Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models bas...Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models based on Markov sampling,which builds upon the traditional framework using i.i.d.samples.Subsequently,we introduce a ueMC-NL algorithm,tailored specifically for nonlinear classification models,facilitating the production of ueMC samples from a finite dataset.Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d.samples.展开更多
Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple l...Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to t...Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from...Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
Cardiac arrest(CA)is a critical condition in the field of cardiovascular medicine.Despite successful resuscitation,patients continue to have a high mortality rate,largely due to post CA syndrome(PCAS).However,the inju...Cardiac arrest(CA)is a critical condition in the field of cardiovascular medicine.Despite successful resuscitation,patients continue to have a high mortality rate,largely due to post CA syndrome(PCAS).However,the injury and pathophysiological mechanisms underlying PCAS remain unclear.Experimental animal models are valuable tools for exploring the etiology,pathogenesis,and potential interventions for CA and PCAS.Current CA animal models include electrical induction of ventricular fibrillation(VF),myocardial infarction,high potassium,asphyxia,and hemorrhagic shock.Although these models do not fully replicate the complexity of clinical CA,the mechanistic insights they provide remain highly relevant,including post-CA brain injury(PCABI),post-CA myocardial dysfunction(PAMD),systemic ischaemia/reperfusion injury(IRI),and the persistent precipitating pathology.Summarizing the methods of establishing CA models,the challenges encountered in the modeling process,and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.展开更多
Concrete material model plays an important role in numerical predictions of its dynamic responses subjected to projectile impact and charge explosion.Current concrete material models could be distinguished into two ki...Concrete material model plays an important role in numerical predictions of its dynamic responses subjected to projectile impact and charge explosion.Current concrete material models could be distinguished into two kinds,i.e.,the hydro-elastoplastic-damage model with independent equation of state and the cap-elastoplastic-damage model with continuous cap surface.The essential differences between the two kind models are vital for researchers to choose an appropriate kind of concrete material model for their concerned problems,while existing studies have contradictory conclusions.To resolve this issue,the constitutive theories of the two kinds of models are firstly overviewed.Then,the constitutive theories between the two kinds of models are comprehensively compared and the main similarities and differences are clarified,which are demonstrated by single element numerical examples.Finally,numerical predictions for projectile penetration and charge explosion experiments on concrete targets are compared to further demonstrate the conclusion made by constitutive comparison.It is found that both the two kind models could be used to simulate the dynamic responses of concrete under projectile impact and blast loadings,if the parameter needed in material models are well calibrated,although some discrepancies between them may exist.展开更多
Reinforced concrete(RC) columns are widely used as supporting structures for high-piled wharfs.The study of damage model of a RC column due to underwater explosion is a critical issue to assess the wharfs antiknock se...Reinforced concrete(RC) columns are widely used as supporting structures for high-piled wharfs.The study of damage model of a RC column due to underwater explosion is a critical issue to assess the wharfs antiknock security.In this study,the dynamic response and damage model of circular RC columns subjected to underwater explosions were investigated by means of scaled-down experiment models.Experiments were carried out in a 10.0 m diameter tank with the water depth of 2.25 m,under different explosive quantities(0.025 kg-1.6 kg),stand-off distances(0.0 m-7.0 m),and detonation depths(0.25 m-2.0 m).The shock wave load and dynamic response of experiment models were measured by configuring sensors of pressure,acceleration,strain,and displacement.Then,the load distribution characteristics,time history of test data,and damage models related to present conditions were obtained and discussed.Three damage models,including bending failure,bending-shear failure and punching failure,were identified.In addition,the experie nce model of shock wave loads on the surface of a RC column was proposed for engineering application.展开更多
In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results sh...In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.展开更多
