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Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests. [ABSTRACT FROM AUTHOR]

Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman's ρ = 0.55–0.86, p≤.01) as well as an accuracy of up to 2.60 mm (95% CI [−3.13, 8.23]) and ≤0.21 Hz (95% CI [−0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings. [ABSTRACT FROM AUTHOR]

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

Detecting and monitoring dental plaque is an important issue in research and clinical practice. In this context, new digital imaging methods that permit permanent documentation of the clinical findings could be promising tools. The aim of the study was therefore to investigate whether disclosed plaque can be reliably visualised on 2D and 3D images captured with digital intraoral imaging devices. Clinical examination was the reference method. Twenty subjects (27.5±1.2 years) were included and plaque was measured at three different stages: habitual plaque (T1), after 72 h without oral hygiene (T2) and after a subsequent habitual brushing exercise (T3). At each time point, plaque was disclosed followed by the clinical examination and capturing the 2D and 3D images (intraoral-camera CS 1500 and intraoral-scanner CS 3600; Carestream Dental, Germany). Plaque amounts were recorded on oral and vestibular surfaces of the Ramfjord-teeth (16, 21, 24, 36, 41, 44) using the Rustogi-modified-Navy-Plaque-Index (RMNPI) and expressed as percentage of plaque-containing RMNPI areas of all RMNPI areas. At T1, percentages (mean±SD) obtained from the clinical examination, 2D and 3D images were 62.2±10.6, 65.1±10.0 and 64.4±10.6 resp. increasing to 76.9±8.0, 77.9±8.6 and 77.5±9.4 resp. at T2. After toothbrushing (T3), values decreased to 56.3±11.1, 58.2±12.1 and 61.2±10.8 resp. All methods were able to show statistically significant changes in plaque amounts at the different time points with in part statistically significant but minor differences between them. The Bland-Altmann analysis revealed a good agreement between values from both 2D and 3D images with the clinical examination. The agreement of the scores obtained with the both image-based methods for the single RMNPI areas with the clinical examination was mainly classified as substantial to almost perfect. Amounts of plaque can be reliably detected and monitored on 2D images from an intraoral camera and on 3D images from an intraoral scanner. [ABSTRACT FROM AUTHOR]

A limited number of gauging stations, especially for nested catchments, hampers a process understanding of the interaction between streamflow, groundwater and water usage during drought. Non‐commercial measurement devices can help overcome this lack of monitoring, but they need to be thoroughly tested. The Dreisam River in the South‐West of Germany was affected by several hydrological drought events from 2015 to 2020 during which parts of the main stream and tributaries fell dry. Therefore it provided a useful case study area for a flexible longitudinal water quality and quantity monitoring network. Among other measurements the setup employs an image‐based method with QR codes as fiducial marker. In order to assess under which conditions the QR‐code based water level loggers (WLL) deliver data according to scientific standards, we compared its performance to conventional capacitive based WLL. The results from 20 monitoring stations reveal that the riverbed was dry for >50% at several locations and even for >70% at most severely affected locations during July and August 2020, with the north western parts of the catchment being especially concerned. Highly variable longitudinal drying patterns of the stream reaches emerged from the monitoring. The image‐based method was found valuable for identification and validation of zero level occurrences. Nevertheless, a simple image processing approach (based on an automatic thresholding algorithm) did not compensate for errors due to natural conditions and technical setup. Our findings highlight that the complexity of measurement environments is a major challenge when working with image‐based methods. [ABSTRACT FROM AUTHOR]

In advanced Rett syndrome (RTT), limited or complete loss of ambulation, nutritional problems and scoliosis are unfavorable factors for bone mineral density (BMD). Still, there are few data available in this research area. Spinal quantitative computed tomography (QCT) allows an exact measurement of the volumetric BMD (vBMD) in this patient group. Two examiners measured vBMD of thoracic and lumbar vertebrae on asynchronous calibrated CTs that were acquired prior to surgical scoliosis correction (n = 21, age 13.6 ± 2.5 years). The values were compared to age- and sex-matched healthy controls to additionally derive Z-scores (n = 22, age 13.8 ± 2.0 years). The results showed the most significant reduction of vBMD values in non-ambulatory RTT patients, with p < 0.001 and average BMD-Z-score −1.5 ± 0.2. In the subgroup comparison, non-ambulatory patients with valproate treatment had significant lower values (p < 0.001) than ambulatory patients without valproate therapy, with an average BMD-Z-score of −2.3 ± 0.2. Comparison of the Z-scores to critical BMD thresholds of 120 and 80 mg/cm3 showed normal Z-scores in case of the ambulatory RTT subgroup, as opposed to BMD-Z-scores of the non-ambulatory RTT subgroups, which were partially below osteopenia-equivalent values. Furthermore, valproate treatment seems to have a direct effect on vBMD in RTT patients and when combined with loss of ambulation, BMD-Z-scores are reduced to osteoporosis-equivalent levels or even further. [ABSTRACT FROM AUTHOR]

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future. [ABSTRACT FROM AUTHOR]

While aging is characterized by neurodegeneration, musical training is associated with experience-driven brain plasticity and protection against age-related cognitive decline. However, evidence for the positive effects of musical training mostly comes from cross-sectional studies while randomized controlled trials with larger sample sizes are rare. The current study compares the influence of six months of piano training with music listening/musical culture lessons in 121 musically naïve healthy elderly individuals with regard to white matter properties using fixel-based analysis. Analyses revealed a significant fiber density decline in the music listening/musical culture group (but not in the piano group), after six months, in the fornix, which is a white matter tract that naturally declines with age. In addition, these changes in fiber density positively correlated to episodic memory task performances and the amount of weekly piano training. These findings not only provide further evidence for the involvement of the fornix in episodic memory encoding but also more importantly show that learning to play the piano at an advanced age may stabilize white matter microstructure of the fornix. [ABSTRACT FROM AUTHOR]

