A multimodal 3D neuro-microphysiological system with neurite-trapping microelectrodes

Three-dimensional cell technologies as pre-clinical models are emerging tools for mimicking the structural and functional complexity of the nervous system. The accurate exploration of phenotypes in engineered 3D neuronal cultures, however, demands morphological, molecular and especially functional measurements. Particularly crucial is measurement of electrical activity of individual neurons with millisecond resolution. Current techniques rely on customized electrophysiological recording set-ups, characterized by limited throughput and poor integration with other readout modalities. Here we describe a novel approach, using multiwell glass microfluidic microelectrode arrays, allowing non-invasive electrical recording from engineered 3D neural tissues. We demonstrate parallelized studies with reference compounds, calcium imaging and optogenetic stimulation. Additionally, we show how microplate compatibility allows automated handling and high-content analysis of human induced pluripotent stem cell–derived neurons. This microphysiological platform opens up new avenues for high-throughput studies on the functional, morphological and molecular details of neurological diseases and their potential treatment by therapeutic compounds.

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

High-level modeling languages facilitate system modeling and the development of control systems. This is mainly achieved by the automated handling of differential algebraic equations which describe the dynamics of the modeled systems across different physical domains. A wide selection of model libraries provides additional support to the modeling process. Nevertheless, deployment on embedded targets poses a challenge and usually requires manual modification and reimplementation of the control system. The novel proposed eFMI Standard (Functional Mock-up Interface for embedded systems) introduces a workflow and an automated toolchain to simplify the deployment of model-based control systems on embedded targets. This contribution describes the application and verification of the eFMI workflow using a vertical dynamics control problem with an automotive application as an example. The workflow is exemplified by a control system design process which is supported by the a-causal, multi-physical, high-level modeling language Modelica. In this process, the eFMI toolchain is applied to a model-based controller for semi-active dampers and demonstrated using an eFMI-based nonlinear prediction model within a nonlinear Kalman filter. The generated code was successfully tested in different validation steps on the dedicated embedded system. Additionally, tests with a low-volume production electronic control unit (ECU) in a series-produced car demonstrated the correct execution of the controller code under real-world conditions. The novelty of our approach is that it automatically derives an embedded software solution from a high-level multi-physical model with standardized eFMI methodology and tooling. We present one of the first full application scenarios (covering all aspects ranging from multi-physical modeling up to embedded target deployment) of the new eFMI tooling.

Automated handling of complex chemical structures in Z-matrix coordinates - the chemcoord library

In this work, we present a fully automated method for the construction of chemically meaningful sets of non-redundant internal coordinates (also commonly denoted as Z-matrices) from the cartesian coordinates of a molecular system. Particular focus is placed on avoiding ill-definitions of angles and dihedrals due to linear arrangements of atoms, to consistently guarantee a well-defined transformation to cartesian coordinates, even after structural changes. The representations thus obtained are particularly well suited for pathway construction in double-ended methods for transition state search and optimisations with non-linear constraints. Analytical gradients for the transformation between the coordinate systems were derived for the first time, which allows analytical geometry optimizations purely in Z-matrix coordinates. The geometry optimisation was coupled with a Symbolic Algebra package to support arbitrary non-linear constraints in Z-matrix coordinates, while retaining analytical energy gradient conversion. Sample applications are provided for a number of common chemical reactions and illustrative examples where these new algorithms can be used to automatically produce chemically reasonable structure interpolations, or to perform non-linearly constrained optimisations of molecules.

CONTAINER TRANSSHIPMENT PROBLEMS AND THE SOLUTION

One of the obstacles to the spread of rail-road intermodal freight transport is the lack of efficient container handling equipment on the rail-road hubs. The known and widely used solutions (gantry crane, reach stackers) are apparently not able to increase the volume of intermodal transport. The goal, which is uniformly desired by the professionals, the growth of rail-road intermodal freight transport, can be served by a container transhipment device that allows unit loads to be transferred between road and rail vehicles even under railway contact line. The new container transhipment technology, proposed in the article can be the missing hardware device for physical internet hubs. The highly automated handling robot meets the requirements of Industry 4.0 and Logistics 4.0.

Recognition and Positioning of Container Lock Holes for Intelligent Handling Terminal Based on Convolutional Neural Network

Container handling is a key link in container transport. In an automated handling terminal, the work efficiency directly depends on the time cost of the alignment between the spreader and the lock holes of the container. This paper attempts to improve the recognition and location of container lock holes with the aid of machine vision. Firstly, a lock hole recognition algorithm was designed based on local binary pattern (LBP) feature and classifier. After feature extraction and classifier training, multi-scale sliding window was used to recognize each lock hole. To realize real-time, accurate recognition of lock holes, the convolutional neural network (CNN) with improved threshold was incorporated to our algorithm. The tests on actual datasets show that our algorithm can effectively locate container lock holes.

