TY - DATA AU - Guseinov, Ruslan ID - 8761 TI - Supplementary data for "Computational design of cold bent glass façades" ER - TY - DATA AB - Supplementary data provided for the provided for the publication: Igor Gridchyn , Philipp Schoenenberger , Joseph O'Neill , Jozsef Csicsvari (2020) Optogenetic inhibition-mediated activity-dependent modification of CA1 pyramidal-interneuron connections during behavior. Elife. AU - Csicsvari, Jozsef L AU - Gridchyn, Igor AU - Schönenberger, Philipp ID - 8563 TI - Optogenetic alteration of hippocampal network activity ER - TY - JOUR AB - Advances in shape-morphing materials, such as hydrogels, shape-memory polymers and light-responsive polymers have enabled prescribing self-directed deformations of initially flat geometries. However, most proposed solutions evolve towards a target geometry without considering time-dependent actuation paths. To achieve more complex geometries and avoid self-collisions, it is critical to encode a spatial and temporal shape evolution within the initially flat shell. Recent realizations of time-dependent morphing are limited to the actuation of few, discrete hinges and cannot form doubly curved surfaces. Here, we demonstrate a method for encoding temporal shape evolution in architected shells that assume complex shapes and doubly curved geometries. The shells are non-periodic tessellations of pre-stressed contractile unit cells that soften in water at rates prescribed locally by mesostructure geometry. The ensuing midplane contraction is coupled to the formation of encoded curvatures. We propose an inverse design tool based on a data-driven model for unit cells’ temporal responses. AU - Guseinov, Ruslan AU - McMahan, Connor AU - Perez Rodriguez, Jesus AU - Daraio, Chiara AU - Bickel, Bernd ID - 7262 JF - Nature Communications KW - Design KW - Synthesis and processing KW - Mechanical engineering KW - Polymers SN - 2041-1723 TI - Programming temporal morphing of self-actuated shells VL - 11 ER - TY - DATA AB - Cryo-electron microscopy (cryo-EM) of cellular specimens provides insights into biological processes and structures within a native context. However, a major challenge still lies in the efficient and reproducible preparation of adherent cells for subsequent cryo-EM analysis. This is due to the sensitivity of many cellular specimens to the varying seeding and culturing conditions required for EM experiments, the often limited amount of cellular material and also the fragility of EM grids and their substrate. Here, we present low-cost and reusable 3D printed grid holders, designed to improve specimen preparation when culturing challenging cellular samples directly on grids. The described grid holders increase cell culture reproducibility and throughput, and reduce the resources required for cell culturing. We show that grid holders can be integrated into various cryo-EM workflows, including micro-patterning approaches to control cell seeding on grids, and for generating samples for cryo-focused ion beam milling and cryo-electron tomography experiments. Their adaptable design allows for the generation of specialized grid holders customized to a large variety of applications. AU - Schur, Florian KM ID - 14592 TI - STL-files for 3D-printed grid holders described in Fäßler F, Zens B, et al.; 3D printed cell culture grid holders for improved cellular specimen preparation in cryo-electron microscopy ER - TY - CONF AB - Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture the topological properties of data succinctly at different spatial resolutions. For graphical data, the shape, and structure of the neighborhood of individual data items (nodes) are an essential means of characterizing their properties. We propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We achieve encouraging results on nine different real-world datasets that attest to the potential of persistent homology-based methods for network analysis. AU - Bhatia, Sumit AU - Chatterjee, Bapi AU - Nathani, Deepak AU - Kaul, Manohar ID - 7213 SN - 1860949X T2 - Complex Networks and their applications VIII TI - A persistent homology perspective to the link prediction problem VL - 881 ER -