@article{13267, abstract = {Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.}, author = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Lyudchik, Julia and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G.N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G}, issn = {1548-7105}, journal = {Nature Methods}, pages = {1256--1265}, publisher = {Springer Nature}, title = {{Dense 4D nanoscale reconstruction of living brain tissue}}, doi = {10.1038/s41592-023-01936-6}, volume = {20}, year = {2023}, } @unpublished{11943, abstract = {Complex wiring between neurons underlies the information-processing network enabling all brain functions, including cognition and memory. For understanding how the network is structured, processes information, and changes over time, comprehensive visualization of the architecture of living brain tissue with its cellular and molecular components would open up major opportunities. However, electron microscopy (EM) provides nanometre-scale resolution required for full in-silico reconstruction1–5, yet is limited to fixed specimens and static representations. Light microscopy allows live observation, with super-resolution approaches6–12 facilitating nanoscale visualization, but comprehensive 3D-reconstruction of living brain tissue has been hindered by tissue photo-burden, photobleaching, insufficient 3D-resolution, and inadequate signal-to-noise ratio (SNR). Here we demonstrate saturated reconstruction of living brain tissue. We developed an integrated imaging and analysis technology, adapting stimulated emission depletion (STED) microscopy6,13 in extracellularly labelled tissue14 for high SNR and near-isotropic resolution. Centrally, a two-stage deep-learning approach leveraged previously obtained information on sample structure to drastically reduce photo-burden and enable automated volumetric reconstruction down to single synapse level. Live reconstruction provides unbiased analysis of tissue architecture across time in relation to functional activity and targeted activation, and contextual understanding of molecular labelling. This adoptable technology will facilitate novel insights into the dynamic functional architecture of living brain tissue.}, author = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G. N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G}, booktitle = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {{Saturated reconstruction of living brain tissue}}, doi = {10.1101/2022.03.16.484431}, year = {2022}, } @inproceedings{6195, abstract = {In the context of robotic manipulation and grasping, the shift from a view that is static (force closure of a single posture) and contact-deprived (only contact for force closure is allowed, everything else is obstacle) towards a view that is dynamic and contact-rich (soft manipulation) has led to an increased interest in soft hands. These hands can easily exploit environmental constraints and object surfaces without risk, and safely interact with humans, but present also some challenges. Designing them is difficult, as well as predicting, modelling, and “programming” their interactions with the objects and the environment. This paper tackles the problem of simulating them in a fast and effective way, leveraging on novel and existing simulation technologies. We present a triple-layered simulation framework where dynamic properties such as stiffness are determined from slow but accurate FEM simulation data once, and then condensed into a lumped parameter model that can be used to fast simulate soft fingers and soft hands. We apply our approach to the simulation of soft pneumatic fingers.}, author = {Pozzi, Maria and Miguel Villalba, Eder and Deimel, Raphael and Malvezzi, Monica and Bickel, Bernd and Brock, Oliver and Prattichizzo, Domenico}, isbn = {9781538630815}, location = {Brisbane, Australia}, publisher = {IEEE}, title = {{Efficient FEM-based simulation of soft robots modeled as kinematic chains}}, doi = {10.1109/icra.2018.8461106}, year = {2018}, } @inproceedings{1364, abstract = {We present a computational method for designing wire sculptures consisting of interlocking wires. Our method allows the computation of aesthetically pleasing structures that are structurally stable, efficiently fabricatable with a 2D wire bending machine, and assemblable without the need of additional connectors. Starting from a set of planar contours provided by the user, our method automatically tests for the feasibility of a design, determines a discrete ordering of wires at intersection points, and optimizes for the rest shape of the individual wires to maximize structural stability under frictional contact. In addition to their application to art, wire sculptures present an extremely efficient and fast alternative for low-fidelity rapid prototyping because manufacturing time and required material linearly scales with the physical size of objects. We demonstrate the effectiveness of our approach on a varied set of examples, all of which we fabricated.}, author = {Miguel Villalba, Eder and Lepoutre, Mathias and Bickel, Bernd}, location = {Anaheim, CA, USA}, number = {4}, publisher = {ACM}, title = {{Computational design of stable planar-rod structures}}, doi = {10.1145/2897824.2925978}, volume = {35}, year = {2016}, } @article{1414, abstract = {In this paper, we present a method to model hyperelasticity that is well suited for representing the nonlinearity of real-world objects, as well as for estimating it from deformation examples. Previous approaches suffer several limitations, such as lack of integrability of elastic forces, failure to enforce energy convexity, lack of robustness of parameter estimation, or difficulty to model cross-modal effects. Our method avoids these problems by relying on a general energy-based definition of elastic properties. The accuracy of the resulting elastic model is maximized by defining an additive model of separable energy terms, which allow progressive parameter estimation. In addition, our method supports efficient modeling of extreme nonlinearities thanks to energy-limiting constraints. We combine our energy-based model with an optimization method to estimate model parameters from force-deformation examples, and we show successful modeling of diverse deformable objects, including cloth, human finger skin, and internal human anatomy in a medical imaging application.}, author = {Miguel Villalba, Eder and Miraut, David and Otaduy, Miguel}, journal = {Computer Graphics Forum}, number = {2}, pages = {385 -- 396}, publisher = {Wiley-Blackwell}, title = {{Modeling and estimation of energy-based hyperelastic objects}}, doi = {10.1111/cgf.12840}, volume = {35}, year = {2016}, }