Building the Pharmacophore Model of HIV-1 Integrase Strand Transfer Inhibitors and Studying Their Inhibition Mechanism

The replication of HIV-1 requires the integration of its cyclic DNA into host DNA by HIV-1 integrase （IN）, which includes two important reactions, 3＇-processing and strand transfer, both catalyzed by HIV-1 IN. Disrupting either of the reactions will fulfill the purpose of inhibiting the replication of HIV-1. In this paper, pharmacophore modeling and molecular docking are employed to investigate the inhibition mechanism of the HIV-1 IN strand transfer inhibitors （INSTIs）. Based on the results, we suggest that the inhibition mechanism of INSTIs involves the inhibitor chelating the cofactors Mg2＋ and its forming hydrogen bonds with some crucial residues adjacent to the DDE active center.

New lead discovery for novel M_1 agonists:pharmacophore model based on DISCO computation and virtual screening

To discover new lead compounds for M1 agonists. Ten typical M1 agonists were superimposed to build a M1 agonists 3D-pharmacophore model using distance-comparisons （DISCO） method without the previous knowledge of the three-dimensional structure of M1 receptor. Virtual screening strategy was used to analyze the Available Chemicals Directory-Screening Compounds （ACD-SC） to identify possible new hits. Twenty-two compounds which fit the pharmacophore model well and are not similar with known M1 agonists were purchased in order to evaluate their M1 receptor agonist activity. One of them shows M1 receptor agonist activity with EC50 of 4.90 μmol/L and maximum response. Multiple of 10.0 which shows it worthy of further study as a new lead compound for M1 agonists.

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.

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.

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. .

成纤维细胞生长因子受体1 抑制剂对胶原诱导关节炎模型大鼠骨破坏的影响

背景:课题组前期的研究表明靶向成纤维细胞生长因子受体1(fibroblast growth factor receptor 1,FGFR1)可能是治疗类风湿性关节炎的有效靶点。目的:探讨FGFR1抑制剂(PD173074)对胶原诱导关节炎模型大鼠骨破坏的影响。方法:将25只雌性SD大鼠随机分为5组,正常对照组、模型组、甲氨蝶呤组、PD173074低剂量组、PD173074高剂量组。除正常对照组外,其余各组大鼠建立Ⅱ型胶原诱导关节炎模型。造模成功后正常组及模型组大鼠腹腔注射无菌PBS,甲氨蝶呤组药物注射剂量为1.04 mg/kg,PD173074低剂量组和高剂量组药物注射剂量分别为5,20 mg/kg,1次/周。给药4周后取材,观察大鼠临床症状以及关节肿胀情况,踝关节Micro-CT三维重建及分析,观察踝关节病理变化,检测关节周围血管生成情况及核因子κB受体活化因子配体的表达,检测关节滑膜中p-FGFR1、血管内皮生长因子A、抗酒石酸酸性磷酸酶的表达,观察肝、脾、肾病理变化并计算肝、脾、肾指数。结果与结论:①PD173074能够减轻模型大鼠踝关节临床症状及关节肿胀,延缓骨质丢失,改善骨结构,减轻关节滑膜侵袭以及软骨骨侵蚀,降低关节周围破骨细胞数量,抑制关节滑膜组织中的血管生成,降低核因子κB受体活化因子配体的表达,抑制FGFR1磷酸化蛋白、抗酒石酸酸性磷酸酶和血管内皮生长因子A的蛋白表达。②大鼠肝、脾、肾病理观察表明经过PD173074治疗后无明显的毒副作用。③研究证明了FGFR1抑制剂能够延缓Ⅱ型胶原诱导关节炎模型大鼠关节炎症及骨破坏的进展,并抑制血管的生成。初步验证了PD173074在Ⅱ型胶原诱导关节炎模型中的治疗作用,其可能是通过抑制FGFR1磷酸化发挥作用,为寻找类风湿性关节炎新的治疗靶点提供了方向。

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.

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.

