基于游牧团体移动模型的网络性能研究

针对节点之间相互独立的网络移动模型不能反映真实应用场景下移动自组网特性的问题,使用在军事领域和车载网中更具可适用性的游牧团体移动模型。在构造了相应的系统模型后,提出一种群组间多副本中继算法,得出该中继模式下基于游牧团体移动模型的网络容量、时延的上限和下限,并依此求得相应的折中率。对节点运动的仿真验证了该模型具备很好的移动特性;绘制出的有关参数的函数曲线证明了在这种移动模型下可以获得更好的网络性能。

团体心理辅导模型构建对护生感恩意识及情绪管理的干预效应研究

探讨团体心理辅导模型构建对护生感恩意识及情绪管理的干预效应。方法 选择60例护理院校学生作为研究对象,随机分为对照组(n=30)与观察组(n=30)。对照组为不干预组,实验组采用团体心理辅导干预。于干预前、干预后对两组护生感恩构建进行评定,采用STAXI(Satate—Trait AngerExpressionScale)量表对两组护生情绪管理进行评定。结果 干预后实验组RCSGQ量表评分、RCSGQ量表各维度评分、总体幸福感量表(GWB)评分均高于干预前,差异有统计学意义(P<0.05);干预后实验组STAXI量表评分均低于干预前,差异有统计学意义(P<0.05)。结论 团体心理辅导模型构建可改善护生感恩意识,提高情绪管理。

数字企业的组织学习模型构建与推进策略

随着政府提出的"互联网+"战略和"数字企业"建设要求,企业e-learning、组织学习及知识管理在互联网技术支撑下相互交织,逐渐促成了数字化组织学习(e-Organization Learning)的综合实践领域,成为数字化环境下的组织学习全新表现形式。借助于信息技术与方法,可以有效地推进企业组织结构变革和内外部生产链优化;拓展企业边界,推进开放学习、情境学习、联结学习,提升组织记忆,从而建构起e-Organization Learning新形态。

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.

基于政策模型公民参与的三种分析范式

政策模型是分析政策过程的理论工具。系统模型、团体模型和精英模型从不同视角对政策过程进行了解析,蕴含了不同的公民参与思想。这些思想在为我们研究公民参与问题提供理论参考的同时也存有诸多理论局限。其中,系统模型重视公民诉求对政策的影响,但未能说明具体的决策过程;团体模型将政策视为团体竞争的产物,但忽视了其他因素对决策的影响,尤其是弱化了政府的作用;精英模型则认为政策体现的是统治精英的价值偏好,但对社会大众的现实诉求无暇顾及。公民参与政策制定与执行不仅能表达其利益诉求和偏好,而且在一定程度上保证了政策的科学性,体现了社会力量对国家权力的监督和制约。在政策决策时,政府要在保持相对自主的同时,充分重视社会大众的公民参与,尤其要关怀社会弱势群体,干预社会不公,更好地维护公共利益,确保社会稳定。

大学生学业拖延的团体干预疗效研究

学业拖延作为当前国内外高校学生群体中的普遍现象,严重制约了学生的学业成功。该问题近年来已引起心理、教育学、甚至社会学领域研究者的广泛关注。但遗憾的是,我国学界在学业拖延研究领域内,目前尚缺乏较为系统的实证研究。研究将尝试引入实验干预方法,通过随机对照研究,深入探索学业拖延的影响因素以及可以调控拖延水平的疗效因子,旨在最终形成针对拖延学术个体的有效治疗方案。研究对27名存在严重拖延问题的大学生进行为期6周的半结构式团体干预。研究发现,团体干预能够显著降低大学生被试的学业拖延症状,降低干预组被试的抑郁水平,有效提升被试的自我控制感;团体干预能够提升大学生被试的自尊、自我效能感和积极完美主义,在一定程度上降低了被试焦虑和消极完美主义倾向。

水污染突发事件应急信息模型构建研究

针对水污染突发事件应急任务的小组特点,运用'小团体'理论中的结构化系统分析和设计方法,将多层级组织与多部门主体参与的应急响应组织抽象为信息分拣实体、决策实体、计划实体、公共事务实体、协调主体五个功能实体,并吻合应急组织的多层级结构特征,构建了水污染突发事件应急响应的信息结构模型。在系统分析水污染突发事件应急信息流向的基础上,构建了应急信息流模型;并基于应急决策目标导向,整合了应急信息结构模型和应急信息流模型,构建了水污染突发事件应急信息管理的基本模型。

