High-resolution Laser Scanning Research Articles

The Drosophila blood–brain barrier invades the nervous system in a GPCR-dependent manner

The blood–brain barrier (BBB) represents a crucial interface between the circulatory system and the brain. In Drosophila melanogaster, the BBB is composed of perineurial and subperineurial glial cells. The perineurial glial cells are small mitotically active cells forming the outermost layer of the nervous system and are engaged in nutrient uptake. The subperineurial glial cells form occluding septate junctions to prevent paracellular diffusion of macromolecules into the nervous system. To address whether the subperineurial glia just form a simple barrier or whether they establish specific contacts with both the perineurial glial cells and inner central nervous system (CNS) cells, we undertook a detailed morphological analysis. Using genetically encoded markers alongside with high-resolution laser scanning confocal microscopy and transmission electron microscopy, we identified thin cell processes extending into the perineurial layer and into the CNS cortex. Interestingly, long cell processes were observed reaching the glia ensheathing the neuropil of the central brain. GFP reconstitution experiments highlighted multiple regions of membrane contacts between subperineurial and ensheathing glia. Furthermore, we identify the G-protein-coupled receptor (GPCR) Moody as negative regulator of the growth of subperineurial cell processes. Loss of moody triggered a massive overgrowth of subperineurial cell processes into the CNS cortex and, moreover, affected the polarized localization of the xenobiotic transporter Mdr65. Finally, we found that GPCR signaling, but not septate junction formation, is responsible for controlling membrane overgrowth. Our findings support the notion that the Drosophila BBB is able to bridge the communication gap between circulation and synaptic regions of the brain by long cell processes.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations

Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.

BEYOND GROUND CONTROL POINTS: COST-EFFECTIVE 3D BUILDING RECONSTRUCTION THROUGH GNSS-INTEGRATED PHOTOGRAMMETRY

Abstract. The process of 3D building modeling serves a multitude of practical and strategic purposes across diverse industries. Building a 3D model involves employing a range of techniques and technologies. Among these, the most used methods include 3D laser scanning and photogrammetry, whether applied at close-range or through the use of Unmanned Aerial Systems (UAS). In photogrammetry, ground control points (GCPs) are generally needed to scale and georeference the digital reconstruction process, but it is a timeconsuming practice or sometimes impractical or dangerous. This paper aims to evaluate the efficiency of two integrated devices to perform photogrammetric 3D reconstruction without GCPs. They are both composed by a Sony ZV1 camera coupled with two different RTK/PPK GNSS system: the Emlid Reach RS2 GNSS receiver and the Emlid Reach M2 module with a multi-band GNSS helical antenna. Different sets of images were acquired with the two proposed devices for the lever-arm estimation and to perform the 3D surveying of the Galata monastery historical monument. The accuracy of the process and derived dense point clouds is assessed by comparing them with GCPs and a reference point cloud derived by fusing an UAS and a high-resolution mobile laser scanning point cloud. The ultimate goal is to obtain a 3D building model without the use of GCPs in the process of bundle block adjustment with centimeter accuracy.

3D visualization of oil-abundant limon flavedo layer under drying

This study investigated the transformations of lemon flavedo tissues during freeze-drying based on high-resolution X-ray micro-computed tomography (μ-CT) and confocal laser scanning microscopy (CLSM). Contrast-enhanced segmentation, traversal statistic algorithmic analysis, and 3D visualization of lemon peel texture are studied. To avoid pseudo-imaging caused by precipitation of contrast enhancer, low concentration cesium iodide was used to improve X-ray contrast and decrease enlarged inter-cellular region. X-ray μ-CT showed the precipitation of contrast enhancers at the intercellular boundary after freeze-drying. The grayscale thresholding method was presented as the most suitable segmentation method for detecting cell walls due to the high noise level caused by low contrast enhancer concentration in the reconstructed images. The faster water loss at the primary drying stage caused the disintegration of oil glands and induced high porosity of 0.7 % in fresh to 0.84 % in 0.5 h freeze-dried. The results suggest that noise filtering based on voxel statistics provides an efficient approach for the inter and intra-cellular microstructural analysis of excessively watery plant tissues with low contrast enhancer concentration.

