Showing papers by "David Martin published in 2006"

Haploidentical stem cell transplantation in patients with pediatric solid tumors: preliminary results of a pilot study and analysis of graft versus tumor effects.

Abstract: Pediatric patients with relapsed metastatic tumors have a poor prognosis and new treatment strategies are warranted. We present preliminary results of a pilot study, evaluating the feasibility and toxicity of transplantation of haploidentical T and B cell depleted grafts with high numbers of NK cells. 6 patients with relapsed metastatic neuroblastomas (n = 4), rhabdomyosarcoma (n = 1) or Ewing's sarcoma (n = 1) after previous autologous transplantation received CD3/CD19 depleted grafts from mismatched family donors with a median number of 16 x 10 (6)/kg stem cells, 167 x 10 (6)/kg Natural Killer cells and only 5.4 x 10 (4)/kg residual T cells. A melphalan-based, reduced intensity conditioning was used. Despite pretransplant chemotherapy, patients entered transplantation with significant tumor burden. Primary engraftment occurred in 6/6 patients. One patient had secondary graft failure. Hematopoietic recovery was rapid (ANC > 0.5 x 10 (9)/L: 11 days (9-12); independence from platelet substitution: 8 days (7-11)). Four patients had acute GvHD grade II, limited chronic GvHD was observed in 2 patients. No transplant-related mortality and only low toxicity occurred. Four patients died from progression, two patients are alive. Overall median survival time is 6 months (2-11) to date. Analysis of posttransplant NK cell function revealed stable cytotoxic activity against K562 targets, whereas activity against neuroblastoma targets was low. Stimulation with cytokines and use of appropriate antibodies clearly enhanced specific lysis in vitro. In summary, these preliminary results indicate the feasibility and low toxicity even in intensively pre-treated patients with neuroblastomas/sarcomas. This approach may form the basis for posttransplant immunomodulation and other therapeutic strategies. Further experience is warranted to evaluate the method.

Issues in Algorithm Characterization for Link Analysis.

Abstract: To meet the intelligence community’s need for link analysis tools that work together, researchers are currently investigating ways of building workflows of these tools using an intelligent system architecture. A key challenge in building a dynamic link analysis workflow environment is representing the behavior of the individual link analysis algorithms being composed. In this paper, we outline techniques for modeling algorithms that allow a system architecture to reason about their behavior and performance, individually and in combination. The algorithm characterization model we propose is based on a layered approach, where the layers range from high-level qualitative descriptions of algorithms to detailed statistical descriptions of their effect on the data. Recent research and development in technology for intelligence analysis has produced a large number of tools, each of which addresses some aspect of the link analysis problem—the challenge of finding events, entities, and connections of interest in large relational data sets. Software developed in recent projects perform many diverse functions within link analysis, including detecting pre-defined patterns (Boner 2005; Coffman, Greenblatt, & Marcus 2004; Piochet al. 2004; Wolvertonet al. 2003), learning these patterns of interest (Holder et al. 2005), classifying individuals according to group membership (Adibi & Chalupsky 2005) or level of threat (Macskassy & Provost 2005), resolving aliases for individuals (Davis et al. 2005), identifying neighborhoods of interest within the data, and others. While these tools often perform complementary functions within the overall link analysis space, there has been limited success getting them to work together. One-time integration efforts have been time-consuming to engineer, and lack flexibility. To address this problem, a recent focus of research has been to link these tools together dynamically, through workflows composed by Grid software (Deelman et al. 2003), a blackboard system (Corkill 2003), or some other intelligent System Architecture (SA). One key challenge in building this kind of dynamic link analysis workflow environment is representing the behavior of the individual link analysis algorithms being composed. In this paper, we outline techniques for modeling algorithms that meet the requirements in the domain of link analysis. Copyright c © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. The algorithm model we propose is characterized by a layered approach, so that different System Architecture candidates can make use of some aspects of the model, even if they are not capable of reasoning about all of them. The layers range from high-level qualitative descriptions of algorithms to detailed statistical descriptions of their effect on the data. Below we outline the challenges in characterizing link analysis algorithms, describe our proposed algorithm characterization approach in more detail, and discuss related research in algorithm characterization and representing capabilities.

Privacy enhancing technologies : 5th International Workshop, PET 2005, Cavtat, Croatia, May 30-June 1, 2005 : revised selected papers

Abstract: Privacy Vulnerabilities in Encrypted HTTP Streams.- An Analysis of Parallel Mixing with Attacker-Controlled Inputs.- Message Splitting Against the Partial Adversary.- Location Privacy for Cellular Systems Analysis and Solution.- Towards Modeling Wireless Location Privacy.- Failures in a Hybrid Content Blocking System.- Anonymity Preserving Techniques in Trust Negotiations.- Unmixing Mix Traffic.- Mix-Network with Stronger Security.- Covert Channels in IPv6.- Towards Privacy-Aware eLearning.- Anonymization of IP Traffic Monitoring Data: Attacks on Two Prefix-Preserving Anonymization Schemes and Some Proposed Remedies.- Privacy Issues in Vehicular Ad Hoc Networks.- High-Power Proxies for Enhancing RFID Privacy and Utility.- Integrating Utility into Face De-identification.- Privacy in India: Attitudes and Awareness.- Economics of Identity Management: A Supply-Side Perspective.