Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis...[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.展开更多
With the increasing demand for understanding skin physiology and advancing regenerative medicine,in vitro three-dimensional(3D)functional skin tissue models have become vital tools in dermatological research.These mod...With the increasing demand for understanding skin physiology and advancing regenerative medicine,in vitro three-dimensional(3D)functional skin tissue models have become vital tools in dermatological research.These models effectively mimic the complex structure and functions of human skin.This review comprehensively discusses the latest advancements in construction techniques,material selection,and applications of 3D skin models.It highlights the advantages and challenges associated with cutting-edge technologies such as layer-by-layer cell coating,3D bioprinting,bio-spray technology,and photolithographic microfabrication in creating highly realistic skin models.Moreover,it examines the wide-ranging applications of 3D skin models,includingelucidation of skin disease mechanisms,investigation of skin barrier functions,studies on skin aging and repair,hair regeneration,efficacy screening of therapeutic agents,cosmetic safety assessment,and personalized medicine.Finally,this review anticipates future trends in developing 3D skin models with greater structural and functional complexity,enhanced multifunctionality,and improved clinical translation.展开更多
Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury...Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury in humans are severely limited by the inaccessibility of living brain tissue,creating a need for in vitro model systems to study cellular mechanisms of degeneration and regeneration following injury.Here we describe methods to establish a 3D human brain tissue model,consisting of a silk-collagen composite scaffold seeded with human neurons,astrocytes,and microglia,to study neuro-regeneration after traumatic brain injury.Step-by-step fabrication,injury,and analytical assessments of the 3D“triculture”system are described.Using this tissue model system,we demonstrate that glial cells promote regeneration of neuronal networks within the injury site over several weeks post-injury.Further,we found that regenerating networks in the 3D triculture tissues did not secrete early markers of neurodegenerative disease,but displayed signs of excitatory/inhibitory imbalance,suggesting that pro-regenerative treatments for traumatic brain injury in the future may need to direct cell differentiation to promote proper function.The mechanical stability of this model system enables physiologically relevant impact injury and long-term culture capability,while its modular design enables modification of cell contents,extracellular matrix composition,and scaffold properties.This adaptability could allow the integration of patient-derived cells and genetic modifications to bridge research and clinical applications focused on personalized targeted therapies.This in vitro system provides a valuable platform for accelerating therapeutic advancements in traumatic brain injury and neurodegenerative disorders,ultimately improving patient outcomes.展开更多
We present a novel nonlinear state transition model for inositol 1,4,5-trisphosphate receptors(IP_(3)Rs)that incorporates a pre-activated state,as suggested by electron microscopy observations.Our model provides a the...We present a novel nonlinear state transition model for inositol 1,4,5-trisphosphate receptors(IP_(3)Rs)that incorporates a pre-activated state,as suggested by electron microscopy observations.Our model provides a theoretical framework for the biphasic Ca^(2+)dependence of IP_(3)Rs and accurately reproduces their experimentally observed state distribution under saturating IP_(3) conditions.By integrating receptor dynamics with cytoplasmic and endoplasmic reticulum(ER)calcium exchange,we simulate IP_(3)R-mediated Ca^(2+)oscillations governed by six key conformational states.A pivotal finding is that IP_(3)regulates these oscillations in a switch-like manner:once a critical IP_(3) concentration is reached,the system abruptly transitions to sustained,constant-amplitude oscillations that quickly terminate when the concentration exceeds a secondary threshold.These results underscore the crucial role of the pre-activated state in modulating calcium signaling.展开更多
The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir upli...The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir uplift.Seismic imaging revealed that the upper slab was scraped and that the lower slab had subducted to a depth of>150 km.These features constitute the tectonic complexity of the Pamirs,as well as the thermal subduction mechanism involved,which remains poorly understood.Hence,in this study,high-resolution three-dimensional(3D)kinematic modeling is applied to investigate the thermal structure and geometry of the subducting slab beneath the Pamirs.The modeled slab configuration reveals distinct along-strike variations,with a steeply dipping slab beneath the southern Pamirs,a more gently inclined slab beneath the northern Pamirs,and apparent upper slab termination at shallow depths beneath the Pamirs.The thermal field reveals a cold slab core after delamination,with temperatures ranging from 400℃to 800℃,enveloped by a hotter mantle reaching~1400℃.The occurrence of intermediate-depth earthquakes aligns primarily with colder slab regions,particularly near the slab tear-off below the southwestern Pamirs,indicating a strong correlation between slab temperature and seismicity.In contrast,the northern Pamirs exhibit reduced seismicity at depth,which is likely associated with thermal weakening and delamination.The central Pamirs show a significant thermal anomaly caused by a concave slab,where the coldest crust does not descend deeply,further suggesting crustal detachment or mechanical failure.The lateral asymmetry in slab temperature possibly explains the mechanism of lateral tearing and differential slab-mantle coupling.展开更多
Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical si...Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.展开更多
Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual pat...Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment.展开更多
Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches ...Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches exhibit high error rates in borderline cases and necessitate expert manual review,which limits scalability and introduces inconsistencies between algorithmic outputs and expert judgment.To address these limitations,we propose a large vision-language model(VLM)designed as an“expert agent”to automatically refine similarity scores,ensuring alignment with expert decisions and overcoming key application bottlenecks.The model consists of two core components:(1)rule-based similarity calculation module generate initial spectral similarity scores,and(2)pre-trained large vision-language model fine-tuned via supervised learning and reinforcement learning with human feedback(RLHF)to emulate expert assessments.To facilitate training and evaluation,we introduce two expert-annotated datasets,Spec1k and SpecReason,which capture both quantitative corrections and qualitative reasoning patterns,allowing the model to emulate expert decision-making processes.Experimental results demonstrate that our method achieves 81.45%source attribution accuracy,38.24%higher than rule-based and machine learning baselines.Real-world deployment further validates its effectiveness.展开更多
Background:Hepatocellular carcinoma(HCC)is a highly lethal malignancy driven by both intrinsic oncogenic pathways and immune microenvironmental regulation.Emerging evidence suggests that DNASE1L3 may influence tumor b...Background:Hepatocellular carcinoma(HCC)is a highly lethal malignancy driven by both intrinsic oncogenic pathways and immune microenvironmental regulation.Emerging evidence suggests that DNASE1L3 may influence tumor biology and immune responses;however,its specific roles in HCC progression and macrophage-mediated regulation remain unclear.This study aimed to elucidate the biological functions of DNASE1L3 in HCC and to determine how it modulates tumor behavior and immune interactions.Methods:Bioinformatics analyses of the GSE41804 and Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC)datasets were used to identify hub genes.Functional assays assessed the impact of DNASE1L3 on HCC cell proliferation,migration,invasion,and cell cycle progression.The effects of DNASE1L3 on macrophage polarization and the Wnt/β-catenin signaling pathway were examined using a co-culture system.An HCC organoid model was established to further validate its regulatory function.Results:Eight prognostic signature genes were identified,with deoxyribonuclease I-like 3(DNase I-like 3)selected as the hub gene.DNASE1L3 overexpression suppressed HCC cell growth,inhibited migration and invasion,induced G1 arrest,and modulated epithelial-mesenchymal transition(EMT)markers.DNASE1L3 knockdown promoted M2-like macrophage polarization.Mechanistically,DNASE1L3 interacted withβ-catenin to enhance its ubiquitination and degradation,thereby inhibiting Wnt/β-catenin signaling and reducing PD-L1 expression.DNASE1L3 overexpression similarly restricted organoid growth and suppressed pathway activity.Conclusion:DNASE1L3 acts as a negative regulator of HCC progression by targeting the Wnt/β-catenin pathway and reducing PD-L1 expression,thereby influencing both tumor cell behavior and macrophage-mediated immune responses.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of arti...With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.展开更多
Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging f...Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging from 278.15 K to 318.15 K.The solubility in each system was found to be positively correlated with temperature.Furthermore,solubility data were analyzed using four equations:the modified Apelblat equation,Van’t Hoff equation,λh equation and CNIBS/R-K equations,and they provided satisfactory results for both two systems.The average root-mean-square deviation(105RMSD)values for these models were less than 13.93.Calculations utilizing the Van’t Hoff equation and Gibbs equations facilitated the derivation of apparent thermodynamic properties of NTO dissolution in the two systems,including values for Gibbs free energy,enthalpy and entropy.The%ζ_(H)is larger than%ζ_(TS),and all the%ζ_(H)data are≥58.63%,indicating that the enthalpy make a greater contribution than entropy to theΔG_(soln)^(Θ).展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not...To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.展开更多
The popularity of deep learning has boosted computer-generated holography(CGH)as a vibrant research field,particularly physics-driven unsupervised learning.Nevertheless,present unsupervised CGH models have not yet exp...The popularity of deep learning has boosted computer-generated holography(CGH)as a vibrant research field,particularly physics-driven unsupervised learning.Nevertheless,present unsupervised CGH models have not yet explored the potential of generating full-color 3D holograms through a unified framework.In this study,we propose a lightweight multiwavelength network model capable of high-fidelity and efficient full-color hologram generation in both 2D and 3D display,called IncepHoloRGB.The high-speed simultaneous generation of RGB holograms at 191 frames per second(FPS)is based on Inception sampling blocks and multi-wavelength propagation module integrated with depth-traced superimposition,achieving an average structural similarity(SSIM)of 0.88 and peak signal-to-noise ratio(PSNR)of 29.00 on the DIV2K test set in reconstruction.Full-color reconstruction of numerical simulations and optical experiments shows that IncepHoloRGB is versatile to diverse scenarios and can obtain authentic full-color holographic 3D display within a unified network model,paving the way for applications towards real-time dynamic naked-eye 3D display,virtual and augmented reality(VR/AR)systems.展开更多
