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One of the main obstacles in applying engineering approaches to biological processes remains dealing with inter- and intra-individual differences. Therefore, it is highly challenging to accurately monitor their individual state (cfr. personalised medicine). The general objective of this PhD is to develop a framework for individualised model-based monitoring for biological processes, as inspired by control engineering concepts. The presented approach addresses four main topics: i) the biological process itself (i.e. bio-process), ii) the process model, iii) model-based features and iv) individualised change detection based on individual thresholds. In order to explore the general objective, six different case studies (cell, embryo, animal, human) were examined: i) individualised monitoring of activity and body weight in the activity-based anorexia rat model, ii) individualised model-based monitoring of interleukin-6 for early detection of infection in pigs, iii) model-based monitoring of heart rate and blood cytokine time series for early detection of infections in critically ill patients, iv) model-based monitoring of mGluR-dependent synaptic plasticity in hippocampal brain slices of rat, v) individualised monitoring of hippocampal theta oscillations and individualised electrical stimulation in the mesencephalic reticular formation for real-time closed-loop suppression of locomotion in rat and vi) individualised model-based monitoring of chicken embryo status during incubation based on eggshell temperature and micro-environmental air temperature. The results showed that the individual bio-processes involved (individual structure, individual dynamics, bio-signals) can be considered as the biological equivalents of clever-designed control engineering components by defining actuator and homeostatic variables for each of the six case studies (case studies i-vi). Although biological processes are known to contain many nonlinearities, compact individual linear models (general Box-Jenkins models) could be used for the specific individualised monitoring applications of the case studies. By using these models we obtained good approximations of the individual bio-process dynamics (case studies ii, iii, iv and vi), since biological systems often show relatively simple responses (expressing the crucial dominant processes that ascertain healthy internal homeostatic or homeodynamic conditions) when exposed to perturbations as illustrated by the bio-processes of the case studies. In addition, we were able to uncover information about the underlying mechanisms/state by applying data-based mechanistic modelling approaches (i.e. case studies iv and vi). Based on the results, we suggest three different model-based features (model parameter changes, changes in model order and changes in the noise model). In addition, more than 20 other generic metrics from the fields of complex systems science, change detection and control engineering were identified that can be used while analysing individual time series (case studies i-vi). This list of metrics can be used for all individual bio-processes in the design of model-based monitoring application. Based on the specific case studies, three possible approaches were proposed for model-based monitoring of bio-processes based on individual thresholds (e.g. case studies v and vi): 1) individual thresholds based on (sub-)population information, 2) individual thresholds based on universal laws and insights from control engineering, complex systems science and biology and 3) individual thresholds based on individual serial baseline measurements, which can be considered as the most individualised way. To conclude, this thesis has led to some innovative individualised monitoring applications based on each of the six specific case studies. Until now the existence of general frameworks for individualised model-based monitoring of biological processes is limited. Each specific case contributed to the development of such general framework inspired by control engineering concepts. The presented general approach could be used in a broad range of application domains, thus stressing the generic power of the suggested framework for individualized model-based monitoring of (complex) bio-processes.

