PURPOSE: To determine the feasibility of upright compared to supine MRI measurements to determine characteristics of the lumbar spine in AA with spondylolisthesis. METHODS: Ten AA (n=10; m/f: 4/6; 14.5±1.7y; 163±7cm; 52±8kg) from various sports, diagnosed with spondylolisthesis grade I-II Meyerding confirmed by x-ray in standing lateral view, were included. Open low-field MRI images (0.25 Tesla) in upright (82°) and supine (0°) position were evaluated by two observers. Medical imaging software was used to measure the anterior translation (AT, mm), lumbosacral joint angle (LSJA, °) and lordosis angle (LA, °). Reliability was analyzed by the intra-rater correlation coefficient (ICC) and standard error of measurements (SEM). RESULTS: Due to motion artifacts during upright position, measures of three participants had to be excluded. Between observers, AT ranged from 4.2±2.7mm to 5.5±1.9mm (ICC=0.94, SEM=0.6mm) in upright and from 4.9±2.4mm to 5.9±3.0mm (ICC=0.89, SEM=0.9mm) in supine position. LSJA varied from 5.1±2.2° to 7.3±1.5° (ICC=0.54, SEM=1.5°) in upright and from 9.8±2.5° to 10±2.4° (ICC=0.73, SEM=1.1°) in supine position. LA differed from 58.8±14.6° to 61.9±6° (ICC=0.94, SEM=1.19°) in upright and from 51.9±11.7° to 52.6±11.1° (ICC=0.98, SEM=1.59°) in supine position. CONCLUSIONS: Determination of AT and LA showed good to excellent reliability in both, upright and supine position. In contrast, reliability of LSJA had only moderate to good correlation between observers and should therefore be interpreted with caution. However, motion artifacts should be taken into consideration during upright imaging procedures.

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.

Background Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has received tremendous attention as a potential neuromodulator of cognitive and affective functions, which likely exerts its effects via activation of the locus coeruleus-noradrenaline (LC-NA) system. Reliable effects of taVNS on markers of LC-NA system activity, however, have not been demonstrated yet. Methods The aim of the present study was to overcome previous limitations by pooling raw data from a large sample of ten taVNS studies (371 healthy participants) that collected salivary alpha-amylase (sAA) as a potential marker of central NA release. Results While a meta-analytic approach using summary statistics did not yield any significant effects, linear mixed model analyses showed that afferent stimulation of the vagus nerve via taVNS increased sAA levels compared to sham stimulation (b = 0.16, SE = 0.05, p = 0.001). When considering potential confounders of sAA, we further replicated previous findings on the diurnal trajectory of sAA activity. Conclusion(s) Vagal activation via taVNS increases sAA release compared to sham stimulation, which likely substantiates the assumption that taVNS triggers NA release. Moreover, our results highlight the benefits of data pooling and data sharing in order to allow stronger conclusions in research.