Functional revascularization is key to stroke recovery and requires remodeling and regeneration of blood vessels around which is located the brain’s only stromal compartment. Stromal progenitor cells (SPCs) are critical for tissue regeneration following injury in many organs, yet their identity in the brain remains elusive. Here we show that the perivascular niche of brain SPCs includes pericytes, venular smooth muscle cells and perivascular fibroblasts that together help cerebral microvasculature regenerate following experimental stroke. Ischemic injury triggers amplification of pericytes and perivascular fibroblasts in the infarct region where they associate with endothelial cells inside a reactive astrocyte border. Fate-tracking of Hic1+ SPCs uncovered a transient functional and transcriptional phenotype of stroke-activated pericytes and perivascular fibroblasts. Both populations of these cells remained segregated, displaying distinct angiogenic and fibrogenic profiles. Therefore, pericytes and perivascular fibroblasts are distinct subpopulations of SPCs in the adult brain that coordinate revascularization and scar formation after injury.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation .

Die Publikation Hacking the Porcelain Industry dokumentiert ein experimentelles Designprojekt, das sich mit den gestalterischen Potenzialen von Porzellan im industriellen Kontext auseinandersetzt. In Kooperation mit der KOBER Steinwiesen GmbH & Co. KG entwickelten Studierende des Studiengangs Integriertes Produktdesign an der Hochschule Coburg visionäre Konzepte und experimentierten mit innovativen Fertigungstechniken. Der Fokus lag auf einer ergebnisoffenen, forschenden Gestaltungspraxis, die traditionelle und digitale Prozesse – darunter den 3D-Druck – neu interpretiert.

Die Publikation gibt Einblicke in die kreativen Arbeitsprozesse, die entstandenen Designobjekte sowie Strategien zur Vermittlung der Ergebnisse. Ergänzt wird sie durch das Beilagenheft Porcelain Prints, das sich speziell den gestalterischen und technischen Möglichkeiten des 3D-Drucks mit Porzellan widmet – einem Feld mit großem Zukunftspotenzial. Die Veröffentlichung zeigt, wie durch interdisziplinäre Zusammenarbeit zwischen Hochschule und Industrie innovative Perspektiven für ein traditionsreiches Material entstehen.

Die zunehmende Bedeutung der Photovoltaik für die deutsche Energieversorgung erfordert eine zuverlässige und effiziente Leistungsverifizierung, um mögliche Ertragseinbußen zu minimieren. Smart-Wechselrichter mit integrierten Ferndiagnosefunktionen bieten eine vielversprechende Lösung für die Überwachung und Optimierung des Anlagenbetriebs.

 Dieser Beitrag stellt einen speziell entwickelten Messaufbau vor, der die Überprüfung der Messfähigkeit von Smart-Wechselrichtern ermöglicht, insbesondere in Hinblick auf die Messung von Strom-Spannungs-Kennlinien (IU-Kennlinien). Der Fokus der Untersuchung liegt auf der Analyse der Messunsicherheit gemessener Leistungsmaxima durch Referenzieren auf kalibrierte IU-Kennlinienschreiber.

 Die durchgeführten Vergleichsmessungen liefern zufriedenstellende Ergebnisse und zeigen einen Trend zu geringen Messunsicherheiten für höhere Bestrahlungsstärken. Dies bestätigt das Potenzial von Smart-Wechselrichtern für die Leistungsüberwachung von Photovoltaikanlagen und unterstreicht den Nutzen für die Optimierung des Anlagenbetriebs.

Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures’ ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n = 8, 104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.