Networks of free-living nematodes and co-extracted fungi, associated with symptoms of apple replant disease

Apple replant disease affects tree nurseries and apple production globally. After repeated planting in the same soil, apple roots show accumulation of phytoalexins, stunting, and blackening. Recently, we showed that nematodes extracted from replanted soil and co-extracted microbes triggered these symptoms, while pathogens or plant-parasitic nematodes could not explain the early disease development. To identify nematode-microbe complexes that coincide with replant disease, apple rootstocks were grown in the greenhouse in soils from five replanted sites for eight weeks. Nematodes were extracted by floatation from pots with stunted or normal plant growth, washed on a 20-μm sieve, and used for DNA extraction. Nematode communities and co-extracted fungi and bacteria were analyzed by high-throughput sequencing of amplified ribosomal fragments. The experiment was repeated in the next year. Regardless of soil type or year, the nematode and fungal communities significantly differed between pots with differential plant growth. Bacteria were not significantly associated with growth depression. Plant-parasitic nematodes or pathogens were not abundant in numbers that could explain the observed root damage. Free-living nematodes Prsimatolaimus, Acrobeles, Tylencholaimus, Acrobeloides, and Aphelenchus, and associated fungi Exophiala, Hohenbuehelia, Naganishia, Psathyrella, and unidentified members of Orbiliales, Helotiales, and Rhytismataceae significantly correlated with reduced plant growth. Isolating and investigating such disease complexes will give a chance to understand external biotic stress of apple roots and design mitigation measures. © 2021 The Author

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/1700

Development and Integration of DOPS as Formative Tests in Head and Neck Ultrasound Education : Proof of Concept Study for Exploration of Perceptions

In Germany, progress assessments in head and neck ultrasonography training have been carried out mainly theoretically and lack standardisation. Thus, quality assurance and comparisons between certified courses from various course providers are difficult. This study aimed to develop and integrate a direct observation of procedural skills (DOPS) in head and neck ultrasound education and explore the perceptions of both participants and examiners. Five DOPS tests oriented towards assessing basic skills were developed for certified head and neck ultrasound courses on national standards. DOPS tests were completed by 76 participants from basic and advanced ultrasound courses (n = 168 documented DOPS tests) and evaluated using a 7-point Likert scale. Ten examiners performed and evaluated the DOPS after detailed training. The variables of “general aspects” (6.0 Scale Points (SP) vs. 5.9 SP; p = 0.71), “test atmosphere” (6.3 SP vs. 6.4 SP; p = 0.92), and “test task setting” (6.2 SP vs. 5.9 SP; p = 0.12) were positively evaluated by all participants and examiners. There were no significant differences between a basic and advanced course in relation to the overall results of DOPS tests (p = 0.81). Regardless of the courses, there were significant differences in the total number of points achieved between individual DOPS tests. DOPS tests are accepted by participants and examiners as an assessment tool in head and neck ultrasound education. In view of the trend toward “competence-based” teaching, this type of test format should be applied and validated in the future

Engineering Genetic Predisposition in Human Neuroepithelial Stem Cells Recapitulates Medulloblastoma Tumorigenesis.

Human neural stem cell cultures provide progenitor cells that are potential cells of origin for brain cancers. However, the extent to which genetic predisposition to tumor formation can be faithfully captured in stem cell lines is uncertain. Here, we evaluated neuroepithelial stem (NES) cells, representative of cerebellar progenitors. We transduced NES cells with MYCN, observing medulloblastoma upon orthotopic implantation in mice. Significantly, transcriptomes and patterns of DNA methylation from xenograft tumors were globally more representative of human medulloblastoma compared to a MYCN-driven genetically engineered mouse model. Orthotopic transplantation of NES cells generated from Gorlin syndrome patients, who are predisposed to medulloblastoma due to germline-mutated PTCH1, also generated medulloblastoma. We engineered candidate cooperating mutations in Gorlin NES cells, with mutation of DDX3X or loss of GSE1 both accelerating tumorigenesis. These findings demonstrate that human NES cells provide a potent experimental resource for dissecting genetic causation in medulloblastoma

