Solar cycles are fundamental to astrophysics,space exploration,technological infrastructure,and Earth's climate.A better understanding of these cycles and their history can aid in risk mitigation on Earth,while al...Solar cycles are fundamental to astrophysics,space exploration,technological infrastructure,and Earth's climate.A better understanding of these cycles and their history can aid in risk mitigation on Earth,while also deepening our knowledge of stellar physics and solar system dynamics.Determining the solar cycles between 1600 and 1700-especially the post-1645 Maunder Minimum,characterized by significantly reduced solar activity-poses challenges to existing solar activity proxies.This study utilizes a new red equatorial auroral catalog from ancient Korean texts to establish solar cycle patterns from 1623 to 1700.Remarkably,a further reevaluation of the solar cycles between 1610 and 1755 identified a total of 13 cycles,diverging from the widely accepted record of 12 cycles during that time.This research enhances our understanding of historical solar activity,and underscores the importance of integrating diverse historical sources into modern analyses.展开更多
BACKGROUND Unraveling the pathogenesis of colorectal cancer(CRC)can aid in developing prevention and treatment strategies.Aurora kinase A(AURKA)is a key participant in mitotic control and interacts with its co-activat...BACKGROUND Unraveling the pathogenesis of colorectal cancer(CRC)can aid in developing prevention and treatment strategies.Aurora kinase A(AURKA)is a key participant in mitotic control and interacts with its co-activator,the targeting protein for Xklp2(TPX2)microtubule nucleation factor.AURKA is associated with poor clinical outcomes and high risks of CRC recurrence.AURKA/TPX2 co-overexpression in cancer may contribute to tumorigenesis.Despite its pivotal role in CRC development and progression,the action mechanism of AURKA remains unclear.Further research is needed to explore the complex interplay between AURKA and TPX2 and to develop effective targeted treatments for patients with CRC.AIM To compare effects of AURKA and TPX2 and their combined knockdown on CRC cells.METHODS We evaluated three CRC gene datasets about CRC(GSE32323,GSE25071,and GSE21510).Potential hub genes associated with CRC onset were identified using the Venn,search tool for the retrieval of interacting genes,and KOBAS platforms,with AURKA and TPX2 emerging as significant factors.Subsequently,cell models with knockdown of AURKA,TPX2,or both were constructed using SW480 and LOVO cells.Quantitative real-time polymerase chain reaction,western blotting,cell counting kit-8,cell cloning assays,flow cytometry,and Transwell assays were used.RESULTS Forty-three highly expressed genes and 39 poorly expressed genes overlapped in cancer tissues compared to controls from three datasets.In the protein-protein interaction network of highly expressed genes,AURKA was one of key genes.Its combined score with TPX2 was 0.999,and their co-expression score was 0.846.In CRC cells,knockdown of AURKA,TPX2,or both reduced cell viability and colony number,while blocking G0/G1 phase and enhancing cell apoptosis.Additionally,they were weakened cell proliferation and migration abilities.Furthermore,the expression levels of B-cell lymphoma-2-Associated X,caspase 3,and tumor protein P53,and E-cadherin increased with a decrease in B-cell lymphoma-2,N-cadherin,and vimentin proteins.These effects were amplified when both AURKA and TPX2 were concurrently downregulated.CONCLUSION Combined knockdown of AURKA and TPX2 was effective in suppressing the malignant phenotype in CRC.Coinhibition of gene expression is a potential developmental direction for CRC treatment.展开更多
Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty i...Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty in precisely defining auroral shape edges,auroral arc skeleton extraction is expected as an alternative representation for studying auroral morphology,resorting skeletons extract key morphological features from complex auroral shapes.Transformer models provide a better understanding of the relationship between the overall morphology and the details when processing image data,so we proposed a Transformer-based method for auroral arc skeleton extraction.Combined with ridge-guided annotation on all-sky images,a Transformer-based skeleton extractor is trained and used to estimate the number of auroral arcs.Experiments demonstrate that the Transformer-based model can more effectively capture structural information and local details of auroral arcs,which is suitable for complex auroral morphologies.展开更多
In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors suc...In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors such as'arc','ab'(aurora but bright),'cloudy','diffuse','discrete',and'clear'.Subsequently,we utilized a state-of-the-art convolutional neural network,ConvNeXt(Convolutional Neural Network Next),deploying deep learning techniques to train the model on a dataset classified into six distinct categories.Remarkably,on the test set our methodology attained an accuracy of 99.4%,a performance metric closely mirroring human visual observation,thereby underscoring the classifier’s competence in paralleling human perceptual accuracy.Building upon this foundation,we embarked on the identification of large-scale auroral optical data,meticulously quantifying the monthly occurrence and Magnetic Local Time(MLT)variations of auroras from stations at different latitudes:RANK(high-latitude),FSMI(mid-latitude),and ATHA(low-latitude),under different solar wind conditions.This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.展开更多
Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora...Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora images to predict local geomagnetic station component,breaking the spatial limitations of geomagnetic stations.Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data.Essentially,the model comprises a visual geometry group(VGG)image feature extraction network,a ViT-based encoder network,and a regression prediction network.Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features.Specifically,the hybrid model achieves a 39.1%reduction in root mean square error compared to the VGG model,a 29.5%reduction compared to the ViT model and a 35.3%reduction relative to the residual network(ResNet)model.Moreover,the fitting accuracy of the model surpasses that of the VGG,ViT,and ResNet models by 2.14%1.58%,and 4.1%,respectively.展开更多
Particle observations of the Defense Meteorological Satellite Program(DMSP) show that discrete auroral structures commonly exist in the region of the plasma mantle, but the optical features of the aurora generated by ...Particle observations of the Defense Meteorological Satellite Program(DMSP) show that discrete auroral structures commonly exist in the region of the plasma mantle, but the optical features of the aurora generated by particles from the plasma mantle(called ‘mantle aurora’ in this paper) have not been established. A comparison of 7-year optical auroral observations made at the Yellow River Station with conjugate particle observations obtained from the DMSP confirm that mantle auroras have common features and can be clearly identified from all-sky imager observations. The mantle auroras normally present as sporadic and weak auroral structures split poleward of the dayside auroral oval. They are observed in both the green and red lines with the intensity of the red line being greater than that of the green line. In this paper, we illustrate typical mantle auroras and provide statistics on 55 mantle aurora cases that are confirmed by particle observation by the DMSP. Statistical results show that the occurrence of the mantle aurora has no clear dependence on the IMF By and Bz conditions, but the motion of the mantle aurora strongly depends on the IMF By, which indicates that the generation of the mantle aurora is intimately related to the dayside magnetopause reconnection. With the fundamental criteria for distinguishing the mantle aurora presented in this paper, we will be able to independently identify the mantle auroras from ground optical observations. This will allow us to investigate the physical processes that occur in the plasma mantle by monitoring the evolution of the auroral forms.展开更多
基金supported by the National Natural Science Foundation of China (42388101)the CAS Youth Interdisciplinary Team (JCTD-2021-05)funded by the Youth Innovation Promotion Association, Chinese Academy of Sciences.
文摘Solar cycles are fundamental to astrophysics,space exploration,technological infrastructure,and Earth's climate.A better understanding of these cycles and their history can aid in risk mitigation on Earth,while also deepening our knowledge of stellar physics and solar system dynamics.Determining the solar cycles between 1600 and 1700-especially the post-1645 Maunder Minimum,characterized by significantly reduced solar activity-poses challenges to existing solar activity proxies.This study utilizes a new red equatorial auroral catalog from ancient Korean texts to establish solar cycle patterns from 1623 to 1700.Remarkably,a further reevaluation of the solar cycles between 1610 and 1755 identified a total of 13 cycles,diverging from the widely accepted record of 12 cycles during that time.This research enhances our understanding of historical solar activity,and underscores the importance of integrating diverse historical sources into modern analyses.
文摘BACKGROUND Unraveling the pathogenesis of colorectal cancer(CRC)can aid in developing prevention and treatment strategies.Aurora kinase A(AURKA)is a key participant in mitotic control and interacts with its co-activator,the targeting protein for Xklp2(TPX2)microtubule nucleation factor.AURKA is associated with poor clinical outcomes and high risks of CRC recurrence.AURKA/TPX2 co-overexpression in cancer may contribute to tumorigenesis.Despite its pivotal role in CRC development and progression,the action mechanism of AURKA remains unclear.Further research is needed to explore the complex interplay between AURKA and TPX2 and to develop effective targeted treatments for patients with CRC.AIM To compare effects of AURKA and TPX2 and their combined knockdown on CRC cells.METHODS We evaluated three CRC gene datasets about CRC(GSE32323,GSE25071,and GSE21510).Potential hub genes associated with CRC onset were identified using the Venn,search tool for the retrieval of interacting genes,and KOBAS platforms,with AURKA and TPX2 emerging as significant factors.Subsequently,cell models with knockdown of AURKA,TPX2,or both were constructed using SW480 and LOVO cells.Quantitative real-time polymerase chain reaction,western blotting,cell counting kit-8,cell cloning assays,flow cytometry,and Transwell assays were used.RESULTS Forty-three highly expressed genes and 39 poorly expressed genes overlapped in cancer tissues compared to controls from three datasets.In the protein-protein interaction network of highly expressed genes,AURKA was one of key genes.Its combined score with TPX2 was 0.999,and their co-expression score was 0.846.In CRC cells,knockdown of AURKA,TPX2,or both reduced cell viability and colony number,while blocking G0/G1 phase and enhancing cell apoptosis.Additionally,they were weakened cell proliferation and migration abilities.Furthermore,the expression levels of B-cell lymphoma-2-Associated X,caspase 3,and tumor protein P53,and E-cadherin increased with a decrease in B-cell lymphoma-2,N-cadherin,and vimentin proteins.These effects were amplified when both AURKA and TPX2 were concurrently downregulated.CONCLUSION Combined knockdown of AURKA and TPX2 was effective in suppressing the malignant phenotype in CRC.Coinhibition of gene expression is a potential developmental direction for CRC treatment.
