Mechano-regulation of collagen architecture in cardiovascular tissue engineering

Clinically available heart valve replacements consist of non-living materials, lacking the ability to grow with the patient. Therefore, several re-operations are necessary to replace the valve with a larger one. In addition, there is a large need for living blood vessel substitutes that overcome the drawbacks of current vascular prostheses. Cardiovascular tissue engineering focuses on the creation of living heart valves and blood vessels that have the potential to grow and remodel in vivo. In brief, cells are acquired from a patient and seeded on a biodegradable material or scaffold. The cell-seeded scaffold is cultured in a bioreactor where mechanical and biochemical stimuli are applied to stimulate tissue development. After several weeks, the scaffold is replaced by tissue produced by the patient’s own cells. Ideally, the tissue can be implanted in the patient to replace dysfunctional tissue. In order to be mechanically functional, such engineered cardiovascular tissues should incorporate load-bearing structures to withstand (changes in) the hemodynamic environment. The mechanical properties of cardiovascular tissues are dictated by a well organized network of collagen fibers. The collagen architecture is influenced by the mechanical environment of the tissue. Hence, mechanical conditioning is considered to be an important regulator to create engineered cardiovascular tissues with defined load-bearing structures and mechanical properties. The aim of the research presented in this thesis is to elucidate the effects of well-defined mechanical conditioning protocols on the collagen architecture in engineered cardiovascular tissues. In this thesis, three main aspects of the collagen architecture in engineered cardiovascular tissues are quantified: collagen amount, collagen cross-link density, and collagen fiber orientation. To systematically investigate the effects of mechanical conditioning on collagen architecture, a model system has been employed. The tissues consist of rectangular strips of rapidly degrading polyglycolic acid based scaffolds seeded with human vascular cells. The advantage of the model system is that it reduces the number of required cells and it allows for the application of pre-defined strain regimes to multiple engineered tissues simultaneously. Static conditioning is applied in longitudinal direction by constraining the tissues at their outer ends. In addition, different uniaxial straining protocols, including continuous dynamic strain (4%, 1Hz, for 10 days and 4 weeks) and intermittent dynamic strain (3 hours on/off, 4%, 1Hz, for 2, 3, and 4 weeks) are applied using a modified straining system. The temporal effects of static and dynamic conditioning on collagen amount and cross-links are assessed up to 10 days of culture from gene and protein measurements. Both conditioning modes upregulate collagen and cross-link expression and protein content with time. Dynamic strain results in lower collagen expression and content, but enhances collagen cross-link expression and density, when compared with static conditioning. To study the effects of static and dynamic conditioning on the mechanical properties of newly formed tissue, the culture period has been extended to 4 weeks. By that time, the initial scaffold has lost its mechanical integrity and the mechanical properties of the constructs are only determined by the newly formed tissue. Compared to 4 weeks of static conditioning, continuous dynamic strain results in similar collagen contents but higher cross-link densities, which correlate to improved mechanical properties. These findings indicate that, despite a similar collagen amount, the quality and structural integrity of the tissue can be improved by dynamic strain via an increase in collagen cross-link densities. A novel technique has been used to quantify the orientations of the newly formed collagen fibers, based on 3D vital imaging using two-photon microscopy combined with image analysis. These collagen fiber orientation analyses reveal that mechanical conditioning induces collagen alignment in the constrained and intermittently strained directions. Importantly, intermittent dynamic strain improves and accelerates the alignment of the collagen fibers in the straining direction compared to constraining only. In addition, intermittent dynamic strain results in increased collagen production, cross-link densities, and mechanical properties at faster rates compared to static conditioning, leading to stronger tissues at shorter culture periods. In conclusion, these studies show that, when compared to constrained tissue culture, continuous dynamic strain does not increase the amount of collagen in the tissue but does enhance cross-link densities and collagen fiber alignment. Intermittent dynamic strain increases and accelerates the production of collagen, cross-links, and collagen fiber alignment. Therefore, intermittent dynamic strain can be used to accelerate the creation of load-bearing tissues with a well organized collagen architecture. This is of considerable importance for cardiovascular tissue engineering, where a functional load-bearing capacity is a prerequisite for in vivo application

Computational analysis of microbial flow cytometry data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research

