Sudden and chronic infection--论文代写范文精选

最近的一篇论文广泛探索使用亚特兰大和科恩的(1998)信息理论诠释治疗免疫认知，两个重要的人口慢性感染的案例记录。简化的生物医学模式已分配不同的免疫反应和疾病之间的表达方式。下面的essay代写范文进行详述。

Abstract 
We continue to study the implications of IR Cohen’s theory of immune cognition, in the presence of both sudden and chronic pathogenic challenge, through a mathematical model derived from the Large Deviations Program of applied probability. The analysis makes explicit the linkage between an individual’s ‘immunocultural condensation’ and embedding social or historical structures and processes, in particular power relations between groups. We use methods adapted from the theory of ecosystem resilience to explore the consequences of the sudden ‘perturbation’ caused by infection in the context of such embedding, and examine a ‘stage’ model for chronic infection involving multiple phase transitions analogous to ‘learning plateaus’ in neural networks or ‘punctuated equilibria’ in adaptive systems.

Introduction
A recent paper by R Wallace and RG Wallace (2001) broadly explored the use of Atlan and Cohen’s (1998) information theory treatment of immune cognition for reinterpreting two important population case histories of chronic infection in the context of ‘cultural variation,’ namely malaria in Burkina Faso and ‘heterosexual AIDS’ in the US. Reductionist biomedical paradigm had assigned differences of immune response and disease expression between populations in a manner so as to reify ‘race’. That is, different responses to pathogenic challenge were attributed entirely to genetic differences between ethnic groups, ignoring the considerable impact of ‘path dependent’ patterns of past and current power relations between coresident communities. 

Reductionist perspective, at least on this matter, thus appears to incorporate a systematic logical fallacy which could significantly, and possibly profoundly, distort attempts to develop effective vaccine strategies against a number of disorders. Here we present details of the mathematical model described in R Wallace and RG Wallace (2001) which allows the interactive condensation of cognitive systems with the ‘selection pressures’ of embedding social constraints. The work relies on foundations supplied by the Large Deviations Program of applied probability, which provides a unified vocabulary for addressing language structures, in a large sense, using mathematical techniques adapted from statistical mechanics and the theory of fluctuations. The essence of the approach is to express the cognitive pattern recognitionand-response described as characterizing immune cognition by Atlan and Cohen (1998) in terms of a ‘language,’ in a broad sense, and then to show how that language can interact and coalesce with similar cognitive languages at lager scales – central nervous system (CNS) and the embedding local sociocultural network. 

The next step is to demonstrate that these may, in turn, interact – and indeed coalesce – with a non-cognitive structured language of externally imposed constraint. The process of developing the formalism will make clear that change in such languages takes place ‘on the surface,’ in a sense, of what has gone before, so that path dependence – the persisting burden of history – becomes a natural outcome. Next we describe the behavior of condensed cognitive systems under the perturbation of a sudden pathogenic challenge, using a generalization of Ives’ (1995) and Holling’s (1973, 1992) ecosystem resilience analysis. Individuals within more resilient social structures – typically those of the powerful rather than of the subordinate – will usually suffer less ‘amplification’ of symptom patterns caused by the infection. 2 Finally we examine chronic pathogenic challenge from this perspective, and propose a ‘stage’ model formally analogous to learning plateaus in cognitive, and ‘punctuated equilibrium’ in adaptive, systems. We begin with a summary of relevant theory.

Information theory preliminaries
Suppose we have an ordered set of random variables, Xk, at ‘times’ k = 1, 2, ... – which we call X – that emits sequences taken from some fixed alphabet of possible outcomes. Thus an output sequence of length n, xn, termed a path, will have the form Thus substrings of xn are not, in general, stochastically independent. That is, there may be powerful serial correlations along the xn. We call X 3 an information source, and are particularly interested in sources for which the long run frequencies of strings converge stochastically to their timeindependent probabilities, generalizing the law of large numbers. 

These we call ergodic (Ash, 1990; Cover and Thomas, 1991; Khinchine, 1957, hereafter known as ACTK). If the probabilities of strings do not change in time, the source is called memoryless. We shall be interested in sources which can be parametized and that are, with respect to that parameter, piecewise adiabatically memoryless, i.e. probabilities closely track parameter changes within a ‘piece,’ but may change suddenly between pieces. 

This allows us to apply the simplest results from information theory, and to use renormalization methods to examine transitions between ‘pieces.’ Learning plateaus represent regions where, with respect to the parameter, the system is, to first approximation, adiabatically memoryless in this sense, analogous to adiabatic physical systems in which rapidly changing phenomena closely track slowly varying driving parameters. In what follows we use the term ‘ergodic,’ to mean ‘piecewise adiabatically memoryless ergodic.’ For any ergodic information source it is possible to divide all possible sequences of output, in the limit of large n, into two sets, S1 and S2, having, respectively, very high and very low probabilities of occurrence. Sequences in S1 we call meaningful.

Languages can affect each other, or, equivalently, systems can translate from one language to another, usually with error. The Rate Distortion Theorem, which is one generalization of the SMT, describes how this can take place. As IR Cohen (2001) has put it, in the context of the cognitive immune system (IR Cohen, 1992, 2000), “An immune response is like a key to a particular lock; each immune response amounts to a functional image of the stimulus that elicited the response. 

Just as a key encodes a functional image of its lock, an effective [immune] response encodes a functional image of its stimulus; the stimulus and the response fit each other. The immune system, for example, has to deploy different types of inflammation to heal a broken bone, repair an infarction, effect neuroprotection, cure hepatitis, or contain tuberculosis. Each aspect of the response is a functional representation of the challenge. Self-organization allows a system to adapt, to update itself in the image of the world it must respond to... The immune system, like the brain... aim[s] at representing a part of the world.”

These considerations suggest that the degree of possible back-translation between the world and its image within a cognitive system represents the profound and systematic coupling between a biological system and its environment, a coupling which may particularly express the way in which the system has ‘learned’ the environment. We attempt a formal treatment, from which it will appear that both cognition and response to systematic patterns of selection pressure are – almost inevitably – highly punctuated by ‘learning plateaus’ in which the two processes can become inextricably intertwined.

Suppose that chain elicits a corresponding chain of responses from the system of interest, producing another path b n = (b1, ..., bn), which has some ‘natural’ translation into the language of the perturbations, although not, generally, in a one-to-one manner. The image is of a continuous analog audio signal which has been ‘digitized’ into a discrete set of voltage values. Thus, there may well be several different y n corresponding to a given ‘digitized’ b n . Consequently, in translating back from the b-language into the y-language, there will generally be information loss. Suppose, however, that with each path b n we specify an inverse code which identifies exactly one path ˆy n . We assume further there is a measure of distortion which compares the real path y n with the inferred inverse ˆy n . Below we follow the nomenclature of Cover and Thomas (1991).（essay代写)

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