- 2 p.m.
- Online PhD defence
- Supervisor: Gerrit Beemster
- Department of Biology
Much is known about the impact of cadmium (Cd) stress on plants and the plant’s response to this form of abiotic stress. However, it is remarkable that the impact of Cd in the growth zone of monocotyledonous leaves remained largely unstudied. This growth zone hosts the two cellular processes driving growth, i.e. cell division and cell elongation. The aim of my PhD study was to assess the impact of Cd in this maize leaf growth zone at several biological levels.
We have found that Cd inhibited leaf growth mainly because it results in a significant reduction of cell production. Cells were halted at the G1-S transition of the cell cycle, which increased the cell cycle duration. In addition, when exposed to Cd, growing leaves had a lower number of meristematic cells and therefore less cells are contributing to cell division. In addition, we have found that Cd accumulated highest in the meristematic tissue, indicating that it could impact processes therein directly. To reveal these processes, we have performed a transcriptome study. This resulted in a broad range of Cd affected processes, which led me to perform biochemical analyses of several phytohormones, minerals, two oxidative stress related parameters and carbohydrates. We showed that Cd caused an increase in stress hormone levels (i.e. salicylic acid, abscisic acid and 1-aminocyclopropane 1-carboxylic acid (ACC, an ethylene precursor)) and a decrease of growth promoting hormones (i.e. gibberellin 1 and trans-zeatin riboside). For gibberellin 1, we were able to directly link changes in the spatial distribution of this phytohormone to changes in transcript levels of key gibberellin synthesis and degradation genes. Regarding the measured minerals, we mainly found manganese to be the most strongly and consistently Cd affected nutrient. Lipid peroxidation and antioxidant potential were increased throughout the entire maize leaf growth zone, demonstrating that Cd resulted in oxidative stress in all developmental stages. Lastly, we found that carbohydrates were increased under Cd stress, perhaps in response to oxidative or osmotic stress.
During my PhD study, we have also published leafkin, an R package that contains
four functions which allow the user to perform all calculations in a kinematic
analysis of monocot leaf growth. In addition, it allows cell length profiles
and leaf elongation rates to be easily extracted, which in turn can be used in
- 4 p.m.
- Online PhD defence
- Supervisor: Paul Scheunders
- Department of Physics
Hyperspectral cameras collect the reflected light of materials in hundreds of narrow, contiguous spectral bands in the visible, near and shortwave infrared wavelengths to provide a continuous reflectance spectrum for each pixel. Due to the complex interaction of light with materials, these spectra are highly nonlinear mixtures of the reflectances of the material constituents. The general goal of this thesis is to estimate the composition of materials from reflectance spectra.
Mixing models describe the reflectance spectrum of a material as a (nonlinear) mixture of the constituent materials. The main disadvantage of these models is that the model parameters are not properly interpretable in terms of the fractions. Moreover, not all spectra necessarily follow the same particular mixing model.
Alternatively, the complex mixing effects can be learned using supervised machine learning methods. This requires ground truth training data, in the form of the actual compositions (i.e., the spectra and fractions of the constituents). One major drawback of these strategies is that the estimated fractions do not comply with their physical constraints, leading to a loss of the physical meaning of the estimated parameters. Another disadvantage of the learned models is that they cannot perform well in case training and test spectra are obtained under different environmental conditions or by different sensors, causing spectral variability of the acquired spectra.
In this thesis, a hybrid framework was developed that combines the physical interpretability of a model and the flexibility of data-driven approaches. The general idea is to learn the complex relation between the nonlinear spectra and spectra that follow a particular mixing model by utilizing advanced machine learning regression algorithms. Based on this strategy, a number of different nonlinear unmixing methods were developed:
1) A supervised method that learns a mapping between the nonlinear spectra and the linear mixing model.
2) A strategy for the estimation of leaf biochemical parameters from leaf reflectance and transmittance spectra, by learning a mapping to a leaf biochemical model (PROSPECT).
3) A semi-supervised method to reduce the number of training samples required to learn the nonlinearities, and additionally does not require the availability of pure pixels.
4) A robust supervised method for nonlinear spectral unmixing that is invariant
to endmember variability.
- 5 p.m.
