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Bias correction of ERA5-Land air temperature in remote tropical regions using participatory monitoring data
(2026) Mitze, Fabian; Jacobs, Suzanne Robin; Breuer, Lutz; Campos Zeballos, Jazmin; Dowling, Thomas P. F.; Weeser, Björn
Many remote tropical mountainous regions lack precise, spatially distributed air temperature data. This limits both scientific research and applied solutions in fields such as hydrology, meteorology, health and agriculture. As regular monitoring networks do not provide sufficient spatial coverage in these regions, alternatives are required. To address this issue, this study proposes a novel approach using participatory monitoring (PM) data for point-to-point bias correction of ERA5-Land air temperature data. ERA5-Land data was trained on PM data from different remote mountainous regions in Ecuador (n = 67), Honduras (n = 23) and Tanzania (n = 275) using simple linear regression models. Validation was conducted using automatically measured air temperature, applying different metrics, including mean absolute error (MAE) and coefficient of determination (R2). Validation demonstrated an improvement across all stations, with the most significant reduction in MAE, from 5.49 °C to 1.76 °C in Ecuador. Concurrently, R2 increased across all stations up to 0.83. The decrease in deviation was also statistically significant for all stations. The study demonstrated that incorporating PM data into simple linear regression bias correction can reduce the regional bias of ERA5-Land air temperature data. This can be regarded as a pragmatic solution for remote regions, where the absence of weather stations is a common issue.
Inhibition of FXIIa attenuates kidney fibrosis in mice with unilateral ureteral obstruction
(2025) Kalina, Daniel; Potaczek, Daniel P.; Zeng-Brouwers, Jinyang; Boehm, Mario; Nolte, Marc W.; Bielohuby, Maximilian; Schermuly, Ralph T.; Schaefer, Liliana; Wygrecka, Malgorzata
Kidney fibrosis is a common manifestation of chronic kidney diseases, with parenchymal tissue scarring serving as a histologic predictor of functional deterioration. Considering the relationships between contact-phase system activation and renal fibrosis as well as potential direct profibrotic activities of factor XII (FXII), we hypothesized that FXII inhibition with an anti-FXII/activated FXII (FXIIa) antibody (3F7) demonstrates therapeutic efficacy in a mouse model of unilateral ureteral obstruction (UUO). Treatment of UUO mice with 3F7 attenuated kidney fibrosis, as evidenced by preserved tissue structure, decreased deposition of collagen, and diminished apoptosis, but increased proliferation of tubular epithelial cells. No effect was observed with the administration of C1 esterase inhibitor, which serves as a primary plasma inhibitor of FXIIa and kallikrein. Transcriptome analysis revealed that 3F7 therapy predominantly affects stress-activated protein kinase signaling cascades and signal transduction in response to DNA damage. Exposure of renal epithelial cells to FXII or FXIIa triggered p21 expression in an Akt- and ERK1/2-dependent manner. Accordingly, treatment of UUO mice with 3F7 reduced numbers of p21+ renal tubular epithelial cells. Our work provides proof-of-concept data supporting efficacy of 3F7 in the UUO model and unravels molecular mechanisms underlying the protective role of FXII inhibition in chronic kidney injury.
Hepatic and intestinal microcirculation and pulmonary inflammation in a model of veno-venous extracorporeal membrane oxygenation in the rat
(2025) Edinger, Fabian; Zajonz, Thomas; Mayer, Nico; Schmidt, Goetz; Schneck, Emmanuel; Sander, Michael; Koch, Christian
Background: Veno-venous (V–V) extracorporeal membrane oxygenation (ECMO) is widely used in critical care but remains associated with high mortality rates (22–68%). In septic shock, increased pulmonary inflammation and impaired intestinal and hepatic microcirculation have been observed during ECMO therapy. To explore the impact of ECMO-induced inflammation, this study used a rat model with varying ECMO blood flows to assess intestinal and hepatic microcirculation and lung inflammation. Methods:Thirty male Lewis rats were randomised into three groups: sham, low-flow ECMO (60 mL/kg/min), and high-flow ECMO (90 mL/kg/min). V–V ECMO was established via femoral drainage and jugular return. Microcirculation in the intestine and liver was measured using micro-light guide spectrophotometry after laparotomy. Systemic and pulmonary inflammation were evaluated through cytokine levels in plasma and bronchoalveolar lavage (BAL), focusing on tumour necrosis factor-alpha (TNF-α), interleukins 6 (IL6) and 10 (IL10), and C–X–C motif chemokine ligands 2 (CXCL2) and 5 (CXCL5). Hemodynamic data were obtained using a left ventricular pressure–volume catheter. Results: Intestinal oxygenation was significantly impaired only during low-flow ECMO therapy (65% [62–70%]) compared to sham therapy (76% [72–79%], p = 0.003), while hepatic microcirculation was reduced during both low-flow (21% [14–26%]) and high-flow (19% [16–21%]) ECMO therapy compared to sham therapy (43% [38–48%], all p < 0.001). Serum TNF-α levels were only significantly elevated during high-flow ECMO therapy (1 h: 14 [12–22] pg/mL; 2 h: 18 [15–38] pg/mL) compared to the sham procedure (1 h: 10 [9–11] pg/mL; 2 h: 10 [9–11] pg/mL; p = 0.033). In contrast, BAL IL6 levels were significantly lower during both high- and low-flow ECMO therapy (32 pg/mL) than sham therapy (81 pg/mL, p ≤ 0.001). IL10, CXCL2, and CXCL5 levels did not differ significantly between the low- and high-flow ECMO and sham therapies. Conclusions: ECMO-induced inflammation is blood flow dependent. In healthy rats, high-flow ECMO did not impair intestinal microcirculation and was associated with reduced pulmonary inflammation, likely due to lung-protective ventilation.
Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species
(2025) Heilmann, Philipp Georg; Grosch, Emanuel; Frisch, Matthias; Herrmann, Matthias; Beuch, Steffen; Kurra, Vivek; Mascher, Martin; Avni, Raz; Oldach, Klaus; Röhrs, Ina; Hanemann, Anja; Mehta, Raja Ram; Reinbrecht, Carsten; Serfling, Albrecht; Stahl, Andreas; Stucke, Marco; Abbadi, Amine; Kox, Tobias; Zenke-Philippi, Carola
Background: In plant breeding, many studies currently investigate the application of machine learning (ML) to genomic prediction, hoping for an improvement in prediction accuracy compared to standard models like Genomic Best Linear Unbiased Prediction (GBLUP). However, ML algorithms require much higher computational resources. This study aims to reduce the computational requirements and speed up training time by developing a novel autoencoder architecture inspired by haplotype blocks. Our approach incorporates prior knowledge on genetic linkage, inspired by haplotype block building, into the autoencoder architecture, resulting in a new encoded variable per haplotype block. We further modified our model into a semi-supervised version by adding available yield information. We used features extracted from the autoencoder’s block layer as inputs for Random Forest and GBLUP models to predict the yield of hybrid and inbred crops. Results: Genomic prediction based on the extracted features maintained prediction accuracies equal to using the original marker data, even with a variable reduction of up to 98% and significantly reduced computation time. Prediction accuracy of the supervised component was in some cases equal to and in some lower than the prediction accuracy achieved using GBLUP. Effects estimated for haplotype block variants using our new method showed a high correlation to the blockwise sum of marker effects, which is the current standard approach for haplotype block effects. Correlation between the two block effect estimation approaches was very low for some blocks, which might indicate the incorporation of non-linear effects by the autoencoder. Conclusions: Our approach introduces a new perspective on processing haplotype blocks for genomic prediction, potentially providing more flexible modelling opportunities without the use of multiple binary dummy variables for each block variant. Additionally, training time for ML models may be significantly reduced by using the reduced feature sets generated using our method. By adding the semi-supervised component, the model is able to estimate values similar to marker effects for each block on yield. In future work, this may provide a new way of quantifying the importance of haplotype blocks for selection and breeding.
Characterization of the performance of a RIT-10 and rf-neutralizer for an ABEP system
(2025) Zorn, Jana; Bräumer, Kalle J.; Rodriguez, Rodrigo S.; Wölki, Andreas R.; Keil, Konstantin; Komjagin, Anna; Bremer, Fiene B.; Hellbert, Aaron; Reuß-Hennschen, Mathis; Kaufmann, Arnold; Krempel-Hesse, Johannes; Chen, Limei; Holste, Kristof; Klar, Peter J.
We investigated a RIT-10 ion thruster and a radio-frequency neutralizer using molecular oxygen, molecular nitrogen and mixtures of both gases as propellants to assess their suitability as components for an air-breathing electric propulsion (ABEP) system. A combination of diagnostics - including Langmuir probes, optical emission spectroscopy (OES), Faraday cups, retarding potential analyzers (RPA), mass spectrometry and Raman spectroscopy - was used to characterize the plasma parameters, plume properties and material contamination. A comparison was made between the experimental results for the single gases and a corresponding global model. Our results show that the dissociation of the molecules and chemical reactions taking place in the plasma have a significant impact on the performance of both tested devices and thus need to be assessed for all operating points. Chemical reactions between oxygen and different material surfaces are identified and safe operating points established. A general optimization strategy to improve the performance of both devices is suggested - an essential requirement for successful missions using ABEP systems. The findings provide critical insight into material suitability and operational regimes for ABEP systems. An optimization based on an in-depth understanding of all parameters involved is only possible by combining multiple diagnostic tools and global modeling.