目的:探究移植肾活组织检查的病理指标与不同时期移植肾功能异常的相关性,评价移植肾病理指标在移植肾功能异常中的诊断价值,建立预测移植肾预后的模型。方法:回顾性分析2015—2018年于南京医科大学第一附属医院接受移植肾活检的同种异...目的:探究移植肾活组织检查的病理指标与不同时期移植肾功能异常的相关性,评价移植肾病理指标在移植肾功能异常中的诊断价值,建立预测移植肾预后的模型。方法:回顾性分析2015—2018年于南京医科大学第一附属医院接受移植肾活检的同种异体肾移植手术受者的临床资料及病理指标。将总体样本基于不同活检后时间(活检时、活检后第1年、活检后第3年、活检后第5年)分别进行统计学分析。采用单因素分析筛选对肾功能异常有影响的指标,通过二元Logistic回归建立模型并绘制列线图;此外,通过混合效应Logistic回归探究在各阶段均与肾功能异常相关因素的动态效应。通过受试者工作特征(receiver operating characteristic,ROC)曲线、对应的曲线下面积(area under the curve,AUC)、校准曲线判断模型的判别效度以及与实际情况的一致性。结果:共纳入121例患者,按照移植肾活检后时间将总体样本分为活检时、活检后1年、3年、5年:i评分≥1分与活检时、活检后第1、3、5年肾功能异常显著相关;动态效应分析结果为i评分≥1分与移植肾穿刺后5年内的肾功能恶化有关。依据上述统计结果绘制出列线图,AUC显示模型具有较好的判别效度。校准曲线显示模型的移植肾功能异常发生的预测概率与实际概率一致性较高。结论:构建了一个预测不同时间点移植肾功能异常的列线图模型,有效提升了移植肾穿刺后患者管理的精准度。展开更多
Motivated by the special theory of gradient elasticity (GradEla), a proposal is advanced for extending it to construct gradient models for interatomic potentials, commonly used in atomistic simulations. Our focus is o...Motivated by the special theory of gradient elasticity (GradEla), a proposal is advanced for extending it to construct gradient models for interatomic potentials, commonly used in atomistic simulations. Our focus is on London’s quantum mechanical potential which is an analytical expression valid until a certain characteristic distance where “attractive” molecular interactions change character and become “repulsive” and cannot be described by the classical form of London’s potential. It turns out that the suggested internal length gradient (ILG) generalization of London’s potential generates both an “attractive” and a “repulsive” branch, and by adjusting the corresponding gradient parameters, the behavior of the empirical Lennard-Jones potentials is theoretically captured.展开更多
目的:研究远隔缺血预适应(RIPC)对大鼠脑缺血模型的保护作用及分子机制。方法:30只成年雄性SD大鼠随机分为4组:假手术组(sham)、RIPC组、缺血再灌注组(MCAO/R)组、RIPC+MCAO/R组;术前通过夹闭双侧股动脉给予相应组RIPC处理,利用大脑中...目的:研究远隔缺血预适应(RIPC)对大鼠脑缺血模型的保护作用及分子机制。方法:30只成年雄性SD大鼠随机分为4组:假手术组(sham)、RIPC组、缺血再灌注组(MCAO/R)组、RIPC+MCAO/R组;术前通过夹闭双侧股动脉给予相应组RIPC处理,利用大脑中动脉栓塞再灌注法(MCAO/R)制备大鼠缺血性脑卒中模型,神经功能评分检测大鼠的神经功能,用2,3,5-三苯四唑氯(TTC)对脑切片进行染色以评估脑梗死的程度。利用real time RT-PCR检测大脑皮质中低氧诱导因子-1α(HIF-1α)和血管内皮生长因子(VEGF) mRNA的表达。结果:与MCAO/R组大鼠相比,RIPC处理组大鼠神经功能缺损症状较轻(P<0.05),脑梗死体积缩小(P<0.01),皮质中HIF-1α和VEGF mRNA的表达表达明显升高(P<0.05)。结论:RIPC处理对减轻缺血性脑卒中大鼠具有保护作用,其分子机制可能与激活HIF-1α/VEGF通路有关。展开更多
Nanoindentation is a useful technique to measure material properties at microscopic level.However,the intrinsically multiscale nature makes it challenging for large-scale simulations to be carried out.It is shown that...Nanoindentation is a useful technique to measure material properties at microscopic level.However,the intrinsically multiscale nature makes it challenging for large-scale simulations to be carried out.It is shown that in molecular statics simulations of nanoindentation,the separated dislocation loops(SDLs)are trapped in simulation box which detrimentally affects the plastic behavior in the plastic zone(PZ);and the long-distance propagation of SDLs consumes much computational cost yet with little contribution to the variation of tip force.To tackle the problem,the dislocation loop erasing(DLE)method is proposed in the work to alleviate the influence of artificial boundary conditions on the SDL–PZ interaction and improve simulation efficiency.Simulation results indicate that the force–depth curves obtained from simulations with and without DLE are consistent with each other,while the method with DLE yields more reasonable results of microstructural evolution and shows better efficiency.The new method provides an alternative approach for large-scale molecular simulation of nanoindentation with reliable results and higher efficiency and also sheds lights on improving existing multiscale methods.展开更多
