854 research outputs found

    Characterization and Bioanalysis of Protein-Based Biopharmaceuticals, Peptides and Amino Acids by Liquid Chromatography and Mass Spectrometry

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    Biopharmazeutika sind zu einer essenziellen Klasse von Therapeutika geworden und werden fĂŒr verschiedene medizinische Indikationen wie Diabetes, Krebs, entzĂŒndliche Erkrankungen und Infektionskrankheiten eingesetzt. Monoklonale Antikörper (mAbs) haben innerhalb der Biopharmazeutika den grĂ¶ĂŸten Anteil bezogen auf die Zulassungszahlen. Den Vorteilen bezĂŒglich hoher SpezifitĂ€t und EffektivitĂ€t stehen jedoch Nachteile durch hohe Kosten und erhöhter KomplexitĂ€t gegenĂŒber. Die KomplexitĂ€t ergibt sich einerseits aufgrund des hohen Molekulargewichts und anderseits aufgrund der strukturellen HeterogenitĂ€t, wodurch die analytische Charakterisierung und QualitĂ€tskontrolle von mAbs und anderer Biopharmazeutika zu einer Herausforderung wird. Neben diesen protein-basierten Biopharmazeutika ist auch die AufklĂ€rung der absoluten Konfiguration von therapeutischen und natĂŒrlichen (Lipo)peptiden von besonderem Interesse fĂŒr die Wirkstoffforschung. Zur BewĂ€ltigung dieser Herausforderungen wurden in der hier prĂ€sentierten Arbeit flĂŒssigchromatographische (LC) und massenspektrometrische (MS) Methoden fĂŒr die umfassende Analyse eingesetzt. Die erste Publikation dieser Dissertation bezog sich auf die Analyse von Ladungsvarianten von mAbs, welche wichtige QualitĂ€tsmerkmale darstellen und die Sicherheit und Wirksamkeit des Arzneimittels beeinflussen können. Zur Charakterisierung der Ladungsvarianten wurden die mAbs auf Ebene des intakten Proteins als auch auf Fragmentebene nach begrenztem Verdau und Reduzierung der DisulfidbrĂŒcken mittels starker KationenaustauschflĂŒssigkeitschromatographie (SCX) analysiert. Die SCX-Methode wurde systematisch mittels statistischer Versuchsplanung (DoE) dahingehend optimiert, die höchstmögliche Anzahl an Ladungsvarianten zu trennen. Die mobile Phase der optimierten SCX-Methode enthielt jedoch eine hohe Konzentration an nicht-flĂŒchtigen Salzen, wodurch sie nicht mit MS Detektion kompatibel ist, welche wiederum entscheidend fĂŒr die Identifikation der Ladungsvarianten ist. Um dieser Herausforderung zu begegnen, wurde erfolgreich eine online zweidimensionale flĂŒssigchromatographische (2D-LC) Methode entwickelt, bei der SCX in der ersten Trenndimension und UmkehrphasenflĂŒssigchromatographie (RP-LC) in der zweiten Trenndimension zur Entsalzung vor der MS Detektion verwendet wurde. Die Entwicklung einer extrem kurzen (≀ 1 min) RP-LC Methode war unabdingbar zur Etablierung einer umfassenden 2D-LC Methode. Dazu wurde eine SĂ€ulenvergleichsstudie mit monolithischen und oberflĂ€chlich porösen PartikelsĂ€ulen (SPP-SĂ€ulen) durchgefĂŒhrt und die Trenneffizienz sowie die Analysengeschwindigkeit untersucht. Eine noch umfassendere SĂ€ulenvergleichsstudie mit Fokus auf das kinetische Leistungsvermögen wurde in der zweiten Arbeit dieser Dissertation durchgefĂŒhrt. Eine Auswahl von 13 RP-ProteintrennsĂ€ulen inklusive monolithischer, SPP und vollporöser PartikelsĂ€ulen (FPP-SĂ€ulen) wurde hinsichtlich ihrer FĂ€higkeit, Peaks in der kĂŒrzest möglichen Zeit zu trennen, untersucht. Es konnte gezeigt werden, dass SPP-SĂ€ulen mit einer PorengrĂ¶ĂŸe von etwa 400 Å und einer dĂŒnnen, porösen Schicht die beste Performance insbesondere fĂŒr grĂ¶ĂŸere Proteinen besitzen. Proteine selbst können auch potenzielle Ziele fĂŒr Arzneistoffe sein, wie z.B. das Tumorsuppressorprotein p53, welches in der dritten Publikation dieser Arbeit untersucht wurde. Intakte Protein LC-MS wurde erfolgreich verwendet, um die Bindungseffizienz und -spezifitĂ€t des kovalenten Inhibitors an p53 nachzuweisen. AminosĂ€uren sind die Bausteine von Proteinen und Peptiden und die Mehrheit dieser AminosĂ€uren sind chiral. Die biologische AktivitĂ€t ist in der Regel abhĂ€ngig von der absoluten Konfiguration der AminosĂ€uren, wodurch die enantiomerenselektive Analyse von höchster Wichtigkeit fĂŒr die StrukturaufklĂ€rung und zur QualitĂ€tskontrolle ist. Daher war die Entwicklung schneller und umfassender Trennmethoden zur Analyse von AminosĂ€uren, deren Enantiomeren, Diastereomeren und konstitutionellen Isomeren ein Ziel dieser Arbeit. Dieses konnte durch Derivatisierung mittels 6-Aminochinolyl-N-hydroxysuccinimidylcarbamat (AQC) und anschließender Analyse durch enantioselektiver flĂŒssigchromatographischer IonenmobilitĂ€ts-Massenspektrometrie (LC-IM-MS) erreicht werden. Eine sehr schnelle dreiminĂŒtige Analysenmethode konnte entwickelt und zur StrukturaufklĂ€rung von therapeutischen Peptiden und eines natĂŒrlichen Lipopeptides eingesetzt werden. Die absolute Konfiguration eines Tetrapeptides