2,200 research outputs found

    Non-invasive and non-intrusive diagnostic techniques for gas-solid fluidized beds – A review

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    Gas-solid fluidized-bed systems offer great advantages in terms of chemical reaction efficiency and temperature control where other chemical reactor designs fall short. For this reason, they have been widely employed in a range of industrial application where these properties are essential. Nonetheless, the knowledge of such systems and the corresponding design choices, in most cases, rely on a heuristic expertise gained over the years rather than on a deep physical understanding of the phenomena taking place in fluidized beds. This is a huge limiting factor when it comes to the design, the scale-up and the optimization of such complex units. Fortunately, a wide array of diagnostic techniques has enabled researchers to strive in this direction, and, among these, non-invasive and non-intrusive diagnostic techniques stand out thanks to their innate feature of not affecting the flow field, while also avoiding direct contact with the medium under study. This work offers an overview of the non-invasive and non-intrusive diagnostic techniques most commonly applied to fluidized-bed systems, highlighting their capabilities in terms of the quantities they can measure, as well as advantages and limitations of each of them. The latest developments and the likely future trends are also presented. Neither of these methodologies represents a best option on all fronts. The goal of this work is rather to highlight what each technique has to offer and what application are they better suited for

    The brown algal genus Fucus : A unique insight into reproduction and the evolution of sex-biased genes

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    Doctoral thesis (PhD) - Nord University, 2023publishedVersio

    Cu-based electrodes for ammonia and urea electrosynthesis

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    ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE

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    In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph

    Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

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    Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations

    Biomolecular NMR spectroscopy in the era of artificial intelligence

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    Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts

    Development of spectroscopic assays for rapid monitoring of estrogen biodegradation

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    Estrogen hormones are well-established environmental micropollutants which have been linked to endocrine disruption in aquatic organisms in wastewater discharge sites. Biological degradation is the primary wastewater treatment mechanism for estrogen removal. However, treatment efficacy is highly variable and difficult to engineer due to the “black box” nature of biological treatment. Microbial strain selection is a critical impediment towards engineering estrogen biodegradation, since isolating endogenous strains with specific metabolic traits requires lengthy enrichment cultures and is limited to culturable organisms. Furthermore, the highly sensitive and selective chemical trace analysis techniques used to measure estrogen removal are relatively expensive and inefficient. In this thesis, we developed rapid, high-throughput spectroscopic methods designed to monitor estrogen biodegradation. The spectroscopic methods include a fluorometric assay based on the uptake of a fluorescently-labelled estrogen and a colorimetric biosensor using gold nanoparticles (AuNPs) and an aptamer bioreceptor. A synthetic microbial community comprised of characterised estrogen-degrading reference strains was used to evaluate the fitness for purpose of the developed methods. A trace analysis method using conventional chromatography was developed to validate the use of the fluorescent probes with the synthetic microbial community. The biochemical fate and distribution of the BODIPY-estrogen in the estrogen-degrading bacteria – specifically, the biotransformation of BODIPY-estradiol to BODIPY-estrone by Caenibius tardaugens – was used to inform the design of the fluorometric assay. The fluorometric assay utilises a cell impermeable halide quencher to suppress the extracellular fluorescence, and thus, the obtained fluorescence response was attributed to the selective internalisation of BODIPY-estrogen by C. tardaugens. While the fluorometric assay was developed to screen for estrogen-degrading bacteria, the colorimetric aptasensor, which was adapted from published AuNP biosensors and aptamers for this application, was developed to quantify 17β-estradiol (E2) in buffered culture media. The developed aptasensor was evaluated against industry guidelines for ligand-binding assays. While the analytical performance of the aptasensor satisfied the majority of the guidelines’ acceptance criteria, the method suffered from biological interferences by the estrogen-degrading bacteria. The work in this thesis contributes towards expanding the available bioanalytical methods in environmental biotechnology

    Aldh1b1-mediated metabolism regulates pancreas progenitor differentiation and β-cell maturation

