Practising social justice: Community organisations, what matters and what counts

This thesis investigates the situated knowing-in-practice of locally-based community organisations, and studies how this practice knowledge is translated and contested in inter-organisational relations in the community services field of practices. Despite participation in government-led consultation processes, community organisations express frustration that the resulting policies and plans inadequately take account of the contributions from their practice knowledge. The funding of locally-based community organisations is gradually diminishing in real terms and in the competitive tendering environment, large nationally-based organisations often attract the new funding sources. The concern of locally-based community organisations is that the apparent lack of understanding of their distinctive practice knowing is threatening their capacity to improve the well-being of local people and their communities. In this study, I work with practitioners, service participants and management committee members to present an account of their knowing-in-practice, its character and conditions of efficacy; and then investigate what happens when this local practice knowledge is translated into results-based accountability (RBA) planning with diverse organisations and institutions. This thesis analyses three points of observation: knowing in a community of practitioners; knowing in a community organisation and knowing in the community services field of practices. In choosing these points of observation, the inquiry explores some of the relations and intra-actions from the single organisation to the institutional at a time when state government bureaucracy has mandated that community organisations implement RBA to articulate outcomes that can be measured by performance indicators. A feminist, performative, relational practice-based approach employs participatory action research to achieve an enabling research experience for the participants. It aims to intervene strategically to enhance recognition of the distinctive contributions of community organisations’ practice knowledge. This thesis reconfigures understandings of the roles, contributions and accountabilities of locally-based community organisations. Observations of situated practices together with the accounts of workers and service participants demonstrate how community organisations facilitate service participants’ struggles over social justice. A new topology for rethinking social justice as processual and practice-based is developed. It demonstrates how these struggles are a dynamic complex of iteratively-enfolded practices of respect and recognition, redistribution and distributive justice, representation and participation, belonging and inclusion. The focus on the practising of social justice in this thesis offers an alternative to the neo-liberal discourse that positions community organisations as sub-contractors accountable to government for delivering measurable outputs, outcomes and efficiencies in specified service provision contracts. The study shows how knowing-in-practice in locally-based community organisations contests the representational conception of knowledge inextricably entangled with accountability and performance measurement apparatus such as RBA. Further, it suggests that practitioner and service participant contributions are marginalised and diminished in RBA through the privileging of knowledge that takes an ‘expert’, quantifiable and calculative form. Thus crucially, harnessing local practice knowing requires re-imagining and enacting knowledge spaces that assemble and take seriously all relevant stakeholder perspectives, diverse knowledges and methods

Predicting the Location of Glioma Recurrence After a Resection Surgery

International audienceWe propose a method for estimating the location of glioma recurrence after surgical resection. This method consists of a pipeline including the registration of images at different time points, the estimation of the tumor infiltration map, and the prediction of tumor regrowth using a reaction-diffusion model. A data set acquired on a patient with a low-grade glioma and post surgery MRIs is considered to evaluate the accuracy of the estimated recurrence locations found using our method. We observed good agreement in tumor volume prediction and qualitative matching in regrowth locations. Therefore, the proposed method seems adequate for modeling low-grade glioma recurrence. This tool could help clinicians anticipate tumor regrowth and better characterize the radiologically non-visible infiltrative extent of the tumor. Such information could pave the way for model-based personalization of treatment planning in a near future

A generative approach for image-based modeling of tumor growth

22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Human infection challenge in the pandemic era and beyond, HIC-Vac annual meeting report, 2022

HIC-Vac is an international network of researchers dedicated to developing human infection challenge studies to accelerate vaccine development against pathogens of high global impact. The HIC-Vac Annual Meeting (3rd and 4th November 2022) brought together stakeholders including researchers, ethicists, volunteers, policymakers, industry partners, and funders with a strong representation from low- and middle-income countries. The network enables sharing of research findings, especially in endemic regions. Discussions included pandemic preparedness and the role of human challenge to accelerate vaccine development during outbreak, with industry speakers emphasising the great utility of human challenge in vaccine development. Public consent, engagement, and participation in human challenge studies were addressed, along with the role of embedded social science and empirical studies to uncover social, ethical, and regulatory issues around human infection challenge studies. Study volunteers shared their experiences and motivations for participating in studies. This report summarises completed and ongoing human challenge studies across a variety of pathogens and demographics, and addresses other key issues discussed at the meeting

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm

We present the first results of the Fermilab Muon g-2 Experiment for the positive muon magnetic anomaly $a_\mu \equiv (g_\mu-2)/2$. The anomaly is determined from the precision measurements of two angular frequencies. Intensity variation of high-energy positrons from muon decays directly encodes the difference frequency $\omega_a$ between the spin-precession and cyclotron frequencies for polarized muons in a magnetic storage ring. The storage ring magnetic field is measured using nuclear magnetic resonance probes calibrated in terms of the equivalent proton spin precession frequency ${\tilde{\omega}'^{}_p}$ in a spherical water sample at 34.7$^{\circ}$C. The ratio $\omega_a / {\tilde{\omega}'^{}_p}$, together with known fundamental constants, determines $a_\mu({\rm FNAL}) = 116\,592\,040(54)\times 10^{-11}$ (0.46\,ppm). The result is 3.3 standard deviations greater than the standard model prediction and is in excellent agreement with the previous Brookhaven National Laboratory (BNL) E821 measurement. After combination with previous measurements of both $\mu^+$ and $\mu^-$, the new experimental average of $a_\mu({\rm Exp}) = 116\,592\,061(41)\times 10^{-11}$ (0.35\,ppm) increases the tension between experiment and theory to 4.2 standard deviationsComment: 10 pages; 4 figure

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects