Screening Arrays of Laminin Peptides on Modified Cellulose for Promotion of Adhesion of Primary Endothelial and Neural Precursor Cells

Neural precursor cells (NPC) are primary cells intensively used in the context of research on adult neurogenesis and modeling of neuronal development in health and diseased states. Substrates that can facilitate NPC adhesion will be very useful for culturing these cells. Due to the presence of laminin in basal lamina as well as their involvement in differentiation, migration, and adhesion of many types of cells, surfaces modified with laminin-derived peptides are focused upon and compared with the widely used fibronectin-derived Arg-Gly-Asp (RGD) peptides. An array of 46 peptides is synthesized on cellulose paper (SPOT) to identify laminin-derived peptides that promote short-term adhesion of murine NPC and human primary endothelial cells. Various previously reported peptide sequences are re-evaluated in this work. Initial adhesion experiments show NPC preferred several laminin-derived peptides by up to 5-time higher cell numbers, compared to the well-known promiscuous integrin binding RGD peptide. Importantly, screening of cell adhesion has revealed a synergetic effect of filamentous matrix, peptide sequence, surface property, ligand density, and the dynamic process of NPC adhesion. © The Authors. Advanced Biology published by Wiley-VCH Gmb

Adaptive sparse sampling for quasiparticle interference imaging

Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes

Adaptive Sparse Sampling for Quasiparticle Interference Imaging

Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes.Comment: 10 pages, 5 figure

Insights from Multimodal Preclinical Imaging in Immunocompetent Nude Mice

Hydrogels based on gelatin have evolved as promising multifunctional biomaterials. Gelatin is crosslinked with lysine diisocyanate ethyl ester (LDI) and the molar ratio of gelatin and LDI in the starting material mixture determines elastic properties of the resulting hydrogel. In order to investigate the clinical potential of these biopolymers, hydrogels with different ratios of gelatin and diisocyanate (3-fold (G10_LNCO3) and 8-fold (G10_LNCO8) molar excess of isocyanate groups) were subcutaneously implanted in mice (uni- or bilateral implantation). Degradation and biomaterial-tissue- interaction were investigated in vivo (MRI, optical imaging, PET) and ex vivo (autoradiography, histology, serum analysis). Multimodal imaging revealed that the number of covalent net points correlates well with degradation time, which allows for targeted modification of hydrogels based on properties of the tissue to be replaced. Importantly, the degradation time was also dependent on the number of implants per animal. Despite local mechanisms of tissue remodeling no adverse tissue responses could be observed neither locally nor systemically. Finally, this preclinical investigation in immunocompetent mice clearly demonstrated a complete restoration of the original healthy tissue

Bringing Molecular Tools into Environmental Resource Management: Untangling the Molecules to Policy Pathway

New advances in molecular biology can be invaluable tools in resource management, but they are best incorporated through a collaborative process with managers who understand the most pressing questions, practical limitations, and political constraints