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
In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training cos...In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.展开更多
CRISPR/Cas9 technology is a powerful genome manipulation tool in insects.However,little is known about whether mRNA and protein of a target gene are completely cleared in homozygous mutants.This study generated homozy...CRISPR/Cas9 technology is a powerful genome manipulation tool in insects.However,little is known about whether mRNA and protein of a target gene are completely cleared in homozygous mutants.This study generated homozygous mutants of the insulin receptor gene 2(NlInR2)in the brown planthopper(Nilaparvata lugens)using CRISPR/Cas9 genome editing.Both frameshift mutants,E5_D17 and E6_I7,differentiated towards long wings,but there were differences in wing morphology,with E5_D17 showing wing deformities.Subsequent investigations revealed the presence of residual expression of NlInR2 mRNA in both mutants,as well as the occurrence of spliceosomes featuring exon skipping splicing in E5_D17.Additionally,the E5_D17 exhibited the detection of N-terminally truncated NlInR2 protein.RNA interference experiments indicated that the knockdown of NlInR2 expression in the E5_D17 mutant line increased the proportion of wing deformities from 11.1 to 65.6%,suggesting that the residual NlInR2 mRNA of the E5_D17 mutant might have retained some genetic functions.Our results imply that systematic characterization of residual protein expression or function in CRISPR/Cas9-generated mutant lines is necessary for phenotypic interpretation.展开更多
Colorectal cancer(CRC)is a prevalent malignant tumor,with the global new cases reaching 1.9316 million and deaths reaching 935,200 in 2022.In China,therewere 555,500 new cases of CRC,with an age-standardized incidence...Colorectal cancer(CRC)is a prevalent malignant tumor,with the global new cases reaching 1.9316 million and deaths reaching 935,200 in 2022.In China,therewere 555,500 new cases of CRC,with an age-standardized incidence rate of 24.07 per 10million,and 286,200 deaths.China accounts for approximately 30%of new cases and deaths from CRC worldwide,with East Asia accounting for over 75%.Initially,CRC presents as local tumor growth,but it has the potential to spread to other body parts over time.Perineural infiltration(PNI)is a relatively less discussed route of diffusion,yet it plays a crucial role in the progression and prognosis of CRC.PNI often occurs alongside local lymph nodes and distant metastases,posing challenges for treatment and management.Clinical symptoms,radiographic findings,and histopathological examination can be used to diagnose PNI with skipmetastasis.Symptoms commonly include local pain,paresthesia,andmotor impairment.Imaging helps identify the mass’s location and relationship to nerves,whereas histopathological examination confirms the diagnosis.Treatment of PNI skipmetastases is similar to other CRC metastases,including surgical resection,chemotherapy,radiotherapy,and targeted therapy.Surgical resection is the primary therapeutic approach,but the wider range of metastasis in PNI skip transfer may limit its feasibility.In cases where surgical resection is not possible,chemotherapy,radiotherapy,and targeted therapy are used to control tumor metastasis.In conclusion,PNI skip metastases increase the risk of poor prognosis for CRC,requiring a comprehensive approach with multiple treatments to prevent disease progression.Early detection and treatment are vital to improving prognosis.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.
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.
基金the National Natural Science Foundation of China(31730073).
文摘CRISPR/Cas9 technology is a powerful genome manipulation tool in insects.However,little is known about whether mRNA and protein of a target gene are completely cleared in homozygous mutants.This study generated homozygous mutants of the insulin receptor gene 2(NlInR2)in the brown planthopper(Nilaparvata lugens)using CRISPR/Cas9 genome editing.Both frameshift mutants,E5_D17 and E6_I7,differentiated towards long wings,but there were differences in wing morphology,with E5_D17 showing wing deformities.Subsequent investigations revealed the presence of residual expression of NlInR2 mRNA in both mutants,as well as the occurrence of spliceosomes featuring exon skipping splicing in E5_D17.Additionally,the E5_D17 exhibited the detection of N-terminally truncated NlInR2 protein.RNA interference experiments indicated that the knockdown of NlInR2 expression in the E5_D17 mutant line increased the proportion of wing deformities from 11.1 to 65.6%,suggesting that the residual NlInR2 mRNA of the E5_D17 mutant might have retained some genetic functions.Our results imply that systematic characterization of residual protein expression or function in CRISPR/Cas9-generated mutant lines is necessary for phenotypic interpretation.
文摘Colorectal cancer(CRC)is a prevalent malignant tumor,with the global new cases reaching 1.9316 million and deaths reaching 935,200 in 2022.In China,therewere 555,500 new cases of CRC,with an age-standardized incidence rate of 24.07 per 10million,and 286,200 deaths.China accounts for approximately 30%of new cases and deaths from CRC worldwide,with East Asia accounting for over 75%.Initially,CRC presents as local tumor growth,but it has the potential to spread to other body parts over time.Perineural infiltration(PNI)is a relatively less discussed route of diffusion,yet it plays a crucial role in the progression and prognosis of CRC.PNI often occurs alongside local lymph nodes and distant metastases,posing challenges for treatment and management.Clinical symptoms,radiographic findings,and histopathological examination can be used to diagnose PNI with skipmetastasis.Symptoms commonly include local pain,paresthesia,andmotor impairment.Imaging helps identify the mass’s location and relationship to nerves,whereas histopathological examination confirms the diagnosis.Treatment of PNI skipmetastases is similar to other CRC metastases,including surgical resection,chemotherapy,radiotherapy,and targeted therapy.Surgical resection is the primary therapeutic approach,but the wider range of metastasis in PNI skip transfer may limit its feasibility.In cases where surgical resection is not possible,chemotherapy,radiotherapy,and targeted therapy are used to control tumor metastasis.In conclusion,PNI skip metastases increase the risk of poor prognosis for CRC,requiring a comprehensive approach with multiple treatments to prevent disease progression.Early detection and treatment are vital to improving prognosis.
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.
