COVIS神经生物学基础

本文论述了COVIS的神经生物学基础.COVIS假设类别学习过程中存在着两个系统的竞争,一个是以额叶为基础,依赖工作记忆和执行注意,采用逻辑推理的外显学习系统;另一个是由基底神经节调控,采用程序学习的内隐学习系统.COVIS外显系统的神经生物模型涉及的关键脑结构是前扣带回(ACC)、前额皮层(PFC)和尾状核头部(CD).模型中有两个独立的分支网络--一个负责在检验过程中将备选规则保持在工作记忆当中,调节注意从一个规则向另一个规则的转换;另一个负责生成或选择新的假设.COVIS内隐程序学习系统的关健结构是尾状核,来自黑质(SN),用于调节奖酬信号的多巴胺促进尾状核中的程序学习.     

医疗器械市场现状、潜力与对策探讨

通过对国内外医疗器械市场现状的描述,分析国内医疗器械市场巨大潜力,从而引发国外医疗器械公司在成功抢占高档医疗器械市场后,又将目光锁定中国医疗器械企业赖依生存的中低档医疗器械和潜在市场。本文只是从经营决策的三个不同角度,即合作对付竞争、再生产品活动,目标市场战略来探讨国内医疗器械企业应对竞争的方法。     

禾-豆混播草地中土壤水分与种间关系研究进展

从植物形态变化和生理反应两方面探讨了禾草和豆科牧草在干旱环境下所采取的不同生存策略,阐述了禾-豆之间存在的水分竞争关系以及豆科植物的水力提升作用对水分资源高效利用的意义,分析了干旱对豆科植物固氮和转氮能力的影响.提出应探讨适于我国干旱区域的深根系豆科牧草与禾草的共存机制,认为揭示水分因子对地上竞争和地下竞争以及对两种竞争间反馈关系的影响是研究其共存机制的一个重要方面.     

青藏高原MODIS NDVI与GIMMS NDVI的对比

连续一致的NDVI时间序列数据是陆地表面特征长期监测的基础和前提.AVHRR NDVI和MODIS NDVI作为时间记录最长和时空分辨率较高数据的典型代表,是未来植被动态监测极为重要的数据源.深入理解两种数据之间的关系,是延续陆地植被长期监测的关键.利用2000—2006年重叠时段的GIMMS NDVI和MODIS NDVI数据,在青藏高原整体、亚区域、植被类型和像元等多尺度对比分析了两种数据的数值差异和动态变化的一致性,并使用495幅20 km×20 km的Landsat影像计算的NDVI,独立地评估了两种数据集的性能.结果表明:GIMMS NDVI和MODIS NDVI捕获青藏高原月尺度物候变化的能力基本相同(显著性水平大多达到0.001);不同植被类型之间两种数据的相似性差异显著,高覆盖的林地一致性较差,均质化较强的草地、农田的一致性较强;像元尺度,两种数据集在82%的研究区域显著一致;在反映植被空间分布方面,MODIS NDVI的数值更接近Landsat NDVI,而GIMMS NDVI在植被动态变化上与Landsat NDVI更相像,不同植被类型之间差异显著,林地MODIS NDVI与Landsat一致性更好,而草地、农田则是GIMMS NDVI更好.融合两种数据,建立一致的NDVI时间序列数据是可行的.在耦合数据时,需要考虑不同植被类型、不同物候期、不同空间尺度对结果的影响.对于针叶林、阔叶林等植被类型,以及物候过渡期的春秋季进行两种数据集成时需要慎重处理.     

脑电图数据的相关性分析

目的:本文对客观记录的脑电图数据进行相关性分析,为正确理解人脑的不同局部以及局部与整体之间的相关性提供基础实验依据和理论根据,为脑电图研究的其它分析做基础分析.方法:分别对10导联电极和21导联电极的两组脑电图数据做互相关和自相关的相关性分析,得出在不同条件、事件下脑的各导联电极的自相关分析结果和左右对称导联电极的互相关分析结果,最后得出相关性值随实验条件和事件的具体变化.数据处理和统计分析采用独立设计的脑电图分析工具箱和相关性分析程序.结果:脑电图数据经过相关性分析后得到的相关图具有和脑电波相似的波形,具有周期性,时间滞后对应的标准化的相关图显示出相关性值的第一个主峰是最大的,主峰频率一般在8到13Hz的脑电波频段内;最后得到的相关性值随事件的变化而改变的结果说明实验事件对相关性的影响.结论:自相关和互相关都是随着时间滞后的增大而逐渐减小;波具有较强的相关性;相关图在从零延迟开始时间滞后所出现的第一个波峰是主峰,反应出相对较强的相关性;从大量数据的分析最后得到的相关性与事件的关系表明相关性是受事件影响的.     

