Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patien...Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patients,caregivers,and healthcare workers.Alzheimer’s disease(AD)and Parkinson’s disease represent the two most common neurodegenerative disorders in the population,affecting over 65 million people,worldwide.展开更多
为了寻求合理简化的流域地形指数水文模型TOPMODEL(Topographic Index model)用于大尺度的陆面模式,推导了土壤表层饱和导水率k0、衰减因子f和地下水补给速率R空间都可变的扩展的TOPMODEL,并将f空间非均匀分布的TOPMODEL与陆面模式SSiB...为了寻求合理简化的流域地形指数水文模型TOPMODEL(Topographic Index model)用于大尺度的陆面模式,推导了土壤表层饱和导水率k0、衰减因子f和地下水补给速率R空间都可变的扩展的TOPMODEL,并将f空间非均匀分布的TOPMODEL与陆面模式SSiB4耦合(SSiB4/GTOP)。通过耦合模型在f空间非均匀条件下进行实际流域的水文模拟,分析f空间非均匀对流域土壤湿度、蒸散发、地表径流、基流和总径流的影响。主要结论有:(1)k0和R的空间变化并不改变经典TOPMODEL原有关系式,只要定义新的地形指数,k0和R空间非均匀TOPMODEL与空间均匀的TOPMODEL并无区别;(2) f空间变化条件下由于局地的地下水埋深还与局地的f值有关,地形指数相同的区域具有水文相似性这一结论不再成立;(3)与f空间均匀的模拟结果相比较,f随海拔高度h i增加而线性减小使模拟的流域土壤湿度、地表径流和流域蒸散减小但使基流和总径流增加;(4) f空间非均匀对流域水文模拟结果有影响,但其影响明显小于流域地形因子的影响。展开更多
UML is widely accepted and applied by the international software industry. UML is a powerful language for Object oriented modeling, designing, and implementing software systems, but its Use Case method for requirem...UML is widely accepted and applied by the international software industry. UML is a powerful language for Object oriented modeling, designing, and implementing software systems, but its Use Case method for requirement analysis and modeling software patterns has some explicit drawbacks. For more complete UML, this paper proposes the Role Use Case modeling and its glyphs, and provides an instance of requirement analysis using Role Use Case method. Uses the Role Model to modeling software pattern at knowledge level. This paper also extends the UML Meta Model and accentuates “RM before UML's class Modeling”.展开更多
We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numeri...We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numerical solution using a recently proposed L1 predictor–corrector method.The given method is based on the L1-type discretization algorithm and the spline interpolation scheme.We perform the error and stability analyses for the given method.We perform graphical simulations demonstrating that the proposed FHN neuron model generates rich electrical activities of periodic spiking patterns,chaotic patterns,and quasi-periodic patterns.The motivation behind proposing a fractional-order improved FHN neuron model is that such a system can provide a more nuanced description of the process with better understanding and simulation of the neuronal responses by incorporating memory effects and non-local dynamics,which are inherent to many biological systems.展开更多
The prophylactic effects of Chinese propolis against cypermethrin toxicity were evaluated by performing ovary and uterus histopathology, as well as by characterizing ovarian function, embryos, and litters. Cypermethri...The prophylactic effects of Chinese propolis against cypermethrin toxicity were evaluated by performing ovary and uterus histopathology, as well as by characterizing ovarian function, embryos, and litters. Cypermethrin induced atypia in the ovary and uterus, and decreased the ovulation sites and the number of embryos. Cypermethrin-induced oxidative stress during pregnancy, decreased the parturition rate as well as the number and weight of offspring and increased the incidence of morphological malformations in the offspring. Administration of propolis to cypermethrin-treated animals mitigated cypermethrin-induced reproductive toxicity.展开更多
The competency model is a widely-used human resource management tool that can be applied to human resource management in different regions,different fields,different enterprises,and positions of different nature,which...The competency model is a widely-used human resource management tool that can be applied to human resource management in different regions,different fields,different enterprises,and positions of different nature,which can improve the objectivity,reliability,authenticity and fairness of enterprise human resource management,give full play to the promotion of human resource management to the development of enterprise operations,and help enterprises achieve development and manage-ment goals.展开更多
AIM To assess the viability of orthotopic and heterotopic patient-derived pancreatic cancer xenografts implanted into nude mice.METHODS This study presents a prospective experimental analytical follow-up of the develo...AIM To assess the viability of orthotopic and heterotopic patient-derived pancreatic cancer xenografts implanted into nude mice.METHODS This study presents a prospective experimental analytical follow-up of the development of tumours in mice upon implantation of human pancreatic adenocarcinoma samples. Specimens were obtained surgically from patients with a pathological diagnosis of pancreatic adenocarcinoma. Tumour samples from pancreatic cancer patients were transplanted into nude mice in three different locations(intraperitoneal, subcutaneous and pancreatic). Histological analysis(haematoxylin-eosin