Glaucoma,a visual thief,is characterized by elevated intraocular pressure(IOP)and the loss of retinal ganglion cells(RGCs).Selecting suitable animals for preclinical models is of great significance in research on the ...Glaucoma,a visual thief,is characterized by elevated intraocular pressure(IOP)and the loss of retinal ganglion cells(RGCs).Selecting suitable animals for preclinical models is of great significance in research on the prevention,early screening,and effective treatments of glaucoma.Rabbit eyeballs possess similar vascularity and aqueous humor outflow pathways to those of humans.Thus,they are among the earliest in vivo models used in glaucoma research.Over the years,rabbit models have made substantial contributions to understanding glaucomatous pathophysiology,surgical adaptations,biomedical device development,and drug development for reducing IOP,protecting RGCs,and inhibiting fibrosis.Compared to other animals,rabbits fit better with surgical operations and cost less.This review summarizes the merits and demerits of different ways to produce glaucomatous rabbit models,such as intracameral injection,vortex vein obstruction,Trendelenburg position,laser photo-coagulation,glucocorticoid induction,limbal buckling induction,retinal ischemia–reperfusion models,and spontaneous models.We analyzed their mechanisms in the hope of providing more references for experimental design and promoting the understanding of glaucoma treatment strategies.展开更多
Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-...Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective.To address these challenges,this research proposes a computer-aided diagnosis(CAD)approach using Artificial Intelligence(AI)techniques for binary and multiclass classification of glaucoma stages.An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network(ConvNet)models–ResNet-50,VGG-16,and InceptionV3 is utilized in this paper.This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models,leveraging their complementary strengths.The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis.Classification is performed on a dataset collected from the Harvard Dataverse repository.With the proposed technique,for Normal vs.Advanced glaucoma classification,a validation accuracy of 98.04%and testing accuracy of 98.03%is achieved,with a specificity of 100%which outperforms stateof-the-art methods.For multiclass classification,the suggested ensemble approach achieved a precision and sensitivity of 97%,specificity,and testing accuracy of 98.57%and 96.82%,respectively.The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma,leading to more reliable,efficient,and timely diagnosis,particularly for early-stage detection and staging of the disease.While the proposed method demonstrates high accuracy and robustness,the study is limited by the evaluation of a single dataset.Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.展开更多
Glutamate excitotoxicity has been shown to play an important role in glaucoma, and glutamate can induce ferroptosis. The p38 mitogenactivated protein kinase(MAPK) pathway inhibitor SB202190 has a potential ability to ...Glutamate excitotoxicity has been shown to play an important role in glaucoma, and glutamate can induce ferroptosis. The p38 mitogenactivated protein kinase(MAPK) pathway inhibitor SB202190 has a potential ability to suppress ferroptosis, and its downstream targets, such as p53, have been shown to be associated with ferroptosis. However, whether ferroptosis also occurs in retinal ganglion cells in response to glutamate excitotoxicity and whether inhibition of ferroptosis reduces the loss of retinal ganglion cells induced by glutamate excitotoxicity remain unclear. This study investigated ferroptosis in a glutamate-induced glaucoma rat model and explored the effects and molecular mechanisms of SB202190 on retinal ganglion cells. A glutamate-induced excitotoxicity model in R28 cells and an N-methyl-D-aspartate-induced glaucoma model in rats were used. In vitro experiments showed that glutamate induced the accumulation of iron and lipid peroxide and morphological changes of mitochondria in R28 cells, and SB202190 inhibited these changes. Glutamate induced the levels of p-p38 MAPK/p38 MAPK and SAT1 and decreased the expression levels of ferritin light chain, SLC7A11, and GPX4. SB202190 inhibited the expression of iron death-related proteins induced by glutamate. In vivo experiments showed that SB202190 attenuated N-methyl-D-aspartate-induced damage to rat retinal ganglion cells and improved visual function. These results suggest that SB202190 can inhibit ferroptosis and protect retinal ganglion cells by regulating ferritin light chain, SAT1, and SLC7A11/Gpx4 pathways and may represent a potential retina protectant.展开更多
In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR i...In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.展开更多
Glaucoma is a group of eye diseases characterized by progressive loss of retinal ganglion cells(RGCs)and optic nerve degeneration.During this process,the visual field is reduced,and blindness may ultimately occur.Worl...Glaucoma is a group of eye diseases characterized by progressive loss of retinal ganglion cells(RGCs)and optic nerve degeneration.During this process,the visual field is reduced,and blindness may ultimately occur.Worldwide,glaucoma is the second leading cause of blindness,with about 80 million people affected.Glaucoma is a multifactorial disease and due to its complexity,the exact pathomechanisms are not fully understood yet.However,different risk factors,such as elevated intraocular pressure(IOP),age,or myopia,have been identified to date(EGS,2021).展开更多
AIM:To explore the therapeutic effect and main molecular mechanisms of acteoside in a glaucoma model in DBA/2J mice.METHODS:Proteomics was used to compare the differentially expressed proteins of C57 and DBA/2J mice.A...AIM:To explore the therapeutic effect and main molecular mechanisms of acteoside in a glaucoma model in DBA/2J mice.METHODS:Proteomics was used to compare the differentially expressed proteins of C57 and DBA/2J mice.After acteoside administration in DBA/2J mice,anterior segment observation,intraocular pressure(IOP)monitoring,electrophysiology examination,and hematoxylin and eosin staining were used to analyze any potential effects.Immunohistochemistry(IHC)assays were used to verify the proteomics results.Furthermore,retinal ganglion cell 5(RGC5)cell proliferation was assessed with cell counting kit-8(CCK-8)assays.Serta domain-containing protein 4(Sertad4)mRNA and protein expression levels were measured by qRT-PCR and Western blot analysis,respectively.RESULTS:Proteomics analysis suggested that Sertad4 was the most significantly differentially expressed protein.Compared with the saline group,the acteoside treatment group showed decreased IOP,improved N1-P1 wave amplitudes,thicker retina,and larger numbers of cells in the ganglion cell layer(GCL).The IHC results showed that Sertad4 expression levels in DBA/2J mice treated with acteoside were significantly lower than in the saline group.Acteoside treatment could improve RGC5 cell survival and reduce the Sertad4 mRNA and protein expression levels after glutamate injury.CONCLUSION:Sertad4 is differentially expressed in DBA/2J mice.Acteoside can protect RGCs from damage,possibly through the downregulation of Sertad4,and has a potential use in glaucoma treatment.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Given the alarmingly increasing rates of glaucoma worldwide and the lack of satisfactory treatments,there is a dire need to explore more feasible treatment options.Magnesium(Mg)is an essential element in maintaining t...Given the alarmingly increasing rates of glaucoma worldwide and the lack of satisfactory treatments,there is a dire need to explore more feasible treatment options.Magnesium(Mg)is an essential element in maintaining the functional and structural integrity of vital ocular tissues,but Mg and its alloys are rarely mentioned in ophthalmic applications.Our previous research found that hydroxyapatite-coated Mg(Mg@HA)shows the best biocompatibility and bioactivity,and exhibits the effect of inhibiting fibrosis after filtration surgery in the rabbit model,which is expected to be a promising material for glaucoma drainage device.In this study,we further demonstrated the anti-fibrosis effect of Mg@HA from the molecular signal level and the efficacy of implantation in the rabbit filtration surgery model.In vitro experiments showed the surface modification of Mg affects the adhesion behavior and the reorganization of cytoskeleton of Human Western blot analysis and immunofluorescence found that Mg@HA regulates the adhesion and motility of human Tenon’s capsule fibroblasts mainly by down-regulating the phosphorylation of Smad2 and Smad3 in the canonical transforming growth factor-beta(TGF-β)signaling pathway.By observing and recording the condition of filtering blebs and intraocular pressure after surgery,the effectiveness of Mg@HA applied in the rabbit filtration surgery model was further evaluated.In conclusion,the application of hydroxyapatite-coated Mg in the eye has good biocompatibility and has the potential to resist postoperative glaucoma filtration fibrosis,which may be mediated by the regulation of the TGFβ/Smad signaling pathway.展开更多
The prevalence of glaucoma, the second leading cause of global blindness, is increasing due to aging populations. In glaucoma, degeneration of the optic nerve and retinal ganglion cells(RGCs) causes visual field defec...The prevalence of glaucoma, the second leading cause of global blindness, is increasing due to aging populations. In glaucoma, degeneration of the optic nerve and retinal ganglion cells(RGCs) causes visual field defects and eventual blindness.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
基金The 64th batch of China Postdoctoral Science Foundation,Grant/Award Number:2018M640754National Natural Science Foundation of China,Grant/Award Number:81860870The“Wang Heng xin Scientific Research Fund”project of Hunan University of Chinese Medicine,Grant/Award Number:2023HX007。
文摘Glaucoma,a visual thief,is characterized by elevated intraocular pressure(IOP)and the loss of retinal ganglion cells(RGCs).Selecting suitable animals for preclinical models is of great significance in research on the prevention,early screening,and effective treatments of glaucoma.Rabbit eyeballs possess similar vascularity and aqueous humor outflow pathways to those of humans.Thus,they are among the earliest in vivo models used in glaucoma research.Over the years,rabbit models have made substantial contributions to understanding glaucomatous pathophysiology,surgical adaptations,biomedical device development,and drug development for reducing IOP,protecting RGCs,and inhibiting fibrosis.Compared to other animals,rabbits fit better with surgical operations and cost less.This review summarizes the merits and demerits of different ways to produce glaucomatous rabbit models,such as intracameral injection,vortex vein obstruction,Trendelenburg position,laser photo-coagulation,glucocorticoid induction,limbal buckling induction,retinal ischemia–reperfusion models,and spontaneous models.We analyzed their mechanisms in the hope of providing more references for experimental design and promoting the understanding of glaucoma treatment strategies.
基金funded by Department of Robotics and Mechatronics Engineering,Kennesaw State University,Marietta,GA 30060,USA.
文摘Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective.To address these challenges,this research proposes a computer-aided diagnosis(CAD)approach using Artificial Intelligence(AI)techniques for binary and multiclass classification of glaucoma stages.An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network(ConvNet)models–ResNet-50,VGG-16,and InceptionV3 is utilized in this paper.This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models,leveraging their complementary strengths.The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis.Classification is performed on a dataset collected from the Harvard Dataverse repository.With the proposed technique,for Normal vs.Advanced glaucoma classification,a validation accuracy of 98.04%and testing accuracy of 98.03%is achieved,with a specificity of 100%which outperforms stateof-the-art methods.For multiclass classification,the suggested ensemble approach achieved a precision and sensitivity of 97%,specificity,and testing accuracy of 98.57%and 96.82%,respectively.The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma,leading to more reliable,efficient,and timely diagnosis,particularly for early-stage detection and staging of the disease.While the proposed method demonstrates high accuracy and robustness,the study is limited by the evaluation of a single dataset.Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.
基金supported by the National Natural Science Foundation of China,Nos.81974132,81770927Hunan Provincial Health Commission,No.20220702839+1 种基金the Natural Science Foundation of Hunan Province of China,No.2022JJ30076National Key R&D Program of China,No.2021YFA1101202(all to WS)。
文摘Glutamate excitotoxicity has been shown to play an important role in glaucoma, and glutamate can induce ferroptosis. The p38 mitogenactivated protein kinase(MAPK) pathway inhibitor SB202190 has a potential ability to suppress ferroptosis, and its downstream targets, such as p53, have been shown to be associated with ferroptosis. However, whether ferroptosis also occurs in retinal ganglion cells in response to glutamate excitotoxicity and whether inhibition of ferroptosis reduces the loss of retinal ganglion cells induced by glutamate excitotoxicity remain unclear. This study investigated ferroptosis in a glutamate-induced glaucoma rat model and explored the effects and molecular mechanisms of SB202190 on retinal ganglion cells. A glutamate-induced excitotoxicity model in R28 cells and an N-methyl-D-aspartate-induced glaucoma model in rats were used. In vitro experiments showed that glutamate induced the accumulation of iron and lipid peroxide and morphological changes of mitochondria in R28 cells, and SB202190 inhibited these changes. Glutamate induced the levels of p-p38 MAPK/p38 MAPK and SAT1 and decreased the expression levels of ferritin light chain, SLC7A11, and GPX4. SB202190 inhibited the expression of iron death-related proteins induced by glutamate. In vivo experiments showed that SB202190 attenuated N-methyl-D-aspartate-induced damage to rat retinal ganglion cells and improved visual function. These results suggest that SB202190 can inhibit ferroptosis and protect retinal ganglion cells by regulating ferritin light chain, SAT1, and SLC7A11/Gpx4 pathways and may represent a potential retina protectant.
基金funded byResearchers Supporting Project Number(RSPD2024R 553),King Saud University,Riyadh,Saudi Arabia.
文摘In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.
基金supported by the Deutsche Forschungsgemeinschaft(Germany,RE-4543/1-1 to SR).
