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Nuclear Localization of α-Synuclein and its Interaction with Histones

2026-05-15T00:00:00Z

Nuclear Localization of α-Synuclein and its Interaction with Histones Rollar, Angela Pathological accumulation of alpha-synuclein (αSyn) is a hallmark of Parkinson's disease (PD). While αSyn primarily localizes at synaptic terminals under physiological conditions, emerging evidence suggests translocation to the nucleus may contribute to PD pathophysiology. Despite growing interest in nuclear αSyn, previous detection methods have lacked reliability, and critical questions regarding the mechanisms of nuclear translocation, intranuclear αSyn behavior, and potential reversibility of nuclear accumulation remain unanswered. This study aimed to establish robust methodologies for detecting nuclear αSyn, to elucidate conditions prompting nuclear αSyn translocation, and to assess intranuclear consequences and clearance dynamics following αSyn nuclear accumulation.
We utilized three complementary in-vivo models: constitutive αSyn overexpression via intraparenchymal injections of adeno-associated viral vectors in the rat substantia nigra; a doxycycline-inducible system allowing for controlled initiation and cessation of αSyn overexpression; and nigrostriatal pathology triggered by systemic paraquat injections. Comprehensive analyses included immunohistochemistry, multiple proximity ligation assays (PLAs), co-immunoprecipitation (Co-IP), and proteasome activity assays.
Across all models, increased neuronal αSyn expression promoted its nuclear translocation. Once it gained access into the nuclei, αSyn directly interacted with histones, as demonstrated by our novel PLA approach and confirmed by Co-IP. αSyn overexpression altered epigenetic modifications, indicating a potential mechanistic link to transcriptional dysregulation. Moreover, our data revealed that nuclear αSyn formed aggregates that also interact with histones. Finally, using the doxycycline-regulated model, we demonstrated for the first time that nuclear αSyn accumulation, aggregation, and αSyn-histone interactions were reversible upon cessation of αSyn overexpression. This reversibility was due, at least in part, to αSyn clearance facilitated by the nuclear ubiquitin-proteasome system.
In summary, using multiple in-vivo approaches and analytical techniques, we provide compelling new evidence for conditions and mechanisms associated with αSyn nuclear translocation and accumulation that may play a role in pathogenic processes in PD and other synucleinopathies. Our findings support the conclusion that nuclear αSyn and αSyn- 82 histone interactions should be considered important therapeutic targets. One strategy for therapeutic intervention could be to reverse the deleterious effects of nuclear αSyn accumulation by promoting specific mechanisms involved in nuclear αSyn clearance.

Towards LiDAR-based Spatio-temporal Scene Understanding for Autonomous Vehicles

2026-05-15T00:00:00Z

Towards LiDAR-based Spatio-temporal Scene Understanding for Autonomous Vehicles Behley, Jens Self-driving cars are expected to reduce the number of casualties caused by traffic accidents, since a machine is always attentive, it can exploit various input modalities, and it always obeys the traffic rules. Liberating people from driving a vehicle will also enable them to do more pleasant activities while getting from one place to another. A fleet of self-driving cars could also lead to less parked cars in the city as cars could efficiently shared and be available on-demand. All these prospects of self-driving cars led to an increasing activity in this area of research and many large automotive companies invested substantially in the research and development of self-driving cars.
A central aspect of self-driving cars is perception to make sense of the different sensory inputs available. Most self-driving car prototypes rely on a combination of different sensors, such as cameras and 3D LiDAR sensors. In particular, 3D LiDAR sensors provide accurate and dense depth measurements of the environment. Since the advent of fast 3D LiDAR sensors that can produce millions of measurements of the 360° field-of-view, research on 3D LiDAR-based perception attracted increasing attention in the recent years.
In this habilitation thesis, we present our contributions in the area of 3D LiDAR-based perception. We cover our work on 3D LiDAR-based spatial perception to enable an autonomous system to localize itself in the environment. We present our approaches for Simultaneous Localization and Mapping (SLAM) for building maps on-the-fly, localization using existing maps, mapping to generate detailed maps, and map compression to efficiently transfer mapping data.
Furthermore, we cover our approaches for semantic interpretation of a single 3D LiDAR scan. All the presented work in semantic perception is based on our dataset, SemanticKITTI, that provides the data needed to train machine learning approaches for semantic interpretation. Furthermore, we present our work on semantic segmentation and panoptic segmentation. Additionally, we present our approach to reduce the need for labeled data.
Lastly, we cover also our work on unifying spatial and semantic interpretation in the area of spatio-temporal interpretation. In this part, we present our approach for moving object segmentation using a sequence of 3D LiDAR scans. We present our approach for semantic SLAM that use semantic information to improve pose estimation. Lastly, we present our work on panoptic segmentation on a sequence of 3D LiDAR scans that provides spatio-temporal interpretation.

