Taking Zhejiang Province as an example,this paper explores the mechanisms and implementation pathways through which the low-altitude economy drives the transformation and upgrading of the tourism industry.It finds tha...Taking Zhejiang Province as an example,this paper explores the mechanisms and implementation pathways through which the low-altitude economy drives the transformation and upgrading of the tourism industry.It finds that the low-altitude economy can effectively promote the development of high-end and diversified tourism in Zhejiang by innovating tourism formats,optimizing resource allocation,and enhancing tourist experiences.Besides,it analyzes the current development status of the low-altitude economy in Zhejiang and its potential for integration with tourism,revealing specific enabling pathways for tourism transformation,including low-altitude sightseeing,aviation tourism,and low-altitude sports.Finally,it proposes policy recommendations such as strengthening policy support,enhancing infrastructure development,and cultivating market entities.The findings aim to provide theoretical references and practical guidance for the high-quality development of tourism in Zhejiang Province.展开更多
Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networ...Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.展开更多
AIM:To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis.METHODS:A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was...AIM:To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis.METHODS:A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied,with all subjects having liver biopsy data and DNA available for testing.This study assessed the association of eight single nucleotide polymorphisms(SNPs)in a total of six genes including toll-like receptor 4(TLR4),transforming growth factor-beta(TGF-β),oxoguanine DNA glycosylase,monocyte chemoattractant protein 1,chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity.Genotyping was performed using high resolution melt analysis and sequencing.The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration.RESULTS:There were significant associations between the cofactors of male gender(P=0.0001),increasing age(P=0.006),alcohol consumption(P=0.0001),steatosis(P=0.03),hepatic iron concentration(P<0.0001)and the presence of hepatic fibrosis.Of the candidate gene polymorphisms studied,none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors.We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied.Importantly,in this large,well characterised cohort of patients there was no association between SNPs for TGF-βor TLR4and the presence of fibrosis,cirrhosis or increasing fibrosis stage in multivariate analysis.CONCLUSION:In our large,well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis.展开更多
Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may de...Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may depend on the pathological process and cell types involved.Voltage-gated sodium channels(VGSCs)are essential ion channels for the generation of action potentials in neurons,and are involved in various neuroexcitation-related diseases.However,the effects of TGF-β1 on the functional properties of VGSCs and firing properties in cortical neurons remain unclear.In this study,we investigated the effects of TGF-β1 on VGSC function and firing properties in primary cortical neurons from mice.We found that TGF-β1 increased VGSC current density in a dose-and time-dependent manner,which was attributable to the upregulation of Nav1.3 expression.Increased VGSC current density and Nav1.3 expression were significantly abolished by preincubation with inhibitors of mitogen-activated protein kinase kinase(PD98059),p38 mitogen-activated protein kinase(SB203580),and Jun NH2-terminal kinase 1/2 inhibitor(SP600125).Interestingly,TGF-β1 significantly increased the firing threshold of action potentials but did not change their firing rate in cortical neurons.These findings suggest that TGF-β1 can increase Nav1.3 expression through activation of the ERK1/2-JNK-MAPK pathway,which leads to a decrease in the firing threshold of action potentials in cortical neurons under pathological conditions.Thus,this contributes to the occurrence and progression of neuroexcitatory-related diseases of the central nervous system.展开更多