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Decreased O2 and slightly increased CO2 partial pressures of controlled atmosphere (CA) storage slow down the postharvest respiration processes of pear and apple fruit and, therefore, ensure their year round availability. While the O2 partial pressures of the storage atmosphere can be precisely controlled, gas diffusion resistance of the fruit tissue and O2 consumption may create too low O2 levels inside the fruit, potentially leading to the development of internal physiological disorders. To understand the effect of hypoxic storage on the physiology of the fruit, the O2 level inside the fruit needs to be known. A modeling approach is so far the best option since the direct measurement of internal gas concentration of fruit is destructive and not practical. Available gas transport models for O2 typically assume a homogeneous gas transport parameter. However, there is now evidence that fruit tissue microstructure is quite heterogeneous over the entire fruit and this would likely result in a heterogeneous gas diffusivity. In this dissertation, the heterogeneity of the tissue microstructure is mapped based on X-ray computed tomography (CT) images and integrated into existing respiratory gas exchange models to investigate its effect on the O2 concentration inside the fruit.A method was developed to create three dimensional (3D) porosity maps. The method was based on a regression model between the grayscale intensity of low resolution CT images and the actual tissue porosity at identical locations. The latter was calculated from high resolution CT images of the same tissue in which pores and cells could be distinguished. The model was constructed for four products that have considerably different tissue microstructures; pear, apple, eggplant and turnip. Grayscale values of juice of the sample and air representing 0 and 100 % porosity, respectively, were included in the data as extreme values. Using the model, the porosity distribution could be mapped based on juice scans only. The constructed porosity maps reflect the heterogeneity of the tissue microstructure both within and between products.The porosity mapping method was then extended to create effective O2 diffusivity maps. A model for relating the effective O2 diffusivity to the total porosity was developed for the same products. The model did not predict the O2 diffusivity well in regions with low connectivity and/or high tortuosity of the pore microstructure. The relationship between the O2 diffusivity and the open porosity and tortuosity was, therefore, also explored. The O2 diffusivity was calculated based on a microscale gas transport model; the total and open porosity and tortuosity were derived from segmented high resolution images of tissues sampled along radial direction of the product. The O2 diffusivity correlated better with the open than total porosity. The addition of the tortuosity in the model did not improve the fit of the correlation. For eggplant and turnip the model based on total porosity was sufficiently accurate as these products consist of more open and less tortuous pores. On the other hand, better results were obtained with the model based on open porosity for pear and apple which have less open channels. An open porosity map was, therefore, estimated for intact apple and pear fruit using a regression model between total porosity and open porosity. Finally, maps of effective O2 diffusivity coefficients were calculated for apple, pear, eggplant and turnip based on the previously created porosity maps.The O2 diffusivity maps were then used to study gas transport in 'Conference' pear and its impact on internal browning. Late harvested pear fruit were stored under CA with browning-inducing conditions: no pre-cooling period, 0.5 kPa O2, 0.7 kPa CO2 at 1 oC. Porosity and effective O2 diffusivity distributions were mapped in pears before and after storage using the developed methods. The effective CO2 diffusivity were used in respiratory gas exchange calculations to obtain the spatial distribution of the O2 and CO2 concentration and the respiratory quotient (RQ) in the fruit. The resulting concentration contours were quite heterogeneous and the low oxygen concentrations and high RQ values found in the core region of the fruit indicated the occurrence of fermentation. Moreover, the gas concentration and RQ contours corresponded well with tissue that was affected by browning and cavities after 8-months storage. In contrast, when homogeneous O2 and CO2 diffusivities were used for computing the internal gas and RQ distributions, smooth contours were obtained that did not correspond to contours of the tissue affected by browning and cavities; nowhere the thresholds indicative for the development of browning were exceeded.In future research, the porosity mapping technique may be integrated into inline systems of fruit quality sorting based on X-ray radiography and tomography, although this will require significant further research in terms of optimizing speed and cost of the systems and development of adequate processing algorithm for inline applications. For the diffusivity mapping, more complex multiphase transport models and orthotropic diffusivity tensors should be considered to describe gas transport more appropriately, compared to a local isotropic diffusivity. Furthermore, the gas transport models should be validated using appropriate methods. The developed and validated models can then be extended to other respiration-related gasses. Finally, browning-related change of respiration, tissue microstructure and, inherently, effective tissue diffusivity during storage should be further investigated to explore the dynamic change of internal gas distribution.