Advancing radiological workflows through AI: Deep learning for automated tissue quantification, disease classification, generating synthetic contrast imaging and free-text report content extraction

2026-05-15T00:00:00Z

Advancing radiological workflows through AI: Deep learning for automated tissue quantification, disease classification, generating synthetic contrast imaging and free-text report content extraction Nowak, Sebastian The following publications are included in this cumulative habilitation thesis
Advances in artificial intelligence (AI) algorithms have raised expectations for the transformation of knowledge-based workflows, also in radiology. In this thesis, the applicability of AI to automate, optimize or support various image- or report-based analysis was investigated. It was the aim to provide insights into the potential of AI for advancing radiological workflows and thereby improving patient care. The scope of the eight original works included in this cumulative thesis can be categorized into three main topics:
Processing of free-text radiological reports
1. Privacy-ensuring, open-weights large language models are competitive with closed GPT-4o in extracting chest X-ray findings from free-text reports. Nowak S, Wulff B, Layer YC, Theis M, Isaak A, Salam B, Block W, Kuetting D, Pieper CCC, Luetkens JA, Attenberger UI, Sprinkart AM. Radiology. 2024; in press.
2. Transformer-based structuring of free-text radiology report databases. Nowak S*, Biesner D*, Layer Y, Theis M, Schneider H, Block W, Wulff B, Attenberger UI*, Sifa R*, Sprinkart AM*. European Radiology. 2023;33(6):4228–4236.
3. Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers. Nowak S*, Schneider H*, Layer YC, Theis M, Biesner D, Block W, Wulff B, Attenberger UI, Sifa R*, Sprinkart AM*. European Radiology. 2024;34(5):2895-2904.
Generating synthetic radiological images
4. Deep learning virtual contrast-enhanced T1 mapping for contrast-free myocardial extracellular volume assessment. Journal of the American Heart Association. Nowak S*, Bischoff LM*, Pennig L, Kaya K, Isaak A, Theis M, Block W, Pieper CC, Kuetting D, Zimmer S, Nickenig G, Attenberger UI, Sprinkart AM*, Luetkens JA*. Journal of the American Heart Association. 2024;13(19):e035599.
Supporting diagnostic and treatment decisions based on radiological imaging
5. Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI. Luetkens JA*, Nowak S*, Mesropyan N, Block W, Praktiknjo M, Chang J, Bauckhage C, Sifa R, Sprinkart AM*, Faron A*, Attenberger UI*. Scientific reports. 2022;12(1):8297.
6. Deep learning–based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment. Nowak S*, Kloth C*, Theis M, Marinova M, Attenberger UI, Sprinkart AM*, Luetkens JA*. European Radiology. 2024;34(1):279–286.
7. Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement. Theis M, Block W, Luetkens JA, Attenberger UI, Nowak S*, Sprinkart AM*. European Journal of Radiology. 2023;168:111150.
8. Computer tomography-based assessment of perivascular adipose tissue in patients with abdominal aortic aneurysms. Ginzburg D*, Nowak S*, Attenberger U, Luetkens J, Sprinkart AM*, Kuetting D*. Scientific Reports 2024;14(1):20512.

* contributed equally

Optimierung und Modellierung der Wachstumsbedingungen für die geschützte Langzeitkultivierung von Lemna minor L.

