,meanwhile the activities ofβ-fructofuranosidase was determined.[Results]BmSuc1 was expressed in the midgut of 5 th-instar silkworm larvae at different developmental stages.Its expression was upregulated at the beginning of the 5 th instar and during the peak feeding period,whereas BmSuc2 expression remained very low throughout the entire 5 th instar.The activity ofβ-fructofuranosidase was relatively high during the peak feeding period of 5 th-instar larvae,showing a trend of increasing first and then decreasing.[Conclusions]The expression pattern of the BmSuc1 gene and the changes inβ-fructofuranosidase activity were generally consistent with the physiological process of sugar nutrient absorption and utilization from mulberry leaves in 5 th-instar silkworms.It suggests that BmSuc1,as a sucrose hydrolase gene,plays a major role in the digestion and absorption of sucrose nutrients from mulberry leaves in the midgut tissue.展开更多
The pathogenesis-related protein PR10 plays a vital role in plant growth,development,and stress responses.This study systematically identified and analyzed PR10 genes in cultivated peanut(Arachis hypogaea L.),examinin...The pathogenesis-related protein PR10 plays a vital role in plant growth,development,and stress responses.This study systematically identified and analyzed PR10 genes in cultivated peanut(Arachis hypogaea L.),examining their phylogenetic relationships,conserved motifs,gene structures,and syntenic relationships.The analysis identified 54 Ah PR10 genes,which were classified into eight groups based on phylogenetic relationships,supported by gene structure and conserved motif characterization.Analysis of chromosomal distribution and synteny demonstrated that segmental duplications played a crucial role in the expansion of the Ah PR10 gene family.The identified Ah PR10 genes exhibited both constitutive and inducible expression patterns.Significantly,Ah PR10-7,Ah PR10-33,and Ah PR10-41 demonstrated potential importance in peanut resistance to Aspergillus flavus.In vitro fungistatic experiments demonstrated that recombinant Ah PR10-33 effectively inhibited A.flavus mycelial growth.These findings provide valuable insights for future investigations into Ah PR10 functions in protecting peanut from A.flavus infection.展开更多
Due to the unique microstructure and diverse opsin genes of the trinocular compound eye,stomatopoda possess an extraordinary ability to perceive multiple properties of light.They not only can detect natural light(NL)a...Due to the unique microstructure and diverse opsin genes of the trinocular compound eye,stomatopoda possess an extraordinary ability to perceive multiple properties of light.They not only can detect natural light(NL)and linearly polarized light(LPL),but also are the only animals capable of recognizing circularly polarized light(CPL).Here,we integrated single-cell RNA sequencing,previously published Illumina data,and in-situ hybridization(ISH)to quantify and localize functional opsin genes in Oratosquilla oratoria,a common stomatopoda species in the China Sea.A total of high-quality 31777 cells were captured for the first time in the O.oratoria compound eye,which were classified into 25 cell subpopulations,and hypothesized that cluster 22 is a critical cell subpopulation responsible for light(whether NL,LPL,or CPL)response in O.oratoria.Furthermore,we propose that the long-wavelengthsensitive opsin gene(lws)gene family,retinol dehydrogenase(rdh),voltage-gated ion channel(vgic),arrestin(arr),and myosin(myo)collectively mediate the light response in O.oratoria.Considering that very few vision-related opsin genes show differential expression in right-handed CPL(RCPL)-vs.-dark(DL),which provides additional evidence that stomatopoda cannot recognize RCPL.Meanwhile,we believe that UV-stimulated scaffold protein A(uvssa)and red pigment concentrating hormone(rpch)play special contributions in the left-handed CPL(LCPL)environment response.ISH revealing that 16 lws,6 middle-wavelength-sensitive(mws),and 2 ultraviolet(uv)opsin genes were expressed in the photoreceptors of the O.oratoria compound eye.Although the inability to determine the functional types of cell subpopulations limits the resolution of opsin genes,these findings systematically elucidate the specific expression patterns of opsin genes in O.oratoria and represent a significant step toward refining the visual ecological theory of O.oratoria and other stomatopod species.展开更多
Botrytis cinerea is a major necrotrophic pathogen responsible for significant crop losses worldwide.Alternative strategies to control B.cinerea are urgently needed to reduce dependence on chemical fungicides,which are...Botrytis cinerea is a major necrotrophic pathogen responsible for significant crop losses worldwide.Alternative strategies to control B.cinerea are urgently needed to reduce dependence on chemical fungicides,which are increasingly ineffective due to resistance and pose environmental risks.In this study,we identified two immunogenic epitopes derived from the B.cinerea cell death-inducing protein BcCrh1 and used them to engineer disease-resistant plants through a novel,spatially compartmentalized dual-epitope immune activation strategy.The first epitope is derived from a 35-amino acid intracellular peptide that exhibits both immunogenicity and cell death-inducing activity,which was mutated to separate these two properties.The second peptide represents an immunogenic portion of the protein that activates extracellular plant immunity.Transcriptomic and metabolomic analyses revealed that these epitopes trigger complementary defense pathways,and their co-expression integrates these responses into a robust,multilayered immunity,providing significantly enhanced protection compared with individual expression.Although constitutive expression of two epitopes conferred resistance,it also led to growth penalties.In contrast,pathogen-inducible expression of two epitopes preserved normal plant development while maintaining strong resistance to both B.cinerea and Pseudomonas syringae in Arabidopsis and tomato.This inducible strategy offers a major advantage by minimizing fitness costs while maximizing protection,highlighting the potential of spatially and temporally targeted epitope-based immune activation for durable and sustainable crop protection.展开更多
Coconut(Cocos nucifera L.),a major oil and fruit crop of the Arecaceae family,is extensively cultivated across the Asia—Pacific region.Despite its agricultural importance,genome assembly in coconut remains challengin...Coconut(Cocos nucifera L.),a major oil and fruit crop of the Arecaceae family,is extensively cultivated across the Asia—Pacific region.Despite its agricultural importance,genome assembly in coconut remains challenging due to its large genome size and high proportion of repetitive sequences.Allele-specific expression(ASE)plays a key role in regulating plant development and evolution,yet research on ASE in coconut is limited(Shao et al.,2019;Li et al.,2021;Zhang et al.,2021;Hu et al.,2022).Among phenotypic traits,fruit color is especially important as an indicator of maturity,guiding harvest timing and post-harvest processes(Kapoor et al.,2022).While prior studies have explored various coconut traits such as salt tolerance,fiber content,and plant height(Wang et al.,2021;Yang et al.,2021),investigations into ASE and fruit color remain scarce.展开更多
The prognostic and therapeutic roles of biological markers in early-stage breast cancer(eBC)warrant further investigation.Non-Breast Cancer(BRCA)genes,along with moderate-and low-penetrance breast cancer risk variant ...The prognostic and therapeutic roles of biological markers in early-stage breast cancer(eBC)warrant further investigation.Non-Breast Cancer(BRCA)genes,along with moderate-and low-penetrance breast cancer risk variant genes,are crucial formaintaining genome stability,yet their prognostic significance in eBCremains unclear.This study aimed to evaluate the impact of non-BRCA genes on clinical outcomes in eBC patients.Significant correlations were observed between the messenger ribonucleic acid(mRNA)expression levels of the genes Ataxia-telangiectasia mutated(ATM),Bloom helicase gene(BLM),and WRN RecQ Like Helicase(WRN)and patient prognosis.High mRNA expression of ATM was associated with longer metastasis-free survival(MFS).Conversely,lower mRNA expression of BLM correlated with favorable outcomes,particularly in triple-negative tumors.Additionally,high levels of WRN mRNA expression were linked to significantly longer MFS compared to low expression levels.This study highlights the prognostic significance of ATM,BLM,and WRN in predicting survival outcomes in eBC patients.Background:The prognostic significance of various biological and non-BRCA genetic in early-stage breast cancer(eBC)remains unclear and warrants further investigation.This study therefore aimed to evaluate the prognostic impact of these genes on clinical outcomes in breast cancer.Methods:Patients included in this study were subdivided into two groups based on low and high messenger ribonucleic acid(mRNA)expression levels.Statistical analysis,including Kaplan-Meier curves,univariable,andmultivariable Cox regression analyses,was performed to assess metastasis-free survival(MFS)of mRNA