Endothelin receptor antagonism versus combined ECE/NEP inhibition in patients with type 2 diabetes and nephropathy

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Hausdorff Dimension of the Record Set of a Fractional Brownian Motion

We prove that the Hausdorff dimension of the record set of a fractional Brownian motion with Hurst parameter $H$ equals $H$

Trapped Ion Mobility Spectrometry coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for the analysis of Complex Mixtures.

Analytical Characterization of complex mixtures, such as crude oil, environmental samples, and biological mixtures, is challenging because of the large diversity of molecular components. Mass spectrometry based techniques are among the most powerful tools for the separation of molecules based on their molecular composition, and the coupling of ion mobility spectrometry has enabled the separation and structural elucidation using the tridimensional structure of the molecule. The present work expands the ability of analytical chemists by furthering the development of IMS-MS instrumentation by coupling Trapped Ion Mobility Spectrometry to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (TIMS-FT-ICR MS). The TIMS-FT-ICR MS platform combines the high-resolution separation of TIMS, which has mobility resolving powers up to 400, and ultra-high mass resolution of FT-ICR MS, with mass resolving power over 1,000,000. This instrumentation allows the assignment of exact chemical composition for compounds in a complex mixture, as well as measurement of the collision cross-section of the molecule. Herein, the principles of the TIMS separation and its coupling to FT-ICR MS are described, as well as how the platform can be applied to targeted analysis of molecules, and untargeted characterization of complex mixtures. Molecular standards were analyzed by TIMS-MS in order to develop a computational workflow that can be utilized to elucidate molecular structure, using the measured collision cross-section of the ion. This workflow enabled identification of structural, cis/trans isomers, and chelated molecules and provides the basis for unsupervised structural elucidation of a complex mixture, and in particular for the elucidation of hydrocarbons from fossil fuels. In summary, this work presents the coupling of TIMS-FT-ICR MS and provides examples of applications as a proof of concept of the potential of this platform for solving complex analytical challenges

A cognitive perspective on learning, decision-making, and technology evaluations in organisations

This dissertation examines how firms’ selection of technological and R&D opportunities shape the performance of their innovation efforts. Managers select R&D investments in complex and uncertain environments where it is difficult to learn from past decisions. I examine this challenge using empirical and agent-based modelling methods and by focusing on three interrelated aspects: managers’ individual learning processes, the adaptation of mental representations in complex environments, and the role of distributed expertise in group evaluations. In the first chapter, I propose an alternative explanation to how managers learn from experience that does not involve feedback and that is thus applicable to contexts where learning from feedback is difficult. I test this novel learning mechanism, termed ‘representation learning’, by analysing a large proprietary dataset of patent evaluations and termination decisions made by managers at a Fortune 500 firm. The second chapter explores further implications for performance of representation learning by means of an agent-based model of representation and policy search in rugged landscapes. This study examines how different representation search strategies affect decision-makers’ adaptation in complex environments. Finally, the third chapter explores the performance of group evaluation processes when evaluators differ in the depth and breadth of their knowledge of the technologies being evaluated. This research contributes to management literature by shedding light on the cognitive processes underlying learning and decision-making in uncertain and complex environments. These findings also have practical implications for strategy research and practice concerning the management of uncertain R&D and technology investments.Open Acces