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The incorporation of proteins in artificial materials such as membranes offers great opportunities to avail oneself the miscellaneous qualities of proteins and enzymes perfected by nature over millions of years. One possibility to leverage proteins is the modification with artificial polymers. To obtain such protein-polymer conjugates, either a polymer can be grown from the protein surface (grafting-from) or a pre-synthesized polymer attached to the protein (grafting-to). Both techniques were used to synthesize conjugates of different proteins with thermo-responsive polymers in this thesis.
First, conjugates were analyzed by protein NMR spectroscopy. Typical characterization techniques for conjugates can verify the successful conjugation and give hints on the secondary structure of the protein. However, the 3-dimensional structure, being highly important for the protein function, cannot be probed by standard techniques. NMR spectroscopy is a unique method allowing to follow even small alterations in the protein structure. A mutant of the carbohydrate binding module 3b (CBM3bN126W) was used as model protein and functionalized with poly(N-isopropylacrylamide). Analysis of conjugates prepared by grafting-to or grafting-from revealed a strong impact of conjugation type on protein folding. Whereas conjugates prepared by grafting a pre-formed polymer to the protein resulted in complete preservation of protein folding, grafting the polymer from the protein surface led to (partial) disruption of the protein structure.
Next, conjugates of bovine serum albumin (BSA) as cheap and easily accessible protein were synthesized with PNIPAm and different oligoethylene glycol (meth)acrylates. The obtained protein-polymer conjugates were analyzed by an in-line combination of size exclusion chromatography and multi-angle laser light scattering (SEC-MALS). This technique is particular advantageous to determine molar masses, as no external calibration of the system is needed. Different SEC column materials and operation conditions were tested to evaluate the applicability of this system to determine absolute molar masses and hydrodynamic properties of heterogeneous conjugates prepared by grafting-from and grafting-to. Hydrophobic and non-covalent interactions of conjugates lead to error-prone values not in accordance to expected molar masses based on conversions and extents of modifications.
As alternative to this method, conjugates were analyzed by sedimentation velocity analytical ultracentrifugation (SV-AUC) to gain insights in the hydrodynamic properties and how they change after conjugation. Within a centrifugal field, a sample moves and fractionates according to the mass, density, and shape of its individual components. Conjugates of BSA with PNIPAm were analyzed below and above the cloud point temperature of the thermo-responsive polymer component. It was identified that the polymer characteristics were transferred to the conjugate molecule which than showed a decreased ideality – defined as increased deviation from a perfect sphere model – below and increased ideality above the cloud point temperature. This effect can be attributed to an arrangement of the polymer chain pointing towards the solvent (expanded state) or snuggling around the protein surface depending on the applied temperature.
The last project dealt with the synthesis of ferric hydroxamate uptake protein component A (FhuA)-polymer conjugates as building blocks for novel membrane materials. The shape of FhuA can be described as barrel and removal of a cork domain inside the protein results in a passive channel aimed to be utilized as pores in the membrane system. The polymer matrix surrounding the membrane protein is composed of a thermo-responsive and a UV-crosslinkable part. Therefore, an external trigger for covalent immobilization of these building blocks in the membrane and switchability of the membrane between different states was incorporated. The overall performance of membranes prepared by a drying-mediated self-assembly approach was evaluated by permeability and size exclusion experiments. The obtained membranes displayed an insufficiency in interchain crosslinking and therefore a lack in performance. Furthermore, the aimed switch between a hydrophilic and hydrophobic state of the polymer matrix did not occur. Correspondingly, size exclusion experiments did not result in a retention of analytes larger than the pores defined by the dimension of the used FhuA variant.
Overall, different paths to generate protein-polymer conjugates by either grafting-from or grafting-to the protein surface were presented paving the way to the generation of new hybrid materials. Different analytical methods were utilized to describe the folding and hydrodynamic properties of conjugates providing a deeper insight in the overall characteristics of these seminal building blocks.
Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.
Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows.
While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture.
There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development.
The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing.
One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods.
The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points.
To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library.
RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below.
Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance.
The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction.
To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time.
Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation.
For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data.
Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.