Three-dimensional common-feature hypotheses for Toll-like receptor 7 agonists

Toll-like receptor 7 （TLR7）, the best known TLRs, has been demonstrated to be useful in fighting against infectious disease. In our study, three-dimensional （3D） pharmacophore models were constructed from a set of 5 TLR7 agonists. Among the 10 common-featured models generated by program Discovery Studio/HipHop, a hypothesis （Hypo2） including one hydrogen-bond donor （D）, one hydrogen-bond acceptor （A）, and two hydrophobic （H） features was considered to be important in evaluating the ligands with TLR7 agonistic activity. The obtained pharmacophore model was further validated using a set of test molecules and the Catalyst TLR7-agonist-subset database. Hypo2 has been shown to identify a range of highly potent TLR7 agonists. Finally, the obtained pharmacophore was further validated using docking studies. Taken together, this model can be utilized as a guide for future studies to design the structurally novel TLR7 agonists.

Pharmacophore identification and validation for human nAChR α7 agonists

Human nAChR u7 is the potential target for schizophrenia cognitive disorders, and it is meaningful to develop selective human nAChR α7 agonists for the clinical treatment of the disease. Because the crystal structure ofα7 receptor has not been resolved, ligand-based drug design strategy was took in this work. A 3D QSAR pharmacophore model was built by HypoGen method, and its quality was evaluated by cost function. Furthermore, the pharmacophore model was validated with activity prediction of test set and was cross-validated based on Fisher＇s Randomization Method. By Enrichment Factor and AU-ROC analysis, the final pharmacophore, which is consisted of one HBA, two Hydrophobic and one PosIonizable, was selected and it fitted well with the docking result of α7 homology model and the ligand. The pharmacophore is expected for the following virtual screening and lead optimization of human nAChR α7 agonists, which is important for the development and discovery of novel antipsychotics.

Predicting hiCE inhibitors based upon pharmacophore models derived from the receptor and its ligands

Human intestinal carboxyl esterase （hiCE） is a drug target for ameliorating irinotecan-induced diarrhea. By reducing irinotecan- induced diarrhea, hiCE inhibitors can improve the anti-cancer efficacy of irinotecan. To find effective hiCE inhibitors, a new virtual screening protocol that combines pharmacophore models derived from the hiCE structure and its ligands has been pro- posed. The hiCE structure has been constructed through homology techniques using hCESI＇s crystal structure. The hiCE structure was optimized via molecular dynamics simulations with the most known active hiCE inhibitors docked into the structure. An optimized pharmacophore, derived from the receptor, was then generated. A ligand-based pharmacophore was also generated from a larger set of known hiCE inhibitors. The final hiCE inhibitor predictions were based upon the virtual screening hits from both ligand-based and receptor-based pharmacophore models. The hit rates from the ligand-based and receptor-based pharmacophore models are 88% and 86%, respectively. The final hit rate is 94%. The two models are highly consistent with one another （85%）. This proves that both models are reliable.

Comparative pharmacophore modeling of human adenosine receptor A1 and A3 antagonists

Adenosine receptors are promising therapeutic targets in drug discovery. In this study, three-dimensional pharmacophore mod- els of human adenosine receptor A1 and A3 antagonists were developed based on 26 and 23 diverse compounds, respectively. The best A1 pharmacophore model （A1-Hopyl） consists of four features： one hydrogen bond donor, one hydrophobic point and two ring aromatics, while the best A3 pharmacophore model （A3_Hopyl） also has four features： one hydrogen bond ac- ceptor, one hydrophobic point and two ring aromatics. The correlation coefficients were 0.840 for A1 test set with 146 diverse compounds and 0.827 for A3 test set with 238 diverse compounds. In the simulated virtual screening experiments, high en- richment factors of 6.51 and 6.90 were obtained for A1_Hopyl and A3_Hopyl models, respectively. Moreover, two models also showed high subtype-selectivity in the simulated virtual screening experiments. These results could be helpful for the dis- covery of novel potent and selective A1 and A3 antagonists.