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101. Automated snow avalanche monitoring for Austria: State of the art and roadmap for future work

Author

Kapper, Kathrin Lisa, primary, Goelles, Thomas, additional, Muckenhuber, Stefan, additional, Trügler, Andreas, additional, Abermann, Jakob, additional, Schlager, Birgit, additional, Gaisberger, Christoph, additional, Eckerstorfer, Markus, additional, Grahn, Jakob, additional, Malnes, Eirik, additional, Prokop, Alexander, additional, and Schöner, Wolfgang, additional

Published
2023
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103. Table S4 from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Espinet, Elisa, primary, Gu, Zuguang, primary, Imbusch, Charles D., primary, Giese, Nathalia A., primary, Büscher, Magdalena, primary, Safavi, Mariam, primary, Weisenburger, Silke, primary, Klein, Corinna, primary, Vogel, Vanessa, primary, Falcone, Mattia, primary, Insua-Rodríguez, Jacob, primary, Reitberger, Manuel, primary, Thiel, Vera, primary, Kossi, Steffi O., primary, Muckenhuber, Alexander, primary, Sarai, Karnjit, primary, Lee, Alex Y.L., primary, Backx, Elyne, primary, Zarei, Soheila, primary, Gaida, Matthias M., primary, Rodríguez-Paredes, Manuel, primary, Donato, Elisa, primary, Yen, Hsi-Yu, primary, Eils, Roland, primary, Schlesner, Matthias, primary, Pfarr, Nicole, primary, Hackert, Thilo, primary, Plass, Christoph, primary, Brors, Benedikt, primary, Steiger, Katja, primary, Weichenhan, Dieter, primary, Arda, H. Efsun, primary, Rooman, Ilse, primary, Kopp, Janel L., primary, Strobel, Oliver, primary, Weichert, Wilko, primary, Sprick, Martin R., primary, and Trumpp, Andreas, primary

Published
2023
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104. Data from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Espinet, Elisa, primary, Gu, Zuguang, primary, Imbusch, Charles D., primary, Giese, Nathalia A., primary, Büscher, Magdalena, primary, Safavi, Mariam, primary, Weisenburger, Silke, primary, Klein, Corinna, primary, Vogel, Vanessa, primary, Falcone, Mattia, primary, Insua-Rodríguez, Jacob, primary, Reitberger, Manuel, primary, Thiel, Vera, primary, Kossi, Steffi O., primary, Muckenhuber, Alexander, primary, Sarai, Karnjit, primary, Lee, Alex Y.L., primary, Backx, Elyne, primary, Zarei, Soheila, primary, Gaida, Matthias M., primary, Rodríguez-Paredes, Manuel, primary, Donato, Elisa, primary, Yen, Hsi-Yu, primary, Eils, Roland, primary, Schlesner, Matthias, primary, Pfarr, Nicole, primary, Hackert, Thilo, primary, Plass, Christoph, primary, Brors, Benedikt, primary, Steiger, Katja, primary, Weichenhan, Dieter, primary, Arda, H. Efsun, primary, Rooman, Ilse, primary, Kopp, Janel L., primary, Strobel, Oliver, primary, Weichert, Wilko, primary, Sprick, Martin R., primary, and Trumpp, Andreas, primary

Published
2023
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105. Extended Data from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Espinet, Elisa, primary, Gu, Zuguang, primary, Imbusch, Charles D., primary, Giese, Nathalia A., primary, Büscher, Magdalena, primary, Safavi, Mariam, primary, Weisenburger, Silke, primary, Klein, Corinna, primary, Vogel, Vanessa, primary, Falcone, Mattia, primary, Insua-Rodríguez, Jacob, primary, Reitberger, Manuel, primary, Thiel, Vera, primary, Kossi, Steffi O., primary, Muckenhuber, Alexander, primary, Sarai, Karnjit, primary, Lee, Alex Y.L., primary, Backx, Elyne, primary, Zarei, Soheila, primary, Gaida, Matthias M., primary, Rodríguez-Paredes, Manuel, primary, Donato, Elisa, primary, Yen, Hsi-Yu, primary, Eils, Roland, primary, Schlesner, Matthias, primary, Pfarr, Nicole, primary, Hackert, Thilo, primary, Plass, Christoph, primary, Brors, Benedikt, primary, Steiger, Katja, primary, Weichenhan, Dieter, primary, Arda, H. Efsun, primary, Rooman, Ilse, primary, Kopp, Janel L., primary, Strobel, Oliver, primary, Weichert, Wilko, primary, Sprick, Martin R., primary, and Trumpp, Andreas, primary