The prevalence of diseases associated with misfolded proteins increases with age. When cellular defense mechanisms become limited, misfolded proteins form aggregates and may also develop more stable cross-β structures ultimately forming amyloid aggregates. Amyloid aggregates are associated with neurodegenerative diseases such as Alzheimer’s disease and Huntington’s disease. The formation of amyloid deposits, their toxicity and cellular defense mechanisms have been intensively studied. However, surprisingly little is known about the effects of protein aggregates on cellular signal transduction. It is also not understood whether the presence of aggregation-prone, but still soluble proteins affect signal transduction.
In this study, the still soluble aggregation-prone HttExon1Q74 and its amyloid aggregates were used to analyze the effect of amyloid aggregates on internalization and receptor activation of G protein-coupled receptors (GPCRs), the largest protein family of mammalian cell surface receptors involved in signal transduction. The aggregated HttExon1Q74, but not its soluble form, could inhibit ligand-induced clathrin-mediated endocytosis (CME) of various GPCRs. Most likely this inhibitory effect is based on a terminal sequestration of the HSC70 chaperone to the aggregates which is necessary for CME. Using the vasopressinV1a receptor (V1aR) and the corticotropin-releasing factor receptor 1 (CRF1R) as a model, it could be shown that the presence of HttExon1Q74 aggregates and the inhibition of ligand-induced CME leads to an accumulation of desensitized receptors at the plasma membrane. In turn, this disrupts Gq-mediated Ca2+ signaling and Gs-mediated cAMP signaling of the V1aR and the CRF1R respectively. In contrast to HttExon1Q74 amyloid aggregates, soluble HttExon1Q74 as well as amorphous aggregates did not inhibit GPCR internalization and signaling demonstrating that cellular signal transduction mechanisms are specifically impaired in response to the formation of amyloid aggregates.
In addition, preliminary experiments could show that HttExon1Q74 aggregates provoke an increase in membrane expression of a protein from a structurally and functionally unrelated membrane protein family, namely the serotonin transporter SERT. As SERT is the main pharmacological target to treat depression this could shed light on this commonly occurring comorbidity in neurodegenerative diseases, in particular in early disease states.
We use ultrafast x-ray diffraction to investigate the effect of expansive phononic and contractive magnetic stress driving the picosecond strain response of a metallic perovskite SrRuO3 thin film upon femtosecond laser excitation. We exemplify how the anisotropic bulk equilibrium thermal expansion can be used to predict the response of the thin film to ultrafast deposition of energy. It is key to consider that the laterally homogeneous laser excitation changes the strain response compared to the near-equilibrium thermal expansion because the balanced in-plane stresses suppress the Poisson stress on the picosecond timescale. We find a very large negative Grüneisen constant describing the large contractive stress imposed by a small amount of energy in the spin system. The temperature and fluence dependence of the strain response for a double-pulse excitation scheme demonstrates the saturation of the magnetic stress in the high-fluence regime.
Conceptual knowledge about objects, people and events in the world is central to human cognition, underlying core cognitive abilities such as object recognition and use, and word comprehension. Previous research indicates that concepts consist of perceptual and motor features represented in modality-specific perceptual-motor brain regions. In addition, cross-modal convergence zones integrate modality-specific features into more abstract conceptual representations.
However, several questions remain open: First, to what extent does the retrieval of perceptual-motor features depend on the concurrent task? Second, how do modality-specific and cross-modal regions interact during conceptual knowledge retrieval? Third, which brain regions are causally relevant for conceptually-guided behavior? This thesis addresses these three key issues using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) in the healthy human brain.
Study 1 - an fMRI activation study - tested to what extent the retrieval of sound and action features of concepts, and the resulting engagement of auditory and somatomotor brain regions depend on the concurrent task. 40 healthy human participants performed three different tasks - lexical decision, sound judgment, and action judgment - on words with a high or low association to sounds and actions. We found that modality-specific regions selectively respond to task-relevant features: Auditory regions selectively responded to sound features during sound judgments, and somatomotor regions selectively responded to action features during action judgments. Unexpectedly, several regions (e.g. the left posterior parietal cortex; PPC) exhibited a task-dependent response to both sound and action features. We propose these regions to be "multimodal", and not "amodal", convergence zones which retain modality-specific information.