As a kind of mathematical model, grey systems predi ct ion model has been widely applied to economy, management and engineering technol ogy. In 1982, Professor Deng Ju-long presented GM prediction model. Then some o t...As a kind of mathematical model, grey systems predi ct ion model has been widely applied to economy, management and engineering technol ogy. In 1982, Professor Deng Ju-long presented GM prediction model. Then some o ther scholars made improvements on GM model. Of course, much still should be don e to develop it. What the scholars have done is to take the first component of X (1) as the starting conditions of the grey differential model. It occ urs that the new information can not be used enough. This paper is addressed to choose the nth component of X (1) as the starting conditions to improv e the models. The main results of the paper is given in Theorem 2: The time response function of the grey differential equation x (0)(k)+az (1)(k)=b is given by x (1)(k)=x (1)(n)-ba e -a(k-n )+ba. and Theorem4: The time response of the grey Verhulst model is given by (1)(k) =ax (1)(n)bx (1)(n)+(a-bx (1)(n))ae a(k-n). As the new information is fully used, the accuracy of prediction is improved gre atly. Therefore, the new model with a certain theoretical and practical value.展开更多
A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models ...A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.展开更多
Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of qua...Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrdinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems, such as momentum, energy and Hamiltonian, so Schrdinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrdinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.展开更多
Underground space utilization and exploration is an irreversible trend for promoting sustainable development especially in megacities.Geotechnical engineering safety is always one of the most important issues in all p...Underground space utilization and exploration is an irreversible trend for promoting sustainable development especially in megacities.Geotechnical engineering safety is always one of the most important issues in all phases,including planning,design,construction and operation,of the underground project.展开更多
Background The mechanical properties are related with many biological functions of cells. Accurate quantification of the mechanical properties of living cells require the combined use of experimental techniques and th...Background The mechanical properties are related with many biological functions of cells. Accurate quantification of the mechanical properties of living cells require the combined use of experimental techniques and theoretical models. Micropipette aspiration (MPA) is one of common techniques in determining mechanical properties of the living cells. The halfspace model (HSM) is employed in MPA technique. However,in the conditions of linear constitutive relations and small deformations,the HSM is inadequate for characterizing the MPA of a spherical cell in two respects. Firstly,the cell size is fairly finite other than semi-infinite to the inner radius of a micropipette;Secondly,cells are compressible,with a Poisson’s ratioνvarying from 0. 2 to 0. 4 (23-25) instead of incompressible (ν=0. 5). Thus,a more accurate model is necessary.In this study,the viscoelastic expressions were derived from our previous MPA test. Then,a sphere model (SM) employed to analyze mechanical properties of rabbit chondrocytes combined with the experimental data. Differences in mechanical properties estimated by different mechanical models were evaluated.Methods A sphere model (SM) was employed. The relative dimension of cell to micropipette and the compressibility of the cell were taken into account,as shown in Fig. 1a.■Fig.1 Sphere model of the MPA of a single cell employing different constitutive relationships The approximate expression for the aspirated length was obtained from our previous study as follows:■Furthermore,assuming that the cell behaves as a homogeneous and isotropic standard linear solid (Fig. 1b),two viscoelastic creep expressions of the aspirated length for incompressible sphere model (ICSM) and for compressible sphere model (CSM) were derived by elastic-viscoelastic correspondence principle and integral transformation as Eqs.(2) and (3)respectively.■Results(1) Comparisons of models The elastic modulus from the ICSM was 47. 4%higher than that of the half-space model (HSM)(P<0. 001). For the CSM,the percentage increase in E over the value for the HSM was 87. 7%,78. 9%,and 64. 9%when the Poisson’s ratio was set to 0. 2,0. 3,and 0. 4,respectively.For the viscoelasticity,the parameters for the ICSM and CSM were significantly larger than those of the HSM (P <0. 001). The k1,k2,andμfor the ICSM were 37. 8%,37. 9%,and 39. 0%higher,respectively,than those of the HSM. For the CSM,the viscoelastic parameters decreased with the increase ofν. Whenν=0. 3,k1,k2,andμincreased by 71. 0%,200%,and 157%,respectively,compared to those of the ICSM (P<0. 001);For the cases ofν=0. 2 andν=0. 4,the above parameters were respectively 102%,243%,and 209%and 35. 3%,97. 5%,and 79%higher than those of the ICSM.(2) Predictions for the relative errors of mechanical parameters caused by HSM e is defined as the relative change of elastic moduli (or relative error) between the HSM and SM. As shown in Fig. 2,when Poisson’s ratioνis 0. 3,in order to let the e less than 30%,relative dimension between the cell and the micropipetteξneeds to be at least 5. 0. Whenνequals 0. 5 (ICSM),ξis about 3. 3 to make the e reach 20%. However,ξis rarely larger than 5. 0 in general MPA experiments,thus the relative error of modulus will exceed 30%. The above results are independent of cell types,thus they are applicable to other spherical solid-like cells.■Fig.2 Thresholds ofξvarying withνwhen e was 10%,20%,and 30%,respectively Another parameter VR was introduced to represent the relative errors of viscoelastic parameters between HSM and SM.With regard to ICSM (Fig. 3a),whenξis 3,VRis nearly 22%. If theξis larger than 8. 0,the relative error will be reduced to less than 10%. For the CSM,the viscoelastic parameters of a typical chondrocyte varying withξandνwere obtained,as shown from Figs. 3b to 3d. Whenνtends to 0. 5,the parameters tend to those of ICSM. When theξexceeds 10,each parameter changes very little. For a certain Poisson’s ratio (ν=0. 3),whenξis 3,the VR of k1,k2,andμare 47. 1%,70. 8%,and 68. 2%,respectively. Whenξequals 5 and 10,the above values are 42. 3%,68. 8%,65%,and 38. 4%,66. 0%,63. 2%,respectively. For a givenξ(ξ=3),whenνis 0. 2,the VR of k1,k2,andμare 53. 6%,73. 3%,and 75. 0%,respectively.Whenνis taken as 0. 3 and 0. 4,the above errors are 47. 7%,71. 1%,68. 2%,and 38. 4%,58. 8%,54. 8%,respectively.Thus,the VR also decreases with the increase ofξandν.Conclusions The effects of the relative dimension between the cell,and micropipette and the Poisson’s ratio of cell were remarkable and should be taken into consideration in the pursuit of more accurate mechanical parameters of cells.展开更多
Von Willebrand Factor(VWF)is a concatameric glycoprotein that plays a key role in rapid hemostasis and thrombosis.VWF has different functional domains that can bind to various molecules such as collagen,hemostatic fac...Von Willebrand Factor(VWF)is a concatameric glycoprotein that plays a key role in rapid hemostasis and thrombosis.VWF has different functional domains that can bind to various molecules such as collagen,hemostatic factorⅧ,integrin,and platelet glycoprotein lbα(GPlbα)to achieve multiple biological functions.During hemostasis,the A1 domain of VWF binds to GPIbαwhere platelets accumulate in the injured vascular endothelium.Due to forces generated by the hemodynamic gradient flow,the relations of bond-dissociation rates versus forces show that the lifetime of molecular bond has multiple states under the external force.We processed the experimental data of receptor-ligand in a single molecule obtained from optical tweezers by two different methods,including a Dudko-Hummer-Szabo equation,and another method combining force4ime history and force induced bond rupture.Then we used a recently developed physical equation regarding protein unfolding rate to fit our results.The lifetime of the bond between A1 and GPlbαobtained by the above mentioned two methods shows a'three-stage'change upon gradually increasing the external force.When the external force was below 8 pN,the lifetime of the bond deceased as the external force increased,which is a typical expression of a catch bond.The lifetime of the bond started to increase when the external force increased from 8 to 11 pN,and then decrease again when the external force increased to above 11 pN.Kim et al.used different processing methods and proposes a'flex-bond'model:the lifetime of the bond will decrease as the external force increases,then suddenly increase to a peak,and continue to decrease with the increase of force.A recently developed model based on the structural-elastic properties of molecules fits our data well,indicating that the bond formed by Al and GPlbαhas a catch-bond phenomenon in a certain interval of external forces,and a flex bond in other force intervals.In conclusion,A1-GPIbαbond will have a'slip-catch-slip'bond tendency.Our result provides a alternative understanding about the role of Al-GPlbαinteractions in the mechanism of hemostasis.展开更多
