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Sentinel-3 and Sentinel-6 orbit validation for 2023 by means of altimetry crossover analysis
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Feasibility of Dark-Field Radiography to Enhance Detection of Nondisplaced Fractures.
No full textBackground Many clinically relevant fractures are occult on conventional radiographs and therefore challenging to diagnose reliably. X-ray dark-field radiography is a developing method that uses x-ray scattering as an additional signal source. Purpose To investigate whether x-ray dark-field radiography enhances the depiction of radiographically occult fractures in an experimental model compared with attenuation-based radiography alone and whether the directional dependence of dark-field signal impacts observer ratings. Materials and Methods Four porcine loin ribs had nondisplaced fractures experimentally introduced. Microstructural changes were visually verified using high-spatial-resolution three-dimensional micro-CT. X-ray dark-field radiographs were obtained before and after fracture, with the before-fracture scans serving as control images. The presence of a fracture was scored by three observers using a six-point scale (6, surely; 5, very likely; 4, likely; 3, unlikely; 2, very unlikely; and 1, certainly not). Differences between scores based on attenuation radiographs alone (n = 96) and based on combined attenuation and dark-field radiographs (n = 96) were evaluated by using the DeLong method to compare areas under the receiver operating characteristic curve. The impact of the dark-field signal directional sensitivity on observer ratings was evaluated using the Wilcoxon test. The dark-field data were split into four groups (24 images per group) according to their sensitivity orientation and tested against each other. Musculoskeletal dark-field radiography was further demonstrated on human finger and foot specimens. Results The addition of dark-field radiographs was found to increase the area under the receiver operating characteristic curve to 1 compared with an area under the receiver operating characteristic curve of 0.87 (95% CI: 0.80, 0.94) using attenuation-based radiographs alone (P < .001). There were similar observer ratings for the four different dark-field sensitivity orientations (P = .16-.65 between the groups). Conclusion These results suggested that the inclusion of dark-field radiography has the potential to help enhance the detection of nondisplaced fractures compared with attenuation-based radiography alone. © RSNA, 2024 See also the editorial by Rubin in this issue
B cells orchestrate tolerance to the neuromyelitis optica autoantigen AQP4.
No full textNeuromyelitis optica is a paradigmatic autoimmune disease of the central nervous system, in which the water-channel protein AQP4 is the target antigen1. The immunopathology in neuromyelitis optica is largely driven by autoantibodies to AQP42. However, the T cell response that is required for the generation of these anti-AQP4 antibodies is not well understood. Here we show that B cells endogenously express AQP4 in response to activation with anti-CD40 and IL-21 and are able to present their endogenous AQP4 to T cells with an AQP4-specific T cell receptor (TCR). A population of thymic B cells emulates a CD40-stimulated B cell transcriptome, including AQP4 (in mice and humans), and efficiently purges the thymic TCR repertoire of AQP4-reactive clones. Genetic ablation of Aqp4 in B cells rescues AQP4-specific TCRs despite sufficient expression of AQP4 in medullary thymic epithelial cells, and B-cell-conditional AQP4-deficient mice are fully competent to raise AQP4-specific antibodies in productive germinal-centre responses. Thus, the negative selection of AQP4-specific thymocytes is dependent on the expression and presentation of AQP4 by thymic B cells. As AQP4 is expressed in B cells in a CD40-dependent (but not AIRE-dependent) manner, we propose that thymic B cells might tolerize against a group of germinal-centre-associated antigens, including disease-relevant autoantigens such as AQP4
Experimentelle und numerische Methoden zur Bewertung des Propagationsverhaltens in Mehrzell-Aufbauten mit Lithium-Ionen-Batterien
No full textIn this thesis, a multi-stage approach that allows to efficiently assess the thermal propagation behavior in multiple cell arrangements, as can be found in high voltage battery packs of prismatic lithium-ion batteries, is developed and presented. A combination of experimental and numerical studies is used to investigate the thermal runaway behavior on single cell level and the thermal propagation behavior on multiple cell level.Im Rahmen dieser Arbeit wird ein mehrstufiger Ansatz entwickelt und vorgestellt, der eine effiziente Bewertung der thermischen Propagation in Mehrzell-Aufbauten ermöglicht, wie sie in Hochvoltspeichern mit prismatischen Lithium-Ionen-Batterien vorkommen. Hierfür werden experimentelle und numerische Studien durchgeführt, die das thermische Durchgehen auf Einzelzell-Ebene und die thermische Propagation auf Mehrzell-Ebene untersuchen
Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation
No full textReinforcement learning (RL) algorithms often require a significant number of experiences to learn a policy capable of achieving desired goals in multi-goal robot manipulation tasks with sparse rewards. Hindsight Experience Replay (HER) is an existing method that improves learning efficiency by using failed trajectories and replacing the original goals with hindsight goals that are uniformly sampled from the visited states. However, HER has a limitation: the hindsight goals are mostly near the initial state, which hinders solving tasks efficiently if the desired goals are far from the initial state. To overcome this limitation, we introduce a curriculum learning method called HERDT (HER with Decision Trees). HERDT uses binary DTs to generate curriculum goals that guide a robotic agent progressively from an initial state toward a desired goal. During the warm-up stage, DTs are optimized using the Grammatical Evolution algorithm. In the training stage, curriculum goals are then sampled by DTs to help the agent navigate the environment. Since binary DTs generate discrete values, we fine-tune these curriculum points by incorporating a feedback value (i.e., the Q-value). This fine-tuning enables us to adjust the difficulty level of the generated curriculum points, ensuring that they are neither overly simplistic nor excessively challenging. In other words, these points are precisely tailored to match the robot's ongoing learning policy. We evaluate our proposed approach on different sparse reward robotic manipulation tasks and compare it with the state-of-the-art HER approach. Our results demonstrate that our method consistently outperforms or matches the existing approach in all the tested tasks