expression of non-BRCA genes.Subgroup analyses were also conducted among four different molecular subtypes of eBC.Results:Our analysis revealed significant correlations between mRNA-expression levels of Ataxiatelangiectasia mutated(ATM),Bloom helicase gene(BLM),and WRN RecQ Like Helicase(WRN)and patient prognosis.High mRNA expression of ATM correlated with longer MFS in the entire cohort(p=0.022,Log Rank),and in luminal-B-like tumors(p=0.036).Lower mRNA expression of BLM was associated with favorable outcomes(p=0.011,Log Rank),particularly in triple-negative eBC(p=0.030,Log Rank).Finally,high levels of WRN mRNA expression correlated with significantly longerMFS compared to lowmRNA expression levels(p=0.009,Log Rank).Conclusions:This study underscores the prognostic significance of moderate penetrance breast cancer risk variant genes,such as ATM,BLM,and WRN,for survival outcomes in eBC.展开更多
Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer ...Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.展开更多
Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ...Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.展开更多
Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based elect...Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.展开更多
基金Project 2005B018 supported by the Science Foundation of China University of Mining and Technology
文摘The multi-scale expression of enormously complicated laneway data requires differentiation of both contents and the way the contents are expressed. To accomplish multi-scale expression laneway data must support multi-scale transformation and have consistent topological relationships. Although the laneway data generated by traverse survey-ing is non-scale data it is still impossible to construct a multi-scale spatial database directly from it. In this paper an al-gorithm is presented to first calculate the laneway mid-line to support multi-scale transformation; then to express topo-logical relationships arising from the data structure; and,finally,a laneway spatial database is built and multi-scale ex-pression is achieved using components GIS-SuperMap Objects. The research result is of great significance for improv-ing the efficiency of laneway data storage and updating,for ensuring consistency of laneway data expression and for extending the potential value of a mine spatial database.
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.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
基金Project(2022YFC2904103)supported by the National Key Research and Development Program of ChinaProjects(52374112,52274108)supported by the National Natural Science Foundation of China+1 种基金Projects(BX20220036,BX20230041)supported by the Postdoctoral Innovation Talents Support Program,ChinaProject(2232080)supported by the Beijing Natural Science Foundation,China。
文摘The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金Supported by General Project of Yunnan Provincial Agricultural Basic Research Joint Special Project(202301BD070001-229)Yunnan Provincial Key R&D Program(202403AK140075)+1 种基金Modern Sericulture Industry Technology System of Yunan Province(KJTX-07)Honghe Comprehensive Test Station of National Sericulture Industry Technology System(CARS-18).
文摘[Objectives]The present study was conducted to investigate the change rule ofβ-fructofuranosidase gene expression and its enzyme activity in the midgut of 5 th instar silkworm(Bombyx mori),in order to provide a reference for illustrating the enzymatic mechanism of usingβ-fructofuranosidase to absorb sucrose nutrition from mulberry leaves.[Methods]Real-time fluorescent quantitative PCR was applied to analyze the expression of BmSuc1 and BmSuc2 in midgut of 5 th-instar silkworm larvae,meanwhile the activities ofβ-fructofuranosidase was determined.[Results]BmSuc1 was expressed in the midgut of 5 th-instar silkworm larvae at different developmental stages.Its expression was upregulated at the beginning of the 5 th instar and during the peak feeding period,whereas BmSuc2 expression remained very low throughout the entire 5 th instar.The activity ofβ-fructofuranosidase was relatively high during the peak feeding period of 5 th-instar larvae,showing a trend of increasing first and then decreasing.[Conclusions]The expression pattern of the BmSuc1 gene and the changes inβ-fructofuranosidase activity were generally consistent with the physiological process of sugar nutrient absorption and utilization from mulberry leaves in 5 th-instar silkworms.It suggests that BmSuc1,as a sucrose hydrolase gene,plays a major role in the digestion and absorption of sucrose nutrients from mulberry leaves in the midgut tissue.
