Sortilin Modulates Schwann Cell Signaling and Remak Bundle Regeneration Following Nerve Injury.

Peripheral nerve regeneration relies on the ability of Schwann cells to support the regrowth of damaged axons. Schwann cells re-differentiate when reestablishing contact with the sprouting axons, with large fibers becoming remyelinated and small nociceptive fibers ensheathed and collected into Remak bundles. We have previously described how the receptor sortilin facilitates neurotrophin signaling in peripheral neurons via regulated trafficking of Trk receptors. This study aims to characterize the effects of sortilin deletion on nerve regeneration following sciatic crush injury. We found that Sort1 - / - mice displayed functional motor recovery like that of WT mice, with no detectable differences in relation to nerve conduction velocities and morphological aspects of myelinated fibers. In contrast, we found abnormal ensheathment of regenerated C-fibers in injured Sort1 - / - mice, demonstrating a role of sortilin for Remak bundle formation following injury. Further studies on Schwann cell signaling pathways showed a significant reduction of MAPK/ERK, RSK, and CREB phosphorylation in Sort1 - / - Schwann cells after stimulation with neurotrophin-3 (NT-3), while Schwann cell migration and myelination remained unaffected. In conclusion, our results demonstrate that loss of sortilin blunts NT-3 signaling in Schwann cells which might contribute to the impaired Remak bundle regeneration after sciatic nerve injury.

Efficient algorithms for solving the non-linear vibrational coupled-cluster equations using full and decomposed tensors.

Vibrational coupled-cluster (VCC) theory provides an accurate method for calculating vibrational spectra and properties of small to medium-sized molecules. Obtaining these properties requires the solution of the non-linear VCC equations which can in some cases be hard to converge depending on the molecule, the basis set, and the vibrational state in question. We present and compare a range of different algorithms for solving the VCC equations ranging from a full Newton-Raphson method to approximate quasi-Newton models using an array of different convergence-acceleration schemes. The convergence properties and computational cost of the algorithms are compared for the optimization of VCC states. This includes both simple ground-state problems and difficult excited states with strong non-linearities. Furthermore, the effects of using tensor-decomposed solution vectors and residuals are investigated and discussed. The results show that for standard ground-state calculations, the conjugate residual with optimal trial vectors algorithm has the shortest time-to-solution although the full Newton-Raphson method converges in fewer macro-iterations. Using decomposed tensors does not affect the observed convergence rates in our test calculations as long as the tensors are decomposed to sufficient accuracy.