In this work, we utilize atomistic simulations and dislocation mechanics to explore the formation of in-verse pileups in CrCoNi model alloys and elucidate their unique impact on the strength and ductilityof multi-prin...In this work, we utilize atomistic simulations and dislocation mechanics to explore the formation of in-verse pileups in CrCoNi model alloys and elucidate their unique impact on the strength and ductilityof multi-principal element alloys (MPEAs). The present atomistic simulations on single crystals revealthat during the deformation of CrCoNi, stress gradients lead to the formation of novel inverse disloca-tion pileup. We find that this unique dislocation pattern in a confined volume is due to the elevatedlattice friction and significant stress gradient present in the material. Furthermore, this phenomenon canbe notably promoted by lowering the temperature, increasing the loading rate, and introducing chemicalshort-range ordering. Additional simulations on bicrystals show that these inverse pileups play a criticalrole in suppressing dislocation transmission, reflection, and grain boundary (GB) migration. As a result,they effectively mitigate stress concentration and reduce damage accumulation at GBs, lowering the riskof catastrophic failure due to GB damages. In our theoretical analysis, we utilize dislocation mechanics topredict the formation of the inverse pileup and its subsequent strengthening effect, considering scenarioswith and without obstacles. Our investigations encompass various lattice frictions and stress gradients.Remarkably, our results shed light on the prevailing impact of dislocation hardening in the plastic de-formation of CrCoNi even under the presence of a linear stress gradient, while the contribution of GBstrengthening is found to be comparatively limited. These findings provide valuable insights into the de-formation mechanisms of MPEAs in general and significantly aid their applications as promising structuralmaterials.展开更多
Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals,and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms.However,existing m...Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals,and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms.However,existing machine learning interatomic potentials(MLIPs)often fall short in adequately describing these defects,as their large characteristic scales exceed the computational limits of firstprinciples calculations.To address this challenge,wepresent acomputational frameworkcombining a defect genome constructed via empirical interatomic potential-guided sampling,with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations.The effectiveness of this approach was validated through simulations of nanoindentation,tensile deformation,and fracture in BCC tungsten.This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.展开更多
