Effect of pore size and cross-linking of a novel collagen-elastin dermal substitute on wound healing

Collagen-elastin (CE) scaffolds are frequently used for dermal replacement in the treatment of full-thickness skin defects such as burn wounds. But little is known about the optimal pore size and level of cross-linking. Different formulations of dermal substitutes with unidirectional pores were tested in porcine full-thickness wounds in combination with autologous split skin mesh grafts (SSG). Effect on wound healing was evaluated both macro- and microscopically. CE scaffolds with a pore size of 80 or 100 μm resulted in good wound healing after one-stage grafting. Application of scaffolds with a larger average pore size (120 μm) resulted in more myofibroblasts and more foreign body giant cells (FBGC). Moderate crosslinking impaired wound healing as it resulted in more wound contraction, more FBGC and increased epidermal thickness compared to no cross-linking. In addition, take rate and redness were negatively affected compared to SSG only. Vascularization and the number of myofibroblasts were not affected by cross-linking. Surprisingly, stability of cross-linked scaffolds was not increased in the wound environment, in contrast to in vitro results. Cross-linking reduced the proliferation of fibroblasts in vitro, which might explain the reduced clinical outcome. The non-cross-linked CE substitute with unidirectional pores allowed one-stage grafting of SSG, resulting in good wound healing. In addition, only a very mild foreign body reaction was observed. Cross-linking of CE scaffolds negatively affected wound healing on several important parameters. The optimal non-cross-linked CE substitute is a promising candidate for future clinical evaluation.     

A stepwise development and validation of a RANS based CFD modelling approach for the hydraulic and thermal-hydraulic analyses of liquid metal flow in a fuel assembly

The demand of reliable and clean energy at affordable prices poses a formidable challenge to the world. The initiatives from various international organizations reserve an important role for liquid metal cooled reactor systems. Assessment of such reactors usually involves unconventional thermal-hydraulics. Consequently, Reynolds Averaged Navier Stokes (RANS) based Computational Fluid Dynamics (CFD) approaches are expected to play a vital role along with various ongoing experiments for the design and the safe operation of these nuclear reactors. One of the major issues is the heat transport in the fuel assembly by the liquid metal. The known difficulties in heat transfer experiments, especially with liquid metals, necessitate the application of RANS in computing details of flow and temperature distribution.Considering these aspects, a four step approach is described in the current paper. As a first step, the heat transfer in a liquid metal flow inside a heated tube is analyzed using a RANS approach and then compared with some of the empirical correlations. The computed Nusselt number reveals the required development length of the thermal boundary layer in liquid metal. Furthermore, these simulations reveal the need of further assessment of this approach and all the existing correlations, and the care that should be taken while applying one of these correlations. In the second step, numerical simulations of the flow of heavy liquid metal around a heated rod in an annular cavity confirm that a RANS strategy can be employed in liquid metal flows. Furthermore, a comparison between computed and experimental non-dimensional axial temperature at the heated rod surface shows that among the considered turbulence models the use of a Baseline-Reynolds Stress Model (BSL-RSM) with automatic wall treatment (AWT) can be preferred for complex geometries. This is also demonstrated in the third step by computing the flow distribution in a triangular arrangement of a fuel assembly and by comparing with an existing hydraulics experiment in rod-bundle. These analyses reveal that the use of the BSL-RSM turbulence model with AWT allows prediction of the cross-flow in this rod-bundle. As the forth and last step, the integral TEGENA (TEmperatur- und GEschwindigkeitsverteilungen in Stabbündel mit turbulenter NAtriumströmung) experiment has been selected for further assessment of RANS based CFD approach in computing both the flow and temperature field. A comparison with literature demonstrates that the use of symmetric boundary condition in such a tightly packed parallel rod-bundle leads to a distorted flow field. The experimentally and the computationally obtained temperature field at a plane in the outlet reveal its acceptable predictive capability. Furthermore, application of two different Reynolds Stress Models yields almost the same temperature distribution as a result of the use of simple first-order gradient model for the turbulent heat fluxes. Consequently, these four steps support the use of this modelling approach for investigating the heat transport in (heavy) liquid metals. Finally, the preferred RANS approach has been applied for thermal-hydraulic evaluation in the square arrangement of a bare rod-bundle as is to be employed in the European Lead-cooled reactor System (ELSY). Also, the influence of rod pitch-to-diameter ratio has been analyzed by numerically re-arranging these rods in the different square lattice. Moreover, arranging the bare rods in triangular lattice at the different rod pitch-to-diameter ratios shows the effect of the square and triangular lattice in thermal-hydraulics. Lastly, comparisons with some of the existing heat transfer correlations for the triangular and the rectangular lattice allows us to identify preferred correlations for these lattice arrangements of bare and liquid metal cooled rod-bundles.     

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.