In this thesis, I focused on studying the above- and belowground interactions of J. vulgaris from a plant-soil feedback (hereafter, PSF) perspective. I investigated the temporal variation of... Show moreIn this thesis, I focused on studying the above- and belowground interactions of J. vulgaris from a plant-soil feedback (hereafter, PSF) perspective. I investigated the temporal variation of negative PSF and examined the effects of root-associated bacteria on plant performance and aboveground herbivores. Additionally, I tested the role of PSF in relation to plant population structure and the significance of soil legacy effects in natural conditions. The findings reveal that temporal dynamics in PSF are driven by changes in plant sensitivity and in the soil microbiome. Although bacteria isolated from J. vulgaris roots can negatively affect plant performance, they can also affect aboveground herbivores and other plant species. Consequently, these bacteria may not be suitable for biological control of J. vulgaris. Moreover, I discovered that soil nematodes can mediate plant-plant interactions, but often favoring J. vulgaris. In my field work, I detected soil legacy effects, but seedling recruitment spatial patterns of J. vulgaris were not soil-mediated. The insights gained from studying PSF and above- and belowground interactions have the potential to reshape traditional approaches employed in controlling invasive plants. This thesis emphasizes the importance of transitioning PSF experiments from indoor to outdoor settings considering various influencing factors simultaneously. Show less
Over several decades, a variety of computational methods for drug discovery have been proposed and applied in practice. With the accumulation of data and the development of machine learning methods... Show moreOver several decades, a variety of computational methods for drug discovery have been proposed and applied in practice. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm, i.e. deep learning methods have attracted particular interest in drug design. In this study, a new deep learning-based method (DrugEx) was proposed to design de novo drug-like molecules. It was proven that candidate molecules designed by DrugEx had a larger chemical diversity, and better covered the chemical space of known ligands. In order to address the issue of polypharmacology, the DrugEx algorithm was updated with multi-objective optimization towards multiple targets. The results of its application demonstrated the generation of compounds with a diverse predicted selectivity profile toward multiple targets, offering the potential of high efficacy and lower toxicity. In order to improve its generality, DrugEx was further updated to have the capability of designing molecules based on given scaffolds. We extended the architecture of Transformer to deal with each molecule as a graph. As a proof, its effectiveness in that 100% valid molecules are generated and most of them had predicted high affinity towards A2AAR with given scaffolds. Moreover, GenUI was developed as a visualizion software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface to facilitate collaboration in the disparate communities interested in computer-aided drug discovery.These studies highlight the overwhelming power of AI methods in drug discovery. Show less
Sunitinib has been approved by FDA in 2006 and became the first-line treatment for patients with clear cell metastatic renal cell carcinoma (cc-mRCC) due to its dramatic improvement in... Show moreSunitinib has been approved by FDA in 2006 and became the first-line treatment for patients with clear cell metastatic renal cell carcinoma (cc-mRCC) due to its dramatic improvement in progression-free survival (PFS) and overall survival (OS) and affordable toxicity. However, the inter-individual variability of sunitinib outcomes is large. Some clinical factors, such as blood pressure, can partly predict sunitinib efficacy, but they are not enough. More insight into the genetic factors underlying sunitinib outcome could also be helpful to improve optimization of treatment. In this thesis, the relevance of single nucleotide polymorphisms (SNPs) to sunitinib treatment in (cc)-mRCC patients was investigated with regard to efficacy and toxicity.We identified SNPs in IL8,IL13, VEGFR, CYP3A5 and CTLA-4 were associated with efficacy, toxicity and clearance.Further validation in independent cohorts is need before the implementation of these genetic biomarkers into clinical practice. Show less
The plant kingdom has evolved an enormous number of chemically diverse metabolites that protect plants from biotic and abiotic stresses. The large number of metabolites in a given plant indicates... Show moreThe plant kingdom has evolved an enormous number of chemically diverse metabolites that protect plants from biotic and abiotic stresses. The large number of metabolites in a given plant indicates interactions between metabolites are very likely. The co-occurrence of plant metabolites comprise a natural background where these metabolites have to function and this is often overlooked or ignored in ecological studies. The main goal of this thesis is to understand the importance of metabolite interactions I used assays with a generalist herbivore to study the interactions between chlorogenic acid (CGA), pyrrolizidine alkaloids (PAs) and fractions from Jacobaea plants. I found that PA free bases, PA N-oxides (the oxidized form of free base) and CGA decreased thrips survival. Although PA free bases and CGA decreased thrips survival, the combination of the two toxins was less toxic than the single toxins. In contrast, the combination of PA N-oxides with CGA enhanced the toxicity against thrips in a synergistic way. Adding PAs to different plant fractions showed that metabolite interactions on thrips survival are common as in all tested combinations we found antagonistic and synergistic effects. Clearly, bioactivity of a metabolite is strongly dependent upon the co-occurrence of metabolites in the plant cell. Show less
Lanthanoid coordination polymers (Ln CPs), self-assembled from organic ligands and lanthanoid ions, combine the promising properties of normal transition metal CPs, such as a well-defined... Show moreLanthanoid coordination polymers (Ln CPs), self-assembled from organic ligands and lanthanoid ions, combine the promising properties of normal transition metal CPs, such as a well-defined structures and large surface areas, with the properties of lanthanoid ions, notably luminescence and magnetism. As the oxidation state and chemical properties of the lanthanoid ions are highly similar, it is possible to prepare mixed-metal Ln CPs which show dual emission from two different lanthanoid ions or lanthanoid ions and ligands. In this research we focused on the preparation of Ln CPs, especially those containing two or more different lanthanoid ions, and the exploration of their applications in sensing. These dual-emission Ln CPs show good performance for temperature sensing: the intensity ratio from two individual emission peaks can be used as parameters for temperature. Furthermore, Gd2O3 was demonstrated to be an excellent substrate for the growth of thin films of Ln CPs. The temperature-sensing properties of these Ln CP films are also reported. Show less