Carbon Losses from Topsoil in Abandoned Peat Extraction Sites Due to Ground Subsidence and Erosion

Peat erosion has a significant impact on soil fertility, agricultural productivity, and climate change dynamics. Through this process, the topsoil rich in organic matter and carbon (C) is removed and can travel long distances, causing a net C loss. Additionally, peat undergoes oxidation, resulting in further C loss. In our study, we evaluated C losses from 11 peat extraction fields in two study sites, abandoned for more than 15 years and overgrown by vegetation of different densities. We used high-resolution airborne laser scanning point clouds and multispectral aerial images acquired periodically within a 9-year period, as well as chemical analyses of the topsoil layer. In our study, we found a strong correlation between peat subsidence, C loss, and the vegetation density (NDVI value). NDVI also determines most of the uncertainty in elevation data. We found also that both erosion and peat subsidence are significant sources of C losses from peat extraction sites. At a site monitored for over 9 years, our estimated ground elevation changes ranged from 0.1 cm y−1 to 0.58 cm y−1; however, at a different site monitored over a 4-year period, the values ranged from 2.14 cm y−1 to 5.72 cm y−1. Accordingly, the mean annual C losses varied from 0.06 to 0.22 kg C m−2 y−1 and from 1.21 to 3.57 kg C m−2 y−1.

Variations in Trochlear Morphology of Contemporary and Legacy Total Knee Arthroplasty Prostheses: A Review of 22 Designs

BackgroundThe morphology of the trochlear compartment of total knee arthroplasty (TKA) prostheses is a major determinant of postoperative patello-femoral kinematics, particularly with unresurfaced patellae. The objective of this study was to quantify and compare the trochlear morphology of a large series of contemporary and legacy TKA designs. MethodsThe 3-dimensional surface models of 22 femoral components (13 contemporary and 9 legacy) were created using high-resolution laser scanning. The trochlear profile of each component was analyzed from proximal to distal in 15° increments around the trochlear axis. In each profile, the following variables were measured: sulcus angle, medio-lateral deviation of the sulcus, the height and width of the facets, and the trochlear groove orientation. ResultsIn the contemporary group, the sulcus angle decreased progressively along the trochlear arc to varying degrees, except for 2 symmetrical designs, whereas the sulcus angle of the legacy designs showed considerable variability. The height of the medial facet was very strongly correlated with that of the lateral facet in the contemporary group (R2 = 0.89), whereas the correlation was weak for the legacy designs (R2 = 0.36). Moreover, the trochlear sulcus deviated laterally from distal to proximal in 10 contemporary designs and 7 legacy designs, resulting in a trochlear groove orientation of 4.2 to 11.1° and 4.3 to 10.5°, respectively. In the remaining 5 designs (3 contemporary and 2 legacy), the sulcus was vertical. ConclusionsThere is more consistency in trochlear morphology of contemporary TKA designs compared to that of legacy designs, yet there are still large variations between different designs.

A multi-scale robotic approach for precise crack measurement in concrete structures

This paper introduces a multi-scale robotic approach for measuring the width, length, shape, and profile of hairline cracks in concrete structures. The approach uses a convolutional neural network to identify potential surface cracks, and then robotically navigates a high-resolution laser scanner to measure the detailed shape of the detected cracks. Finally, 3D point cloud registration techniques fuse the laser scans with LiDAR-based scan of the surrounding environment. The proposed method is validated with computer simulations and physical experiments on a concrete specimen. The results are compared against the state-of-the-art, vision-based methods as well as readings of a transparent crack width ruler. The comparison demonstrates the superiority and effectiveness of the proposed multi-scale robotic approach in measuring hairline cracks providing vital data for assessing the conditions of civil infrastructures.