Universal machine-learning interatomic potentials(uMLIPs)are emerging as foundation models for atomistic simulation,offering near-ab initio accuracy at far lower cost.Their safe,broad deployment is limited by the abse...Universal machine-learning interatomic potentials(uMLIPs)are emerging as foundation models for atomistic simulation,offering near-ab initio accuracy at far lower cost.Their safe,broad deployment is limited by the absence of reliable,general uncertainty estimates.We present a unified,scalable uncertainty metric,U,built from a heterogeneous ensemble that reuses existing pretrained MLIPs.Across diverse chemistries and structures,U strongly tracks true prediction errors and robustly ranks configuration-level risk.Using U,we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels:for tungsten,we match full density-functional-theory(DFT)training using 4%of the DFT data;for MoNbTaW,a dataset distilled by U supports high-accuracy potential training.By filtering numerical label noise,the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data.This framework provides a practical reliability monitor and guides data selection and fine-tuning,enabling cost-efficient,accurate,and safer deployment of foundation models.展开更多
文摘目的:探究移植肾活组织检查的病理指标与不同时期移植肾功能异常的相关性,评价移植肾病理指标在移植肾功能异常中的诊断价值,建立预测移植肾预后的模型。方法:回顾性分析2015—2018年于南京医科大学第一附属医院接受移植肾活检的同种异体肾移植手术受者的临床资料及病理指标。将总体样本基于不同活检后时间(活检时、活检后第1年、活检后第3年、活检后第5年)分别进行统计学分析。采用单因素分析筛选对肾功能异常有影响的指标,通过二元Logistic回归建立模型并绘制列线图;此外,通过混合效应Logistic回归探究在各阶段均与肾功能异常相关因素的动态效应。通过受试者工作特征(receiver operating characteristic,ROC)曲线、对应的曲线下面积(area under the curve,AUC)、校准曲线判断模型的判别效度以及与实际情况的一致性。结果:共纳入121例患者,按照移植肾活检后时间将总体样本分为活检时、活检后1年、3年、5年:i评分≥1分与活检时、活检后第1、3、5年肾功能异常显著相关;动态效应分析结果为i评分≥1分与移植肾穿刺后5年内的肾功能恶化有关。依据上述统计结果绘制出列线图,AUC显示模型具有较好的判别效度。校准曲线显示模型的移植肾功能异常发生的预测概率与实际概率一致性较高。结论:构建了一个预测不同时间点移植肾功能异常的列线图模型,有效提升了移植肾穿刺后患者管理的精准度。
文摘Motivated by the special theory of gradient elasticity (GradEla), a proposal is advanced for extending it to construct gradient models for interatomic potentials, commonly used in atomistic simulations. Our focus is on London’s quantum mechanical potential which is an analytical expression valid until a certain characteristic distance where “attractive” molecular interactions change character and become “repulsive” and cannot be described by the classical form of London’s potential. It turns out that the suggested internal length gradient (ILG) generalization of London’s potential generates both an “attractive” and a “repulsive” branch, and by adjusting the corresponding gradient parameters, the behavior of the empirical Lennard-Jones potentials is theoretically captured.
文摘目的:研究远隔缺血预适应(RIPC)对大鼠脑缺血模型的保护作用及分子机制。方法:30只成年雄性SD大鼠随机分为4组:假手术组(sham)、RIPC组、缺血再灌注组(MCAO/R)组、RIPC+MCAO/R组;术前通过夹闭双侧股动脉给予相应组RIPC处理,利用大脑中动脉栓塞再灌注法(MCAO/R)制备大鼠缺血性脑卒中模型,神经功能评分检测大鼠的神经功能,用2,3,5-三苯四唑氯(TTC)对脑切片进行染色以评估脑梗死的程度。利用real time RT-PCR检测大脑皮质中低氧诱导因子-1α(HIF-1α)和血管内皮生长因子(VEGF) mRNA的表达。结果:与MCAO/R组大鼠相比,RIPC处理组大鼠神经功能缺损症状较轻(P<0.05),脑梗死体积缩小(P<0.01),皮质中HIF-1α和VEGF mRNA的表达表达明显升高(P<0.05)。结论:RIPC处理对减轻缺血性脑卒中大鼠具有保护作用,其分子机制可能与激活HIF-1α/VEGF通路有关。
基金Supports from the National Natural Science Foundation of China(Grant Nos.11790292,11672298,and 11432014)the Strategic Priority Research Program(B)of the Chinese Academy of Sciences(XDB22040501)are gratefully acknowledged.Computations are performed on the ScGrid of Supercomputing Center,Computer Network Information Center of Chinese Academy of Sciences and LNMGrid of the State Key Laboratory of Nonlinear Mechanics.