Published
2023
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106. Supplementary Figures S1-S15 from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Espinet, Elisa, primary, Gu, Zuguang, primary, Imbusch, Charles D., primary, Giese, Nathalia A., primary, Büscher, Magdalena, primary, Safavi, Mariam, primary, Weisenburger, Silke, primary, Klein, Corinna, primary, Vogel, Vanessa, primary, Falcone, Mattia, primary, Insua-Rodríguez, Jacob, primary, Reitberger, Manuel, primary, Thiel, Vera, primary, Kossi, Steffi O., primary, Muckenhuber, Alexander, primary, Sarai, Karnjit, primary, Lee, Alex Y.L., primary, Backx, Elyne, primary, Zarei, Soheila, primary, Gaida, Matthias M., primary, Rodríguez-Paredes, Manuel, primary, Donato, Elisa, primary, Yen, Hsi-Yu, primary, Eils, Roland, primary, Schlesner, Matthias, primary, Pfarr, Nicole, primary, Hackert, Thilo, primary, Plass, Christoph, primary, Brors, Benedikt, primary, Steiger, Katja, primary, Weichenhan, Dieter, primary, Arda, H. Efsun, primary, Rooman, Ilse, primary, Kopp, Janel L., primary, Strobel, Oliver, primary, Weichert, Wilko, primary, Sprick, Martin R., primary, and Trumpp, Andreas, primary

Published
2023
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107. Supplementary Methods Figure from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Espinet, Elisa, primary, Gu, Zuguang, primary, Imbusch, Charles D., primary, Giese, Nathalia A., primary, Büscher, Magdalena, primary, Safavi, Mariam, primary, Weisenburger, Silke, primary, Klein, Corinna, primary, Vogel, Vanessa, primary, Falcone, Mattia, primary, Insua-Rodríguez, Jacob, primary, Reitberger, Manuel, primary, Thiel, Vera, primary, Kossi, Steffi O., primary, Muckenhuber, Alexander, primary, Sarai, Karnjit, primary, Lee, Alex Y.L., primary, Backx, Elyne, primary, Zarei, Soheila, primary, Gaida, Matthias M., primary, Rodríguez-Paredes, Manuel, primary, Donato, Elisa, primary, Yen, Hsi-Yu, primary, Eils, Roland, primary, Schlesner, Matthias, primary, Pfarr, Nicole, primary, Hackert, Thilo, primary, Plass, Christoph, primary, Brors, Benedikt, primary, Steiger, Katja, primary, Weichenhan, Dieter, primary, Arda, H. Efsun, primary, Rooman, Ilse, primary, Kopp, Janel L., primary, Strobel, Oliver, primary, Weichert, Wilko, primary, Sprick, Martin R., primary, and Trumpp, Andreas, primary

Published
2023
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108. The Airn lncRNA does not require any DNA elements within its locus to silence distant imprinted genes.

Author

Daniel Andergassen, Markus Muckenhuber, Philipp C Bammer, Tomasz M Kulinski, Hans-Christian Theussl, Takahiko Shimizu, Josef M Penninger, Florian M Pauler, and Quanah J Hudson

Subjects
Genetics, QH426-470
Abstract

Long non-coding (lnc) RNAs are numerous and found throughout the mammalian genome, and many are thought to be involved in the regulation of gene expression. However, the majority remain relatively uncharacterised and of uncertain function making the use of model systems to uncover their mode of action valuable. Imprinted lncRNAs target and recruit epigenetic silencing factors to a cluster of imprinted genes on the same chromosome, making them one of the best characterized lncRNAs for silencing distant genes in cis. In this study we examined silencing of the distant imprinted gene Slc22a3 by the lncRNA Airn in the Igf2r imprinted cluster in mouse. Previously we proposed that imprinted lncRNAs may silence distant imprinted genes by disrupting promoter-enhancer interactions by being transcribed through the enhancer, which we called the enhancer interference hypothesis. Here we tested this hypothesis by first using allele-specific chromosome conformation capture (3C) to detect interactions between the Slc22a3 promoter and the locus of the Airn lncRNA that silences it on the paternal chromosome. In agreement with the model, we found interactions enriched on the maternal allele across the entire Airn gene consistent with multiple enhancer-promoter interactions. Therefore, to test the enhancer interference hypothesis we devised an approach to delete the entire Airn gene. However, the deletion showed that there are no essential enhancers for Slc22a2, Pde10a and Slc22a3 within the Airn gene, strongly indicating that the Airn RNA rather than its transcription is responsible for silencing distant imprinted genes. Furthermore, we found that silent imprinted genes were covered with large blocks of H3K27me3 on the repressed paternal allele. Therefore we propose an alternative hypothesis whereby the chromosome interactions may initially guide the lncRNA to target imprinted promoters and recruit repressive chromatin, and that these interactions are lost once silencing is established.

