Zusammenfassung
Am Standort der ehemaligen Munitionsfabrik in Hessisch-Lichtenau/Hirschhagen in Nordhessen wurde im Zuge von Sanierungs- und Sicherungsmaßnahmen eine große Anzahl von ...Grundwasseraufschlüssen hergestellt. Pumpversuche, Probennahmen und Wasserstandsmessungen über einen Zeitraum von 20 Jahren verbesserten die Kenntnisse des hydrogeologischen Systems. Die Wechselfolge von Sand-, Schluff- und Tonsteinen des Mittleren Bundsandsteins am Standort stellt hydrogeologisch gesehen eine Abfolge von Grundwasserleitern, -geringleitern und -nichtleitern dar. Der Untergrund ist durch Staffelbrüche gegliedert und von Störungen und Klüften durchzogen. Er bildet ein inhomogenes, anisotropes System, in dem Wasserbewegungen vor allem entlang von Klüften und wasserwegsamen Störungsbereichen erfolgen. Das Grundwasser befindet sich großräumig betrachtet in drei unterschiedlichen Zonen, der Hirschhagenzone, der Zwischenzone und dem Hauptgrundwasserstockwerk. Zwischen den Zonen bilden sich bevorzugt an Störungen Fließwege aus. Die oberen Zonen bilden schwebende Grundwasserstockwerke. Das Grundwasser sickert aus diesen Zonen der jeweilig tieferliegenden Zone zu. Bevorzugte Fließverbindungen entlang von Nord-Süd verlaufenden Störungen wurden in allen drei Zonen nachgewiesen.
A systematic study on the asymmetric allylation of aldehydes on solid support is reported. Different kinds of chiral allylboron reagents with complementary direction of stereoinduction were applied ...successfully in this reagent-controlled transformation. The homoallylic alcohol products are generated with high levels of stereoselectivity and in high yields. The crotylation of aldehydes on solid support employing (
E)- and (
Z)-Ipc
2crotylborane also proceeds with very high levels of stereoinduction and in high yields. Applications of this methodology for the synthesis of compound collections by subsequent modifications of the allylic moiety are described. In particular, a collection of γ- and δ-lactones has been synthesized by means of a cyclo-release approach including a natural product. In addition, a procedure for the long-standing problem of the hydrogenation of double bonds on solid support is reported. We have also demonstrated the feasibility of applying the stereoselective allylation of aldehydes on solid support in an iterative fashion to generate polyol structures.
The first systematic study on the asymmetric allylboration of aldehydes on solid support is reported. Applications of this methodology are also described.
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α,β‐Unsaturated δ‐lactones
with multiply oxygenated side chains are a substructure found in a group of natural products with a broad range of biological activity. In their Full Paper on page 3305 ...ff., H. Waldmann et al. describe the parallel synthesis of all eight diastereomers of cryptocarya diacetate by asymmetric allylation reactions on a solid support with chiral allylboranes.
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional ...registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below \(1.2\) mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/.
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image ...registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical ...decision-support systems for diagnosis, surgery planning, and population-based analysis on spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms towards labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The content and code concerning VerSe can be accessed at: https://github.com/anjany/verse.
The underlying frameworks of natural product classes with multiple biological activities can be regarded as biologically selected and prevalidated starting points in vast chemical structure space in ...the development of compound collections for chemical biology and medicinal chemistry research. For the synthesis of natural product-derived and -inspired compound collections, the development of enantioselective transformations in a format amenable to library synthesis, e.g., on the solid support, is a major and largely unexplored goal. We report on the enantioselective solid-phase synthesis of a natural product-inspired α,β-unsaturated δ-lactone collection and its investigation in cell-based screens monitoring cell cycle progression and viral entry into cells. The screens identified modulators of both biological processes at a high hit rate. The screen for inhibition of viral entry opens up avenues of research for the identification of compounds with antiviral activity.
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