Impact of root observation windows on plant performance, root development, and rhizosphere-microbial communities during late vegetative growth of field-grown winter wheat - Loreen Sommermann; Jan Helge Behr; Narges Moradtalab; Saskia Windisch; Soumitra Paul Chowdhury; Michael Rothballer; Ingo Schellenberg; Doreen Babin; Kornelia Smalla; Lorrie Maccario; SØren J. SØrensen; Uwe Ludewig; et al. (Plant and Soil, 2026)
Background and Aims Root observation windows (RW) installed in the field provide a tool for non-destructive monitoring of root development and rhizosphere processes. However, the highly invasive installation process, which requires cutting soil profiles, may affect plant development and, ultimately, the outcome of the experiments. This study systematically compares plant development with and without the installation of RW. Methods Using the location of a long-term field experiment, the responses of winter wheat to different intensities of tillage, N-fertilization, and use of fungicides were compared for plants grown along root windows and in undisturbed control plots. Sampling was performed during late vegetative growth, six weeks after RW installation, with comparisons of shoot and root biomass, root length, mineral nutritional status, stress-related gene expression, stress metabolites, and the composition of microbial communities reflecting rhizosphere (RH) conditions. Results The presence of RW did not significantly affect most of the parameters analyzed. As an exception, RW plants showed increased foliar concentrations of several nutrients (K, Mg, Ca, Cu, Mn), potentially attributable to reduced inter-plant competition because RW were installed along border rows of the experimental plots. By contrast, N-fertilization intensity and fungicide use affected plant biomass, root growth, and fungal communities. Tillage intensity primarily affected fine-root production, the composition of RH-microbial communities, and physiological stress indicators in leaf tissue. Conclusion The results suggest only a limited influence on performance of winter wheat six weeks after RW installation under the selected experimental conditions, with plant responses and experimental results comparable to undisturbed controls.

Collaborative ring trial for the detection of cow, sheep and goat milk in dairy samples by real-time PCR targeting mitochondrial DNA - Kristina Kappel; Maik Döring; Stefan Nöbel; Katrin Neumann; Claudia Brünen-Nieweler; Ingo Krujatz; Bert Pöpping; Kathrin Szabo; Ilka Haase (Journal of Consumer Protection and Food Safety, 2026)
Authentication of dairy product composition is vital for consumer protection and food safety. This study validated 4 real-time PCR methods targeting mitochondrial DNA from cattle, sheep, goat, and total mammals in a collaborative ring trial. Twelve well-characterised dairy samples – including milk, yoghurt, Picodon-type cheese, and Feta-type cheese ‒ were produced from sheep and goat milk with 1–33.3% cow milk admixture for use in a ring trial involving 15 laboratories. Additional samples from various production stages increased the total to 50, which were also analysed by a single laboratory. All 4 real-time PCR assays demonstrated high specificity and sensitivity, with no false positives or negatives observed in the collaborative trial. Quantitative analysis of species proportions showed good precision across laboratories. However, the proportion of cattle DNA was consistently underestimated due to lower somatic cell counts in cow milk. This bias was effectively reduced by calibrating with milk-based reference samples rather than DNA standards. The methods performed reliably across diverse dairy matrices and production stages. Duplex PCR formats matched the performance of singleplex assays while reducing both time and reagent use. Quantitative results support differentiation between economically motivated adulteration and incidental contamination, although precise determination of cow milk admixture remains challenging due to biological variability. The validated real-time PCR methods described in here are included in the Official Collection of Methods of Analysis (ASU), published by the German Federal Office of Consumer Protection and Food Safety (BVL).

Predicting Air Flow in Calendered Paper Sheets from μ-CT Data: Combining Physics with Morphology - Phillip Gräfensteiner; Andoni Rodriguez; Peter Leitl; Ekaterina Baikova; Maximilian Fuchs; Eduardo Machado Charry; Ulrich Hirn; André Hilger; Ingo Manke; Robert Schennach; Matthias Neumann; Volker Schmidt; et al. (Transport in Porous Media, 2026)
Predicting the macroscopic properties of thin fiber-based porous materials from their microscopic morphology remains challenging because of the structural heterogeneity of these materials. In this study, computational fluid dynamics simulations were performed to compute volume air flow based on tomographic image data of uncompressed and compressed paper sheets. To reduce computational demands, a pore network model was employed, allowing volume air flow to be approximated with less computational effort. To improve prediction accuracy, geometric descriptors of the pore space, such as porosity, surface area, median pore radius, and geodesic tortuosity, were combined with predictions of the pore network model. This integrated approach significantly improves the predictive power of the pore network model and indicates which aspects of the pore space morphology are not accurately represented within the pore network model. In particular, we illustrate that a high correlation among descriptors does not necessarily imply redundancy in a combined prediction.