Laser Scanning Method for High-Resolution Thickness Mapping of Lithium-Ion Pouch Cells

A precise understanding of the physical properties of lithium-ion cells including the cell thickness distribution during cycling and its connection with lifetime is important for cell improvement. A laser scanning instrument has been developed to perform contact-free thickness measurements in operando for small 250 mAh lithium-ion pouch cells and a large 60 Ah automotive format pouch cell. LiNi0.83Mn0.07Co0.10O2/silicon-graphite cells with 20 and 10 wt% micron-sized silicon particles, a LiNi0.8Mn0.1Co0.1O2/natural graphite cell, and a medium-Ni/graphite automotive cell were either cycled in the laser scanning setup with continuous operando thickness mapping or aged at elevated temperature on separate battery cyclers with intermittent operando thickness mapping using the new laser scanning instrument. During the operando cycles, the cell thickness was measured periodically every 1 h and a graphical quantification method was developed to determine reversible and irreversible swelling of the silicon-containing and silicon-free cells. Using the high-resolution laser scanning technique, irreversible cell swelling could be correlated with capacity loss, especially in cells with high silicon content. Graphite-based cells with mature interface like the large automotive pouch cell showed a fully reversible swelling profile indicative of a long-lived cell.

Structure-function correlation of retinal photoreceptors in PRPH2-associated central areolar choroidal dystrophy patients assessed by high-resolution scanning laser imaging and microperimetry.

High Magnification Module (HMM™, Heidelberg Engineering, Heidelberg, Germany) imaging is a novel technique, designed to visualize the retina at a cellular level. To assess the potential of HMM™-based metrics as endpoints for future trials, we evaluated correlations between structural HMM™ cone metrics, spectral-domain OCT (SD-OCT, Heidelberg Engineering, Heidelberg, Germany) and retinal sensitivity on microperimetry (MP, MAIA, CenterVue, Padova, Italy) in healthy subjects and p.(Arg142Trp) PRPH2-associated Central Areolar Choroidal Dystrophy (CACD) patients. We projected a default 10° MP grid on composite HMM™ images and performed automated cone density (CD), intercell distance (ICD) and nearest neighbour distance (NND) analysis at stimuli located at 3° and 5° retinal eccentricity. We manually measured intrasubject outer retinal thickness on SD-OCT in absolute and relative scotomas, located outside of focal atrophy. We included 15 CACD patients and five healthy subjects. We found moderate-to-strong correlations of HMM™ metrics and MP sensitivity at 3° eccentricity from the fovea. We found the outer retina at the locations of absolute scotomas to be statistically significant thinner (p = 0.000003, one-sample t-test), as the outer retinal thickness at locations of relative scotomas. Interestingly, HMM™ metrics of these areas did not differ significantly. We found significant correlations between structural photoreceptors metrics on HMM™ imaging and retinal sensitivity on MP in healthy subjects and CACD patients. A multimodal approach, combining SD-OCT, MP and HMM™ imaging, allows for detailed mapping of retinal photoreceptor integrity and restitution potential, important data that could serve as biomarkers in future clinical trials.

Discrete wavelet analysis of external corrosion clusters on pipelines for burst capacity prediction

The present study applies the two-dimensional discrete wavelet transform to analyze naturally occurring corrosion clusters on external surfaces of in-service steel transmission pipelines. The corrosion depths in a cluster are measured by high-resolution laser scans. The Daubechies wavelet with two vanishing moments (DB2) is adopted in the analysis, and the widely used RSTRENG model is employed to compute the burst capacity of pipelines with corrosion clusters. A methodology is proposed to determine level- and sub-band-dependent thresholds such that those wavelet coefficients below the thresholds have a negligible impact on the burst capacity and can be ignored for the reconstruction of the cluster. Statistical analyses of the preserved wavelet coefficients indicate that most of the features in the natural corrosion clusters are circumferential features, but their influence on the burst capacity is less significant than longitudinal features. Features that have large impacts on the burst capacity prediction are approximately 4–16 mm in width and 4–32 mm in length. It is unnecessary to decompose the natural corrosion cluster to a scale greater than 32 mm because the wavelet coefficients at higher levels have a negligible impact on the burst capacity prediction.