Introduction Glioblastoma multiforme(GBM),a malignant brain tumor,is highly invasive and use brain microvessels to migrate and invade.Studying the perivascular invasion/migration of GBM may enable new possibilities in...Introduction Glioblastoma multiforme(GBM),a malignant brain tumor,is highly invasive and use brain microvessels to migrate and invade.Studying the perivascular invasion/migration of GBM may enable new possibilities in GBM therapy.However,the lack proper 3D study models that recapitulate GBM hallmarks restricts investigating cell-cell/cell-molecular interactions in tumor microenvironments.In this study,we created GBM-vascular niche models through 3D bioprinting [1-2] using patient-derived GBM cells with sternness(GSC:glioblastoma stem cells),vasculature endothelial cells(ECs),mural cells,and various hydrogels.Materials and methods Three GBM-vascular models were designed:Model A with large vessels and GBM spheroid;Model B with large-and micro-vessels,and GBM spheroid;Model C with large-and micro-vessels and scattered GBM cells.Large channels were created by sacrificial bioprinting.Microvessel network was formed through self-assembly of ECs(HUVEC or brain EC)and mural cells(fibroblast,pericytes,and/or astrocytes).Three GBM cell types were used in the study:SD02 and SD03 are GSCs;U87MG is a commercially-available GBM cell line.Collagen type I or fibrin hydrogel have been used as major scaffold materials.For drug treatment,Temozolomide in culture medium was perfused through large vasculatures in Model A.Results and discussion Three different GBM-vascular models were successfully fabricated and culture for 2-10.GSCs cultured in these models maintained sternness and heterogeneity during the long-term cultures.In Model A,GSCs actively invaded into the surrounding tissues(~Day26),initially regressed in response to the drug(~Day50),then developed therapeutic resistance and resumed aggressive invasion(~Day57).In Model B and C,three GBM types presented distinctive invasion patterns and EC-interactions.SD02 cells showed a spiky invasion pattern with elongated morphology.SD03 cells showed a more dispersed invasion pattern with many single cell migrations towards surrounding microvessels.U87MG cells showed a blunt invasion pattern,caused EC death in the spheroid form;however,the EC death was significantly reduced in the scattered single cell form.Conclusions In this study,we have created GBM-vascular niche models that can recapitulate various GBM characteristics such as cancer sternness,tumor type-specific invasion patterns,and drug responses with therapeutic resistance.Our models have a great potential in investigating patient-specific tumor behaviors under chemo-/radio-therapy conditions and consequentially helping to tailor personalized treatment strategy.The model platform is capable of modifying multiples variables including ECMs,cell types,vascular structures,and dynamic culture condition.Thus,it can be adapted to other biological systems and serve as a valuable tool for generating customized microenvironments.展开更多
文摘Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models based on Markov sampling,which builds upon the traditional framework using i.i.d.samples.Subsequently,we introduce a ueMC-NL algorithm,tailored specifically for nonlinear classification models,facilitating the production of ueMC samples from a finite dataset.Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d.samples.
文摘Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金supported by the National Natural Science Foundation of China(62222117).
文摘Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金National Natural Science Foundation of China Key Project(No.42050103)Higher Education Disciplinary Innovation Program(No.B25052)+2 种基金the Guangdong Pearl River Talent Program Innovative and Entrepreneurial Team Project(No.2021ZT09H399)the Ministry of Education’s Frontiers Science Center for Deep-Time Digital Earth(DDE)(No.2652023001)Geological Survey Project of China Geological Survey(DD20240206201)。
文摘Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金supported by the National Key Research and Development Program(2021YFC3002205)the Postgraduate Research and Innovation Program of Tianjin Municipal Education Commission(2022BKY113),China.
文摘Cardiac arrest(CA)is a critical condition in the field of cardiovascular medicine.Despite successful resuscitation,patients continue to have a high mortality rate,largely due to post CA syndrome(PCAS).However,the injury and pathophysiological mechanisms underlying PCAS remain unclear.Experimental animal models are valuable tools for exploring the etiology,pathogenesis,and potential interventions for CA and PCAS.Current CA animal models include electrical induction of ventricular fibrillation(VF),myocardial infarction,high potassium,asphyxia,and hemorrhagic shock.Although these models do not fully replicate the complexity of clinical CA,the mechanistic insights they provide remain highly relevant,including post-CA brain injury(PCABI),post-CA myocardial dysfunction(PAMD),systemic ischaemia/reperfusion injury(IRI),and the persistent precipitating pathology.Summarizing the methods of establishing CA models,the challenges encountered in the modeling process,and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.
