Human development and log-periodic law

We suggest applying the log-periodic law formerly used to describe various crisis phenomena, in biology (evolutionary leaps), inorganic systems (earthquakes), societies and economy (economic crisis, market crashes) to the various steps of human ontogeny. We find a statistically significant agreement between this model and the data.     

Metapopulation dynamics with quasi-local competition

Stepping-stone models for the ecological dynamics of metapopulations are often used to address general questions about the effects of spatial structure on the nature and complexity of population fluctuations. Such models describe an ensemble of local and spatially isolated habitat patches that are connected through dispersal. Reproduction and hence the dynamics in a given local population depend on the density of that local population, and a fraction of every local population disperses to neighboring patches. In such models, interesting dynamic phenomena, e.g. the persistence of locally unstable predator-prey interactions, are only observed if the local dynamics in an isolated patch exhibit non-equilibrium behavior. Therefore, the scope of these models is limited. Here we extend these models by making the biologically plausible assumption that reproductive success in a given local habitat not only depends on the density of the local population living in that habitat, but also on the densities of neighboring local populations. This would occur if competition for resources occurs between neighboring populations, e.g. due to foraging in neighboring habitats. With this assumption of quasi-local competition the dynamics of the model change completely. The main difference is that even if the dynamics of the local populations have a stable equilibrium in isolation, the spatially uniform equilibrium in which all local populations are at their carrying capacity becomes unstable if the strength of quasi-local competition reaches a critical level, which can be calculated analytically. In this case the metapopulation reaches a new stable state, which is, however, not spatially uniform anymore and instead results in an irregular spatial pattern of local population abundance. For large metapopulations, a huge number of different, spatially non-uniform equilibrium states coexist as attractors of the metapopulation dynamics, so that the final state of the system depends critically on the initial conditions. The existence of a large number of attractors has important consequences when environmental noise is introduced into the model. Then the metapopulation performs a random walk in the space of all attractors. This leads to large and complicated population fluctuations whose power spectrum obeys a red-shifted power law. Our theory reiterates the potential importance of spatial structure for ecological processes and proposes new mechanisms for the emergence of non-uniform spatial patterns of abundance and for the persistence of complicated temporal population fluctuations.     

BINOMIAL COUNT ANALYSIS BASED ON THE SPATIAL DISTRIBUTION AND POPULATION DENSITY ESTIMATION OF COTTON APHIDS1)

Abstract  Based on field population sampling of Aphis gossypii on cotton seedlings in Quzhou Experiment Station of China Agricultural University in Hebei Province in 1991, we obtained a data set consisting of 24 estimates of mean aphid density ( m , number of aphids per plant), variance (s²) and the proportion of plants (P_T) with no more than T aphids (T=0, 1, 2,…, 8, 10, 15, 20, respectively and defined as tally threshold). Taylor's power law fitted the data well (r²= 0. 958). The resulting slope (1. 515) was significantly greater than 1, indicating that the spatial distribution of this aphid was in aggregated pattern. An empirical relationship between m and Pr was developed for each T value using the parameters from the linear regression In( m )= a +bln[- ln( P_T )}]. The importance of the T values in reduction of sampling errors and their application to binomial sampling plans are discussed. Small T values, particularly aphid-free plant (T = 0, conventional binomial sample), could lead to spurious estimates of m from P_T . A value of T from 10 to 15 was recommended to develop binomial sampling plans for the aphids on cotton seedlings because of the relatively small sampling errors.     

Causal Regularities in the Biological World of Contingent Distributions

Former discussions of biological generalizations have focused on the question of whether there are universal laws of biology. These discussions typically analyzed generalizations out of their investigative and explanatory contexts and concluded that whatever biological generalizations are, they are not universal laws. The aim of this paper is to explain what biological generalizations are by shifting attention towards the contexts in which they are drawn. I argue that within the context of any particular biological explanation or investigation, biologists employ two types of generations. One type identifies causal regularities exhibited by particular kinds of biological entities. The other type identifies how these entities are distributed in the biological world.     

人类基因组多样性保护与伦理学和法律问题初探

人类基因组计划（ｈｕｍａｎ ｇｅｎｏｍｅ ｐｒｏｊｅｃｔ,ＨＧＰ）不研究中的一项重要内容－－基因组研究中的社会学、伦理学和法律问题已引起了我们的普遍关注。本文针对基因组多样性保护中所涉及的人类本身的生存与发展、人与自然及与其他物种的相互关系中的伦理道德的法律问题进行初步探讨。     

Image correction method for the colour contrast effect using inverse processes of the brain

In the colour contrast effect, the impression of a colour changes according to the situation; cases occur in which the colour appearance is misunderstood. We propose an image signal processing method for preventing such misperception of colour. Many conventional image improving methods emphasize the contrast of images as same as the brain does. However, by their processes, the colour contrast effect is not canceled; we misunderstand the colour. The objective of this study is to perceive original colour. Therefore, we propose an image correction method using inverse processes of the brain in order to cancel the processes of the brain, the colour contrast effect. We verified whether the proposed method corrected the colour contrast effect by conducting a psychological experiment. The results show that the method succeeds in canceling the colour contrast effect.     

Some model based considerations on observing generation times for communicable diseases

The generation time of an infectious disease is usually defined as the time from the moment one person becomes infected until that person infects another person. The concept is similar to “generation gap” in demography, with new infections replacing births in a population. Originally applied to diseases such as measles where at least the first generations are clearly discernible, the concept has recently been extended to other diseases, such as influenza, where time order of infections is usually much less apparent.By formulating the relevant statistical questions within a simple yet basic mathematical model for infection spread, it is possible to derive theoretical properties of observations in various situations e.g. in “isolation”, in households, or during large outbreaks. In each case, it is shown that the sampling distribution of observations depends on a number of factors, usually not considered in the literature and that must be taken into account in order to achieve unbiased inference about the generation time distribution. Some implications of these findings for statistical inference methods in epidemic spread models are discussed.     

Sensitivity summation theorems for stochastic biochemical reaction systems

We investigate how stochastic reaction processes are affected by external perturbations. We describe an extension of the deterministic metabolic control analysis (MCA) to the stochastic regime. We introduce stochastic sensitivities for mean and covariance values of reactant concentrations and reaction fluxes and show that there exist MCA-like summation theorems among these sensitivities. The summation theorems for flux variances is shown to depend on the size of the measurement time window (?) within which reaction events are counted for measuring a single flux. It is found that the degree of the ?-dependency can become significant for processes involving multi-time-scale dynamics and is estimated by introducing a new measure of time-scale separation. This ?-dependency is shown to be closely related to the power-law scaling observed in flux fluctuations in various complex networks.