Published
2019
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109. A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy.

Author

Georgios Kaissis, Sebastian Ziegelmayer, Fabian Lohöfer, Katja Steiger, Hana Algül, Alexander Muckenhuber, Hsi-Yu Yen, Ernst Rummeny, Helmut Friess, Roland Schmid, Wilko Weichert, Jens T Siveke, and Rickmer Braren

Subjects
Medicine, Science
Abstract

PURPOSE:Development of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features. METHODS:The retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm was trained on 70% of the patients (N = 28) and tested on 30% (N = 17) to predict KRT81+ vs. KRT81- tumor subtypes. A gradient-boosted survival regression model was fit to the disease-free and overall survival data. Chemotherapy response and survival were assessed stratified by subtype and radiomic signature. Radiomic feature importance was ranked. RESULTS:The mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. The mean±STDEV concordance indices between the disease-free and overall survival predicted by the model based on the radiomic parameters and actual patient survival were 0.76±0.05 and 0.71±0.06, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81- patients (7.0 vs. 22.6 months, HR 4.03, log-rank-test P =

Published
2019
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110. Alemtuzumab induction combined with reduced maintenance immunosuppression is associated with improved outcomes after lung transplantation: A single centre experience.

Author

Alberto Benazzo, Stefan Schwarz, Moritz Muckenhuber, Thomas Schweiger, Gabriela Muraközy, Bernhard Moser, José Matilla Sigüenza, György Lang, Shahrokh Taghavi, Walter Klepetko, Konrad Hoetzenecker, Peter Jaksch, and Cristopher Lambers

Subjects
Medicine, Science
Abstract

Question addressed by the studyThe value of induction therapy in lung transplantation is controversial. According to the ISHLT, only about 50% of patients transplanted within the last 10 years received induction therapy. We reviewed our institutional experience to investigate the impact of induction therapy on short- and long-term outcomes.Materials/patients and methodsBetween 2007 and 2015, 446 patients with a complete follow-up were included in this retrospective analysis. Analysis comprised long-term kidney function, infectious complications, incidence of rejection and overall survival.ResultsA total of 231 patients received alemtuzumab, 50 patients antithymocyte globulin (ATG) and 165 patients did not receive induction therapy (NI). The alemtuzumab group revealed the lowest rate of chronic kidney insufficiency (NI: 52.2%; ATG: 60%; alemtuzumab: 36.6%; p = 0.001). Both, the NI group (pConclusionAlemtuzumab induction therapy followed by reduced maintenance immunosuppression is associated with a better kidney function compared to no induction and ATG. Survival rate as well as freedom from ACR and CLAD were comparable between alemtuzumab and ATG.

Published
2019
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112. The potential of automotive perception sensors for local snow avalanche monitoring

Author

Stefan Muckenhuber, Thomas Goelles, Birgit Schlager, Kathrin Lisa Kapper, Alexander Prokop, and Wolfgang Schöner