Local DRLs and automated risk estimation in paediatric interventional cardiology

Introduction : Cardiac catheterization procedures result in high radiation doses and often multiple procedures are necessary for congenital heart disease patients. However, diagnostic reference levels (DRL) remain scarce. Our first goal was finding the optimal DRL parameter and determining appropriate DRLs. The second goal was to calculate organ doses (OD), effective doses (ED) and lifetime attributable risks (LAR) per procedure and to provide conversion factors based on dose area product (DAP). Materials and methods : DRLs are calculated for each procedure type, as the 75th percentile of the cumulative value per procedure from the corresponding parameter. All irradiation events in the DICOM Structured Reports were automatically processed and simulated using PCXMC, resulting in OD, ED and LAR. Using a Kruskal Wallis H test and subsequent pairwise comparisons, differences in median values of the DRL parameter between procedure types were assessed. Results : Linear regression showed a strong correlation and narrow confidence interval between DAP and product of body weight and fluoroscopy time (BWxFT), even when all procedures (diagnostic and interventional) are combined. Only 15% of the pairwise comparisons were statistically significant for DAP normalized to BWxFT (DAP(BWxFT)). The latter pairs contained less frequent procedure types with significant outliers. For DAP normalized to BW (DAP(BW)), 38% of the pairwise comparisons showed statistically significant differences. Conversion factors from DAP(BW) to OD and ED were reported for various weight groups, due to the higher correlation between DAP(BW) and both OD and ED than between DAP and both OD and ED. Conclusions : The P75 of DAP(BWxFT) for all procedures combined serves as an appropriate DRL value. This facilitates local DRL determination in smaller paediatric centres, which often have insufficient data to produce appropriate DRLs for different procedure types. Conversion factors are more reliable starting from DAP(BW) instead of DAP and should be used according to the appropriate BW group

Simple Fixpoint Iteration To Solve Parity Games

A naive way to solve the model-checking problem of the mu-calculus uses fixpoint iteration. Traditionally however mu-calculus model-checking is solved by a reduction in linear time to a parity game, which is then solved using one of the many algorithms for parity games. We now consider a method of solving parity games by means of a naive fixpoint iteration. Several fixpoint algorithms for parity games have been proposed in the literature. In this work, we introduce an algorithm that relies on the notion of a distraction. The idea is that this offers a novel perspective for understanding parity games. We then show that this algorithm is in fact identical to two earlier published fixpoint algorithms for parity games and thus that these earlier algorithms are the same. Furthermore, we modify our algorithm to only partially recompute deeper fixpoints after updating a higher set and show that this modification enables a simple method to obtain winning strategies. We show that the resulting algorithm is simple to implement and offers good performance on practical parity games. We empirically demonstrate this using games derived from model-checking, equivalence checking and reactive synthesis and show that our fixpoint algorithm is the fastest solution for model-checking games.Comment: In Proceedings GandALF 2019, arXiv:1909.0597

La3TaO7 derivatives with Weberite structure type: Possible electrolytes for solid oxide fuel cells and high temperature electrolysers

In this study, with the aim to enhance the ionic conduction of known structures by defect chemistry, the La2O3-Ta2O5 system was considered with a focus on the La3TaO7 phase whose structure is of Weberite type. In order to predict possible preferential substitution sites and substitution elements, atomistic simulation was used as a first approach. A solid solution La3−xSrxTaO7−x/2 was confirmed by X-ray diffraction and Raman spectroscopy; it extends for a substitution ratio up to x = 0.15. Whereas La3TaO7 is a poor oxide ion conductor (σ700 °C = 2 × 10−5S.cm−1), at 700 °C, its ionic conductivity is increased by more than one order of magnitude when 3.3% molar strontium is introduced in the structure (σ700 °C = 2 × 10−4S.cm−1)

Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Flow cytometry is able measure up to 50.000 cells in various dimensions in seconds of time. This large amount of data gives rise to the possibility of making predictions at the single-cell level, however, applied to bacterial populations a systemic investigation lacks. In order to combat this deficiency, we cultivated twenty individual bacterial populations and measured them through flow cytometry. By creating in silico communities we are able to use supervised machine learning techniques in order to examine to what extent single-cell predictions can be made; this can be used to identify the community composition. We show that for more than half of the communities consisting out of two bacterial populations we can identify single cells with an accuracy >90%. Furthermore we prove that in silico communities can be used to identify their in vitro counterpart communities. This result leads to the conclusion that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of bacterial flow cytometric data and for the experimental study of low-complexity communities

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R-2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. IMPORTANCE A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production

Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Single cells can be characterized in terms of their phenotypic properties using flow cytometry. However, up to our knowledge there has not yet been a thorough survey concerning the classification of bacterial species based on flow cytometric data. This paper aims to perform a thorough investigation concerning the identification of bacterial communities of various complexities in species richness. We do this by creating so-called in silico communities, communities created by aggregating the data coming from individual cultures; moreover we show that it is possible to use in silico communities to identify in vitro created communities as well, proving the biological relevance and usability of bacterial in silico communities