G protein-coupled receptors (GPCRs), one of the largest families of membrane proteins, are responsive to a diverse set of physiological endogenous ligands including hormones and neurotransmitters.... Show moreG protein-coupled receptors (GPCRs), one of the largest families of membrane proteins, are responsive to a diverse set of physiological endogenous ligands including hormones and neurotransmitters. Due to the various GPCR ligand binding domains present on GPCRs and their sensitivities to a diverse array of ligands, these proteins have shown to be very ‘druggable’ as they are the main target for an estimated 30% of approved drugs. A growing body of evidence shows a prominent role of GPCRs in all phases of cancer with a mutation frequency of approximately 20% in all cancers. Mutations occurring in GPCRs can severely alter their normal function and may ultimately convert their physiological and pathological roles. One particular class of rhodopsin-like GPCRs included in this thesis are the adenosine receptors (ARs). Due to the accumulation of adenosine in the tumor microenvironment, all four subtypes of ARs might be targets for the development of novel approaches for the treatment of cancer. For each of the four subtypes, a number of somatic mutations have been identified in patient isolates. In this thesis, we examined them on receptor activation and ligand binding using reference adenosine receptor ligands, and determined the impact mutations have on these pharmacological readouts. Show less
Humans perceive the real world through their sensory organs: vision, taste, hearing, smell, and touch. In terms of information, we consider these different modesalso referred to as different... Show moreHumans perceive the real world through their sensory organs: vision, taste, hearing, smell, and touch. In terms of information, we consider these different modesalso referred to as different channels of information or modals. Considering multiple channels of information, at the same time, is referred to as multimodal and the input as multimedia. By their very nature, multimedia data are complex and often involve intertwined instances of different kinds of information. We can leverage this multimodal perspective to extract meaning and understanding of theworld. This is comparable to how our brain processes these multiple channels, we learn how to combine and extract meaningful information from them. In this thesis, the learning is done by computer programs and smart algorithms. This is referred to as artificial intelligence. To that end, in this thesis, we have studied multimedia information, with a focus on vision and language information representation for semantic mapping. The aims of the semantic mapping learning in this thesis are: (1) visually supervised word embedding learning; (2) fine-grained labellearning for vision representation; (3) kernel-based transformation for image and text association; (4) visual representation learning via a cross-modal contrastivelearning framework. Show less
