Establishment of a humanized ST6GAL1 mouse model for influenza research

Background:This study aimed to construct and characterize a humanized influenza mouse model expressing hST6GAL1.Methods:Humanized fragments,consisting of the endothelial cell-specific K18 promoter,human ST6GAL1-encoding gene,and luciferase gene,were microinjected into the fertilized eggs of mice.The manipulated embryos were transferred into the oviducts of pseudopregnant female mice.The offspring were identified using PCR.Mice exhibiting elevated expression of the hST6GAL1 gene were selectively bred for propagation,and in vivo analysis was performed for screening.Expression of the humanized gene was tested by performing immunohistochemical(IHC)analysis.Hematologic and biochemical analyses using the whole blood and serum of humanized hST6GAL1 mice were performed.Results:Successful integration of the human ST6GAL1 gene into the mouse genome led to the overexpression of human SiaT ST6GAL1.Seven mice were identified as carrying copies of the humanized gene,and the in vivo analysis indicated that hST6GAL1gene expression in positive mice mirrored influenza virus infection characteristics.The IHC results revealed that hST6GAL1 was expressed in the lungs of humanized mice.Moreover,the hematologic and biochemical parameters of the positive mice were within the normal range.Conclusion:A humanized influenza mouse model expressing the hST6GAL1 gene was successfully established and characterized.

An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Numerical Models and Methods of Atmospheric Parameters Originating in the Formation of the Earth’s Climatic Cycle

Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in2/s for humidity, 2.993 in2/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

基于Markov模型的胰高血糖素样肽1受体激动剂联合二甲双胍治疗2型糖尿病药物经济学评价

目的 对胰高血糖素样肽1受体激动剂(GLP-1RA)联合二甲双胍治疗2型糖尿病(T2DM)进行经济学评价。方法 从我国卫生体系角度出发,基于7项GLP-1RA联合二甲双胍治疗T2DM的随机对照试验(RCT),构建二甲双胍单药或联合GLP-1RA治疗T2DM的Markov模型,模拟治疗期间T2DM无并发症、T2DM伴并发症以及死亡3种状态的动态变化。模型以质量调整生命年(QALYs)为健康产出指标、以3倍我国2023年人均国内生产总值(GDP)为意愿支付(WTP)阈值。模型循环周期设定为1年,共计模拟20年,采用Markov模型进行队列模拟,以增量成本-效用比(ICUR)为评价指标,从而获得每种治疗策略的长期成本、效用及其经济性。通过对成本、效用及贴现的敏感性分析,检验研究结果的稳定性。结果 与二甲双胍单药治疗相比,5种GLP-1RA类药物(利拉鲁肽、度拉糖肽、艾塞那肽、聚乙二醇洛塞那肽、司美格鲁肽)联合二甲双胍治疗方案的ICUR均小于3倍我国2023年人均GDP,增加的成本可接受。敏感性分析中各参数在设定的范围内变化,或将模拟时间延长至30年或50年,对研究结论无显著影响;概率敏感性分析结果表明,WTP阈值为3倍我国2023年人均GDP值(268 074元)时,二甲双胍联合司美格鲁肽0.5 mg方案具有成本-效用优势的概率最高,约为99.7%。结论 对于T2DM患者,相比于二甲双胍单药治疗,利拉鲁肽、度拉糖肽、艾塞那肽、聚乙二醇洛塞那肽、司美格鲁肽以说明书推荐剂量联合二甲双胍治疗方案均属于优势方案,具有经济性。

Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(UT1-UTC)which cannot be directly estimated through Global Navigation Satellite System(GNSS)techniques,significantly affects the rapid and ultra-rapid orbit determination of GNsS satellites.Pres-ently,the traditional LS(least squares)+AR(autoregressive)and LS+MAR(multivariate autoregressive)hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions(1-10 days).The LS+MAR hybrid method relies on the UT1-UTC and LOD(length of day)series.However,the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and UT1-UTC.In light of this,and with the aid of the first-order-difference UT1-UTC,we propose an enhanced LS+MAR hybrid method to UT1-UTC ultra-short-term prediction.By using the UT1-UTC and LOD data series of the IERS(International Earth Rotation and Reference Systems Service)EOP 14 C04 product,we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS+AR and LS+MAR hybrid methods.According to the numerical results over more than 210 days,they demonstrate that,when considering the correlation information between the LoD and the first-order-difference UT1-UTC,the mean absolute errors(MAEs)of the improved LS+MAR hybrid method range from 21 to 934μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs in 1-10 days predictions,with corresponding improvement percentages ranging from 1 to 28%.Similarly,when compared to the traditional LS+MAR hybrid method,the MAEs have a reduction of 5-42μs in 1-10 days predictions,with corresponding improvement percentages ranging from 4-20%.Additionally,when aided by GNSS-derived LOD data series,the MAEs of improved LS+MAR hybrid method experience further reduction.