and Masson's trichrome staining) and immunohistochemical assessment of apoptosis(TUNEL), proliferation(Ki-67), angiogenesis(CD31) and fibrogenesis(α-SMA) were performed. When a tumour xenograft reached the target size, it was reimplanted in a new nude mouse. Three sequential tumour xenograft generations were generated(F1, F2 and F3).RESULTS The overall tumour engraftment rate was 61.1%. The subcutaneous model was most effective in terms of tissue growth(69.9%), followed by intraperitoneal(57.6%) and pancreatic(55%) models. Tumour development was faster in the subcutaneous model(17.7 ± 2.6 wk) compared with the pancreatic(23.1 ± 2.3 wk) and intraperitoneal(25.0 ± 2.7 wk) models(P = 0.064). There was a progressive increase in the tumour engraftment rate over successive generations for all three models(F1 28.1% vs F2 71.4% vs F3 80.9%, P < 0.001). There were no significant differences in tumour xenograft differentiation and cell proliferation between human samples and the three experimental models among the sequential generations of tumour xenografts. However, a progressive decrease in fibrosis, fibrogenesis, tumour vascularisation and apoptosis was observed in the three experimental models compared with the human samples. All three pancreatic patient-derived xenograft models presented similar histological and immunohistochemical characteristics.CONCLUSION In our experience, the faster development andgreatest number of viable xenografts could make the subcutaneous model the best option for experimentation in pancreatic cancer.展开更多
We introduce the general AC( atlribure certificate), the role specificationAC and the rolt assignment AC We discuss the rolt-based PMI(Privilege Management Infrastructure)architecture. The role-based PMT(Public-Kty In...We introduce the general AC( atlribure certificate), the role specificationAC and the rolt assignment AC We discuss the rolt-based PMI(Privilege Management Infrastructure)architecture. The role-based PMT(Public-Kty In-frastructure) secure model forE-govcrnment isresearehed by combining the role-bastd PMI with PKI architeclure (Public Key Infrastructure). Themodel has advantages of flexibility, convenience, less storage space and less network consumptionetc. We are going to ust iht secure modelin the E-govern-ment system.展开更多
AIM To evaluate the angiogenic effect of platelet-rich plasma(PRP)-preconditioned adipose-derived stem cells(ADSCs) both in vitro and in a mouse ischemic hindlimb model.METHODS ADSCs were divided based on culture medi...AIM To evaluate the angiogenic effect of platelet-rich plasma(PRP)-preconditioned adipose-derived stem cells(ADSCs) both in vitro and in a mouse ischemic hindlimb model.METHODS ADSCs were divided based on culture medium: 2.5% PRP, 5% PRP, 7.5% PRP, and 10% PRP. Cell proliferation rate was analyzed using the MTS assay. The gene expression of CD31, vascular endothelial growth factor, hypoxia-inducible factors, and endothelial cell nitric oxide synthase was analyzed using reverse transcription polymerase chain reaction. Cell markers and structural changes were assessed through immunofluorescence staining and the tube formation assay. Subsequently, we studied the in vivo angiogenic capabilities of ADSCs by a mouse ischemic hindlimb model.RESULTS The proliferation rate of ADSCs was higher in the 2.5%, 5%, and 7.5% PRP groups. The expression of hypoxia-inducible factor, CD31, vascular endothelial growth factor, and endothelial cell nitric oxide synthase in the 5% and 7.5% PRP groups increased. The 5%, 7.5%, and 10% PRP groups showed higher abilities to promote both CD31 and vascular endothelial growth factor production and tubular structure formation in ADSCs. According to laser Doppler perfusion scan, the perfusion ratios of ischemic limb to normal limb were significantly higher in 5% PRP, 7.5% PRP, and human umbilical vein endothelial cells groups compared with the negative control and fetal bovine serum(FBS) groups(0.88 ± 0.08, 0.85 ± 0.07 and 0.81 ± 0.06 for 5%, 7.5% PRP and human umbilical vein endothelial cells compared with 0.42 ± 0.17 and 0.54 ± 0.14 for the negative control and FBS, P < 0.01).CONCLUSION PRP-preconditioned ADSCs presented endothelial cell characteristics in vitro and significantly improved neovascularization in ischemic hindlimbs. The optimal angiogenic effect occurred in 5% PRP-and 7.5% PRPpreconditioned ADSCs.展开更多
Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH pati...Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.展开更多
Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model...Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.展开更多