文摘Glaucoma is a group of eye diseases characterized by progressive loss of retinal ganglion cells(RGCs)and optic nerve degeneration.During this process,the visual field is reduced,and blindness may ultimately occur.Worldwide,glaucoma is the second leading cause of blindness,with about 80 million people affected.Glaucoma is a multifactorial disease and due to its complexity,the exact pathomechanisms are not fully understood yet.However,different risk factors,such as elevated intraocular pressure(IOP),age,or myopia,have been identified to date(EGS,2021).
基金Supported by Tianjin Key Medical Discipline(Specialty)Construction Project(No.TJYXZDXK-037A).
文摘AIM:To explore the therapeutic effect and main molecular mechanisms of acteoside in a glaucoma model in DBA/2J mice.METHODS:Proteomics was used to compare the differentially expressed proteins of C57 and DBA/2J mice.After acteoside administration in DBA/2J mice,anterior segment observation,intraocular pressure(IOP)monitoring,electrophysiology examination,and hematoxylin and eosin staining were used to analyze any potential effects.Immunohistochemistry(IHC)assays were used to verify the proteomics results.Furthermore,retinal ganglion cell 5(RGC5)cell proliferation was assessed with cell counting kit-8(CCK-8)assays.Serta domain-containing protein 4(Sertad4)mRNA and protein expression levels were measured by qRT-PCR and Western blot analysis,respectively.RESULTS:Proteomics analysis suggested that Sertad4 was the most significantly differentially expressed protein.Compared with the saline group,the acteoside treatment group showed decreased IOP,improved N1-P1 wave amplitudes,thicker retina,and larger numbers of cells in the ganglion cell layer(GCL).The IHC results showed that Sertad4 expression levels in DBA/2J mice treated with acteoside were significantly lower than in the saline group.Acteoside treatment could improve RGC5 cell survival and reduce the Sertad4 mRNA and protein expression levels after glutamate injury.CONCLUSION:Sertad4 is differentially expressed in DBA/2J mice.Acteoside can protect RGCs from damage,possibly through the downregulation of Sertad4,and has a potential use in glaucoma treatment.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金supported by the National Natural Science Foundation of China(Grant No.81670860 and 81700836)Natural Science Foundation of Chongqing(Grant No.cstc2018jcyjAX0034 and cstc2018jcyjAX0016).
文摘Given the alarmingly increasing rates of glaucoma worldwide and the lack of satisfactory treatments,there is a dire need to explore more feasible treatment options.Magnesium(Mg)is an essential element in maintaining the functional and structural integrity of vital ocular tissues,but Mg and its alloys are rarely mentioned in ophthalmic applications.Our previous research found that hydroxyapatite-coated Mg(Mg@HA)shows the best biocompatibility and bioactivity,and exhibits the effect of inhibiting fibrosis after filtration surgery in the rabbit model,which is expected to be a promising material for glaucoma drainage device.In this study,we further demonstrated the anti-fibrosis effect of Mg@HA from the molecular signal level and the efficacy of implantation in the rabbit filtration surgery model.In vitro experiments showed the surface modification of Mg affects the adhesion behavior and the reorganization of cytoskeleton of Human Western blot analysis and immunofluorescence found that Mg@HA regulates the adhesion and motility of human Tenon’s capsule fibroblasts mainly by down-regulating the phosphorylation of Smad2 and Smad3 in the canonical transforming growth factor-beta(TGF-β)signaling pathway.By observing and recording the condition of filtering blebs and intraocular pressure after surgery,the effectiveness of Mg@HA applied in the rabbit filtration surgery model was further evaluated.In conclusion,the application of hydroxyapatite-coated Mg in the eye has good biocompatibility and has the potential to resist postoperative glaucoma filtration fibrosis,which may be mediated by the regulation of the TGFβ/Smad signaling pathway.
基金supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grants-in-Aid for Scientific Research (JP22K09804 to CHJP21K09688 to XG+3 种基金JP19KK0229, JP21H04786, JP21H02819 and JP21K18279 to TH)the Shiseido Female Researcher Science Grant (to XG)Mitsubishi FoundationTakeda Science Foundation (to TH)。
文摘The prevalence of glaucoma, the second leading cause of global blindness, is increasing due to aging populations. In glaucoma, degeneration of the optic nerve and retinal ganglion cells(RGCs) causes visual field defects and eventual blindness.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
