The emergence of complex diseases resulting from abnormal cell-cell signaling and the spread of infectious diseases caused by pathogens are significant threats to humanity. Unraveling the dynamic... Show moreThe emergence of complex diseases resulting from abnormal cell-cell signaling and the spread of infectious diseases caused by pathogens are significant threats to humanity. Unraveling the dynamic mechanisms underlying cell-cell signaling and infectious disease spreading is crucial for effective disease prevention and treatment. As science and technology advance, the availability and diversity of observational and experimental data related to these biological processes continue to grow. In this thesis, we integrate multisource data with dynamic modeling to investigate the biological mechanisms of Notch signaling in biological development and to develop prevention and control strategies for infectious diseases. Show less
Carbon dioxide capture and utilization technologies are necessary to create a truly circular economy. The electrochemical reduction of carbon dioxide to formate is an appealing carbon utilization... Show moreCarbon dioxide capture and utilization technologies are necessary to create a truly circular economy. The electrochemical reduction of carbon dioxide to formate is an appealing carbon utilization method as it can be performed at room temperature and pressure, it only requires two electrons, and it has a high atom efficiency. This reaction has been known and studied for decades, but no commercial process is currently practiced.This thesis reviews work that has been performed in the field of electrocehmical reduction of CO2 toward formate and reviews how a gas diffusion electrode functions. A gas diffusion layer production method is explored for ways to tune the characteristics of the gas diffusion layer. A design of experiments is used to explore how the catalyst layer can interact with the gas diffusion layer. The best results (100% CE at 400mA/cm2) are scaled-up from 10 cm2 to 200 cm2. Contaminants in an industrial CO2 stream are studied using density funcitonal theory to determine their potential to poison electrocatalysts known to convert CO2 to formate. Show less
Stein. N. van; Winter, R. de; Bäck, T.H.W.; Kononova, A. V. 2023
Radiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the... Show moreRadiography is an important technique to inspect objects, with applications in airports and hospitals. X-ray imaging is also essential in industry, for instance in food safety checks for the presence of foreign objects. Computed tomography (CT) enables more accurate visualizations of an object in 3D, but requires more computation time. Spectral X-ray imaging is an important recent development to optimize these conflicting goals of speed and accuracy. This technique enables separation of detected X-ray photons in terms of energy. More information can be extracted from spectral images, which allows for better separation of materials. Deep learning is another important recent technique enabling machines to quickly carry out processing tasks, by training these with large volumes of data for these specific tasks.In this dissertation we present new processing methods that use spectral imaging and machine learning, with a special focus on industrial processes. We design a workflow using CT to efficiently generate large volumes of machine learning training data. In addition, we develop a compression method for efficient processing of large volumes of spectral data and two new spectral CT methods to produce more accurate reconstructions. The presented methods are designed for effective use in industry. Show less
Performing simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate)... Show morePerforming simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance. In fact, the Convolutional Neural Network was able to emulate the behavior of the auditory nerve fiber model with extremely high similarity ( R 2 > 0 . 99 ), tested under a wide range of experimental conditions, whilst reducing the simulation time by five orders of magnitude. In addition, a method for randomly generating charge-balanced waveforms using hyperplane projection is introduced. In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms of energy efficiency. The resulting waveforms resemble a positive Gaussian-like peak, preceded by an elongated negative phase. When comparing the energy of the waveforms generated by the Evolutionary Algorithm with the commonly used square wave, energy decreases of 8%-45% were observed for differ-ent pulse durations. These results were validated with the original auditory nerve fiber model, which demonstrates that the proposed surrogate model can be used as its accurate and efficient replacement.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Show less
Geelhoed, W.J.; Boonekamp, M.; Stadt, H. van de; Badulescu, S.; Lalai, R.A.; Groeneweg, K.E.; ... ; Rotmans, J.I. 2021
The cannulation of blood vessels is one of the most basic and essential interventions in medical practice. A common adverse event of this procedure is miscannulation with infiltration of the second... Show moreThe cannulation of blood vessels is one of the most basic and essential interventions in medical practice. A common adverse event of this procedure is miscannulation with infiltration of the second part of the vessel wall, often resulting in a perivascular hematoma. In hemodialysis patients, surgically created arteriovenous conduits are cannulated 3-4 times per week to provide sufficient blood supply to the hemodialysis machine. However, the high blood flow and pressure in these vascular access sites increase the risk of complications upon miscannulation. A novel needle system that allows for rapid automatic retraction of the needle in response to contact with blood after positioning the cannula in the blood vessel was developed to reduce the risk of miscannulation. The device can easily be incorporated into existing needle designs. The mechanical functionality of the device was validated by testing prototypes in an ex vivo system. Optimization of the needle system was performed to enhance response time and piston shape. A final prototype design was manufactured and validated. The optimal membrane composition and piston shape were determined, which resulted in a needle response time of 40 ms upon contact with fluid at a pressure of 100 mmHg (arterial pressure). Here, we have successfully designed, mechanically validated, and tested a novel automated rapid needle retraction system that allows incorporation into existing needle systems. This device could notably decrease the difficulty of vessel cannulation and the prevalence of hematoma formation. Show less
The next generation of high-contrast imaging instruments on space-based observatories requires sophisticated wavefront sensing and control in addition to a high-performance coronagraph. This thesis... Show moreThe next generation of high-contrast imaging instruments on space-based observatories requires sophisticated wavefront sensing and control in addition to a high-performance coronagraph. This thesis aims to further our knowledge of coronagraphs and their integration into high-contrast imaging instruments. Chapter 2 presents a new algorithm for global optimization of the apodizing phase plate coronagraph. Chapters 3 and 4 present the theory, design and laboratory results of the SCAR coronagraph, which uses a phase plate and single-mode fibers. Chapter 5 presents the development of HCIPy, a software package in Python for high-contrast imaging. Chapters 7 and 8 present the theory, design and laboratory results of the PAPLC coronagraph, which uses a phase plate, knife-edge focal-plane mask and Lyot stop, and an integrated high-order wavefront sensor. These new coronagraphy and wavefront sensing concepts pave the way for improved high-contrast imaging instruments, both from ground-based and space-based observatories. Show less
