Maximal mouth opening in infants and toddlers with spinal muscular atrophy: a prospective controlled study

Background: Bulbar function is frequently impaired in patients with spinal muscular atrophy (SMA). Although extremely important for the patient’s quality of life, it is difficult to address therapeutically. Due to bulbar dysfunction, maximum mouth opening (MMO) is suspected to be reduced in children with SMA. However, no published MMO values exist for SMA children younger than 24 months. This study presents a novel approach to measuring MMO in infants and toddlers with SMA and compares it with healthy controls. Methods: Children with SMA (0–24 months) who received disease-modifying therapy at a single neuropediatric center and similarly aged healthy children were prospectively recruited. MMO was measured using a cardboard scale and a custom-designed instrument. Results: A total of 115 children were included (SMA = 24, healthy controls = 91). Inter-rater reliability between two examiners was excellent (ICC = 0.987, 95% CI 0.959 to 0.995), as was the reliability between the cardboard scale and the custom-designed instrument (ICC = 0.986, 95% CI 0.968 to 0.994). A mixed linear model showed a significant increase of MMO with age, and a significantly wider mouth opening in healthy controls (p <.001). Conclusion: For future research, MMO can provide valuable information about the involvement of cranial nerves, particularly in the context of disease-modifying therapies, even at a very early age

Sustainability regulations for PtX projects: scope and impact analysis

The utilization of power-to-X (PtX) technologies as a substitute for fossil fuels is a key instrument in defossilizing the global economy, potentially making a significant contribution to sustainability efforts. This must be ensured or at least supported by legal measures to maximize the full sustainability potential. Thus, in recent years, regulations have been created to ensure sustainable PtX production on the one hand and to promote the use of PtX products on the other. The diversity and complexity of these regulations, differing in their scope of application, the applied instruments, and the respective sustainability requirements, among other things, create potential hurdles for PtX projects and complicate determining the respective influence on technical, economic, and environmental aspects. This review aims to summarize, classify, and assess current sustainability regulations and to evaluate the impact on potential PtX projects. The paper underscores the significance of the EU Renewable Energy Directive (REDIII) as the blueprint or reference for the legal regulations within the EU member states as well as the respective certification schemes and incentive and support schemes. The impact of sustainability criteria (power supply and GHG threshold) on PtX projects is analyzed and discussed, highlighting the importance of the electricity provision concept and sustainable carbon supply

On algorithmical methods facilitating clinical translation of magnetic particle imaging