基金supported by the National Natural Science Foundation of China(Grant no.41874173)。
文摘Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty in precisely defining auroral shape edges,auroral arc skeleton extraction is expected as an alternative representation for studying auroral morphology,resorting skeletons extract key morphological features from complex auroral shapes.Transformer models provide a better understanding of the relationship between the overall morphology and the details when processing image data,so we proposed a Transformer-based method for auroral arc skeleton extraction.Combined with ridge-guided annotation on all-sky images,a Transformer-based skeleton extractor is trained and used to estimate the number of auroral arcs.Experiments demonstrate that the Transformer-based model can more effectively capture structural information and local details of auroral arcs,which is suitable for complex auroral morphologies.
基金supported by the General Program of the National Natural Science Foundation of China(Grant No.42374212)the National Magnetic Confinement Fusion Energy Research and Development Program of China(Grant No.2024YFE03020004).
文摘In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors such as'arc','ab'(aurora but bright),'cloudy','diffuse','discrete',and'clear'.Subsequently,we utilized a state-of-the-art convolutional neural network,ConvNeXt(Convolutional Neural Network Next),deploying deep learning techniques to train the model on a dataset classified into six distinct categories.Remarkably,on the test set our methodology attained an accuracy of 99.4%,a performance metric closely mirroring human visual observation,thereby underscoring the classifier’s competence in paralleling human perceptual accuracy.Building upon this foundation,we embarked on the identification of large-scale auroral optical data,meticulously quantifying the monthly occurrence and Magnetic Local Time(MLT)variations of auroras from stations at different latitudes:RANK(high-latitude),FSMI(mid-latitude),and ATHA(low-latitude),under different solar wind conditions.This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.
基金supported by the National Natural Science Foundation of China(No.41471381)the General Project of Jiangsu Natural Science Foundation(No.BK20171410)the Major Scientific and Technological Achievements Cultivation Fund of Nanjing University of Aeronautics and Astronautics(No.1011-XBD23002)。
文摘Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora images to predict local geomagnetic station component,breaking the spatial limitations of geomagnetic stations.Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data.Essentially,the model comprises a visual geometry group(VGG)image feature extraction network,a ViT-based encoder network,and a regression prediction network.Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features.Specifically,the hybrid model achieves a 39.1%reduction in root mean square error compared to the VGG model,a 29.5%reduction compared to the ViT model and a 35.3%reduction relative to the residual network(ResNet)model.Moreover,the fitting accuracy of the model surpasses that of the VGG,ViT,and ResNet models by 2.14%1.58%,and 4.1%,respectively.
基金supported by the National Natural Science Foundation of China(NSFC)(Grants nos.41831072,41774174,41431072,41474146,and 41674169)
文摘Particle observations of the Defense Meteorological Satellite Program(DMSP) show that discrete auroral structures commonly exist in the region of the plasma mantle, but the optical features of the aurora generated by particles from the plasma mantle(called ‘mantle aurora’ in this paper) have not been established. A comparison of 7-year optical auroral observations made at the Yellow River Station with conjugate particle observations obtained from the DMSP confirm that mantle auroras have common features and can be clearly identified from all-sky imager observations. The mantle auroras normally present as sporadic and weak auroral structures split poleward of the dayside auroral oval. They are observed in both the green and red lines with the intensity of the red line being greater than that of the green line. In this paper, we illustrate typical mantle auroras and provide statistics on 55 mantle aurora cases that are confirmed by particle observation by the DMSP. Statistical results show that the occurrence of the mantle aurora has no clear dependence on the IMF By and Bz conditions, but the motion of the mantle aurora strongly depends on the IMF By, which indicates that the generation of the mantle aurora is intimately related to the dayside magnetopause reconnection. With the fundamental criteria for distinguishing the mantle aurora presented in this paper, we will be able to independently identify the mantle auroras from ground optical observations. This will allow us to investigate the physical processes that occur in the plasma mantle by monitoring the evolution of the auroral forms.
