Objective:To investigate the changes and clinical significance of auxiliary diagnosis, cell survival, angiogenesis and nutritional support in patients with oral cancer.Methods: 50 patients with oral cancer treated in ...Objective:To investigate the changes and clinical significance of auxiliary diagnosis, cell survival, angiogenesis and nutritional support in patients with oral cancer.Methods: 50 patients with oral cancer treated in our hospital from June 2016 to September 2017 were selected as the observation group and 50 healthy people as the control group. The expression levels of auxiliary diagnosis [including secretory immunoglobulin A (SIgA), catalase (CAT)], cell survival [including survivin, focal adhesion kinase (FAK)], angiogenesis [including vascular endothelial growth factor (VEGF), hepatocyte growth (HGF), urokinase-type plasminogen activator (uPA)] and nutritional support [including lead (Pb), magnesium (Mg), calcium (Ca), iron (Fe), zinc (Zn) and copper (Cu)] related indicators in the two groups were observed and compared.Results:The levels of SIgA [(83.30±6.05) ug/mL], Mg [(1.21±0.17) mmol/L], Fe [(6.75±1.03)mmol/L] and Zn [(87.11±15.31) ug/L] in the observation group were significantly lower than those in the control group (P<0.05), while the levels of CAT [(39.87±9.18) U/mL], survivin [(131.63±10.53) ng/L], FAK [(62.27±5.20) ng/mL], VEGF[(533.73±150.63)ng/L], HGF[(411.32±181.72)ng/L], uPA[(5.12±1.31)mg/L], Pb[(65.55±20.76)μg/L], Ca[(1.55±0.20)mmol/L] and Cu[(14.90±5.30)μmol/L] were significantly higher than that of the control group. The difference was statistically significant (P<0.05).Conclusions:Patients with oral cancer, the immune function of salivary mucosa decreased and cell survival was abnormal. Oral cancer patients are easy to regenerate tumor blood vessels. Tumor cells and vascular endothelial cells are active in proliferation, migration and invasion. The expression of trace elements is also abnormal, which is not conducive to the nutritional support of the body. The relevant indicators should be strengthened in clinical practice, so as to provide evidence for early diagnosis and treatment of the disease.展开更多
利用脑电图信号,结合深度学习方法进行抑郁症辅助诊断目前仍存在特征提取不足及模型诊断准确率不高的问题。为了提取更具抑郁症表征的特征,提高抑郁症辅助诊断的准确率,本文从特征提取和网络框架两个方面进行改进,提出一种结合改进VGG–...利用脑电图信号,结合深度学习方法进行抑郁症辅助诊断目前仍存在特征提取不足及模型诊断准确率不高的问题。为了提取更具抑郁症表征的特征,提高抑郁症辅助诊断的准确率,本文从特征提取和网络框架两个方面进行改进,提出一种结合改进VGG–16(visual geometry group–16)和基于压缩激励网络的通道注意力机制(modified VGG–16 network based on SE–NET,SEMod–VGG)的抑郁症辅助检测模型。首先,提取脑电图信号中α(Alpha)、θ(Theta)和β(Beta)频段的微分熵特征,与对应通道的功率谱密度特征相融合,构成一种同时具有时频属性和能量属性的4维融合特征;其次,针对该4维特征,改进现有的VGG–16模型,同时采用5×5和7×7两种不同尺度的卷积核,在提取脑电信号的时频信息和功率信息的同时,提高特征的泛化表征能力;再将基于压缩激励网络的通道注意力机制与改进的检测模型相结合,对电极通道的权重进行2次标定;最后采用10折交叉验证使得最小二乘支持向量机取得最佳检测准确率。对所提模型在准确率,召回率以及网络性能这3个方面进行实验评估,在MODMA数据集上的结果表明:当使用4维融合特征作为输入时,SEMod–VGG可达到最佳检测性能,其抑郁症检测准确率在3通道、16通道及128通道分别为92.21%、93.47%和95.76%;检测召回率在3通道、16通道以及128通道分别为91.57%、92.46%和96.80%。相较于现有的抑郁症辅助检测模型,本研究所提出的融合特征对抑郁症的表征性更强,且所提出的模型在检测准确率,召回率以及模型效率上均取得明显提升。展开更多
With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power...With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.展开更多