The mostly subterranean nature of district heating system pipelines makes pinpointing any occurring leakages a challenge. Airborne thermography offers a means for widespread monitoring, allowing thermal anomalies to be identified within multitudes of infrared images. This paper details a program developed to automate the entire image analysis process using mainly open-source software. Thermal images are acquired via unmanned aircraft system (UAS), pre-processed, and georeferenced individually or combined to orthomosaics. The search space is minimized to areas around the pipelines. Regions of interest are determined by image segmentation via tailored triangle histogram thresholding. The majority of resulting false alarms are removed by comparison with characteristic traits and results classified by their severity. The algorithm is applied to images newly acquired in Germany as part of a case study. The implemented methodology allows for a reduction of between 92 and 99% of thermal anomalies to a manageable amount of potential leakages for network operators to view. The use of orthomosaiking software in this context, though helpful in coalescing data, is found to lack robustness, precision and therefore reliability. Despite some limitations, the developed program is able to confidently detect and categorize leakages of varying severity and can be used directly by network operators. Future research will focus on further data pre-processing to eliminate thermal drift and remove the remaining false alarms, which mostly pertain to common urban features. [ABSTRACT FROM AUTHOR]

A new semi-automated method is proposed in this study that detects joint traces from digital images and calculates the length distribution through three-dimensional data structuring. The method comprises detecting the pixels corresponding to the joint trace in digital images and calculating the length distribution through three-dimensional data structuring of the pixels detected as the joint trace. Semantic segmentation based on deep learning techniques is applied to the detection of joint trace pixels to overcome the limitations of rule-based image processing techniques. For training a deep learning network, various rock joint images were sampled and labeled, following which the data sets for training, validation, and testing were prepared through classification and augmentation. Using the prepared datasets, the performance of the classifiers based on widely used semantic segmentation networks, namely U-Net (University of Freiburg, Germany) network and DeepLabV3+ (Google, USA) network, was compared. The classifier based on the DeepLabV3+ network was ultimately selected and applied. The trained classifier successfully detected pixels corresponding to the joint trace in images of both flat and uneven rock surfaces, indicating that the deep learning technique could be applied effectively to joint trace detection from digital images. Further, a data structuring technique is proposed to calculate the joint trace length using pixelwise data detected through the trained classifier. This technique uses point cloud data obtained by photogrammetry, and comprises two-dimensional thinning and segmentation, three-dimensional projection, segmentation, and segment linking. The entire proposed method, from detecting the joint trace pixels to three-dimensional data structuring, was verified by applying it to both simple flat and uneven rock surface models. • New technique for joint trace detection using deep learning network is proposed. • New data structuring technique for pixelwise data is proposed. • Semi-automatic calculation of the trace length is possible using the two techniques. • Proposed techniques is verified by applying it to both flat and uneven rock surfaces. [ABSTRACT FROM AUTHOR]

Copyright of Journal for Religion in Education / Religionspädagogische Beiträge is the property of Arbeitsgemeinschaft Katholische Religionspadagogik & Katechetik (AKRK) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rooftop solar photovoltaics can significantly contribute to global energy transitions by providing clean, decentralized energy without the need for new land, thereby avoiding land-use conflicts. It serves as a valuable complement to other renewable-energy sources and is expected to play a crucial role in future electricity systems. Due to the spatiotemporal variability in the solar radiation on roof surfaces, it is essential to determine the potentials of the rooftop photovoltaics and its variations in specific regions. In Germany, this potential was assessed in 5 km × 5 km zones, as well as at the federal-state and national levels. High-quality satellite and reanalysis data were used to determine the power output of the solar photovoltaics. Additionally, high-resolution (2 m × 2 m) European Settlement Map data, calibrated with solar cadastre data, were utilized to evaluate different scenarios. The results show that the potential is concentrated in larger urban areas and the southwestern part of Germany due to the availability of rooftop space and solar radiation. Overall, the national rooftop areas are substantial across all scenarios, ranging from 2100 to 4500 km2. The applied methods and scenarios provide a straightforward way to reveal the spatiotemporal variability and define realistic ranges of the solar photovoltaic potential without requiring detailed information about each building. However, assessing the rooftop photovoltaic potential remains challenging and uncertain due to the lack of large-scale, high-resolution data on building characteristics and the complexity of the solar radiation distribution in urban environments. [ABSTRACT FROM AUTHOR]

NOx is an important primary air pollutant of major environmental concern which is predominantly produced by anthropogenic combustion activities. NOx needs to be accounted for in national emission inventories, according to international treaties. Constructing accurate inventories requires substantial time and effort, resulting in reporting delays of 1 to 5 years. In addition to this, difficulties can arise from temporal and country-specific legislative and protocol differences. To address these issues, satellite-based atmospheric composition measurements offer a unique opportunity for the independent and large-scale estimation of emissions in a consistent, transparent, and comprehensible manner. Here we test the multi-source plume method (MSPM) to assess the NOx emissions over Germany in the COVID-19 period from 2019–2021. For the years where reporting is available, the differences between satellite estimates and inventory totals were within 75–100 kt (NO2) NOx (<10 % of inventory values). The large reduction in the NOx emissions (∼15 %) concurrent with the COVID-19 lockdowns was observed in both the inventory and satellite-derived emissions. The recent projections for the inventory emissions of 2021 pointed to a recovery of the 2021 emissions towards pre-COVID-19 levels. In the satellite-derived emissions, however, such an increase was not observed. While emissions from the larger power plants did rebound to pre-COVID-19 levels, other sectors such as road transport did not, and the change in emissions is likely due to a reduction in the number of heavier transport trucks compared to the pre-COVID-19 numbers. This again illustrates the value of having a consistent satellite-based methodology for faster emission estimates to guide and check the conventional emission inventory reporting. The method described in this work also meets the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues, bolstering transparency and comparability, which are two key values for emission reporting. [ABSTRACT FROM AUTHOR]