基金supported by the National Key R&D Program of China(2022YFD1200400)the National Natural Science Foundation of China(32301851)。
文摘The pathogenesis-related protein PR10 plays a vital role in plant growth,development,and stress responses.This study systematically identified and analyzed PR10 genes in cultivated peanut(Arachis hypogaea L.),examining their phylogenetic relationships,conserved motifs,gene structures,and syntenic relationships.The analysis identified 54 Ah PR10 genes,which were classified into eight groups based on phylogenetic relationships,supported by gene structure and conserved motif characterization.Analysis of chromosomal distribution and synteny demonstrated that segmental duplications played a crucial role in the expansion of the Ah PR10 gene family.The identified Ah PR10 genes exhibited both constitutive and inducible expression patterns.Significantly,Ah PR10-7,Ah PR10-33,and Ah PR10-41 demonstrated potential importance in peanut resistance to Aspergillus flavus.In vitro fungistatic experiments demonstrated that recombinant Ah PR10-33 effectively inhibited A.flavus mycelial growth.These findings provide valuable insights for future investigations into Ah PR10 functions in protecting peanut from A.flavus infection.
基金Supported by the Natural Science Foundation of Shandong Province(No.ZR2021QD110)the National Natural Science Foundation of China(No.42106128)。
文摘Due to the unique microstructure and diverse opsin genes of the trinocular compound eye,stomatopoda possess an extraordinary ability to perceive multiple properties of light.They not only can detect natural light(NL)and linearly polarized light(LPL),but also are the only animals capable of recognizing circularly polarized light(CPL).Here,we integrated single-cell RNA sequencing,previously published Illumina data,and in-situ hybridization(ISH)to quantify and localize functional opsin genes in Oratosquilla oratoria,a common stomatopoda species in the China Sea.A total of high-quality 31777 cells were captured for the first time in the O.oratoria compound eye,which were classified into 25 cell subpopulations,and hypothesized that cluster 22 is a critical cell subpopulation responsible for light(whether NL,LPL,or CPL)response in O.oratoria.Furthermore,we propose that the long-wavelengthsensitive opsin gene(lws)gene family,retinol dehydrogenase(rdh),voltage-gated ion channel(vgic),arrestin(arr),and myosin(myo)collectively mediate the light response in O.oratoria.Considering that very few vision-related opsin genes show differential expression in right-handed CPL(RCPL)-vs.-dark(DL),which provides additional evidence that stomatopoda cannot recognize RCPL.Meanwhile,we believe that UV-stimulated scaffold protein A(uvssa)and red pigment concentrating hormone(rpch)play special contributions in the left-handed CPL(LCPL)environment response.ISH revealing that 16 lws,6 middle-wavelength-sensitive(mws),and 2 ultraviolet(uv)opsin genes were expressed in the photoreceptors of the O.oratoria compound eye.Although the inability to determine the functional types of cell subpopulations limits the resolution of opsin genes,these findings systematically elucidate the specific expression patterns of opsin genes in O.oratoria and represent a significant step toward refining the visual ecological theory of O.oratoria and other stomatopod species.
基金supported by the National Natural Science Foundation of China(grant no.32372514)the Research and Innovation Initiatives of WHPU(grant no.2024J02)+1 种基金Y.L.(202108280009)was funded by the China Scholarship Councilsupported by BARD(grant no.5261-20C)to A.S and T.M.
文摘Botrytis cinerea is a major necrotrophic pathogen responsible for significant crop losses worldwide.Alternative strategies to control B.cinerea are urgently needed to reduce dependence on chemical fungicides,which are increasingly ineffective due to resistance and pose environmental risks.In this study,we identified two immunogenic epitopes derived from the B.cinerea cell death-inducing protein BcCrh1 and used them to engineer disease-resistant plants through a novel,spatially compartmentalized dual-epitope immune activation strategy.The first epitope is derived from a 35-amino acid intracellular peptide that exhibits both immunogenicity and cell death-inducing activity,which was mutated to separate these two properties.The second peptide represents an immunogenic portion of the protein that activates extracellular plant immunity.Transcriptomic and metabolomic analyses revealed that these epitopes trigger complementary defense pathways,and their co-expression integrates these responses into a robust,multilayered immunity,providing significantly enhanced protection compared with individual expression.Although constitutive expression of two epitopes conferred resistance,it also led to growth penalties.In contrast,pathogen-inducible expression of two epitopes preserved normal plant development while maintaining strong resistance to both B.cinerea and Pseudomonas syringae in Arabidopsis and tomato.This inducible strategy offers a major advantage by minimizing fitness costs while maximizing protection,highlighting the potential of spatially and temporally targeted epitope-based immune activation for durable and sustainable crop protection.
