In climacteric fruits,the role of ethylene in promoting ripening process and its molecular regulatory mechanisms have been well elucidated.However,research into ethylene's roles in non-climacteric fruits has only ...In climacteric fruits,the role of ethylene in promoting ripening process and its molecular regulatory mechanisms have been well elucidated.However,research into ethylene's roles in non-climacteric fruits has only advanced in recent years,largely because these fruits produce much less ethylene than climacteric fruits.Consequently,reports on its molecular regulatory involvement are still limited.Grape(Vitis vinifera L.),one of the most economically valuable fruits,is regarded as a classical non-climacteric fruit.In this study,an enzyme participating in the last step of ethylene biosynthesis,VvACO1,has been identified as a key enzyme controlling ethylene release in grape fruits(Vitis vinifera‘Jingyan’and‘Red Balado’)using correlation analysis and enzymatic experiments.The transcriptional regulation of VvACO1 was investigated by integrating multiple methods such as DNA pull-down assays,co-expression analysis,dual luciferase reporting system,yeast one-hybrid assays,and transgenic experiments.Our findings revealed that the upregulation of VvACO1 in grape fruits was primarily caused by the removal of transcriptional inhibition.Remarkably,seven transcription factors(TFs)were identified as inhibitors of VvACO1,including VvHY5 from bZIP family,VvWIP2 from C2H2 family,VvBLH1 from Homeobox family,VvAG1 and VvCMB1 from MADS-box family,VvASIL1 and VvASIL2 from Trihelix family.These seven TFs were located in nuclei and exhibited transcriptional inhibition activity.Notably,VvAG1 and VvASIL2 could inhibit VvACO1 expression when overexpressed in grape leaves.Our findings provided theoretical clues for differences of ethylene release regulation between climacteric and non-climacteric fruits,also the identified seven TFs could be potential targets for grape molecular breeding.展开更多
Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making ...Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.展开更多
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule...Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.展开更多
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.展开更多
为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规...为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规划效果。实验结果表明,与单独的改进PSO和ACO算法相比,本研究的融合改进种群算法和多因素地形适应度的路径规划算法具有更佳的路径规划效果,在3 km、6 km、9 km、12 km的路径规模下的转折点个数仅为33,105,239,543,路径长度仅为3.25 km, 6.33 km, 10.26 km, 13.44 km,明显低于改进PSO和ACO算法;与其他路径规划算法相比,本研究算法在复杂场景下能够得到最佳的路径,同时还能够保持路径的平滑性。以上结果表明,本研究设计的路径规划算法具有优秀的路径规划效果,能够应用于实际场景中的路径规划,具有较高的可行性和使用价值。展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.32025032 and 32202415)the Agricultural Breeding Project of Ningxia Hui Autonomous Region(Grant No.NXNYYZ20210104)the National Key Research and Development Program of China(Grant No.2022YFE0116400).
文摘In climacteric fruits,the role of ethylene in promoting ripening process and its molecular regulatory mechanisms have been well elucidated.However,research into ethylene's roles in non-climacteric fruits has only advanced in recent years,largely because these fruits produce much less ethylene than climacteric fruits.Consequently,reports on its molecular regulatory involvement are still limited.Grape(Vitis vinifera L.),one of the most economically valuable fruits,is regarded as a classical non-climacteric fruit.In this study,an enzyme participating in the last step of ethylene biosynthesis,VvACO1,has been identified as a key enzyme controlling ethylene release in grape fruits(Vitis vinifera‘Jingyan’and‘Red Balado’)using correlation analysis and enzymatic experiments.The transcriptional regulation of VvACO1 was investigated by integrating multiple methods such as DNA pull-down assays,co-expression analysis,dual luciferase reporting system,yeast one-hybrid assays,and transgenic experiments.Our findings revealed that the upregulation of VvACO1 in grape fruits was primarily caused by the removal of transcriptional inhibition.Remarkably,seven transcription factors(TFs)were identified as inhibitors of VvACO1,including VvHY5 from bZIP family,VvWIP2 from C2H2 family,VvBLH1 from Homeobox family,VvAG1 and VvCMB1 from MADS-box family,VvASIL1 and VvASIL2 from Trihelix family.These seven TFs were located in nuclei and exhibited transcriptional inhibition activity.Notably,VvAG1 and VvASIL2 could inhibit VvACO1 expression when overexpressed in grape leaves.Our findings provided theoretical clues for differences of ethylene release regulation between climacteric and non-climacteric fruits,also the identified seven TFs could be potential targets for grape molecular breeding.
文摘Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.
基金supported by the National Natural Science Foundation of China(No.12104141).
文摘Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
文摘为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规划效果。实验结果表明,与单独的改进PSO和ACO算法相比,本研究的融合改进种群算法和多因素地形适应度的路径规划算法具有更佳的路径规划效果,在3 km、6 km、9 km、12 km的路径规模下的转折点个数仅为33,105,239,543,路径长度仅为3.25 km, 6.33 km, 10.26 km, 13.44 km,明显低于改进PSO和ACO算法;与其他路径规划算法相比,本研究算法在复杂场景下能够得到最佳的路径,同时还能够保持路径的平滑性。以上结果表明,本研究设计的路径规划算法具有优秀的路径规划效果,能够应用于实际场景中的路径规划,具有较高的可行性和使用价值。