Three-dimensional configuration of crystal plasticity in stainless steel assessed by high resolution digital image correlation and confocal microscopy

In polycrystalline austenitic stainless steels, a comprehensive three-dimensional configuration of crystal plasticity in relation to microstructure is required to elucidate the deformation behaviors during mechanical loading. In the present work, a combination of backscattered electron image (BSE) based high-resolution digital image correlation (HR-DIC) and laser scanning confocal microscopy (LSCM) was employed to simultaneously investigate in-plane and out-of-plane deformations at the sub-grain scale in 316 stainless steel. The high spatial resolution and precise alignment provided by the three-dimensional deformation assessment enable the identification of specific slip and twinning systems. This is achieved through the minimization of the residual L2 norm between the in-plane components of experimental and theoretical strain tensors. Dominant slip and few mechanical twinning activities are initiated at the early-stage of yielding, and their frequency and intensity increase with the applied load. The hierarchical networks formed by nanotwins, along with the presence of grain and annealing twin boundaries, serve as obstacles for slip glide, leading to substantial twin–slip interaction and grain boundary strengthening. As a consequence of wide mechanical twins formed by parallel nanotwins coalescence and strain localization at the twin boundary, slip transmission occurs, resulting in an effective accommodation of external stress. Furthermore, the consistent observation of out-of-plane roughness, in alignment with twinning Burgers vector predictions, offers valuable insights into the significant deformation gradient occurring in the vicinity of boundaries. This deformation gradient is influenced by the neighboring mechanical twinning activities, indicating strong obstacle to twin transmission and high back-stresses resulting from the highly misoriented grain boundary.

Can a Hand-Held 3D Scanner Capture Temperature-Induced Strain of Mortar Samples? Comparison between Experimental Measurements and Numerical Simulations

The expected lifespan of cement-based materials, particularly concrete, is at least 50 years. Changes in the pore structure of the material need to be considered due to external influences and associated transport processes. The expansion behaviour of concrete and mortar during freeze–thaw attacks, combined with de-icing salt agents, is crucial for both internal and external damage. It is essential to determine and simulate the expansion behaviour of these materials in the laboratory, as well as detect the slow, long-term expansion in real structures. This study measures the expansion of mortar samples during freeze–thaw loading using a high-resolution hand-held 3D laser scanner. The specimens are prepared with fully or partially saturated pore structures through water storage or drying. During freeze–thaw experiments, the specimens are exposed to pure water or a 3% sodium chloride solution (NaCl). Results show contraction during freezing and subsequent expansion during thawing. Both test solutions exhibit similar expansion behaviour, with differences primarily due to saturation levels. Further investigations are required to explore the changing expansion behaviour caused by increasing microcracking resulting from continuous freeze–thaw cycles. A numerical analysis using a 3D coupled hygro-thermo-mechanical (HTM) model is conducted to examine the freeze–thaw behaviour of the mortar. The model accurately represents the freezing deformation during the freeze–thaw cycle.

Experimental investigation of a fissured rock with thermal storage potential subjected to descending amplitude low cycle impacts