Magnetic Particle Imaging (MPI) is an emerging tomographic imaging modality that has demonstrated significant potential in pre-clinical applications. As MPI moves towards clinical translation, the field faces a variety of interdisciplinary challenges that must be addressed through advancements in hardware, algorithmic methodologies, and application-driven refinements. This dissertation explores algorithmic methods that facilitate the transition of MPI from a research-oriented technology to a clinically viable imaging tool. The work presented in this dissertation highlights the crucial interplay between applicational demands, hardware design and limitations, and methodological development. Hardware innovations define the fundamental capabilities of MPI, such as sensitivity, resolution, and acquisition speed. However, these capabilities alone are insufficient to meet the stringent requirements of clinical imaging. Algorithmic research plays a pivotal role in bridging this gap by optimizing imaging performance, improving reconstruction accuracy, and addressing practical constraints in system usability. From accelerating system calibration to refining signal processing techniques and optimizing field encoding strategies, computational advancements serve as a critical link between hardware constraints and the needs of real-world medical applications. By focusing on the synergies between these three domains, this dissertation demonstrates how methodological improvements can drive the clinical adoption of MPI. Building on this foundation, the dissertation presents key methodological contributions that address specific challenges in MPI. The research contributions of the author are embedded within this broader framework, showcasing innovations that improve the efficiency and feasibility of MPI for clinical use. The core of the dissertation is composed of three peer-reviewed research articles, each addressing a distinct methodological challenge in MPI. The first contribution introduces a novel approach to system calibration by leveraging extrapolation techniques for system matrices. Traditional MPI calibration is time-intensive and requires extensive measurements, limiting the scalability of the method for clinical applications. By employing an extrapolation-based methodology, this work significantly reduces the calibration burden while maintaining reconstruction accuracy, thereby improving efficiency and reducing experimental effort. The second contribution focuses on regularization techniques in MPI reconstruction, presenting a method with automatically tuned parameters. Image reconstruction in MPI requires balancing noise suppression with the preservation of fine structural details, which is particularly crucial for medical imaging applications. This research introduces an adaptive regularization framework that dynamically adjusts parameters based on image characteristics, enhancing robustness, improving reconstruction quality, and reducing the need for manual parameter selection. The third contribution explores the use of ellipsoidal harmonic expansions to efficiently represent magnetic fields, addressing a fundamental challenge in MPI system modeling. Accurate field representations are essential for precise spatial encoding and the development of tailored field-cameras for MPI scanners. Compared to spherical harmonic representations, ellipsoidal harmonics provide greater geometric flexibility, enabling optimized field encoding strategies that are better suited for specific scanner designs. This study develops a mathematically rigorous yet efficient method for representing complex field distributions, offering improvements in encoding efficiency and adaptability to application-specific requirements. Additional research contributions contextualize these advancements within collaborative efforts involving multiple research institutions. These collaborations have furthered the development of MPI modeling techniques, investigated the MPI performance of the clinically approved tracer Resotran, and contributed to studies on the development of novel MPI tracers. The findings presented in this dissertation have been disseminated through various peer-reviewed journal articles and conference proceedings, reinforcing the significance of algorithmic research in driving the clinical translation of MPI. In conclusion, this dissertation demonstrates that algorithmic innovations are fundamental to bridging the gap between pre-clinical research and clinical implementation in MPI. By enhancing calibration processes, improving reconstruction methodologies, and optimizing field representations, these advancements facilitate the practical deployment of MPI in medical imaging. The final chapter provides a summary of the key findings and an outlook on future algorithmic research directions that will further support the integration of MPI into clinical workflows.Die Magnetpartikelbildgebung (MPI) ist eine aufstrebende tomographische Bildgebungsmethode, die bereits großes Potenzial in präklinischen Anwendungen gezeigt hat. Mit der zunehmenden klinischen Translation von MPI steht das Forschungsfeld vor einer Vielzahl interdisziplinärer Herausforderungen, die durch Fortschritte in der Hardwareentwicklung, algorithmischen Methoden und anwendungsgetriebenen Optimierungen bewältigt werden müssen. Diese Dissertation untersucht algorithmische Methoden, die den Übergang von MPI von einer forschungsorientierten Technologie zu einem klinisch einsetzbaren Bildgebungsverfahren erleichtern. Die in dieser Arbeit vorgestellten Untersuchungen betonen das entscheidende Zusammenspiel zwischen anwendungsspezifischen Anforderungen, Hardwaredesign und - beschränkungen so - wie algorithmischer und methodischer Entwicklung. Hardware-Innovationen definieren die grundlegenden Leistungsmerkmale von MPI, wie Sensibilität, Auflösung und Bildaufnahmerate. Diese Eigenschaften allein reichen jedoch nicht aus, um die hohen Anforderungen der klinischen Bildgebung zu erfüllen. Algorithmische Forschung spielt eine Schlüsselrolle bei der Überbrückung dieser Lücke, indem sie die Bildgebungsleistung optimiert, die Rekonstruktionsgenauigkeit verbessert und praktische Einschränkungen in der Systemanwendbarkeit adressiert. Von der Beschleunigung der Systemkalibrierung über die Verfeinerung der Signalverarbeitung bis hin zur Optimierung der Feldkodierung stellen algorithmische Entwicklungen eine essenzielle Verbindung zwischen Hardwaregrenzen und den Anforderungen realer medizinischer Anwendungen dar. Durch die Fokussierung auf die Synergien zwischen diesen drei Bereichen zeigt diese Dissertation, wie methodische Verbesserungen die klinische Etablierung von MPI vorantreiben können. Aufbauend auf dieser Grundlage präsentiert die Dissertation wesentliche methodische Beiträge, die spezifische Herausforderungen in der MPI-Technologie adressieren. Die Forschungsarbeiten des Autors sind in dieses übergeordnete Rahmenwerk eingebettet und demonstrieren Innovationen, die die Effizienz und Realisierbarkeit von MPI für den klinischen Einsatz verbessern. Der Kern dieser Dissertation besteht aus drei begutachteten Forschungsartikeln, die jeweils eine spezifische methodische Fragestellung in MPI behandeln. Der erste Beitrag stellt einen neuartigen Ansatz zur Systemkalibrierung vor, der auf Extrapolationstechniken für Systemmatrizen basiert. Die herkömmliche Kalibrierung von MPI ist zeitaufwendig und erfordert umfangreiche Messungen, was die Skalierbarkeit der Methode für klinische Anwendungen einschränkt. Durch die Verwendung eines extrapolationsbasierten Verfahrens reduziert diese Arbeit die Kalibrierungsanforderungen erheblich, während die Rekonstruktionsgenauigkeit erhalten bleibt. Die steigert die Effizienz und senkt den experimentellen Aufwand. Der zweite Beitrag konzentriert sich auf Regularisierungstechniken in der MPI-Rekonstruktion und stellt eine Methode mit automatisch gewählten Parametern vor. Die Bildrekonstruktion in MPI erfordert eine sorgfältige Balance zwischen Rauschunterdrückung und der Erhaltung feiner struktureller Details, was insbesondere für medizinische Anwendungen von entscheidender Bedeutung ist. Diese Arbeit entwickelt ein adaptives Regularisierungsverfahren, das Parameter dynamisch an die Bildcharakteristika anpasst, wodurch die Robustheit erhöht, die Rekonstruktionsqualität verbessert und der Bedarf an manueller Parameterauswahl reduziert wird. Der dritte Beitrag untersucht den Einsatz von ellipsoidischen Harmonischen zur effizienten Darstellung magnetischer Felder, um eine zentrale Herausforderung in der MPI-Systemmodel- lierung zu bewältigen. Eine präzise Feldbeschreibung ist essenziell für die räumliche Kodierung und die Entwicklung maßgeschneiderter Feldkameras für MPI-Scanner. Im Vergleich zu sphärischen harmonischen Darstellungen bieten ellipsoidische Harmonische eine größere geometrische Flexibilität, die optimierte Feldkodierungsstrategien ermöglicht und spezifische Scannerdesigns besser unterstützt. Diese Studie entwickelt eine mathematisch fundierte und effiziente Methode zur Darstellung komplexer Feldverteilungen, die Verbesserungen in der Kodierungseffizienz und der Anpassungsfähigkeit an anwendungsspezifische Anforderungen bietet. Weitere Forschungsarbeiten betten diese Entwicklungen in den Kontext gemeinschaftlicher Projekte mit verschiedenen Forschungseinrichtungen ein. Diese Kooperationen umfassten neben der Entwicklung von Modellierungstechniken für MPI auch Studien zur Leistungsfähigkeit des klinisch zugelassenen Tracers Resotran sowie Untersuchungen zur Entwicklung neuer MPI-Tracer. Die in dieser Dissertation vorgestellten Ergebnisse wurden durch verschiedene begutachtete Fachartikel und Konferenzbeiträge verbreitet und unterstreichen die Bedeutung der algorithmischen Forschung für die klinische Translation von MPI. Zusammenfassend zeigt diese Dissertation, dass algorithmische Innovationen entscheidend sind, um die Lücke zwischen präklinischer Forschung und klinischer Anwendung in MPI zu schließen. Durch die Verbesserung der Kalibrierungsprozesse, die Optimierung von Rekonstruktionsmethoden und die Weiterentwicklung von Feldrepräsentationen erleichtern diese Fortschritte die praktische Implementierung von MPI in der medizinischen Bildgebung. Das abschließende Kapitel der Arbeit bietet eine Zusammenfassung der zentralen Erkenntnisse sowie einen Ausblick auf zukünftige algorithmische Forschungsrichtungen, welche die Integration von MPI in klinische Arbeitsabläufe weiter vorantreiben werden