To investigate the temperature susceptibility and nonlinear memory effects of artificially frozen soil creep behavior,this study conducted uniaxial step-loading creep tests under controlled temperatures ranging from-1...To investigate the temperature susceptibility and nonlinear memory effects of artificially frozen soil creep behavior,this study conducted uniaxial step-loading creep tests under controlled temperatures ranging from-10℃to-20℃.The transient creep characteristics and steady-state creep rates of artificially frozen soils were systematically examined with respect to variations in temperature and stress.Experimental results demonstrate that decreasing temperatures lead to a decaying trend in the steady-state creep rate of silty frozen soil,confirming that low-temperature environments significantly inhibit plastic flow while enhancing material stiffness.Based on fractional calculus theory,a fractional derivative creep model was established.By incorporating temperature dependencies,the model was further improved to account for both stress and temperature effects.The model predictions align closely with experimental data,achieving over 91%agreement(standard deviation±1.8%),and effectively capture the stress-strain behavior of artificially frozen soil under varying thermal conditions.This research provides a reliable theoretical foundation for studying deformation characteristics in cold-regions engineering.展开更多
Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Me...Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice.展开更多
Offline policy evaluation,evaluating and selecting complex policies for decision-making by only using offline datasets is important in reinforcement learning.At present,the model-based offline policy evaluation(MBOPE)...Offline policy evaluation,evaluating and selecting complex policies for decision-making by only using offline datasets is important in reinforcement learning.At present,the model-based offline policy evaluation(MBOPE)is widely welcomed because of its easy to implement and good performance.MBOPE directly approximates the unknown value of a given policy using the Monte Carlo method given the estimated transition and reward functions of the environment.Usually,multiple models are trained,and then one of them is selected to be used.However,a challenge remains in selecting an appropriate model from those trained for further use.The authors first analyse the upper bound of the difference between the approximated value and the unknown true value.Theoretical results show that this difference is related to the trajectories generated by the given policy on the learnt model and the prediction error of the transition and reward functions at these generated data points.Based on the theoretical results,a new criterion is proposed to tell which trained model is better suited for evaluating the given policy.At last,the effectiveness of the proposed criterion is demonstrated on both benchmark and synthetic offline datasets.展开更多
Fractional differential equations(FDEs)provide a powerful tool for modeling systems with memory and non-local effects,but understanding their underlying structure remains a significant challenge.While numerous numeric...Fractional differential equations(FDEs)provide a powerful tool for modeling systems with memory and non-local effects,but understanding their underlying structure remains a significant challenge.While numerous numerical and semi-analytical methods exist to find solutions,new approaches are needed to analyze the intrinsic properties of the FDEs themselves.This paper introduces a novel computational framework for the structural analysis of FDEs involving iterated Caputo derivatives.The methodology is based on a transformation that recasts the original FDE into an equivalent higher-order form,represented as the sum of a closed-form,integer-order component G(y)and a residual fractional power seriesΨ(x).This transformed FDE is subsequently reduced to a first-order ordinary differential equation(ODE).The primary novelty of the proposed methodology lies in treating the structure of the integer-order component G(y)not as fixed,but as a parameterizable polynomial whose coefficients can be determined via global optimization.Using particle swarm optimization,the framework identifies an optimal ODE architecture by minimizing a dual objective that balances solution accuracy against a high-fidelity reference and the magnitude of the truncated residual series.The effectiveness of the approach is demonstrated on both a linear FDE and a nonlinear fractional Riccati equation.Results demonstrate that the framework successfully identifies an optimal,low-degree polynomial ODE architecture that is not necessarily identical to the forcing function of the original FDE.This work provides a new tool for analyzing the underlying structure of FDEs and gaining deeper insights into the interplay between local and non-local dynamics in fractional systems.展开更多
