Investigation of the 2010 rock avalanche onto the regenerated glacier Brenndalsbreen, Norway

Rock avalanches onto glaciers are rare in Norway. Here, we examine a rock avalanche that spread onto the regenerated Brenndalsbreen, an outlet glacier from Jostedalsbreen ice cap. The rock avalanche is intriguing in that limited information exists with respect to the exact time of failure, location of detachment area, and preparatory and triggering processes. Based on an analysis of ice stratigraphy and photographic documentation, we assess that the event happened between mid-March and June 4, 2010. A potential triggering factor could have been heavy snow and rainfall combined with above freezing air temperatures on March 18–19, 2010. We use digital terrain models to determine that the detachment area is at an almost vertical rock slope in a narrow gorge above Lower Brenndalsbreen. The deposit volume is estimated to 0.130 ± 0.065 Mm3, and the H/L ratio and fahrböschung are 0.45 and 24°, respectively. We apply a Voellmy flow model to confirm the detachment location and volume estimate by producing realistic runout lengths. Although glacial debuttressing may have been a likely preparatory process, the detachment area was exposed for 45–70 years before the rock avalanche occurred. The supraglacial rock avalanche debris was separated into two branches with a distinct melt-out line across the glacier. The debris reached the glacier front in 2019 and 2020, where it started being deposited proglacially while Lower Brenndalsbreen kept receding. The 2010 Brenndalsbreen rock avalanche may not be a unique event, as deposits constituting evidence of an old rock avalanche are currently melting out at the glacier front.publishedVersio

Deep learning outperforms existing algorithms in glacier surface velocity estimation with high-resolution data – the example of Austerdalsbreen, Norway

Remote sensing is a key tool to derive glacier surface velocities but existing mapping methods, such as cross-correlation techniques, can fail where surface properties change temporally or where large velocity variations occur spatially. High-resolution datasets, such as UAV imagery, offer a promising solution to tackle these issues and to study small-scale glacier dynamics, but new workflows are required to handle such data. Therefore, we tested the potential of new deep learning-based image-matching algorithms for deriving glacier surface velocities across the ablation area of a glacier with strong spatial variability in surface velocities (<5 m/yr to >100 m/yr) and substantial changes in surface properties between image acquisitions. For a thorough comparison of state-of-the-art methods and sensors, we applied three different techniques (cross-correlation using geoCosiCorr3D, feature tracking with ORB using SeaIceDrift and the new deep learning-based method using ICEpy4D) and three different platforms (Sentinel-2, PlanetScope, UAVs) to estimate glacier surface velocities. Results showed lowest errors for velocities derived with the deep learning-based approach applied to UAV imagery (RMSE = 2.17 m/yr, R2 = 0.99), followed by cross-correlation using Sentinel-2 images (RMSE = 21.0 m/yr, R2 = 0.59) and the deep learning-based approach with PlanetScope data (RMSE = 21.28 m/yr, R2 = 0.36). Cross-correlation with geoCosiCorr3D resulted in comparably high errors with the UAV dataset (RMSE = 36.22 m/yr, R2 = 0.24), whereas ORB-based feature tacking showed lowest performance with all sensors. Spatial patterns of computed velocities indicate that applying existing cross-correlation methods for areas with regular displacements or low glacier velocities yields suitable results on UAV data, but innovative deep learning-based approaches are required for resolving rapid changes in velocities or in surface properties. This novel method benefits from improved keypoint detection and matching through training using neural networks and data characterized by challenging geometries, outlier minimization and more robust descriptors by applying cross-attention layers. We conclude that continued development of deep learning-based feature tracking approaches for glacier velocity computations may substantially improve UAV-based velocity derivations applied to challenging situations. This method is able to deliver reliable displacement data in situations where traditional methods fail, which implies a new level of detail in understanding and interpreting glacier dynamics

Efficient production of multi-modified pigs for xenotransplantation by ‘combineering’, gene stacking and gene editing

Xenotransplantation from pigs could alleviate the shortage of human tissues and organs for transplantation. Means have been identified to overcome hyperacute rejection and acute vascular rejection mechanisms mounted by the recipient. The challenge is to combine multiple genetic modifications to enable normal animal breeding and meet the demand for transplants. We used two methods to colocate xenoprotective transgenes at one locus, sequential targeted transgene placement - ‘gene stacking’, and cointegration of multiple engineered large vectors - ‘combineering’, to generate pigs carrying modifications considered necessary to inhibit short to mid-term xenograft rejection. Pigs were generated by serial nuclear transfer and analysed at intermediate stages. Human complement inhibitors CD46, CD55 and CD59 were abundantly expressed in all tissues examined, human HO1 and human A20 were widely expressed. ZFN or CRISPR/Cas9 mediated homozygous GGTA1 and CMAH knockout abolished α-Gal and Neu5Gc epitopes. Cells from multi-transgenic piglets showed complete protection against human complement-mediated lysis, even before GGTA1 knockout. Blockade of endothelial activation reduced TNFα-induced E-selectin expression, IFNγ-induced MHC class-II upregulation and TNFα/cycloheximide caspase induction. Microbial analysis found no PERV-C, PCMV or 13 other infectious agents. These animals are a major advance towards clinical porcine xenotransplantation and demonstrate that livestock engineering has come of age

Design and mechanical construction of a hot-corrosion testing system

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Fülöp Grünvald (1887-1964)

Porodica Fülöp-a Grünvald-a poreklom je iz Ceceja. Za vreme oslobodilačkog rata 1848. njegov deda bio je nakupac konja za vojsku. Njegov otac, Mano Grünvald, došao je u Šopron kao mladić i bio rabin Ortodoksne jevrejske opštine sve do smrti 1929. godine. Fülop se školovao u Šopronu. Bio je učitelj u Muškoj građanskoj školi, a od 1919. prešao je u novootvorenu Jevrejsku mušku gimnaziju. Ovoj školi posvetio je sav svoj život. Od 1948. do penzionisanja 1958, bio je direktor Jevrejske gimnazije. Poslednjih godina života vodio je katedru istorije na Zemaljskom rabinskom seminaru u Budimpešti, a posebno je predavao istoriju mađarskih Jevreja. Bio je kustos, a zatim direktor Mađarskog jevrejskog muzeja. Eksponate je najvećim delom sam nalazio i sakupljao, a tokom nemačke okupacije i sačuvao. Ostao je veran svom rodnom gradu Šopronu kome je izuzetno doprineo stvaranjem Zbirke jevrejske umetnosti u zgradi srednjovekovne sinagoge.Fülöp Grünvald's family is originally from Cecej. During the liberation war of 1848, his grandfather was a horse buyer for the army. His father, Mano Grünwald, came to Sopron as a young man and was a rabbi of the Orthodox Jewish Community until his death in 1929. Fülop was educated in Sopron. He was a teacher at the Men's Civic School, and since 1919, he transferred to the newly opened Jewish Men's High School. He has dedicated his life to this school. From 1948 until his retirement in 1958, he was the director of the Jewish High School. In the last years of his life, he held the Chair of History at the Land Rabbinical Seminar in Budapest and especially taught the history of Hungarian Jews. He was a curator and then director of the Hungarian Jewish Museum. For the most part, I found and collected the exhibits, and preserved them during the German occupation. He remained true to his hometown of Sopron, to whom he made a tremendous contribution by creating the Collection of Jewish Art in the Medieval Synagogue Building