Abstract

Monitoring of local snow avalanche releases are indispensable for many use cases. Existing lidar and radar technologies for monitoring local avalanche activity are costly and require closed source commercial software. These systems are often inflexible for exploring new use cases and too expensive for large scale applications, e.g., 100-1000 slopes. Therefore, developing reliable and inexpensive measurement and monitoring techniques with cutting- edge lidar and radar technology are highly required. Today, the automotive industry is a leading technology driver for lidar and radar sensors, because the largest challenge for achieving the next level of vehicle automation is to improve the reliability of its perception system. Automotive lidar sensors record high-resolution point clouds with very high acquisition frequencies of 10-20Hz and a range of up to 400m. High costs of mechanically spinning lidars (5-20kEUR) are still a limiting factor, but prices have already dropped significantly during the last decade and are expected to drop by another order of magnitude in the upcoming years. Modern automotive radar sensors operate at 24GHz and 77GHz, have a range of up to 300m, and provide raw data formats that allow the development of algorithms for detecting changes in the backscatter caused by avalanches. To exploit the potential of these newly emerging, cost- effective technologies for geoscientific applications, a stand-alone, modular sensor system called MOLISENS (MObile LIdar SENsor System) was developed in a cooperation between Virtual Vehicle Research Center and University of Graz. MOLISENS allows the modular incorporation of cutting-edge radar and lidar sensors. The open-source python package ‘pointcloudset’ was developed for handling, analyzing, and visualizing large datasets that consist of multiple point clouds recorded over time. This python package is designed to enable the development of new point cloud algorithms, and it is planned to extend the functionality to radar cluster data. Based on MOLISENS and pointcloudset, a strategy for their operational use in local avalanche monitoring is being developed.

Published
2023
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113. pointcloudset - A Python package to analyze large datasets of point clouds recorded over time

Author

Thomas Goelles, Birgit Schlager, and Stefan Muckenhuber

Abstract

Point clouds can be acquired by different sensor types and methods, such as lidar (light detection and ranging), radar (radio detection and ranging), RGB-D (red, green, blue, depth) cameras, SfM (structure from motion), etc. In many cases multiple point clouds are recorded over time, sometimes also referred to as 4D point clouds. For example, automotive lidars from Ouster or Velodyne record point clouds at around 10-20Hz resulting in millions of points per second. In addition, monitoring with terrestrial laser scanners is becoming used more often. Producing similar datasets than the automotive lidars, although with larger individual point clouds at a lower frame rate.Analyzing such a large collection of point clouds is a big challenge due the size and unstructured nature of the data. The Python package "pointcloudset" provides a way to store, analyze, and visualize large datasets consisting of multiple point clouds recorded over time. Pointcloudset features lazy evaluation, parallel processing and is designed to enable the development of new point cloud algorithms and their application on big datasets. The package is based on the Python packages pandas, pytncloud, dask and open3D. Its API is easy to use and high level and the package is open source and available on GitHub.

Published
2023
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114. Next steps to a modular machine learning-based data pipeline for automated snow avalanche detection in the Austrian Alps

Author

Kathrin Lisa Kapper, Thomas Goelles, Stefan Muckenhuber, Andreas Trügler, Jakob Abermann, Birgit Schlager, Christoph Gaisberger, Jakob Grahn, Eirik Malnes, Alexander Prokop, and Wolfgang Schöner

Abstract

Snow avalanches pose a significant danger to the population and infrastructure in the Austrian Alps. Although rigorous prevention and mitigation mechanisms are in place in Austria, accidents cannot be prevented, and victims are mourned every year. A comprehensive mapping of avalanches would be desirable to support the work of local avalanche commissions to improve future avalanche predictions. In recent years, mapping of avalanches from satellite images has been proven to be a promising and fast approach to monitor the avalanche activity. The Copernicus Sentinel-1 mission provides weather independent synthetic aperture radar data, free of charge since 2014, that has been shown to be suitable for avalanche mapping in a test region in Norway. Several recent approaches of avalanche detection make use of deep learning-based algorithms to improve the detection rate compared to conventional segmentation algorithms. Building upon the success of these deep learning-based approaches, we are setting up a modular data pipeline to map previous avalanche cycles in Sentinel-1 imagery in the Austrian Alps. As segmentation algorithm we make use of a common U-Net approach as a baseline and compare it to mapping results from an additional algorithm that has originally been applied to an autonomous driving problem. As a first test case, the extensive labelled training dataset of around 25 000 avalanche outlines from Switzerland will be used to train the U-Net; further test cases will include the training dataset of around 3 000 avalanches in Norway and around 800 avalanches in Greenland. To obtain training data of avalanches in Austria we tested an approach by manually mapping avalanches from Sentinel-2 satellite imagery and aerial photos. In a new approach, we will introduce high-resolution weather data, e.g., weather station data, to the learning-based algorithm to improve the detection performance. The avalanches detected with the algorithm will be quantitatively evaluated against held-out test sets and ground-truth data where available. Detection results in Austria will additionally be validated with in situ measurements from the MOLISENS lidar system and the RIEGL VZ-6000 laser scanner. Moreover, we will assess the possibilities of learning-based approaches in the context of avalanche forecasting.

