一个新的多序列比对算法

多序列比对是生物信息学中基础而又重要的序列分析方法.本文提出一种新的多序列比对算法,该算法综合了渐进比对方法和迭代策略,采用加权函数以调整序列的有偏分布,用neighbor-joining方法构建指导树以确定渐进比对的顺序.通过对BAlibASE中142组蛋白质序列比对的测试,验证了本算法的有效性.与Multalin算法比较的结果表明,本算法能有效地提高分歧较大序列的比对准确率.     

微生物降解多环芳烃的研究进展

多环芳烃是一类长久存在于环境中,具有毒性、致突变与致癌等特性的环境优先污染物。本文对降解多环芳烃的微生物类群进行了阐述,介绍了在土壤与厌氧条件下细菌降解多环芳烃的研究情况,最后介绍了降解多环芳烃的相关酶类以及分子生物学的研究,并对消除环境中多环芳烃的相关生物技术提出展望。     

基于Progressive多序列比对方法的求解多序列比对的启发式算法

在生物信息学研究中,生物序列比对问题占有重要的地位。多序列比对问题是一个NPC问题,由于时间和空间的限制不能够求出精确解。文中简要介绍了Feng和Doolittle提出的多序列比对算法的基本思想,并改进了该算法使之具有更好的比对精度。实验结果表明,新算法对解决一般的progressive多序列比对方法中遇到的局部最优问题有较好的效果。     

基于多目标的杨树速生丰产林主伐决策分析

经济效益综合评价是产业决策研究的重要内容.从系统工程观点来看,经济效益综合评价本质上是一个有限方案多目标决策问题.因此,本文采用多目标决策法对杨树速生丰产林的主伐决策进行研究,以年均净现值、内部收益率、土地期望价、效益成本比、成本利润率为指标,提出用熵技术法确定评价指标权重,结合"理想点"法、TOPSIS法和线性加权和法三种方法对决策方案进行排序择优,得到不同排序结果,最后用平均值法得到最终综合评价结果.结果表明,对所研究的密度为10×10m~2、8×8m~2、6×8m~2、5×6m~2的杨速生丰产林进行分析得到综合评价结果,其最优主伐龄分别为:13年、10年、9年、9年.随着密度的增大,经济成熟龄提前.本研究表明该方法对杨树速生丰产林的主伐决策研究是有效可行的.     

基于多判据决策的水体营养状态评价

为了准确地评价水生态系统营养状态和综合决策,通过最大熵原理耦合模糊性与随机性,建立了最大熵模糊评价模型(FAME);利用逼近理想解排序法(TOPSIS),以待决策水体样本的实测值为理想解,以评价结果中与实测值相差最大的为负理想解,建立了多判据决策模型(MCDM).经12个湖泊实测数据验证,最大熵模糊评价与随机评价、模糊评价和灰色评价的结果较为一致,但提高了评价水体营养状态问题各层次的分辨力.多判据决策模型可解决多种方法评价结果不相容问题,使评价结果更接近水体实际情况.FAME和MCDM适用于各种水质的综合评价及决策.     

螺旋CT的多排多层探测器应用设计

目的设计螺旋CT机的多排多层探测器,提高影像质量。方法将常规多排探测器改进为多排多层探测器,输出端接超高倍光电耦合放大器,可成倍提高探测器与采集系统的信噪比。结果多排多层探测器比同样排数的单层探测器输出的信噪比高、省时、剂量少,且图像质量高。结论多排多层探测器输出的数据量大幅增加,使CT系统的成像质量显著提高。     

生态系统管理的多目标体系和方法

20世纪90年代以来,人们开始采用“生态系统管理”这种基于生态系统原理的综合方法管理自然资源和生态环境,促进人类与自然的和谐发展。然而,生态系统自身的复杂性、动态性及不确定性特点使得“生态系统管理”难以形成明确的定义和方法体系,系统的多重尺度和目标也增加了管理的难度。近20年兴起的系统工程主要针对大规模复杂系统进行研究,实现系统的总体优化。多目标决策和决策支持系统是系统工程的常用技术方法,生态系统管理是“基于目标”的管理模式。本文概述了生态系统管理的概念和要素,从构建管理体系的角度着重阐述了生态系统管理目标体系的结构、构建过程及目标间的相互关系,针对生态系统的复杂特性探讨了多目标决策方法和决策支持系统在生态系统管理中的应用,以期能够对形成具有普遍意义和实际操作性的生态系统管理方法体系起到借鉴作用。     

