Coral reefs are complex ecosystems that provide several ecological, environmental, and economic services. However, climate change has heavily threatened these ecosystems, particularly due to increasing sea surface temperature. Innovative solutions to improve coral tolerance to climate change are therefore urgently needed. Vitamin and trace element supplements can improve the fitness of several animals (e.g., fish and crustaceans) in aquaculture systems, and could represent an alternative treatment to improve coral health and growth in coral nurseries. Here, we tested whether the supplementation of vitamins B6, B12, and zinc could boost coral growth, and health. For this purpose, fragments (n=10) of colonies of five Acropora hemprichii were collected from the central Red Sea were treated with B6, B12, zinc, and a combination of these supplements for 21 days. Coral fragments were collected before and after the experiment. Calcification and oxygen metabolism (respiration, photosynthesis) were measured, while symbiont density, chlorophyll, total protein, and carbohydrate were quantified in the lab. Our data showed that corals’ symbionts density, chlorophyll c2, net productivity, and total protein were significantly increased due to zinc supplementation when compared to control colonies. In addition, the multi-treatment also increased the corals’ total proteins. In contrast, none of the other treatments showed a significant effect on the tested coral’s physiological traits. The results of this study may provide data to support alternative approaches to improve coral growth for restoration efforts.

Shewanella spp. are model electroactive bacteria (EAB) and well-known for their broad metabolic capabilities and extracellular electron transfer (EET) properties, which allow them to utilize a diverse range of carbon substrates, such as formate, lactate, pyruvate, and amino acids. However, the majority of Shewanella spp. cannot metabolize glucose, a naturally occuring carbon and energy source. Here, we examine the electrode respiring potential of Shewanella chilikensis JC5T- and its coexistence with acetoclastic EAB, Geobacter sulfurreducens in glucose-fed microbial electrolysis cells (MECs) operated at a set anode potential condition of 0 V vs. Ag/AgCl. Chronoamperometry analysis revealed that the maximum current density was 0.04523 mA/cm2 (CE: 14.55%) in S-MEC (only S. chilikensis JC5T) and 0.299 mA/cm2 (CE: 53.85%) in CC-MEC (S. chilikensis JC5T and G. sulfurreducens) which is 6.6-folds higher in current density and 3.7-folds higher in coulombic efficiency than S-MEC. Cyclic voltammetry analysis demonstrates presence of biofilm and redox mediator for EET mechanisms. Metabolic analysis showed that S. chilikensis as a monoculture and co-culture with G. sulfurreducens can metabolize glucose and produce intermediates such as acetate, formate, and lactate. These intermediates are likely used to generate electrical current. Collectively, these results provide novel insights on electrode respiring properties of S. chilikensis and its coexistence with acetoclastic EAB, G. sulfurreducens to enhance the current density and coulombic efficiency from glucose-fed MEC.

Water desalination has the potential to alleviate a significant part of the world’s thirst, with a majority of desalinated water capacity coming from seawater reverse osmosis (SWRO). However, SWRO membrane systems suffer from the loss of performance due to biofouling leading to economic costs. There is no control or preventive strategy for SWRO biofouling and current industry practices recommend chemical treatments to restore membrane performance. Chemical cleaning results in high economic costs due to chemical acquisition, storage, transportation, long plant downtimes and ultimately shorter membrane lifetime and early replacement; in addition to the environmental impact associated with disposing of chemicals. Therefore, there is a need for novel effective green cleaning strategies for SWRO to meet the increasing demand for desalinated water while taking care of the environment. Micronanobubbles (MNBs) consist of small gas cavities formed in aqueous solutions. This study evaluates the efficiency of both air-filled micronanobubbles (AMNBs) and CO2 nucleated MNBs as: i) curative cleaning-in-place (CIP) treatments and ii) preventive daily treatments for biofouling over long-term studies. Experiments were performed using the membrane fouling simulator (MFS) under conditions that are representative of SWRO membrane systems. Pressure drop was implemented as the main biofilm growth monitoring parameter as used by standard industry practices. Curative studies showed that both MNBs CIP treatments had high cleaning efficiencies of 49-56% pressure drop recovery. MNBs pressure drop recovery values were close to the conventional chemical cleaning (NaOH/HCl) at 51% and were significantly higher than the hydraulic flush (HF) physical cleaning control at 24%. The pressure drop recovery results were supported by the optical coherence tomography (OCT) images before and after CIP and biomass autopsy results. Similarly, preventive MNBs daily treatments showed a significant delay in the system’s performance decline. This delay was 5.1 days for the CO2 MNBs experiments, 4 days for the AMNBs, and only 0.6 days for the hydraulic flushing treatments compared to the control. Compared to the control the duration of the operation was doubled in time before the cleaning criteria was met. OCT images confirmed biofilm growth delay with lower biomass occurrence.