Published
2023
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116. Table S7 from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Andreas Trumpp, Martin R. Sprick, Wilko Weichert, Oliver Strobel, Janel L. Kopp, Ilse Rooman, H. Efsun Arda, Dieter Weichenhan, Katja Steiger, Benedikt Brors, Christoph Plass, Thilo Hackert, Nicole Pfarr, Matthias Schlesner, Roland Eils, Hsi-Yu Yen, Elisa Donato, Manuel Rodríguez-Paredes, Matthias M. Gaida, Soheila Zarei, Elyne Backx, Alex Y.L. Lee, Karnjit Sarai, Alexander Muckenhuber, Steffi O. Kossi, Vera Thiel, Manuel Reitberger, Jacob Insua-Rodríguez, Mattia Falcone, Vanessa Vogel, Corinna Klein, Silke Weisenburger, Mariam Safavi, Magdalena Büscher, Nathalia A. Giese, Charles D. Imbusch, Zuguang Gu, and Elisa Espinet

Abstract

MC2vsMC1 DMRs

Published
2023
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117. Supplementary Figures S1-S15 from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Andreas Trumpp, Martin R. Sprick, Wilko Weichert, Oliver Strobel, Janel L. Kopp, Ilse Rooman, H. Efsun Arda, Dieter Weichenhan, Katja Steiger, Benedikt Brors, Christoph Plass, Thilo Hackert, Nicole Pfarr, Matthias Schlesner, Roland Eils, Hsi-Yu Yen, Elisa Donato, Manuel Rodríguez-Paredes, Matthias M. Gaida, Soheila Zarei, Elyne Backx, Alex Y.L. Lee, Karnjit Sarai, Alexander Muckenhuber, Steffi O. Kossi, Vera Thiel, Manuel Reitberger, Jacob Insua-Rodríguez, Mattia Falcone, Vanessa Vogel, Corinna Klein, Silke Weisenburger, Mariam Safavi, Magdalena Büscher, Nathalia A. Giese, Charles D. Imbusch, Zuguang Gu, and Elisa Espinet

Abstract

Supplementary Figures S1 to S15

Published
2023
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118. Extended Data from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Andreas Trumpp, Martin R. Sprick, Wilko Weichert, Oliver Strobel, Janel L. Kopp, Ilse Rooman, H. Efsun Arda, Dieter Weichenhan, Katja Steiger, Benedikt Brors, Christoph Plass, Thilo Hackert, Nicole Pfarr, Matthias Schlesner, Roland Eils, Hsi-Yu Yen, Elisa Donato, Manuel Rodríguez-Paredes, Matthias M. Gaida, Soheila Zarei, Elyne Backx, Alex Y.L. Lee, Karnjit Sarai, Alexander Muckenhuber, Steffi O. Kossi, Vera Thiel, Manuel Reitberger, Jacob Insua-Rodríguez, Mattia Falcone, Vanessa Vogel, Corinna Klein, Silke Weisenburger, Mariam Safavi, Magdalena Büscher, Nathalia A. Giese, Charles D. Imbusch, Zuguang Gu, and Elisa Espinet

Abstract

Extended Data Figures 1-3

Published
2023
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119. Data from Aggressive PDACs Show Hypomethylation of Repetitive Elements and the Execution of an Intrinsic IFN Program Linked to a Ductal Cell of Origin

Author

Andreas Trumpp, Martin R. Sprick, Wilko Weichert, Oliver Strobel, Janel L. Kopp, Ilse Rooman, H. Efsun Arda, Dieter Weichenhan, Katja Steiger, Benedikt Brors, Christoph Plass, Thilo Hackert, Nicole Pfarr, Matthias Schlesner, Roland Eils, Hsi-Yu Yen, Elisa Donato, Manuel Rodríguez-Paredes, Matthias M. Gaida, Soheila Zarei, Elyne Backx, Alex Y.L. Lee, Karnjit Sarai, Alexander Muckenhuber, Steffi O. Kossi, Vera Thiel, Manuel Reitberger, Jacob Insua-Rodríguez, Mattia Falcone, Vanessa Vogel, Corinna Klein, Silke Weisenburger, Mariam Safavi, Magdalena Büscher, Nathalia A. Giese, Charles D. Imbusch, Zuguang Gu, and Elisa Espinet

