Pregnancy is a unique event in which a fetus, despite being genetically and immunologically different from the mother (a hemi-allograft), develops in the uterus. Successful pregnancy implies avoidance of rejection by the maternal immune system. Fetal and maternal immune cells come into direct contact at the decidua, which is a highly specialized mucous membrane that plays a key role in fetal tolerance. Uterine dendritic cells (DC) within the decidua have been implicated in pregnancy maintenance. DC serve as antigen-presenting cells with the unique ability to induce primary immune responses. Just as lymphocytes comprise different subsets, DC subsets have been identified that differentially control lymphocyte function. DC may also act to induce immunologic tolerance and regulation of T cell-mediated immunity. Current understanding of DC immunobiology within the context of mammalian fetal-maternal tolerance is reviewed and discussed herein.

This chapter reviews measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem.Emergence is defined as the information a system or process produces. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at different scales, which can be studied with multi-scale profiles.

Single-particle tracking (SPT) was used to determine the mobility characteristics of MHC (major histocompatibility complex) class I molecules at the surface of HeLa cells at 22 degrees C and on different time scales. MHC class I was labeled using the Fab fragment of a monoclonal antibody (W6/32), covalently bound to either R-phycoerythrin or fluorescent microspheres, and the particles were tracked using high-sensitivity fluorescence imaging. Analysis of the data for a fixed time interval suggests a reasonable fit to a random diffusion model. The best fit values of the diffusion coefficient D decreased markedly, however, with increasing time interval, demonstrating the existence of anomalous diffusion. Further analysis of the data shows that the diffusion is anomalous over the complete time range investigated, 4-300 s. Fitting the results obtained with the R-phycoerythrin probe to D = D0talpha-1, where Do is a constant and t is the time, gave D0 = (6.7 +/- 4.5) x 10(-11) cm2 s-1 and alpha = 0.49 +/- 0.16. Experiments with fluorescent microspheres were less reproducible and gave slower anomalous diffusion. The R-phycoerythrin probe is considered more reliable for fluorescent SPT because it is small (11 x 8 nm) and monovalent. The type of motion exhibited by the class I molecules will greatly affect their ability to migrate in the plane of the membrane. Anomalous diffusion, in particular, greatly reduces the distance a class I molecule can travel on the time scale of minutes. The present data are discussed in relation to the possible role of diffusion and clustering in T-cell activation.

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales. Nontechnical AbstractThere several measures and definitions of complexity, emergence, self-organization, and homeostasis. This has led to confusion reflected on the carefree use of these concepts. We provide precise definitions based on information theory, attempting to clarify the meaning of these concepts with simple but formal measures. We illustrate the measures with experiments on discrete dynamical systems.