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Bacteriophages, viruses infecting bacteria, disrupt the bacterial cell wall at the end of their replication cycle to release newly produced virions. The major constituent of the bacterial cell wall is the peptidoglycan. To degrade this rigid layer, bacteriophages encode peptidoglycan hydrolases, called endolysins, that hydrolyze specific bonds in the peptidoglycan. This dissertation specifically focuses on endolysins, isolated from phages infecting Gram-negative bacterial species, including Pseudomonas aeruginosa, Salmonella Typhimurium, Escherichia coli, Klebsiella pneumoniae and Citrobacter rodentii. Most of these bacteria are opportunistic pathogens that are of increasing concern in hospitals due to their high intrinsic and acquired antibiotic resistance. In a first part of this study, we extend the pool of available endolysins from Gram-negative origin and analyze their potential and applicability as alternative antibacterial agents for antibiotics to combat these Gram-negative pathogens. The Gram-negative outer membrane prevents exogenously applied endolysins from reaching the peptidoglycan layer and protects bacteria against their lytic activity. We therefore evaluate in the second part of this dissertation an approach that allows the endolysin to efficiently destabilize the outer membrane and subsequently reach the peptidoglycan. This approach consists of the fusion of a set of outer membrane-permeabilizing antimicrobial peptides to the endolysin to allow for an autonomous interaction with the outer membrane. To sketch the background, this dissertation starts with an overview of the literature concerning bacteriophage endolysins (history and structural diversity), antimicrobial peptides (types and mode of action) and outer membrane diversity present among Gram-negative bacteria.From an in silico analysis of fully sequenced phage genomes, a selection of fifteen interesting candidate endolysins is made (Chapter 4). Six single-domain (Chapter 5) and three modular (Chapter 6) endolysins with the highest maximal muralytic activity under physiological conditions, are selected for extensive characterization on biochemical (pH-dependency, enzymatic activity, activity upon heating) and antibacterial level. In this way, we aim to prove their lytic role and to reveal enzyme-specific characteristics interesting from an application perspective. In silico, the single-domain endolysins consist of a catalytic domain, whereas the modular ones feature an N-terminal peptidoglycan binding domain and a C-terminal catalytic domain, hitherto a unique property present in a few endolysins from Gram-negative origin. In addition, the predicted peptidoglycan binding domains are experimentally verified.The modular endolysins in this study are shown to be enzymatically more active than the single-domain endolysins, an observation that was translated into their in vitro antibacterial activity. Of all tested endolysins, the modular endolysin from Pseudomonas fluorescens phage OBP, OBPgp279, shows the highest muralytic and antibacterial activity, followed by PVP-SE1gp146, the endolysin from Salmonella Enteritidis phage PVP-SE1. The peptidoglycan binding domain present in modular endolysins accounts for their strong lytic action since the contribution of this domain (38 to 56 %) to the total enzymatic activity is considerable. In addition, the enzymatic activity is consistent for the different Gram-negative bacterial species due to their conserved peptidoglycan (A1gamma chemotype). This characterization also revealed various interesting biochemical properties. OBPgp279 shows intrinsic antibacterial activity on P. aeruginosa PAO1 (± 1 log unit), probably by destabilizing the Pseudomonas outer membrane. PVP-SE1gp146 remains active up to temperatures of 90°C with 60 % residual enzymatic activity after 40 minutes. This last property makes the enzyme a potential candidate as antibacterial component in hurdle technology for food preservation. At the start of the second part, OBPgp279 and PVP-SE1gp146, the two most promising endolysins, are selected to evaluate the proposed fusion approach for passage of the outer membrane (Chapter 7). The N-terminal fusion of a polycationic PK peptide (KRKKRKKRK) composed of lysine and arginine residues, turns out to be the most effective fusion to improve the antibacterial activity of both endolysins. The highest activity is reached for P. aeruginosa with maximal 2.61 log units. Addition of minor EDTA concentrations enhances activity and extends the activity range with E. coli (maximal 1.70 log units) and S. Typhimurium (maximal 0.91 log units). This fused PK peptide is believed to compete with the Achilles heel of the outer membrane: the stabilizing divalent cations. A double N-terminal fusion of this promising PK peptide with other antimicrobial peptides only increases the antibacterial activity of OBPgp279 against E. coli to maximal 2.22 log units (for PP-PK double fusion), but is detrimental for the activity against other Gram-negative species. Analysis for the impact of the N-terminal PK fusion on endolysin characteristics reveals a protein-dependent inhibition of the enzymatic activity (with 52 to 94 %), a reduced heat resistance and a switch in pH-dependency to slightly more alkaline values (Chapter 8). Due to a more hydrophobic outer membrane, the antibacterial efficacy of the PK fusion is limited for Enterobacteriaceae. Additionally, the PK fusion also confers biofilm-degrading activity to PVP-SE1gp146. Extension of the linker length between the PK peptide and endolysin partly reconstitutes the reduced muralytic activity due to the PK peptide fusion, leading to an improved antibacterial activity against Pseudomonads and Enterobacteriaceae. Switching the PK peptide to the C-terminal end does not improve activity. These data nicely illustrate that endolysins can be turned into effective anti-Gram-negative compounds by an N-terminalfusion approach and subsequent optimization of the linker length.In the last part, we evaluate the PK-fused endolysin approach on an in vitro human keratinocyte monolayer and an in vivo Caenorhabditis elegans model. PK-PVP-SE1gp146 is able to protect the keratinocyte monolayer from a P. aeruginosa PA14 infection (Chapter 9). In addition, PK-PVP-SE1gp146 improves the survival of PA14-infected C. elegans with 60 % after five days of treatment (Chapter 10). These results prove the in vitro and in vivo applicability of the PK-fused endolysin approach against P. aeruginosa, offering promising perspectives towards prophylactic and therapeutic applications in human health and veterinary and towards microbial decontamination purposes in the food industry.

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Brain damage is caused by loss or deterioration of brain cells and it is a prevalent type of injury that may be fatal, or may result in severe impairments, with devastating consequences on the quality of life of survivors. Loss of neural cells may lead to the formation of abnormal brain cavities (aBC), that are most often the result of stroke and traumatic brain injuries (TBI), but can also occur after surgical resection of tumors or abscesses. The chronic debilitating symptoms caused by neuronal damage are currently untreatable and largely depend on the degree and location of damage. In this work, we investigated the potential of neuromodulation as a treatment option, driven in a novel target - directly within the aBC wall, so as to alter aBC-related symptoms, and to improve behavioral outcome after neurological damage. We used a generic model for brain damage, developed in a rat model. A foldable electrode array was implanted against the aBC wall, in a motor cortical rat model. The electrode implant was used both to interact with neural populations as well as record their activity. Much of the current understanding about motor system function is based on correlations between brain and behavioral tasks. Thus, the general objective of this thesis was to develop algorithms that identify behavioral and neural signal features with multiple aims: (i) to validate the aBC wall as a target for recording meaningful brain activity, (ii) to better quantify motor impairments and (iii) to investigate how modulating brain activity can improve behavioral outcome. In a first step, we recorded electrical brain activity, as field potentials (FPs) on the surface of motor cortical aBC of freely moving rats. We showed that FPs are dominated by oscillatory activity in the theta range (4-9 Hz) and gamma range (30-100 Hz) and can be an informative biomarker for behavioral features, as it allowed us to discriminate between behavioral state: active versus resting. In a second step, we analyzed in detail impairment state. We developed an automated computer algorithm for analyzing reaching and grasping in impaired animals, subsequent to induction of a motor cortical aBC. The algorithm automatically tracks the movement of the rat's forelimb using image processing methods. We classified endpoint behavior, achieving accuracy rates of 86%-92%. With this extended analysis we captured perturbation of skill after a motor cortical lesion was induced. Kinematics of reaching revealed that rats developed individual strategies to achieve the task. In a third phase, we used the automatic algorithm to evaluate kinematics and endpoint outcome of skilled reaching during stimulation in different targets within the lesion wall. We stimulated either over the entire 16-electrode array (non-selective stimulation) or over subsets of electrodes (selective stimulation), proving that both strategies could alter kinematics of movement, with selective stimulation being at least as effective as non-selective stimulation. Neurostimulation has been proven effective in the past recent years in treating psychiatric disorders. Finally, we used a rat model of obsessive compulsive disorder (OCD) in which deep brain stimulation was used to diminish compulsive symptoms. We showed that responders to deep brain stimulation presented specific brain modulations in the frequency bands of delta (1- 4 Hz), theta (4-8 Hz), beta (12-30 Hz), and lower gamma (30-45 Hz) that were otherwise not present in the non-responders or in the control subjects. Our strategy is not limited by the cause of insult, be it ischemic stroke or traumatic brain injury, and allows direct interaction with the lesion wall via an invasive array of electrodes that can be used both for stimulation and for recording brain electrical activity. Access to neural activity allowed us to investigate neural mechanisms that may be relevant for impaired brain function, both in rat models of motor disability and psychiatric disorders. Finally, our strategy aims for an individual-focus treatment, that circumvents patient-dependent differences like severity and location of the brain damage.