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated... Show moreQuantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table) as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects (T-1, T-2), proton density (PD), receiver phase ( phi(0)), transmit field inhomogeneity (B-1(+)), and slice profile. Parametermapswere comparedwith those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension (T-1, T-2, B-1(+)) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from 1.47GB to 464KB. Furthermore, the proposed method was equally robust against undersampling artifacts as dictionarymatching. Dictionary fittingwith B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi- parametric mapping. Show less
Pezzotti, N.; Yousefi, S.; Elmahdy, M.S.; Gemert, J.H.F. van; Schuelke, C.; Doneva, M.; ... ; Staring, M. 2020
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR... Show moreAdaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil tracks. This demonstrates the superior performance and wide applicability of the method. Show less
Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems. In the case of badly scaled problems, SGD, however, only exhibits sublinear convergence... Show moreStochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems. In the case of badly scaled problems, SGD, however, only exhibits sublinear convergence properties. In this paper, we propose an efficient preconditioner estimation method to improve the convergence rate of SGD. Based on the observed distribution of voxel displacements in the registration, we estimate the diagonal entries of a preconditioning matrix, thus rescaling the optimization cost function. The preconditioner is efficient to compute and employ and can be used for mono-modal as well as multi-modal cost functions, in combination with different transformation models, such as the rigid, the affine, and the B-spline model. Experiments on different clinical datasets show that the proposed method, indeed, improves the convergence rate compared with SGD with speedups around 2 similar to 5 in all tested settings while retaining the same level of registration accuracy. Show less
Ribeiro de Almeida, L.; Emmerich, M.T.M.; Da Silva Soares, A.; Woerle de Lima, T. 2019
Industrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality... Show moreIndustrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine parameters and quality indicators. To optimize these complex processes, for example by reducing the number of defects or increasing the throughput, a great number of requirements need to be taken into consideration. In this dissertation a framework for monitoring and optimizing these complex industrial processes is presented. The framework is specifically tailored to the production processes of Tata Steel and BMW Group. Both are industrial partners of the PROMIMOOC project. The framework consists of several components of which; preprocessing, outlier detection, predictive modeling and optimization are the main technical components that are the focus of this work. For each of these components a possible implementation is proposed and the challenges in implementing these components in an industrial manufacturing setting are discussed Show less
In this work we describe three methods to improve the performance of Quantum Field Theory calculations. First, we simplify large expressions to speed up numerical integrations. Second, we design... Show moreIn this work we describe three methods to improve the performance of Quantum Field Theory calculations. First, we simplify large expressions to speed up numerical integrations. Second, we design Forcer, a program for the reduction of four-loop massless propagator integrals. Third, we extend the R* method to quickly compute the poles of Feynman integrals. With these methods, we compute several four-loop splitting functions and the five-loop beta function for Yang-Mills theory with fermions. Show less
Steubing, B.R.P.; Mutel, C.; Suter, F.; Hellweg, S. 2016
Software architecting is a non-trivial and demanding task for software engineers to perform. The architecture is a key enabler for software systems. Besides being crucial for user functionality,... Show moreSoftware architecting is a non-trivial and demanding task for software engineers to perform. The architecture is a key enabler for software systems. Besides being crucial for user functionality, the software architecture has deep impact on software qualities such as performance, safety, and cost. In this dissertation, an automated approach for software architecture design is proposed that supports analysis and optimization of multiple quality attributes:First of all, we demonstrate an optimization approach for automated software architecture design. It reports the results of applying our architecture optimization framework to an automotive sub-system that was conducted based on a large-scale real world case study. Moreover, we introduce two novel degrees of freedom which demonstrate how the number of processing nodes and their interconnecting network can be codified to fit into a genetic algorithm. Our studies show that these extra degrees of freedom lead to better overall software architecture optimization. Finally, we propose a new search-based approach for generating a set of optimal software architectural solutions for use in software product lines. Our new approach analyses the commonality of the found optimal solutions and proposes a set of solutions which are suitable for the range of products defined by various feature combinations. Show less
Streptomyces are Gram-positive, soil dwelling bacteria that raised interest in the last 50 years for their high potential in antibiotic and protein production. Thanks to their saprophytic nature,... Show moreStreptomyces are Gram-positive, soil dwelling bacteria that raised interest in the last 50 years for their high potential in antibiotic and protein production. Thanks to their saprophytic nature, streptomycetes secrete a massive amount of industrial enzymes. They have a relatively low level of endogenous extracellular proteolytic activity when compared to other expression hosts (e.g. Bacillus), they are generally more suited to produce proteins encoded by high G+C actinomycete genes in their native form, coupled to efficient secretion so as to avoid that the proteins end up in inclusion bodies (often a problem when using e.g. E. coli) and making downstream processes easier. Despite their attractive potential, Streptomyces present several constraints which so far limit their application in industry. The first constraint is morphology: by growing as a network of hyphae, they produce dense pellets in liquid cultures that hold Streptomyces back from being one of the first choice cell factories in large scale fermentations. In addition, the limited availability of efficient expression systems for high-level transcription/translation and subsequent secretion is a further bottleneck. This thesis presents the work done to address these issues for the optimization of Streptomyces lividans for future industrial applications and enzyme production. Show less