The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly impro...The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards.Utilising the SKUA GoCAD software,we constructed detailed seismic fault models for the 2021 M_(S)6.4 Yangbi earthquake in Yunnan,China,using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow.Our analysis revealed a NW-striking main fault with a high-angle SW dip,accompanied by two branch faults.Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault,whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern.Additionally,a third ENE-striking short fault was identified NE of the main fault.In combination with the spatial distribution of pre-existing faults,our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW-and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone.The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones,through either cascade or conjugate rupture modes,can cause unexpected moderate-large earthquakes and severe disasters,necessitating attention in regions like southeast Xizang,which have complex fault systems.展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
文摘[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.
文摘With the increasing demand for understanding skin physiology and advancing regenerative medicine,in vitro three-dimensional(3D)functional skin tissue models have become vital tools in dermatological research.These models effectively mimic the complex structure and functions of human skin.This review comprehensively discusses the latest advancements in construction techniques,material selection,and applications of 3D skin models.It highlights the advantages and challenges associated with cutting-edge technologies such as layer-by-layer cell coating,3D bioprinting,bio-spray technology,and photolithographic microfabrication in creating highly realistic skin models.Moreover,it examines the wide-ranging applications of 3D skin models,includingelucidation of skin disease mechanisms,investigation of skin barrier functions,studies on skin aging and repair,hair regeneration,efficacy screening of therapeutic agents,cosmetic safety assessment,and personalized medicine.Finally,this review anticipates future trends in developing 3D skin models with greater structural and functional complexity,enhanced multifunctionality,and improved clinical translation.
基金supported by funding from the U.S.Department of Defense,Nos.W911NF-23-1-0276,W81XWH2211065the NIH,No.P41EB027062(all to DLK).
文摘Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury in humans are severely limited by the inaccessibility of living brain tissue,creating a need for in vitro model systems to study cellular mechanisms of degeneration and regeneration following injury.Here we describe methods to establish a 3D human brain tissue model,consisting of a silk-collagen composite scaffold seeded with human neurons,astrocytes,and microglia,to study neuro-regeneration after traumatic brain injury.Step-by-step fabrication,injury,and analytical assessments of the 3D“triculture”system are described.Using this tissue model system,we demonstrate that glial cells promote regeneration of neuronal networks within the injury site over several weeks post-injury.Further,we found that regenerating networks in the 3D triculture tissues did not secrete early markers of neurodegenerative disease,but displayed signs of excitatory/inhibitory imbalance,suggesting that pro-regenerative treatments for traumatic brain injury in the future may need to direct cell differentiation to promote proper function.The mechanical stability of this model system enables physiologically relevant impact injury and long-term culture capability,while its modular design enables modification of cell contents,extracellular matrix composition,and scaffold properties.This adaptability could allow the integration of patient-derived cells and genetic modifications to bridge research and clinical applications focused on personalized targeted therapies.This in vitro system provides a valuable platform for accelerating therapeutic advancements in traumatic brain injury and neurodegenerative disorders,ultimately improving patient outcomes.
基金supported by the National Natural Science Foundation of China(Grant No.12475080)。
文摘We present a novel nonlinear state transition model for inositol 1,4,5-trisphosphate receptors(IP_(3)Rs)that incorporates a pre-activated state,as suggested by electron microscopy observations.Our model provides a theoretical framework for the biphasic Ca^(2+)dependence of IP_(3)Rs and accurately reproduces their experimentally observed state distribution under saturating IP_(3) conditions.By integrating receptor dynamics with cytoplasmic and endoplasmic reticulum(ER)calcium exchange,we simulate IP_(3)R-mediated Ca^(2+)oscillations governed by six key conformational states.A pivotal finding is that IP_(3)regulates these oscillations in a switch-like manner:once a critical IP_(3) concentration is reached,the system abruptly transitions to sustained,constant-amplitude oscillations that quickly terminate when the concentration exceeds a secondary threshold.These results underscore the crucial role of the pre-activated state in modulating calcium signaling.