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Several approaches and methodologies have been proposed in the literature to address the technical aspects of resource optimization and the improvement of management practices in agricultural systems. Similarly, field experimentation has benefited from multispectral imagery, obtained by unmanned aerial vehicles (UAVs), which allow to obtain crop data with cost-efficient nondestructive measurements. The overall objective of this PhD thesis was to develop and extend generic and multi-target quantitative methods to study the technical sustainability of agricultural systems (at the system level), and the statistical modeling of UAV-multispectral imagery (at field level) leading to the optimization of open-field agricultural experiments. This PhD applies the developed methodologies to different study cases.The system analysis sections examine the potential environmental impacts and the most relevant biophysical factors explaining the yield gap and yield variability for potato cropping systems in the Central Peruvian Andes. The methods used were life cycle assessment, multivariate statistics, data envelopment analysis and crop simulation modelling. Taking into account the variability in potato production strategies, important environmental impact values were found for acidification and eutrophication (per ton of potato fresh weight), caused by the inappropriate or sub-optimal use of fertilizer sources. The k-means clustering algorithm identified three groups mainly defined by the nature of the inputs used for fertilization: inorganic, organic and mixed oriented. Exploratory factor analysis demonstrated that the first and second latent variables were correlated with an inorganic- and organic-oriented agriculture respectively; the inorganic system was associated with high values of potential environmental impacts. Relative environmental efficiency was linked to the quantity and source of the inputs, showing that potential environmental savings can be reached if more balanced input sources (mix of organic and inorganic) are employed. Similarly, the average potato yield gap was 42.1%, showing there is an important difference that needs to be reduced. The heterogeneous crop management practices of smallholders resulted in high variability in the dry weight production (710 to 18885 kg ha-1). The classification tree identified that inorganic N is the main factor characterizing the yield gap. The methodology identified that large yield gaps (Fourth quantile) are described by low inorganic N and scarce human labour energy, while small yield gap (First quantile) were mainly described by high N-inputs (inorganic and organic). This classification will be helpful to target inputs and site-specific agronomic recommendations towards closing the potato yield gaps.The field analysis sections examine the feasibility of nonlinear mixed models to analyze UAV-multispectral canopy vegetation index from cassava (without treatment effect) and tomato (with irrigation treatment effect) experiments. Diverse methods were adapted for this purpose: segmentation algorithms (simple linear iterative clustering), affinity propagation, mixed modelling and resampling techniques. Object-based image analysis based on oversegmented multispectral imagery represented a good approach to extract canopy information of individual plants (experimental unit). A three-parameter logistic growth curve (non-linear mixed model) was found to be well-suited to fit the cassava and tomato canopy curves of the normalized difference vegetation index (NDVI). Resampling analysis showed that a suitable accuracy in parameter estimation can be achieved with fewer experimental units which could result in smaller agricultural experimental designs (cassava experiment). Similarly, differences were observed from 100% of the actual evapotranspiration (ETc) for all the treatments (75, 125 and 150% ETc) at maturity stage, when the cumulative effect of the water doses was well-defined and reached its asymptote for the tomato experiment. The diagnosis plots and the root mean squared error of the observed and fitted NDVI indicated the suitability of using the three-parameter logistic mixed model.This PhD research contributed to enhancing and extending the agricultural systems approaches towards technical sustainability. Likewise, it was also demonstrated that UAV-multispectral imagery, analyzed by nonlinear mixed models, provided useful insights towards field experiment optimization and treatment effect studies at field level.