TY - JOUR A1 - Schneider, Matthias A1 - Fritzsche, Nora A1 - Puciul-Malinowska, Agnieszka A1 - Baliś, Andrzej A1 - Mostafa, Amr A1 - Bald, Ilko A1 - Zapotoczny, Szczepan A1 - Taubert, Andreas T1 - Surface etching of 3D printed poly(lactic acid) with NaOH BT - a systematic approach JF - Polymers N2 - The article describes a systematic investigation of the effects of an aqueous NaOH treatment of 3D printed poly(lactic acid) (PLA) scaffolds for surface activation. The PLA surface undergoes several morphology changes and after an initial surface roughening, the surface becomes smoother again before the material dissolves. Erosion rates and surface morphologies can be controlled by the treatment. At the same time, the bulk mechanical properties of the treated materials remain unaltered. This indicates that NaOH treatment of 3D printed PLA scaffolds is a simple, yet viable strategy for surface activation without compromising the mechanical stability of PLA scaffolds. KW - surface modification KW - sodium hydroxide etching KW - poly(lactic acid) KW - 3D KW - printing KW - roughness KW - wettability KW - erosion Y1 - 2020 U6 - https://doi.org/10.3390/polym12081711 SN - 2073-4360 VL - 12 IS - 8 PB - MDPI CY - Basel ER - TY - JOUR A1 - Engel, Robert A1 - Micheel, Burkhard A1 - Hanack, Katja T1 - Three-dimensional cell culture approach for in vitro immunization and the production of monoclonal antibodies JF - Biomedical materials : materials for tissue engineering and regenerative medicine N2 - The generation of monoclonal antibodies using an in vitro immunization approach is a promising alternative to conventional hybridoma technology. As recently published, the in vitro approach enables an antigen-specific activation of B lymphocytes within 10-12 d followed by immortalization and subsequent selection of hybridomas. This in vitro process can be further improved by using a three-dimensional surrounding to stabilize the complex microenvironment required for a successful immune reaction. In this study, the suitability of Geltrex as a material for the generation of monoclonal antigen-specific antibodies by in vitro immunization was analyzed. We could show that dendritic cells, B cells, and T cells were able to travel through and interact inside of the matrix, leading to the antigen-specific activation of T and B cells. For cell recovery and subsequent hybridoma technique the suitability of dispase and Corning cell recovery solution (CRS) was compared. In our experiments, the use of dispase resulted in a severe alteration of cell surface receptor expression patterns and significantly higher cell death, while we could not detect an adverse effect of Corning CRS. Finally, an easy approach for high-density cell culture was established by printing an alginate ring inside a cell culture vessel. The ring was filled with Geltrex, cells, and medium to ensure a sufficient supply during cultivation. Using this approach, we were able to generate monoclonal hybridomas that produce antigen-specific antibodies against ovalbumin and the SARS-CoV-2 nucleocapsid protein. KW - monoclonal antibody KW - hybridoma technology KW - in vitro immunization KW - 3D KW - cell culture KW - Geltrex Y1 - 2022 U6 - https://doi.org/10.1088/1748-605X/ac7b00 SN - 1748-6041 SN - 1748-605X VL - 17 IS - 5 PB - Inst. of Physics CY - London ER - TY - JOUR A1 - Döllner, Jürgen Roland Friedrich T1 - Geospatial artificial intelligence BT - potentials of machine learning for 3D point clouds and geospatial digital twins JF - Journal of photogrammetry, remote sensing and geoinformation science : PFG : Photogrammetrie, Fernerkundung, Geoinformation N2 - Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models. N2 - Georäumliche Künstliche Intelligenz: Potentiale des Maschinellen Lernens für 3D-Punktwolken und georäumliche digitale Zwillinge. Künstliche Intelligenz (KI) verändert grundlegend die Art und Weise, wie IT-Lösungen in allen Anwendungsbereichen, einschließlich dem Geoinformationsbereich, implementiert und betrieben werden. In diesem Beitrag stellen wir KI-basierte Techniken für 3D-Punktwolken als einen Baustein der georäumlichen KI vor. Zunächst werden kurz der Begriff "KI” und die technologischen Entwicklungen skizziert, die für die Anwendung von KI auf IT-Lösungen aus der Sicht der Softwaretechnik erforderlich sind. Als nächstes charakterisieren wir 3D-Punktwolken als Schlüsselkategorie von Geodaten und ihre Rolle für den Aufbau von räumlichen digitalen Zwillingen; wir erläutern die Machbarkeit der Ansätze für Maschinelles Lernen (ML) und Deep Learning (DL) in Bezug auf 3D-Punktwolken. Insbesondere argumentieren wir, dass 3D-Punktwolken als Korpus mit ähnlichen Eigenschaften wie natürlichsprachliche Korpusse gesehen werden können und formulieren eine "Natürlichkeitshypothese” für 3D-Punktwolken. Im Hauptteil stellen wir einen Workflow zur Interpretation von 3D-Punktwolken auf der Grundlage von ML/DL-Ansätzen vor, die eine domänenspezifische und anwendungsspezifische Semantik für 3D-Punktwolken ableiten, ohne explizite räumliche 3D-Modelle oder explizite Regelsätze erstellen zu müssen. Abschließend wird an Beispielen gezeigt, wie ML/DL es ermöglichen, Basisdaten für räumliche digitale Zwillinge, wie z.B. für virtuelle 3D-Stadtmodelle, Innenraummodelle oder Gebäudeinformationsmodelle, effizient aufzubauen und zu pflegen. KW - geospatial artificial intelligence KW - machine learning KW - deep learning KW - 3D KW - point clouds KW - geospatial digital twins KW - 3D city models Y1 - 2020 U6 - https://doi.org/10.1007/s41064-020-00102-3 SN - 2512-2789 SN - 2512-2819 VL - 88 IS - 1 SP - 15 EP - 24 PB - Springer International Publishing CY - Cham ER -