Simple Summary: Folate receptors can serve to increase folate uptake in cancer cells through significant overexpression, which is ultimately critical for cell proliferation and tumor growth. Therefore, the characterization of folate receptor expression in cancer presents exciting opportunities in multiple dimensions. On the one hand, the folate receptor can be used as a target for selective transfer of therapeutic agents. On the other hand, it can be used for risk stratification. We found that in prostate cancer (PCa), activation and membrane integration by GPI–transamidase exhibit significant changes. By image analysis, we could demonstrate these differences to healthy control cells and tissues, and we expect high potential in this approach to identify biological subtypes and allow risk stratification based on these alterations in PCa. Due to the proliferation-induced high demand of cancer cells for folic acid (FA), significant overexpression of folate receptors 1 (FR1) is detected in most cancers. To our knowledge, a detailed characterization of FR1 expression and regulation regarding therapeutic and diagnostic feasibilities in prostate cancer (PCa) has not been described. In the present study, cell cultures, as well as tissue sections, were analyzed using Western blot, qRT-PCR and immunofluorescence. In addition, we utilized FA-functionalized lipoplexes to characterize the potential of FR1-targeted delivery into PCa cells. Interestingly, we detected a high level of FR1-mRNA in healthy prostate epithelial cells and healthy prostate tissue. However, we were able to show that PCa cells in vitro and PCa tissue showed a massively enhanced FR1 membrane localization where the receptor can finally gain its function. We were able to link these changes to the overexpression of GPI–transamidase (GPI-T) by image analysis. PCa cells in vitro and PCa tissue show the strongest overexpression of GPI-T and thereby induce FR1 membrane localization. Finally, we utilized FA-functionalized lipoplexes to selectively transfer pDNA into PCa cells and demonstrate the therapeutic potential of FR1. Thus, FR1 represents a very promising candidate for targeted therapeutic transfer pathways in PCa and in combination with GPI-T, may provide predictive imaging in addition to established diagnostics. [ABSTRACT FROM AUTHOR]

• The experiment obtained the crack data of the completely life cycle of crossing nose. • A new algorithm combining four feature selection algorithms, which screens out the 8 crack features most relevant to rolling fatigue. • The supervised learning of support vector machine regression (SVR) has achieved the best results compared to other methods in the regression fitting of the crack feature data. • The results represent a simple system to evaluate the life cycle of crossing nose and the early prediction of fatigue. • The system finds the location of cracks appearance that can create dangerous defects in the crossing nose surface. It is essential to assess the rolling contact fatigue (RCF) of turnouts and maintain them in advance. It saves a lot of money while protecting the safety of railway operations. In Germany, the damage on rails, especially crossing noses, mainly depends on the subjective judgment of experts. There are no objective and comprehensive evaluation criteria. This paper presents the application of image processing and supervised machine learning algorithms to crossing nose fatigue judgment. The fatigue characteristics of the crossing nose rolling contact surface along the life cycle of the crossing nose are analyzed. The study used crack information from magnetic particle inspection (MPI) images of crossing nose surfaces. It uses basic image processing methods to collect physical information about features of fatigue cracks in images. Existing feature selection methods are used to exclude irrelevant features and retain valuable features. And we select the best feature selection method through the regression results. Statistically significant crack features and combinations that depict the surface fatigue state are found. In this paper, by comparing several usually machine learning regression algorithms, it is found that the supervised learning of support vector machine regression (SVR) has achieved the best results in the regression fitting of the crack feature data in this paper. The regression results form a simple system to evaluate the life cycle of crossing nose. The system finds the location of cracks that can create dangerous defects in the crossing nose surface. The research result consists of the early prediction of rail contact fatigue. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to evaluate to the masticatory function performance and Oral Health-related Quality of Life (OHRQoL) in implant-retained overdenture compared with different implant number placements in the edentulous mandible. Methods: From 2013 to 2015, each patients received 3 implants (iSy-Implant, Camlog, Wimsheim, Germany) in intraforaminal mandible (34, 41/31, 44). After operation, inserted implants were gradually loaded and incorporated into an overdenture with a self-aligning attachment system (Locator abutments) in 3 + 3 + 3 months. Five checked points were performed chewing cycle test with multicolored chewing gum and OHIP-G14 questionnaire and a sum score questionnaire as following: pre-operation, one implant load (41/31), two implants loaded (33,43), three implants loaded and 1-year follow up. Result: A total of 10 patients with 30 implants were placed, the survival rate of the implants was 100% within 1-year follow-up. Regarding the masticatory function analysis, for the higher number of chewing cycles, the higher mixing rate was observed. After 1 year, the inter-mixing rate without significant changes was found compared to the time after three implants were loaded with attachment system. The mean value of OHIP-G14 was 30.4 preoperatively, 21.1 after loading the first locator, 10.7 after loading two locator abutments, and 3.2 after loading all three locator abutments. After 1 year, OHIP-G14 was 2.6 without significantly changed. The mean of the sum score was 15.5 preoperatively, 27.8 after activation of the first locator, 39.4 after activation of two locators, 46.2 after activation of all three locators, and 47.3 after 1 year. An increase of 0.7 sum score units per time point was observed. No significance was detectable, analogous to OHIP-G14, compared to the time of activation of all three locator setups (p-value = 0.22). Conclusions: A significant improvement in masticatory function performance and OHRQoL was evaluated with the increasing number of implants with locator attachment in edentulous mandible. With the investigation of the OHIP-G14 and sum score, the results of patient report outcome might be associated with the increase in the number of implants. [ABSTRACT FROM AUTHOR]

To determine the intraspecific variability of microscopic organisms such as dinophytes is challenging, but can be achieved using cultured material. Unusual morphologies of Peridinium tuberosum assigned to the Peridinium cinctum species group have been described as bulges on the posterior end of the cell a hundred years ago and more, but its taxonomic significance is unclear at present. We collected field material in Germany and Poland in order to establish strains to study cell morphology using light and scanning electron microscopy. For the cultured material, DNA sequence data from the rRNA operon was gained as well and included in molecular phylogenetics (including 22 new partial rRNA sequences). Two new, closely related ribotypes were detected, and all strains showed the principle morphology of P. cinctum having an asymmetric epitheca, a large first apical plate and a sulcus extending onto the epitheca. In the single-strain GeoM*979 assigned to one of the two new ribotypes, cells with bulges appeared rarely but consistently, mostly on the hypotheca, but other variations also occurred. Overall, cells of this strain display traits not observed before while studying cultured P. cinctum, and this distinction is further supported by molecular data and additional details of epithecal opening. However, there does not remain enough information to determine strain GeoM*979 as a separate species (namely P. tuberosum) and therefore, it is identified as Peridinium aff. cinctum until further notice. [ABSTRACT FROM AUTHOR]

Copyright of Selcuk Dental Journal is the property of Selcuk Dental Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software, and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age‐related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls. [ABSTRACT FROM AUTHOR]

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows. [ABSTRACT FROM AUTHOR]