基金supported by the National Natural Science Foundations of China (Grant Nos. 52178515, 52078133)
文摘Concrete material model plays an important role in numerical predictions of its dynamic responses subjected to projectile impact and charge explosion.Current concrete material models could be distinguished into two kinds,i.e.,the hydro-elastoplastic-damage model with independent equation of state and the cap-elastoplastic-damage model with continuous cap surface.The essential differences between the two kind models are vital for researchers to choose an appropriate kind of concrete material model for their concerned problems,while existing studies have contradictory conclusions.To resolve this issue,the constitutive theories of the two kinds of models are firstly overviewed.Then,the constitutive theories between the two kinds of models are comprehensively compared and the main similarities and differences are clarified,which are demonstrated by single element numerical examples.Finally,numerical predictions for projectile penetration and charge explosion experiments on concrete targets are compared to further demonstrate the conclusion made by constitutive comparison.It is found that both the two kind models could be used to simulate the dynamic responses of concrete under projectile impact and blast loadings,if the parameter needed in material models are well calibrated,although some discrepancies between them may exist.
基金funded by the National Natural Science Foundation of China(Grant Nos.51578543)。
文摘Reinforced concrete(RC) columns are widely used as supporting structures for high-piled wharfs.The study of damage model of a RC column due to underwater explosion is a critical issue to assess the wharfs antiknock security.In this study,the dynamic response and damage model of circular RC columns subjected to underwater explosions were investigated by means of scaled-down experiment models.Experiments were carried out in a 10.0 m diameter tank with the water depth of 2.25 m,under different explosive quantities(0.025 kg-1.6 kg),stand-off distances(0.0 m-7.0 m),and detonation depths(0.25 m-2.0 m).The shock wave load and dynamic response of experiment models were measured by configuring sensors of pressure,acceleration,strain,and displacement.Then,the load distribution characteristics,time history of test data,and damage models related to present conditions were obtained and discussed.Three damage models,including bending failure,bending-shear failure and punching failure,were identified.In addition,the experie nce model of shock wave loads on the surface of a RC column was proposed for engineering application.
基金supported by the National Natural Science Foundation of China(1147105951375517+5 种基金71271226)the China Postdoctoral Science Foundation Funded Project(2014M560712)Chongqing Frontier and Applied Basic Research Project(cstc2014jcyj A00024)the Ministry of Education of Humanities and Social Sciences Youth Foundation(14YJAZH033)the Chongqing Municipal Education Scientific Planning Project(2012-GX-142)the Higher School Teaching Reform Research Project in Chongqing(1202010)
文摘In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.
文摘As a kind of mathematical model, grey systems predi ct ion model has been widely applied to economy, management and engineering technol ogy. In 1982, Professor Deng Ju-long presented GM prediction model. Then some o ther scholars made improvements on GM model. Of course, much still should be don e to develop it. What the scholars have done is to take the first component of X (1) as the starting conditions of the grey differential model. It occ urs that the new information can not be used enough. This paper is addressed to choose the nth component of X (1) as the starting conditions to improv e the models. The main results of the paper is given in Theorem 2: The time response function of the grey differential equation x (0)(k)+az (1)(k)=b is given by x (1)(k)=x (1)(n)-ba e -a(k-n )+ba. and Theorem4: The time response of the grey Verhulst model is given by (1)(k) =ax (1)(n)bx (1)(n)+(a-bx (1)(n))ae a(k-n). As the new information is fully used, the accuracy of prediction is improved gre atly. Therefore, the new model with a certain theoretical and practical value.
基金Project(T201221207)supported by the Fundamental Research Fund for the Central Universities,ChinaProject(2012CB725301)supported by National Basic Research and Development Program,China
文摘A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.
文摘Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrdinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems, such as momentum, energy and Hamiltonian, so Schrdinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrdinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.
文摘Underground space utilization and exploration is an irreversible trend for promoting sustainable development especially in megacities.Geotechnical engineering safety is always one of the most important issues in all phases,including planning,design,construction and operation,of the underground project.
