Global warming has modified the distribution of some forests growing at their altitudinal limit (treelines): some have been advancing (leading to loss of alpine biodiversity and changes in surface reflectance of mountain and polar areas), others remained stable leaving a lag between the position of uppermost tree stands and the treeline isotherm (TI, common seasonal mean temperature indicative for potential treeline position). Despite these striking worldwide patterns, there is a lack of systematic observation to understand the drivers of differential treeline response to global warming. The extent of the treeline lag behind warming may be associated with seedlings survival. However, as temperature is considered the main factor responsible for treeline, the role of seedling drought sensitivity as driver of differential treeline response to global warming is poorly understood. Certain xylem anatomical traits (XA) such as conduits wall thickness-area ratio, conduits area and layers, pits, and parenchyma area may confer drought adaptations, ultimately affecting survival and growth under increasing warming conditions. The proposed research will help understand the drivers of differential treeline response to global warming. I will analyse the effect of XA on the distance of the uppermost tree stands to TI in representative European treeline tree species. We will determine the treelines’ lag across several regions. I will quantify XA and their plasticity, and correlate them with treeline position. My contribution to achieve the project objectives relies on quantitative wood anatomy experience for studying drought adaptations in tree species, and examining patterns between drought resistance and anatomical variation. This project brings me experience abroad to get a permanent position at a high-quality research institution, with a high-life quality, focusing on independent research on wood anatomical influence on plant function under stressful environments.
Intestinal fungi are an important component of the microbiome and their homeostasis/dysbiosis constantly shapes the immune responses. The human commensal fungus Candida albicans colonizes the gut of 40-80% of individuals. However, life-threatening infections caused by this fungus are relatively rare since the unwinding of its pathogenic potential is kept in check by the immune system. In line with this, immunodeficient patients suffer often from Candidiasis, which is typically difficult to treat. Candida albicans induce strong Th17 response which is the main protective mechanism against pathogenic fungi. Candida-induced Th17 cells can protect the host not only against other fungal but also against extracellular pathogens. Besides the positive effects of Th17 in defence against pathogens, recent studies connect fungi-elicited Th17 response with local and gut-distal pathologies such as asthma, multiple sclerosis, Crohn’s disease, or rheumatoid arthritis. Despite that the role of Th17 is relatively well studied, the early events at the initiation of innate and adaptive immune responses against intestinal fungi are much less known. Various mutations in the adaptive immune system are clearly associated with impaired antifungal immunity. The proposed project will fill this knowledge gap by systematic studies of antigen-presenting cells involved in initiating Th17 response upon Candida colonization. We propose to use single-cell RNA sequencing techniques to determine Candida responsive populations followed by deletion of MHCII on selected APCs populations to prove that the effect is mediated via antigen presentation. We will further study the bidirectional interaction between Candida and APCs in these mice. This study will lead to the understanding of events at the beginning of antifungal response via the identification of cell type(s) responsible for Th17 induction as well as the regulation of fungal homeostasis and control of Candida virulence.
This project aims to overcome the major hurdles that prevent current state-of-the-art models for natural language generation (NLG) from real-world deployment. While deep learning and neural networks brought considerable progress in many areas of natural language processing, neural approaches to NLG remain confined to experimental use and production NLG systems are handcrafted. The reason for this is that despite the very natural and fluent outputs of recent neural systems, neural NLG still has major drawbacks: (1) the behavior of the systems is not transparent and hard to control (the internal representation is implicit), which leads to incorrect or even harmful outputs, (2) the models require a lot of training data and processing power do not generalize well, and are mostly English-only. On the other hand, handcrafted models are safe, transparent and fast, but produce less fluent outputs and are expensive to adapt to new languages and domains (topics). As a result, usefulness of NLG models in general is limited. In addition, current methods for automatic evaluation of NLG outputs are unreliable, hampering system development. The main aims of this project, directly addressing the above drawbacks, are: 1) Develop new approaches for NLG that combine neural approaches with explicit symbolic semantic representations, thus allowing greater control over the outputs and explicit logical inferences over the data. 2) Introduce approaches to model compression and adaptation to make models easily portable across domains and languages. 3) Develop reliable neural-symbolic approaches for evaluation of NLG systems. We will test our approaches on multiple NLG applications—data-to-text generation (e.g., weather or sports reports), summarization, and dialogue response generation. For example, our approach will make it possible to deploy a new data reporting system for a given domain based on a few dozen example input-output pairs, compared to thousands needed by current methods.