以“凤丹”牡丹鲜花为原料,研究酶处理制备牡丹鲜花汁工艺;采用高效液相色谱与四极杆飞行时间串联质谱(high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry,HPLC-Q-TOF-MS-MS)...以“凤丹”牡丹鲜花为原料,研究酶处理制备牡丹鲜花汁工艺;采用高效液相色谱与四极杆飞行时间串联质谱(high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry,HPLC-Q-TOF-MS-MS)联用技术分析牡丹鲜花汁中化学物质。结果表明,由果胶酶、纤维素酶和木瓜蛋白酶组成的复合酶制剂(质量比1∶2∶1)能提高牡丹鲜花出汁率,达22.3%。复合酶处理的条件为∶酶添加量0.15%(质量分数),酶解温度50℃,酶解时间90 min。鉴定了牡丹鲜花汁中的20个主要成分,包括3个有机酸、8个酚酸、7个黄酮以及2个单萜糖苷。酚酸和黄酮苷是牡丹鲜花汁中主要活性成分,主要以没食子酸、没食子酰葡萄糖、没食子酰单宁、芹菜素葡萄糖苷、芹菜素新橘皮糖苷和山奈酚二葡萄糖苷等形式存在。研究结果为牡丹鲜花制汁方法以及牡丹鲜花液态功能食品开发提供理论依据。展开更多
Ferroptosis is a newly proposed type of programmed cell death,which has been associated with a variety of diseases including tumors.Researchers have thereby presented nanoplatforms to mediate ferroptosis for anti-canc...Ferroptosis is a newly proposed type of programmed cell death,which has been associated with a variety of diseases including tumors.Researchers have thereby presented nanoplatforms to mediate ferroptosis for anti-cancer therapy.However,the development of ferroptosis-based nanotherapeutics is generally hindered by the limited penetration depth in tumors,poor active pharmaceutical ingredient(API)loading content and the systemic toxicity.Herein,self-propelled ferroptosis nanoinducers composed of two endogenous proteins,glucose oxidase and ferritin,are presented to show enhanced tumor inhibition via ferroptosis while maintaining high API and biocompatibility.The accumulation of our proteomotors at tumor regions is facilitated by the active tumor-targeting effect of ferritin.The enhanced diffusion of proteomotors is then actuated by efficiently decomposing glucose into gluconic acid and H_(2)O_(2),leading to deeper penetration and enhanced uptake into tumors.Under the synergistic effect of glucose oxidase and ferritin,the equilibrium between reactive oxygen species and GSH is damaged,leading to lipid peroxidation.As a result,by inducing ferroptosis,our self-propelled ferroptosis nanoinducers exhibit enhanced tumor inhibitory effects.This work paves a way for the construction of a biocompatible anticancer platform with enhanced diffusion utilizing only two endogenous proteins,centered around the concept of ferroptosis.展开更多
Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-ra...Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.展开更多
Maintaining community stability has profound positive impacts on the ecological functions and sustainable utilization of grassland ecosystems.Numerous studies have explored how community stability responds to climate ...Maintaining community stability has profound positive impacts on the ecological functions and sustainable utilization of grassland ecosystems.Numerous studies have explored how community stability responds to climate change and its relationship with plant species diversity.Nevertheless,the impact and underlying mechanisms of belowground ecosystem multifunctionality(BGEMF)on community stability along a precipitation gradient in alpine grasslands remain poorly understood.To address this knowledge gap,we conducted field surveys from 2015 to 2020,measuring plant species diversity,annual net primary productivity(ANPP),and soil physicochemical properties across 79 sites in alpine grassland ecosystems on the Qinghai-Xizang Plateau.Our findings highlight both plant species diversity(standardized total effect:32%)and BGEMF(standardized total effect:75%)had an indirect effect on stability viaregulating mean ANPP within alpine grasslands.Furthermore,mean annual precipitation substantially impacted both plant species diversity and BGEMF,subsequently affecting community stability.However,temperature had a strong negative regulatory effect on species diversity,the mean and variability of ANPP.Thus,we emphasized the pivotal role of plant species diversity and BGEMF in shaping community stability,and stated the imperative need for species conservation and BGEMF improvement to sustain alpine ecosystems in the face of ongoing climate change.展开更多