基金the Chinese Academy of Sciences Pioneer Hundred Talents Program and the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0708)supported by a MEXT(Ministry of Education,Culture,Sports,Science and Technology)KAKENHI(Grants-in-Aid for Scientific Research)grant(Grant No.21H05203)Kobe University Strategic International Collaborative Research Grant(Type B Fostering Joint Research).
文摘The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir uplift.Seismic imaging revealed that the upper slab was scraped and that the lower slab had subducted to a depth of>150 km.These features constitute the tectonic complexity of the Pamirs,as well as the thermal subduction mechanism involved,which remains poorly understood.Hence,in this study,high-resolution three-dimensional(3D)kinematic modeling is applied to investigate the thermal structure and geometry of the subducting slab beneath the Pamirs.The modeled slab configuration reveals distinct along-strike variations,with a steeply dipping slab beneath the southern Pamirs,a more gently inclined slab beneath the northern Pamirs,and apparent upper slab termination at shallow depths beneath the Pamirs.The thermal field reveals a cold slab core after delamination,with temperatures ranging from 400℃to 800℃,enveloped by a hotter mantle reaching~1400℃.The occurrence of intermediate-depth earthquakes aligns primarily with colder slab regions,particularly near the slab tear-off below the southwestern Pamirs,indicating a strong correlation between slab temperature and seismicity.In contrast,the northern Pamirs exhibit reduced seismicity at depth,which is likely associated with thermal weakening and delamination.The central Pamirs show a significant thermal anomaly caused by a concave slab,where the coldest crust does not descend deeply,further suggesting crustal detachment or mechanical failure.The lateral asymmetry in slab temperature possibly explains the mechanism of lateral tearing and differential slab-mantle coupling.
基金financially supported by the National Key Research and Development Program of China (2022YFB3706802)。
文摘Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.
基金supported by the Natural Science Foundation of Guangdong Province(No.2021B1515120053)Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515140166).
文摘Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment.
文摘Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches exhibit high error rates in borderline cases and necessitate expert manual review,which limits scalability and introduces inconsistencies between algorithmic outputs and expert judgment.To address these limitations,we propose a large vision-language model(VLM)designed as an“expert agent”to automatically refine similarity scores,ensuring alignment with expert decisions and overcoming key application bottlenecks.The model consists of two core components:(1)rule-based similarity calculation module generate initial spectral similarity scores,and(2)pre-trained large vision-language model fine-tuned via supervised learning and reinforcement learning with human feedback(RLHF)to emulate expert assessments.To facilitate training and evaluation,we introduce two expert-annotated datasets,Spec1k and SpecReason,which capture both quantitative corrections and qualitative reasoning patterns,allowing the model to emulate expert decision-making processes.Experimental results demonstrate that our method achieves 81.45%source attribution accuracy,38.24%higher than rule-based and machine learning baselines.Real-world deployment further validates its effectiveness.
基金funded by Shanghai Science and Technology Innovation Action Plan Project(22140901100)Shanghai Key Laboratory of Molecular Imaging(18DZ2260400)Shanghai University of Medicine and Health Science Seed Fund(SSF-24-21-01).
文摘Background:Hepatocellular carcinoma(HCC)is a highly lethal malignancy driven by both intrinsic oncogenic pathways and immune microenvironmental regulation.Emerging evidence suggests that DNASE1L3 may influence tumor biology and immune responses;however,its specific roles in HCC progression and macrophage-mediated regulation remain unclear.This study aimed to elucidate the biological functions of DNASE1L3 in HCC and to determine how it modulates tumor behavior and immune interactions.Methods:Bioinformatics analyses of the GSE41804 and Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC)datasets were used to identify hub genes.Functional assays assessed the impact of DNASE1L3 on HCC cell proliferation,migration,invasion,and cell cycle progression.The effects of DNASE1L3 on macrophage polarization and the Wnt/β-catenin signaling pathway were examined using a co-culture system.An HCC organoid model was established to further validate its regulatory function.Results:Eight prognostic signature genes were identified,with deoxyribonuclease I-like 3(DNase I-like 3)selected as the hub gene.DNASE1L3 overexpression suppressed HCC cell growth,inhibited migration and invasion,induced G1 arrest,and modulated epithelial-mesenchymal transition(EMT)markers.DNASE1L3 knockdown promoted M2-like macrophage polarization.Mechanistically,DNASE1L3 interacted withβ-catenin to enhance its ubiquitination and degradation,thereby inhibiting Wnt/β-catenin signaling and reducing PD-L1 expression.DNASE1L3 overexpression similarly restricted organoid growth and suppressed pathway activity.Conclusion:DNASE1L3 acts as a negative regulator of HCC progression by targeting the Wnt/β-catenin pathway and reducing PD-L1 expression,thereby influencing both tumor cell behavior and macrophage-mediated immune responses.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)。
文摘With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.
