Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predic...Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing the application of trametinib in glioma patients.展开更多
In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies ofte...In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables,lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces,and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics.To address these issues,this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction.First,a novel evaluation model integrating feature importance and correlation is developed,and a comprehensive feature importance assessment method is proposed.Then,a multi-dimensional weighting extraction framework is designed,from which a multi-dimensional weight matrix and its multi-layer input structure are constructed.Finally,a multi-scale fusion model driven by a multi-channel convolutional neural network is developed.The backbone network is a multi-channel convolutional structure,consisting of a multilevel feature extraction module in the front,a multi-scale sampling mechanism in the middle,and a multiscale feature fusion architecture in the rear.Based on the proposed comprehensive feature importance assessment method,a multi-factor collaborative load forecasting model is established,achieving accurate load prediction.Experimental results demonstrate that,compared with various state-of-the-art forecasting models,the proposed method reduces Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)by up to 28.30%,24.14%,and 30.35%,respectively.展开更多
Developing new therapeutic agents for cancer immunotherapy is highly demanding due to the low response ratio of PD-1/PD-L1 blockade in cancer patients.Here,we discovered that the novel immune checkpoint VISTA is highl...Developing new therapeutic agents for cancer immunotherapy is highly demanding due to the low response ratio of PD-1/PD-L1 blockade in cancer patients.Here,we discovered that the novel immune checkpoint VISTA is highly expressed on a variety of tumor-infiltrating immune cells,especially myeloid derived suppressor cells(MDSCs)and CD8^(+)T cells.Then,peptide C1 with binding affinity to VISTA was developed by phage displayed bio-panning technique,and its mutant peptide VS3 was obtained by molecular docking based mutation.Peptide VS3 could bind VISTA with high affinity and block its interaction with ligand PSGL-1 under acidic condition,and elicit anti-tumor activity in vivo.The peptide DVS3-Pal was further designed by D-amino acid substitution and fatty acid modification,which exhibited strong proteolytic stability and significant anti-tumor activity through enhancing CD8^(+)T cell function and decreasing MDSCs infiltration.This is the first study to develop peptides to block VISTA/PSGL-1 interaction,which could act as promising candidates for cancer immunotherapy.展开更多
Rechargeable aqueous aluminum batteries(AABs)with high energy-to-price ratios,abundant element reserves,and intrinsic safety are promising candidates for large-scale energy storage.However,the inherent hydrogen evolut...Rechargeable aqueous aluminum batteries(AABs)with high energy-to-price ratios,abundant element reserves,and intrinsic safety are promising candidates for large-scale energy storage.However,the inherent hydrogen evolution reaction(HER)of aluminum(Al)metal anode with inferior kinetics irreversibly hinders their practical implementation.Herein,we propose,for the first time,a double interfacial layer on the Al anode with drastically reduced HER and accelerated kinetics for AABs.Benefiting from the large band gap of the dual-interfacial layer(integration of Sn and SnS(SS-Al)),the stable voltage window of the electrolyte is remarkably expanded with the potential negatively shifting from−2.34 to−2.98 V at−5.0 mA/cm^(2).Fur-thermore,the synergistic effect from both the SnS outer layer(lower desolvation energy barrier)and the Sn interlayer with improved aluminumophilic properties contributes to accelerated kinetics.Consequently,the optimized SS-Al electrode main-tains one of the best long-term stability among interface-modified Al anodes(more than 700 h at 0.05 mA/cm^(2)with a low initial overpotential of 50.0 mV)in symmetric batteries.Practically,the large-size full-cell prototypes deliver high performance over 1,000 cycles at 1.0 A/g.Overall,this novel interface modification strategy provides a promising pathway for the anode devel-opment in AABs.展开更多
基金funded by the Scientific Research Project of the Higher Education Department of Guizhou Province[Qianjiaoji 2022(187)]Department of Education of Guizhou Province[Guizhou Teaching and Technology(2023)015]+1 种基金Guizhou Medical University National Natural Science Foundation Cultivation Project(22NSFCP45)China Postdoctoral Science Foundation Project(General Program No.2022M720929).