This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations ove...This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals.The model incorporates three key fractional derivatives:the Caputo-Fabrizio fractional derivative with a non-singular kernel,the Caputo proportional constant fractional derivative with a singular kernel,and the Atangana-Baleanu fractional derivative with a non-singular kernel.We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model.To achieve this,the approximation of Caputo proportional constant fractional derivative with Grünwald-Letnikov nonstandard finite difference method is used for the deterministic model with a singular kernel,while the Toufik-Atangana method is employed for models involving a non-singular Mittag-Leffler kernel.Additionally,the integral Caputo-Fabrizio approximation and a two-step Lagrange polynomial are utilized to approximate the model with a non-singular exponential decay kernel.For the stochastic component,the Milstein method is implemented to approximate the stochastic differential equations.The stability and effectiveness of the proposed model and methodologies are validated through numerical simulations and comparisons with real-world cholera data from Yemen.The results confirm the reliability and practical applicability of the model,providing strong theoretical and empirical support for the approach.展开更多
Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolutionizing drug dis...Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolutionizing drug discovery and treatment strategies.However,the links between in vitro and in vivo data are poorly understood,hindering a comprehensive understanding of the absorption,distribution,metabolism,and excretion(ADME)of PROTACs.In this work,14C-labeled vepdegestrant(ARV-471),which is currently in phase III clinical trials for breast cancer,was synthesized as a model PROTAC to characterize its preclinical ADME properties and simulate its clinical pharmacokinetics(PK)by establishing a physiologically based pharmacokinetics(PBPK)model.For in vitro–in vivo extrapolation(IVIVE),hepatocyte clearance correlated more closely with in vivo rat PK data than liver microsomal clearance did.PBPK models,which were initially developed and validated in rats,accurately simulate ARV-471's PK across fed and fasted states,with parameters within 1.75-fold of the observed values.Human models,informed by in vitro ADME data,closely mirrored postoral dose plasma profiles at 30 mg.Furthermore,no human-specific metabolites were identified in vitro and the metabolic profile of rats could overlap that of humans.This work presents a roadmap for developing future PROTAC medications by elucidating the correlation between in vitro and in vivo characteristics.展开更多
Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In ...Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages： primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image （MRI）. Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.展开更多
Based on dominant degree of role model among the viewpoints for object oriented modeling process, it dissertates that role modeling is a modeling method for software pattern at knowledge level. After giving some examp...Based on dominant degree of role model among the viewpoints for object oriented modeling process, it dissertates that role modeling is a modeling method for software pattern at knowledge level. After giving some examples for modeling design pattern and analysis pattern at knowledge level using role model, it presents a process for refining design pattern from role model to class model and event trace diagram of UML. In this paper, we advocate the opinion that role modeling before object modeling of UML.展开更多
As more nurses embrace precision science,there is a tendency to utilize theoretical frameworks from other disciplines thus,placing nursing at risk of losing its autonomy and independence.The discipline has fallen prey...As more nurses embrace precision science,there is a tendency to utilize theoretical frameworks from other disciplines thus,placing nursing at risk of losing its autonomy and independence.The discipline has fallen prey to internal binary opposition,eliminating opportunities to engage in civil discourse.To explore how the roles nurses select might fit together in a theoretical framework and help nurses understand how the roles they choose to support their identity as nurses,this paper introduced a model of nursing that includes the bench scientists,the policy activists,and bedside nurses,using the Neuman Systems Model(NSM).The Nurse Role Integration Model(NRIM)espouses the basic tenets of NSM:prevention counteracts stressors from penetrating the client's lines of defense thus,reducing stress response.Primary prevention reflects the work of the nurse bench scientists,investigating the underlying mechanisms behind pathophysiology;secondary prevention is applied nurse scientists who build upon nurse researchers'work,identifying and testing potential interventions;tertiary prevention is nurse policy activists,the fulcrum,who leverage primary and secondary findings to argue policy change at all levels.Once policy change is adopted,bedside nurses are educated and implement the change.This lens provides an opportunity to create greater solidarity,strengthening the unity and autonomy of the discipline.展开更多
基金supported by the Canadian Institutes of Health Research(DFD-181599)the National Institutes of Health(T32AG058527)to RJB and R0190106435 to VM.