Maximum initial primary wave model for low-Froude-number reservoir landslides based on wave theory

The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking the Shuipingzi 1#landslide that occurred in the Baihetan Reservoir area of the Jinsha River in China as an engineering example,this study established a large-scale physical model(with dimensions of 30 m×29 m×3.5 m at a scale of 1:150)and conducted scaled experiments on 3D landslide-induced impulse waves.During the process in which a sliding mass displaced and compressed a body of water to generate waves,the maximum initial wave amplitude was found to be positively correlated with the sliding velocity and the volume of the landslide.With the increase in the water depth,the wave amplitude initially increased and then decreased.The duration of pressure exertion by the sliding mass at its maximum velocity directly correlated with an elevated wave amplitude.Based on the theories of low-amplitude waves and energy conservation,while considering the energy conversion efficiency,a predictive model for the initial wave amplitude was derived.This model could fit and validate the functions of wavelength and wave velocity.The accuracy of the initial wave amplitude was verified using physical experiment data,with a prediction accuracy for the maximum initial wave amplitude reaching 90%.The conversion efficiency(η)directly determined the accuracy of the estimation formula.Under clear conditions for landslide-induced impulse wave generation,estimating the value ofηthrough analogy cases was feasible.This study has derived the landslide-induced impulse waves amplitude prediction formula from the standpoints of wave theory and energy conservation,with greater consideration given to the intrinsic characteristics in the formation process of landslide-induced impulse waves,thereby enhancing the applicability and extensibility of the formula.This can facilitate the development of empirical estimation methods for landslide-induced impulse waves toward universality.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

The Improving Effects of Diabetes Education on Diabetes Awareness and Management in Children and Adolescents with T1DM

Background: Diabetes education is crucial in empowering persons with Type 1 diabetes (T1DM) and their families to properly manage the condition by providing comprehensive knowledge, tools, and support. It boosts one’s belief in their ability to succeed, encourages following medical advice, and adds to the general enhancement of health. Objective: This study is to investigate the effectiveness of diabetes education in empowering individuals with Type 1 Diabetes Mellitus (T1DM) and their families to effectively manage the condition. Furthermore, it strives to improve nursing care for families whose children have been diagnosed with Type 1 Diabetes Mellitus (T1DM). Design: This research study investigates the efficacy of diabetes education in empowering individuals with Type 1 Diabetes Mellitus (T1DM) and their families to effectively handle the condition. Materials and Methods: A systematic search was conducted between the years 2000 and 2022, utilizing the Medline and Google Scholar databases. The purpose of the search was to uncover relevant papers pertaining to diabetes education, management of Type 1 Diabetes Mellitus (T1DM), nurse care, and empowerment. The search focused on peer-reviewed research, clinical trials, and scholarly articles that evaluated the efficacy of diabetes education in empowering individuals and families. Results: Diabetes education is crucial for understanding and controlling T1DM. It includes personalized sessions, webinars, group classes, and clinics that provide customized therapies. Comprehensive education enhances glycemic control and family dynamics. Nevertheless, the implementation of diabetes education for families requires specific standards, especially in the field of nursing. Conclusion: Diabetes education is essential for effectively managing Type 1 Diabetes Mellitus (T1DM), providing patients and families with crucial knowledge, resources, and confidence. It encourages independence in-home care and provides explicit guidelines for diabetic nurses to improve nursing care.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson's disease,with insights into Pink1 knockout models

Parkinson’s disease(PD)relates to defective mitochondrial quality control in the dopaminergic motor network.Genetic studies have revealed that PINK1 and Parkin mutations are indicative of a heightened propensity to PD onset,pinpointing mitophagy and inflammation as the culprit pathways involved in neuronal loss in the substantia nigra(SNpc).In a reciprocal manner,LRRK2 functions in the regulation of basal flux and inflammatory responses responsible for PINK1/Parkin-dependent mitophagy activation.Pharmacological intervention in these diseasemodifying pathways may facilitate the development of novel PD therapeutics,despite the current lack of an established drug evaluation model.As such,we reviewed the feasibility of employing the versatile global Pink1knockout(KO)rat model as a self-sufficient,spontaneous PD model for investigating both disease etiology and drug pharmacology.These rats retain clinical features encompassing basal mitophagic flux changes with PD progression.We demonstrate the versatility of this PD rat model based on the incorporation of additional experimental insults to recapitulate the proinflammatory responses observed in PD patients.