Potentialanalyse von SAR-basierten Oberflächenmodellen in Monitoring- und Analyseprozessen in tidebeeinflussten Gebieten

Zahlreiche notwendige Monitoringaufgaben im Nationalpark deutsches Wattenmeer basieren auf regelmäßig erstellten, hoch aufgelösten Geodaten in Form von digitalen Gelände- und Oberflachenmodellen. Das Wassermanagement im Wattenmeerbereich der Nordsee erfordert das stetige Monitoring der unter anderem tidebedingten Vegetations- und Topographie Veränderungen, welche derzeit zumeist mit dem etablierten Verfahren des Airborne Laserscanning erfasst werden. Ergänzend zur etablierten Laserscanmethode wird im Projekt GeoWAM die Erfassungsmethode der flugzeuggestutzten Radarinterferometrie anhand von drei Befliegungskampagnen in zwei Testgebieten optimiert und das Potential der abgeleiteten, radarbasierten Oberflachenmodelle für die notwendigen Monitoringaufgaben im Küstengebiet aus Anwendersicht untersucht. Die im Projektverlauf erzielte Qualitatsverbesserung wird im Folgenden präsentiert und eine kritische Betrachtung der Projektergebnisse führt zum Fazit, dass eine ähnlich gute Datenqualität, wie sie in der letzten Befliegungskampagne erreicht werden konnte, als Voraussetzung für eine erfolgreiche Veranderungsanalyse angesehen werden muss. Werden Oberflachenmodelle mit einer geringeren Datenqualität in die Analysen einbezogen, fuhren Datenlücken und Artefakte zu erschwerten Analysebedingungen. Die Ergebnisse der letzten Befliegungskampagne zeigen jedoch ein vielversprechendes Potential für zukünftige Monitoringsaufgaben im tidebeeinflussten Kustengebiet

Guidelines of the European Resuscitation Council (ERC) on cardiopulmonary resuscitation 2021: update and comments

The European guidelines on cardiopulmonary resuscitation, which are divided into 12 chapters, have recently been published. In addition to the already known chapters, the topics epidemiology and life-saving systems have been integrated for the first time. For each chapter five practical key statements were formulated. In the present article the revised recommendations on basic measures and advanced resuscitation measures in adults as well as on postresuscitation treatment are summarized and commented on

Guidelines of the European Resuscitation Council (ERC) on cardiopulmonary resuscitation 2021: update and comments

The European guidelines on cardiopulmonary resuscitation, which are divided into 12 chapters, have recently been published. In addition to the already known chapters, the topics epidemiology and life-saving systems have been integrated for the first time. For each chapter five practical key statements were formulated. In the present article the revised recommendations on basic measures and advanced resuscitation measures in adults as well as on postresuscitation treatment are summarized and commented on

GeoWAM: Neue Geodaten zur Verbesserung des Wassermanagements tidebeeinflusster Küstenbereiche

Es wird die für GeoWAM genutzte, flugzeuggestützte InSAR Technik vorgestellt, insbesondere der speziell für GeoWAM-Zwecke entwickelte Zwei-Basislinien Ansatz mit zwei Frequenzen (dual-frequency, dual-baseline - DFDB). Die Testgebiete und die einzelnen Befliegungen werden gelistet, sowie die InSAR Datenverarbeitung zur Erzeugung von hochgenauen, digitalen Oberflächenmodellen erläutert. Folgende Klassifikationsprodukte werden beschrieben: Boden-Nichtboden, Wasser-Landgrenzen, Muschelbänke. Es folgt die Beschreibung der Erzeugung von digitalen Oberflächenmodellen des Wasserlaufs (DGM-W), sowie Valiedierungsuntersuchungen zu allen Produkten. Die Beschreibung der Bereitstellung der Daten über ein neu entwickeltes Web-Interface schließt den Bericht ab