OBJECTIVES:To investigate the effect of Bushen Tongluo recipe(BSTLR, 补肾通络方) on rats with diabetic kidney disease(DKD) and to explore the underlying mechanism of action. METHODS:The rat model of DKD was establishe...OBJECTIVES:To investigate the effect of Bushen Tongluo recipe(BSTLR, 补肾通络方) on rats with diabetic kidney disease(DKD) and to explore the underlying mechanism of action. METHODS:The rat model of DKD was established, and rats were treated with different doses of BSTLR. Body weight and the levels of urinary protein, α1-microglobulin, glucose, blood urea nitrogen, creatinine, Cystatin C, superoxide dismutase, malondialdehyde, and catalase were analyzed biochemically or by enzyme-linked immunosorbent assay. The pathological damage to renal tissues was assessed by hematoxylin-eosin staining. Immunohistochemical staining was carried out to detect the expression levels of fibronectin, E-cadherin, α-smooth muscle actin, laminin, vimentin, collagen type Ⅳ in kidney tissues. Western blot analysis was conducted to analyze the expression levels of Nephrin, Desmin, Podocin, transforming growth factor-β1, mothers against decapentaplegic homolog 3(Smad3), Notch1, jagged, hairy and enhancer of split 1(Hes1) in kidney tissues, and the expression levels of maternally expressed gene 3(MEG3) and mi R-145 were measured by quantitative reverse transcription-polymerase chain reaction. Moreover, dual-luciferase reporter assay was employed to verify the binding of mi R-145 to MEG3. RESULTS:BSTLR increased the body weight of DKD rats, effectively ameliorated the renal function and pathological injury in DKD, regulated the balance of renal oxidative stress, inhibited the TGF/Notch signaling pathway, and affected the variations in the lnc RNA MEG3/mi R-145 axis. CONCLUSION:BSTLR improved oxidative stress homeostasis, inhibited the TGF/Notch signaling pathway, and regulated the lnc RNA MEG3/mi R-145 axis, effectively delaying the progression of DKD.展开更多
In-situ observations on α/γ phase transformation were made to study the effects of grain boundary microstructures on the formation of a new phase and the migration of α/γ interphase boundary in an iron4. 2%Cr allo...In-situ observations on α/γ phase transformation were made to study the effects of grain boundary microstructures on the formation of a new phase and the migration of α/γ interphase boundary in an iron4. 2%Cr alloy. It was found that triple junctions with more random boundaries could be the primary nucleation sites for a new phase, while triple junctions with low angle or low ∑ coincidence boundaries did not play any role as preferential sites. The migration of α/γ interphase boundary during heating over the transformation temperature range showed the two stage behaviour characterized by a stage with a migration velocity of 0. 33-0. 75 mm/s and secondly by a stage with 3. 7-7. 6 mm/s. It was also found that abnormal grain growth and a high density of ∑3 coincidence boundaries could occur in a phase with bcc structure after cycling of α/γ phase transformation. A new mechanism of nucleation and growth of a new phase in α/γ phase transformation is proposed on the basis of roles of plane-matching interphase boundaries, as previously discussed on the origin of anisotropy of grain growth due to the migration of {110} plane-matching boundaries in Fe-3z%Si alloy. The most recent theoretical work on the distribution of plane-matching boundaries in solids with different crystal structures was found to be useful for the understanding of nucleation and growth during α/γ phase transformation.展开更多
Battery technology plays a crucial role across various sectors,powering devices from smartphones to electric vehicles and supporting grid-scale energy storage.To ensure their safety and efficiency,batteries must be ev...Battery technology plays a crucial role across various sectors,powering devices from smartphones to electric vehicles and supporting grid-scale energy storage.To ensure their safety and efficiency,batteries must be evaluated under diverse operating conditions.Traditional modeling techniques,which often rely on first principles and atomic-level calculations,struggle with practical applications due to incomplete or noisy data.Furthermore,the complexity of battery dynamics,shaped by physical,chemical,and electrochemical interactions,presents substantial challenges for precise and efficient modeling.The Transformer model,originally designed for natural language processing,has proven effective in time-series analysis and forecasting.It adeptly handles the extensive,complex datasets produced during battery cycles,efficiently filtering out noise and identifying critical features without extensive preprocessing.This capability positions Transformers as potent tools for tackling the intricacies of battery data.This review explores the application of customized Transformers in battery state estimation,emphasizing crucial aspects such as charging,health assessment,lifetime prediction,and safety monitoring.It highlights the distinct advantages of Transformer-based models and addresses ongoing challenges and future opportunities in the field.By combining data-driven AI techniques with empirical insights from battery analysis,these pre-trained models can deliver precise diagnostics and comprehensive monitoring,enhancing performance metrics like health monitoring,anomaly detection,and early-warning systems.This integrated approach promises significant improvements in battery technology management and application.展开更多
Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herb...Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herbaceous plants,with notable applications in species such as Arabidopsis(Yin et al.,2024),soybean(Zhang et al.,2024),rice(Zhang et al.,2020),and Chinese cabbage(Li et al.,2021).However,its application in fruit trees is limited.This is primarily due to their long growth cycles and lack of rapid,efficient,and stable transgenic systems,which severely hinders foundational research involving plant genetic transformation(Mei et al.,2024).Furthermore,for subtropical fruit trees,the presence of recalcitrant seeds adds an extra layer of difficulty to genetic transformation(Umarani et al.,2015),as most methods rely on seed germination as a basis for transformation.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
We reported in this manuscript that TGF-beta1 induces apoptosis in AML12 murine hepatocytes, which is associated with the activation of p38 MAPK signaling pathway. SB202190, a specific inhibitor of p38 MAPK, strongly ...We reported in this manuscript that TGF-beta1 induces apoptosis in AML12 murine hepatocytes, which is associated with the activation of p38 MAPK signaling pathway. SB202190, a specific inhibitor of p38 MAPK, strongly inhibited the TGF-beta1-induced apoptosis and PAI-1 promoter activity. Treatment of cells with TGF-beta1 activates p38. Furthermore, over-expression of dominant negative mutant p38 also reduced the TGF-beta1-induced apoptosis. The data indicate that the activation of p38 is involved in TGF-beta1-mediated gene expression and apoptosis.展开更多
Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that...Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
文摘Taking Zhejiang Province as an example,this paper explores the mechanisms and implementation pathways through which the low-altitude economy drives the transformation and upgrading of the tourism industry.It finds that the low-altitude economy can effectively promote the development of high-end and diversified tourism in Zhejiang by innovating tourism formats,optimizing resource allocation,and enhancing tourist experiences.Besides,it analyzes the current development status of the low-altitude economy in Zhejiang and its potential for integration with tourism,revealing specific enabling pathways for tourism transformation,including low-altitude sightseeing,aviation tourism,and low-altitude sports.Finally,it proposes policy recommendations such as strengthening policy support,enhancing infrastructure development,and cultivating market entities.The findings aim to provide theoretical references and practical guidance for the high-quality development of tourism in Zhejiang Province.
基金the Deanship of Graduate Studies and Scientific Research at Najran University,Saudi Arabia,for their financial support through the Easy Track Research program,grant code(NU/EFP/MRC/13).
文摘Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.
基金Supported by NHMRC Medical Postgraduate Scholarship and the Royal Brisbane and Women’s Hospital Research Foundation to Wood MJthe National Health and Medical Research Council(NHMRC)to Ramm GA and Powell LW+1 种基金the recipient of an NHMRC Senior Research Fellowship,1024672 to Subramaniam VNan NHMRC Senior Research Fellowship,No.552409 to Ramm GA
文摘AIM:To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis.METHODS:A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied,with all subjects having liver biopsy data and DNA available for testing.This study assessed the association of eight single nucleotide polymorphisms(SNPs)in a total of six genes including toll-like receptor 4(TLR4),transforming growth factor-beta(TGF-β),oxoguanine DNA glycosylase,monocyte chemoattractant protein 1,chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity.Genotyping was performed using high resolution melt analysis and sequencing.The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration.RESULTS:There were significant associations between the cofactors of male gender(P=0.0001),increasing age(P=0.006),alcohol consumption(P=0.0001),steatosis(P=0.03),hepatic iron concentration(P<0.0001)and the presence of hepatic fibrosis.Of the candidate gene polymorphisms studied,none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors.We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied.Importantly,in this large,well characterised cohort of patients there was no association between SNPs for TGF-βor TLR4and the presence of fibrosis,cirrhosis or increasing fibrosis stage in multivariate analysis.CONCLUSION:In our large,well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis.
