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Studies on neuroinvasion, susceptibility and long-term persistence of alphaherpesviruses in the murine central nervous system
(2025) Korff, Viktoria
Alphaherpesviruses such as HSV-1 and PrV exhibit pronounced neurotropism and the ability to establish acute and latent infections within the nervous system. To elucidate key determinants of alphaherpesviral neuroinvasion and persistence, this thesis employed the attenuated mutant PrV-ΔUL21/US3Δkin in CD-1 mice, which reproduces hallmark features of human HSE. Through a combination of targeted stereotactic and intranasal infection models, three principal aspects were addressed: (i) regional and cell type–specific neuronal susceptibility, (ii) anatomical pathways of early CNS entry, and (iii) long-term infection dynamics including latency and reactivation. The studies identified the mesial temporal, piriform, and prefrontal cortices as core targets, linked to high nectin-1 expression and specific anatomical connectivity. Neuroanatomical mapping revealed that PrV gains CNS access via a multimodal cranial-nerve network encompassing trigeminal, olfactory, glossopharyngeal-vagal, and hypoglossal routes converging in the brainstem. Long-term analyses demonstrated persistent, low-grade infection and episodic abortive or subclinical reactivation associated with neuroinflammatory responses, indicating a predominantly central establishment of viral latency that extends the current understanding of alphaherpesviral persistence to include central neuronal reservoirs in addition to peripheral ganglia. Collectively, these findings establish the PrV-ΔUL21/US3Δkin model as a robust and translationally relevant system for dissecting regional and receptor-dependent neurotropism, multimodal cranial-nerve-mediated CNS entry, and the central dynamics of alphaherpesviral latency and reactivation.
Makroskopische und molekularbiologische Evaluation des Einheilens neu entwickelter Polymerimplantate bei osteochondralen Kniedefekten am Schafmodell
(2025) Tüngler, Tim Ludwig
Die Behandlung osteochondraler Läsionen ist eine große klinische Herausforderung. Etablierte Verfahren führen häufig lediglich zur Bildung von minderwertigem Faserknorpel. Im Rahmen dieses BMBF-geförderten Projekts wurden daher mittels eines laserassistierten 3D-Druckverfahrens biphasische Polymerimplantate entwickelt. Diese Implantate imitieren mit ihren interkonnektierenden Poren die osteochondrale Zone. Als Polymere kamen LCM3 (Abbau über Milchsäure) und ACM (Abbau über Aminosäuren) zum Einsatz. Um die Zellbesiedlung zu fördern, wurden zwei Implantatgruppen zusätzlich mit einem kollagenbasierten Biogel versehen. Ziel dieser Studie war die makroskopische und molekularbiologische Analyse der Defektheilung in einem Großtiermodell. Dazu wurden je zwei zylindrische Implantate (7 mm Durchmesser, 10 mm Höhe) in das distale Femur von adulten, weiblichen Schafen (n = 5 pro Gruppe; Gewicht 66,28 ± 1,72 kg) eingesetzt. Ein Implantat wurde in der medialen Femurkondyle platziert und das zweite proximal davon. Die Versuchsgruppen erhielten entweder LCM-Implantate ohne Biogel (L-OB), LCM-Implantate mit Biogel (L-B) oder ACM-Implantate mit Biogel (A-B). Nach drei Monaten erfolgte die makroskopische und molekularbiologische Evaluation der Defektzonen. Makroskopisch zeigten die meisten Defekte eine gute Implantatintegration (Grad II nach ICRS), jedoch ohne signifikante Unterschiede zwischen den Gruppen. Die molekularbiologische Analyse hingegen, welche für die qualitative Beurteilung der Gewebeheilung entscheidend war, offenbarte signifikante Effekte: Die Gruppe L-B induzierte die stärkste Chondrogenese, was durch eine signifikant erhöhte Expression der hyalinen Knorpelmarker Sox9 und Col2 belegt wurde. Im Gegensatz dazu deutete eine erhöhte Col1-Expression in der Gruppe L-OB auf eine fibröse Regeneration hin. Darüber hinaus war der Knochenresorptionsmarker CtsK in der Gruppe L-OB erhöht, woraus ein stabilisierender Effekt des Biogels auf die Knochenphase abgeleitet werden kann. Die Studie belegt somit die molekularbiologische Überlegenheit der LCM-Biogel-Kombination hinsichtlich der Bildung von qualitativ hochwertigem, hyalinähnlichem Knorpel. Obwohl die Implantate vielversprechend erscheinen, sind für eine abschließende Bewertung weitere immunhistochemische und histologische Analysen erforderlich.