BACKGROUND The prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms(PDGNENs)is dismal and related research is limited.AIM To investigate the prognostic factors,and validate a novel prognos...BACKGROUND The prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms(PDGNENs)is dismal and related research is limited.AIM To investigate the prognostic factors,and validate a novel prognostic nomogram for PDGNEN patients.METHODS We conducted a retrospective study using clinical and pathological data from PDGNEN patients treated at the First Medical Center of the Chinese PLA General Hospital from January 2000 to June 2023.Overall survival(OS)differences were assessed with the Log-rank test and Kaplan-Meier survival curves.Cox regression analysis identified independent risk factors for prognosis.Model performance was evaluated using Harrell’s concordance index,receiver operating characteristic analysis,area under the curve,calibration curves,and decision curve analysis(UDC),including the area under the UDC.RESULTS The study included 336 patients(227 with neuroendocrine carcinoma and 109 with mixed adenoneuroendocrine carcinoma).The average age was 62.7 years.The cohort comprised 80(24.7%)patients in stage I,146(42.9%)in stage II,62(18.1%)in stage III,and 48(14.3%)in stage IV.Significant differences in OS were observed across tumor-node-metastasis stages(P<0.001).Multivariate analysis showed age,Ki-67 index,invasion depth,lymph node metastasis,distant metastasis,and platelet-to-lymphocyte ratio as independent risk factors.We developed a nomogram with a concordance index of 0.779(95%confidence interval:0.743-0.858).Receiver operating characteristic analysis showed area under the curves for 1-year,3-year,and 5-year OS predictions of 0.865,0.850,and 0.890,respectively.The calibration curve demonstrated good agreement with actual outcomes.The area under the UDC for the nomogram vs the 8th American Joint Committee on Cancer tumor-node-metastasis staging system were 0.047 vs 0.027,0.291 vs 0.179,and 0.376 vs 0.216 for 1-year,3-year,and 5-year OS,respectively.CONCLUSION PDGNENs are predominantly found in older men,often in advanced stages at diagnosis,resulting in poor prognosis.The established nomogram demonstrates strong predictive capability and clinical utility.展开更多
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co...Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.展开更多
文摘以“凤丹”牡丹鲜花为原料,研究酶处理制备牡丹鲜花汁工艺;采用高效液相色谱与四极杆飞行时间串联质谱(high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry,HPLC-Q-TOF-MS-MS)联用技术分析牡丹鲜花汁中化学物质。结果表明,由果胶酶、纤维素酶和木瓜蛋白酶组成的复合酶制剂(质量比1∶2∶1)能提高牡丹鲜花出汁率,达22.3%。复合酶处理的条件为∶酶添加量0.15%(质量分数),酶解温度50℃,酶解时间90 min。鉴定了牡丹鲜花汁中的20个主要成分,包括3个有机酸、8个酚酸、7个黄酮以及2个单萜糖苷。酚酸和黄酮苷是牡丹鲜花汁中主要活性成分,主要以没食子酸、没食子酰葡萄糖、没食子酰单宁、芹菜素葡萄糖苷、芹菜素新橘皮糖苷和山奈酚二葡萄糖苷等形式存在。研究结果为牡丹鲜花制汁方法以及牡丹鲜花液态功能食品开发提供理论依据。
基金supported by National Key Research and Development Program of China(No.2022YFA1206900)National Natural Science Foundation of China(Nos.22175083,82204415,51973241,22375224)GuangDong Basic and Applied Basic Research Foundation(No.2021A1515220187)。
文摘Ferroptosis is a newly proposed type of programmed cell death,which has been associated with a variety of diseases including tumors.Researchers have thereby presented nanoplatforms to mediate ferroptosis for anti-cancer therapy.However,the development of ferroptosis-based nanotherapeutics is generally hindered by the limited penetration depth in tumors,poor active pharmaceutical ingredient(API)loading content and the systemic toxicity.Herein,self-propelled ferroptosis nanoinducers composed of two endogenous proteins,glucose oxidase and ferritin,are presented to show enhanced tumor inhibition via ferroptosis while maintaining high API and biocompatibility.The accumulation of our proteomotors at tumor regions is facilitated by the active tumor-targeting effect of ferritin.The enhanced diffusion of proteomotors is then actuated by efficiently decomposing glucose into gluconic acid and H_(2)O_(2),leading to deeper penetration and enhanced uptake into tumors.Under the synergistic effect of glucose oxidase and ferritin,the equilibrium between reactive oxygen species and GSH is damaged,leading to lipid peroxidation.As a result,by inducing ferroptosis,our self-propelled ferroptosis nanoinducers exhibit enhanced tumor inhibitory effects.This work paves a way for the construction of a biocompatible anticancer platform with enhanced diffusion utilizing only two endogenous proteins,centered around the concept of ferroptosis.