Solvation free energies of anions: from curated reference data to predictive models

Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited availability of high-quality reference data poses a challenge in developing accurate, inexpensive computational prediction methods. In this study, we address both issues of data quality and availability. We present three databases and models related to ionic phenomena: (1) 8,241 pKa data points across 8 solvents, (2) 5,536 gas-phase acidities from DLPNO-CCSD(T) QM calculations, and (3) 6,090 solvation free energies of anions across 8 solvents obtained from a thermodynamic cycle. We also report 6,088 solvation free energies of neutral conjugate solutes computed using the COSMO-RS method. The pKa data were obtained from the iBonD database, cleaned, and combined with a separate compilation of trustworthy reference pKa data. Gas-phase acidities were computed for most of the acids present in the pKa corpus. Leveraging these data, we compiled values for solvation free energies of anions. We then trained several graph neural network models, which can be used as an alternative to QM approaches to quickly estimate these properties. The pKa and gas-phase acidity models accept reaction SMILES strings of the acid dissociation as inputs, whereas the solvation energy model accepts the SMILES string of the anion. Our microscopic pKa model achieves good accuracy, with an overall test mean average error of 0.58 units on unseen solutes and 0.59 on the SAMPL7 challenge (the lowest error so far among multisolvent models). Our gas-phase acidity model had mean absolute errors slightly above 2 kcal mol-1 when evaluated against experimental data. The anionic solvation free energy model had mean absolute errors of less than 3 kcal mol-1 in several test evaluations, comparable to (though less reliable than) several widely used QM-based solvation models. The models and data are free and publicly available at doi.org/10.5281/zenodo.13987781

Effect of stent struts angle on body vessel shear stress at different heat fluxes

Today, there are many methods for treating coronary artery blockage. Doctors determine the type of treatment according to many parameters, such as the patient's age and the degree of arterial blockage. A method of arteriosclerosis treatment is stenting. The body's activity in different conditions causes different thermal heat fluxes on the artery. This study was to obtain the optimum angle of the stent struts to get the suitable wall shear stress at different thermal heat fluxes of 5, 10, and 15 W m−2, which were applied in two conditions on the artery and stent; the first, the heat flux was applied on entire geometry of the artery and the stent, the second, the heat flux was applied only on the stent. For simulation, the finite volume method (FVM) and simple schemes were employed; furthermore, the Carreau viscosity model was selected for the non-Newtonian viscosity model. In addition, PLA stent material was considered in this study. The best optimum angle for stent struts was 61 degrees. It was also observed that the heat flux does not affect wall shear stress, axial and radial blood velocity profiles, and blood temperature on the artery centerline

Development of a model-based tool for the design of biotechnological processes under consideration of effects caused by heterogeneities

Heterogeneous conditions caused by non-ideal mixing could cause worse process performances in large-scale reactors compared to lab-scale processes. To date, the industrial scale-up process often relies on empirical rules and costly experiments. Mathematical models and physical scale-down systems are rarely used for industrial scale up, especially for bioeconomic processes, despite their potential to address scale up challenges. This thesis employs a physical scale-down system with two interconnected stirred tank reactors, and a novel mathematical model using a network of zones approach for the simulation of non-ideally mixed conditions. Model-based techniques are utilised to design the operational strategies in processes under controlled non-ideal conditions. Two different research questions were formulated and answered in this thesis: 1. Is a structured, mechanistic mathematical model, calculating several interconnected ideally mixed zones, able to approximate the flow patterns in stirred tank reactors and describe the effects caused by non-ideal mixing in biotechnological processes with complex kinetics? 2. How can feeding strategies for biotechnological processes in non-ideally mixed reactors be systematically designed? To answer the first question, a comprehensive experimental study was conducted with 19 cultivations in an ideally mixed lab-scale reactor and the non-ideally mixed scale-down system. After parameterising the mathematical model with data from 16 experiments, the model described the experimental data with high accuracy and was successfully validated with data of three experiments. The model was also used in a modified model-based design of experiments to maximise biomass density under controlled heterogeneous conditions, predicting an experiment with high accuracy (R² = 0.94). In a second study, process differences between laboratory and pilot scale were investigated. Non-linear model-based predictive control was employed for the first time to design a process in the scale-down system, aiming to reduce differences to pilot-scale data. The mathematical model was then used to find potential explanations for these differences, identifying the different zones (and their volumes) in the large-scale system as the probable reason for the performance differences between scales. In the future, this combination of physical and mathematical modelling techniques with model-based control methods may accelerate process development and scale-up, while increasing efficiency and reliability.Heterogene Bedingungen, die durch nicht ideale Durchmischung verursacht werden, können dazu führen, dass die Prozessperformance in großtechnischen Reaktoren schlechter ist als im Labormaßstab. Zudem basiert der industrielle Scale-Up häufig auf empirischen Regeln und kostspieligen Experimenten. Mathematische Modelle und physikalische Scale-Down Systeme werden nur selten für das industrielle Scale-up verwendet, insbesondere für bioökonomische Prozesse, obwohl sie das Potenzial besitzen, die Problematik des Scale-up zu bewältigen. In dieser Thesis wird ein physikalisches Scale-Down System eingesetzt, welches aus zwei mit¬einander verbundenen Rührkessel besteht und mit einem neuartigen mathematischen Modell kombiniert wird, welches ein „Network of Zones Modell“ verwendet, um nicht ideale Misch¬bedingungen zu simulieren. Modellgestützte Techniken werden eingesetzt, um Betriebs-strategien unter kontrollierten, nicht-ideal durchmischten Bedingungen zu designen. In dieser Arbeit wurden zwei verschiedene Forschungsfragen formuliert und beantwortet: 1. Ist ein strukturiertes, mechanistisches mathematisches Modell mit mehreren ver-bundenen ideal durchmischten Zonen in der Lage, Strömungsmuster in Rührreaktoren zu approximieren und die Auswirkungen nicht idealer Durchmischung in biotechnischen Prozessen mit komplexer Kinetik zu beschreiben? 2. Wie können Fütterungsstrategien für biotechnologische Prozesse in nicht ideal durchmischten Reaktoren systematisch gestaltet werden? Zur Beantwortung der ersten Frage wurde eine umfassende experimentelle Studie mit 19 Kultivierungen in ideal gemischten Reaktoren und dem nicht ideal gemischten Scale-down-System durchgeführt. Nach der Parametrierung des mathematischen Modells mit Daten von 16 Experimenten beschreibt das Modell die experimentellen Daten mit hoher Genauigkeit und wurde zudem erfolgreich mit Daten aus drei Experimenten validiert. Das Modell wurde auch in einer modifizierten modellbasierten Versuchsplanung eines Experiments mit dem Ziel der Maximierung der Biomassedichte im Scale-Down System einge¬setzt werden und konnte dies mit hoher Genauigkeit (R² = 0,94) voraussagen. In einer zweiten Studie wurden Prozessunterschiede zwischen Labor- und Pilotmaßstab untersucht. Zum ersten Mal wurde adaptive, modellbasierte Prozesssteuerung genutzt, um einen Prozess in einem Scale-Down System zu entwickeln, der die Unterschiede zwischen dem Scale-Down System und dem Pilotmaßstab reduzieren sollte. Das mathematische Modell wurde anschließend verwendet, um potenzielle Erklärungen für Unterschiede zu finden, wobei verschiedene Zonen (und deren Volumina) im Produktions¬maßstab als mögliche Ursache für unterschiedliche Performance identifiziert wurden. In Zukunft könnte diese Kombination physikalischer und mathematischer Modellierungs-techniken mit modellbasierten Strategien die Prozessentwicklung und den Scale-up beschleunigen und gleichzeitig deren Effizienz und Zuverlässigkeit erhöhen

