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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.
Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.
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.
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.