基金supported by Central Public-interest Scientific Institution Basal Research Fund(CATAS-Nos.1630152023007,1630152023011,1630152023012,1630152023013)the National Natural Science Foundation of China(Grant No.32071805).
文摘Coconut(Cocos nucifera L.),a major oil and fruit crop of the Arecaceae family,is extensively cultivated across the Asia—Pacific region.Despite its agricultural importance,genome assembly in coconut remains challenging due to its large genome size and high proportion of repetitive sequences.Allele-specific expression(ASE)plays a key role in regulating plant development and evolution,yet research on ASE in coconut is limited(Shao et al.,2019;Li et al.,2021;Zhang et al.,2021;Hu et al.,2022).Among phenotypic traits,fruit color is especially important as an indicator of maturity,guiding harvest timing and post-harvest processes(Kapoor et al.,2022).While prior studies have explored various coconut traits such as salt tolerance,fiber content,and plant height(Wang et al.,2021;Yang et al.,2021),investigations into ASE and fruit color remain scarce.
文摘The prognostic and therapeutic roles of biological markers in early-stage breast cancer(eBC)warrant further investigation.Non-Breast Cancer(BRCA)genes,along with moderate-and low-penetrance breast cancer risk variant genes,are crucial formaintaining genome stability,yet their prognostic significance in eBCremains unclear.This study aimed to evaluate the impact of non-BRCA genes on clinical outcomes in eBC patients.Significant correlations were observed between the messenger ribonucleic acid(mRNA)expression levels of the genes Ataxia-telangiectasia mutated(ATM),Bloom helicase gene(BLM),and WRN RecQ Like Helicase(WRN)and patient prognosis.High mRNA expression of ATM was associated with longer metastasis-free survival(MFS).Conversely,lower mRNA expression of BLM correlated with favorable outcomes,particularly in triple-negative tumors.Additionally,high levels of WRN mRNA expression were linked to significantly longer MFS compared to low expression levels.This study highlights the prognostic significance of ATM,BLM,and WRN in predicting survival outcomes in eBC patients.Background:The prognostic significance of various biological and non-BRCA genetic in early-stage breast cancer(eBC)remains unclear and warrants further investigation.This study therefore aimed to evaluate the prognostic impact of these genes on clinical outcomes in breast cancer.Methods:Patients included in this study were subdivided into two groups based on low and high messenger ribonucleic acid(mRNA)expression levels.Statistical analysis,including Kaplan-Meier curves,univariable,andmultivariable Cox regression analyses,was performed to assess metastasis-free survival(MFS)of mRNA expression of non-BRCA genes.Subgroup analyses were also conducted among four different molecular subtypes of eBC.Results:Our analysis revealed significant correlations between mRNA-expression levels of Ataxiatelangiectasia mutated(ATM),Bloom helicase gene(BLM),and WRN RecQ Like Helicase(WRN)and patient prognosis.High mRNA expression of ATM correlated with longer MFS in the entire cohort(p=0.022,Log Rank),and in luminal-B-like tumors(p=0.036).Lower mRNA expression of BLM was associated with favorable outcomes(p=0.011,Log Rank),particularly in triple-negative eBC(p=0.030,Log Rank).Finally,high levels of WRN mRNA expression correlated with significantly longerMFS compared to lowmRNA expression levels(p=0.009,Log Rank).Conclusions:This study underscores the prognostic significance of moderate penetrance breast cancer risk variant genes,such as ATM,BLM,and WRN,for survival outcomes in eBC.
基金Opening Foundation of Key Laboratory of Explosive Energy Utilization and Control,Anhui Province(BP20240104)Graduate Innovation Program of China University of Mining and Technology(2024WLJCRCZL049)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2701)。
文摘Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.
基金supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).
文摘Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.
基金funded by the Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U21A20143)the National Science Fund for Excellent Young Scholars(52322607)the Excellent Youth Foundation of Heilongjiang Scientific Committee(YQ2022E028)。
文摘Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.