生物协同学,Lorenz模型和种群动力学

由协同学方程出发,可以描述种群的大迁徙,由此又能够得到Lorenz模型,它可以描述两种种群的变化关系．当取绝热近似时,还可以导致种群动力学的不同模型．因此,生物协同学能够深刻揭示不同物种之间,既竞争又协同的复杂的非线性关系．     

双集合卡尔曼滤波LAI同化结合BEPS模型的竹林生态系统碳通量模拟

叶面积指数(LAI)是森林生态系统碳循环研究的重要观测数据,也是驱动森林生态系统模型模拟碳循环的重要参数.本文以毛竹林和雷竹林为研究对象,首先利用双集合卡尔曼滤波,同化两种竹林生态系统观测站点2014—2015年MODIS LAI时间序列数据,然后将同化的高质量毛竹LAI和雷竹LAI作为输入数据驱动BEPS模型,模拟两种竹林生态系统总初级生产力(GPP)、净生态系统碳交换量(NEE)和总生态系统呼吸(TER)等碳循环数据,并用通量站实际观测值评价模拟结果;另外,还对比不同质量LAI对碳循环模拟的影响.结果表明: 双集合卡尔曼滤波同化得到的毛竹林和雷竹林LAI与实测LAI之间的相关关系极为显著,R²分别为0.81和0.91,且均方根误差和绝对偏差均较小,极大地提高了MODIS LAI的产品精度;在同化得到的LAI驱动下,BEPS模型模拟的毛竹林GPP、NEE和TER与实际观测值之间的R²分别为0.66、0.47和0.64,雷竹林分别为0.66、0.45和0.73,模拟结果均好于三次样条帽盖算法平滑LAI模拟得到的GPP、NEE和TER,其中,毛竹林、雷竹林NEE的模拟精度提高幅度最大,分别为11.2%和11.8%.     

长白山区森林景观格局动态

本文以长白山国家级自然保护区及其外围的4个林业局为研究区,基于2004年和2015年的Landsat遥感影像数据,分析了保护区与周边林业局的森林景观格局在空间分布、形状、破碎化程度和多样性等方面的变化。结果表明:长白山保护区与周边林业局之间的森林景观格局变化既有一致性,也存在着差异。与2004年相比,2015年保护区与周边林业局的林地景观形状均变得简单规则,针叶林景观面积减少,主要转变成了针阔混交林。保护区内林地总面积基本不变,而保护区周边林业局内的林地总面积共减少了7326 hm2,转出的林地主要变成了耕地,其次是裸地及采伐地和建设用地。保护区及周边林业局普遍表现为景观破碎化加重,景观异质性和景观多样性增加,受人为干扰明显。另外,不同林业局之间的景观格局变化不一致。例如,白河林业局内林地景观形状趋于简单规则,破碎化得到改善,而泉阳和松江河林业局却表现出相反的趋势。     

基于关联基因本体论注释的蛋白质相互作用预测

细胞中的生理活动主要是通过蛋白质 - 蛋白质之间的相互作用来调控完成 . 详尽细致的蛋白质 - 蛋白质相互作用网络的解析对于理解细胞中复杂的调控、代谢和信号通路有重要的意义 . 近年来,关于新的蛋白质 - 蛋白质相互作用预测领域进展快速,这里,利用贝叶斯算法结合关联的 GO (Gene Ontology) ,来预测蛋白质的相互作用 . 利用非冗余的蛋白质相互作用数据来观察 GO 对的特性,得到 GO 关联的概率 . 通过阳性的和阴性的标准对照数据证实这个新方法可以很好地区别这两类不同的数据,显示出较好的灵敏度和非常低的假阳性预测率 . 通过与已知的高通量的实验数据比较,这个方法具有灵敏度高、速度快的优点 . 而且,运用这个新方法可以提供一些新的关于细胞内蛋白质之间相互作用的信息,为进一步的实验提供理论依据 .     

南京城市森林干扰及恢复自动制图

使用1987—2011年Landsat TM/ETM⁺稠密时间序列数据,以南京市老山林场和紫金山森林为研究对象,通过Ledaps预处理系统生成地表反射率数据集,采用植被变化追踪模型(VCT)得到南京城市森林的干扰及恢复历史数据库产品,并对产品进行验证.结果表明： 空间一致性为65.4%～95.0%,VCT产品监测森林干扰具有较高的空间一致性.2个研究区的森林干扰和恢复随着时间变化波动明显,干扰变化规律相似,但森林恢复规律明显不同.紫金山的森林覆盖率小于老山林场,但总体上,老山林场的森林干扰率和恢复率大于紫金山.     

