Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postn...Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postnatal neurogenesis remains unclear.In this study,to define the precise role of transforming growth factor-βsignaling in postnatal neurogenesis at distinct stages of the neurogenic cascade both in vitro and in vivo,we developed two novel inducible and cell type-specific mouse models to specifically silence transforming growth factor-βsignaling in neural stem cells in(mGFAPcre-ALK5fl/fl-Ai9)or immature neuroblasts in(DCXcreERT2-ALK5fl/fl-Ai9).Our data showed that exogenous transforming growth factor-βtreatment led to inhibition of the proliferation of primary neural stem cells while stimulating their migration.These effects were abolished in activin-like kinase 5(ALK5)knockout primary neural stem cells.Consistent with this,inhibition of transforming growth factor-βsignaling with SB-431542 in wild-type neural stem cells stimulated proliferation while inhibited the migration of neural stem cells.Interestingly,deletion of transforming growth factor-βreceptor in neural stem cells in vivo inhibited the migration of postnatal born neurons in mGFAPcre-ALK5fl/fl-Ai9 mice,while abolishment of transforming growth factor-βsignaling in immature neuroblasts in DCXcreERT2-ALK5fl/fl-Ai9 mice did not affect the migration of these cells in the hippocampus.In summary,our data supports a dual role of transforming growth factor-βsignaling in the proliferation and migration of neural stem cells in vitro.Moreover,our data provides novel insights on cell type-specific-dependent requirements of transforming growth factor-βsignaling on neural stem cell proliferation and migration in vivo.展开更多
Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.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
Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a...Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.展开更多
基金supported by NIH grants,Nos.R01NS125074,R01AG083164,R01NS107365,and R21NS127177(to YL),1F31NS129204-01A1(to KW)and Albert Ryan Fellowship(to KW).
文摘Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postnatal neurogenesis remains unclear.In this study,to define the precise role of transforming growth factor-βsignaling in postnatal neurogenesis at distinct stages of the neurogenic cascade both in vitro and in vivo,we developed two novel inducible and cell type-specific mouse models to specifically silence transforming growth factor-βsignaling in neural stem cells in(mGFAPcre-ALK5fl/fl-Ai9)or immature neuroblasts in(DCXcreERT2-ALK5fl/fl-Ai9).Our data showed that exogenous transforming growth factor-βtreatment led to inhibition of the proliferation of primary neural stem cells while stimulating their migration.These effects were abolished in activin-like kinase 5(ALK5)knockout primary neural stem cells.Consistent with this,inhibition of transforming growth factor-βsignaling with SB-431542 in wild-type neural stem cells stimulated proliferation while inhibited the migration of neural stem cells.Interestingly,deletion of transforming growth factor-βreceptor in neural stem cells in vivo inhibited the migration of postnatal born neurons in mGFAPcre-ALK5fl/fl-Ai9 mice,while abolishment of transforming growth factor-βsignaling in immature neuroblasts in DCXcreERT2-ALK5fl/fl-Ai9 mice did not affect the migration of these cells in the hippocampus.In summary,our data supports a dual role of transforming growth factor-βsignaling in the proliferation and migration of neural stem cells in vitro.Moreover,our data provides novel insights on cell type-specific-dependent requirements of transforming growth factor-βsignaling on neural stem cell proliferation and migration in vivo.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
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.
基金supported by Shandong Provincial Natural Science Foundation(ZR2024ME229,ZR2024ZD29).
文摘Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.