As the advancement of deep earth energy engineering in the 21st century, the challenges imposed by dynamic loads and high temperatures will be increasingly regular. This paper studied the dynamic mechanical behaviors and fracture characteristics of defective red sandstone (single fissure inclination of 0°, 30°, 60°, and 90°) subjected to thermal treatments (25 °C, 200 °C, 400 °C, 600 °C, and 800 °C) under descending amplitude low cycle (DALC) impact loading. The experiments were conducted using a modified split Hopkinson pressure bar (SHPB) system, and the fracture surfaces of the failed specimens were characterized through the use of high-resolution 3D laser scanning to obtain point cloud data. Subsequently, the fractal, roughness, texture, and elevation features of fracture surfaces were analyzed using the boxing-counting method, grayscale co-occurrence matrix (GLCM), and elevation-based statistics. The scanning electron microscopy (SEM) was utilized to study the fracture micromorphology as well. Additionally, the mechanisms of damage and fracture evolution were discussed based on the results of X-ray diffractometry (XRD), synchronous thermal analysis (STA), discrete element method (DEM), and strain energy density theory. The results demonstrate that the peak stress and dynamic elastic modulus deteriorate with rising temperatures and increase with larger fissure angles. While the peak strain, strain rate, and peak stress increase factor (PIF) exhibit the opposite trend. The threshold values for the intensification of these events are 400 °C and 60°. The damage is dominated by tensile cracks, which transition to shear at 30° and 60° due to temperature dependence. Meanwhile, the fractal dimension (DC), joint roughness coefficient (JRC), second-order statistics of GLCM, and elevation eigenvalues have been modeled using multiple regression analysis to investigate their interaction with temperature and fissure angle. The best-fit models illustrate significant linear or non-linear correlations between the aforementioned parameters and the mechanical parameters. Furthermore, the magnitude and distribution pattern of thermal cracks, as well as the growth of shear fractures, are governed by the dehydration, thermal expansion, phase transition, plasticization, thermal fracture, and thermal decomposition. Finally, strain energy density theory manifests promising prospects for understanding and predicting DALC impact fracture patterns of specimens.

Relationship between Lidar-Derived Canopy Densities and the Scattering Phase Center of High-Resolution TanDEM-X Data

The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations.

In-situ identification of ice accumulation profile and vibration response at composite blades under various icing conditions

Icing challenges the safety and performance of wind turbines in low temperature environments. Detailed information on the amount and location of ice on rotor blades can facilitate decision making and mitigate hazardous effects. Therefore, a better understanding of the relation between ice accumulation and frequency response is required. Small scale experiments are conducted employing rotor blade specimens, experiencing natural icing of different configuration in terms of temperature and ice extent. The ice distribution is measured with a high-resolution laser scanner, and an algorithm based on alpha shapes is developed to determine the growth of the ice volume. Experimental modal analyses are performed periodically during the icing process, detecting consequent effects in the corresponding frequency spectra. The collected data is assessed regarding its suitability for an ice detection system and serves as a starting point for further developments.

Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern analysis.

Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expertise. In this study, an advanced machine learning algorithm was proposed to produce an accurate and precise high-resolution land use map that includes mangroves in the Al Wajh Bank habitat in northeastern Saudi Arabia. To achieve this, high-resolution multispectral images were generated using an image fusion technique, and machine learning algorithms were applied, including artificial neural networks, random forests and support vector machine algorithms. The performance of the models was evaluated using various matrices, and changes in mangrove distribution and connectivity were assessed using the landscape fragmentation model and Getis-Ord statistics. The research gap that this study aims to address is the lack of accurate and precise mapping and assessment of mangrove status in the Red Sea area, particularly in data-scarce regions. Our study produced high-resolution mobile laser scanning (MLS) imagery of 15-m length for 2014 and 2022, and trained 5, 6 and 9 models for artificial neural networks, support vector machines and random forests (RF) to predict land use and land cover maps using 15-m and 30-m resolution MLS images. The best models were identified using error matrices, and it was found that RF outperformed other models. According to the 15-m resolution map of 2022 and the best models of RF, the mangrove cover in the Al Wajh Bank is 27.6 km2, which increased to 34.99 km2 in the case of the 30-m resolution image of 2022, and was 11.94 km2 in 2014, indicating a doubling of the mangrove area. Landscape structure analysis revealed an increase in small core and hotspot areas, which were converted into medium core and very large hotspot areas in 2014. New mangrove areas were identified in the form of patches, edges, potholes and coldspots. The connectivity model showed an increase in connectivity over time, promoting biodiversity. Our study contributes to the promotion of the protection, conservation and planting of mangroves in the Red Sea area.