基于灰色综合关联分析法的粤港澳大湾区生物医药产业效益研究*

生物医药产业是中国医药制造业的第三大产业,也是粤港澳大湾区重点扶持的新兴高技术产业之一。透视产业主营业务收入与各类指标的关联情况是进一步指导粤港澳大湾区生物医药产业建设规划的必要之举。而灰色综合关联分析法是探索指标间关联程度的重要工具。通过多种权威途径获得粤港澳大湾区生物医药产业规模以上企业数量、企业孵化器数量、新增专利数、自然科学基金立项投入总额、非自然科学基金立项数量、产学研合作项数量六大指标数据。采用灰色关联分析法,将所得数据进行建模并求解,得出这六大指标与粤港澳大湾区生物医药产业主营业务收入的关联度。结果显示,规模以上企业数量与粤港澳大湾区生物医药产业主营业务收入的关联度最高,其次为自然科学基金立项投入总额,再次为产学研合作项数量,非自然科学基金立项数量位列第四,企业孵化器数量、新增专利数分别位列第五、第六。因此,谋求粤港澳大湾区生物医药产业的进一步发展应着重从提高规模以上企业数量、重视自然科学基金投入、加强政产学研合作3个层面入手。     

Engineering the Virtual Enterprise: An Architecture-Driven Modeling Approach

The managerial and organization practices required by an increasingly dynamic competitive manufacturing, business, and industrial environment include the formation of “virtual enterprises.” A major concern in the management of virtual enterprises is the integration and coordination of business processes contributed by partner enterprises. The traditional methods of process modeling currently used for the design of business processes do not fully support the needs of the virtual enterprise. The design of these virtual enterprises imposes requirements that make it more complex than conventional intraorganizational business process design. This paper first describes an architecture that assists in the design of the virtual enterprise. Then it discusses business process reengineering (BPR) as a methodology for modeling and designing virtual organizations. While BPR presents many useful tools, the approach itself and the modeling tools commonly used for redesign have fundamental shortcomings when dealing with the virtual enterprise. However, several innovative modeling approaches provide promise for this problem. The paper discusses some of these innovative modeling approaches, such as object-oriented modeling of business processes, agent modeling of organizational players, and the use of ontological modeling to capture and manipulate knowledge about the players and processes. The paper concludes with a conceptual modeling methodology that combines these approaches under the enterprise architecture for the design of virtual enterprises.     

生物技术的商业化原则及其未来

生物技术就是应用自然科学及工程学的原理 ,依靠微生物、动物、植物作为反应器 ,将物料进行加工以提高产品来为社会服务的技术。文章在重点分析了世界生物技术研究开发、产业发展状况及其管理策略基础上 ,探讨了生物技术及其产业的商业化系统模型以及系统各要素之间的关系 ,阐明了建立生物技术商业化系统的 4项原则 ,即相对优势的竞争原则 ,行业的乘数效应原则 ,投资的获得与运营原则和协调管理的价值实现原则 ,并提出了生物技术未来发展的设想。     

Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants

A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.     

LCA experiences in Danish industry

A study has been performed on Danish industry’s experiences with LCA. Twenty-six enterprises from different sectors conpleted a questionnaire. The enterprises are still in an adoption and learning phase, and experiences with full-blown LCA’s are sparse. Expectations of future market pressure to supply more environmentally friendly products is the most important incentive for the enterprises to engage in LCA activities. This pressure, however, has not yet emerged and the enterprises have not achieved the expected competitive advantages. LCA work has revealed new environmental aspects of the products with subsequent new priorities in the environmental efforts. Only a few enterprises have built up in-house LCA competence, whereas consultants are heavily involved in LCA work. In large enterprises, LCA work is predominantly carried out by environmental staff members, but the product development staff is also involved. The nature of the co-operation and distribution of roles between these two actors is not clear, and should therefore be studied further.     

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications.     

An investigation of competitive learning for autonomous cluster identification in embedded systems.

Robust signal processing for embedded systems requires the effective identification and representation of features within raw sensory data. This task is inherently difficult due to unavoidable long-term changes in the sensory systems and/or the sensed environment. In this paper we explore four variations of competitive learning and examine their suitability as an unsupervised technique for the automated identification of data clusters within a given input space. The relative performance of the four techniques is evaluated through their ability to effectively represent the structure underlying artificial and real-world data distributions. As a result of this study it was found that frequency sensitive competitive learning provides both reliable and efficient solutions to complex data distributions. As well, frequency sensitive and soft competitive learning are shown to exhibit properties which may permit the evolution of an appropriate network structure through the use of growing or pruning procedures.     

Banknote recognition: investigating processing and cognition framework using competitive neural network

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria’s Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.     

A self-stabilizing learning rule for minor component analysis

The paper reviews single-neuron learning rules for minor component analysis and suggests a novel minor component learning rule. In this rule, the weight vector length is self-stabilizing, i.e., moving towards unit length in each learning step. In simulations with low- and medium-dimensional data, the performance of the novel learning rule is compared with previously suggested rules.     

Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data

In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently, many researchers are analyzing the gene expression data using various machine learning techniques. However, due to a very small number of training samples compared to the huge number of genes and class imbalance, most of these methods suffer from overfitting. In this paper, we present a majority voting genetic programming classifier (MVGPC) for the classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming (GP) and then apply those rules to test samples to determine their labels with majority voting technique. By performing experiments on four different public cancer data sets, including multiclass data sets, we have found that the test accuracies of MVGPC are better than those of other methods, including AdaBoost with GP. Moreover, some of the more frequently occurring genes in the classification rules are known to be associated with the types of cancers being studied in this paper.