Wide bandgap perovskites are emerging as suitable candidates for the technology of tandem solar cells. Understanding their optical properties is a prerequisite for improving the corresponding solar cells’ efficiencies. In this thesis, we employ various steady-state spectroscopies to reveal the optical properties of two wide bandgap perovskites: FA0.83Cs0.17Pb(I0.7Br0.3)3 or PVK1 and FA0.83Cs0.17Pb(I0.5Br0.5)3 or PVK2. The optical properties of interest are the semiconductors’ absorption spectra, the sub-bandgap absorption features, the bandgap energy, the Urbach energy, and the excitonic binding energy. We find that the sub-bandgap absorption can be characterized by a single exponential function. We also find that the Urbach energies and the excitonic binding energies are below the thermal energy at room temperature, which signals that PVK1 and PVK2 are excellent nominees for photovoltaic absorbers. Finally, the bandgap energy is red shifted due to excitonic effects as revealed by the Elliot model.

One of the primary challenges in clinical genetics is the interpretation of the numerous genetic variants identified through sequencing applications. Assessing the impact of missense variants where only one amino acid is substituted is particularly difficult. In this study, we examined the structural characteristics of amino acids affected by missense substitutions in 26,690 pathogenic variants and compared them to 11,302 common variants found in the general population. This analysis was conducted across 6,747 protein structures. The residues were annotated using 7 protein features with a total of 35 feature subtypes. Subsequently, we assessed the burden of both common and pathogenic missense variants across these features. Additionally, we carried out separate analyses relative to protein function (with variants grouped in 24 protein functional classes) and relative to diseases (with variants grouped in 86 diseases). Through a comprehensive analysis of the entire dataset, we identified 25 pathogenic features that play a crucial role in the overall fitness and stability of proteins. Additionally, when we conducted individual analyses for 24 protein functional classes, we discovered specific features that are relevant to each function. For the disease analysis we identified 3 main clusters. Type I diseases primarily result from ordered mutations and are mainly affected by charge loss. This cluster is dominated by transporter protein class and includes diseases linked to X-chromosome. Type II diseases involve hydrolases and are characterized by enriched variants at the protein core, resulting in protein destabilization. Type III diseases involve extracellular matrix proteins (mainly collagen), are predominantly found in disordered regions, and are affected by charge gain and introduction of polar residues. Gly variants are particularly relevant in this cluster, as collagen proteins require Gly in every third residue in the collagen triple-helix. Considering the structural aspects when interpreting mutations associated with diseases offers valuable insights into their underlying mechanisms. Our work can serve as resource to delineate and understand variant pathogenicity by mapping a genetic variant into its structural context.

In this study, we employed CRISPR-Cas9 technology to generate isogenic induced pluripotent stem cells (iPSCs) with a corrected GLP1-R gene, serving as a control to patient-derived iPSCs harboring GLP1-R mutations. We tested these isogenic iPSCs and their pluripotency potential by measuring the expression of two common pluripotency markers Tra-1-81 and Tra-1-60. Moreover, we performed quantitative PCR (qPCR) experiments to measure the transcription levels of the GLP1-R gene in isogenic and mutant iPSC clones. Our findings revealed significant differences in GLP1-R transcription levels and mRNA structures between the mutant and isogenic clones. These insights contribute to a better understanding of the mosaic nature of mutant iPSCs and the functional consequences of GLP1-R mutations.

Acute Lymphoblastic Leukemia (ALL) is one of the most common type of hematologic malignancy in children, characterized by an excessive proliferation of unfunctional immature lymphoblasts in the blood and the bone marrow, which leads to a range of severe blood-related complications. Given the remarkable increase in the prevalence of leukemia in the past 20 years, there has been a particular interest in the development of in vitro experimental models for cancer research. Ultra-short self-assembling peptides have shown to be a promising class of synthetic biomaterials due to their biocompatibility, tunable mechanical properties, and the possibility of controlling the scaffold composition. The objective of this study was to create a bioactive but well-defined synthetic 3D model of the bone marrow (BM) microenvironment for the simulation of ALL using biofunctionalized ultrashort self-assembling peptide scaffolds. Different bioactive motifs derived from integral extracellular matrix (ECM) constituents that are known to enhance cell-matrix adhesion, including RGDS from fibronectin, YIGSR from laminin, and GFOGER from collagen, were incorporated into the parent peptide IIZK. These peptides demonstrated to be capable of generating stable hydrogel structures composed of fibrous porous networks, each with unique nanofiber morphology and mechanical properties. All the peptide scaffolds that were investigated in this study exhibited optimal characteristics concerning the cytocompatibility of multiple BM niche cells, including human bone marrow mesenchymal stem cells (MSCs), human umbilical vein endothelial cells (HUVECs), and patient derived ALL cells. The suitability of the scaffolds as drug screening platforms was evaluated, demonstrating their potential as versatile tools for the assessment of drug efficacy.