Abstract 1084: AI-aided drug development for disconnecting glioma tumor microtube networks

Abstract Diffuse infiltrating gliomas, especially glioblastomas, are the most common incurable primary brain tumors in adults. The aggressive growth and high resistance against cytotoxic agents are mediated by ultra-long tubular membrane protrusions, Tumor Microtubes (TMs), that interconnect single glioblastoma cells via gap junctions to multicellular communicating and resistant networks. Early studies showed that targeting these malignant networks increases response to therapy. Thus, the disconnection of such networks has emerged as a new therapeutic strategy to decrease radioresistance. However, the development of drugs that interfere with TM- and network formation is compromised by the lack of established drug screening pipelines to study a compound‘s specific anti-TM and network-inhibiting activity in combination with radiotherapy. To identify compounds with anti-TM activity, we first developed an in vitro medium-throughput imaging-based drug screening. Human-derived primary glioblastoma cells were seeded in a newly developed 2D monolayer where tumor cells form TMs and networks. The cells were treated with compounds with potential anti-TM activity. Using high-resolution laser scanning microscopy, morphological readouts were obtained and analyzed. The most promising compounds were then evaluated in a xenograft chronic cranial window mouse model. Treatments were administered with concomitant radiotherapy (6Gy per day for 3 days). By observing TM and tumor cell network changes via longitudinal in vivo 2-photon microscopy, tumor cell count, growth, and TM parameters were measured in the live mouse over up to 12 weeks. Among the 87 compounds tested in vitro, 42 significantly inhibited TMs. Two of these hits showed a higher count of dead cells in combination with radiotherapy as opposed to glioblastoma cells treated with radiotherapy alone and were therefore studied in the in vivo pipeline. One of these two agents showed reductions of TM length and striking responses in a combinatorial treatment regime with radiotherapy in individual mice that were not observed in control animals treated with radiotherapy only; however, the responses observed were heterogeneous between regions and mice. Further studies revealed this compound directly phosphorylates the gap junction protein Connexin 43 on S368, which contributes to a decrease in intercellular communication of glioblastoma cells. By applying different imaging techniques and AI algorithms we successfully established a novel in-vitro-to-in-vivo drug screening pipeline for the development of disconnecting agents. Most importantly, our data shows that disconnecting multicellular brain tumor networks increases response to radiotherapy. Further studies will be needed to develop reliable anti-TM agents as novel co-treatment options to sensitize glioblastomas to cytotoxic therapy. Citation Format: Daniel D. Azorín, Sophie Weil, Dirk C. Hoffmann, David Hausmann, Erik Jung, Matthias Osswald, Jill Reckless, Nigel Ramsden, Simon Thompson, David Grainger, Wolfgang Wick, Frank Winkler. AI-aided drug development for disconnecting glioma tumor microtube networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1084.

Identification and Quantification of Surface Depressions on Grassy Land Surfaces of Different Topographic Attributes Using High-Resolution Terrestrial Laser Scanning Point Cloud and Triangulated Irregular Network

The objective of this study was to identify and quantify surface depressions on grass-covered land surfaces using a high-resolution terrestrial laser scanning (TLS) point cloud, and a triangulated irregular network (TIN). The entire grassy land surface in the study area was divided into five subwatersheds of different topographic attributes (i.e., depression depth and surface slope). Surface depressions were identified and quantified using a TIN generated from a high-resolution TLS point cloud. The results indicated that microtopography of the grassy land surface was well-characterized within each subwatershed in comparison with field observations. With the terrestrial light detection and ranging (LIDAR) point cloud of 15-mm point spacing and the TIN method, surface depression storage depths of the five subwatersheds ranged from 1.73 to 14.28 mm in the study area. The surface depression storage depth, as expected, increased with the maximum depth of surface depression. It was also found to increase when the land surface slope became milder. A sensitivity analysis indicated that a point cloud with a point spacing of 30 mm was sufficient to obtain an accurate representation of the terrain surface in the study area. This study also indicated the TIN method can represent the ground surface and the surface depression more realistically than the commonly used digital elevation model (DEM) method due to the TIN method’s higher capability of identifying and filtering out surface obstructions such as blades of grass. Moreover, by using the high-resolution TLS technology and the TIN method, our study provides an important and broad range of reference data on the surface depression storage depth commonly needed in application of the Storm Water Management Model (SWMM) and other watershed models.

Transmission-type high-resolution laser scanning microscope

Transmission-type high-resolution laser scanning microscope