针对道路交通环境中车辆和行人目标较小或被遮挡导致的检测精度低以及误检、漏检问题,提出道路目标检测算法RO-YOLOv9。增加小目标检测层,增强算法对小目标的特征学习能力。设计双向与自适应尺度融合特征金字塔网络(bidirectional and a...针对道路交通环境中车辆和行人目标较小或被遮挡导致的检测精度低以及误检、漏检问题,提出道路目标检测算法RO-YOLOv9。增加小目标检测层,增强算法对小目标的特征学习能力。设计双向与自适应尺度融合特征金字塔网络(bidirectional and adaptive scale fusion feature pyramid network,BiASF-FPN)结构,优化多尺度特征融合,保证算法有效捕捉从小尺度到大尺度目标的详细信息。提出OR-RepN4模块,通过重参数化策略,复杂算法结构简单化,提高推理速度。引用Shape-NWD(shape neighborhood weighted decomposition)损失函数,专注边界框形状与尺寸,采用归一化高斯Wasserstein距离平滑回归,实现跨尺度不变性,降低小尺度与遮挡目标的检测误差。实验结果表明,在优化后的SODA10M和BDD100K数据集下,RO-YOLOv9算法的mAP@0.5(mean average precision)分别达到68.1%和56.8%,比YLOLOv9算法提高5.6个百分点和4.4个百分点,并且检测帧率分别达到了55.3帧/s和54.2帧/s,达到检测精度和检测速度的平衡。展开更多
针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-...针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-YOLOv9)。首先,设计CoT-CAFRNet(Chain-of-Thought prompted Content-Aware Feature Reassembly Network)图像增强网络,以提升图像质量,并优化输入特征;其次,加入通道自适应特征融合(iCAFF)模块,以增强小目标及重叠遮挡目标的提取能力;再次,提出特征融合金字塔结构BiHS-FPN(Bi-directional High-level Screening Feature Pyramid Network),以增强多尺度特征的融合能力;最后,设计IF-MPDIoU(Inner-Focaler-Minimum Point Distance based Intersection over Union)损失函数,以通过调整变量因子,聚焦关键样本,并增强泛化能力。实验结果表明,在自制数据集和SODA10M数据集上,ITD-YOLOv9算法的检测精度分别为83.8%和56.3%,检测帧率分别为64.8 frame/s和57.4 frame/s。与YOLOv9算法相比,ITD-YOLOv9算法的检测精度分别提升了3.9和2.7个百分点。可见,所提算法有效实现了交通路口的多目标检测。展开更多
Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated ...Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.展开更多
文摘针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-YOLOv9)。首先,设计CoT-CAFRNet(Chain-of-Thought prompted Content-Aware Feature Reassembly Network)图像增强网络,以提升图像质量,并优化输入特征;其次,加入通道自适应特征融合(iCAFF)模块,以增强小目标及重叠遮挡目标的提取能力;再次,提出特征融合金字塔结构BiHS-FPN(Bi-directional High-level Screening Feature Pyramid Network),以增强多尺度特征的融合能力;最后,设计IF-MPDIoU(Inner-Focaler-Minimum Point Distance based Intersection over Union)损失函数,以通过调整变量因子,聚焦关键样本,并增强泛化能力。实验结果表明,在自制数据集和SODA10M数据集上,ITD-YOLOv9算法的检测精度分别为83.8%和56.3%,检测帧率分别为64.8 frame/s和57.4 frame/s。与YOLOv9算法相比,ITD-YOLOv9算法的检测精度分别提升了3.9和2.7个百分点。可见,所提算法有效实现了交通路口的多目标检测。
基金supported by grants from The National Natural Science Foundation of China(12361104)Yunnan Fundamental Research Projects(202301AT070016,202401AT070036)+2 种基金the Youth Talent Program of Xingdian Talent Support Plan(XDYC-QNRC-2022-0514)the Yunnan Province International Joint Laboratory for Intelligent Integration and Application of Ethnic Multilingualism(202403AP140014)the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University(SMDAYB2023004)。
文摘Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.
文摘亮桥是太阳黑子内部一种常见的结构,通常在亮桥区域会产生一系列的爆发活动,并对黑子演化产生影响。利用太阳动力学天文台(Solar Dynamics Observatory,SDO)搭载的大气成像阵列(Atmospheric Imaging Assembly,AIA)极紫外成像数据和日震学与磁场成像仪(Helioseismic and Magnetic Imager,HMI)视向磁图和矢量磁图,通过统计大数据样本,表明亮桥与冕环之间的抑制特征具有一定的普遍性。冕环缺失区域主要位于亮桥锚定点附近的本影-半影边界上,在该区域,随着亮桥的演化和形态的改变,冕环也表现出显现和消失现象。通过分析HMI磁图数据,亮桥锚定区通常伴随着极性相反的小尺度磁场结构,可以推断亮桥锚定点区域的磁力线与周围相反极性的小尺度磁场结构相连,并形成短程闭合磁环,由于磁环的长度较短,无法延伸到日冕层,因此在日冕层无法观测到冕环。根据研究结果,提出了亮桥与冕环关系模型,用来解释亮桥与冕环之间的磁连接性,这个模型可以解释许多与亮桥相关的物理过程。
