Design,synthesis, biological activity evaluation and mechanism of action of myricetin derivatives containing thioether quinazolinone

Plant bacteria and viruses have a huge negative impact on food crops in the world. Therefore, it is important to create new and efficient green pesticides. In this paper, a series of myricetin derivatives containing quinazolinone sulfide were introduced. Good antibacterial and antiviral activities of the drug molecules 2-((3-((5,7-dimethoxy-4-oxo-2-(3,4,5-trimethoxyphenyl)-4H-chromen-3-yl)oxy)propyl)thio)-6-fluoro-3-phenylquinazolin-4(3H)-one (T5) and 2-((4-((5,7-dimethoxy-4-oxo-2-(3,4,5-trimethoxyphenyl)-4H-chromen-3-yl)oxy)butyl)thio)-6-methyl-3-phenylquinazolin-4(3H)-one (T15) respectively were found by biological activity screening. The value of dissociation constant (K_d) of compound T15 to TMV CP was 0.024 ± 0.006 μM, determined by Microscale thermophoresis (MST), which was far less than the value of 8.491 ± 2.027 μM of commercial drug ningnanmycin (NNM). The interaction between compound T15 and TMV CP was further verified by molecular docking. Compound T15 formed strong hydrogen bonds with residues SER:49 and SER:15 (1.92 Å, 2.20 Å, respectively), which were superior to the traditional hydrogen bonds formed by NNM with residue SER:215 (3.64 Å). In addition, the effects of compound T15 on the contents of chlorophyll and peroxidase (POD) in tobacco were studied, and the results indicated that compound T15 could enhance the disease resistance of tobacco plants to a certain extent.     

物流服务供应商联盟构建及分析

在分析供应链环境下物流服务运作的基础上,建立了信息对称情况下物流服务供应商分散控制和集中控制策略模型,并从物流服务联盟的总体产出效果和物流服务联盟对盟员利润的影响两个方面对其进行了定量分析,结果表明,建立物流服务联盟有利于增加联盟产出,抑制竞争对手的产出;如果物流服务企业之间有增加合作利润的需要,则都有建立联盟的积极性.最后对物流服务联盟的实现机理进行了分析.     

Alliance free and alliance cover sets

A defensive (offensive) k-alliance in Γ = (V,E) is a set S ⊆ V such that every υ in S (in the boundary of S) has at least k more neighbors in S than it has in V / S. A set X ⊆ V is defensive (offensive) k-alliance free, if for all defensive (offensive) k-alliance S, S/X ≠ ∅, i.e., X does not contain any defensive (offensive) k-alliance as a subset. A set Y ⊆ V is a defensive (offensive) k-alliance cover, if for all defensive (offensive) k-alliance S, S ∩ Y ≠ ∅, i.e., Y contains at least one vertex from each defensive (offensive) k-alliance of Γ. In this paper we show several mathematical properties of defensive (offensive) k-alliance free sets and defensive (offensive) k-alliance cover sets, including tight bounds on their cardinality.     

Using an Iterated Prisoner's Dilemma with Exit Option to Study Alliance Behavior: Results of a Tournament and Simulation

Nearly half of all strategic alliances fail (Park and Russo, 1996; Dyer et al., 2001), often because of opportunistic behavior by one party or the other. We use a tournament and simulation to study strategies in an iterated prisoner's dilemma game with exit option to shed light on how a firm should react to an opportunistic partner. Our results indicate that a firm should give an alliance partner a second chance following an opportunistic act but that subsequent behavior should be contingent on the value of the next best opportunity outside the alliance. Firms should be more forgiving if the potential benefits from the alliance exceed other opportunities. The strategies were also found to be robust across a wide range of game lengths. The implications of these results for alliance strategies are discussed. Steven E. Phelan received his PhD in economics from La Trobe University (Australia) in 1998. Following five years at the University of Texas at Dallas, he joined the faculty of the University of Nevada Las Vegas in 2003. Dr. Phelan's research interests include competitive dynamics, organizational efficiency, acquisition and alliance performance, and entrepreneurial competence. His methods of choice to study these phenomena include agent-based modelling, experimental game theory, and event studies. Prior to joining academia, Dr. Phelan held executive positions in the telecommunications and airline industries and was a principal partner in Bridges Management Group, a consultancy specializing in strategic investment decisions. Richard J. Arend is a graduate of the University of British Columbia's doctoral program in Policy Analysis and Strategy. He is on the Management faculty of the University of Nevada, Las Vegas, arriving most recently from the Management faculty of New York University's Stern School of Business. Dr. Arend's interests lie in the analysis of unusual modes of firm value creation and destruction, where he has published in several top journals. He is a professional engineer with work and consulting experience in aerospace and computing. Darryl A. Seale joined the faculty of UNLV in 1999, following three years at Kent State University and the University of Alabama in Huntsville. Prior to Alabama, he completed his Ph.D. and M.S. degrees in Business Administration at the University of Arizona, his M.B.A. from Penn State University, and spent over ten years in management and market planning positions in the health care industry. Professor Seale's research interests include strategic decision making, bargaining and negotiation, and behavioral game theory. His research has been funded by the National Science Foundation and has been published in top-tier journals including Management Science, OBHDP, Games and Economic Behavior, and Strategic Management Journal. His teaching interests include business policy/strategy, managerial decision making, and bargaining and negotiation.     