2026-05-15T00:00:00Z

Optimierung und Modellierung der Wachstumsbedingungen für die geschützte Langzeitkultivierung von Lemna minor L. Schmidt, Karl-Michael Unter geeigneten Bedingungen produzieren Lemna spp. rasch große Mengen wertvoller Biomasse, die als alternative Quelle für Futter- und Lebensmittel gilt. Für eine kontinuierliche und langfristige Indoor-Produktion unter kontrollierten Bedingungen müssen Umwelt- und Ernteparameter optimiert werden, um das Algenwachstum einzudämmen und stets ein qualitativ hochwertiges Produkt zu liefern. Eine experimentelle Bewertung des Einflusses einer größeren Anzahl von Parametern auf die Wachstumsrate r_i ist aufgrund der theoretisch hohen Anzahl an Parameterkombinationen unmöglich. Daher wurde ein SIMILE^®-basiertes Modell entwickelt. Dieses ermöglicht es, Produktionsparameter einzeln auf ihren Einfluss auf die Wachstumsrate r_i mittels einer Differentialgleichung zu untersuchen. Die Startwerte für die numerische Integration wurden aus Messdaten und analytischen Lösungen der differentiellen Wachstumsgleichung entnommen. Bei 400 ppm CO₂ betrug die Wachstumsrate r_i in einem optimierten Laboraufbau 216 g FM·m⁻²d⁻¹, wobei ein Drittel der Biomasse in Abständen von 5 Tagen geerntet wurde. In großtechnischen Versuchen wurden niedrigere Nachwachsraten r_i von etwa 173 g FM·m⁻²d⁻¹ (Kalkar) und 190 g FM·m⁻²d⁻¹ (Berlin) erzielt, da die Temperatur- und Lichtbedingungen unter dem Optimum lagen. Bei 3.500 ppm CO₂ stieg die Wachstumsrate r_i im Laborversuch durch Verkürzung des Ernteintervalls auf drei Tage auf 323 g FM·m⁻²d⁻¹. Maximale Wachstumsraten r_i wurden bei einem NH₄⁺/NO₃^- Verhältnis von 1/9 und einer Gesamtstickstoffkonzentration von 1,14 mM erzielt. Um das durchschnittliche Blattalter der Lemna-Population aufgrund der Auswirkungen der zyklisch wiederholten Teilernte zu berücksichtigen, wurde ein zusätzliches Teilmodell erstellt. Bei jüngerem durchschnittlichem Blattalter war ein Anstieg des Proteingehalts sowie eine Abnahme der Oxalsäure- und Schwermetallakkumulation zu beobachten. Diese Ergebnisse zeigen auf, wie die Kulturbedingungen und die Teilernteintervalle optimiert werden können. Die Modellläufe stimmen weitgehend mit den experimentellen Daten aus den drei verschiedenen Ansätzen überein und bestätigen somit die Validität des Modells.; Optimizing and Modelling of Growth Conditions for Protected Long-term Cultivation of Lemna minor L.
Given the proper conditions, Lemna spp. rapidly produce a high amount of valuable biomass which is considered as an alternative source for feed and food. For a continuous and long-term indoor production under controlled conditions, environmental and harvest parameters have to be optimized to suppress algal growth and constantly yield a high-quality product. Experimentally assessing the effect of a larger number of parameters on the growth rate ri is impossible due to the theoretically high number of parameter combinations. Thus, a SIMILE^® - based model has been developed. This enables production parameters to be assessed individually for its effect on the growth rate ri by a differential equation. Start values for numerical integration were taken from measured data and analytical solutions of the differential growth equation. At 400 ppm CO₂, the regrowth rate r_i in an optimized laboratory set-up amounted to 216 g FM·m⁻²d⁻¹, harvesting one third of the biomass at intervals of 5 days. In up-scaled set-ups, lower regrowth rates r_i of about 173 g FM·m⁻²d⁻¹ (Kalkar) and 190 g FM·m⁻²d⁻¹ (Berlin) were obtained, because temperature and light conditions were below optimum. At 3,500 ppm CO₂, the regrowth rate r_i in laboratory set-up increased to 323 g FM·m⁻²d⁻¹ by shortening the harvest interval to three days. Maximum growth rates r_i were obtained with an NH₄⁺/NO₃^- ratio of 1/9 at 1.14 mM total N concentration. In order to account for the average frond age of the Lemna population due to the impact of cyclically repeated partial harvesting, an additional sub-model was created. There was an increase in protein content and a decrease in oxalic acid and heavy metal accumulation for younger average frond ages. These results indicate how to optimize culture conditions and partial harvest intervals. Model runs closely match the experimental data taken from the three different approaches and thus confirm the validity of the model.