基金supported by the Natural Science Foundation of Guangdong Province,Nos.2019A1515010649(to WC),2022A1515012044(to JS)the China Postdoctoral Science Foundation,No.2018M633091(to JS).
文摘Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may depend on the pathological process and cell types involved.Voltage-gated sodium channels(VGSCs)are essential ion channels for the generation of action potentials in neurons,and are involved in various neuroexcitation-related diseases.However,the effects of TGF-β1 on the functional properties of VGSCs and firing properties in cortical neurons remain unclear.In this study,we investigated the effects of TGF-β1 on VGSC function and firing properties in primary cortical neurons from mice.We found that TGF-β1 increased VGSC current density in a dose-and time-dependent manner,which was attributable to the upregulation of Nav1.3 expression.Increased VGSC current density and Nav1.3 expression were significantly abolished by preincubation with inhibitors of mitogen-activated protein kinase kinase(PD98059),p38 mitogen-activated protein kinase(SB203580),and Jun NH2-terminal kinase 1/2 inhibitor(SP600125).Interestingly,TGF-β1 significantly increased the firing threshold of action potentials but did not change their firing rate in cortical neurons.These findings suggest that TGF-β1 can increase Nav1.3 expression through activation of the ERK1/2-JNK-MAPK pathway,which leads to a decrease in the firing threshold of action potentials in cortical neurons under pathological conditions.Thus,this contributes to the occurrence and progression of neuroexcitatory-related diseases of the central nervous system.
文摘OBJECTIVES:To investigate the effect of Bushen Tongluo recipe(BSTLR, 补肾通络方) on rats with diabetic kidney disease(DKD) and to explore the underlying mechanism of action. METHODS:The rat model of DKD was established, and rats were treated with different doses of BSTLR. Body weight and the levels of urinary protein, α1-microglobulin, glucose, blood urea nitrogen, creatinine, Cystatin C, superoxide dismutase, malondialdehyde, and catalase were analyzed biochemically or by enzyme-linked immunosorbent assay. The pathological damage to renal tissues was assessed by hematoxylin-eosin staining. Immunohistochemical staining was carried out to detect the expression levels of fibronectin, E-cadherin, α-smooth muscle actin, laminin, vimentin, collagen type Ⅳ in kidney tissues. Western blot analysis was conducted to analyze the expression levels of Nephrin, Desmin, Podocin, transforming growth factor-β1, mothers against decapentaplegic homolog 3(Smad3), Notch1, jagged, hairy and enhancer of split 1(Hes1) in kidney tissues, and the expression levels of maternally expressed gene 3(MEG3) and mi R-145 were measured by quantitative reverse transcription-polymerase chain reaction. Moreover, dual-luciferase reporter assay was employed to verify the binding of mi R-145 to MEG3. RESULTS:BSTLR increased the body weight of DKD rats, effectively ameliorated the renal function and pathological injury in DKD, regulated the balance of renal oxidative stress, inhibited the TGF/Notch signaling pathway, and affected the variations in the lnc RNA MEG3/mi R-145 axis. CONCLUSION:BSTLR improved oxidative stress homeostasis, inhibited the TGF/Notch signaling pathway, and regulated the lnc RNA MEG3/mi R-145 axis, effectively delaying the progression of DKD.
