Culture and cancer--论文代写范文精选

收集的细胞和其他机制限制肿瘤发生可能积极的认知,从而成为结构性压力,被动地反映了以扭曲的方式选择压力。对人类来说,远远超过癌症的病因。下面的essay代写范文进行详述。

Abstract 
Genetic mechanisms, since they broadly involve information transmission, should be translatable into information dynamics formalism. From this perspective we reconsider the adaptive mutator, one possible means of ‘second order selection’ by which a highly structured ‘language’ of environment and development writes itself onto the variation upon which evolutionary selection and tumorigenesis operate. Our approach uses recent results in the spirit of the Large Deviations Program of applied probability that permit transfer of phase transition approaches from statistical mechanics to information theory, generating evolutionary and developmental punctuation in what we claim to be a highly natural manner. Allowing ‘universality class tuning’ of the phase transition-analog generates a mutator by altering the rate at which an internal genetic picture of adaptive pressures comes to match them. 

The analysis has particular implications for understanding cancer etiology, as the collection of cellular and other mechanisms which limits tumorigenesis may be actively cognitive, and thus become linked with an embedding system of structured stress, in contrast to evolutionary process which passively reflects selection pressures in a distorted way. The punctuated interpenetration between this ‘socio-cellular’ cognition and tumorigenec clonal adaptation then accounts for the staged nature of the disease. ‘Social exposures’ are, for human populations, far more than simply incidental cofactors in the etiology of cancer, jointly affecting both rates of mutation and the failure of cognitive processes of mutation control. Evolutionary anthropologist Robert Boyd’s metaphorical aphorism that ‘culture is as much a part of human biology as the enamel on our teeth’ appears to be literally true at a very basic level. 
KEY WORDS: Cancer, cellular cognition, culture, evolution, information theory, interpenetration, mutator

Introduction 
Adami et al. (2000) envision genomic complexity as the amount of information a gene sequence stores about its environment. Something similar can be said of a reverse process: environmental complexity is the amount of information organisms introduce into the environment as a result of their collective actions and interactions (Lewontin, 2000). Extending that perspective (Wallace, 2002b), we have invoked an information theory formalism, imposing invariance under renormalization on the mutual information characterizing the Rate Distortion Theorem applied to Adami’s mapping. The result is a description of how a structured environment, through adaptation, literally writes a (necessarily) distorted image of itself onto the genetic structure of an organism in a punctuated manner.

Arguing by abduction from physical theory, to use Hodgson’s (1993) terminology, we adopted a version of Wilson’s (1971) classic renormalization strategy (Wallace and Wallace, 1998, 1999; Wallace, 2000, 2002a, b) to treat the dynamics of such ‘languages-on-networks’, finding their punctuated splittings and coagulations to represent, respectively, speciation and coevolution. Application of the Rate Distortion and Joint Asymptotic Equipartition Theorems produced a theory whose qualitative behavior was free of the details of the chosen renormalization relations (Wallace, 2002a, b). Here we use those details to extend that theory.

Review of formalism
Before beginning the formal treatment, we highlight several important points: First, information theory is notorious for providing ‘existence theorems’ whose application is arduous indeed. For example, while the Shannon Coding Theorem implied the possibility of very efficient coding schemes as early as 1949, it took more than forty years for practical ‘turbo codes’ to be created. Our adaptation of the Shannon Source Coding Theorem is unlikely to be less difficult. Second, we are invoking information theory variants of the fundamental asymptotic limit theorems of probability. These are independent of exact mechanism, but constrain the collective behavior of such mechanisms. For example, although not all processes involve long sums of individual stochastic variables, those that do, regardless of the individual variable distributions, follow a Normal distribution as a consequence of the Central Limit Theorem. 

Similarly, the games of chance in a Las Vegas casino are all quite different, but nonetheless the possible success of ‘strategies’ for playing them is strongly and systematically constrained by the Martingale Theorem, regardless of game details. We similarly propose that languages-on-networks and languages-that-interact, as a consequence of the limit theorems of information theory, will be subject to regularities of punctuation and ‘generalized Onsager relations’, regardless of detailed mechanism, as important as the latter may be. Finally, just as we often impose parametric statistics on sometimes questionable experimental situations, relying on the robustness of the Central Limit Theorem to carry us through, we will invoke a similar heuristic approach in our applications of the information theory limit theorems. The essential homology relating information theory to statistical mechanics and nonlinear dynamics has been described elsewhere (Wallace and Wallace, 1998, 1999; Rojdestevnski and Cottam, 2000; Wallace, 2000, 2002a, b), and we truncate the discussion here.

The ‘adiabatic’ nature of the information source means that probabilities defining H closely track parameter changes, remaining as ‘memoryless’ as is necessary for the mathematics to work, along a ‘piece’ of underlying structure. Between such pieces, we impose ‘phase transition’ regularities described by renormalization dynamics. See Wallace (2002a, b) for further discussion. While information systems do not have ‘Hamiltonians’ allowing definition of a ‘partition function’ and a free energy density, they may have a source uncertainty obeying a limiting relation like that of free energy density. 

Importing ‘renormalization’ symmetry gives phase transitions at critical points (or surfaces), and importing a Legendre transform in a ‘natural’ manner gives dynamic behavior far from criticality. As neural networks demonstrate so well, it is possible to build larger pattern recognition systems from assemblages of smaller ones. We abstract this process in terms of a generalized linked array of subcomponents which ‘talk’ to each other in two different ways. These we take to be ‘strong’ and ‘weak’ ties between subassemblies. ‘Strong’ ties are, following arguments from sociology (Granovetter, 1973), those which permit disjoint partition of the system into equivalence classes. Thus the strong ties are associated with some reflexive, symmetric, and transitive relation between components. ‘Weak’ ties do not permit such disjoint partition. In a physical system these might be viewed, respectively, as ‘local’ and ‘mean field’ coupling We are, thus, concerned with languages ‘spoken’ on an underlying network, be it chemical, neural, social, ecological, or some mix of these. The network will be manifest in the properties of any language ‘spoken’ on it, and vice versa, if language process can affect network properties. It is this composite, interactive phenomenon we wish to model.

Conclusions 
We have applied an elaborate mathematical modeling strategy, in the spirit of the Large Deviations Program of applied probability, to the adaptive mutator, and to possible linkage with a cognitive, but culturally-linked, process of socio-cellular mutation control in humans. As ecologist E.C. Pielou has argued (Pielou, 1977, p. 106), a severe limit to any such approach is that mathematical models do not create new knowledge, they create new speculation. Thus their often considerable utility lies almost entirely in raising questions for subsequent empirical study, which, in a scientific context, is the only true source of new knowledge. The speculations we have raised are of some interest. We have expressed tumorigenesis in terms of a synergistic linkage of a ‘language’ of structured external stress with the mutator, and with the opposing cognitive process of mutation control. Elsewhere we describe at some length the mediating role that an enveloping local cognitive socio-cultural network plays in linking an individual to an embedding system of structured stress (Wallace and Wallace, 2002; Wallace, 2002a), a matter we have not emphasized here to simplify the development.（essay代写)

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