文摘Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patients,caregivers,and healthcare workers.Alzheimer’s disease(AD)and Parkinson’s disease represent the two most common neurodegenerative disorders in the population,affecting over 65 million people,worldwide.
基金Supported the Middleware Software Division of Software Group of F ujitsu L imited in Japanthe National Depart-ment of Educat
文摘UML is widely accepted and applied by the international software industry. UML is a powerful language for Object oriented modeling, designing, and implementing software systems, but its Use Case method for requirement analysis and modeling software patterns has some explicit drawbacks. For more complete UML, this paper proposes the Role Use Case modeling and its glyphs, and provides an instance of requirement analysis using Role Use Case method. Uses the Role Model to modeling software pattern at knowledge level. This paper also extends the UML Meta Model and accentuates “RM before UML's class Modeling”.
文摘We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numerical solution using a recently proposed L1 predictor–corrector method.The given method is based on the L1-type discretization algorithm and the spline interpolation scheme.We perform the error and stability analyses for the given method.We perform graphical simulations demonstrating that the proposed FHN neuron model generates rich electrical activities of periodic spiking patterns,chaotic patterns,and quasi-periodic patterns.The motivation behind proposing a fractional-order improved FHN neuron model is that such a system can provide a more nuanced description of the process with better understanding and simulation of the neuronal responses by incorporating memory effects and non-local dynamics,which are inherent to many biological systems.
文摘The prophylactic effects of Chinese propolis against cypermethrin toxicity were evaluated by performing ovary and uterus histopathology, as well as by characterizing ovarian function, embryos, and litters. Cypermethrin induced atypia in the ovary and uterus, and decreased the ovulation sites and the number of embryos. Cypermethrin-induced oxidative stress during pregnancy, decreased the parturition rate as well as the number and weight of offspring and increased the incidence of morphological malformations in the offspring. Administration of propolis to cypermethrin-treated animals mitigated cypermethrin-induced reproductive toxicity.
文摘The competency model is a widely-used human resource management tool that can be applied to human resource management in different regions,different fields,different enterprises,and positions of different nature,which can improve the objectivity,reliability,authenticity and fairness of enterprise human resource management,give full play to the promotion of human resource management to the development of enterprise operations,and help enterprises achieve development and manage-ment goals.