生态系统多稳态研究进展

在多稳态的生态系统中,外力可能导致生态系统状态突然之间发生不可逆转的转变,从而达到一个新的平衡状态。但目前对多稳态理论的系统研究很少,如何使用预警信号来预测生态系统的状态转变依旧是个难题。通过多稳态理论的梳理提出了一个更加综合的多稳态定义,并以放牧模型为例,系统总结了多稳态理论的相关概念,将多稳态理论应用在生态系统演替和扰沌理论的解释中;通过对生态系统稳态转换预警信号的原理、优缺点和应用条件的分析,对不同尺度下多稳态的研究方法进行了归纳;最后提出了目前多稳态领域的研究问题和未来的研究重点。结果表明:(1)将时间和空间预警信号结合在一起,并量化正确预警信号的概率,对错误预警信号的比例进行加权,可能会提供更准确的稳态转换的预报。(2)定量观测试验适用于小尺度的研究,而较大尺度的研究则采用简化的模型来模拟研究,选择正确的尺度极有可能改变预警信号的可靠性。(3)结合多稳态理论研究生态系统临界转换和反馈控制机制,并将基于性状的特征指标和进化动力学纳入其中,是生态系统修复实践的重要研究方向。(4)将多稳态相关理论和生态保护管理政策的实践相结合,是多稳态理论未来应用的前景。本研究为多稳态理论和实践的...     

基于NDVI的社会-生态系统多尺度干扰分析——以甘肃省榆中县为例

作为人类与自然界耦合的社会-生态系统是一种动态等级结构,需要以多尺度的视角进行分析。恢复力作为社会．生态系统的重要属性是指系统进入新状态前可以承受的干扰大小,由此可见系统所受到的干扰在恢复力的研究中至关重要,但由于社会一生态系统的复杂性,目前尚缺乏对于扰定量化研究的案例。选取社会．生态系统运行的驱动因子——干扰作为研究对象,将遥感技术中对植被覆被／变化敏感的NDVI指数作为干扰反馈变量,采用移动窗口运算法则,以一个行政单元（甘肃省榆中县）为研究对象,提出了社会-生态系统多尺度干扰的一种计算方法,从而为评价社会-生态系统恢复力、构建生态保护网络、增强生态系统对环境变化（土地覆被／变化、生境破碎化）的适应能力、探索可操作的社会．生态系统案例研究模式提供了技术支持。     

鸟苷发酵过程的多尺度问题研究

以鸟苷发酵为对象,在生物反应器中以多尺度问题的角度,对在线计算机数据进行相关分析,结合中间代谢物及有关酶活性的检测,建立了以代谢流为核心的多尺度调控方法;又通过与代谢流迁移有关的动态化学计量分析,实现了对批式发酵过程的时变系统估算,建立了主体代谢与产物形成的支路代谢间,包括能量与物质流的关系模型;最后,把具有不同生产能力菌株的关键基因与基因库数据进行了序列比较,发现了本文研究的菌株已具备高产能力,即编码sAMP合成酶的基因发生移码突变以及pur操纵子的多处突变,而中心代谢流迁移是过程优化的关键。对开展微生物功能基因组学的研究和工业过程优化的问题进行了探讨,作者提出有必要从生物反应器的系统生物学高度来认识和解决所面临的问题。     

桔园植保协调管理的多目标群决策研究

植保系统工程是植保工作科学化的必然趋势,有关系统工程与具体植保工作相结合的理论研究较为丰富,然而,在更高的层次上,植保系统是多决策者、多目标的复杂系统,这就要求植保工作者必须学会用系统工程理论解决植保系统的协调管理问题。迄今这方面的研究     

Learning and stabilization of altruistic behaviors in multi-agent systems by reciprocity

Optimization of performance in collective systems often requires altruism. The emergence and stabilization of altruistic behaviors are difficult to achieve because the agents incur a cost when behaving altruistically. In this paper, we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. This strategy in conjunction with learning capabilities make altruistic agents cooperate only between themselves, thus preventing their exploitation by selfish agents, if future benefits are greater than the current cost of altruistic acts. Our multi-agent system is made up of agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so achieving optimal (or near-to-optimal) performances in unknown and changing environments. Received: 1 August 1997 / Accepted in revised form: 28 November 1997     

Decentralized Opportunistic Spectrum Resources Access Model and Algorithm toward Cooperative Ad-Hoc Networks

Limited communication resources have gradually become a critical factor toward efficiency of decentralized large scale multi-agent coordination when both system scales up and tasks become more complex. In current researches, due to the agent’s limited communication and observational capability, an agent in a decentralized setting can only choose a part of channels to access, but cannot perceive or share global information. Each agent’s cooperative decision is based on the partial observation of the system state, and as such, uncertainty in the communication network is unavoidable. In this situation, it is a major challenge working out cooperative decision-making under uncertainty with only a partial observation of the environment. In this paper, we propose a decentralized approach that allows agents cooperatively search and independently choose channels. The key to our design is to build an up-to-date observation for each agent’s view so that a local decision model is achievable in a large scale team coordination. We simplify the Dec-POMDP model problem, and each agent can jointly work out its communication policy in order to improve its local decision utilities for the choice of communication resources. Finally, we discuss an implicate resource competition game, and show that, there exists an approximate resources access tradeoff balance between agents. Based on this discovery, the tradeoff between real-time decision-making and the efficiency of cooperation using these channels can be well improved.     