基金supported by the joint funds of the Chinese National Natural Science Foundation(NSFC)(Grant No.U2242213)the funds of the NSFC(Grant No.42341209)+2 种基金the National Key Research and Development(R&D)Program of the Ministry of Science and Technology of China(Grant No.2021YFC3000902)the National Science Foundation for Young Scholars(Grant No.42205166)the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSQ008)。
文摘Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
基金supported financially by the National Natural Science Foundation of China(Grant No.32271774).
文摘Maintaining community stability has profound positive impacts on the ecological functions and sustainable utilization of grassland ecosystems.Numerous studies have explored how community stability responds to climate change and its relationship with plant species diversity.Nevertheless,the impact and underlying mechanisms of belowground ecosystem multifunctionality(BGEMF)on community stability along a precipitation gradient in alpine grasslands remain poorly understood.To address this knowledge gap,we conducted field surveys from 2015 to 2020,measuring plant species diversity,annual net primary productivity(ANPP),and soil physicochemical properties across 79 sites in alpine grassland ecosystems on the Qinghai-Xizang Plateau.Our findings highlight both plant species diversity(standardized total effect:32%)and BGEMF(standardized total effect:75%)had an indirect effect on stability viaregulating mean ANPP within alpine grasslands.Furthermore,mean annual precipitation substantially impacted both plant species diversity and BGEMF,subsequently affecting community stability.However,temperature had a strong negative regulatory effect on species diversity,the mean and variability of ANPP.Thus,we emphasized the pivotal role of plant species diversity and BGEMF in shaping community stability,and stated the imperative need for species conservation and BGEMF improvement to sustain alpine ecosystems in the face of ongoing climate change.
文摘BACKGROUND The prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms(PDGNENs)is dismal and related research is limited.AIM To investigate the prognostic factors,and validate a novel prognostic nomogram for PDGNEN patients.METHODS We conducted a retrospective study using clinical and pathological data from PDGNEN patients treated at the First Medical Center of the Chinese PLA General Hospital from January 2000 to June 2023.Overall survival(OS)differences were assessed with the Log-rank test and Kaplan-Meier survival curves.Cox regression analysis identified independent risk factors for prognosis.Model performance was evaluated using Harrell’s concordance index,receiver operating characteristic analysis,area under the curve,calibration curves,and decision curve analysis(UDC),including the area under the UDC.RESULTS The study included 336 patients(227 with neuroendocrine carcinoma and 109 with mixed adenoneuroendocrine carcinoma).The average age was 62.7 years.The cohort comprised 80(24.7%)patients in stage I,146(42.9%)in stage II,62(18.1%)in stage III,and 48(14.3%)in stage IV.Significant differences in OS were observed across tumor-node-metastasis stages(P<0.001).Multivariate analysis showed age,Ki-67 index,invasion depth,lymph node metastasis,distant metastasis,and platelet-to-lymphocyte ratio as independent risk factors.We developed a nomogram with a concordance index of 0.779(95%confidence interval:0.743-0.858).Receiver operating characteristic analysis showed area under the curves for 1-year,3-year,and 5-year OS predictions of 0.865,0.850,and 0.890,respectively.The calibration curve demonstrated good agreement with actual outcomes.The area under the UDC for the nomogram vs the 8th American Joint Committee on Cancer tumor-node-metastasis staging system were 0.047 vs 0.027,0.291 vs 0.179,and 0.376 vs 0.216 for 1-year,3-year,and 5-year OS,respectively.CONCLUSION PDGNENs are predominantly found in older men,often in advanced stages at diagnosis,resulting in poor prognosis.The established nomogram demonstrates strong predictive capability and clinical utility.
基金supported by the Sichuan Science and Technology Program(Nos.2024JDRC0100 and 2023YFQ0091)the National Natural Science Foundation of China(Nos.U21A20167 and 52475138)the Scientific Research Foundation of the State Key Laboratory of Rail Transit Vehicle System(No.2024RVL-T08).
文摘Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.
基金Doctoral Scientific Research Foundation of Shanxi Datong University(2014-B-11)National Natural Science Foundation of China(21174114)+3 种基金National Natural Science Foundation of China(21363021)Program for Changjiang Scholars and Innovative Research Team in University of M inistry of Education of China(IRT1177)Scientific and Technical plan project of Gansu province(1204GKCA006)Scientific and Technical Innovation Project of Northw est Normal University(nw nu-kjcxgc-03-63)~~