Volume 21 (2021), Issue 1 of PAMM "Proceedings in Applied Mathematics and Mechanics" assembles the contributions to the 92nd Annual Meeting of the Gesellschaft für Angewandte Mathematik und Mechanik (GAMM), organized in 2021 by Universität Kassel, Germany, and held as a virtual conference. The contributions are grouped according to the minisymposia and sessions of the conference. Overview of the Sections GAMM related DFG Priority Programmes DFG‐PP 1886 Polymorphic Uncertainty Modelling for the Numerical Design of Structures DFG‐PP 1897 Calm, Smooth and Smart – Novel Approaches for Influencing Vibrations by Means of Deliberately Introduced Dissipation DFG‐PP 2020 Cyclic Deterioration of High‐Performance Concrete in an Experimental‐Virtual Lab DFG‐PP 2256 Variational Methods for Predicting Complex Phenomena in Engineering Structures and Materials Minisymposia MS 1 Computational Photonics MS 2 Symbolic computation methods for differential equations, dynamical systems, and control theory MS 4 Advanced experimental and computational techniques in polymer mechanics Sections 1–24 S1 Multi‐body dynamics S2 Biomechanics S3 Damage and fracture mechanics S4 Structural mechanics S5 Nonlinear oscillations S6 Material modelling in solid mechanics S7 Coupled problems S8 Multiscales and homogenization S9 Laminar flows and transition S10 Turbulence and reactive flows S11 Interfacial flows S12 Waves and acoustics S13 Flow control S14 Applied analysis S15 Uncertainty quantification S16 Optimization S17 Applied and numerical linear algebra S18 Numerical methods of differential equations S19 Optimization of differential equations S20 Dynamics and control S21 Mathematical signal and image processing S22 Scientific computing S24 History of mechanics Young Researchers' Minisymposia YRM1 Algorithms for coupled multi‐physics problems YRM3 Recent developments in isogeometric analysis for flow problemes YRM6 Novel discretization methods for phase‐field modeling in fracture mechanics [ABSTRACT FROM AUTHOR]

Sustainable weed management strategies are critical to feeding the world's population while preserving ecosystems and biodiversity. Therefore, site-specific weed control strategies based on automation are needed to reduce the additional time and effort required for weeding. Machine vision-based methods appear to be a promising approach for weed detection, but require high quality data on the species in a specific agricultural area. Here we present a dataset, the Moving Fields Weed Dataset (MFWD), which captures the growth of 28 weed species commonly found in sorghum and maize fields in Germany. A total of 94,321 images were acquired in a fully automated, high-throughput phenotyping facility to track over 5,000 individual plants at high spatial and temporal resolution. A rich set of manually curated ground truth information is also provided, which can be used not only for plant species classification, object detection and instance segmentation tasks, but also for multiple object tracking. [ABSTRACT FROM AUTHOR]

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. [ABSTRACT FROM AUTHOR]

In recent years, numerous multitemporal global land use and land cover products have been published acting as valuable source for training spatially explicit geosimulation models forecasting urban growth. However, there is a notable gap in research that specifically addresses the sensitivity of models traing with those data sets when it comes to regional modeling purposes. Accordingly, the objectives of this study were to calibrate, validate, and employ global urban input datasets for the regional simulation of urban growth by the year 2030. The SLEUTH urban growth model, focused on the metropolitan area of the Ruhr, Germany, was calibrated using the Global Human Settlement Layer, World Settlement Footprint Evolution, historical OpenStreetMap data, and a Digital Land Cover Model for Germany. The goal was to compare the results in terms of accuracy, certainty, quantity, and allocation, particularly in urban areas susceptible to floods and heat. While all models achieved high accuracy levels concerning quantity and allocation, the extent of new settlements varied from 40.77 km² to 477.91 km². The models based on World Settlement Footprint and OpenStreetMap exhibited higher certainty and lower stochasticity. As the simulated urban growth increased, there was a corresponding rise in the likelihood of allocating new settlements in areas affected by natural hazards. While all models presented a similar relative portion of new settlement areas impacted by floods, variations emerged in terms of areas affected by unfavorable thermal conditions. This study underscored the potential use of historical OpenStreetMap data in training cellular automation for geosimulating future settlement growth. Furthermore, it highlighted the applicability of global Earth observation-based urban datasets for regional geosimulation and explored the impacts of diverse input data on the accuracy, certainty, quantity, and allocation performances in simulating future conditions. [ABSTRACT FROM AUTHOR]

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In cultivated landscapes, grasslands are an important land use type for insect life. Grassland management practices can have a significant impact on insect ecology. For example, intense fertilization and frequent cutting can reduce the diversity and abundance of insects by destroying their habitat and food sources. Thus, the quality of grassland habitat for insect development depends on its management intensity. The intensification of grassland production is discussed as one factor contributing to the decline in insect biomass over recent decades. Characterizing grassland changes over time provides one piece to the larger puzzle of insect decline. We analyzed landscape-level trends in grassland biomass near Orbroich and Wahnbachtal in North Rhine-Westphalia, Germany, over a 25-year period. In both areas, pronounced insect biomass decline had been observed. More than 430 Landsat images were used. An image normalization process was developed and employed to ensure that observed changes over time were attributed to grassland changes and not systemic changes inherent within image time series. Distinct clusters of grassland parcels were identified based on intensity and temporal changes in biomass using Normalized Difference Vegetation Index (NDVI) as an indicator. Cluster separability was confirmed using the Transform Divergence method. The results showed clusters having periods of distinct trends in vegetation biomass, indicating changes in grassland agronomic and/or management practices over time (e.g., fertilization, increased silage production). Changes in management practices coincided with regional trends in cultivation as documented by official statistics. We demonstrated the feasibility of using 100+ images over multiple decades to perform a long-term remote sensing analysis examining grassland change. These temporally expansive and spatially detailed trends of grassland change can be included as factors in the multi-variate analysis of insect decline. The methodology can be applied to other geographic areas. Such improved insights can support informed landscape design and cultivation patterns in relation to insect ecology and the broader context of biodiversity enhancement. [ABSTRACT FROM AUTHOR]