Segmentation 3D multi-objets d’images scanner cardiaques : une approche multi-agents

We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed.     

Two game-based solution concepts for a two-agent scheduling problem

In the current research papers on multi-agent (multi-person) scheduling, a person’s objective function is always considered as a cost function on scheduling, whereas a cooperative profit function is defined to serve as his objective one in this paper. In the two-person scheduling problem addressed in this paper, the two persons jointly order a common operational time interval of a single machine. Each person needs to process a set of his own jobs in that time window. The same objective function of each person still relies on the sequence of all the jobs of both persons since each part of the function is determined by some given parameters except one part assumed to be a given multiple of the total completion time of his own jobs. The two persons have to negotiate a job sequence and determine the (related) final solution on cooperative profit allocation. Such a two-person scheduling problem is essentially a cooperative game. An algorithm is designed to yield the cooperative-profit-based Pareto efficient solution set acting as the first game-based solution concept in this paper. The parallelized version of the algorithm is also developed. The second game-based solution concept is the Shapley value appropriate for the above cooperative-game situation on two-person scheduling. Several instances are presented and analyzed to reveal the necessity to employ the two solution concepts together.     

Social setting,intuition and experience in laboratory experiments interact to shape cooperative decision-making

Recent studies suggest that cooperative decision-making in one-shot interactions is a history-dependent dynamic process: promoting intuition versus deliberation typically has a positive effect on cooperation (dynamism) among people living in a cooperative setting and with no previous experience in economic games on cooperation (history dependence). Here, we report on a laboratory experiment exploring how these findings transfer to a non-cooperative setting. We find two major results: (i) promoting intuition versus deliberation has no effect on cooperative behaviour among inexperienced subjects living in a non-cooperative setting; (ii) experienced subjects cooperate more than inexperienced subjects, but only under time pressure. These results suggest that cooperation is a learning process, rather than an instinctive impulse or a self-controlled choice, and that experience operates primarily via the channel of intuition. Our findings shed further light on the cognitive basis of human cooperative decision-making and provide further support for the recently proposed social heuristics hypothesis.     

Effective norm emergence in cell systems under limited communication

Background

The cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems. Research findings in multi-agent systems literature can provide valuable inspirations to biological research. The well-coordinated states in cell systems can be viewed as desirable social norms in cooperative multi-agent systems. One important research question is how a norm can rapidly emerge with limited communication resources.

Results

In this work, we propose a learning approach which can trade off the agents’ performance of coordinating on a consistent norm and the communication cost involved. During the learning process, the agents can dynamically adjust their coordination set according to their own observations and pick out the most crucial agents to coordinate with. In this way, our method significantly reduces the coordination dependence among agents.

Conclusion

The experiment results show that our method can efficiently facilitate the social norm emergence among agents, and also scale well to large-scale populations.

     

Cooperation and the evolution of intelligence

The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the 'social intelligence hypothesis'), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa.     

Stimulation of dopamine D1 receptor improves learning capacity in cooperating cleaner fish

Accurate contextual decision-making strategies are important in social environments. Specific areas in the brain are tasked to process these complex interactions and generate correct follow-up responses. The dorsolateral and dorsomedial parts of the telencephalon in the teleost fish brain are neural substrates modulated by the neurotransmitter dopamine (DA), and are part of an important neural circuitry that drives animal behaviour from the most basic actions such as learning to search for food, to properly choosing partners and managing decisions based on context. The Indo-Pacific cleaner wrasse Labroides dimidiatus is a highly social teleost fish species with a complex network of interactions with its ‘client’ reef fish. We asked if changes in DA signalling would affect individual learning ability by presenting cleaner fish two ecologically different tasks that simulated a natural situation requiring accurate decision-making. We demonstrate that there is an involvement of the DA system and D₁ receptor pathways on cleaners'' natural abilities to learn both tasks. Our results add significantly to the growing literature on the physiological mechanisms that underlie and facilitate the expression of cooperative abilities.     

Ballast Blockade: Stopping Aquatic Immigrants

This article emphasizes not only an important environmental issue for the Great Lakes but also the importance of decision-making skills in scientific thinking. The activity allows students to acquaint themselves with current Great Lakes topics while simultaneously partaking in decision-making processes that could affect them. As students work through the outlined steps of making a decision, they also participate in cooperative learning, scientific reasoning, and interdisciplinary processing. Students are presented with six ballast water treatment methods that they must evaluate on the basis of provided criteria. They assess the treatments using the decision-making skills of rating, weighing, and discussing.