文摘Background The mechanical properties are related with many biological functions of cells. Accurate quantification of the mechanical properties of living cells require the combined use of experimental techniques and theoretical models. Micropipette aspiration (MPA) is one of common techniques in determining mechanical properties of the living cells. The halfspace model (HSM) is employed in MPA technique. However,in the conditions of linear constitutive relations and small deformations,the HSM is inadequate for characterizing the MPA of a spherical cell in two respects. Firstly,the cell size is fairly finite other than semi-infinite to the inner radius of a micropipette;Secondly,cells are compressible,with a Poisson’s ratioνvarying from 0. 2 to 0. 4 (23-25) instead of incompressible (ν=0. 5). Thus,a more accurate model is necessary.In this study,the viscoelastic expressions were derived from our previous MPA test. Then,a sphere model (SM) employed to analyze mechanical properties of rabbit chondrocytes combined with the experimental data. Differences in mechanical properties estimated by different mechanical models were evaluated.Methods A sphere model (SM) was employed. The relative dimension of cell to micropipette and the compressibility of the cell were taken into account,as shown in Fig. 1a.■Fig.1 Sphere model of the MPA of a single cell employing different constitutive relationships The approximate expression for the aspirated length was obtained from our previous study as follows:■Furthermore,assuming that the cell behaves as a homogeneous and isotropic standard linear solid (Fig. 1b),two viscoelastic creep expressions of the aspirated length for incompressible sphere model (ICSM) and for compressible sphere model (CSM) were derived by elastic-viscoelastic correspondence principle and integral transformation as Eqs.(2) and (3)respectively.■Results(1) Comparisons of models The elastic modulus from the ICSM was 47. 4%higher than that of the half-space model (HSM)(P<0. 001). For the CSM,the percentage increase in E over the value for the HSM was 87. 7%,78. 9%,and 64. 9%when the Poisson’s ratio was set to 0. 2,0. 3,and 0. 4,respectively.For the viscoelasticity,the parameters for the ICSM and CSM were significantly larger than those of the HSM (P <0. 001). The k1,k2,andμfor the ICSM were 37. 8%,37. 9%,and 39. 0%higher,respectively,than those of the HSM. For the CSM,the viscoelastic parameters decreased with the increase ofν. Whenν=0. 3,k1,k2,andμincreased by 71. 0%,200%,and 157%,respectively,compared to those of the ICSM (P<0. 001);For the cases ofν=0. 2 andν=0. 4,the above parameters were respectively 102%,243%,and 209%and 35. 3%,97. 5%,and 79%higher than those of the ICSM.(2) Predictions for the relative errors of mechanical parameters caused by HSM e is defined as the relative change of elastic moduli (or relative error) between the HSM and SM. As shown in Fig. 2,when Poisson’s ratioνis 0. 3,in order to let the e less than 30%,relative dimension between the cell and the micropipetteξneeds to be at least 5. 0. Whenνequals 0. 5 (ICSM),ξis about 3. 3 to make the e reach 20%. However,ξis rarely larger than 5. 0 in general MPA experiments,thus the relative error of modulus will exceed 30%. The above results are independent of cell types,thus they are applicable to other spherical solid-like cells.■Fig.2 Thresholds ofξvarying withνwhen e was 10%,20%,and 30%,respectively Another parameter VR was introduced to represent the relative errors of viscoelastic parameters between HSM and SM.With regard to ICSM (Fig. 3a),whenξis 3,VRis nearly 22%. If theξis larger than 8. 0,the relative error will be reduced to less than 10%. For the CSM,the viscoelastic parameters of a typical chondrocyte varying withξandνwere obtained,as shown from Figs. 3b to 3d. Whenνtends to 0. 5,the parameters tend to those of ICSM. When theξexceeds 10,each parameter changes very little. For a certain Poisson’s ratio (ν=0. 3),whenξis 3,the VR of k1,k2,andμare 47. 1%,70. 8%,and 68. 2%,respectively. Whenξequals 5 and 10,the above values are 42. 3%,68. 8%,65%,and 38. 4%,66. 0%,63. 2%,respectively. For a givenξ(ξ=3),whenνis 0. 2,the VR of k1,k2,andμare 53. 6%,73. 3%,and 75. 0%,respectively.Whenνis taken as 0. 3 and 0. 4,the above errors are 47. 7%,71. 1%,68. 2%,and 38. 4%,58. 8%,54. 8%,respectively.Thus,the VR also decreases with the increase ofξandν.Conclusions The effects of the relative dimension between the cell,and micropipette and the Poisson’s ratio of cell were remarkable and should be taken into consideration in the pursuit of more accurate mechanical parameters of cells.