A multimodal 3D neuro-microphysiological system with neurite-trapping microelectrodes

Three-dimensional cell technologies as pre-clinical models are emerging tools mimicking the structural and functional complexity of the nervous system. The accurate exploration of phenotypes in engineered 3D neuronal cultures, however, demands morphological, molecular and especially functional measurements. Particularly crucial is measurement of electrical activity of individual neurons with millisecond resolution. Current techniques rely on customized electrophysiological recording set-ups, characterized by limited throughput and poor integration with other readout modalities. Here we describe a novel approach, using multiwell glass microfluidic microelectrode arrays, allowing non-invasive electrical recording from engineered 3D neural tissues. We demonstrate parallelized studies with reference compounds, calcium imaging and optogenetic stimulation. Additionally, we show how microplate compatibility allows automated handling and high-content analysis of human-induced pluripotent stem cell-derived neurons. This microphysiological platform opens up new avenues for high-throughput studies on the functional, morphological and molecular details of neurological diseases and their potential treatment by therapeutic compounds.

Numerical and experimental investigations for distortion-reduced laser heat treatment of aluminum

In the field of mobility, increased safety and emission requirements lead to steadily rising demands on materials used and their performance. Over the last decades, 5000 and 6000 series aluminum alloys have become more and more attractive as lightweight material due to their beneficial weight to strength ratio. The 7000 series offers extended lightweight potential due to its high strength. Until now, this class of alloys has not been widely used in mass production due to its limited corrosion resistance and poor forming behavior. By using so-called Tailor Heat Treated Blanks, it is possible to set increased forming limits of previously locally heat treated components. The reason for the enhanced formability is the local softening, with the resulting improved material flow and the reduced critical forming stresses of the sheet metal before the forming operation. Despite these advantages, the use of previously heat treated materials has been very limited so far. For example, the distortion that occurs during local heat treatment reduces geometrical accuracy and thus automated handling. Therefore, the focus of this thesis is the investigation of tailored heat treatment strategies, permitting a distortion-reduced local short-term heat treatment. For this purpose, the distortion behavior is represented and quantified both numerically and experimentally. The generated knowledge is then transferred to a large volume component and characterized.

Research and development in robotics with potential to automate handling of biological collections

This report investigates the current state of physical (mechanical) robotics, automated warehousing approaches and assistive technologies in relation to the storage, handling and processing (particularly digitisation) of natural history collections. Robotics can sound futuristic, however we provide case studies that show many and growing examples of physical automation in the natural history and cultural heritage sectors, including barcodes and conveyor belts for digitisation; robots that handle multiple vials for molecular and genetic work; robots for use in in display or exhibition contexts; and automated warehousing of library collections. We provide a non-exhaustive example of an end to end workflow of storage, retrieval and processing and discuss aspects of the tools and challenges relevant to these stages. The Distributed System of Scientific Collections (DiSSCo), a new Research Infrastructure for natural science collections, should build on this, leading a future programme of pilots that develop understanding of independent stages, and can be connected to make progress towards end-to-end solutions. Robots, or automated systems, excel at repetitive tasks, and are developing rapidly to be able to handle more complex object types, at lower cost. High volume, high variety of objects, and considerations such as fragility are not unique to the natural history sector - they apply for example to major retail operations - however natural history collections do offer some of the more extreme examples of these challenges, and in particular are not replaceable. Increased consistency of storage units is likely to be a critical factor in enabling automated handling in future, as well as looking at automation possibilities when new collections storage spaces are developed and built. Engagement with industry and subject matter experts has been patchy and again we recommend that DiSSCo help to ensure a joined up engagement with the right incentives in place, and with clear communication of requirements and challenges for shared R&D. When examining return on investment for particular automation, collections-holding institutions need to consider not only time and cost of automation compared to human labour, but wider factors including: health and safety such as physical environment and repetitve strain injury; security; quality and consistency of outputs; degree of criticality in response times (e.g. if digitising on demand); effective use of spaces; and freeing up staff to conduct other tasks. Purely software-based automation is outside the scope of this report, but is also in increasing use and has enormous potential, for example to transform the extraction of label and specimen data at scale from images. The challenges of managing and digitising collections at scale under DiSSCo are likely to require a combination of hardware and software automation approaches.

Soft nanocomposite electroadhesives for digital micro- and nanotransfer printing

Automated handling of microscale objects is essential for manufacturing of next-generation electronic systems. Yet, mechanical pick-and-place technologies cannot manipulate smaller objects whose surface forces dominate over gravity, and emerging microtransfer printing methods require multidirectional motion, heating, and/or chemical bonding to switch adhesion. We introduce soft nanocomposite electroadhesives (SNEs), comprising sparse forests of dielectric-coated carbon nanotubes (CNTs), which have electrostatically switchable dry adhesion. SNEs exhibit 40-fold lower nominal dry adhesion than typical solids, yet their adhesion is increased >100-fold by applying 30 V to the CNTs. We characterize the scaling of adhesion with surface morphology, dielectric thickness, and applied voltage and demonstrate digital transfer printing of films of Ag nanowires, polymer and metal microparticles, and unpackaged light-emitting diodes.