文摘Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging from 278.15 K to 318.15 K.The solubility in each system was found to be positively correlated with temperature.Furthermore,solubility data were analyzed using four equations:the modified Apelblat equation,Van’t Hoff equation,λh equation and CNIBS/R-K equations,and they provided satisfactory results for both two systems.The average root-mean-square deviation(105RMSD)values for these models were less than 13.93.Calculations utilizing the Van’t Hoff equation and Gibbs equations facilitated the derivation of apparent thermodynamic properties of NTO dissolution in the two systems,including values for Gibbs free energy,enthalpy and entropy.The%ζ_(H)is larger than%ζ_(TS),and all the%ζ_(H)data are≥58.63%,indicating that the enthalpy make a greater contribution than entropy to theΔG_(soln)^(Θ).
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
基金Supported by the China National Oil and Gas Major Project(2016ZX05010-003)PetroChina Science and Technology Major Project(2019B1210,2021DJ1201).
文摘To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.
基金supports from National Natural Science Foundation of China(Grant No.62205117,52275429)National Key Research and Development Program of China(Grant No.2021YFF0502700)+2 种基金Young Elite Scientists Sponsorship Program by CAST(Grant No.2022QNRC001)West Light Foundation of the Chinese Academy of Sciences(Grant No.xbzg-zdsys-202206)Hubei Natural Science Foundation Innovative Research Group Project(2024AFA025).
文摘The popularity of deep learning has boosted computer-generated holography(CGH)as a vibrant research field,particularly physics-driven unsupervised learning.Nevertheless,present unsupervised CGH models have not yet explored the potential of generating full-color 3D holograms through a unified framework.In this study,we propose a lightweight multiwavelength network model capable of high-fidelity and efficient full-color hologram generation in both 2D and 3D display,called IncepHoloRGB.The high-speed simultaneous generation of RGB holograms at 191 frames per second(FPS)is based on Inception sampling blocks and multi-wavelength propagation module integrated with depth-traced superimposition,achieving an average structural similarity(SSIM)of 0.88 and peak signal-to-noise ratio(PSNR)of 29.00 on the DIV2K test set in reconstruction.Full-color reconstruction of numerical simulations and optical experiments shows that IncepHoloRGB is versatile to diverse scenarios and can obtain authentic full-color holographic 3D display within a unified network model,paving the way for applications towards real-time dynamic naked-eye 3D display,virtual and augmented reality(VR/AR)systems.
基金financial support from the National Key R&D Program of China (No. 2021YFC3000600)National Natural Science Foundation of China (No. 41872206)National Nonprofit Fundamental Research Grant of China, Institute of Geology, China, Earthquake Administration (No. IGCEA2010)
文摘The three-dimensional(3D)geometry of a fault is a critical control on earthquake nucleation,dynamic rupture,stress triggering,and related seismic hazards.Therefore,a 3D model of an active fault can significantly improve our understanding of seismogenesis and our ability to evaluate seismic hazards.Utilising the SKUA GoCAD software,we constructed detailed seismic fault models for the 2021 M_(S)6.4 Yangbi earthquake in Yunnan,China,using two sets of relocated earthquake catalogs and focal mechanism solutions following a convenient 3D fault modeling workflow.Our analysis revealed a NW-striking main fault with a high-angle SW dip,accompanied by two branch faults.Interpretation of one dataset revealed a single NNW-striking branch fault SW of the main fault,whereas the other dataset indicated four steep NNE-striking segments with a left-echelon pattern.Additionally,a third ENE-striking short fault was identified NE of the main fault.In combination with the spatial distribution of pre-existing faults,our 3D fault models indicate that the Yangbi earthquake reactivated pre-existing NW-and NE-striking fault directions rather than the surface-exposed Weixi-Qiaohou-Weishan Fault zone.The occurrence of the Yangbi earthquake demonstrates that the reactivation of pre-existing faults away from active fault zones,through either cascade or conjugate rupture modes,can cause unexpected moderate-large earthquakes and severe disasters,necessitating attention in regions like southeast Xizang,which have complex fault systems.