Vehicle detection using data fusion techniques from overhead platforms (RGB/MSI imagery and LiDAR point clouds) with vector and shape data can be a powerful tool in a variety of fields, including, but not limited to, national security, disaster relief efforts, and traffic monitoring. Knowing the location and number of vehicles in a given area can provide insight into the surrounding activities and patterns of life, as well as support decision-making processes. While researchers have developed many approaches to tackling this problem, few have exploited the multi-data approach with a classical technique. In this paper, a primarily LiDAR-based method supported by RGB/MSI imagery and road network shapefiles has been developed to detect stationary vehicles. The addition of imagery and road networks, when available, offers an improved classification of points from LiDAR data and helps to reduce false positives. Furthermore, detected vehicles can be assigned various 3D, relational, and spectral attributes, as well as height profiles. This method was evaluated on the Houston, TX dataset provided by the IEEE 2018 GRSS Data Fusion Contest, which includes 1476 ground truth vehicles from LiDAR data. On this dataset, the algorithm achieved a 92% precision and 92% recall. It was also evaluated on the Vaihingen, Germany dataset provided by ISPRS, as well as data simulated using an image generation model called DIRSIG. Some known limitations of the algorithm include false positives caused by low vegetation and the inability to detect vehicles (1) in extremely close proximity with high precision and (2) from low-density point clouds. [ABSTRACT FROM AUTHOR]

Purpose: Anti-retinal autoantibodies are assumed to be associated with age-related macular degeneration (AMD). To our knowledge, this is the first evaluation of autoantibodies in human sera of participants with different stages of AMD in a large population-based, observational cohort study in Germany. Methods: The Gutenberg Health Study (GHS) is a population-based, observational cohort study in Germany, including 15,010 participants aged between 35 and 74. Amongst others, non-mydriatic fundus photography (Visucam PRO NM™, Carl Zeiss Meditec AG, Jena, Germany) was performed. Fundus images of the first 5000 participants were graded based on the Rotterdam Eye Study classification. Sera of participants with AMD (n=541) and sera of age-matched participants without AMD (n=490) were analyzed by antigen-microarrays. Besides descriptive statistics, autoantibody-levels were compared by Mann-Whitney-U test and the associations of level of autoantibodies with AMD were calculated by logistic regression analysis. Likewise, possible associations of the autoantibodies and both clinical and laboratory parameters on AMD subjects were analyzed. Results: Autoantibodies against transferrin (p<0.001) were significantly downregulated in participants with early AMD and soft, distinct drusen (≥63 μm) or pigmentary abnormalities only compared to Controls. Mitogen-activated protein kinase 3 (p=0.041), glutathione peroxidase 4 (p=0.048), clusterin (p=0.045), lysozyme (p=0.19), protein kinase C substrate 80K-H (p=0.02), heat shock 70 kDa protein 1A (p=0.04) and insulin (p=0.018) show a trend between Control and participants with early AMD and soft, distinct drusen (≥63 μm) or pigmentary abnormalities only. Conclusions: This study contributes to a growing knowledge of autoantibodies in association with different AMD stages compared to controls in the context of a large population-based study in Germany. Especially autoantibodies against inflammatory proteins were downregulated in participants with early AMD and soft, distinct drusen (≥63 μm) or pigmentary abnormalities only. [ABSTRACT FROM AUTHOR]

Climate models face limitations in their ability to accurately represent highly variable atmospheric phenomena. To resolve fine‐scale physical processes, allowing for local impact assessments, downscaling techniques are essential. We propose spateGAN, a novel approach for spatio‐temporal downscaling of precipitation data using conditional generative adversarial networks. Our method is based on a video super‐resolution approach and trained on 10 years of country‐wide radar observations for Germany. It simultaneously increases the spatial and temporal resolution of coarsened precipitation observations from 32 to 2 km and from 1 hr to 10 min. Our experiments indicate that the ensembles of generated temporally consistent rainfall fields are in high agreement with the observational data. Spatial structures with plausible advection were accurately generated. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution‐dependent extreme value distribution with high skill. It showed a high fractions skill score of 0.6 (spatio‐temporal scale: 32 km and 1 hr) for rainfall intensities over 15 mm h−1 and a low relative bias of 3.35%. A power spectrum analysis confirmed that the probabilistic downscaling ability of our model further increased its skill. We observed that neural network predictions may be interspersed by recurrent structures not related to rainfall climatology, which should be a known issue for future studies. We were able to mitigate them by using an appropriate model architecture and model selection process. Our findings suggest that spateGAN offers the potential to complement and further advance the development of climate model downscaling techniques, due to its performance and computational efficiency. Plain Language Summary: Natural disasters like floods, hail, or landslides originate from precipitation. Global climate models are an important tool to understand these hazards and derive expected changes in a future climate. However, they operate on spatial and temporal scales that limit the regional ability to reflect their small‐scale characteristics. This has led to the development of dynamical and statistical downscaling methods. Due to their computational efficiency, machine learning algorithms recently got increased attention as a method for improving the spatial resolution of climate data. Here, we describe a new deep learning model that allows to simultaneously increasing both the temporal and spatial resolution of precipitation data. Our presented approach enhances the spatial resolution by a factor of 16 and the temporal resolution by a factor of 6. The generated rain fields are hardly identifiable as artificially generated and exhibit the typical structure, movement, and distribution of observed rain fields. Key Points: High performance simultaneous spatial and temporal precipitation downscaling enabled by 3D convolution approachGeneration of realistic high‐resolution ensembles using probabilistic conditional generative adversarial networksLow computational effort compared to dynamical downscaling approaches [ABSTRACT FROM AUTHOR]

The ability to fly is crucial for migratory insects. Consequently, the accumulation of damage on the wings over time can affect survival, especially for species that travel long distances. We examined the frequency of irreversible wing damage in the migratory butterfly Vanessa cardui to explore the effect of wing structure on wing damage frequency, as well as the mechanisms that might mitigate wing damage. An exceptionally high migration rate driven by high precipitation levels in their larval habitats in the winter of 2018–2019 provided us with an excellent opportunity to collect data on the frequency of naturally occurring wing damage associated with long‐distance flights. Digital images of 135 individuals of V. cardui were collected and analyzed in Germany. The results show that the hindwings experienced a greater frequency of damage than the forewings. Moreover, forewings experienced more severe damage on the lateral margin, whereas hindwings experienced more damage on the trailing margin. The frequency of wing margin damage was higher in the painted lady butterfly than in the migrating monarch butterfly and in the butterfly Pontia occidentalis following artificially induced wing collisions. The results of this study could be used in future comparative studies of patterns of wing damage in butterflies and other insects. Additional studies are needed to clarify whether the strategies for coping with wing damage differ between migratory and nonmigratory species. [ABSTRACT FROM AUTHOR]

