受损背根节神经元自发放电节律的整数倍模式及其动力学变化

本文记录了大鼠损伤背根节神经元的自发放电活动。采用背根节慢性压迫动物模型,记录慢性压迫手术后３－１０ｄ背根节的自发放电。在记录的１５６根纤维中,观察到１７根（占１１Ａ％）出现的动作电位峰峰间期以某一基础间期的整数倍模式出现的整数倍时间节律形式,其回归映射图为晶格状点阵结构,并且该时间形式受细胞膜上钠,钾通道的调控。     

沉默信息调节因子1在消化系统肿瘤中的作用

沉默信息调节因子1(SIRT1)是Sirtuin 家族中的一员,属于烟酰胺（NAD⁺）依赖的Ⅲ类组蛋白去乙酰化酶,能通过对多种非组蛋白及组蛋白赖氨酸残基进行去乙酰化修饰调节基因表达。近来的研究发现,SIRT1不仅能使肿瘤抑制因子去乙酰化,促进肿瘤发生,还能使肿瘤促进因子去乙酰化,抑制肿瘤发生。SIRT1与肿瘤的生物学特性密切相关,影响肿瘤分期及患者预后。在消化系统肿瘤中,SIRT1具有双面性,既可作为抑癌因子,也可发挥癌因子的作用。近年来,许多研究对SIRT1在肿瘤中的作用靶点及相关信号通路做了深入研究,关于SIRT1在肿瘤中作用机制的新研究不断出现。SIRT1已成为人们攻克肿瘤的一个研究热点。本文通过对SIRT1在肿瘤中的双重作用,尤其是在消化系统肿瘤中的不同作用靶点和参与的信号通路作一综述,希望为临床上治疗消化系统肿瘤提供更有说服力的证据。     

我国动物地理学研究的前景─—方法论探讨

本文着重从方法论的角度探讨在新形势下我国动物地理学的前景，提出建立两类资料地图──动物学资料图（ Ｚｏｏｌｏｇｉｃａｌ　ｉｎｆｏｒｍａｔｉｏｎ　ｍａｐ）和地理学资料图（Ｇｅｏｇｒａｐｈｉｃａｌ ｉｎｆｏｒｍａｔｉｏｎ ｍａｐ），以比较地理学分析法（Ｃｏｍｐａｒａｔｉｖｅ ｇｅｏｇｒａｐｈｉｃａｌ ａｎａｌｙｓｉｓ），进行不同时空尺度的研究，并建议了在最近时期内应优先选择开展研究的关键性地区及其中心问题。     

Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.     

The control of copy number of IS6110 in Mycobacterium tuberculosis

Insertion sequence (IS) elements are bacterial genes that are able to transpose to different locations in the genome. These elements are often used in molecular epidemiology as genetic markers that track the spread of pathogens. Transposable elements have frequently been described as "selfish DNA" because they facilitate their own transposition, causing damage when they insert into coding regions, while contributing little if anything to the bacterial host. According to this hypothesis, the expansion of copy number of insertion sequences is opposed by negative selection against high copy numbers. From an alternative point of view, we might expect IS elements to intrinsically regulate transposition within cells, thereby limiting damage to their bacterial host. Here, we report evidence that the copy number of IS6110 in Mycobacterium tuberculosis is controlled by selection against the element. We first construct 12 different models of marker change resulting from a combination of possible transposition functions and selective regimes. We then compute the Akaike Information Criterion for each model to identify the models that best explain data consisting of serial isolates of M. tuberculosis genotyped with IS6110. We find that the best performing models all include selection against the accumulation of copies. Specifically, our analysis points to the interaction of separate copies of the element causing lethal effects. We discuss the implications of these findings for genome evolution and molecular epidemiology.     