文摘Nanoindentation is a useful technique to measure material properties at microscopic level.However,the intrinsically multiscale nature makes it challenging for large-scale simulations to be carried out.It is shown that in molecular statics simulations of nanoindentation,the separated dislocation loops(SDLs)are trapped in simulation box which detrimentally affects the plastic behavior in the plastic zone(PZ);and the long-distance propagation of SDLs consumes much computational cost yet with little contribution to the variation of tip force.To tackle the problem,the dislocation loop erasing(DLE)method is proposed in the work to alleviate the influence of artificial boundary conditions on the SDL–PZ interaction and improve simulation efficiency.Simulation results indicate that the force–depth curves obtained from simulations with and without DLE are consistent with each other,while the method with DLE yields more reasonable results of microstructural evolution and shows better efficiency.The new method provides an alternative approach for large-scale molecular simulation of nanoindentation with reliable results and higher efficiency and also sheds lights on improving existing multiscale methods.
文摘In this work, we utilize atomistic simulations and dislocation mechanics to explore the formation of in-verse pileups in CrCoNi model alloys and elucidate their unique impact on the strength and ductilityof multi-principal element alloys (MPEAs). The present atomistic simulations on single crystals revealthat during the deformation of CrCoNi, stress gradients lead to the formation of novel inverse disloca-tion pileup. We find that this unique dislocation pattern in a confined volume is due to the elevatedlattice friction and significant stress gradient present in the material. Furthermore, this phenomenon canbe notably promoted by lowering the temperature, increasing the loading rate, and introducing chemicalshort-range ordering. Additional simulations on bicrystals show that these inverse pileups play a criticalrole in suppressing dislocation transmission, reflection, and grain boundary (GB) migration. As a result,they effectively mitigate stress concentration and reduce damage accumulation at GBs, lowering the riskof catastrophic failure due to GB damages. In our theoretical analysis, we utilize dislocation mechanics topredict the formation of the inverse pileup and its subsequent strengthening effect, considering scenarioswith and without obstacles. Our investigations encompass various lattice frictions and stress gradients.Remarkably, our results shed light on the prevailing impact of dislocation hardening in the plastic de-formation of CrCoNi even under the presence of a linear stress gradient, while the contribution of GBstrengthening is found to be comparatively limited. These findings provide valuable insights into the de-formation mechanisms of MPEAs in general and significantly aid their applications as promising structuralmaterials.
基金sponsored by Nederlandse Organisatie voor WetenschappelijkOnderzoek(The Netherlands Organization for Scientific Research,NWO)domain Science for the use of supercomputer facilities.
文摘Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals,and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms.However,existing machine learning interatomic potentials(MLIPs)often fall short in adequately describing these defects,as their large characteristic scales exceed the computational limits of firstprinciples calculations.To address this challenge,wepresent acomputational frameworkcombining a defect genome constructed via empirical interatomic potential-guided sampling,with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations.The effectiveness of this approach was validated through simulations of nanoindentation,tensile deformation,and fracture in BCC tungsten.This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.
基金sponsored by Nederlandse Organisatie voor WetenschappelijkOnderzoek (The Netherlands Organization for Scientific Research, NWO) domain Science for the use of supercomputer facilities. The authors also acknowledge the use of DelftBlue supercomputer, provided by Delft High Performance Computing Center (https://www.tudelft.nl/dhpc).
文摘Universal machine-learning interatomic potentials(uMLIPs)are emerging as foundation models for atomistic simulation,offering near-ab initio accuracy at far lower cost.Their safe,broad deployment is limited by the absence of reliable,general uncertainty estimates.We present a unified,scalable uncertainty metric,U,built from a heterogeneous ensemble that reuses existing pretrained MLIPs.Across diverse chemistries and structures,U strongly tracks true prediction errors and robustly ranks configuration-level risk.Using U,we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels:for tungsten,we match full density-functional-theory(DFT)training using 4%of the DFT data;for MoNbTaW,a dataset distilled by U supports high-accuracy potential training.By filtering numerical label noise,the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data.This framework provides a practical reliability monitor and guides data selection and fine-tuning,enabling cost-efficient,accurate,and safer deployment of foundation models.