Study 2 - an fMRI connectivity study - investigated the functional interaction between modality-specific and multimodal areas during conceptual knowledge retrieval. Using the above fMRI data, we asked (1) whether modality-specific and multimodal regions are functionally coupled during sound and action feature retrieval, (2) whether their coupling depends on the task, (3) whether information flows bottom-up, top-down, or bidirectionally, and (4) whether their coupling is behaviorally relevant. We found that functional coupling between multimodal and modality-specific areas is task-dependent, bidirectional, and relevant for conceptually-guided behavior. Left PPC acted as a connectivity "switchboard" that flexibly adapted its coupling to task-relevant modality-specific nodes.
Hence, neuroimaging studies 1 and 2 suggested a key role of left PPC as a multimodal convergence zone for conceptual knowledge. However, as neuroimaging is correlational, it remained unknown whether left PPC plays a causal role as a multimodal conceptual hub. Therefore, study 3 - a TMS study - tested the causal relevance of left PPC for sound and action feature retrieval. We found that TMS over left PPC selectively impaired action judgments on low sound-low action words, as compared to sham stimulation. Computational simulations of the TMS-induced electrical field revealed that stronger stimulation of left PPC was associated with worse performance on action, but not sound, judgments. These results indicate that left PPC causally supports conceptual processing when action knowledge is task-relevant and cannot be compensated by sound knowledge. Our findings suggest that left PPC is specialized for action knowledge, challenging the view of left PPC as a multimodal conceptual hub.
Overall, our studies support "hybrid theories" which posit that conceptual processing involves both modality-specific perceptual-motor regions and cross-modal convergence zones. In our new model of the conceptual system, we propose conceptual processing to rely on a representational hierarchy from modality-specific to multimodal up to amodal brain regions. Crucially, this hierarchical system is flexible, with different regions and connections being engaged in a task-dependent fashion. Our model not only reconciles the seemingly opposing grounded cognition and amodal theories, it also incorporates task dependency of conceptually-related brain activity and connectivity, thereby resolving several current issues on the neural basis of conceptual knowledge retrieval.
This paper-based dissertation aims to contribute to the open innovation (OI) and technology management (TM) research fields by investigating their mechanisms, and potentials at the operational level. The dissertation connects the well-known concept of technology management with OI formats and applies these on specific manufacturing technologies within a clearly defined setting.
Technological breakthroughs force firms to continuously adapt and reinvent themselves. The pace of technological innovation and their impact on firms is constantly increasing due to more connected infrastructure and accessible resources (i.e. data, knowledge). Especially in the manufacturing sector it is one key element to leverage new technologies to stay competitive. These technological shifts call for new management practices.
TM supports firms with various tools to manage these shifts at different levels in the firm. It is a multifunctional and multidisciplinary field as it deals with all aspects of integrating technological issues into business decision-making and is directly relevant to a number of core business processes. Thus, it makes sense to utilize this theory and their practices as a foundation of this dissertation. However, considering the increasing complexity and number of technologies it is not sufficient anymore for firms to only rely on previous internal R&D and managerial practices. OI can expanse these practices by involving distributed innovation processes and accessing further external knowledge sources. This expansion can lead to an increasing innovation performance and thereby accelerate the time-to-market of technologies.
Research in this dissertation was based on the expectations that OI formats will support the R&D activities of manufacturing technologies on the operational level by providing access to resources, knowledge, and leading-edge technology. The dissertation represents uniqueness regarding the rich practical data sets (observations, internal documents, project reviews) drawn from a very large German high-tech firm. The researcher was embedded in an R&D unit within the operational TM department for manufacturing technologies. The analyses include 1.) an exploratory in-depth analysis of a crowdsourcing initiative to elaborate the impact on specific manufacturing technologies, 2.) a deductive approach for developing a technology evaluation score model to create a common understanding of the value of selected manufacturing technologies at the operational level, and 3.) an abductive reasoning approach in form of a longitudinal case study to derive important indicator for the in-process activities of science-based partnership university-industry collaboration format. Thereby, the dissertation contributed to research and practice 1.) linkages of TM and OI practices to assimilate technologies at the operational level, 2.) insights about the impact of CS on manufacturing technologies and a related guideline to execute CS initiatives in this specific environment 3.) introduction of manufacturing readiness levels and further criteria into the TM and OI research field to support decision-makers in the firm in gaining a common understanding of the maturity of manufacturing technologies and, 4.) context-specific important indicators for science based university-industry collaboration projects and a holistic framework to connect TM with the university-industry collaboration approach
The findings of this dissertation illustrate that OI formats can support the acceleration of time-to-market of manufacturing technologies and further improve the technical requirements of the product by leveraging external capabilities. The conclusions and implications made are intended to foster further research and improve managerial practices to evolve TM into an open collaborative context with interconnectivities between all internal and external involved technologies, individuals and organizational levels.