Purpose: Dental implant abutments are defined as medical devices by their intended use. Surfaces of custom-made CAD/CAM two-piece abutments may become contaminated during the manufacturing process in the dental lab. Inadequate reprocessing prior to patient care may contribute to implant-associated complications. Risk-adapted hygiene management is required to meet the requirements for medical devices. Methods: A total of 49 CAD/CAM-manufactured zirconia copings were bonded to prefabricated titanium bases. One group was bonded, polished, and cleaned separately in dental laboratories throughout Germany (LA). Another group was left untreated (NC). Five groups received the following hygiene regimen: three-stage ultrasonic cleaning (CP and FP), steam (SC), argon–oxygen plasma (PL), and simple ultrasonic cleaning (UD). Contaminants were detected using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) and segmented and quantified using interactive machine learning (ML) and thresholding (SW). The data were statistically analysed using non-parametric tests (Kruskal–Wallis test, Dunn's test). Results: Significant differences in contamination levels with the different cleaning procedures were found (p ≤ 0.01). The FP–NC/LA groups showed the most significant difference in contamination levels for both measurement methods (ML, SW), followed by CP–LA/NC and UD–LA/NC for SW and CP–LA/NC and PL–LA/NC for ML (p ≤ 0.05). EDS revealed organic contamination in all specimens; traces of aluminum, silicon, and calcium were detected. Conclusions: Chemothermal cleaning methods based on ultrasound and argon–oxygen plasma effectively removed process-related contamination from zirconia surfaces. Machine learning is a promising assessment tool for quantifying and monitoring external contamination on zirconia abutments. [ABSTRACT FROM AUTHOR]

Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction. [ABSTRACT FROM AUTHOR]

The inherent complexity of underground mining requires highly selective ore extraction and adaptive mine planning. Repeated geological face mapping and reinterpretation throughout mine life is therefore routine in underground mines. Hyperspectral imaging (HSI) has successfully been applied to enhance geological mapping in surface mining environments, but remains a largely unexplored opportunity in underground operations due to challenges associated with illumination, wet surfaces and data corrections. In this study, we propose a workflow that paves the way for the operational use of HSI in active underground mines. In a laboratory set‐up, we evaluated different hyperspectral sensors and lighting set‐ups as well as the effect of surface moisture. We then acquired hyperspectral data in an underground mine of the Zinnwald/Cínovec Sn‐W‐Li greisen‐type deposit in Germany. These data were corrected for illumination effects, back‐projected into three dimensions and then used to map mineral abundance and estimate Li content across the mine face. We validated the results with handheld laser‐induced breakdown spectroscopy. Despite remaining challenges, we hope this study will help establish hyperspectral sensors in the extractive industry as a means to increase the volume and efficiency of raw material supply, advance digitalisation, and reduce the environmental footprint and other risks associated with underground mining. [ABSTRACT FROM AUTHOR]

Vegetation classification is an essential prerequisite for understanding vegetation‐water relations at a range of spatial scales. However, in site‐specific applications, such classifications were mostly based on a single Unmanned Aerial Vehicle (UAV) flight, which can be challenging in grasslands and/or herbaceous‐dominated systems, as those communities are small in size and highly mixed. Here, we conducted monthly UAV flights for two years in a riparian wetland in Germany, with acquired imagery used for vegetation classification on a monthly basis under different strategies (with or without auxiliary information from other flights) to increase understanding in ecohydrology. The results show that multi‐flight‐based classification outperformed single‐flight‐based classification due to the higher classification accuracy. Moreover, improved sensitivity of temporal changes in community distribution highlights the benefits of multi‐flight‐based classification ‐ providing a more comprehensive picture of community evolution. From reference to the monthly community distribution, we argue that a combination of two or three flights in early‐ and late‐summer is enough to achieve comparable results to monthly flights, while mid‐summer would be a better timing in case only one flight is scheduled. With such detailed vegetation mapping, we further interpreted the complex spatio‐temporal heterogeneity in NDVI and explored the dominant areas and developmental progress of each community. Impacts from management (mowing events) were also evaluated based on the different responses between communities in two years. Finally, we explored how such vegetation mapping could help understand landscape ecohydrology, and found that the spatio‐temporal distribution of minimal soil moisture was related to NDVI peaks of local community, while grass distribution was explained by both topography and low moisture conditions. Such bi‐directional relationships proved that apart from contributing to an evidence base for wetland management, multi‐flight UAV vegetation mapping could also provide fundamental insights into the ecohydrology of wetlands. [ABSTRACT FROM AUTHOR]

Heterogeneous flow pathways through the soil determine the transport of dissolved and particle-bound nutritional elements like phosphorus (P) to ground and surface waters. This study was designed to understand the spatial patterns of P in agriculturally used soils and the mechanisms causing P accumulation and depletion at the centimetre scale. We conducted dye tracer experiments using Brilliant Blue on a loamy Stagnosol in North-Eastern-Germany. The plant-available P was analysed using double lactate extraction (DL-P). The plant-available P content of the topsoil was significantly higher than that of the subsoil in all three replicates (p < 0.001). The topsoil's stained areas showed significantly higher P contents than unstained areas (p < 0.05), while the opposite was found for the subsoil. The P content varied enormously across all observed soil profiles (4 to 112 mg P kg−1 soil) and different categories of flow patterns (matrix flow, flow fingers, macropore flow, and no visible transport pathways). The P contents of these transport pathways differed significantly and followed the order: Pmatrix flow > Pfinger flow > Pno visible transport pathways > Pmacropore flow. We conclude that P tends to accumulate along flow pathways in the topsoil in the observed fertilized and tilled mineral soil. In contrast, in the subsoil at a generally lower P level, P is depleted from the prominent macroporous flow domains. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduced two datasets from different markets, one with over 300,000 entries from Germany to serve as a training basis for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we included extensive cross-market analyses by comparing the emerging Romanian market versus one of the world's largest and most developed car markets, Germany. Our study used several neural network architectures that captured complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset was limited. Our experiments highlighted the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision making and provide valuable guidance in the used car market. [ABSTRACT FROM AUTHOR]