基金supported by the National Science Foundation of China ( 11772133, 11372116)the Fundamental Research Funds for the Central Universities ( HUST 0118012051)
文摘Von Willebrand Factor(VWF)is a concatameric glycoprotein that plays a key role in rapid hemostasis and thrombosis.VWF has different functional domains that can bind to various molecules such as collagen,hemostatic factorⅧ,integrin,and platelet glycoprotein lbα(GPlbα)to achieve multiple biological functions.During hemostasis,the A1 domain of VWF binds to GPIbαwhere platelets accumulate in the injured vascular endothelium.Due to forces generated by the hemodynamic gradient flow,the relations of bond-dissociation rates versus forces show that the lifetime of molecular bond has multiple states under the external force.We processed the experimental data of receptor-ligand in a single molecule obtained from optical tweezers by two different methods,including a Dudko-Hummer-Szabo equation,and another method combining force4ime history and force induced bond rupture.Then we used a recently developed physical equation regarding protein unfolding rate to fit our results.The lifetime of the bond between A1 and GPlbαobtained by the above mentioned two methods shows a'three-stage'change upon gradually increasing the external force.When the external force was below 8 pN,the lifetime of the bond deceased as the external force increased,which is a typical expression of a catch bond.The lifetime of the bond started to increase when the external force increased from 8 to 11 pN,and then decrease again when the external force increased to above 11 pN.Kim et al.used different processing methods and proposes a'flex-bond'model:the lifetime of the bond will decrease as the external force increases,then suddenly increase to a peak,and continue to decrease with the increase of force.A recently developed model based on the structural-elastic properties of molecules fits our data well,indicating that the bond formed by Al and GPlbαhas a catch-bond phenomenon in a certain interval of external forces,and a flex bond in other force intervals.In conclusion,A1-GPIbαbond will have a'slip-catch-slip'bond tendency.Our result provides a alternative understanding about the role of Al-GPlbαinteractions in the mechanism of hemostasis.
基金supported mainly by grants from American Heart Association Scientist Development Grant ( 12SDG12050083 to G.D.)National Institute of Health ( R21HL102773,R21HD090680,R01HL118245 to G.D.)National Science Foundation ( CBET-1263455,CBET-1350240 to G.D.)
文摘Introduction Glioblastoma multiforme(GBM),a malignant brain tumor,is highly invasive and use brain microvessels to migrate and invade.Studying the perivascular invasion/migration of GBM may enable new possibilities in GBM therapy.However,the lack proper 3D study models that recapitulate GBM hallmarks restricts investigating cell-cell/cell-molecular interactions in tumor microenvironments.In this study,we created GBM-vascular niche models through 3D bioprinting [1-2] using patient-derived GBM cells with sternness(GSC:glioblastoma stem cells),vasculature endothelial cells(ECs),mural cells,and various hydrogels.Materials and methods Three GBM-vascular models were designed:Model A with large vessels and GBM spheroid;Model B with large-and micro-vessels,and GBM spheroid;Model C with large-and micro-vessels and scattered GBM cells.Large channels were created by sacrificial bioprinting.Microvessel network was formed through self-assembly of ECs(HUVEC or brain EC)and mural cells(fibroblast,pericytes,and/or astrocytes).Three GBM cell types were used in the study:SD02 and SD03 are GSCs;U87MG is a commercially-available GBM cell line.Collagen type I or fibrin hydrogel have been used as major scaffold materials.For drug treatment,Temozolomide in culture medium was perfused through large vasculatures in Model A.Results and discussion Three different GBM-vascular models were successfully fabricated and culture for 2-10.GSCs cultured in these models maintained sternness and heterogeneity during the long-term cultures.In Model A,GSCs actively invaded into the surrounding tissues(~Day26),initially regressed in response to the drug(~Day50),then developed therapeutic resistance and resumed aggressive invasion(~Day57).In Model B and C,three GBM types presented distinctive invasion patterns and EC-interactions.SD02 cells showed a spiky invasion pattern with elongated morphology.SD03 cells showed a more dispersed invasion pattern with many single cell migrations towards surrounding microvessels.U87MG cells showed a blunt invasion pattern,caused EC death in the spheroid form;however,the EC death was significantly reduced in the scattered single cell form.Conclusions In this study,we have created GBM-vascular niche models that can recapitulate various GBM characteristics such as cancer sternness,tumor type-specific invasion patterns,and drug responses with therapeutic resistance.Our models have a great potential in investigating patient-specific tumor behaviors under chemo-/radio-therapy conditions and consequentially helping to tailor personalized treatment strategy.The model platform is capable of modifying multiples variables including ECMs,cell types,vascular structures,and dynamic culture condition.Thus,it can be adapted to other biological systems and serve as a valuable tool for generating customized microenvironments.
