Background Unhealthy lifestyles have a considerable impact on the incidence of dementia.Skipping breakfast disturbs energy homeostasis and impairs brain function.In this study,we investigated the association between b...Background Unhealthy lifestyles have a considerable impact on the incidence of dementia.Skipping breakfast disturbs energy homeostasis and impairs brain function.In this study,we investigated the association between breakfast skipping and cognitive performance among community-dwelling adults.Methods We recruited 859 community-dwelling adults aged≥60 years from January 1 to December 31,2021.Participants’sociodemographic information and breakfast skipping habits were self-reported.Participants were followed up for 36 months and cognitive function was assessed using the Mini-Mental State Examination(MMSE)with an interval of 18 months.Trajectories of cognitive change were compared between individuals with and without breakfast skipping.To reduce the risk of bias owing to unmatched sample sizes between the groups,we conducted 1:1 propensity score matching(PSM)based on age,sex,education level,and ApoE genotype.Results At baseline and 18-month follow-up,no difference was found in MMSE scores between participants with and without breakfast skipping.However,those who habitually skipped breakfast had significantly lower MMSE scores than those who did not at 36-month follow-up.Individuals with habitual breakfast skipping had a steeper rate of cognitive decline than those without habitual breakfast skipping during follow-up.Breakfast skipping was a risk factor for longitudinal cognitive decline,defined as a decrease in MMSE scores of≥3,adjusted for age,sex,education,body mass index,ApoEε4 carrier status,hypertension,diabetes,and hyperlipidemia.At the last follow-up,participants who habitually skipped breakfast had significantly higher levels of ptau181 and NfL than those who did not.In the PSM cohort,similar findings were obtained regarding cognitive trajectories and plasma biomarkers.Conclusion Breakfast skipping was linked to an increased risk of long-term cognitive decline and neurodegeneration among older adults.The link between unhealthy dietary habits and cognitive decline may be attributed to a deficiency in neurorestoration resulting from inadequate energy consumption.展开更多
Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions,which is a typical manifestation of this trend.With the increasing complexity of im...Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions,which is a typical manifestation of this trend.With the increasing complexity of image in tasks and the growth of data scale,existing deep learning methods still have some limitations.For example,they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal.To solve this problem,the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network.The encoder of the proposed model generator consists of a dual-branch parallel structure with stacked CNN blocks and Transformer blocks,aiming to extract global and local feature information from images.Furthermore,a dual-branch fusion module is adopted to combine the features obtained from both branches.Additionally,a gated full-scale skip connection module is proposed to further enhance the coherence of the inpainting results and alleviate information loss.Finally,experimental results from the three public datasets demonstrate the superior performance of the proposed method.展开更多
Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional a...Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.展开更多
The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this...The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV,inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface.The novel tandem twin-rotor AAV was employed as the research subject and a strategybased ADRC control method for validation,comparing it with a strategy-based PID control method.The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability.The strategy-based ADRC control method exhibits a certain advantage in controlling height,pitch angle,and reducing impact force.This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.展开更多
Alterations in the mesenchymal-epithelial transition factor(MET)gene are critical drivers of non-small cell lung cancer(NSCLC).In recent years advances in precision therapies targeting MET alterations have significant...Alterations in the mesenchymal-epithelial transition factor(MET)gene are critical drivers of non-small cell lung cancer(NSCLC).In recent years advances in precision therapies targeting MET alterations have significantly expanded treatment options for NSCLC patients.These alterations include MET exon 14 skipping mutations(MET exon 14 skipping),MET gene amplifications,MET point mutations(primarily kinase domain mutations),and MET protein overexpression.Accurate identification of these alterations and appropriate selection of patient populations and targeted therapies are essential for improving clinical outcomes.The East China Lung Cancer Group,Youth Committee(ECLUNG YOUNG,Yangtze River Delta Lung Cancer Cooperation Group)has synthesized insights from China’s innovative drug development landscape and clinical practice to formulate an expert consensus on the diagnosis and treatment of NSCLC patients with MET alterations.This consensus addresses key areas,such as optimal testing timing,testing methods,testing strategies,quality control measures,and treatment approaches.By offering standardized recommendations,this guidance aims to streamline diagnostic and therapeutic processes and enhance clinical decision-making for NSCLC with MET alterations.展开更多
Circuit design of 32 bit logarithmic skip adder (LSA) is introduced to implement high performance,low power addition.ELM carry lookahead adder is included into groups of carry skip adder and the hybrid structure cost...Circuit design of 32 bit logarithmic skip adder (LSA) is introduced to implement high performance,low power addition.ELM carry lookahead adder is included into groups of carry skip adder and the hybrid structure costs 30% less hardware than ELM.At circuit level,a carry incorporating structure to include the primary carry input in carry chain and an 'and xor' structure to implement final sum logic in 32 bit LSA are designed for better optimization.For 5V,1μm process,32 bit LSA has a critical delay of 5 9ns and costs an area of 0 62mm 2,power consumption of 23mW at 100MHz.For 2 5V,0 25μm process,critical delay of 0 8ns,power dissipation of 5 2mW at 100MHz is simulated.展开更多