Pharmacologic inhibition of mTORC1 mimics dietary protein restriction in a mouse model of lactation

Background:Understanding the mechanisms of N utilization for lactation can lead to improved requirement estimates and increased efficiency,which modern dairy diets currently fail to maximize.The mechanistic target of rapamycin complex 1(mTORC1)is a central hub of translation regulation,processing extra-and intra-cellular signals of nutrient availability and physiological state,such as amino acids and energy.We hypothesized that dietary amino acids regulate lactation through mTORC1,such that inhibition of mTORC1 will lead to decreased lactation performance when amino acids are not limiting.Our objectives were to assess lactation performance in lactating mice undergoing dietary and pharmacologic interventions designed to alter mTORC1 activity.Methods:First lactation mice(N=18;n=6/treatment)were fed an adequate protein diet(18%crude protein),or an isocaloric protein-restricted diet(9%crude protein)from the day after parturition until lactation day 13.A third group of mice was fed an adequate protein diet and treated with the mTORC1 inhibitor rapamycin(4 mg/kg every other day)intraperitoneally,with the first two groups treated with vehicle as control.Dams and pups were weighed daily,and feed intake was recorded every other day.Milk production was measured every other day beginning on lactation day 4 by the weigh-suckle-weigh method.Tissues were collected after fasting and refeeding.Results:Milk production and pup weight were similarly decreased by both protein restriction and rapamycin treatment,with final production at 50%of control(P=0.008)and final pup weight at 85%of control(P<0.001).Mammary phosphorylation of mTORC1’s downstream targets were decreased by protein restriction and rapamycin treatment(P<0.05),while very little effect was observed in the liver of rapamycin treated mice,and none by protein restriction.Conclusions:Overall,sufficient supply of dietary amino acids was unable to maintain lactation performance status in mice with pharmacologically reduced mammary mTORC1 activity,as evidenced by diminished pup growth and milk production,supporting the concept that mTORC1 activation rather than substrate supply is the primary route by which amino acids regulate synthesis of milk components.

Simulating Particle Swarm Optimization Algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

基于ARMA(1,1)需求的多级供应链牛鞭效应仿真

在市场需求信息为ARMA(1,1)平稳可逆时间序列模型的前提下,首次建立了多级供应链各级成员以均方误差优化预测技术预测市场需求,以订货点法来确定订货量时多级供应链牛鞭效应理论及仿真模型,并利用仿真模型对多级供应链的整体牛鞭效应及其影响因素进行了详细的分析,研究表明供应链牛鞭效应和相关系数ρ与滑动平均系数θ之间的相互关系有关。

基于ARMA模型的我国政府行政成本支出研究(1978—2009)

为抵御百年罕见的金融危机,我国财政赤字预算陡然增加,经济面临前所未有的严峻考验,确保政府行政部门开支不再超常增长对平安度过巨额赤字周期至关重要。文章针对1978—2009年中国政府行政管理支出数据,进行了时间序列分析,研究证明ARMA(1,2)模型适合中国政府行政管理支出建模;进而对政府成本进行预测,并提出了控制政府成本的相关建议,对控制其增长规模具有重要的现实意义。

MGM(1,n)-ARMA模型在大坝监测系统中的应用

针对大坝监测系统监测过程中受到各种随机因素干扰的情况,详细地讨论了灰色预测MGM(1,n)和MGM(1,n)-ARMA模型的基本内容及建模过程,成功地将MGM(1,n)-ARMA预测模型应用于大坝变形的预测预报。实践证明,MGM(1,n)-ARMA预测模型由于考虑了各变量相互关联、共同发展的关系,并建立了ARMA模型对残差进行了拟合修正,提高了灰区间的白色度,预测效果比传统的MGM(1,n)模型效果好。因此,MGM(1,n)-ARMA预测模型在大坝变形的预测预报中比MGM(1,n)预测模型具有更高的应用价值。

Study on the Prediction of Rice Blast Based on the Unbiased GM (1,1) Model

To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.

缺失数据下ARMA(1,1)模型的估计方法

近几十年以来,国际上在对"风险的处理和效益的优化"这两个现代金融学的中心议题的分析和处理过程中,金融时间序列的计量学模型及其相应的分析越来越起到非常重要的作用。对于线性时间序列模型如AR(p),MA(q),ARMA(p,q)等,已经为我们所熟知。具体到模型的参数估计在数据没有缺失时,也有很多经典的办法,如最小二乘法、极大似然法等。但是当数据在中间有缺失时,上述方法将无能为力。本文将详细讨论在数据有缺失时的ARMA(1,1)模型,即Zt=αZt-1+tε-βεt-1的参数的估计方法。

