Accuracy and Mechanical Properties of Open-Cell Microstructures Fabricated by Selective Laser Sintering

This paper investigates the applicability of selective laser sintering (SLS) for the manufacture of scaffold geometries for bone tissue engineering applications. Porous scaffold geometries with open-cell structure and relative density of 10-60 v% were computationally designed and fabricated by selective laser sintering using polyamide powder. Strut and pore sizes ranging from 0.4 - 1 mm and 1.2 -2 mm are explored. The effect of process parameters on compressive properties and accuracy of scaffolds was examined and outline laser power and scan spacing were identified as significant factors. In general, the designed scaffold geometry was not accurately fabricated on the micron-scale. The smallest successfully fabricated strut and pore size was 0.4 mm and 1.2 mm, respectively. It was found that selective laser sintering has the potential to fabricate hard tissue engineering scaffolds. However the technology is not able to replicate exact geometries on the micron-scale but by accounting for errors resulting from the diameter of the laser and from the manufacturing induced geometrical deformations in different building directions, the exact dimensions of the manufactured scaffolds can be predicted and controlled indirectly, which corresponds favorably with its application in computer aided tissue engineering.Mechanical Engineerin

TNF signaling inhibition in the CNS: implications for normal brain function and neurodegenerative disease

The role of tumor necrosis factor (TNF) as an immune mediator has long been appreciated but its function in the brain is still unclear. TNF receptor 1 (TNFR1) is expressed in most cell types, and can be activated by binding of either soluble TNF (solTNF) or transmembrane TNF (tmTNF), with a preference for solTNF; whereas TNFR2 is expressed primarily by microglia and endothelial cells and is preferentially activated by tmTNF. Elevation of solTNF is a hallmark of acute and chronic neuroinflammation as well as a number of neurodegenerative conditions including ischemic stroke, Alzheimer's (AD), Parkinson's (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). The presence of this potent inflammatory factor at sites of injury implicates it as a mediator of neuronal damage and disease pathogenesis, making TNF an attractive target for therapeutic development to treat acute and chronic neurodegenerative conditions. However, new and old observations from animal models and clinical trials reviewed here suggest solTNF and tmTNF exert different functions under normal and pathological conditions in the CNS. A potential role for TNF in synaptic scaling and hippocampal neurogenesis demonstrated by recent studies suggest additional in-depth mechanistic studies are warranted to delineate the distinct functions of the two TNF ligands in different parts of the brain prior to large-scale development of anti-TNF therapies in the CNS. If inactivation of TNF-dependent inflammation in the brain is warranted by additional pre-clinical studies, selective targeting of TNFR1-mediated signaling while sparing TNFR2 activation may lessen adverse effects of anti-TNF therapies in the CNS

False discovery rate smoothing

We present false discovery rate smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false-discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a non-standard high-dimensional optimization problem, for which an efficient augmented-Lagrangian algorithm is presented. In simulation studies, FDR smoothing exhibits state-of-the-art performance at modest computational cost. In particular, it is shown to be far more robust than existing methods for spatially dependent multiple testing. We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods. All code for FDR smoothing is publicly available in Python and R.Comment: Added misspecification analysis, added pathological scenario discussions, additional comparisons, new graph fused lasso algorith