Life cycle assessment of circular economy strategies for high-strength connection bolts in wind turbines

Wind turbines are an important element of renewable energy systems, but their manufacturing requires significant amounts of materials such as steel, copper, aluminum, rare earth metals, fiberglass, and concrete, with considerable environmental impacts. One approach to reduce the environmental impacts of wind turbines and their components is the application of circular economy strategies. This study focuses on high-strength connection bolts, specifically M36 threaded steel bolts with a length of 660 mm. A comparative life cycle assessment is conducted to analyze the environmental impacts of two scenarios: recycling all bolts at the end of the wind turbine's life cycle, and reusing some bolts after inspection. The product system is modeled in openLCA, where the processes of the foreground system are linked to the Ecoinvent database in the background system. For life cycle impact assessment, the ReCiPe 2016 method is applied, focusing on climate impact. Results show that after five life cycles, reusing bolts reduces the climate impact by 48%, compared to a 42% reduction through recycling alone. Sensitivity analyses reveal that the potential for impact reduction through reuse is primarily driven by the achievable reuse rate, while factors such as production technology, electricity mix, and transportation distances play a smaller role

Revisiting directed disjoint paths on tournaments (and relatives)

In the Directed Disjoint Paths problem (k-DDP), we are given a digraph and k pairs of terminals, and the goal is to find k pairwise vertex-disjoint paths connecting each pair of terminals. Bang-Jensen and Thomassen [SIAM J. Discrete Math. 1992] claimed that k-DDP is NP-complete on tournaments, and this result triggered a very active line of research about the complexity of the problem on tournaments and natural superclasses. We identify a flaw in their proof, which has been acknowledged by the authors, and provide a new NP-completeness proof. From an algorithmic point of view, Fomin and Pilipczuk [J. Comb. Theory B 2019] provided an FPT algorithm for the edge-disjoint version of the problem on semicomplete digraphs, and showed that their technique cannot work for the vertex-disjoint version. We overcome this obstacle by showing that the version of k-DDP where we allow congestion c on the vertices is FPT on semicomplete digraphs provided that c is greater than k/2. This is based on a quite elaborate irrelevant vertex argument inspired by the edge-disjoint version, and we show that our choice of c is best possible for this technique, with a counterexample with no irrelevant vertices when c ≤ k/2. We also prove that k-DDP on digraphs that can be partitioned into h semicomplete digraphs is W[1]-hard parameterized by k + h, which shows that the XP algorithm presented by Chudnovsky, Scott, and Seymour [J. Comb. Theory B 2019] is essentially optimal

How students (mis)understand simple control structures - an attempt at a taxonomy

Being able to read and correctly trace code is an essential skill in computer science. This poster presents a taxonomy of the misconceptions and difficulties concerning this skill. We developed our taxonomy with the aim of creating a good basis for developing effective assessment tools and teaching interventions