Maturation of humoral immune responses : Studies on the effects of antigen type, apoptosis and age

The humoral immune response is dependent on the formation of antibodies. Antibodies are produced by terminally differentiated B cells, plasma cells. Plasma cells are generated either directly from antigen challenged B cells, memory cells or from cells that have undergone the germinal center (GC) reaction. The GC is the main site for class switch, somatic hypermutation and generation of memory cells. Different factors, both internal and external, shape the outcome of the immune response. In this thesis, we have studied a few factors that influence the maturation of the humoral response. We have studied how age affects the response, and we show that responses against thymus dependent antigens (TD) are more affected than responses to thymus independent (TI) antigens, in concordance with the view that the T cell compartment is more affected by age than the B cell compartment. Furthermore, we demonstrate that priming early in life have a big influence on the immune response in the aged individual. Priming with a TI form of the carbohydrate dextran B512 (Dx) induces a reduction of IgG levels in later TD responses against Dx. We have evaluated possible mechanisms for this reduction. The reduction does not seem to be caused by clonal exhaustion or antibody mediated mechanisms. We also showed that the reduced TD response after TI priming can be induced against another molecule than Dx. With the hypothesis that TI antigens induce a plasma cell biased maturation of the responding B cells, we examined the presence of Blimp-1, a master regulator of plasma cell differentiation, in GCs induced by TD and TI antigen. Blimp-1 was found earlier in GCs induced by TI antigen and the staining intensity in these GCs was stronger than in TD antigen induced GCs, indicating that plasma cells might be continuously recruited from these GCs. B cells undergoing the GC reaction are thought to be under a strict selection pressure that removes cells with low affinity for the antigen and also cells that have acquired self-reactivity. We investigated the effect of apoptotic deficiencies on the accumulation of somatic mutations in GC B cells. In mice lacking the death receptor Fas, lpr mice, the frequency of mutations was increased but the pattern of the mutations did not differ from wild type mice. In contrast, mice over-expressing the anti-apoptotic protein Bcl-2, had a lowered frequency of mutations and the mutations introduced had other characteristics.

The Power of the Palestinian Landscape : An exploratory study of the functions of power using aerial image interpretation

The Palestinian region is changing rapidly, with both economic and cultural consequences. One way of approaching this very political process is thru the concept of landscape. By viewing the region as a multiprocessual, dynamic landscape the analysis allows for a holistic read where historical and contemporary projections, interpretations and notions of power are fused. This thesis draws on the scholarly fields of humanistic landscape research and aerial image interpretation as well as theories of orientalism and power. A case study of two regions of the West Bank is performed; interviews and observations provide localized knowledge that is then used in open-access image interpretation. By performing image interpretations this thesis explores the power embedded in mapping and the possible inclinations the development towards open-access geospatial analytic tools could have on the functions of power in the Palestinian landscape. By investigating the spatial configuration of the Palestinian landscape and tracing its roots this thesis finds four major themes that are particularly pivotal in the processual change of the Palestinian landscape: the Israeli/Palestinian time-space, the blurring of the conflict, the dynamics of the frontier region and the orientalist gaze. 

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.