Direct bone marrow HSC transplantation enhances local engraftment at the expense of systemic engraftment in NSG mice

Direct bone marrow (BM) injection has been proposed as a strategy to bypass homing inefficiencies associated with intravenous (IV) hematopoietic stem cell (HSC) transplantation. Despite physical delivery into the BM cavity, many donor cells are rapidly redistributed by vascular perfusion, perhaps compromising efficacy. Anchoring donor cells to 3-dimensional (3D) multicellular spheroids, formed from mesenchymal stem/stromal cells (MSC) might improve direct BM transplantation. To test this hypothesis, relevant combinations of human umbilical cord blood-derived CD34(+) cells and BM-derived MSC were transplanted into NOD/SCID gamma (NSG) mice using either IV or intrafemoral (IF) routes. IF transplantation resulted in higher human CD45(+) and CD34(+) cell engraftment within injected femurs relative to distal femurs regardless of cell combination, but did not improve overall CD45(+) engraftment at 8 weeks. Analysis within individual mice revealed that despite engraftment reaching near saturation within the injected femur, engraftment at distal hematopoietic sites including peripheral blood, spleen and non-injected femur, could be poor. Our data suggest that the retention of human HSC within the BM following direct BM injection enhances local chimerism at the expense of systemic chimerism in this xenogeneic model.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.