Measurements of protein sequence-structure correlations

Correlations between protein structures and amino acid sequences are widely used for protein structure prediction. For example, secondary structure predictors generally use correlations between a secondary structure sequence and corresponding primary structure sequence, whereas threading algorithms and similar tertiary structure predictors typically incorporate interresidue contact potentials. To investigate the relative importance of these sequence-structure interactions, we measured the mutual information among the primary structure, secondary structure and side-chain surface exposure, both for adjacent residues along the amino acid sequence and for tertiary structure contacts between residues distantly separated along the backbone. We found that local interactions along the amino acid chain are far more important than non-local contacts and that correlations between proximate amino acids are essentially uninformative. This suggests that knowledge-based contact potentials may be less important for structure predication than is generally believed.     

Neural Population Structures and Consequences for Neural Coding

Researchers studying neural coding have speculated that populations of neurons would more effectively represent the stimulus if the neurons "cooperated:" by interacting through lateral connections, the neurons would process and represent information better than if they functioned independently. We apply our new theory of information processing to determine the fidelity limits of simple population structures to encode stimulus features. We focus on noncooperative populations, which have no lateral connections. We show that they always exhibit positively correlated responses and that as population size increases, they perfectly represent the information conveyed by their inputs regardless of the individual neuron's coding scheme. Cooperative populations, which do have lateral connections, can, depending on the nature of the connections, perform better or worse than their noncooperative counterparts. We further show that common notions of synergy fail to capture the level of cooperation and to reflect the information processing properties of populations.     

Information gain for genetic parameter estimation with incorporation of marker data

Genetic marker data has been increasingly incorporated into segregation analysis, as combined segregation and linkage analysis has been performed more frequently. In this article, we study the extent of information gains with incorporation of marker data in segregation analysis, a topic that has not been investigated rigorously. Specifically, the current study is to investigate the influence of marker data on genetic model parameter estimation. A variance matrix criterion (as the inverse of the Fisher information matrix) and a relative entropy criterion (a measure of flatness of expected log-likelihood surface) are used to quantify the information gains. Our results indicate that substantial information gain can be achieved with the incorporation of marker data. The amount of variance reduction increases as the heterozygosity of the linked marker increases and as the trait gets closer to the linked marker(s). Incorporation of marker data in larger pedigrees also yields greater information gains based on both criteria. The effect of pedigree structure is also studied.     

Multiple unloadings by nectar foragers in honey bees: a matter of information improvement or crop fullness?

Summary When a honey bee forager returns to her hive and unloads nectar, she sometimes transfers her entire load to one bee, but other times she makes a series of unloadings to several bees. One intriguing hypothesis for why foragers make multiple unloadings is the Information Improvement hypothesis: multiple unloadings improve a foragers estimate of the difficulty in finding a receiver bee, and thus of the allocation of labor between nectar collecting and nectar processing. In this paper, we discuss a possible weakness in the empirical evidence in support of the Information Improvement hypothesis. We also present a competing hypothesis, the Crop Fullness hypothesis: multiple unloadings arise from a mismatch between the amount of nectar a forager has to unload and the amount of nectar a receiver can imbibe. Finally, we test the two hypotheses by checking their predictions regarding the conditions under which multiple unloadings occur and which bee (forager or receiver) breaks off the first unloading when a forager makes multiple unloadings. We find that multiple unloadings are common only at times of high nectar influx and that most often it is the receiver, not the forager, who breaks off the first unloading. We argue that both of these findings are contradictory to the Information Improvement hypothesis but are consistent with the Crop Fullness hypothesis. Furthermore, we relate our findings to a recent theoretical study (Gregson et al., 2003) which shows, by means of a simulation model, that the level of multiple unloadings observed can be accounted for by a mismatch between the crop loads of foragers and the crop capacities of receiver bees. We combine our measurements with the Gregson et al. model to identify the rule used by receiver bees in deciding when to stop receiving more nectar. We conclude that receivers make this decision during the course of an unloading, not after completing an unloading. Finally, with this conclusion in hand, we test the Gregson et al. model by comparing predictions and observations on how full receivers are when they decide to break off an unloading. We find a remarkable agreement (prediction: 60%, observation: 52–59%), in strong support of the model.