基金Supported by the Andalusian Public Foundation for the Management of Health Research in Seville(FISEVI)
文摘AIM To assess the viability of orthotopic and heterotopic patient-derived pancreatic cancer xenografts implanted into nude mice.METHODS This study presents a prospective experimental analytical follow-up of the development of tumours in mice upon implantation of human pancreatic adenocarcinoma samples. Specimens were obtained surgically from patients with a pathological diagnosis of pancreatic adenocarcinoma. Tumour samples from pancreatic cancer patients were transplanted into nude mice in three different locations(intraperitoneal, subcutaneous and pancreatic). Histological analysis(haematoxylin-eosin and Masson's trichrome staining) and immunohistochemical assessment of apoptosis(TUNEL), proliferation(Ki-67), angiogenesis(CD31) and fibrogenesis(α-SMA) were performed. When a tumour xenograft reached the target size, it was reimplanted in a new nude mouse. Three sequential tumour xenograft generations were generated(F1, F2 and F3).RESULTS The overall tumour engraftment rate was 61.1%. The subcutaneous model was most effective in terms of tissue growth(69.9%), followed by intraperitoneal(57.6%) and pancreatic(55%) models. Tumour development was faster in the subcutaneous model(17.7 ± 2.6 wk) compared with the pancreatic(23.1 ± 2.3 wk) and intraperitoneal(25.0 ± 2.7 wk) models(P = 0.064). There was a progressive increase in the tumour engraftment rate over successive generations for all three models(F1 28.1% vs F2 71.4% vs F3 80.9%, P < 0.001). There were no significant differences in tumour xenograft differentiation and cell proliferation between human samples and the three experimental models among the sequential generations of tumour xenografts. However, a progressive decrease in fibrosis, fibrogenesis, tumour vascularisation and apoptosis was observed in the three experimental models compared with the human samples. All three pancreatic patient-derived xenograft models presented similar histological and immunohistochemical characteristics.CONCLUSION In our experience, the faster development andgreatest number of viable xenografts could make the subcutaneous model the best option for experimentation in pancreatic cancer.
文摘We introduce the general AC( atlribure certificate), the role specificationAC and the rolt assignment AC We discuss the rolt-based PMI(Privilege Management Infrastructure)architecture. The role-based PMT(Public-Kty In-frastructure) secure model forE-govcrnment isresearehed by combining the role-bastd PMI with PKI architeclure (Public Key Infrastructure). Themodel has advantages of flexibility, convenience, less storage space and less network consumptionetc. We are going to ust iht secure modelin the E-govern-ment system.
基金Supported by grant from the National Sci-Tech Program,Ministry of Science and Technology,No.NRMPG3E0471 and No.NMRPG3D0231a Chang Gung Memorial Hospital grant,No.CMRPGBH0011
文摘AIM To evaluate the angiogenic effect of platelet-rich plasma(PRP)-preconditioned adipose-derived stem cells(ADSCs) both in vitro and in a mouse ischemic hindlimb model.METHODS ADSCs were divided based on culture medium: 2.5% PRP, 5% PRP, 7.5% PRP, and 10% PRP. Cell proliferation rate was analyzed using the MTS assay. The gene expression of CD31, vascular endothelial growth factor, hypoxia-inducible factors, and endothelial cell nitric oxide synthase was analyzed using reverse transcription polymerase chain reaction. Cell markers and structural changes were assessed through immunofluorescence staining and the tube formation assay. Subsequently, we studied the in vivo angiogenic capabilities of ADSCs by a mouse ischemic hindlimb model.RESULTS The proliferation rate of ADSCs was higher in the 2.5%, 5%, and 7.5% PRP groups. The expression of hypoxia-inducible factor, CD31, vascular endothelial growth factor, and endothelial cell nitric oxide synthase in the 5% and 7.5% PRP groups increased. The 5%, 7.5%, and 10% PRP groups showed higher abilities to promote both CD31 and vascular endothelial growth factor production and tubular structure formation in ADSCs. According to laser Doppler perfusion scan, the perfusion ratios of ischemic limb to normal limb were significantly higher in 5% PRP, 7.5% PRP, and human umbilical vein endothelial cells groups compared with the negative control and fetal bovine serum(FBS) groups(0.88 ± 0.08, 0.85 ± 0.07 and 0.81 ± 0.06 for 5%, 7.5% PRP and human umbilical vein endothelial cells compared with 0.42 ± 0.17 and 0.54 ± 0.14 for the negative control and FBS, P < 0.01).CONCLUSION PRP-preconditioned ADSCs presented endothelial cell characteristics in vitro and significantly improved neovascularization in ischemic hindlimbs. The optimal angiogenic effect occurred in 5% PRP-and 7.5% PRPpreconditioned ADSCs.
文摘Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.
基金supported by the National Natural Science Foundation of China(62473020).
文摘Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.
