Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions to...Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?展开更多
Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding appro...Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.展开更多
In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and comput...In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.展开更多
目的:探讨罗汉果皂苷V(MV)对铁死亡诱导剂RAS选择性致死分子3(RSL3)诱导的人神经母细胞瘤SH-SY5Y细胞铁死亡的抑制作用及可能机制。方法:用RSL3诱导SH-SY5Y细胞建立铁死亡模型。MTT法检测细胞活力;倒置显微镜观察细胞形态;亚铁离子荧光...目的:探讨罗汉果皂苷V(MV)对铁死亡诱导剂RAS选择性致死分子3(RSL3)诱导的人神经母细胞瘤SH-SY5Y细胞铁死亡的抑制作用及可能机制。方法:用RSL3诱导SH-SY5Y细胞建立铁死亡模型。MTT法检测细胞活力;倒置显微镜观察细胞形态;亚铁离子荧光探针FerroFarRed检测细胞内亚铁离子含量;线粒体红色荧光探针MitoTracker Red CMXRos检测线粒体膜电位(MMP);超氧化物阴离子荧光探针二氢乙啶和线粒体超氧化物红色荧光探针MitoSoX Red分别检测细胞内和线粒体内活性氧(ROS)。微板法检测细胞谷胱甘肽(GSH)和丙二醛(MDA)水平。Western blot检测脂酰辅酶A合成酶长链家族成员4(ACSL4)、环加氧酶2(COX-2、)谷胱甘肽过氧化物酶4(GPX4)和溶质载体家族7成员11(SLC7A11)蛋白表达水平。分子对接技术预测MV与ACSL4、COX-2、GPX4和SLC7A11的靶向关系。结果:与control组相比,RSL3组SH-SY5Y细胞活力显著降低(P<0.01),细胞内亚铁离子含量、细胞内和线粒体内ROS水平及MDA水平显著升高(P<0.05或P<0.01),MMP和GSH水平显著降低(P<0.01),ACSL4和COX-2蛋白表达水平显著升高,而GPX4和SLC7A11蛋白表达水平显著降低(P<0.01),提示成功建立了细胞铁死亡模型。MV处理使细胞活力显著升高(P<0.05),细胞内亚铁离子含量、细胞内和线粒体内ROS水平及MDA水平显著降低(P<0.01),MMP和GSH水平显著升高(P<0.05或P<0.01);ACSL4和COX-2蛋白水平显著降低,而GPX4和SLC7A11蛋白水平显著升高(P<0.05或P<0.01)。分子对接结果显示,MV与铁死亡核心蛋白ACSL4、COX-2、GPX4和SLC7A11存在结合位点。结论:MV可抑制RSL3诱导的SH-SY5Y细胞铁死亡的发生,其机制可能与激活SLC7A11/GPX4和抑制ACSL4/COX-2有关。展开更多
文摘Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?
基金Supported by National Nature Science Foudation of China(61976160,61906137,61976158,62076184,62076182)Shanghai Science and Technology Plan Project(21DZ1204800)。
文摘Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.
基金Supported by Sichuan Science and Technology Program(2021YFQ0003,2023YFSY0026,2023YFH0004).
文摘In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.
文摘目的:探讨罗汉果皂苷V(MV)对铁死亡诱导剂RAS选择性致死分子3(RSL3)诱导的人神经母细胞瘤SH-SY5Y细胞铁死亡的抑制作用及可能机制。方法:用RSL3诱导SH-SY5Y细胞建立铁死亡模型。MTT法检测细胞活力;倒置显微镜观察细胞形态;亚铁离子荧光探针FerroFarRed检测细胞内亚铁离子含量;线粒体红色荧光探针MitoTracker Red CMXRos检测线粒体膜电位(MMP);超氧化物阴离子荧光探针二氢乙啶和线粒体超氧化物红色荧光探针MitoSoX Red分别检测细胞内和线粒体内活性氧(ROS)。微板法检测细胞谷胱甘肽(GSH)和丙二醛(MDA)水平。Western blot检测脂酰辅酶A合成酶长链家族成员4(ACSL4)、环加氧酶2(COX-2、)谷胱甘肽过氧化物酶4(GPX4)和溶质载体家族7成员11(SLC7A11)蛋白表达水平。分子对接技术预测MV与ACSL4、COX-2、GPX4和SLC7A11的靶向关系。结果:与control组相比,RSL3组SH-SY5Y细胞活力显著降低(P<0.01),细胞内亚铁离子含量、细胞内和线粒体内ROS水平及MDA水平显著升高(P<0.05或P<0.01),MMP和GSH水平显著降低(P<0.01),ACSL4和COX-2蛋白表达水平显著升高,而GPX4和SLC7A11蛋白表达水平显著降低(P<0.01),提示成功建立了细胞铁死亡模型。MV处理使细胞活力显著升高(P<0.05),细胞内亚铁离子含量、细胞内和线粒体内ROS水平及MDA水平显著降低(P<0.01),MMP和GSH水平显著升高(P<0.05或P<0.01);ACSL4和COX-2蛋白水平显著降低,而GPX4和SLC7A11蛋白水平显著升高(P<0.05或P<0.01)。分子对接结果显示,MV与铁死亡核心蛋白ACSL4、COX-2、GPX4和SLC7A11存在结合位点。结论:MV可抑制RSL3诱导的SH-SY5Y细胞铁死亡的发生,其机制可能与激活SLC7A11/GPX4和抑制ACSL4/COX-2有关。