Tidal modeling based on satellite altimetry observations of TOPEX/ Poseidon, Jason1, Jason2, and Jason3 with high prediction capability: A case study of the Baltic Sea

This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon,Jason1,Jason2,and Jason3 altimetry missions for tidal modeling.We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location.Our tidal modeling methodology follows an iterative process:partitioning sea surface height(SSH)observations into analysis/training and prediction/validation parts and ultimately identi-fying the set of tidal constituents that provide the best predictions at each time series location.The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea,each with its own set of tidal constituents.Verification of the developed tidal models against the sSH observations within the prediction/validation part reveals mean absolute error(MAE)values ranging from 0.0334 m to 0.1349 m,with an average MAE of 0.089 m.The same validation process is conducted on the FES2014 and EOT20 global tidal models,demonstrating that our tidal model,referred to as BT23(short for Baltic Tide 2023),outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m,respectively.In addition to providing details on the development of the time series and the tidal modeling procedure,we offer the 1256 along-track time series and their associated tidal models as supplementary materials.We encourage the satellite altimetry community to utilize these resources for further research and applications.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Influence of Angiotensin II on α1-Adrenergic Receptors Function in Rat Aorta and Expression in Vascular Smooth Muscle Cells

Angiotensin II (Ang II) is the main mediator of the Renin-Angiotensin-System acting on AT1 and other AT receptors. It is regarded as a pleiotropic agent that induces many actions, including functioning as a growth factor, and as a contractile hormone, among others. The aim of this work was to examine the impact of Ang II on the expression and function of α1-adrenergic receptors (α1-ARs) in cultured rat aorta, and aorta-derived smooth muscle cells. Isolated Wistar rat aorta was incubated for 24 h in DMEM at 37˚C, then subjected to isometric tension and to the action of added norepinephrine, in concentration-response curves. Ang II was added (1 × 10−5 M), and in some experiments, 5-Methylurapidil (α1A-AR antagonist), AH11110A (α1B-AR antagonist), or BMY-7378 (α1D-AR antagonist), were used to identify the α1-AR involved in the response. Desensitization of the contractile response to norepinephrine was observed due to incubation time, and by the Ang II action. α1D-AR was protected from desensitization by BMY-7378;while RS-100329 and prazosin partially mitigated desensitization. In another set of experiments, isolated aorta-derived smooth muscle cells were exposed to Ang II and α1-ARs proteins were evaluated. α1D-AR increased at 30 and 60 min post Ang II exposure, the α1A-AR diminished from 1 to 4 h, while α1B-AR remained unchanged over 24 h of Ang II exposure. Ang II induced an increase of α1D-AR at short times, and BMY-7378 protected α1D-AR from desensitization.

Application of Modelica Based Multi- Domain Modeling and Simulation for Gravity-1

In the R&D phase of Gravity-1(YL-1), a multi-domain modeling and simulation technology based on Modelica language was introduced, which was a recent attempt in the practice of modeling and simulation method for launch vehicles in China. It realizes a complex coupling model within a unified model for different domains, so that technologists can work on one model. It ensured the success of YL-1 first launch mission, supports rapid iteration, full validation, and tight design collaboration.

基于负二项稀疏算子和推广的负二项稀疏算子的INAR(1)模型的比较

本文利用条件极大似然估计的方法比较利用预设新息分布法基于推广的负二项稀疏算子INAR(1)模型和负二项稀疏算子的INAR(1)模型的参数估计情况。通过对取不同新息项分布的两个模型分别进行数值模拟,并对两个模型数值模拟的结果进行对比来直观地表明由于基于负二项稀疏算子的INAR(1)模型在x=0点时概率质量不存在进而模型的所有边际分布不存在,从而证明利用预设新息分布法基于负二项稀疏算子的INAR(1)模型不存在,也表明推广的负二项稀疏算子在利用预设新息分布法构建INAR(1)模型中的必要性。