ARMA(1,1)需求条件下供应链需求提前承诺的影响效果分析

为了分析供应链需求提前承诺的影响效果,考虑供应链所面临的顾客需求满足ARMA(1,1)过程。首先从理论上建立正常顾客需求与顾客需求提前承诺时零售商订单量波动程度和平均库存的定量描述模型,通过两种情形下的比较分析,得出在顾客需求自回归系数大于零时,顾客需求提前承诺将减小牛鞭效应和平均库存水平;同时得出在顾客需求提前承诺时,如果顾客需求自回归系数大于零,顾客提前承诺的需求比例越高,则牛鞭效应和平均库存水平越低;顾客需求提前承诺的时间跨度越长,则牛鞭效应和平均库存水平也越低。反之亦然。其次运用仿真方法分析了顾客需求提前承诺对零售商平均库存成本的影响,得出在顾客需求自回归系数大于零时,顾客需求提前承诺将有效降低零售商的平均库存成本。

带有ARMA(0,1,0)误差的非线性模型影响诊断

在带有AR IMA(0,1,0)误差的非线性回归模型中,利用梯度方向研究了观测数据对于其异方差检验的Score统计量的局部影响,分别得到了因变量及自变量的微小扰动情况下度量最大局部影响的诊断统计量.最后,给出了具体的数值实例,说明了本文结论的有效性.

Protective Effects of Oral Fructose-1, 6-diphosphate on Liver Injury in Animal Models

Aim To investigate the effects of FDP on different liver injury models to explore the possibility of FDP used as an oral liver protective agent. Methods Chronic liver injury model in rats was induced by carbon tetrachloride （ CCl4 ） ; Acute liver injury model in mice was induced by aminogalactose （GAIN） or lipopolysaccharide （LPS）. Results In CCl4-induced chronic liver injury model, FDP （1 , 4 g·kg^-1·d^-1, q.d., for 10 weeks） significantly lowered ALT, AST,γ-glutamyl transpeptidase （γ-GT）, alkaline phosphatase （ALP）, and total bilirubin （T-BIL） in serum compared with vehicle; simultaneously it evidently elevated abnormal total protein （TP）, albumin （ALB） and total cholesterol （ T-CHO ） levels in serum; it also dose-dependently reduced hydroxyproline contents in hepatic tissue. 4 g·kg^-1·d^-1 of FDP apparently decreased incidence of hepatic cirrhosis, and alleviated pathological changes of liver tissue. In GaiN-induced model, 1.0 - 4. 0 g·kg^-1·d^-1 of FDP （ bid, for 3 d ） significantly lowered alanine aminotransferase （ ALT ） and aspartate aminotransferase （ AST ） levels in serum ; it also decreased liver coefficient. 4. 0 g·kg^-1·d^-1 of FDP significantly alleviated pathological changes of cell ultra-structures. In LPS-induced model, only high dose of FDP （4. 0 g·kg^-1·d^-1, bid, for 12 d） significantly decreased ALT level in serum. Conclusion This study first demonstrated the protective effect of oral FDP on chronic liver injury caused by CCl4, and confirmed its effect on acute liver injury at the same time, suggesting that Long-term oral FDP is efficacious against liver injury induced by different factors and can be used as an oral liver protective agent in clinic.

基于Sentinel-1/2改进极化指数和纹理特征的土壤含盐量反演模型

目前Sentinel-1/2协同反演植被土壤含盐量的研究大多是基于Sentinel-2光谱信息和Sentinel-1后向散射系数,没有考虑Sentinel-2光谱信息容易受土壤亮度等信息影响,Sentinel-1后向散射系数容易受土壤粗糙度和水分影响。为进一步提高Sentinel-1/2协同反演植被土壤含盐量的精度,用水云模型对雷达卫星后向散射系数进行校正,消除植被影响;然后协同Sentinel-2纹理特征,基于VIP、OOB、PCA 3种变量筛选和RF、ELM、Cubist 3种机器学习回归模型构建植被土壤含盐量反演模型。研究结果表明:经过水云模型去除植被影响后的雷达后向散射系数及其极化组合指数与土壤含盐量的相关性有一定程度的提高。不同变量选择方法与不同机器学习方法耦合模型在反演土壤含盐量中,OOB变量筛选方法与RF、ELM和Cubist 3种机器学习方法的耦合模型精度最佳,建模集和验证集的R2都在0.750以上,且验证集的RMSE和MAE均最小;其中OOB-Cubist耦合模型精度最高,且R_(v)^(2)/R_(c)^(2)为0.955,具有良好的鲁棒性。研究可为机器学习协同物理模型、光学卫星协同雷达卫星在土壤含盐量反演中的进一步应用提供思路。