Detecting change points in time series of inSAR persistent scatterers using deep learning models - Kourosh Shahryarinia; Mohammad Omidalizarandi; Mohammadreza Heidarianbaei; Mohammad Ali Sharifi; Ingo Neumann (Applied Geomatics, 2025)
Accurately detecting significant changes in the Earth’s surface is essential for timely intervention. As a key techniques in Interferometric Synthetic Aperture Radar (InSAR), Persistent Scatterer Interferometry (PSI) generates time series data of Persistent Scatterers (PS), which are stable points on the Earth’s surface that enable precise displacement measurements over time. While many studies have focused on statistical methods for identifying anomalies in PS time series, few have explored the potential of deep learning for change point (CP) detection. A major challenge with supervised deep learning is the need for large labeled datasets. To overcome this, we implemented a simulation algorithm to generate an extensive set of PS points with corresponding target CPs, reflecting the statistical characteristics of PS time series. To identify changes in slope and intercept, We used two deep learning models: Bidirectional Long Short-Term Memory (BiLSTM), designed for time series data, and U-Net, developed for image data. A spectral analysis technique is applied to remove seasonal components from the time series data before feeding into the networks. The models were evaluated using metrics such as F1-score, precision, and recall, and were compared to a Bayesian-based approach. Additionally, the methodology was applied to real PS time series from a study area in Germany. We analyzed the detected CPs along with the neighboring PS time series within a 15-meter radius. The results indicated that the deep learning models outperformed the Bayesian approach in terms of precision, recall, and F1-score with simulated PS time series, highlighting their potential for precise CP detection. Furthermore, the models demonstrated their effectiveness when applied to the real PS time series.

Robust algorithm for automatic surface-based outlier detection in MBES point clouds - Bahareh Mohammadivojdan; Felix Lorenz; Thomas Artz; Robert Weiß; Frederic Hake; Yazan Alkhatib; Ingo Neumann; Hamza Alkhatib (Marine Geodesy, 2025)

Automated damage detection for port structures using machine learning algorithms in heightfields - Frederic Hake; Paula Lippmann; Hamza Alkhatib; Vincent Oettel; Ingo Neumann (Applied Geomatics, 2023)
Marine infrastructures such as harbours, bridges, and locks are particularly exposed to salt water and are therefore subject to increasing deterioration. This makes regular inspection of the structures necessary. The inspection is carried out manually, using divers under water. To improve this costly and time-consuming process, we propose to scan the surface and underwater structure of the port with a multi-sensor system (MSS) and classify the obtained point cloud into damaged and undamaged areas fully automatically. The MSS consists of a high-resolution hydro-acoustic underwater multi-beam echo-sounder, an above-water profile laser scanner, and five HDR cameras. In addition to the IMU-GPS/GNSS method known from various applications, hybrid referencing with automatically tracking total stations is used for positioning. The key research idea relies on 3D data from TLS, multi-beam or dense image matching. For this purpose, we build a rasterised heightfield of the point cloud of a harbour structure by reducing the CAD-based geometry from the measured 3D point cloud. To do this, we fit regular shapes into the point cloud and determine the distance of the points to the geometry. To detect anomalies in the data, we use two methods in our approach. First, we use the VGG19 Deep Neural Network (DNN), and second, we use the Local-Outlier-Factors (LOF) method. To test and validate the developed methods, training data was simulated. Afterwards, the developed methods were evaluated on real data set in Lübeck, Germany, which were acquired with the developed Multi-Sensor-System (MSS). In contrast to the traditional, manual method by divers, we have presented an approach that allows for automated, consistent, and complete damage detection. We have achieved an accuracy of 90.5% for the method. The approach can also be applied to other infrastructures such as tunnels and bridges.