- Online PhD defence
- Supervisors: Floris Wuyts, Angelique Van Ombergen, Ben Jeurissen & Athena Demertzi
- Department of Physics
In over half a century
of crewed missions to space, many different effects of spaceflight on the human
body have been uncovered so far. However, little focus has been directed to
investigating how space stressors affect the human brain. The largest body of
work in this dissertation describes pioneering findings on brain structural and
functional changes after spaceflight in Roscosmos cosmonauts by means of
multi-modal magnetic resonance imaging (MRI) in a longitudinal and prospective
design. Structural MRI modalities, such as T1-weighted and diffusion MRI, were
used to unravel macroscopic volume and microstructural brain tissue composition
changes. We found a widespread redistribution of the cerebrospinal fluid (CSF)
with secondary mechanistic effects on the grey matter (GM) tissue. We also
revealed increased neural tissue volume in motor regions of the brain that
suggest evidence for structural brain adaptations, also known as
neuroplasticity, associated with altered motor strategies in space. Most CSF
changes after spaceflight were still detectable more than half a year after
return to Earth, while the GM changes after spaceflight partially reversed in
the long term. In addition, functional MRI data was acquired in these
cosmonauts to study functional reorganisation of the brain after spaceflight,
showing numerous functional connectivity (FC) alterations after spaceflight.
Some of these changes persisted in the longer-term, whereas other changes
returned back to pre-flight levels. Furthermore, this work also describes the
experimental work and preliminary analyses of several Earth-based models. One
is a longitudinal MRI pilot study in hindlimb-unloaded (HLU) mice, inducing
fluid shifts to the head region, in order to better understand the consequence of
these fluid shifts on the brain. A second study was performed in fighter pilots
as a model for exposure to high g-levels and sensory conflicts, in which FC was
compared to that in a control group. This work rendered a vast increase in
available information on structural and functional brain changes after
spaceflight compared to several years ago. In the future, the underlying
mechanisms of the observed findings need to be understood in more detail.
Ultimately, we aim to characterise the effects space stressors have on the
brain, to then attempt to mitigate these changes through countermeasures and
characterise beneficial coping mechanisms that we can enhance, in order to be
fully prepared for future exploration missions into deep space.
- 3 p.m.
- Online PhD defence
- Supervisors: Annemie Bogaerts & Erik Neyts
- Department of Chemistry
CO2 conversion, CH4
conversion and NH3 synthesis are three essential processes that can help to
reduce greenhouse gas emissions. However, these processes typically require
harsh reaction conditions when performed thermally, because of the strong
chemical bonds of the reactants. Plasma catalysis can provide alternative
methods to activating chemical bonds at ambient conditions. Due to the
complexity of plasma-catalytic systems, fundamental understanding of the
underlying mechanisms is still lacking, impeding the optimization of the
technology and holding back its full potential. The aim of this dissertation is
to provide fundamental understanding, needed to unlock the full potential of
As a tool to acquire the fundamental understanding, we introduced microkinetic modelling to provide detailed information on reaction mechanisms, kinetics and thermodynamics of the processes. In this way, we identified the limitations of thermal processes, but also unraveled if and how plasma-catalytic processes can overcome these limitations. The main difficulty of CO2 hydrogenation is to selectively produce CH3OH at sufficient rates. In plasma catalysis, the contribution of the plasma is twofold: excitation of the reactant molecules, lowering the barrier of dissociation and increasing the conversion rates of the thermal pathways, and generation of reactive radicals and intermediates, allowing new, unique pathways that potentially lead to CH3OH (often in a much faster way).
In the study on the conversion of CH4, we showed the limitations of transition metal catalysts to produce C2-hydrocarbons under thermal conditions. Thermally, the more noble catalysts are not able to dissociate the strong chemical bonds of the CH4 molecule, while the less noble catalysts suffer from cokes formation. In plasma catalysis, dissociation rates on noble catalysts can be increased by vibrationally exciting the reactants, or catalytic dissociation can be avoided by adsorption of plasma-generated radicals. Whether the adsorbed species couple directly to C2-hydrocarbons or undergo further dehydrogenation before coupling, can be controlled by the catalyst binding strength.
Lastly, the potential of the plasma-catalytic NH3 synthesis is locked in the enhanced catalytic rates, caused by plasma-induced excitation and plasma-generated radicals. Again, both vibrationally excited species and plasma-generated radicals are found to improve the NH3 synthesis rates. Due to the contribution of ER reactions, rates are not only increased on noble catalysts, but also on more strongly binding catalysts, making the choice of the catalyst material much less impactful.