In this article, we introduce and investigate polynomial curvelets on spheres, which form a class of Parseval frames for L^2(𝕊^d-1), d ≥ 3. The proposed construction offers a directionally sensitive multiscale decomposition and provides a sparse representation of spherical data. As a main result, we derive a sharp pointwise localization bound which shows that the frame elements decay rapidly away from their center of mass, making them a powerful tool for position-based analyses. In contrast to previous constructions, polynomial curvelets are not limited in their directional resolution. Consequently, the frames established in this article are particularly powerful when it comes to the analysis of localized anisotropic features, such as edges. To illustrate this point, we show that, given a suitable test signal that exhibits (higher-order) discontinuities at the boundary ∂ A of a spherical cap A⊂𝕊^d-1, the corresponding curvelet coefficients peak precisely when the analysis function matches some segment of the boundary ∂ A, both in terms of position and orientation. Otherwise, the coefficients decay rapidly.
BackgroundIdentifying relevant biomarkers is critical in clinical research and precision medicine, particularly when analysing high-dimensional data. Random forests (RFs) are promising for such settings due to their flexibility, ease of use, and their ability to handle data sets with more variables than samples. RFs assess the importance of each variable in predicting the outcome using variable importance (VIMP) scores. However, since the distribution of VIMP scores is intricate, standard statistical testing and multiple testing adjustments for variable selection are challenging.MethodsWe propose shadowVIMP, a novel method for multiple testing-controlled variable selection, based on an approach similar to permutation testing. It generates permuted counterparts for each variable and compares their VIMPs with those of the original variables over multiple iterations to calculate p-values. Unlike conventional permutation testing, shadowVIMP preserves the correlation structure between variables, mitigating biases caused by the over-selection of correlated variables in RFs. We evaluated shadowVIMP against three competing RF variable selection approaches using simulation designs previously employed in studies considering VIMPs and variable selection for RFs. These designs included high- and low-dimensional data, as well as correlated and categorical variables. For illustration, we also applied the method to a real-world example on Alzheimer's disease.ConclusionsOur results showed that, compared to competing approaches, shadowVIMP offers advantages in high-dimensional settings, improving sensitivity while enabling multiple testing-adjusted results. Additionally, it demonstrated robustness against VIMP biases induced by correlated and categorical variables when using permutation-based VIMP. The method can be used to annotate standard VIMP plots, visually presenting selected variable sets based on different types of multiple testing adjustments and significance levels. Overall, shadowVIMP is a promising approach for providing multiple testing-adjusted variable selection while explicitly addressing known biases of RF's permutation-based VIMP measure. The shadowVIMP method is implemented in an R package shadowVIMP, which is available on CRAN.
Steigende Einsatzzahlen, demographische Veränderungen und eine zunehmende Inanspruchnahme des Rettungsdienstes bei nicht lebensbedrohlichen Hilfeersuchen verdeutlichen die Notwendigkeit vorbeugender Versorgungsansätze in der Außerklinischen Akut- und Notfallversorgung. Im Rahmen des 1. Deggendorfer Workshops zur akademischen Ausbildung in der Außerklinischen Akut- und Notfallversorgung wurde diskutiert, welche erweiterten Kompetenzen und Qualifikationsprofile künftig für akademisch qualifizierte Notfallsanitäter:innen in diesem Handlungsfeld erforderlich sind. Im Februar 2026 erarbeiteten 24 Expert:innen aus Rettungsdienst, Wissenschaft und Berufsverbänden aus Deutschland, Österreich und der Schweiz Grundlagen für und Anforderungen an ein bundeseinheitliches Bachelor-Curriculum für Notfallsanitäter:innen im vorbeugenden Rettungsdienst mit entsprechenden Kompetenzprofilen und Ausbildungsinhalten. Es konnten zwei komplementäre Teil-Curricula für den Kompetenzerwerb im Rahmen von Aufbaustudiengängen für Notfallsanitäter:innen im vorbeugenden Rettungsdient entwickelt werden. Diese decken die Bereiche Forschung und Entwicklung sowie Patientenversorgung ab. Ein bundeseinheitlich abgestimmtes Kompetenzprofil kann dazu beitragen, die Professionalisierung des Rettungsdienstes fortzuführen und die Patientensicherheit in der Außerklinischen Akut- und Notfallversorgung zu stärken. Es ist Grundlage für eine medikolegal auszudefinierende Tätigkeit von akademisch qualifizierten Notfallsanitäter:innen im vorbeugenden Rettungsdienst.
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundational task in neuroimaging, has similarly advanced through the Learn2Reg initiative. Building on this, we introduce the Large-scale Unsupervised Brain MRI Image Registration (LUMIR) challenge, a next-generation benchmark for unsupervised brain MRI registration. Previous challenges relied upon anatomical label maps, however LUMIR provides 4,014 unlabeled T1-weighted MRIs for training, encouraging biologically plausible deformation modeling through self-supervision. Evaluation includes 590 in-domain test subjects and extensive zero-shot tasks across disease populations, imaging protocols, and species. Deep learning methods consistently achieved state-of-the-art performance and produced anatomically plausible, diffeomorphic deformation fields. They outperformed several leading optimization-based methods and remained robust to most domain shifts. These findings highlight the growing maturity of deep learning in neuroimaging registration and its potential to serve as a foundation model for general-purpose medical image registration.
