Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying thes...Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology.展开更多
Benzotriazole UV stabilizers (BT-UVs) have attracted concems due to their ubiquitous occurrence in the aquatic environment,and their bioaccumulative and toxic properties.However,little is known about their aquatic env...Benzotriazole UV stabilizers (BT-UVs) have attracted concems due to their ubiquitous occurrence in the aquatic environment,and their bioaccumulative and toxic properties.However,little is known about their aquatic environmental degradation behavior.In this study,photodegradation of a representative of BT-UVs,2-(2-hydroxy-5-methylphenyl) benzotriazole (UV-P),was investigated under simulated sunlight irradiation.Results show that UV-P photodegrades slower under neutral conditions (neutral form) than under acidic or alkaline conditions (cationic and anionic forms).Indirect photodegradation is a dominant elimination pathway of UV-P in coastal seawaters.Dissolved organic matter (DOM) from seawaters accelerate the photodegradation rates mainly through excited triplet DOM (3DOM*),and the roles of singlet oxygen and hydroxyl radical are negligible in the matrixes.DOM from seawaters impacted by mariculture exhibits higher steady-state concentration of 3DOM*([3DOM*]) relative to those from pristine seawaters,leading to higher photosensitizing effects on the photodegradation.Halide ions inhibit the DOM-sensitized photodegradation of UV-P by decreasing [3DOM*].Photodegradation half-lives of UV-P are estimated to range from 24.38 to 49.66 hr in field water bodies of the Yellow River estuary.These results are of importance for assessing environmental fate and risk UV-P in coastal water bodies.展开更多
Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important paramete...Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.展开更多
The detection of dust devils on Mars poses significant challenges,primarily due to the substantial variability in target scales,the susceptibility of small-scale features to loss or distortion during feature extractio...The detection of dust devils on Mars poses significant challenges,primarily due to the substantial variability in target scales,the susceptibility of small-scale features to loss or distortion during feature extraction and fusion,and the interference from complex Martian backgrounds.To tackle these issues,we propose the Dynamic Triplet Fusion Attentive Net(DTFA-Net),a framework tailored for Martian dust devil detection.Within DTFA-Net,we design a Multi-Dimensional Dynamic Feature Pyramid Network(MDFPN),which is based on the Bi-directional Feature Pyramid Network(BiFPN)and enhances multi-level feature fusion by incorporating shallow-layer features and employing a cross-scale connection strategy.Additionally,we propose three innovative lightweight and plug-and-play modules:the Local-Channel Cross-Stage Module(LCCS)to boost feature diversity,the Progressive Feature Enhancement Module(PFE)to increase the focus on critical features,and the Triplet-Aware Cross-Stage Module(TACS)for capturing interactions across spatial and channel dimensions.Furthermore,the framework incorporates the Dynamic Head(DyHead),which uses multi-dimensional attention mechanisms to dynamically adjust to various scales,spatial positions,and detection challenges.Experimental results show that DTFA-Net achieved a detection Precision of 94.3%,a Recall of 92.8%,and mAP50 of 96.6%on the Amazonis Planitia dust devil dataset.Its overall performance significantly surpasses that of existing mainstream methods,while also demonstrating strong generalization capability on cross-regional datasets.Beyond detection,this framework was further applied to analyze the seasonal activity and spatial distribution patterns of Martian dust devils in Amazonis Planitia,and the prevailing winds of each season were examined to explore the mechanisms underlying the formation of activity hotspots.In addition,the model was extended to the ten core candidate landing sites of the Tianwen-3 mission to systematically assess dust devil distribution across these regions.Based on the detection results and previous studies,we suggest that four sites—Kasei Valles,Oxia Planum,McLaughlin Crater,and Mawrth Vallis—offer a more balanced trade-off among scientific value,dust-cleaning,and engineering safety,making them relatively ideal landing sites for the Tianwen-3 mission.Overall,this study provides important insights into the spatiotemporal distribution,activity patterns,and potential environmental risks of Martian dust devils,thereby offering valuable references for Mars exploration missions.Furthermore,it provides guidance for the optimized design and safe operation of spacecraft,and contributes scientific support for the planning and implementation of future Mars exploration endeavors.展开更多