Parkinson’s disease is a neurodegenerative disorder characterized by the loss of motor skills and cognitive impairment. The hallmark of this disease is the presence of Lewy bodies in the substantia nigra of the brain, where the accumulation of alpha-synuclein amyloid fibrils lead to the death of dopaminergic neurons. Understanding the factors influencing AS aggregation and developing effective strategies for its inhibition is of paramount importance for developing potential therapeutic interventions. Previous studies suggest that flavonoids in green and black tea have neuroprotective properties that decrease the fibrillization rate of AS. This thesis investigates the inhibitory effects of two flavonoids coming from green and black tea, namely: EGCG, TFDG, and HSA on AS aggregation, shedding light on their potential as therapeutic candidates. The study employed a combination of biochemical and biophysical experimental techniques to elucidate the inhibitory mechanisms of EGCG, TFDG, and HSA on AS aggregation. Initial experiments involved the characterization of AS fibrillation kinetics using ThT assays, TEM, and CD. Results revealed that flavonoids exhibited similar inhibitory effects on AS aggregation, with more than 90% inhibitory potency. Interestingly, when aggregated AS was exposed to EGCG or TFDG, the amyloid fibrils changed conformation and formed non-toxic amorphous oligomers. The 13C-detected NMR experiments, adapted to probe the AS dynamic conformation and interactions at the atomic level at physiologically relevant conditions, further provided insights into the binding interactions between flavonoids and AS, revealing interaction with hydrophobic residues involved in the inhibition process. Furthermore, the role of HSA, a major protein component of the blood plasma, in modulating α-synuclein aggregation in the presence of tea-derived flavonoids was investigated. The study demonstrated, in line with the previous reports, that HSA can significantly suppress AS fibrillation, and moreover, the presence of HSA further enhances the flavonoids’ inhibitory effect. My findings provide valuable atomic level mechanistic insights into the inhibitory effects of EGCG and TFDG on alpha-synuclein aggregation. The comprehensive spectroscopic and biophysical investigation provides a solid foundation for further developing flavonoid-based inhibitors, subsequently drug candidates blocking the AS toxic oligomers formation and aggregation.

A wide range of contemporary technologies leveraging ubiquitous mobile phones have addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. This problem has found applications in various areas, including smart city transportation, carbon footprint calculation, and context-aware mobile assistants. Previous research has primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones. However, these approaches often necessitate the installation of specialized mobile applications on users’ devices to collect sensor data, resulting in power inefficiency and privacy concerns. In this study, we tackle these issues by presenting a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. Our system was developed using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. The underlying concept is to correlate phone speed with features extracted from Channel State Information (CSI), which includes information about Physical Cell ID, received signal strength, and other relevant data. The feature extraction process involves utilizing sliding windows over both the time and frequency domains. By employing statistical classification and boosting techniques, we achieved remarkable F-scores of 85%, 95%, 88%, and 70% for the car, bus, walking, and train modes, respectively. Moreover, we conducted an analysis of the handover rate in a one-tier network and compared the analytical results with real data. This investigation provided novel insights into the influence of transportation modes on handover rate, revealing the correlation between different modes of mobility and network connectivity. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.

Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. The combination of ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This process requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain. This thesis presents use cases where we applied ontology composition, integration, and alignment of phenotype ontologies, and evaluated the resulting ontologies and alignment. First, we analyzed a large aging dataset of inbred laboratory mice, using Mouse Anatomy and Mouse Pathology ontologies. Second, we integrated phenotype ontologies for human and model organism phenotypes to enable comparisons of phenotypes between and within individual species. We developed Pheno-e, an extension of PhenomeNet. We identified novel abnormal anatomical classes for fly phenotypes, allowing the annotation of fly genes that were not annotated before. We demonstrate the distinct contributions of each species' phenotypic data to detecting human diseases using Pheno-e, and show that mouse phenotypic data contributes the most to the discovery of gene--disease associations. This work could guide the selection of model organisms when building methods to find gene-disease associations. Additionally, we refined class definitions in phenotypic ontologies, specifically targeting cell cardinality phenotypes. This representation resolved incorrect inferences in the utilized ontologies, enabling accurate interpretation of phenotypic descriptions. Our findings reveal that this correction enhances gene-disease prediction for diseases associated with cardinality phenotypes. Third, we introduce a novel neural-symbolic method that combines logic fundamentals with machine learning for ontology alignment. This method begins with symbolic representation, followed by iterative neural learning for alignment and symbolic representation consistency checking and reasoning, and back to neural learning. We demonstrate that our system generates noncontroversial alignments first and these alignments are coherent with respect to OWL EL. This novel method can pave the way for more accurate and efficient ontology-based methods, which can have significant implications for various semantic web applications.