Death of dopaminergic (DAergic) neurons in the substantia nigra pars compacta of the human brain is the characteristic pathological feature of Parkinson's disease (PD). On exposure to neurotoxicants, Drosophila too exhibits mobility defects and diminished levels of brain dopamine. In the fly model of sporadic PD, our laboratory has demonstrated that there is no loss of DAergic neuronal number, however, a significant reduction in fluorescence intensity (FI) of secondary antibodies that target the primary antibody-anti-tyrosine hydroxylase (TH). Here, we present a sensitive, economical, and repeatable assay to characterize neurodegeneration based on the quantification of FI of the secondary antibody. As the intensity of fluorescence correlates with the amount of TH synthesis, its reduction under PD conditions denotes the depletion in the TH synthesis, suggesting DAergic neuronal dysfunction. Reduction in TH protein synthesis is further confirmed through Bio-Rad Stain-Free Western Blotting. Quantification of brain DA and its metabolites (DOPAC and HVA) using HPLC-ECD further demonstrated the depleted DA level and altered DA metabolism as evident from enhanced DA turnover rate. Together all these PD marker studies suggest that FI quantification is a refined and sensitive method to understand the early stages of DAergic neurodegeneration. FI quantification is performed using ZEN 2012 SP2, a licensed software from Carl Zeiss, Germany. This method will be of good use to biologists, as it with few modifications, can also be implemented to characterize the extent of degeneration of different cell types. Unlike the expensive and cumbersome confocal microscopy, the present method using fluorescence microscopy will be a feasible option for fund-constrained neurobiology laboratories in developing countries. [ABSTRACT FROM AUTHOR]

The chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry – through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies. [ABSTRACT FROM AUTHOR]

Purpose : To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). Material and methods: Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. Results: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. Conclusion: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions. [ABSTRACT FROM AUTHOR]

Konservat‐Lagerstätten, such as the Toarcian (Early Jurassic) Posidonia Shale of southwestern Germany, are renowned for their spectacular fossils. Ichthyosaur skeletons recovered from this formation are frequently associated with soft tissues; however, the preserved material ranges from three‐dimensional, predominantly phosphatized structures to dark films of mainly organic matter. We examined soft‐tissue residues obtained from two ichthyosaur specimens using an integrated ultrastructural and geochemical approach. Our analyses revealed that the superficially‐looking 'films' in fact comprise sections of densely aggregated melanosome (pigment) organelles sandwiched between phosphatized layers containing fibrous microstructures. We interpret this distinct layering as representing condensed and incompletely degraded integument from both sides of the animal. When compared against previously documented ichthyosaur fossils, it becomes readily apparent that a range of preservational modes exists between presumed 'phosphatic' and 'carbonized' soft‐tissue remains. Some specimens show high structural fidelity (e.g. distinct integumentary layering), while others, including the fossils examined in this study, retain few original anatomical details. This diversity of soft‐tissue preservational modes among Posidonia Shale ichthyosaurs offers a unique opportunity to examine different biostratinomic, taphonomic and diagenetic variables that potentially could affect the process of fossilization. It is likely that soft‐tissue preservation in the Posidonia Shale was regulated by a multitude of factors, including decay efficiency and speed of phosphatic mineral nucleation; these in turn were governed by a seafloor with sustained microbial mat activity fuelled by high organic matter input and seasonally fluctuating oxygen levels. [ABSTRACT FROM AUTHOR]

The goal of the current study was to determine the mechanical proprieties of polymethylmethacrylate (PMMA) and the improved compound, the graphene-based PMMA, with Zn and Ag and to compare the results. Scanning electron microscopy analysis of the samples before and after the mechanical test was conducted. The compression behavior, flexural properties, tensile strength, and shape of the samples were all investigated and compared between the variants of PMMA. Commercially available polymethylmethacrylate was used (Orthocryl®—Dentaurum, Ispringen, Germany) with the salt and pepper technique according to the manufacturer's instructions to produce 20 samples for each mechanical trial with standard cylinders (4 mm diameter × 8 mm length) for compression, parallelepipedal prisms for flexing (2 mm × 2 mm × 25 mm) and flat samples for traction. There was no statistical difference in the mechanical proprieties of the samples evaluated, although there were values that could suggest significance. The graphene-based PMMA demonstrated good mechanical proprieties, like the commercially available PMMA, and appears promising for future clinical use based on its multiple advantages. [ABSTRACT FROM AUTHOR]

We demonstrate how machine learning can be easily applied to support the analysis of large quantities of excited hydroxyl (OH *) airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories "dynamic" (images where small-scale motions like turbulence are likely to be found), "calm" (clear-sky images with weak airglow variations) and "cloudy" (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany, between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the dynamic category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m per pixel) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similarly to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of the energy dissipation rate range from 0.03 to 3.18 W kg -1. [ABSTRACT FROM AUTHOR]