Powerful alliances in graphs

For a graph G=(V,E), a non-empty set S⊆V is a defensive alliance if for every vertex v in S, v has at most one more neighbor in V−S than it has in S, and S is an offensive alliance if for every v∈V−S that has a neighbor in S, v has more neighbors in S than in V−S. A powerful alliance is both defensive and offensive. We initiate the study of powerful alliances in graphs.     

Security in graphs

Let G=(V,E) be a graph. A set S⊆V is a defensive alliance if |N[x]∩S|?|N[x]-S| for every x∈S. Thus, each vertex of a defensive alliance can, with the aid of its neighbors in S, be defended from attack by its neighbors outside of S. An entire set S is secure if any subset X⊆S can be defended from an attack from outside of S, under an appropriate definition of what such a defense implies. Necessary and sufficient conditions for a set to be secure are determined.     

Computing Normalizing Constants for Finite Mixture Models via Incremental Mixture Importance Sampling (IMIS)

This article proposes a method for approximating integrated likelihoods in finite mixture models. We formulate the model in terms of the unobserved group memberships, z, and make them the variables of integration. The integral is then evaluated using importance sampling over the z. We propose an adaptive importance sampling function which is itself a mixture, with two types of component distributions, one concentrated and one diffuse. The more concentrated type of component serves the usual purpose of an importance sampling function, sampling mostly group assignments of high posterior probability. The less concentrated type of component allows for the importance sampling function to explore the space in a controlled way to find other, unvisited assignments with high posterior probability. Components are added adaptively, one at a time, to cover areas of high posterior probability not well covered by the current importance sampling function. The method is called incremental mixture importance sampling (IMIS).

IMIS is easy to implement and to monitor for convergence. It scales easily for higher dimensional mixture distributions when a conjugate prior is specified for the mixture parameters. The simulated values on which the estimate is based are independent, which allows for straightforward estimation of standard errors. The self-monitoring aspects of the method make it easier to adjust tuning parameters in the course of estimation than standard Markov chain Monte Carlo algorithms. With only small modifications to the code, one can use the method for a wide variety of mixture distributions of different dimensions. The method performed well in simulations and in mixture problems in astronomy and medical research.     

Partitioning a graph into alliance free sets

A strong defensive alliance in a graph G=(V,E) is a set of vertices A⊆V, for which every vertex v∈A has at least as many neighbors in A as in V−A. We call a partition A,B of vertices to be an alliance-free partition, if neither A nor B contains a strong defensive alliance as a subset. We prove that a connected graph G has an alliance-free partition exactly when G has a block that is other than an odd clique or an odd cycle.     

Global defensive alliances in star graphs

A defensive alliance in a graph G=(V,E) is a set of vertices S⊆V satisfying the condition that, for each v∈S, at least one half of its closed neighbors are in S. A defensive alliance S is called a critical defensive alliance if any vertex is removed from S, then the resulting vertex set is not a defensive alliance any more. An alliance S is called global if every vertex in V(G)?S is adjacent to at least one member of the alliance S. In this paper, we shall propose a way for finding a critical global defensive alliance of star graphs. After counting the number of vertices in the critical global defensive alliance, we can derive an upper bound to the size of the minimum global defensive alliances in star graphs.