Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensiv...Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation,survival,and therapy resistance.Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1(LAT1)and enzymes including branched chain amino acid transaminase 1(BCAT1),branched chain amino acid transaminase 2(BCAT2),branched-chain alpha-keto acid dehydrogenase(BCKDH),and branched chain alpha-keto acid dehydrogenase kinase(BCKDK).These alterations sustain energy production,biosynthesis,redox homeostasis,and oncogenic signaling(especially mammalian target of rapamycin complex 1[mTORC1]).Crucially,tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment,impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer(NK)cells.Innovative therapeutic strategies targeting BCAA pathways—ranging from selective small-molecule inhibitors(e.g.,LAT1 and BCAT1/2)to dietary modulation—have shown promising preclinical and early clinical efficacy,highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses.By integrating multi-omics data and precision targeting approaches,this review underscores the translational significance of BCAA metabolic reprogramming,positioning it as a novel frontier in cancer treatment.展开更多
Multi-objective machine learning(ML)methods are widely used in the field of materials because material optimizations are always multi-objective.Traditional multi-objective optimization methods mainly use a combination...Multi-objective machine learning(ML)methods are widely used in the field of materials because material optimizations are always multi-objective.Traditional multi-objective optimization methods mainly use a combination of hierarchical single-objective optimization.However,this strategy often has difficulty in finding features that can optimize multiple objectives simultaneously.In this work,taking the two objectives of ductility and thermoelectric performance as examples,interpretable and explainable ML strategies are used to find features that can simultaneously optimize multiple objectives.Specifically,SHAP and SISSO are applied for qualitative analysis and quantitative analysis between key features and target values.Both SISSO and SHAP show that EN(ab)A/B and V are both positively correlated with zT and negatively correlated with Pugh's ratio.Furthermore,domain knowledge helps to rationalize the two favorable features.The compounds with large EN(ab)A/B tend to have high band degeneracies,resulting in high zT.High EN(ab)A/B correspond to weak BeX bonds,reducing the G and Pugh's ratio,and improving the ductility of materials.On the other hand,large V will cause small G,which is beneficial to small Pugh's ratio and large zT(via low kL).The present work demonstrates the significance of multiobjective optimization and domain knowledge in the development of materials informatics.展开更多
Nanophotonic waveguides hold great promise to achieve chip-scale gas sensors. However, their performance is limited by a short light path and small light–analyte overlap. To address this challenge, silicon-based, slo...Nanophotonic waveguides hold great promise to achieve chip-scale gas sensors. However, their performance is limited by a short light path and small light–analyte overlap. To address this challenge, silicon-based, slow-lightenhanced gas-sensing techniques offer a promising approach. In this study, we experimentally investigated the slow light characteristics and gas-sensing performance of 1D and 2D photonic crystal waveguides(PCWs) in the near-IR(NIR) region. The proposed 2D PCW exhibited a high group index of up to 114, albeit with a high propagation loss. The limit of detection(LoD) for acetylene(C_(2)H_(2)) was 277 parts per million(ppm) for a1 mm waveguide length and an averaging time of 0.4 s. The 1D PCW shows greater application potential compared to the 2D PCW waveguide, with an interaction factor reaching up to 288%, a comparably low propagation loss of 10 dB/cm, and an LoD of 706 ppm at 0.4 s. The measured group indices of the 2D and 1D waveguides are104 and 16, respectively, which agree well with the simulation results.展开更多
Following the Materials Genome Initiative project,materials research has embarked a new research paradigm centered around material repositories,significantly accelerating the discovery of novel materials,such as therm...Following the Materials Genome Initiative project,materials research has embarked a new research paradigm centered around material repositories,significantly accelerating the discovery of novel materials,such as thermoelectrics.Thermoelectric materials,capable of directly converting heat into electricity,are garnering increasing attention in applications like waste heat recovery and refrigeration.To facilitate research in this emerging paradigm,we have established the Materials Hub with Three-Dimensional Structures(MatHub-3d)repository,which serves as the foundation for high-throughput(HTP)calculations,property analysis,and the design of thermoelectric materials.In this review,we summarize recent advancements in thermoelectric materials powered by the MatHub-3d,specifically HTP calculations of transport properties and material design on key factors.For HTP calculations,we develop the electrical transport package for HTP purpose,and utilize it for materials screening.In some works,we investigate the relationship between transport properties and chemical bonds for particular types of thermoelectric compounds based on HTP results,enhancing the fundamental understanding about interested compounds.In our work associated with material design,we primarily utilize key factors beyond transport properties to further expedite materials screening and speedily identify specific materials for further theoretical/experimental analyses.Finally,we discuss the future developments of the MatHub-3d and the evolving directions of database-driven thermoelectric research.展开更多
基金supported by a grant from the Dalian Science and Technology Innovation Fund Program(No.2024JJ13PT070)United Foundation for Dalian Institute of Chemical Physics,Chinese Academy of Sciences and the Second Hospital of Dalian Medical University(No.DMU-2&DICP UN202410)Dalian Life and Health Field Guidance Program Project(No.2024ZDJH01PT084).