Berufliche Zukunftsprofile und studentische Rahmenbedingungen im Medizinstudium: Lebenslagen von Studierenden der Landarztquote und regulär Zugelassenen sowie motivationale Wirkung des Wahlfachs in der Allgemeinmedizin – Eine empirische Evaluation an der Justus-Liebig-Universität Gießen
(2026-06-01) Yalcin, Cem Serkan
Integrating floral morphogenesis and transcriptomics in eudicots
(2025-08) Kong, Doudou
Comparative transcriptomics reveals how conserved regulators and flexible gene expression programmes shape the stability and diversity of carpel identity and differentiation. Taking this perspective further, we integrate floral morphogenesis with cross-species transcriptome data across eudicots to test how regulatory change accompanies morphological innovation.An orthogroup (OG) is a set of genes across species that descend from a single gene in their most recent common ancestor (MRCA), encompassing orthologs. On this basis, We mapped OGs to expression profiles and identified conserved and lineage-specific patterns. These patterns are then linked to morphological traits. The findings suggest that a small number of deeply conserved factors are fundamental to carpel development, and that shifts in expression and timing are associated with lineage-specific carpel morphologies. In the first part of this thesis, floral morphogenesis in eudicots is summarized with an emphasis on the origin and diversity of ring meristems. Ring meristems, which generate multiple whorls of stamens, are widespread in Ranunculales and exhibit multiple patterns of initiation. Subsequently, the floral morphogenesis of *Pteridophyllum racemosum* (Papaveraceae, Ranunculales), a sister lineage to the remaining Papaveraceae, is described for the first time. Its floral organs are relatively simple and lack a ring meristem. *P. racemosum* produces flowers with two sepals, four petals in two whorls, four stamens, and a syncarpous gynoecium of two carpels, a combination rare within Papaveraceae but consistent with reconstructions of the family’s ancestral flower. The second part focuses on transcriptomics of carpel development in eudicots. Transcriptomes of carpels are generated for *Arabidopsis thaliana*, *Eschscholzia californica*, and *Solanum lycopersicum* across four developmental stages. Comparison of OGs revealed that most regulators of carpel development are present in all three species at the genome level, but their expression pattern often differs. Only a few regulators, like *HECATE* (*HEC*) and *FRUITFULL* (*FUL*), follow conserved expression patterns. Detailed mapping from expression of OGs to published regulatory pathways showed that the *NGATHA* (*NGA*) is conserved both in expression and in function, representing a core component of the regulatory network for stigma and style development, while other network, such as those involving polarity establishment, is divergent. These results indicate that carpel development relies on both core regulators and flexible components whose evolutionary role may mediate through expression. In conclusion, this thesis integrates morphological studies with comparative transcriptomics to investigate the genomics and expression of floral organ evolution in eudicots. The results show that conserved carpel regulators maintained in genome, while flexible expression patterns may be inferred to contribute to differentiation. These results provide valuable gene resources for future functional studies once stable transformation systems are established in non-model systems.
Data, Dataset and Code Repository for AI-based shrimp fingerprinting study
(2026-05-28) Bendag, Slim
This repository contains python code that benchmarks local feature–based matching models against a classic computer vision baseline for shrimp re-identification across moulting events. The objective is to evaluate whether modern feature extractors and matchers can reliably re-identify the same shrimp before and after a moult. Each method is evaluated using two main criteria: • Re-identification accuracy • Percentage of shrimps correctly matched between moults. • Computational efficiency • Runtime per image pair and GPU usage. ***Repository structure** The repository is organized by method, with each folder containing a standalone fingerprinting pipeline: • d2-net/ – D2-Net feature extraction and shrimp fingerprinting pipeline • LoFTR/ – LoFTR-based matching pipeline • LightGlue/ – LightGlue + SuperPoint pipeline • SuperGluePretrainedNetwork/ – SuperGlue + SuperPoint pipeline • EDM/ – EDM model code and fingerprinting pipeline • xfeat/ – XFeat pipeline and training utilities • SIFT_OCV/ – SIFT baseline implemented using OpenCV Additional folders: • database/ – ROI cropped imaged dataset , organized by moult stage and shrimp ID • results/ – Output folders for each matching method • Stats/ – Jupyter notebooks, plots, and summary tables for analysis **Dataset layout** The dataset structure is: • database/moult1/<shrimp_id>/*.jpg • database/moult2/<shrimp_id>/*.jpg Notes: • Each <shrimp_id> directory may contain multiple images. • Each shrimp ID is treated as a class. • The scripts perform class-to-class matching, linking shrimp from moult 1 to moult 2. **Setup** 1. Create a Python 3.10 environment. 2. Install dependencies: 3. pip install -r requirements.txt 4. (Optional) Install a CUDA-enabled PyTorch build for GPU acceleration. The original setup used PyTorch 2.0.1 with CUDA 11.7. 5. Activate the virtual environment (venv). **Running the experiments** Each method has its own standalone script, with a configuration block at the top. Paths can be updated directly in the script. Example commands: SIFT • python SIFT_OCV/SIFT_OCV_fingerpritning.py LightGlue • python LightGlue/lightGlue_fingerprint.py LoFTR • python LoFTR/LoFTR_fingerprinting.py SuperGlue • python SuperGluePretrainedNetwork/superglue_fingerprinting.py D2-Net • python d2-net/D2-net_fingerprint.py EDM • python EDM/EDM_fingerprint.py XFeat • python xfeat/Xfeat.py **Outputs** Each method writes its results to a dedicated output folder, including: • score_matrix.csv – Class-to-class similarity matrix • mapping.csv – Hungarian assignment results with inlier counts and match rates • matched_keypoints.csv – Inlier correspondences for the best image pair per assigned class • vis/ – Visualizations of inlier matches after RANSAC filtering • run_resource_report.txt – Runtime statistics and GPU usage **Analysis and visualization** The Stats/ folder contains Jupyter notebooks that: • Aggregates results across all methods • Computes re-identification accuracy and efficiency metrics • Generates plots and CSV summary tables used in the final analysis