基金National Key Research and Development Program of China“Structural Stability Assessment Techniques and Demonstration for Masonry Ancient Pagodas”(2023YFF0906005)。
文摘To investigate the temperature susceptibility and nonlinear memory effects of artificially frozen soil creep behavior,this study conducted uniaxial step-loading creep tests under controlled temperatures ranging from-10℃to-20℃.The transient creep characteristics and steady-state creep rates of artificially frozen soils were systematically examined with respect to variations in temperature and stress.Experimental results demonstrate that decreasing temperatures lead to a decaying trend in the steady-state creep rate of silty frozen soil,confirming that low-temperature environments significantly inhibit plastic flow while enhancing material stiffness.Based on fractional calculus theory,a fractional derivative creep model was established.By incorporating temperature dependencies,the model was further improved to account for both stress and temperature effects.The model predictions align closely with experimental data,achieving over 91%agreement(standard deviation±1.8%),and effectively capture the stress-strain behavior of artificially frozen soil under varying thermal conditions.This research provides a reliable theoretical foundation for studying deformation characteristics in cold-regions engineering.
基金supported by the Young and Middle-aged Research Fund Project of Shenzhen People's Hospital(Grant No.SYHL2024-N0010)the Shenzhen Basic Research Program(General Program,Grant No.JCYJ20240813104409013)。
文摘Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice.
文摘Offline policy evaluation,evaluating and selecting complex policies for decision-making by only using offline datasets is important in reinforcement learning.At present,the model-based offline policy evaluation(MBOPE)is widely welcomed because of its easy to implement and good performance.MBOPE directly approximates the unknown value of a given policy using the Monte Carlo method given the estimated transition and reward functions of the environment.Usually,multiple models are trained,and then one of them is selected to be used.However,a challenge remains in selecting an appropriate model from those trained for further use.The authors first analyse the upper bound of the difference between the approximated value and the unknown true value.Theoretical results show that this difference is related to the trajectories generated by the given policy on the learnt model and the prediction error of the transition and reward functions at these generated data points.Based on the theoretical results,a new criterion is proposed to tell which trained model is better suited for evaluating the given policy.At last,the effectiveness of the proposed criterion is demonstrated on both benchmark and synthetic offline datasets.
基金Research Council of Lithuania(LMTLT),agreement No.S-PD-24-120Research Council of Lithuania(LMTLT),agreement No.S-PD-24-120funded by the Research Council of Lithuania.
文摘Fractional differential equations(FDEs)provide a powerful tool for modeling systems with memory and non-local effects,but understanding their underlying structure remains a significant challenge.While numerous numerical and semi-analytical methods exist to find solutions,new approaches are needed to analyze the intrinsic properties of the FDEs themselves.This paper introduces a novel computational framework for the structural analysis of FDEs involving iterated Caputo derivatives.The methodology is based on a transformation that recasts the original FDE into an equivalent higher-order form,represented as the sum of a closed-form,integer-order component G(y)and a residual fractional power seriesΨ(x).This transformed FDE is subsequently reduced to a first-order ordinary differential equation(ODE).The primary novelty of the proposed methodology lies in treating the structure of the integer-order component G(y)not as fixed,but as a parameterizable polynomial whose coefficients can be determined via global optimization.Using particle swarm optimization,the framework identifies an optimal ODE architecture by minimizing a dual objective that balances solution accuracy against a high-fidelity reference and the magnitude of the truncated residual series.The effectiveness of the approach is demonstrated on both a linear FDE and a nonlinear fractional Riccati equation.Results demonstrate that the framework successfully identifies an optimal,low-degree polynomial ODE architecture that is not necessarily identical to the forcing function of the original FDE.This work provides a new tool for analyzing the underlying structure of FDEs and gaining deeper insights into the interplay between local and non-local dynamics in fractional systems.