文摘Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation,survival,and therapy resistance.Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1(LAT1)and enzymes including branched chain amino acid transaminase 1(BCAT1),branched chain amino acid transaminase 2(BCAT2),branched-chain alpha-keto acid dehydrogenase(BCKDH),and branched chain alpha-keto acid dehydrogenase kinase(BCKDK).These alterations sustain energy production,biosynthesis,redox homeostasis,and oncogenic signaling(especially mammalian target of rapamycin complex 1[mTORC1]).Crucially,tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment,impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer(NK)cells.Innovative therapeutic strategies targeting BCAA pathways—ranging from selective small-molecule inhibitors(e.g.,LAT1 and BCAT1/2)to dietary modulation—have shown promising preclinical and early clinical efficacy,highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses.By integrating multi-omics data and precision targeting approaches,this review underscores the translational significance of BCAA metabolic reprogramming,positioning it as a novel frontier in cancer treatment.
基金supported by the National Natural Science Foundation of China(Grant Nos.11975100,92163212,and 52172216)JY acknowledges the support from Hefei advanced computing center and Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials.
文摘Multi-objective machine learning(ML)methods are widely used in the field of materials because material optimizations are always multi-objective.Traditional multi-objective optimization methods mainly use a combination of hierarchical single-objective optimization.However,this strategy often has difficulty in finding features that can optimize multiple objectives simultaneously.In this work,taking the two objectives of ductility and thermoelectric performance as examples,interpretable and explainable ML strategies are used to find features that can simultaneously optimize multiple objectives.Specifically,SHAP and SISSO are applied for qualitative analysis and quantitative analysis between key features and target values.Both SISSO and SHAP show that EN(ab)A/B and V are both positively correlated with zT and negatively correlated with Pugh's ratio.Furthermore,domain knowledge helps to rationalize the two favorable features.The compounds with large EN(ab)A/B tend to have high band degeneracies,resulting in high zT.High EN(ab)A/B correspond to weak BeX bonds,reducing the G and Pugh's ratio,and improving the ductility of materials.On the other hand,large V will cause small G,which is beneficial to small Pugh's ratio and large zT(via low kL).The present work demonstrates the significance of multiobjective optimization and domain knowledge in the development of materials informatics.
基金supported by the National Key Research and Development Program of China(2021YFB3502200,2018YFB0703600,and 2019YFA0704901)the National Natural Science Foundation of China(52172216,92163212,and 12174242)+4 种基金the Key Research Project of Zhejiang Laboratory(2021PE0AC02)the support from Guangdong Innovation Research Team Project(2017ZT07C062)Guangdong Provincial Key-Lab program(2019B030301001)Shenzhen Municipal Key-Lab program(ZDSYS20190902092905285)supported by the Center for Computational Science and Engineering at Southern University of Science and Technology。
基金National Natural Science Foundation of China(62175087, 62235016, 61960206004)Key Science and Technology RD Program of Jilin Province,China(20200401059GX, 20230201054GX)+1 种基金Science and Technology Research Project of Department of Education,Jilin Province,China (JJKH20211088KJ)Program for JLU Science and Technology Innovative Research Team(JLUSTIRT, 2021TD-39)。
文摘Nanophotonic waveguides hold great promise to achieve chip-scale gas sensors. However, their performance is limited by a short light path and small light–analyte overlap. To address this challenge, silicon-based, slow-lightenhanced gas-sensing techniques offer a promising approach. In this study, we experimentally investigated the slow light characteristics and gas-sensing performance of 1D and 2D photonic crystal waveguides(PCWs) in the near-IR(NIR) region. The proposed 2D PCW exhibited a high group index of up to 114, albeit with a high propagation loss. The limit of detection(LoD) for acetylene(C_(2)H_(2)) was 277 parts per million(ppm) for a1 mm waveguide length and an averaging time of 0.4 s. The 1D PCW shows greater application potential compared to the 2D PCW waveguide, with an interaction factor reaching up to 288%, a comparably low propagation loss of 10 dB/cm, and an LoD of 706 ppm at 0.4 s. The measured group indices of the 2D and 1D waveguides are104 and 16, respectively, which agree well with the simulation results.
基金supported by the National Key Research and Development Program of China(2021YFB3502200,2018YFB0703600,and 2019YFA0704901)the National Natural Science Foundation of China(52172216,92163212,and 12174242)+3 种基金the Key Research Project of Zhejiang Laboratory(2021PE0AC02)Zhang W also acknowledges the support from Guangdong Innovation Research Team Project(2017ZT07C062)Guangdong Provincial Key-Lab program(2019B030301001)Shenzhen Municipal Key-Lab program(ZDSYS20190902092905285).
文摘Following the Materials Genome Initiative project,materials research has embarked a new research paradigm centered around material repositories,significantly accelerating the discovery of novel materials,such as thermoelectrics.Thermoelectric materials,capable of directly converting heat into electricity,are garnering increasing attention in applications like waste heat recovery and refrigeration.To facilitate research in this emerging paradigm,we have established the Materials Hub with Three-Dimensional Structures(MatHub-3d)repository,which serves as the foundation for high-throughput(HTP)calculations,property analysis,and the design of thermoelectric materials.In this review,we summarize recent advancements in thermoelectric materials powered by the MatHub-3d,specifically HTP calculations of transport properties and material design on key factors.For HTP calculations,we develop the electrical transport package for HTP purpose,and utilize it for materials screening.In some works,we investigate the relationship between transport properties and chemical bonds for particular types of thermoelectric compounds based on HTP results,enhancing the fundamental understanding about interested compounds.In our work associated with material design,we primarily utilize key factors beyond transport properties to further expedite materials screening and speedily identify specific materials for further theoretical/experimental analyses.Finally,we discuss the future developments of the MatHub-3d and the evolving directions of database-driven thermoelectric research.