The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers t...The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.展开更多
文摘Objective:To investigate the changes and clinical significance of auxiliary diagnosis, cell survival, angiogenesis and nutritional support in patients with oral cancer.Methods: 50 patients with oral cancer treated in our hospital from June 2016 to September 2017 were selected as the observation group and 50 healthy people as the control group. The expression levels of auxiliary diagnosis [including secretory immunoglobulin A (SIgA), catalase (CAT)], cell survival [including survivin, focal adhesion kinase (FAK)], angiogenesis [including vascular endothelial growth factor (VEGF), hepatocyte growth (HGF), urokinase-type plasminogen activator (uPA)] and nutritional support [including lead (Pb), magnesium (Mg), calcium (Ca), iron (Fe), zinc (Zn) and copper (Cu)] related indicators in the two groups were observed and compared.Results:The levels of SIgA [(83.30±6.05) ug/mL], Mg [(1.21±0.17) mmol/L], Fe [(6.75±1.03)mmol/L] and Zn [(87.11±15.31) ug/L] in the observation group were significantly lower than those in the control group (P<0.05), while the levels of CAT [(39.87±9.18) U/mL], survivin [(131.63±10.53) ng/L], FAK [(62.27±5.20) ng/mL], VEGF[(533.73±150.63)ng/L], HGF[(411.32±181.72)ng/L], uPA[(5.12±1.31)mg/L], Pb[(65.55±20.76)μg/L], Ca[(1.55±0.20)mmol/L] and Cu[(14.90±5.30)μmol/L] were significantly higher than that of the control group. The difference was statistically significant (P<0.05).Conclusions:Patients with oral cancer, the immune function of salivary mucosa decreased and cell survival was abnormal. Oral cancer patients are easy to regenerate tumor blood vessels. Tumor cells and vascular endothelial cells are active in proliferation, migration and invasion. The expression of trace elements is also abnormal, which is not conducive to the nutritional support of the body. The relevant indicators should be strengthened in clinical practice, so as to provide evidence for early diagnosis and treatment of the disease.
文摘利用脑电图信号,结合深度学习方法进行抑郁症辅助诊断目前仍存在特征提取不足及模型诊断准确率不高的问题。为了提取更具抑郁症表征的特征,提高抑郁症辅助诊断的准确率,本文从特征提取和网络框架两个方面进行改进,提出一种结合改进VGG–16(visual geometry group–16)和基于压缩激励网络的通道注意力机制(modified VGG–16 network based on SE–NET,SEMod–VGG)的抑郁症辅助检测模型。首先,提取脑电图信号中α(Alpha)、θ(Theta)和β(Beta)频段的微分熵特征,与对应通道的功率谱密度特征相融合,构成一种同时具有时频属性和能量属性的4维融合特征;其次,针对该4维特征,改进现有的VGG–16模型,同时采用5×5和7×7两种不同尺度的卷积核,在提取脑电信号的时频信息和功率信息的同时,提高特征的泛化表征能力;再将基于压缩激励网络的通道注意力机制与改进的检测模型相结合,对电极通道的权重进行2次标定;最后采用10折交叉验证使得最小二乘支持向量机取得最佳检测准确率。对所提模型在准确率,召回率以及网络性能这3个方面进行实验评估,在MODMA数据集上的结果表明:当使用4维融合特征作为输入时,SEMod–VGG可达到最佳检测性能,其抑郁症检测准确率在3通道、16通道及128通道分别为92.21%、93.47%和95.76%;检测召回率在3通道、16通道以及128通道分别为91.57%、92.46%和96.80%。相较于现有的抑郁症辅助检测模型,本研究所提出的融合特征对抑郁症的表征性更强,且所提出的模型在检测准确率,召回率以及模型效率上均取得明显提升。
基金supported by the Science and Technology Project of the State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.
基金This work was supported in part by the National Natural Science Foundation of China(Grants No.72025404 and No.71621002)Bei-jing Natural Science Foundation(L192012)Beijing Nova Program(Z201100006820085).
文摘The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.