Background:Pressure ulcers(PUs)are a major clinical problem that constitutes a tremendous economic burden on healthcare systems.Deep tissue injury(DTI)is a unique serious type of pressure ulcer that arises in skeletal...Background:Pressure ulcers(PUs)are a major clinical problem that constitutes a tremendous economic burden on healthcare systems.Deep tissue injury(DTI)is a unique serious type of pressure ulcer that arises in skeletal muscle tissue.DTI arises in part because skeletal muscle tissues are more susceptible than skin to external compression.Unfortunately,few effective therapies are currently available for muscle injury.Basic fibroblast growth factor(bFGF),a potent mitogen and survival factor for various cells,plays a crucial role in the regulation of muscle development and homeostasis.The main purpose of this study was to test whether local administration of bFGF could accelerate muscle regeneration in a rat DTI model.Methods:Male Sprague Dawley(SD)rats(age 12 weeks)were individually housed in plastic cages and a DTI PU model was induced according to methods described before.Animals were randomly divided into three groups:a normal group,a PU group treated with saline,and a PU group treated with bFGF(10μg/0.1 ml)subcutaneously near the wound.Results:We found that application of bFGF accelerated the rate of wound closure and promoted cell proliferation and tissue angiogenesis.In addition,compared to saline administration,bFGF treatment prevented collagen deposition,a measure of fibrosis,and up-regulated the myogenic marker proteins MyHC and myogenin,suggesting bFGF promoted injured muscle regeneration.Moreover,bFGF treatment increased levels of myogenesis-related proteins p-Akt and p-mTOR.Conclusions:Our findings show that bFGF accelerated injured skeletal muscle regeneration through activation of the PI3K/Akt/mTOR signaling pathway and suggest that administration of bFGF is a potential therapeutic strategy for the treatment of skeletal muscle injury in PUs.展开更多
An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste(MSW).This is because the common methods of manual and semi-mechanical screenings not only consume la...An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste(MSW).This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission.As the categories of MSW are diverse considering their compositions,chemical reactions,and processing procedures,etc.,resulting in low efficiencies in MSW sorting using the traditional methods.Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode.This study for the first time applied MSWNet in MSW sorting,a ResNet-50 with transfer learning.The method of cyclical learning rate was taken to avoid blind finding,and tests were repeated until accidentally encountering a good value.Measures of visualization were also considered to make the MSWNet model more transparent and accountable.Results showed transfer learning enhanced the efficiency of training time(from 741 s to 598.5 s),and improved the accuracy of recognition performance(from 88.50%to 93.50%);MSWNet showed a better performance in MSW classsification in terms of sensitivity(93.50%),precision(93.40%),F1-score(93.40%),accuracy(93.50%)and AUC(92.00%).The findings of this study can be taken as a reference for building the model MSW classification by deep learning,quantifying a suitable learning rate,and changing the data from high dimensions to two dimensions.展开更多
基金funded by the National Natural Science Foundation of China(12363009 and 12103020)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191 and 2023CYZ26970)Jiangxi Province Graduate Innovation Special Funds Project(YC2024-S529 and YC2023-S672).
文摘Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology.
基金supported by the National Key R&D Program of China(No.2018YFC1801604)the National Natural Science Foundation of China(No.21661142001)
文摘Benzotriazole UV stabilizers (BT-UVs) have attracted concems due to their ubiquitous occurrence in the aquatic environment,and their bioaccumulative and toxic properties.However,little is known about their aquatic environmental degradation behavior.In this study,photodegradation of a representative of BT-UVs,2-(2-hydroxy-5-methylphenyl) benzotriazole (UV-P),was investigated under simulated sunlight irradiation.Results show that UV-P photodegrades slower under neutral conditions (neutral form) than under acidic or alkaline conditions (cationic and anionic forms).Indirect photodegradation is a dominant elimination pathway of UV-P in coastal seawaters.Dissolved organic matter (DOM) from seawaters accelerate the photodegradation rates mainly through excited triplet DOM (3DOM*),and the roles of singlet oxygen and hydroxyl radical are negligible in the matrixes.DOM from seawaters impacted by mariculture exhibits higher steady-state concentration of 3DOM*([3DOM*]) relative to those from pristine seawaters,leading to higher photosensitizing effects on the photodegradation.Halide ions inhibit the DOM-sensitized photodegradation of UV-P by decreasing [3DOM*].Photodegradation half-lives of UV-P are estimated to range from 24.38 to 49.66 hr in field water bodies of the Yellow River estuary.These results are of importance for assessing environmental fate and risk UV-P in coastal water bodies.
基金supported by the National Natural Science Foundation of China(nos.62i01100 and 62262015).
文摘Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.
