Unnatural Amino Acids Improve Affinity and Modulate Immunogenicity: Developing Peptides to Treat MHC Type II Autoimmune Disorders

Many autoimmune diseases, including multiple sclerosis (MS), rheumatoid arthritis (RA), and celiac disease (CD), arise from improper immune system recognition of self or benign peptides as threats. No autoimmune disease currently has a cure. Many treatments suppress the entire immune system to decrease symptom severity. The core molecular interaction underlying these diseases involves specific alleles of the human leukocyte antigen (HLA) receptor hosting the immunodominant peptides associated with the disease (i.e. myelin basic protein, Type II collagen, or α-gliadin) in their binding groove. Once bound, circulating T-cells can recognize the HLA-antigen complex and initiate the complex cascade that forms an adaptive immune response. This initial HLA-antigen interaction is a promising target for therapeutic intervention. Two general strategies have been pursued: altered peptide ligands (APLs) that attempt to recruit a different class of T-cell to induce an anti-inflammatory response to balance the pro-inflammatory response associated with the antigen; and HLA blockers (HLABs), peptides that, due to a much higher affinity for the HLA receptor, quantitatively displace the antigen, inhibiting the immune response. Both approaches would benefit from improved HLA-drug binding, but as the HLA receptors are highly promiscuous, the binding sites are not specific for any natural amino acid. Unnatural amino acids, either designed or screened through high-throughput assays, may provide a solution. This review summarizes the nascent field of using non-canonical residues to treat MS, RA and CD, focusing on the importance of specific molecular interactions, and provides some examples of the synthesis of these unnatural residues

Titanium microalloyed steels

This review considers the compositions, the main process routes, microstructure and structural properties of microalloyed steels. The background and brief history are followed by sections dealing with aspects of precipitation, which control grain size and dispersion strengthening in ferrite–pearlite steels, the approaches to modelling thermomechanical processing and the influence of multiple additions of transition metals on properties. High strength acicular ferrite/bainite steels used for linepipe are included and lead to super bainite steels. Around 12% of the world strip production is processed by the thin slab direct charging route, which is considered in some detail. The weldability of microalloyed steels now embraces joining using friction stir welding, which is discussed. Over the years, many approaches have been developed to predict the structural properties of these steels. They comprise several quantifiable microstructural features including possible atom clusters, relatively recently identified through atom probe tomography. A comprehensive collection of references is provided

Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways

Faced with the complexity of diseases such as cancer which has 1012 mutations, altering gene expression, and disrupting regulatory networks, there has been a paradigm shift in the biological sciences and what has emerged is a much more quantitative field of biology. Mathematical modeling can aid in biological discovery with the development of predictive models that provide future direction for experimentalist. In this work, I have contributed to the development of novel computational approaches which explore mechanisms of receptor aggregation and predict the effects of downstream signaling. The coupled spatial non-spatial simulation algorithm, CSNSA is a tool that I took part in developing, which implements a spatial kinetic Monte Carlo for capturing receptor interactions on the cell membrane with Gillespies stochastic simulation algorithm, SSA, for temporal cytosolic interactions. Using this framework we determine that receptor clustering significantly enhances downstream signaling. In the next study the goal was to understand mechanisms of clustering. Cytoskeletal interactions with mobile proteins are known to hinder diffusion. Using a Monte Carlo approach we simulate these interactions, determining at what cytoskeletal distribution and receptor concentration optimal clustering occurs and when it is inhibited. We investigate oligomerization induced trapping to determine mechanisms of clustering, and our results show that the cytoskeletal interactions lead to receptor clustering. After exploring the mechanisms of clustering we determine how receptor aggregation effects downstream signaling. We further proceed by implementing the adaptively coarse grained Monte Carlo, ACGMC to determine if \u27receptor-sharing\u27 occurs when receptors are clustered. In our proposed \u27receptor-sharing\u27 mechanism a cytosolic species binds with a receptor then disassociates and rebinds a neighboring receptor. We tested our hypothesis using a novel computational approach, the ACGMC, an algorithm which enables the spatial temporal evolution of the system in three dimensions by using a coarse graining approach. In this framework we are modeling EGFR reaction-diffusion events on the plasma membrane while capturing the spatial-temporal dynamics of proteins in the cytosol. From this framework we observe \u27receptor-sharing\u27 which may be an important mechanism in the regulation and overall efficiency of signal transduction. In summary, I have helped to develop predictive computational tools that take systems biology in a new direction.\u2

Tensor lattice field theory with applications to the renormalization group and quantum computing

We discuss the successes and limitations of statistical sampling for a sequence of models studied in the context of lattice QCD and emphasize the need for new methods to deal with finite-density and real-time evolution. We show that these lattice models can be reformulated using tensorial methods where the field integrations in the path-integral formalism are replaced by discrete sums. These formulations involve various types of duality and provide exact coarse-graining formulas which can be combined with truncations to obtain practical implementations of the Wilson renormalization group program. Tensor reformulations are naturally discrete and provide manageable transfer matrices. Combining truncations with the time continuum limit, we derive Hamiltonians suitable to perform quantum simulation experiments, for instance using cold atoms, or to be programmed on existing quantum computers. We review recent progress concerning the tensor field theory treatment of non-compact scalar models, supersymmetric models, economical four-dimensional algorithms, noise-robust enforcement of Gauss's law, symmetry preserving truncations and topological considerations. We discuss connections with other tensor network approaches.Comment: Review article, 71 pages, 47 figures, connections to other tensor network approaches and references adde

Towards the Implementation of Nature-based Solutions for Climate Change Mitigation