文摘Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing the application of trametinib in glioma patients.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62133008)the Key Research and Development Program of Shandong Province(No.2025CXPT076).
文摘In power systems,environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors,posing significant challenges to reliable energy planning.Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables,lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces,and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics.To address these issues,this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction.First,a novel evaluation model integrating feature importance and correlation is developed,and a comprehensive feature importance assessment method is proposed.Then,a multi-dimensional weighting extraction framework is designed,from which a multi-dimensional weight matrix and its multi-layer input structure are constructed.Finally,a multi-scale fusion model driven by a multi-channel convolutional neural network is developed.The backbone network is a multi-channel convolutional structure,consisting of a multilevel feature extraction module in the front,a multi-scale sampling mechanism in the middle,and a multiscale feature fusion architecture in the rear.Based on the proposed comprehensive feature importance assessment method,a multi-factor collaborative load forecasting model is established,achieving accurate load prediction.Experimental results demonstrate that,compared with various state-of-the-art forecasting models,the proposed method reduces Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)by up to 28.30%,24.14%,and 30.35%,respectively.
基金supported by grants from the National Natural Science Foundation of China (U1904147,U20A20369)Shenzhen Science and Technology Program (KQTD20190929173853397,China)“Pearl River Talent Plan”Innovation and Entrepreneurship Team Project of Guangdong Province (2019ZT08Y464,China)。
文摘Developing new therapeutic agents for cancer immunotherapy is highly demanding due to the low response ratio of PD-1/PD-L1 blockade in cancer patients.Here,we discovered that the novel immune checkpoint VISTA is highly expressed on a variety of tumor-infiltrating immune cells,especially myeloid derived suppressor cells(MDSCs)and CD8^(+)T cells.Then,peptide C1 with binding affinity to VISTA was developed by phage displayed bio-panning technique,and its mutant peptide VS3 was obtained by molecular docking based mutation.Peptide VS3 could bind VISTA with high affinity and block its interaction with ligand PSGL-1 under acidic condition,and elicit anti-tumor activity in vivo.The peptide DVS3-Pal was further designed by D-amino acid substitution and fatty acid modification,which exhibited strong proteolytic stability and significant anti-tumor activity through enhancing CD8^(+)T cell function and decreasing MDSCs infiltration.This is the first study to develop peptides to block VISTA/PSGL-1 interaction,which could act as promising candidates for cancer immunotherapy.
基金supported by the National Natural Science Foundation of China(52374295)the National Key Research and Development Program of China(2022YFB2402400).
文摘Rechargeable aqueous aluminum batteries(AABs)with high energy-to-price ratios,abundant element reserves,and intrinsic safety are promising candidates for large-scale energy storage.However,the inherent hydrogen evolution reaction(HER)of aluminum(Al)metal anode with inferior kinetics irreversibly hinders their practical implementation.Herein,we propose,for the first time,a double interfacial layer on the Al anode with drastically reduced HER and accelerated kinetics for AABs.Benefiting from the large band gap of the dual-interfacial layer(integration of Sn and SnS(SS-Al)),the stable voltage window of the electrolyte is remarkably expanded with the potential negatively shifting from−2.34 to−2.98 V at−5.0 mA/cm^(2).Fur-thermore,the synergistic effect from both the SnS outer layer(lower desolvation energy barrier)and the Sn interlayer with improved aluminumophilic properties contributes to accelerated kinetics.Consequently,the optimized SS-Al electrode main-tains one of the best long-term stability among interface-modified Al anodes(more than 700 h at 0.05 mA/cm^(2)with a low initial overpotential of 50.0 mV)in symmetric batteries.Practically,the large-size full-cell prototypes deliver high performance over 1,000 cycles at 1.0 A/g.Overall,this novel interface modification strategy provides a promising pathway for the anode devel-opment in AABs.