Anthropogenic climate change alters the hydrological cycle. While certain areas experience more intense precipitation events, others will experience droughts and increased evaporation, affecting water storage in long-term reservoirs, groundwater, snow, and glaciers. High elevation environments are especially vulnerable to climate change, which will impact the water supply for people living downstream. The Himalaya has been identified as a particularly vulnerable system, with nearly one billion people depending on the runoff in this system as their main water resource. As such, a more refined understanding of spatial and temporal changes in the water cycle in high altitude systems is essential to assess variations in water budgets under different climate change scenarios.
However, not only anthropogenic influences have an impact on the hydrological cycle, but changes to the hydrological cycle can occur over geological timescales, which are connected to the interplay between orogenic uplift and climate change. However, their temporal evolution and causes are often difficult to constrain. Using proxies that reflect hydrological changes with an increase in elevation, we can unravel the history of orogenic uplift in mountain ranges and its effect on the climate.
In this thesis, stable isotope ratios (expressed as δ2H and δ18O values) of meteoric waters and organic material are combined as tracers of atmospheric and hydrologic processes with remote sensing products to better understand water sources in the Himalayas. In addition, the record of modern climatological conditions based on the compound specific stable isotopes of leaf waxes (δ2Hwax) and brGDGTs (branched Glycerol dialkyl glycerol tetraethers) in modern soils in four Himalayan river catchments was assessed as proxies of the paleoclimate and (paleo-) elevation. Ultimately, hydrological variations over geological timescales were examined using δ13C and δ18O values of soil carbonates and bulk organic matter originating from sedimentological sections from the pre-Siwalik and Siwalik groups to track the response of vegetation and monsoon intensity and seasonality on a timescale of 20 Myr.
I find that Rayleigh distillation, with an ISM moisture source, mainly controls the isotopic composition of surface waters in the studied Himalayan catchments. An increase in d-excess in the spring, verified by remote sensing data products, shows the significant impact of runoff from snow-covered and glaciated areas on the surface water isotopic values in the timeseries.
In addition, I show that biomarker records such as brGDGTs and δ2Hwax have the potential to record (paleo-) elevation by yielding a significant correlation with the temperature and surface water δ2H values, respectively, as well as with elevation. Comparing the elevation inferred from both brGDGT and δ2Hwax, large differences were found in arid sections of the elevation transects due to an additional effect of evapotranspiration on δ2Hwax. A combined study of these proxies can improve paleoelevation estimates and provide recommendations based on the results found in this study.
Ultimately, I infer that the expansion of C4 vegetation between 20 and 1 Myr was not solely dependent on atmospheric pCO2, but also on regional changes in aridity and seasonality from to the stable isotopic signature of the two sedimentary sections in the Himalaya (east and west).
This thesis shows that the stable isotope chemistry of surface waters can be applied as a tool to monitor the changing Himalayan water budget under projected increasing temperatures. Minimizing the uncertainties associated with the paleo-elevation reconstructions were assessed by the combination of organic proxies (δ2Hwax and brGDGTs) in Himalayan soil. Stable isotope ratios in bulk soil and soil carbonates showed the evolution of vegetation influenced by the monsoon during the late Miocene, proving that these proxies can be used to record monsoon intensity, seasonality, and the response of vegetation. In conclusion, the use of organic proxies and stable isotope chemistry in the Himalayas has proven to successfully record changes in climate with increasing elevation. The combination of δ2Hwax and brGDGTs as a new proxy provides a more refined understanding of (paleo-)elevation and the influence of climate.
Fluids in the Earth's crust can move by creating and flowing through fractures, in a process called `hydraulic fracturing’. The tip-line of such fluid-filled fractures grows at locations where stress is larger than the strength of the rock. Where the tip stress vanishes, the fracture closes and the fluid-front retreats. If stress gradients exist on the fracture's walls, induced by fluid/rock density contrasts or topographic stresses, this results in an asymmetric shape and growth of the fracture, allowing for the contained batch of fluid to propagate through the crust.
The state-of-the-art analytical and numerical methods to simulate fluid-filled fracture propagation are two-dimensional (2D). In this work I extend these to three dimensions (3D). In my analytical method, I approximate the propagating 3D fracture as a penny-shaped crack that is influenced by both an internal pressure and stress gradients. In addition, I develop a numerical method to model propagation where curved fractures can be simulated as a mesh of triangular dislocations, with the displacement of faces computed using the displacement discontinuity method. I devise a rapid technique to approximate stress intensity and use this to calculate the advance of the tip-line. My 3D models can be applied to arbitrary stresses, topographic and crack shapes, whilst retaining short computation times.
I cross-validate my analytical and numerical methods and apply them to various natural and man-made settings, to gain additional insights into the movements of hydraulic fractures such as magmatic dikes and fluid injections in rock. In particular, I calculate the `volumetric tipping point’, which once exceeded allows a fluid-filled fracture to propagate in a `self-sustaining’ manner. I discuss implications this has for hydro-fracturing in industrial operations. I also present two studies combining physical models that define fluid-filled fracture trajectories and Bayesian statistical techniques. In these studies I show that the stress history of the volcanic edifice defines the location of eruptive vents at volcanoes. Retrieval of the ratio between topographic to remote stresses allows for forecasting of probable future vent locations. Finally, I address the mechanics of 3D propagating dykes and sills in volcanic regions. I focus on Sierra Negra volcano in the Gal\'apagos islands, where in 2018, a large sill propagated with an extremely curved trajectory. Using a 3D analysis, I find that shallow horizontal intrusions are highly sensitive to topographic and buoyancy stress gradients, as well as the effects of the free surface.
The mitochondrial chaperone complex HSP60/HSP10 facilitates mitochondrial protein homeostasis by folding more than 300 mitochondrial matrix proteins. It has been shown previously that HSP60 is downregulated in brains of type 2 diabetic (T2D) mice and patients,
causing mitochondrial dysfunction and insulin resistance. As HSP60 is also decreased in peripheral tissues in T2D animals, this thesis investigated the effect of overall reduced HSP60 in the development of obesity and associated co-morbidities.
To this end, both female and male C57Bl/6N control (i.e. without further alterations in their genome, Ctrl) and heterozygous whole-body Hsp60 knock-out (Hsp60+/-) mice, which exhibit a 50 % reduction of HSP60 in all tissues, were fed a normal chow diet (NCD) or a highfat diet (HFD, 60 % calories from fat) for 16 weeks and were subjected to extensive metabolic phenotyping including indirect calorimetry, NMR spectroscopy, insulin, glucose and pyruvate tolerance tests, vena cava insulin injections, as well as histological and molecular analysis.
Interestingly, NCD feeding did not result in any striking phenotype, only a mild increase in energy expenditure in Hsp60+/- mice. Exposing mice to a HFD however revealed an increased body weight due to higher muscle mass in female Hsp60+/- mice, with a simultaneous decrease in energy expenditure. Additionally, these mice displayed decreased fasting glycemia. Opposingly, male Hsp60+/- compared to control mice showed lower body weight gain due to decreased fat mass and an increased energy expenditure, strikingly independent of lean mass. Further, only male Hsp60+/- mice display improved HOMA-IR and Matsuda
insulin sensitivity indices.
Despite the opposite phenotype in regards to body weight development, Hsp60+/- mice of both sexes show a significantly higher cell number, as well as a reduction in adipocyte size in the subcutaneous and gonadal white adipose tissue (sc/gWAT). Curiously, this adipocyte hyperplasia – usually associated with positive aspects of WAT function – is disconnected from metabolic improvements, as the gWAT of male Hsp60+/- mice shows mitochondrial dysfunction, oxidative stress, and insulin resistance. Transcriptomic analysis of gWAT shows an up
regulation of genes involved in macroautophagy. Confirmatory, expression of microtubuleassociated protein 1A/1B light chain 3B (LC3), as a protein marker of autophagy, and direct measurement of lysosomal activity is increased in the gWAT of male Hsp60+/- mice.
In summary, this thesis revealed a novel gene-nutrient interaction. The reduction of the crucial chaperone HSP60 did not have large effects in mice fed a NCD, but impacted metabolism during DIO in a sex-specific manner, where, despite opposing body weight and
body composition phenotypes, both female and male Hsp60+/- mice show signs of protection from high fat diet-induced systemic insulin resistance.