文摘This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals.The model incorporates three key fractional derivatives:the Caputo-Fabrizio fractional derivative with a non-singular kernel,the Caputo proportional constant fractional derivative with a singular kernel,and the Atangana-Baleanu fractional derivative with a non-singular kernel.We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model.To achieve this,the approximation of Caputo proportional constant fractional derivative with Grünwald-Letnikov nonstandard finite difference method is used for the deterministic model with a singular kernel,while the Toufik-Atangana method is employed for models involving a non-singular Mittag-Leffler kernel.Additionally,the integral Caputo-Fabrizio approximation and a two-step Lagrange polynomial are utilized to approximate the model with a non-singular exponential decay kernel.For the stochastic component,the Milstein method is implemented to approximate the stochastic differential equations.The stability and effectiveness of the proposed model and methodologies are validated through numerical simulations and comparisons with real-world cholera data from Yemen.The results confirm the reliability and practical applicability of the model,providing strong theoretical and empirical support for the approach.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82373938,82104275,and 82204585)Key Technologies R&D Program of Guangdong Province,China(Grant No.:2023B1111030004)National Key R&D Program of China(Grant No.:2022YFF1202600).
文摘Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolutionizing drug discovery and treatment strategies.However,the links between in vitro and in vivo data are poorly understood,hindering a comprehensive understanding of the absorption,distribution,metabolism,and excretion(ADME)of PROTACs.In this work,14C-labeled vepdegestrant(ARV-471),which is currently in phase III clinical trials for breast cancer,was synthesized as a model PROTAC to characterize its preclinical ADME properties and simulate its clinical pharmacokinetics(PK)by establishing a physiologically based pharmacokinetics(PBPK)model.For in vitro–in vivo extrapolation(IVIVE),hepatocyte clearance correlated more closely with in vivo rat PK data than liver microsomal clearance did.PBPK models,which were initially developed and validated in rats,accurately simulate ARV-471's PK across fed and fasted states,with parameters within 1.75-fold of the observed values.Human models,informed by in vitro ADME data,closely mirrored postoral dose plasma profiles at 30 mg.Furthermore,no human-specific metabolites were identified in vitro and the metabolic profile of rats could overlap that of humans.This work presents a roadmap for developing future PROTAC medications by elucidating the correlation between in vitro and in vivo characteristics.
基金supported by the National Natural Science Foundation of China(Grant No.61271323)the Open Project from State Key Laboratory of MillimeterWaves,China(Grant No.K200913)
文摘Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages： primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image （MRI）. Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.
基金The research has gained the stake of Middleware Software Division of Software Group of F ujitsu L imitedJapanThe Project T
文摘Based on dominant degree of role model among the viewpoints for object oriented modeling process, it dissertates that role modeling is a modeling method for software pattern at knowledge level. After giving some examples for modeling design pattern and analysis pattern at knowledge level using role model, it presents a process for refining design pattern from role model to class model and event trace diagram of UML. In this paper, we advocate the opinion that role modeling before object modeling of UML.
基金Research reported in this discussion paper was supported by the National Institute for Nursing Research of the National Institutes of Health under award number[1 F32 NR01859101].Special thanks to Dr.E.Carol Polifroni,EdD,NEA-BC,CNE RN,ANEF for her unwavering support,guidance,and encouragement to see this disc scussion published.
文摘As more nurses embrace precision science,there is a tendency to utilize theoretical frameworks from other disciplines thus,placing nursing at risk of losing its autonomy and independence.The discipline has fallen prey to internal binary opposition,eliminating opportunities to engage in civil discourse.To explore how the roles nurses select might fit together in a theoretical framework and help nurses understand how the roles they choose to support their identity as nurses,this paper introduced a model of nursing that includes the bench scientists,the policy activists,and bedside nurses,using the Neuman Systems Model(NSM).The Nurse Role Integration Model(NRIM)espouses the basic tenets of NSM:prevention counteracts stressors from penetrating the client's lines of defense thus,reducing stress response.Primary prevention reflects the work of the nurse bench scientists,investigating the underlying mechanisms behind pathophysiology;secondary prevention is applied nurse scientists who build upon nurse researchers'work,identifying and testing potential interventions;tertiary prevention is nurse policy activists,the fulcrum,who leverage primary and secondary findings to argue policy change at all levels.Once policy change is adopted,bedside nurses are educated and implement the change.This lens provides an opportunity to create greater solidarity,strengthening the unity and autonomy of the discipline.