Practice on Gravity-1 Rocket Dynamic Characteristics Design

Since the Dongfeng-2 missile, full-vehicle modal testing has been established as an indispensable part of the development and testing of rocket and missile models. However, as rockets have been developed larger, the cost and duration of such tests have significantly increased, magnifying their impact on model development. This article follows the process of the modal testing practice of the Gravity-1 rocket, reviewing and summarizing the design process of the rocket's dynamic characteristics. Initially, the article introduces common modeling techniques for launch rockets, including the mass-beam model and the hybrid element model. It then discusses the relationship between the structural dynamics model of the launch rocket and modal testing, aiming to reduce testing costs through refined structural dynamics modeling methods. Subsequently, the article describes the dynamic characteristics design process of the Gravity-1 carrier rocket, categorizes structural parameters, and studies how the selection of structural parameters affects the predicted dynamic characteristics of the rocket. Finally, it elaborates on the design of the modal testing scheme and the dynamic characteristics design based on the test data.

血清c5ar1、ALP及25(OH)D3的表达规律对股骨颈骨折术后骨折愈合情况的预测价值

目的:探讨血清补体成分5a受体1(Complement component 5a receptor,c5ar1)、碱性磷酸酶(Alkaline phosphatase,ALP)及25羟基维生素D(Vitamin D325-hydroxy monohydrate,25(OH)D3)的表达规律对股骨颈骨折术后骨折愈合情况的预测价值.方法:选取2019年1月至2022年12月本院治疗的股骨颈发生骨折且实施全髋关节置换术的98例患者作为研究对象.依据其术后6 m的影像学方面检查结果评估患者的骨折愈合情况,并将患者分成骨折愈合组(77例)和未愈合组(21例).于入院及术后42 d分别对两组的血清c5ar1、ALP、25(OH)D3水平进行测定,采用Pearson相关性分析c5ar1、ALP、25(OH)D3的相关性.采用受试者工作(Receiver operating characteristic,ROC)曲线分析血清c5ar1、ALP及25(OH)D3对股骨颈骨折术后骨折的愈合情况的预测价值.结果:骨折愈合组术后血清c5ar1、ALP及25(OH)D3的表达水平均高于术前(P<0.05),未愈合组术后血清c5ar1、ALP及25(OH)D3的表达水平均低于术前(P<0.05);Pearson相关性分析结果显示,c5ar1与25(OH)D3呈正相关关系(P<0.05);c5ar1、ALP、25(OH)D3诊断股骨颈骨折术后骨折愈合情况的敏感度分别为92.20%、62.30%、76.60%,特异度分别为90.50%、47.60%、52.40%,ROC曲线分析显示,c5ar1、ALP、25(OH)D3诊断股骨颈骨折术后骨折愈合情况的曲线下面积(area under curve,AUC)分别为0.953、0.515、0.765.结论:血清c5ar1、ALP及25(OH)D3的表达水平升高可促进股骨颈骨折术后骨折愈合,c5ar1与25(OH)D3呈正相关关系,c5ar1、25(OH)D3诊断股骨颈骨折术后骨折愈合情况具有较高的准确性,而ALP预测股骨颈骨折术后骨折愈合情况的准确性较低.

基于L_(1)范数混合主动轮廓的河流SAR图像分割

为解决现有主动轮廓模型难以准确分割河流SAR图像的问题,提出一种基于L_(1)范数的混合主动轮廓模型。首先,计算轮廓曲线内外区域像素灰度的中值作为区域拟合中心,以抑制SAR图像中干扰区域对其准确性的影响;然后,利用L_(1)范数构建新的能量约束项并在模型能量泛函中引入边缘指示函数,进一步提升模型的分割性能;最后,将基于L_(1)范数的中值和均值能量约束项结合起来并添加额外的区域拟合中心约束项,以提高模型的整体稳定性。针对实际河流SAR图像进行分割试验,结果表明,与现有分割方法相比,本文模型能更准确、稳定地分割河流SAR图像。

Lie symmetry analysis and invariant solutions for the(3+1)-dimensional Virasoro integrable model

Lie symmetry analysis is applied to a(3+1)-dimensional Virasoro integrable model and the corresponding similarity reduction equations are obtained with the different infinitesimal generators.Invariant solutions with arbitrary functions for the(3+1)-dimensional Virasoro integrable model,including the interaction solution between a kink and a soliton,the lump-type solution and periodic solutions,have been studied analytically and graphically.