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive desmoplasia, which challenges the molecular analyses of bulk tumor samples. Here we FACS-purified epithelial cells from human PDAC and normal pancreas and derived their genome-wide transcriptome and DNA methylome landscapes. Clustering based on DNA methylation revealed two distinct PDAC groups displaying different methylation patterns at regions encoding repeat elements. Methylationlow tumors are characterized by higher expression of endogenous retroviral transcripts and double-stranded RNA sensors, which lead to a cell-intrinsic activation of an interferon signature (IFNsign). This results in a protumorigenic microenvironment and poor patient outcome. Methylationlow/IFNsignhigh and Methylationhigh/IFNsignlow PDAC cells preserve lineage traits, respective of normal ductal or acinar pancreatic cells. Moreover, ductal-derived KrasG12D/Trp53−/− mouse PDACs show higher expression of IFNsign compared with acinar-derived counterparts. Collectively, our data point to two different origins and etiologies of human PDACs, with the aggressive Methylationlow/IFNsignhigh subtype potentially targetable by agents blocking intrinsic IFN signaling.Significance:The mutational landscapes of PDAC alone cannot explain the observed interpatient heterogeneity. We identified two PDAC subtypes characterized by differential DNA methylation, preserving traits from normal ductal/acinar cells associated with IFN signaling. Our work suggests that epigenetic traits and the cell of origin contribute to PDAC heterogeneity.This article is highlighted in the In This Issue feature, p. 521

Published
2023
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127. Supplementary Table 1 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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128. Supplementary Figure 2 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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129. Supplementary Figure 1 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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131. Supplementary Figure 5 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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132. Supplementary Table 3 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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133. Supplementary Table 2 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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134. Supplementary Figure 3 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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135. Supplementary Figure 4 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Muckenhuber, Alexander, primary, Berger, Anne Katrin, primary, Schlitter, Anna Melissa, primary, Steiger, Katja, primary, Konukiewitz, Björn, primary, Trumpp, Andreas, primary, Eils, Roland, primary, Werner, Jens, primary, Friess, Helmut, primary, Esposito, Irene, primary, Klöppel, Günter, primary, Ceyhan, Güralp O., primary, Jesinghaus, Moritz, primary, Denkert, Carsten, primary, Bahra, Marcus, primary, Stenzinger, Albrecht, primary, Sprick, Martin R., primary, Jäger, Dirk, primary, Springfeld, Christoph, primary, and Weichert, Wilko, primary

Published
2023
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136. Charge‐State‐Enhanced Ion Sputtering of Metallic Gold Nanoislands

Author

Szabo, Gabriel L., primary, Jany, Benedykt R., additional, Muckenhuber, Helmut, additional, Niggas, Anna, additional, Lehner, Markus, additional, Janas, Arkadiusz, additional, Szabo, Paul S., additional, Gan, Ziyang, additional, George, Antony, additional, Turchanin, Andrey, additional, Krok, Franciszek, additional, and Wilhelm, Richard A., additional

Published
2023
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137. Next steps to a modular machine learning-based data pipeline for automated snow avalanche detection in the Austrian Alps

Author

Kapper, Kathrin Lisa, primary, Goelles, Thomas, additional, Muckenhuber, Stefan, additional, Trügler, Andreas, additional, Abermann, Jakob, additional, Schlager, Birgit, additional, Gaisberger, Christoph, additional, Grahn, Jakob, additional, Malnes, Eirik, additional, Prokop, Alexander, additional, and Schöner, Wolfgang, additional

Published
2023
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144. Optimum timing of antithymocyte globulin in relation to adoptive regulatory T cell therapy

Author

Muckenhuber, Moritz, primary, Mucha, Jasmin, additional, Mengrelis, Konstantinos, additional, How, Christopher, additional, Reindl-Schwaighofer, Roman, additional, Heinzel, Andreas, additional, Kainz, Verena, additional, Worel, Nina, additional, Berlakovich, Gabriela, additional, Edinger, Matthias, additional, Oberbauer, Rainer, additional, and Wekerle, Thomas, additional

Published
2023
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145. Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery

Author

S. Muckenhuber, A. A. Korosov, and S. Sandven

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

A computationally efficient, open-source feature-tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR (Synthetic Aperture Radar) images. The most suitable setting and parameter values have been found using four Sentinel-1 image pairs representative of sea ice conditions between Greenland and Severnaya Zemlya during winter and spring. The performance of the algorithm is compared to two other feature-tracking algorithms, namely SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features). Having been applied to 43 test image pairs acquired over Fram Strait and the north-east of Greenland, the tuned ORB (Oriented FAST and Rotated BRIEF) algorithm produces the highest number of vectors (177 513, SIFT: 43 260 and SURF: 25 113), while being computationally most efficient (66 s, SIFT: 182 s and SURF: 99 s per image pair using a 2.7 GHz processor with 8 GB memory). For validation purposes, 314 manually drawn vectors have been compared with the closest calculated vectors, and the resulting root mean square error of ice drift is 563 m. All test image pairs show a significantly better performance of the HV (horizontal transmit, vertical receive) channel due to higher informativeness. On average, around four times as many vectors have been found using HV polarization. All software requirements necessary for applying the presented feature-tracking algorithm are open source to ensure a free and easy implementation.

Published
2016
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146. Sea ice cover in Isfjorden and Hornsund, Svalbard (2000–2014) from remote sensing data

Author

S. Muckenhuber, F. Nilsen, A. Korosov, and S. Sandven

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

A satellite database including 16 555 satellite images and ice charts displaying the area of Isfjorden, Hornsund, and the Svalbard region has been established with focus on the time period 2000–2014. 3319 manual interpretations of sea ice conditions have been conducted, resulting in two time series dividing the area of Isfjorden and Hornsund into "fast ice" (sea ice attached to the coastline), "drift ice", and "open water". The maximum fast ice coverage of Isfjorden is > 40 % in the periods 2000–2005 and 2009–2011 and stays 40 % in all considered years, except for 2012 and 2014, where the maximum stays

Published
2016
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147. A Tale of Integration? The Migrant Wealth Gap in Austria

Author

Mattias Muckenhuber, Miriam Rehm, and Matthias Schnetzer

Subjects
Soziologie, Sozialwissenschaften, Demography
Abstract

We investigate how previous generations of migrants and their children integrated into Austrian society, as measured by their wealth ownership. Using individual-level data from the Household Finance and Consumption Survey (HFCS), we document (1) a positive average migrant wealth gap between migrants and natives—that is, migrants owning less wealth than natives, especially in the upper half of the distribution, (2) substantial within-group inequality for migrants, and (3) evidence for catch-up, since second-generation migrants are much more similar to natives in terms of wealth and socio-economic characteristics than first-generation migrants. Using a RIF regression, we confirm an economically significant migrant wealth gap for first-generation migrants after controlling for socio-economic characteristics especially for the upper middle of the distribution, where housing wealth is a particularly relevant asset category. Second-generation migrants’ wealth gap is fully explained by our covariates in the middle of the distribution, whereas at the top where business wealth is more salient, their characteristics predict them to have higher wealth than natives. Decomposing the partial effects of covariates suggests that inheritances have the highest explanatory power for the migrant wealth gap of both first- and second-generation migrants, further buttressing the case for progressive integration in terms of wealth, while the composition of the migrant population, and in particular migrants’ heritage may continue to play a role in their wealth ownership.

Published
2022
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148. Supplementary Figure 4 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Wilko Weichert, Christoph Springfeld, Dirk Jäger, Martin R. Sprick, Albrecht Stenzinger, Marcus Bahra, Carsten Denkert, Moritz Jesinghaus, Güralp O. Ceyhan, Günter Klöppel, Irene Esposito, Helmut Friess, Jens Werner, Roland Eils, Andreas Trumpp, Björn Konukiewitz, Katja Steiger, Anna Melissa Schlitter, Anne Katrin Berger, and Alexander Muckenhuber

Abstract

Differences between molecular subtypes with regard to initial tumor control irrespective of chemotherapeutic regimen (p-value 0.033). Y-axis is representing total patient count.

Published
2023
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150. Supplementary Figure 1 from Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response

Author

Wilko Weichert, Christoph Springfeld, Dirk Jäger, Martin R. Sprick, Albrecht Stenzinger, Marcus Bahra, Carsten Denkert, Moritz Jesinghaus, Güralp O. Ceyhan, Günter Klöppel, Irene Esposito, Helmut Friess, Jens Werner, Roland Eils, Andreas Trumpp, Björn Konukiewitz, Katja Steiger, Anna Melissa Schlitter, Anne Katrin Berger, and Alexander Muckenhuber

Abstract

Frequency distributions of IHC-derived subtypes of PDAC and unclassifiable cases (purple) in all cohorts investigated in this study.

Published
2023
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