In den letzten Jahren hat die Rolle naturbasierter Lösungen (Nature-based Solutions, NbS) –Aktivitäten, die in und mit der Natur arbeiten, um globale gesellschaftliche Herausforderungen zu bewältigen – bei der Abschwächung und Anpassung an den Klimawandel, dem Schutz der Artenvielfalt und der Verbesserung des menschlichen Wohlbefindens an Bedeutung gewonnen. Die Einführung von NbS verläuft jedoch weiterhin schleppend, und es besteht nach wie vor eine deutliche Kluft zwischen den langsamen Maßnahmen in großem Maßstab und den vielversprechenden Forschungsergebnissen und politischen Aussagen. Dies zeigt, dass es dringend notwendig ist, die Umsetzungsbedingungen für NbS besser zu verstehen. Derzeit ist die Evidenzbasis zu NbS noch unvollständig, insbesondere wenn es um die systematische Bewertung der Wirksamkeit und der Umsetzungsanforderungen geht. Insbesondere fehlen bei groß angelegten Bewertungen wichtige kontextbezogene Informationen über Kultur, Verhalten und andere soziale und institutionelle Faktoren. Auch die vielfältigen Vorteile von NbS werden nach wie vor unterschätzt oder in einigen Fällen überhaupt nicht gewürdigt. Ziel dieser Arbeit ist es daher, politikrelevante Forschung zu betreiben, die dazu beitragen kann, die Lücke zwischen dem großen Potenzial naturbasierter Lösungen zur Bewältigung globaler Herausforderungen, insbesondere der Eindämmung des Klimawandels, und der langsamen Umsetzung in der Praxis zu schließen. Ausgehend von der vorhandenen Literatur zu NbS argumentiere ich, dass drei Bausteine für die erfolgreiche Umsetzung jeder NbS-Aktivität, insbesondere in großem Maßstab, wesentlich sind: (1) Wissenssynthese; (2) Planung und Entscheidungsfindung; (3) Politik und Finanzierungsmechanismen. In dieser Arbeit untersuche ich diese Bereiche, indem ich Nachweise und Überlegungen zu theoretischen und methodischen Lücken in ihrer Bewertung sowie neue Perspektiven beisteuere.Recent years have seen increased attention to the role that nature-based solutions (NbS) – activities that work in and with nature to address global societal challenges – can play in mitigating and adapting to climate change, protecting biodiversity, and improving human well-being. Natural climate solutions (NCS) – a subset of NbS – can contribute up to a third of the cost-effective carbon dioxide mitigation needed to hold global warming below 2 degrees Celsius. To have the biggest effect on reducing global temperatures, however, NbS must be scaled up now and designed for the long-term. Yet, uptake of NbS continues to be slow and there remains a clear gap between the lagging action at scale and the promising research and policy narratives. This demonstrates an urgent need to better understand the implementation conditions for NbS. Currently, the evidence base on NbS remains incomplete, especially when it comes to systematically assessing effectiveness and implementation requirements. In particular, important contextual information on culture, behavior, and other social and institutional factors are lacking in large-scale assessments. The multiple benefits of NbS also remain undervalued, or in some cases are not valued at all. As such, the objective of this thesis is to conduct policy-relevant research that can contribute to closing the gap between the high potential for nature-based solutions to address global challenges, particularly climate change mitigation, and the realities of slow implementation in practice. Drawing on the existing literature on NbS, I argue that three building blocks are essential to driving successful implementation of any NbS activity, in particular at scale: (1) knowledge synthesis; (2) planning & decision-making; (3) policy & financing mechanisms. I explore these in this thesis, contributing evidence and reflection on theoretical and methodological gaps in their assessment, as well as new perspectives

Phase-change materials, systems and applications for low- and medium-temperature thermal energy storage

Determining the ideal size of compact thermal energy storage containers has been an issue for many building designers due to the difficulty of determining the transient performance of the thermal storage systems. Research and development of compact thermal energy storage systems has been ongoing for more than 80 years with phase change materials (PCMs) used to replace conventional water based thermal stores. PCMs have the potential to store larger amounts of energy when compared to water-based thermal stores over a narrow temperature range, providing a greater thermal storage capacity for the same available volume. This research was undertaken to investigate theoretically and experimentally the thermal behaviour of various PCMs and the overall decarbonisation potential when integrated into current heating and cooling systems. The overall aim was to develop algorithms that could determine optimal and cost effective compact thermal storage geometries and their system integration into the various heating and cooling applications studied. Three operating temperatures were selected based on the application: office space cooling (10 to 24$^\circ$C), residential domestic hot water and space heating (40 to 65$^\circ$C) and district heating (55 to 80$^\circ$C). The algorithms developed predict the energy performance and $CO_2$ emissions reduction for each application with a latent heat thermal storage system compared to a reference (current system design) case in each application. Previous research has focused on the melting behaviour of the PCM within a specific geometry, modelling the heat transfer fluid (HTF) in a separate analysis. The algorithms developed focus on the modelling of these 2 elements simultaneously within the respective application. This provided a useful tool to evaluate the thermal performance of each storage technology compared to the reference case in each application studied. The levelized costs of energy (LCOE) in each application were compared. It was found that in all cases studied, the latent heat thermal energy storage system is an expensive solution, compared to the reference case in each application (72\% more expensive in the office space cooling study, 69\% more expensive in the domestic hot water and space heating study and 9\% more expensive in the district heating study); although the obtained emission reductions are considerable (36\% by shifting daily cooling loads, 57\% by shifting domestic hot water and space heating loads and 11\% by utilizing industrial waste heat via a compact portable thermal store). Further integration of renewable energy sources and the electrification of current heating and cooling applications with the possibility of shifting heating and cooling loads into periods with lower carbon emissions can significantly contribute to meet the UK s 80\% carbon emissions reduction targets by 2050