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    Pancreatic β-cells have a central function in the regulation of glucose homeostasis by releasing the blood sugar-lowering hormone insulin. Disruption of this process results in diabetes, which has a tremendous impact on the quality of life and requires lifelong treatment. Elucidating the mechanisms of pancreatic progenitor cell differentiation into fully functional β-cells will contribute to identifying the underlying reasons for β-cell dysfunction and to finding a cure for diabetes. Aldh1b1 was identified by our research group as a regulator of pancreas development and β-cell functionality. Aldh1b1 is a mitochondrial enzyme, expressed in all embryonic pancreas progenitors. Its expression is switched off during the process of differentiation and is undetectable in differentiated cells. Functional inactivation of Aldh1b1 in the mouse leads to premature differentiation of progenitor cells in the embryo and dysfunctional β-cells in the adult. However, the enzymatic function of Aldh1b1 in pancreas progenitors and how it ultimately affects β-cell functionality remained to be elucidated. In this study, I analyzed the role of Aldh1b1 in the metabolism of embryonic pancreas progenitor cells and its impact on chromatin structure and gene expression in both, progenitors and postnatal β-cells. Flow cytometry analysis of freshly isolated Aldh1b1 null embryonic pancreas progenitors showed a significant increase in ROS levels as well as a significant decrease in mitochondrial mass, whereas the mitochondrial membrane potential was not affected. To elucidate the impact of Aldh1b1 on cellular metabolism, I conducted metabolic flux experiments and untargeted metabolomics studies using FACS-isolated embryonic pancreas progenitors expanded in a 3D spheroid culture. Analyses following metabolic labeling with either 13C6-Glucose or 13C2-Glutamine showed that the absence of Aldh1b1 lead to an increase of the reductive glutamine metabolism towards citrate, a reaction that channels carbon units into the acetyl-CoA biosynthesis. However, the ACLy-dependent flux towards acetyl-coA synthesis was reduced and this was consistent with reduced expression of ACLy as well as the citrate transporter SLC25a1. A decrease in cellular acetyl-CoA would reduce histone acetylation. Untargeted metabolomics showed an increase in the concentration of S-adenosyl-methionine, suggesting increased DNA and histone methylation. Consistent with these findings, ATAC-Seq analyses on freshly isolated pancreatic progenitors showed reduced chromatin accessibility at genes implicated in chromatin organization, protein acetylation and histone modification. Transcription motif analysis showed that the affected genomic sites were mainly associated with the binding of Klf/Sp and Nrf1 transcription factors. Transcriptome analyses displayed that the expression of genes implicated in progenitor differentiation, ECM organization and transcriptional regulation was affected. Furthermore, transcriptome analyses of early postnatal β-cells uncovered early signs of oxidative stress and increased proliferation, thus providing the basis to explain the β-cell phenotype in Aldh1b1 null mice. I then used organotypic cultures of embryonic pancreata to investigate the connection between high ROS levels and aberrant differentiation in the Aldh1b1 null pancreata. Reducing ROS levels using NAC enabled the reversal of the aberrant transcription factor expression and increased viability of Aldh1b1 null explants, thus identifying high ROS levels as a driving force in this process. To investigate how persisting Aldh1b1 expression would affect progenitor differentiation, I generated ROSA26LSLAldh1b1, an inducible constitutive Aldh1b1 expression line. Progenitors with continuous Aldh1b1 expression avoided the endocrine cell fate, underscoring the importance of timely Aldh1b1 downregulation in the course of β-cell differentiation. Altogether, my work provides strong evidence for the role of Aldh1b1 as a metabolic regulator in the process of progenitor cell differentiation and identifies a link between metabolism and gene regulation through chromatin accessibility during development. Aldh1b1 inactivity causes defects in embryonic progenitor cells as well as postnatal β-cells and could therefore contribute, as genetic risk factor, to the development of hyperglycemia and diabetes later in life. Comprehending the mechanisms underlying the process of pancreas progenitor differentiation as well as the origins of β cell dysfunction should assist in the design of novel therapeutic interventions for diabetes
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