绿色产品零售企业与市场消费者策略选择演化分析

线上线下结合营销模式有效缓解了流通渠道不畅和营销方式单一的问题,进而促进绿色产品消费。在相关假设的基础上,通过构建绿色产品零售企业和市场消费者的演化博弈模型,重现博弈主体的动态演进过程。研究结果表明,在线上线下结合营销模式中,绿色产品零售企业与消费者演化博弈系统存在两个可能的均衡状态,分别是O{维持传统营销策略,使用传统渠道购买产品}、C{建立线上营销策略,使用线上渠道购买产品}。演化博弈系统的演进受到9个相关因素的干扰,这些因素通过影响博弈主体的收益、成本及潜在的风险损失等发生作用。在理论推导的基础上,以被调研的A绿色产品零售企业为样本进行数值模拟,验证理论推导的正确性,给出上述影响因素的作用强度。     

基于风险-均值分析的供应商选择与订单分配集成优化模型与求解

针对不确定环境下具有不同供应合约的供应商选择与订单分配问题,本文构建了基于风险-均值分析的模糊两阶段多周期集成优化模型。与传统的该问题研究并未充分考虑供应商选择与订单分配两阶段决策的交互影响不同,在该模型中,第一阶段供应商选择的评价目标依赖于后期实际运营中的订单分配决策;并考虑未来需求和实际运营成本的不确定性,引入在险价值和期望值两种决策准则对供应商选择方案的绩效进行评价。提出了该模型的分析求解方法,在险价值得以精确评估,期望值被控制在确定的误差范围内,并可以达到足够的精度要求。     

A rapid and simple method for the isolation of mutant variants regulating tissue-specific expression of the tni gene through drug selection

TnINEO fusion gene was constructed by fusing 3.4-kbp of quailTnI genomic DNA sequences spanning the promoter to exon 5 and aneo gene in frame. A myoblast cell line was established after transfection of pTnINEO. Since this cell line was passaged several times, a high frequency of neomycin (G418) sensitivity conversion was detected. Two drug-resistant variants were analyzed through genomic Southern blot and S1 nuclease protection assay. One variant has a mutation(s) in the regulatory element that activated the dormantTnI promoter-enhancer in myoblast, and the other has shown the genomic rearrangement. This result presented the possibility of isolating factor(s) that activate the muscle-specificTnI promoter simply by screening drug-resistant cells having appropriate mutations.     

PP回归方法在预测模型中的应用－用PP回归建立“积累”“消费”模型

本文利用ＰＰ回归方法,建立“积累”“消费”回归模型,并与最小二乘法拟合的线性回归模型相比较,说明用ＰＰ回归建立的模型预测精度较高．     

Thermodynamics of natural selection

It is shown that biological-natural-selection-like behavior can occur, as a general type of time evolution, in a statistical system where detailed balance is violated owing to the presence of metastable energy states. A model of a non-equilibrium phase transition corresponding to the spontaneous origin of self-reproduction in the system is suggested. After a phase transition, the system passes from one quasistationary distribution of self-reproducing subsystems to another, with an increase in the total organization, as long as the growth of the energy flow through the system or a reduction of energy dissipation in the system is possible. The entropy production is calculated for this process in terms of selective values of Eigen's theory for self-organization in autocatalytic systems. Correspondence of the extremal principle of Eigen's theory with the criterion of evolution in Prigogine's thermodynamics is established.     

STUDIES ON PREPARATION OF INACTIVATED VACCINES FROM CELL CULTURES OF MINK ENTERITIS VIRUS (MEV) AND THEIR IMMUNITY

In this paper, superhigh reproductive rate strains of MEV with titre more than HA8192* or TCID50 log9.7 10 have been achieved both by cultivation in cell lines with different susceptibility to MEV and by isolating and identifying in field by the author. The systematic tests proved that S18 and L12 strains of MEV are the best strains for vaccine preparation. In this study, the best means for the tissue cultivation of MEV and the most advanced technological process for the production and detection of serum-free cell-cultured MEV fluids with super-high HA titre in batches in large quantities have been established for the first time. Optimum conditions for MEV inactivation were determined, and safe and effective inactivated vaccines with mineral oil or A1(OH)3 gel adjuvant were successfully prepared with serum-free cell-cultured MEV fluids. Both vaccines with different adjuvants can be manufactured in batches in large quantities and have been widely used all over China since 1986. The change laws of the imm     

QSAR Studies on the COX-2 Inhibition by 3,4-Diarylcycloxazolones Based on MEDV Descriptor

Selective inhibition of cyclooxygenase-2 (COX-2) might avoid the side effects of current available nonsteroidal antiinflammatory drugs while retaining their therapeutic efficacy. A novel variable selection and modeling method based on prediction is developed to construct the quantitative structure-activity relationships (QSAR) between the molecular electronegativity distance vector (MEDV) based on 13 atomic types and the biological activities of a set of selective cyclooxygenase-2 inhibitory molecules, 3,4-diarylcycloxazolones (DAA) plus indomethacin,naproxen, and celecoxib. Using multiple linear regression, a 5-variable linear model is developed with the calibrated correlation coefficient of 0.9271 and root mean square error of 0.17 in modeling stage and the validated correlation coefficient of 0.9030 and root mean square error of 0.20 in leave-one-out validation step, respectively. To further test the predictive ability of the model, 20 DAA compounds are picked up to construct a training set which is used to build a QSAR model and then the model is employed to predict the biological activities of the balance compounds. The predicted correlation coefficient and root mean square error are 0.9332 and 0.19, respectively.     

Generic method for systematic phase selection and method development of biochromatographic processes. Part I. Selection of a suitable cation-exchanger for the purification of a pharmaceutical protein

Even if the first protein therapeutics are now for more than 20 years on the market the selection of suitable adsorbents for the preparative downstream processing (DSP) of these biomolecules as well as the method development towards process conditions are still based mainly on 'trial and error'. Therefore, theses processes are not perfectly efficient, but indeed very time consuming and laborious. In this study a novel systematic method is introduced to find a suitable adsorbent (not necessarily the best one) with appropriate separation parameters for a specific separation with reduced effort. Following this strategy, the adsorbents must first be packed into columns under preparative conditions and then characterized completely with regard to, e.g. pressure drop, k'-values, plate heights (HETP curves), selectivity and capacity by using test substances, which are similar in their characteristics (molecular mass, size, charge distribution, hydrophobicity) to the target proteins. With the database once determined, a preselection of most suitable adsorbents including separation parameters is made regarding chromatographic and also economical properties. After this, preparative experiments must be conducted with a reduced number of adsorbents to figure out the individual influence of side components. This approach is demonstrated for the separation of an exemplary industrial protein mixture using cation-exchange chromatography (CEX). Characterization of different weak CEX-adsorbents is illustrated. After comparing these phases with each other, a first preselection and a prediction of suitable adsorbents is made. In the following preparative separation conditions (load, velocity, gradient) are determined for the preparative separations using the database and results of some additional experiments. The final comparison of separation performance in preparative scale confirms this selection and so the applicability of the new method.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.