The Rules of Human T Cell Fate in vivo.

The processes governing lymphocyte fate (division, differentiation, and death), are typically assumed to be independent of cell age. This assumption has been challenged by a series of elegant studies which clearly show that, for murine cells in vitro, lymphocyte fate is age-dependent and that younger cells (i.e., cells which have recently divided) are less likely to divide or die. Here we investigate whether the same rules determine human T cell fate in vivo. We combined data from in vivo stable isotope labeling in healthy humans with stochastic, agent-based mathematical modeling. We show firstly that the choice of model paradigm has a large impact on parameter estimates obtained using stable isotope labeling i.e., different models fitted to the same data can yield very different estimates of T cell lifespan. Secondly, we found no evidence in humans in vivo to support the model in which younger T cells are less likely to divide or die. This age-dependent model never provided the best description of isotope labeling; this was true for naïve and memory, CD4+ and CD8+ T cells. Furthermore, this age-dependent model also failed to predict an independent data set in which the link between division and death was explored using Annexin V and deuterated glucose. In contrast, the age-independent model provided the best description of both naïve and memory T cell dynamics and was also able to predict the independent dataset

Explicit kinetic heterogeneity: mechanistic models for interpretation of labeling data of heterogeneous cell populations

Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three models that are most commonly used are in fact mathematically identical and differ only in their interpretation of the estimated parameters. By extending these previous models, we here propose a more mechanistic approach for the analysis of data from deuterium labeling experiments. We construct a model of "kinetic heterogeneity" in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a mechanistic way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length

Selection of multiple donor gauges via Graphical Lasso for estimation of daily streamflow time series

A fundamental challenge in estimations of daily streamflow time series at sites with incomplete records is how to effectively and efficiently select reference or donor gauges from an existing gauge network to infer the missing data. While research on estimating missing streamflow time series is not new, the existing approaches either use a single reference streamflow gauge or employ a set of "ad-hoc" reference gauges, leaving a systematic selection of reference gauges as a long-standing open question. In this work, a novel method is introduced that facilitates systematical selection of multiple reference gauges from any given streamflow network. The idea is to mathematically characterize the network-wise correlation structure of a streamflow network via graphical Markov modeling, and further transforms a dense network into a sparsely connected one. The resulted underlying sparse graph from the graphical model encodes conditional independence conditions among all reference gauges from the streamflow network, allowing determination of an optimum subset of the donor gauges. The sparsity is discovered by using the Graphical Lasso algorithm with an L1-norm regularization parameter and a thresholding parameter. These two parameters are determined by a multi-objective optimization process. Furthermore, the graphical modeling approach is employed to solve another open problem in gauge removal planning decision (e.g., due to operation budget constraints): which gauges to remove would statistically guarantee the least loss of information by estimations from the remaining gauges? Our graphical model-based method is demonstrated with daily streamflow data from a network of 34 gauges over the Ohio River basin.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0137

Childhood and the politics of scale: Descaling children's geographies?

This is the post-print version of the final published paper that is available from the link below. Copyright @ 2008 SAGE Publications.The past decade has witnessed a resurgence of interest in the geographies of children's lives, and particularly in engaging the voices and activities of young people in geographical research. Much of this growing body of scholarship is characterized by a very parochial locus of interest — the neighbourhood, playground, shopping mall or journey to school. In this paper I explore some of the roots of children's geographies' preoccupation with the micro-scale and argue that it limits the relevance of research, both politically and to other areas of geography. In order to widen the scope of children's geographies, some scholars have engaged with developments in the theorization of scale. I present these arguments but also point to their limitations. As an alternative, I propose that the notion of a flat ontology might help overcome some difficulties around scalar thinking, and provide a useful means of conceptualizing sociospatiality in material and non-hierarchical terms. Bringing together flat ontology and work in children's geographies on embodied subjectivity, I argue that it is important to examine the nature and limits of children's spaces of perception and action. While these spaces are not simply `local', they seldom afford children opportunities to comment on, or intervene in, the events, processes and decisions that shape their own lives. The implications for the substance and method of children's geographies and for geographical work on scale are considered

Chaos, containment and change: responding to persistent offending by young people

This article reviews policy developments in Scotland concerning 'persistent young offenders' and then describes the design of a study intended to assist a local planning group in developing its response. The key findings of a review of casefiles of young people involved in persistent offending are reported. It emerges that youth crime and young people involved in offending are more complex and heterogeneous than is sometimes assumed. This, along with a review of some literature about desistance from offending, reaffirms the need for properly individualised interventions. Studies of 'desisters' suggest the centrality of effective and engaging working relationships in this process. However, these studies also re-assert the significance of the social contexts of workers’ efforts to bring 'change' out of 'chaos'. We conclude therefore that the 'new correctionalism' must be tempered with appreciation of the social exclusion of young people who offend