Platelets play an essential role in hemostasis but are also critically involved in acute arterial thrombotic occlusions leading to myocardial infarction or ischemic stroke and associated tissue fibrosis which are still the major cause of morbidity and disability in the European Union thus causing enormous costs in the health care system. In the last years there is increasing evidence that primary hemostasis and inflammatory atherothrombosis are crucially affected by leukocytes. Thereby the neutrophils represent the most abundant type of immune celly as almost 50% of all leukocytes belong to the neutrophil subset. The neutrophil extracellular trap (NET) formation is mainly know as pro-thrombotic factor in arterial thrombosis and is characterized by release of decondensed chromatin with incorporated histones and neutrophil elastases after neutrophil activation. Beside their pro-thrombotic effect, NETs were also recently described as inducer of tissue fibrosis in vivo thus contributing to cardiac tissue damage. Although tubulin and intermediate filament rearrangements in the cytoskeleton and nuclear envelope are a prerequisite for NET formation and chromatin release, nothing is know about the underlying molecular mechanisms and targets hitherto. Tubulin dynamics and microtubules are known regulators of intermediate filaments in the nuclear envelope thus maintaining the nuclear integrity of cells. Thereby, the ubiquitous Casein kinase 2 (CK2) is an acknowledged upstream molecule of microtubule dynamics and stability in a wide variety of cells. For this reason, the role of the CK2 in microtubule and intermediate filament dynamics during NET formation and its impact on thrombo-occlusive tissue fibrosis in cardiovascular diseases will be investigated resulting in the identification of new molecular structures suitable for improved and personalized treatment of thrombo-occlusive events like myocardial infarction and ischemic stroke.

In antiquity and the early Middle Ages, a network of trade routes known as the Silk Road connected east Asia and the Мiddle East. The Silk Road was not just an economic link, but also the avenue for cultural and even genetic exchanges between these regions. Recent genetic discoveries have hinted that such connections might have begun much earlier, during the Pleistocene. The Pleistocene period is of fundamental importance for human history. It is then that our ancestors evolved and colonised the entire Old World, surviving a suite of major extinction events – and they did so against a dramatic backdrop of ice ages and warmer interglacial phases which substantially altered their habitats. Conquering the extreme environments of arid central Asia to eventually settle the entire Asian mainland and beyond is one of the most impressive feats in this story. Unfortunately, there are too few known Pleistocene archaeological sites in central Asia to allow us to piece together when and how this happened. PALAEOSILKROAD will resolve this deficit by surveying central Asian mountain foothills as both corridors for human and animal movements and archives of past climate change. The project will discover new sites in the Tian Shan, Dzungar, and southern Altai foothills (Kazakhstan) and use them to examine if and how 1) humans were able to survive in the foothills throughout the last glacial cycle (110-11 500 years ago), and 2) periodic advances of mountain glaciers motivated dispersals, population segmentation, and behavioural adaptations. To address these questions, PALAEOSILKROAD will take an ambitious approach rooted in archaeology and contextualised by palaeoenvironmental reconstruction. The results of this project will change the way we understand human dispersals on a global scale and the resilience of early humans in the face of environmental challenges, providing a major missing link to explain how Homo sapiens became the only surviving species of our genus.

Deep learning (DL) has recently achieved remarkable success due to the continuous growth in model sizes. However, this growth has led to increased energy consumption. Hardware implementation of digital DL can help reduce energy usage, but the Von Neumann architecture of current DL has hindered its practical realization. In contrast, the brain exhibits energy-efficient multiscale spatiotemporal processing. Biologically plausible (BiP) frameworks have emerged as alternatives to mainstream DL. These methods use bottom-up and top-down signals, incorporating feedforward and feedback mechanisms, and local objectives instead of global error. Recently, I demonstrated that a BiP opto-analog hardware can achieve competitive performance compared to digital DL for feedforward networks. However, transformers, the backbone of current DL, are challenging to implement due to the input-dependent quadratic complexity in the transformer's attention. This project leverages the multiscale dynamics in the primary vision system to explore BiP architectures for transformers. The project is hosted at the University of Tübingen under Matthias Bethge and Thomas Euler, who have a long-standing effort in the system identification of mouse retina via DL. The project has three objectives. First, I will extract top-down information from neural recordings of ganglion cells in the mouse retina, focusing on unique spatiotemporal features that maximally activate specific cell types. Next, I will combine top-down signals with bottom-up models of the retina using recurrent architectures with linear complexity and compare their performance in classification tasks to a vision transformer for the retina. Lastly, I propose a BiP transformer with local weight updates. I will examine the robustness of models under data distribution shifts and noise injection. A positive outcome of the project will address energy and cost issues of AI and help me progress my academic career in this interdisciplinary field.

While in many countries women have been increasing their participation in higher education, fields of study continue to be segregated by gender. Men dominate the technical fields which include mathematics, engineering and the physical sciences, whereas women tend to engage in areas that have a social and caring dimension. Despite numerous policies to enhance gender equalities in society, gender segregation in higher education, ironically, appears to be more pronounced in post-industrial countries, such as Germany, than in developing/transforming societies. Although gender differences in field of study and occupational choices are shaped by multifarious influences, the role of parents might be particularly worth attention because parents are the primary and one of the most influential socialising agents in childhood and adolescence. In fact, one possible source of students’ knowledge about and interest in a particular field of study and career early on stems from parental education and occupation. The impact of parents’ occupational fields and its gender typicality has received less attention than parental education in early research. Even though some studies have identified the association of parental occupations with students’ engagement in gender-atypical fields of study, they do not provide any empirical evidence of the mechanism for the relationship. Despite the possibly significant influence of parental occupation on the educational and career choices of adolescent boys and girls, previous quantitative studies rarely examined the mechanisms for the relationship between such gender differences and parental occupations. With the proposed research “Understanding the impact of parental occupation on gender differences in field of study and occupational choices in Germany”, that comprises 3 projects using secondary and primary data, I seek to offer new evidence on how parental occupations influence students’ choices of a gender-atypical field of study and career.