als Bestandteil des natĂŒrlichen, antimikrobiellen Peptidpolyens‘ Epifadin konnte mittels chiraler LC-MS bestimmt werden, was wiederum entscheidend fĂŒr die StrukturaufklĂ€rung war. In dieser Arbeit konnten alle acht Enantiomerenpaare erfolgreich getrennt werden und die Diastereomerentrennung wurde optimiert.Biopharmaceuticals have become an essential class of therapeutics and are used for different medical indications such as diabetes, cancer, inflammatory diseases, and infectious diseases. Monoclonal antibodies (mAbs) have the biggest share within the biopharmaceuticals regarding the drug approval numbers. However, the benefits in terms of high specificity and efficacy come with the drawback of higher cost and higher complexity. This complexity arises from the high molecular weight on the one hand and high structural heterogeneity on the other hand, making the analytical characterization and quality control of mAbs and other biopharmaceuticals a significant challenge. In addition to these protein-based biopharmaceuticals, the elucidation of the absolute configuration of therapeutic peptides and natural (lipo)peptides is also of particular interest for drug discovery. To address these challenges, different liquid chromatography (LC) and mass spectrometric (MS) methods were used for the more comprehensive analysis in the presented work. The first publication of this dissertation was dedicated to the analysis of charge variants of mAbs, which is an important quality attribute that might affect safety and efficacy of the drug product. To characterize the charge variants, the mAbs were analysed at the intact protein level and the subunit level after limited digestion and disulphide reduction using strong cation-exchange chromatography (SCX). The SCX method was systematically optimized to enable the separation of the maximum number of charge variants using a design of experiments (DoE) approach. The optimized SCX mobile phase, however, contains high concentrations of non-volatile salt in the mobile phase, which is incompatible with MS detection. On the other hand, MS analysis is essential for the identification of the charge variants. To overcome this limitation, an online two-dimensional liquid chromatographic (2D-LC) method was successfully developed, which uses SCX in the first separation dimension and reversed-phase (RP) LC in the second separation dimension, which can be used for de-salting prior MS analysis. An ultra-short analysis time (≀ 1 min) of the second dimension RP method was essential to establish a full comprehensive 2D-LC analysis. For this purpose, a column comparison study was performed using a set of monolithic and superficially porous particle (SPP) columns, and the separation efficiency and analysis speed were investigated. An even more comprehensive column comparison study focusing on the kinetic performance was done for the second work presented in this dissertation. A set of 13 RP protein separation columns including monolithic, SPP, and fully porous particle (FPP) columns was investigated regarding their capability to separate peaks in the shortest possible time. It could be demonstrated that SPP columns with a pore size of 400 Å and a thin, porous shell provided the best performance especially for large proteins such as mAbs. Proteins themselves can also be the potential targets of drug products such as the tumour suppressor protein p53 studied in publication III. Intact protein LC-MS was successfully used to investigate the binding efficiency and specificity of covalent inhibitors. Amino acids are the building blocks of proteins and peptides and most of these amino acids are chiral. As the biological activity is usually dependent on the absolute configuration of the amino acids, the enantioselective analysis is of utmost importance for structural elucidation and quality control. Therefore, one goal of the presented work was to develop a fast and comprehensive method to separate amino acids, their enantiomers, diastereomers, and constitutional isomers. This was achieved by derivatization using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) and subsequent analysis by enantioselective liquid chromatography ion mobility-mass spectrometry (LC-IM-MS). A very fast three minutes short analysis method could be developed and was applied for the successful structure elucidation of a therapeutic peptide and a natural lipopeptide. The absolute configuration of a tetrapeptide originating from the natural antimicrobial peptide-polyene epifadin could be determined using chiral LC-MS, which was crucial for the structure elucidation. In this work, all eight enantiomer peak pairs could be successfully separated and the separation of the diastereomers was optimized

    Optimization of Beyond 5G Network Slicing for Smart City Applications

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    Transitioning from the current fifth-generation (5G) wireless technology, the advent of beyond 5G (B5G) signifies a pivotal stride toward sixth generation (6G) communication technology. B5G, at its essence, harnesses end-to-end (E2E) network slicing (NS) technology, enabling the simultaneous accommodation of multiple logical networks with distinct performance requirements on a shared physical infrastructure. At the forefront of this implementation lies the critical process of network slice design, a phase central to the realization of efficient smart city networks. This thesis assumes a key role in the network slicing life cycle, emphasizing the analysis and formulation of optimal procedures for configuring, customizing, and allocating E2E network slices. The focus extends to catering to the unique demands of smart city applications, encompassing critical areas such as emergency response, smart buildings, and video surveillance. By addressing the intricacies of network slice design, the study navigates through the complexities of tailoring slices to meet specific application needs, thereby contributing to the seamless integration of diverse services within the smart city framework. Addressing the core challenge of NS, which involves the allocation of virtual networks on the physical topology with optimal resource allocation, the thesis introduces a dual integer linear programming (ILP) optimization problem. This problem is formulated to jointly minimize the embedding cost and latency. However, given the NP-hard nature of this ILP, finding an efficient alternative becomes a significant hurdle. In response, this thesis introduces a novel heuristic approach the matroid-based modified greedy breadth-first search (MGBFS) algorithm. This pioneering algorithm leverages matroid properties to navigate the process of virtual network embedding and resource allocation. By introducing this novel heuristic approach, the research aims to provide near-optimal solutions, overcoming the computational complexities associated with the dual integer linear programming problem. The proposed MGBFS algorithm not only addresses the connectivity, cost, and latency constraints but also outperforms the benchmark model delivering solutions remarkably close to optimal. This innovative approach represents a substantial advancement in the optimization of smart city applications, promising heightened connectivity, efficiency, and resource utilization within the evolving landscape of B5G-enabled communication technology

    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    Resilient cooling of buildings: state of the art review

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    Name of the research project : IEA Annex 80 – Resilient Cooling of Buildings Publisher: Institute of Building Research & Innovation ZT GmbH, AustriaThis report summarizes an assessment of current State-of-the Art resilient cooling strategies and technologies. It is a result of a collaborative work conducted by participants members of IEA EBC Annex 80. This report consists of four chapters. In the first chapter are included relevant technologies and strategies that contribute to reducing heat loads to people and indoor environments. These technologies/strategies include Advanced window/glazing and shading technologies, Cool envelope materials, Evaporative Envelope Surfaces, Ventilated Envelope Surfaces and Heat Storage and Release. In the second chapter are assessed cooling strategies and technologies that are responsible for removing sensible heat in indoor environments: Ventilative cooling, Evaporative Cooling, Compression refrigeration, Desiccant cooling system, Ground source cooling, Night sky radiative cooling and High-temperature cooling systems. In the third chapter various typologies of cooling strategies and technologies are assessed inside the framework of enhancing personal comfort apart from space cooling. This group of strategies/technologies comprise of: Vertical-axis ceiling fans and horizontal-axis wall fans (such fixed fans differ from pure PCS in that they may be operated under imposed central control or under group or individual control), Small desktop-scale fans or stand fans, Furnitureintegrated fan jets, Devices combining fans with misting/evaporative cooling, Cooled chairs, with convective/conductive cooled heat absorbing surfaces, Cooled desktop surfaces, Workstation micro-air-conditioning units, some including phase change material storage, Radiantly cooled panels (these are currently less for PCS than for room heat load extraction), Conductive wearables, Fan-ventilated clothing ensembles, Variable clothing insulation: flexible dress codes and variable porosity fabrics. In the fourth chapter technologies and strategies pertinent to removing latent heat from indoor environments are assessed. This group includes Desiccant dehumidification, Refrigeration dehumidification, Ventilation dehumidification, and Thermos-electric dehumidification.Preprin

    Data Collection and Information Freshness in Energy Harvesting Networks

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    An Internet of Things (IoT) network consists of multiple devices with sensor(s), and one or more access points or gateways. These devices monitor and sample targets, such as valuable assets, before transmitting their samples to an access point or the cloud for storage or/and analysis. A critical issue is that devices have limited energy, which constrains their operational lifetime. To this end, researchers have proposed various solutions to extend the lifetime of devices. A popular solution involves optimizing the duty cycle of devices; equivalently, the ratio of their active and inactive/sleep time. Another solution is to employ energy harvesting technologies. Specifically, devices rely on one or more energy sources such as wind, solar or Radio Frequency (RF) signals to power their operations. Apart from energy, another fundamental problem is the limited spectrum shared by devices. This means they must take turns to transmit to a gateway. Equivalently, they need a transmission schedule that determines when they transmit their samples to a gateway. To this end, this thesis addresses three novel device/sensor selection problems. It first aims to determine the best devices to transmit in each time slot in an RF Energy-Harvesting Wireless Sensor Network (EH-WSN) in order to maximize throughput or sum-rate. Briefly, a Hybrid Access Point (HAP) is responsible for charging devices via downlink RF energy transfer. After that, the HAP selects a subset of devices to transmit their data. A key challenge is that the HAP has neither channel state information nor energy level information of device. In this respect, this thesis outlines two centralized algorithms that are based on cross-entropy optimization and Gibbs sampling. Next, this thesis considers information freshness when selecting devices, where the HAP aims to minimize the average Age of Information (AoI) of samples from devices. Specifically, the HAP must select devices to sample and transmit frequently. Further, it must select devices without channel state information. To this end, this thesis outlines a decentralized Q-learning algorithm that allows the HAP to select devices according to their AoI. Lastly, this thesis considers targets with time-varying states. As before, the aim is to determine the best set of devices to be active in each frame in order to monitor targets. However, the aim is to optimize a novel metric called the age of incorrect information. Further, devices cooperate with one another to monitor target(s). To choose the best set of devices and minimize the said metric, this thesis proposes two decentralized algorithms, i.e., a decentralized Q-learning algorithm and a novel state space free learning algorithm. Different from the decentralized Q-learning algorithm, the state space free learning algorithm does not require devices to store Q-tables, which record the expected reward of actions taken by devices

    Book of Abstracts:9th International Conference on Smart Energy Systems

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    A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock

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    The construction industry has witnessed an increase in the use of digital tools and smart solutions, particularly in the realm of building energy automation. While realising the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, which are considered critical urban development units. To support the interplay of smart solutions between buildings and neighbourhoods, this study aimed to collect and review all the smart solutions presented in existing scientific articles, the technical literature, and realised European projects. These solutions were classified into two main sections, buildings and neighbourhoods, which were investigated through five domains: building-energy-related uses, renewable energy sources, water, waste, and open space management. The quantitative outcomes demonstrated the potential benefits of implementing smart solutions in areas ranging from buildings to neighbourhoods. Moreover, this research concluded that the true enhancement of energy conservation goes beyond the building’s energy components and can be genuinely achieved by integrating intelligent neighbourhood elements owing to their strong interdependencies. Future research should assess the effectiveness of these solutions in resource conservation

    Review of Serious Energy Games : Objectives, Approaches, Applications, Data Integration, and Performance Assessment

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    In recent years, serious energy games (SEGs) garnered increasing attention as an innovative and effective approach to tackling energy-related challenges. This review delves into the multifaceted landscape of SEG, specifically focusing on their wide-ranging applications in various contexts. The study investigates potential enhancements in user engagement achieved through integrating social connections, personalization, and data integration. Among the main challenges identified, previous studies overlooked the full potential of serious games in addressing emerging needs in energy systems, opting for oversimplified approaches. Further, these studies exhibit limited scalability and constrained generalizability, which poses challenges in applying their findings to larger energy systems and diverse scenarios. By incorporating lessons learned from prior experiences, this review aims to propel the development of SEG toward more innovative and impactful directions. It is firmly believed that positive behavior changes among individuals can be effectively encouraged by using SEG

    Understanding building and urban environment interactions: An integrated framework for building occupancy modelling

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    Improving building energy efficiency requires accurate modelling and a comprehensive understanding of how occupants use building space. This thesis focuses on modelling building occupancy to enhance the predictive accuracy of occupancy patterns and gain a better understanding of the causal reasons for occupancy behaviour. A conceptual framework is proposed to relax the restriction of isolated building analysis, which accounts for interactions between buildings, its occupants, and other urban systems, such as the effects of transport incidents on occupancy and circulation in buildings. This thesis also presents a counterpart mapping of the framework that elaborates the links between modelling of transport and building systems. To operationalise the proposed framework, a novel modelling approach which has not been used in the current context, called the hazard-based model, is applied to model occupancy from a single building up to a district area. The proposed framework is further adapted to integrate more readily with transport models, to ensure that arrivals and departures to and from the building are consistent with the situation of the surrounding transport systems. The proposed framework and occupancy models are calibrated and validated using Wi-Fi data and other variables, such as transport and weather parameters, harvested from the South Kensington campus of Imperial College London. In addition to calibrating the occupancy model, integrating a travel simulator produces synthetic arrivals into or around the campus, which are further distributed over campus buildings via an adapted technique and feed the occupancy simulations. The model estimation results reveal the causal reasons for or exogenous effects on individual occupancy states. The validation results confirm the ability of the proposed models to predict building occupancy accurately both on average and day by day across the future dataset. Finally, evaluating occupancy simulations for various hypothetical scenarios provides valuable suggestions for efficient building design and facility operation.Open Acces
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