文摘Background Unhealthy lifestyles have a considerable impact on the incidence of dementia.Skipping breakfast disturbs energy homeostasis and impairs brain function.In this study,we investigated the association between breakfast skipping and cognitive performance among community-dwelling adults.Methods We recruited 859 community-dwelling adults aged≥60 years from January 1 to December 31,2021.Participants’sociodemographic information and breakfast skipping habits were self-reported.Participants were followed up for 36 months and cognitive function was assessed using the Mini-Mental State Examination(MMSE)with an interval of 18 months.Trajectories of cognitive change were compared between individuals with and without breakfast skipping.To reduce the risk of bias owing to unmatched sample sizes between the groups,we conducted 1:1 propensity score matching(PSM)based on age,sex,education level,and ApoE genotype.Results At baseline and 18-month follow-up,no difference was found in MMSE scores between participants with and without breakfast skipping.However,those who habitually skipped breakfast had significantly lower MMSE scores than those who did not at 36-month follow-up.Individuals with habitual breakfast skipping had a steeper rate of cognitive decline than those without habitual breakfast skipping during follow-up.Breakfast skipping was a risk factor for longitudinal cognitive decline,defined as a decrease in MMSE scores of≥3,adjusted for age,sex,education,body mass index,ApoEε4 carrier status,hypertension,diabetes,and hyperlipidemia.At the last follow-up,participants who habitually skipped breakfast had significantly higher levels of ptau181 and NfL than those who did not.In the PSM cohort,similar findings were obtained regarding cognitive trajectories and plasma biomarkers.Conclusion Breakfast skipping was linked to an increased risk of long-term cognitive decline and neurodegeneration among older adults.The link between unhealthy dietary habits and cognitive decline may be attributed to a deficiency in neurorestoration resulting from inadequate energy consumption.
基金supported by Scientific Research Fund of Hunan Provincial Natural Science Foundation under Grant 20231J60257Hunan Provincial Engineering Research Center for Intelligent Rehabilitation Robotics and Assistive Equipment under Grant 2025SH501Inha University and Design of a Conflict Detection and Validation Tool under Grant HX2024123.
文摘Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions,which is a typical manifestation of this trend.With the increasing complexity of image in tasks and the growth of data scale,existing deep learning methods still have some limitations.For example,they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal.To solve this problem,the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network.The encoder of the proposed model generator consists of a dual-branch parallel structure with stacked CNN blocks and Transformer blocks,aiming to extract global and local feature information from images.Furthermore,a dual-branch fusion module is adopted to combine the features obtained from both branches.Additionally,a gated full-scale skip connection module is proposed to further enhance the coherence of the inpainting results and alleviate information loss.Finally,experimental results from the three public datasets demonstrate the superior performance of the proposed method.
基金Open Access funding provided by the National Institutes of Health(NIH)The funding for this project was provided by NCATS Intramural Fund.
文摘Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.
基金supported by Southern Marine Science and Guangdong Laboratory(Zhuhai)(Grant No.SML2023SP229)。
文摘The maneuverability and stealth of aerial-aquatic vehicles(AAVs)is of significant importance for future integrated air-sea combat missions.To improve the maneuverability and stealth of AAVs near the water surface,this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV,inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface.The novel tandem twin-rotor AAV was employed as the research subject and a strategybased ADRC control method for validation,comparing it with a strategy-based PID control method.The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability.The strategy-based ADRC control method exhibits a certain advantage in controlling height,pitch angle,and reducing impact force.This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.
文摘Alterations in the mesenchymal-epithelial transition factor(MET)gene are critical drivers of non-small cell lung cancer(NSCLC).In recent years advances in precision therapies targeting MET alterations have significantly expanded treatment options for NSCLC patients.These alterations include MET exon 14 skipping mutations(MET exon 14 skipping),MET gene amplifications,MET point mutations(primarily kinase domain mutations),and MET protein overexpression.Accurate identification of these alterations and appropriate selection of patient populations and targeted therapies are essential for improving clinical outcomes.The East China Lung Cancer Group,Youth Committee(ECLUNG YOUNG,Yangtze River Delta Lung Cancer Cooperation Group)has synthesized insights from China’s innovative drug development landscape and clinical practice to formulate an expert consensus on the diagnosis and treatment of NSCLC patients with MET alterations.This consensus addresses key areas,such as optimal testing timing,testing methods,testing strategies,quality control measures,and treatment approaches.By offering standardized recommendations,this guidance aims to streamline diagnostic and therapeutic processes and enhance clinical decision-making for NSCLC with MET alterations.
文摘Circuit design of 32 bit logarithmic skip adder (LSA) is introduced to implement high performance,low power addition.ELM carry lookahead adder is included into groups of carry skip adder and the hybrid structure costs 30% less hardware than ELM.At circuit level,a carry incorporating structure to include the primary carry input in carry chain and an 'and xor' structure to implement final sum logic in 32 bit LSA are designed for better optimization.For 5V,1μm process,32 bit LSA has a critical delay of 5 9ns and costs an area of 0 62mm 2,power consumption of 23mW at 100MHz.For 2 5V,0 25μm process,critical delay of 0 8ns,power dissipation of 5 2mW at 100MHz is simulated.
