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

A Variational Reconstruction Method for Undersampled Dynamic X-ray Tomography based on Physical Motion Models

In this paper we study the reconstruction of moving object densities from undersampled dynamic x-ray tomography in two dimensions. A particular motivation of this study is to use realistic measurement protocols for practical applications, i.e. we do not assume to have a full Radon transform in each time step, but only projections in few angular directions. This restriction enforces a space-time reconstruction, which we perform by incorporating physical motion models and regularization of motion vectors in a variational framework. The methodology of optical flow, which is one of the most common methods to estimate motion between two images, is utilized to formulate a joint variational model for reconstruction and motion estimation. We provide a basic mathematical analysis of the forward model and the variational model for the image reconstruction. Moreover, we discuss the efficient numerical minimization based on alternating minimizations between images and motion vectors. A variety of results are presented for simulated and real measurement data with different sampling strategy. A key observation is that random sampling combined with our model allows reconstructions of similar amount of measurements and quality as a single static reconstruction.Peer reviewe

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

Imaginary-time quantum many-body theory out of equilibrium I: Formal equivalence to Keldysh real-time theory and calculation of static properties

We discuss the formal relationship between the real-time Keldysh and imaginary-time theory for nonequilibrium in quantum dot systems. The latter can be reformulated using the recently proposed Matsubara voltage approach. We establish general conditions for correct analytic continuation procedure on physical observables, and apply the technique to the calculation of static quantities in steady-state non-equilibrium for a quantum dot subject to a finite bias voltage and external magnetic field. Limitations of the Matsubara voltage approach are also pointed out.Comment: 24 pages, 10 figure

Immunomodulation with romiplostim as a second-line strategy in primary immune thrombocytopenia: The iROM study.

Thrombopoietin receptor agonists (TPO-RAs) stimulate platelet production, which might restore immunological tolerance in primary immune thrombocytopenia (ITP). The iROM study investigated romiplostim's immunomodulatory effects. Thirteen patients (median age, 31 years) who previously received first-line treatment received romiplostim for 22 weeks, followed by monitoring until week 52. In addition to immunological data, secondary end-points included the sustained remission off-treatment (SROT) rate at 1 year, romiplostim dose, platelet count and bleedings. Scheduled discontinuation of romiplostim and SROT were achieved in six patients with newly diagnosed ITP, whereas the remaining seven patients relapsed. Romiplostim dose titration was lower and platelet count response was stronger in patients with SROT than in relapsed patients. In all patients, regulatory T lymphocyte (Treg) counts increased until study completion and the counts were higher in patients with SROT. Interleukin (IL)-4, IL-9 and IL-17F levels decreased significantly in all patients. FOXP3 (Treg), GATA3 (Th2) mRNA expression and transforming growth factor-β levels increased in patients with SROT. Treatment with romiplostim modulates the immune system and possibly influences ITP prognosis. A rapid increase in platelet counts is likely important for inducing immune tolerance. Better outcomes might be achieved at an early stage of autoimmunity, but clinical studies are needed for confirmation

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

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