家禽作为全球畜牧业的重要经济物种,表观遗传调控对其生长、繁殖、抗病能力具有关键影响。近年来,染色质转座酶可及性测序(Assay for Transposase Accessible Chromatin With High-Throughput Sequencing,ATAC-seq)技术的兴起,为解析家...家禽作为全球畜牧业的重要经济物种,表观遗传调控对其生长、繁殖、抗病能力具有关键影响。近年来,染色质转座酶可及性测序(Assay for Transposase Accessible Chromatin With High-Throughput Sequencing,ATAC-seq)技术的兴起,为解析家禽表观遗传的调控机制提供了关键技术支撑。本文综述了ATAC-seq技术在家禽生长发育、繁殖性状及疾病研究中的进展,旨在为染色质可及性及基因表达调控的相关研究提供参考资料。展开更多
微服务的按需伸缩对提高集群的资源利用率至关重要,而按需伸缩的前提是集群能够对资源需求进行精准预测。当前基于规则响应式的资源管理策略仍是产业界的主流方式,学术界结合机器学习的资源负载预测方法仍存在预测不够精准等问题。因此...微服务的按需伸缩对提高集群的资源利用率至关重要,而按需伸缩的前提是集群能够对资源需求进行精准预测。当前基于规则响应式的资源管理策略仍是产业界的主流方式,学术界结合机器学习的资源负载预测方法仍存在预测不够精准等问题。因此,提出一种基于微服务依赖程度的负载预测模型。通过基于DTW(Dynamic Time Warping)改进的容器依赖程度检测算法,对容器进行依赖程度评估。分析存在强依赖关系的容器之间指标的相关性,选择相关性较高的指标作为模型的输入特征变量。预测模型采用Seq2Seq(Sequence to Sequence)编解码模型,并结合注意力机制和残差LSTM来提升模型预测的精准性和稳定性。实验表明,该模型预测效果显著,误差评价指标MAE、RMSE、MAPE相较于另外两个深度学习模型平均降低了48%、35%、51%,能够有效预测出存在强依赖关系容器的短时负载。展开更多
为解决以往船舶轨迹预测模型在训练过程中未能合理应对多步预测任务间关联性的问题,构建了一种融合频域分析方法和Seq2Seq模型的船舶轨迹预测算法(Time-Frequency based Seqence to Seqence Model,TF-Seq2Seq)。基于自动识别系统数据特...为解决以往船舶轨迹预测模型在训练过程中未能合理应对多步预测任务间关联性的问题,构建了一种融合频域分析方法和Seq2Seq模型的船舶轨迹预测算法(Time-Frequency based Seqence to Seqence Model,TF-Seq2Seq)。基于自动识别系统数据特点设计了系列预处理操作;用融合频域分析方法和Seq2Seq模型的改进算法捕获船舶轨迹序列的关联性。基于真实数据进行实例分析,结果表明基于长短时记忆单元的Seq2Seq改进算法多种指标上表现最优,均方误差较原算法降低了12.9%。算法改进能更好地发挥模型优势,提高船舶轨迹预测精度。展开更多
Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware los...Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.展开更多
Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other...Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.展开更多
文摘家禽作为全球畜牧业的重要经济物种,表观遗传调控对其生长、繁殖、抗病能力具有关键影响。近年来,染色质转座酶可及性测序(Assay for Transposase Accessible Chromatin With High-Throughput Sequencing,ATAC-seq)技术的兴起,为解析家禽表观遗传的调控机制提供了关键技术支撑。本文综述了ATAC-seq技术在家禽生长发育、繁殖性状及疾病研究中的进展,旨在为染色质可及性及基因表达调控的相关研究提供参考资料。
文摘微服务的按需伸缩对提高集群的资源利用率至关重要,而按需伸缩的前提是集群能够对资源需求进行精准预测。当前基于规则响应式的资源管理策略仍是产业界的主流方式,学术界结合机器学习的资源负载预测方法仍存在预测不够精准等问题。因此,提出一种基于微服务依赖程度的负载预测模型。通过基于DTW(Dynamic Time Warping)改进的容器依赖程度检测算法,对容器进行依赖程度评估。分析存在强依赖关系的容器之间指标的相关性,选择相关性较高的指标作为模型的输入特征变量。预测模型采用Seq2Seq(Sequence to Sequence)编解码模型,并结合注意力机制和残差LSTM来提升模型预测的精准性和稳定性。实验表明,该模型预测效果显著,误差评价指标MAE、RMSE、MAPE相较于另外两个深度学习模型平均降低了48%、35%、51%,能够有效预测出存在强依赖关系容器的短时负载。
文摘为解决以往船舶轨迹预测模型在训练过程中未能合理应对多步预测任务间关联性的问题,构建了一种融合频域分析方法和Seq2Seq模型的船舶轨迹预测算法(Time-Frequency based Seqence to Seqence Model,TF-Seq2Seq)。基于自动识别系统数据特点设计了系列预处理操作;用融合频域分析方法和Seq2Seq模型的改进算法捕获船舶轨迹序列的关联性。基于真实数据进行实例分析,结果表明基于长短时记忆单元的Seq2Seq改进算法多种指标上表现最优,均方误差较原算法降低了12.9%。算法改进能更好地发挥模型优势,提高船舶轨迹预测精度。
基金supported by the National Natural Science Foundation of China(No.52171284)。
文摘Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.
文摘Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.
文摘针对新能源大规模并网带来的消纳问题,提出一种考虑源荷双侧弹性资源的日前调度方法.首先,对深度调峰机组、可平移负荷和可削减负荷的弹性调节能力进行分析,建立含弹性资源的电力系统调度模型;然后,提出一种基于Conv-Seq2Seq (convolutional sequence to sequence)模型的日前调度方法,使用多层卷积神经网络作为编码器对负荷预测数据等信息进行提取,改进深度学习网络信息提取的能力和速度,并使用门控循环单元作为解码器对编码器提取的信息进行解码,以输出调度计划;最后,通过辅助决策修正来确保调度计划的安全性.基于改进的IEEE39节点算例验证所提出方法的有效性和正确性.