基金supported by the National Natural Science Foundation of China(Grant Nos.12103020&12363009)the Science and Technology Development Fund(FDCT)of Macao(Grant Nos.0021/2024/RIA1,0158/2024/AFJ and 0034/2024/AMJ)+4 种基金the Open Project Program of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology)(Grant No.002/2024/SKL)the Key Technology Research Project of TW-3(Grant No.TW3004)the Youth Talent Project of the Science and Technology Plan of Ganzhou(Grant No.2023CYZ26970)the Graduate Innovation Special Fund Project of Jiangxi University of Science and Technology(Grant No.XY2024-S213)the Key Laboratory of Low Dimensional Quantum Materials and Sensor Devices of Jiangxi Education Institutes(Grant No.Gan Jiao Ke Zi-20241301)。
文摘The detection of dust devils on Mars poses significant challenges,primarily due to the substantial variability in target scales,the susceptibility of small-scale features to loss or distortion during feature extraction and fusion,and the interference from complex Martian backgrounds.To tackle these issues,we propose the Dynamic Triplet Fusion Attentive Net(DTFA-Net),a framework tailored for Martian dust devil detection.Within DTFA-Net,we design a Multi-Dimensional Dynamic Feature Pyramid Network(MDFPN),which is based on the Bi-directional Feature Pyramid Network(BiFPN)and enhances multi-level feature fusion by incorporating shallow-layer features and employing a cross-scale connection strategy.Additionally,we propose three innovative lightweight and plug-and-play modules:the Local-Channel Cross-Stage Module(LCCS)to boost feature diversity,the Progressive Feature Enhancement Module(PFE)to increase the focus on critical features,and the Triplet-Aware Cross-Stage Module(TACS)for capturing interactions across spatial and channel dimensions.Furthermore,the framework incorporates the Dynamic Head(DyHead),which uses multi-dimensional attention mechanisms to dynamically adjust to various scales,spatial positions,and detection challenges.Experimental results show that DTFA-Net achieved a detection Precision of 94.3%,a Recall of 92.8%,and mAP50 of 96.6%on the Amazonis Planitia dust devil dataset.Its overall performance significantly surpasses that of existing mainstream methods,while also demonstrating strong generalization capability on cross-regional datasets.Beyond detection,this framework was further applied to analyze the seasonal activity and spatial distribution patterns of Martian dust devils in Amazonis Planitia,and the prevailing winds of each season were examined to explore the mechanisms underlying the formation of activity hotspots.In addition,the model was extended to the ten core candidate landing sites of the Tianwen-3 mission to systematically assess dust devil distribution across these regions.Based on the detection results and previous studies,we suggest that four sites—Kasei Valles,Oxia Planum,McLaughlin Crater,and Mawrth Vallis—offer a more balanced trade-off among scientific value,dust-cleaning,and engineering safety,making them relatively ideal landing sites for the Tianwen-3 mission.Overall,this study provides important insights into the spatiotemporal distribution,activity patterns,and potential environmental risks of Martian dust devils,thereby offering valuable references for Mars exploration missions.Furthermore,it provides guidance for the optimized design and safe operation of spacecraft,and contributes scientific support for the planning and implementation of future Mars exploration endeavors.
基金This study was supported by research grants from the Zhejiang Provincial Natural Science Funding(LY14H150008)the National Natural Science Funding of China(81372064,81472165,and 81572237)+2 种基金the Zhejiang Provincial Program of Medical and Health Science(2014KYA131)the Wenzhou Program of Science and Technology(Y20140003)the State Key Basic Research Development Program(2012CB518105)
文摘Background:Pressure ulcers(PUs)are a major clinical problem that constitutes a tremendous economic burden on healthcare systems.Deep tissue injury(DTI)is a unique serious type of pressure ulcer that arises in skeletal muscle tissue.DTI arises in part because skeletal muscle tissues are more susceptible than skin to external compression.Unfortunately,few effective therapies are currently available for muscle injury.Basic fibroblast growth factor(bFGF),a potent mitogen and survival factor for various cells,plays a crucial role in the regulation of muscle development and homeostasis.The main purpose of this study was to test whether local administration of bFGF could accelerate muscle regeneration in a rat DTI model.Methods:Male Sprague Dawley(SD)rats(age 12 weeks)were individually housed in plastic cages and a DTI PU model was induced according to methods described before.Animals were randomly divided into three groups:a normal group,a PU group treated with saline,and a PU group treated with bFGF(10μg/0.1 ml)subcutaneously near the wound.Results:We found that application of bFGF accelerated the rate of wound closure and promoted cell proliferation and tissue angiogenesis.In addition,compared to saline administration,bFGF treatment prevented collagen deposition,a measure of fibrosis,and up-regulated the myogenic marker proteins MyHC and myogenin,suggesting bFGF promoted injured muscle regeneration.Moreover,bFGF treatment increased levels of myogenesis-related proteins p-Akt and p-mTOR.Conclusions:Our findings show that bFGF accelerated injured skeletal muscle regeneration through activation of the PI3K/Akt/mTOR signaling pathway and suggest that administration of bFGF is a potential therapeutic strategy for the treatment of skeletal muscle injury in PUs.
基金This work was financially supported by the China Scholarship Council(No.202206260111)the Research Project of Shanghai Chengtou Group Co.,Ltd.(China)(CTKY-LGZX-2020-005-02).
文摘An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste(MSW).This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission.As the categories of MSW are diverse considering their compositions,chemical reactions,and processing procedures,etc.,resulting in low efficiencies in MSW sorting using the traditional methods.Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode.This study for the first time applied MSWNet in MSW sorting,a ResNet-50 with transfer learning.The method of cyclical learning rate was taken to avoid blind finding,and tests were repeated until accidentally encountering a good value.Measures of visualization were also considered to make the MSWNet model more transparent and accountable.Results showed transfer learning enhanced the efficiency of training time(from 741 s to 598.5 s),and improved the accuracy of recognition performance(from 88.50%to 93.50%);MSWNet showed a better performance in MSW classsification in terms of sensitivity(93.50%),precision(93.40%),F1-score(93.40%),accuracy(93.50%)and AUC(92.00%).The findings of this study can be taken as a reference for building the model MSW classification by deep learning,quantifying a suitable learning rate,and changing the data from high dimensions to two dimensions.