文摘In-situ observations on α/γ phase transformation were made to study the effects of grain boundary microstructures on the formation of a new phase and the migration of α/γ interphase boundary in an iron4. 2%Cr alloy. It was found that triple junctions with more random boundaries could be the primary nucleation sites for a new phase, while triple junctions with low angle or low ∑ coincidence boundaries did not play any role as preferential sites. The migration of α/γ interphase boundary during heating over the transformation temperature range showed the two stage behaviour characterized by a stage with a migration velocity of 0. 33-0. 75 mm/s and secondly by a stage with 3. 7-7. 6 mm/s. It was also found that abnormal grain growth and a high density of ∑3 coincidence boundaries could occur in a phase with bcc structure after cycling of α/γ phase transformation. A new mechanism of nucleation and growth of a new phase in α/γ phase transformation is proposed on the basis of roles of plane-matching interphase boundaries, as previously discussed on the origin of anisotropy of grain growth due to the migration of {110} plane-matching boundaries in Fe-3z%Si alloy. The most recent theoretical work on the distribution of plane-matching boundaries in solids with different crystal structures was found to be useful for the understanding of nucleation and growth during α/γ phase transformation.
基金the support provided by the California Department of Transportation(Caltrans)through the Fiscal Year 2023-24 grant(65A0686)for the research project titled‘Revolutions in Battery technologies and Future Electric Vehicles’。
文摘Battery technology plays a crucial role across various sectors,powering devices from smartphones to electric vehicles and supporting grid-scale energy storage.To ensure their safety and efficiency,batteries must be evaluated under diverse operating conditions.Traditional modeling techniques,which often rely on first principles and atomic-level calculations,struggle with practical applications due to incomplete or noisy data.Furthermore,the complexity of battery dynamics,shaped by physical,chemical,and electrochemical interactions,presents substantial challenges for precise and efficient modeling.The Transformer model,originally designed for natural language processing,has proven effective in time-series analysis and forecasting.It adeptly handles the extensive,complex datasets produced during battery cycles,efficiently filtering out noise and identifying critical features without extensive preprocessing.This capability positions Transformers as potent tools for tackling the intricacies of battery data.This review explores the application of customized Transformers in battery state estimation,emphasizing crucial aspects such as charging,health assessment,lifetime prediction,and safety monitoring.It highlights the distinct advantages of Transformer-based models and addresses ongoing challenges and future opportunities in the field.By combining data-driven AI techniques with empirical insights from battery analysis,these pre-trained models can deliver precise diagnostics and comprehensive monitoring,enhancing performance metrics like health monitoring,anomaly detection,and early-warning systems.This integrated approach promises significant improvements in battery technology management and application.
基金funded by the Key-Area Research and Development Program of Guangdong Province(Grant No.2022B0202070002)the Guangxi Science and Technology Major Program(Grant No.GuikeAA23023007-2)+1 种基金the Guangdong Province Modern Agricultural Industry Technology System Innovation Team Construction Project(2024CXTD19)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515010303)。
文摘Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herbaceous plants,with notable applications in species such as Arabidopsis(Yin et al.,2024),soybean(Zhang et al.,2024),rice(Zhang et al.,2020),and Chinese cabbage(Li et al.,2021).However,its application in fruit trees is limited.This is primarily due to their long growth cycles and lack of rapid,efficient,and stable transgenic systems,which severely hinders foundational research involving plant genetic transformation(Mei et al.,2024).Furthermore,for subtropical fruit trees,the presence of recalcitrant seeds adds an extra layer of difficulty to genetic transformation(Umarani et al.,2015),as most methods rely on seed germination as a basis for transformation.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
基金grants fromthe Chinese Academy of Sciences (No. KJ951-BI608), the National Natural Sciences FOundation ofChina (No. 39625007 and
文摘We reported in this manuscript that TGF-beta1 induces apoptosis in AML12 murine hepatocytes, which is associated with the activation of p38 MAPK signaling pathway. SB202190, a specific inhibitor of p38 MAPK, strongly inhibited the TGF-beta1-induced apoptosis and PAI-1 promoter activity. Treatment of cells with TGF-beta1 activates p38. Furthermore, over-expression of dominant negative mutant p38 also reduced the TGF-beta1-induced apoptosis. The data indicate that the activation of p38 is involved in TGF-beta1-mediated gene expression and apoptosis.
文摘Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