Genome sequencing in families with congenital limb malformations - Jonas Elsner; Martin A. Mensah; Manuel Holtgrewe; Jakob Hertzberg; Stefania Bigoni; Andreas Busche; Marie Coutelier; Deepthi C. de Silva; Nursel Elçioglu; Isabel Filges; Erica Gerkes; Katta M. Girisha; et al. (Human Genetics, 2021)
The extensive clinical and genetic heterogeneity of congenital limb malformation calls for comprehensive genome-wide analysis of genetic variation. Genome sequencing (GS) has the potential to identify all genetic variants. Here we aim to determine the diagnostic potential of GS as a comprehensive one-test-for-all strategy in a cohort of undiagnosed patients with congenital limb malformations. We collected 69 cases (64 trios, 1 duo, 5 singletons) with congenital limb malformations with no molecular diagnosis after standard clinical genetic testing and performed genome sequencing. We also developed a framework to identify potential noncoding pathogenic variants. We identified likely pathogenic/disease-associated variants in 12 cases (17.4%) including four in known disease genes, and one repeat expansion in HOXD13 . In three unrelated cases with ectrodactyly, we identified likely pathogenic variants in UBA2 , establishing it as a novel disease gene. In addition, we found two complex structural variants (3%). We also identified likely causative variants in three novel high confidence candidate genes. We were not able to identify any noncoding variants. GS is a powerful strategy to identify all types of genomic variants associated with congenital limb malformation, including repeat expansions and complex structural variants missed by standard diagnostic approaches. In this cohort, no causative noncoding SNVs could be identified.

Georeferencing of an Unmanned Aerial System by Means of an Iterated Extended Kalman Filter Using a 3D City Model - Johannes Bureick; Sören Vogel; Ingo Neumann; Jakob Unger; Hamza Alkhatib (PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2019)
In engineering geodesy, the technical progress leads to various kinds of multi-sensor systems (MSS) capturing the environment. Multi-sensor systems, especially those mounted on unmanned aerial vehicles, subsequently called unmanned aerial system (UAS), have emerged in the past decade. Georeferencing for MSS and UAS is an indispensable task to obtain further products of the data captured. Georeferencing comprises at least the determination of three translations and three rotations. The availability and accuracy of Global Navigation Satellite System (GNSS) receivers, inertial measurement units, or other sensors for georeferencing is not or not constantly given in urban scenarios. Therefore, we utilize UAS-based laser scanner measurements on building facades. The building latter are modeled as planes in a three-dimensional city model. We determine the trajectory of the UAS by combining the laser scanner measurements with the plane parameters. The resulting implicit measurement equations and nonlinear equality constraints are covered within an iterated extended Kalman filter (IEKF). We developed a software simulation for testing the IEKF using different scenarios to evaluate the functionality, performance, strengths, and remaining challenges of the IEKF implemented.

Microbial communities along biogeochemical gradients in a hydrocarbon‐contaminated aquifer - Karolin Tischer; Sabine Kleinsteuber; Kathleen M. Schleinitz; Ingo Fetzer; Oliver Spott; Florian Stange; Ute Lohse; Janett Franz; Franziska Neumann; Sarah Gerling; Christian Schmidt; Eyk Hasselwander; et al. (Environmental Microbiology, 2013)
Summary Micro‐organisms are known to degrade a wide range of toxic substances. How the environment shapes microbial communities in polluted ecosystems and thus influences degradation capabilities is not yet fully understood. In this study, we investigated microbial communities in a highly complex environment: the capillary fringe and subjacent sediments in a hydrocarbon‐contaminated aquifer. Sixty sediment sections were analysed using terminal restriction fragment length polymorphism ( T‐RFLP ) fingerprinting, cloning and sequencing of bacterial and archaeal 16 S rRNA genes, complemented by chemical analyses of petroleum hydrocarbons, methane, oxygen and alternative terminal electron acceptors. Multivariate statistics revealed concentrations of contaminants and the position of the water table as significant factors shaping the microbial community composition. Micro‐organisms with highest T‐RFLP abundances were related to sulphate reducers belonging to the genus D esulfosporosinus , fermenting bacteria of the genera S edimentibacter and S mithella , and aerobic hydrocarbon degraders of the genus A cidovorax . Furthermore, the acetoclastic methanogens M ethanosaeta , and hydrogenotrophic methanogens M ethanocella and M ethanoregula were detected. Whereas sulphate and sulphate reducers prevail at the contamination source, the detection of methane, fermenting bacteria and methanogenic archaea further downstream points towards syntrophic hydrocarbon degradation.

Down-regulation of achaete-scute complex homolog 1 (ASCL1) in neuroblastoma cells induces up-regulation of insulin-like growth factor 2 (IGF2) - Jialing Li; Ingo Neumann; Ines Volkmer; Martin Sebastian Staege (Molecular Biology Reports, 2011)

Purification and characterisation of a jacalin-related, coleoptile specific lectin from Hordeum vulgare - Ingo Grunwald; Ines Heinig; Hubert H. Thole; Dieter Neumann; Uwe Kahmann; Klaus Kloppstech; Achim E. Gau (Planta, 2007)