Many geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a priori information is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally constrained smooth inversion as well as to more standard (i.e., not structurally constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Previous studies have found that C turnover is bound to hotspots of microbial activity. The objective of this study was to analyze the effects of pure energy substrate (glucose), nutrient (mineral N or P) and combined substrate and nutrient (glucose + N, glucose + P, sterile DOC, artificial root exudate extract) additions to enzyme activity inside and outside hotspots as a proxy for microbial C turnover in a subsoil. By means of different substrate and nutrient additions, we tested how the limitations of our site were distributed on a small scale and depth-dependently to contribute to an increase in knowledge of subsoil mechanistics. The study site is a sandy Dystric Cambisol under an over 100-year-old beech forest stand in Lower Saxony, Germany. Forty-eight undisturbed soil samples from two depth increments (15–27 cm and 80–92 cm) of three profiles were sprayed homogeneously with easily available C, N and P sources to investigate the impacts of substrates and nutrients on three enzyme activities (acid phosphatase, β-glucosidase and N-acetylglucosaminidase) by using the soil zymography approach. Comparisons of upper and lower subsoils showed significantly fewer and smaller hotspots in the lower subsoil but with a high degree of spatial variation in comparison to the upper subsoil. Different patterns of enzyme distribution between upper and lower subsoil suggest microbial communities with a lower diversity are found in deeper soil regions of the site. Both substrate and nutrient additions stimulated enzyme activities significantly more outside the initial hotspots than within. Because of this, we conclude that microorganisms in the initial hotspots are less limited than in the surrounding bulk soil. Changes in enzyme activities owing to both substrate and nutrient addition were stronger in the lower subsoil than in the upper subsoil, showing differences in limitations and possible changes in microbial community structure with increasing depth. The results of our study emphasize the need to consider spatial factors in microbial turnover processes, especially in lower subsoil regions where stronger substrate and nutrient limitations occur. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions; Hemangioma and hepatocellular carcinoma (HCC). Ultrasound images, which are commonly available and have low cost and low risk compared to computerized tomography (CT) scan images, will be used as input for the model. A total of 350 ultrasound images belonging to 59 patients are used. The number of images with HCC is 202 and 148, respectively. These images were collected from ultrasound cases.info (28 Hemangiomas patients and 11 HCC patients), the department of radiology, the University of Washington (7 HCC patients), the Atlas of ultrasound Germany (3 HCC patients), and Radiopedia and others (10 HCC patients). The ultrasound images are divided into 225, 52, and 73 for training, validation, and testing. A data augmentation technique is used to enhance the validation performance. We proposed an approach based on ensembles of the best-selected deep transfer models from the on-the-shelf models: VGG16, VGG19, DenseNet, Inception, InceptionResNet, ResNet, and EfficientNet. After tuning both the feature extraction and the classification layers, the best models are selected. Validation accuracy is used for model tuning and selection. The accuracy, sensitivity, specificity and AUROC are used to evaluate the performance. The experiments are concluded in five stages. The first stage aims to evaluate the base model performance by training the on-the-shelf models. The best accuracy obtained in the first stage is 83.5%. In the second stage, we augmented the data and retrained the on-the-shelf models with the augmented data. The best accuracy we obtained in the second stage was 86.3%. In the third stage, we tuned the feature extraction layers of the on-the-shelf models. The best accuracy obtained in the third stage is 89%. In the fourth stage, we finetuned the classification layer and obtained an accuracy of 93% as the best accuracy. In the fifth stage, we applied the ensemble approach using the best three-performing models and obtained an accuracy, specificity, sensitivity, and AUROC of 94%, 93.7%, 95.1%, and 0.944, respectively. [ABSTRACT FROM AUTHOR]

The creeping perennial weed species Cirsium arvense (L.) Scop. occurs in patches. Expanding creeping roots allow these patches to increase their covered area. This characteristic has rarely been addressed when investigating the effects of control options in arable fields. We designed a three-year field experiment (2019–2021) in north-eastern Germany, accounting for existing patch patterns. The experimental setup included an untreated control, a competition treatment (cover crop, CC), two disturbance treatments by mouldboard ploughing (PL), root cutting (RC), and four combined applications (RC + CC, PL + CC, PL + RC, PL + RC + CC). Root cutting was performed by a prototype tillage machine produced by "Kverneland". Plots were defined by the species growth pattern and mapped by GPS and UAV. The experiment investigates the thistle response variables: "Expansion", "Density", "Coverage", and "Height". Treatments including disturbance by ploughing (PL, PL + CC, PL + RC, PL + RC + CC) reduced "Density" by the factor 0.15 and "Expansion" by 0.25, while those without ploughing (CC, RC, RC + CC) only reduced "Density" by the factor 0.68 and "Expansion" by 0.71. Adding root cuttings or cover crops did not further increase the reduction effect of ploughing. Treatments with competition by cover crops impacted "Expansion" more clearly than "Density". When cover crops were combined with root cutting (RC + CC), "Expansion" was almost additively reduced, resulting in a reduction comparable to that of ploughing. The "Height" of the shoots was significantly reduced in four treatments (PL, RC + CC, PL + RC, PL + RC + CC), while "Coverage" did not change significantly. UAV patch monitoring proved to be accurate enough for thistle "Expansion" but not for thistle "Density" within the patch. The results of this study demand innovative research when controlling patch-forming creeping perennial weeds. The need for patches will limit small-scale experimental set ups. [ABSTRACT FROM AUTHOR]

Flooding annually causes thousands of fatalities and billions of dollars in damage globally. Predicting future floods has become increasingly challenging due to changing urban environments and land surface conditions. Simultaneously, severe floods are likely to increase due to climate change and associated shifts in rain patterns, resulting into potentially stronger and more consequential flood events. High-water marks represent key information to be collected after flooding for advancing the understanding of flood impacts and the development of mitigation strategies. However, high-water marks often become increasingly difficult to detect with time passing after a flood event due to drying. In addition, access into flooded areas can be complicated by destroyed infrastructure, leading to significant loss of data or risk to personnel entering these recently flooded areas. Here, initial data are presented demonstrating the application of multispectral imagery in rapidly collecting and mapping high-water marks after flooding. The multispectral images were collected 3–4 weeks after the July 14, 2021, western European flood events in the town of Mayschoss, Germany, along the Ahr River. At that time, affected buildings, walls, and soil were exposed to high summer temperatures and solar radiation, as well as dust from surrounding emergency response and repair works. High-water marks were barely visible by eye. Preliminary results showed the high-water mark is significantly enhanced in the blue band (wavelength 443 to 507 nm) and can be modally isolated through linear combination of the blue band and red-edge band (wavelength 705 to 729 nm). The results illustrate the potential to apply this technique in postdisaster reconnaissance to quickly and safely map high-water levels to identify the magnitude and extent of flooding in urban areas. [ABSTRACT FROM AUTHOR]

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products' high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. [ABSTRACT FROM AUTHOR]

Emerging contaminants are produced globally at high rates and often ultimately find their way into the aquatic environment. These include substances contained in anti-seizure medication (ASM), which are currently appearing in surface waters at increasing concentrations in Germany. Unintentional and sublethal, chronic exposure to pharmaceuticals such as ASMs has unknown consequences for aquatic wildlife. Adverse effects of ASMs on the brain development are documented in mammals. Top predators such as Eurasian otters (Lutra lutra) are susceptible to the bioaccumulation of environmental pollutants. Still little is known about the health status of the otter population in Germany, while the detection of various pollutants in otter tissue samples has highlighted their role as an indicator species. To investigate potential contamination with pharmaceuticals, Eurasian otter brain samples were screened for selected ASMs via high-performance liquid chromatography and mass spectrometry. Via histology, brain sections were analyzed for the presence of potential associated neuropathological changes. In addition to 20 wild otters that were found dead, a control group of 5 deceased otters in human care was studied. Even though none of the targeted ASMs were detected in the otters, unidentified substances in many otter brains were measured. No obvious pathology was observed histologically, although the sample quality limited the investigations. [ABSTRACT FROM AUTHOR]