Assessment of the Exposure to Pyrrolizidine Alkaloids from Tea Consumption in Lebanon

Pyrrolizidine Alkaloids (PAs) are naturally occurring phytotoxins that can contaminate Camellia sinensis, posing health risks when present in tea. This study aimed to quantify six major PAs in loose black and green teas sold in the Beirut suburbs, compare obtained PA levels with current European regulatory guidelines and literature, and conduct an exposure assessment to evaluate health risks from PAs through tea consumption. PA levels were determined by sample extraction followed by UPLC-MS/MS analysis. A total of 28 tea samples, including black teas (n=14) and green teas (n=14), were analyzed, with sum PA concentrations averaging 120.1 µg/kg, and ranging between 25.8 – 607.7 µg/kg. Black tea averaged higher contamination levels (148.7 µg/kg) compared to green tea (91.2 µg/kg) (P˃0.05). Comparison by tea origin showed that Vietnamese-origin green tea had the highest average contamination, with 171.9 µg/kg. Exposure assessments revealed that 21.4% of samples from each tea type exceeded the EU adult limit, while more than half of the samples from each tea type surpassed the EU limit for children. In a short-term risk assessment, rarely did the samples pose a concern for adults, while a much larger proportion posed a concern for children. A long-term risk assessment indicated minimal risk for adults but moderate concern for children under average consumption, with higher risks associated with more frequent consumption habits for both age groups. These findings highlight potential dietary PA exposure exceeding safety limits, particularly for children and frequent tea consumers. Stricter regulations and enhanced monitoring of teas are needed to ensure food safety for tea consumption

Investigating the Effects of Fosfomycin on Cognition, Sensory Processing, Locomotor Function and Hippocampal Neurogenesis in Healthy Rats

Neurogenesis is primarily defined as the development of neurons, which occur in several locations of the brain including the dentate gyrus of the hippocampus. This region is responsible for learning and memory, along with spatial processing and cognition. The proliferation stage of neurogenesis is the earliest stage in which the neural stem cells produced can be affected by a variety of factors, such as antibiotic toxicity. Fosfomycin (FOS) is a very reactive broad-spectrum antibiotic that works by disrupting the early stages of bacterial cell wall synthesis and is commonly prescribed for urinary tract infections. Our laboratory has previously published a study that entails a decrease in hippocampal neurogenesis upon administering the required dosage of FOS to treat induced-UTIs in Sprague-Dawley rats. In this study, we will delve in a deeper focus on the potential neurotoxic effects of Fosfomycin on proliferating neural stem cells in the absence of infection

Political Parties’ Positions on Women Quota in the Lebanese Parliament: Lessons from the Iraqi and Algerian Cases

The persistent underrepresentation of women in Lebanese politics has spurred discussions on women quota as a potential solution. This thesis examines the positions of Lebanese political parties on the adoption of women quota in parliament through five political parties taken as case studies and interviews with political and civil society actors. Also, this thesis followed a comparative approach with two Arab countries that have adopted women quota to analyze what lessons Lebanon can take from the experience of these two countries. Key themes emerging from the interviews include the influence of lobbying, the effectiveness of quotas in a patriarchal context, the need for women to expand their focus beyond issues of women and children, and the enriching experience of working within a political party structure. The comparison with the two Arab countries highlights the importance of bottom-up mobilization and the role of women’s organizations. The findings emphasize the necessity of uniting efforts between women in political parties and external women’s organizations and gender activists to create a comprehensive, sustainable approach to enhancing women’s participation in the Lebanese parliament

Bridging Cultures: Harnessing Large Language Models for Translation of Lebanese Dialect

Machine translation (MT) of Arabic dialects presents unique challenges, mainly due to their rich cultural context, and the scarcity of linguistic resources. While Large Language Models (LLMs) such as ChatGPT, LLaMA, and BLOOM have demonstrated remarkable capabilities across a range of MT tasks, their effectiveness in translating culturally embedded dialects remains largely unexplored. This thesis specifically investigates the effectiveness of LLMs in translating the Lebanese dialect, a prominent Arabic variant in the Levant region, known for its rich cultural heritage and complex idiomatic language. A key limitation in dialectal MT is the scarcity of culturally representative datasets needed to develop effective models. The few existing Lebanese-English parallel datasets suffer from cultural misalignment due to their translation from non-native resources. To address this gap, this research introduces two culturally aware resources- LW, and LebEval- derived from authentic Lebanese podcasts, and professionally translated to English. It further investigates the advantage of collecting such authentic datasets by conducting comprehensive experiments comparing the performance of Arabic-centric LLMs against NMT systems. Findings reveal that while both architectures perform similarly on non-native datasets, LLMs demonstrate superior capabilities in preserving cultural nuances, outperforming NMTs by a significant margin on the LW data. Additionally, while fine-tuning LLMs with instructional data has shown promising results in MT tasks, there has been little to no effort dedicated to adapting these techniques specifically for Arabic or its diverse dialects. This work explores fine-tuning the open-source Aya23 model on three types of instructions: 1) parallel Lebanese/English instructions, 2) contrastive instructions, and 3) Grammar-hint instructions. Results demonstrate that models fine-tuned on a smaller but culturally aware Lebanese dataset (LW) consistently outperform those trained on larger, non-native data. They also show the superiority of fine-tuning using contrastive instructions, highlighting the value of leveraging translation errors. Finally, while most studies on the translation of Arabic dialects rely on the statistical evaluation metric BLEU, despite its well-documented limitations, this research takes a different approach by conducting a human correlation analysis with different evaluation metrics. Findings validate the shortcomings of BLEU and showcase xCOMET as a more reliable and culturally sensitive metric for evaluating the quality of MT in this domain. Overall, this thesis makes significant contributions to culturally aware dialectal MT, highlighting the potential of leveraging LLMs and challenging the prevailing "more data is better" paradigm

On the Linear and Nonlinear Dynamics of the Hasegawa-Mima Equation

Understanding turbulence in plasmas is essential for studying transport properties in magnetically confined fusion devices. This thesis studies the Hasegawa-Mima (HM) model, which describes the evolution of electrostatic potential fluctuations in a plasma. First, we derive the HM model from first principles using fundamental conservation equations. Analytical studies of the HM model are then performed to investigate the role of the linear term and its effect on the system dynamics. In particular, this study explores the evolution of an initial electrostatic potential perturbation initialized as white noise. The effect of the linear term on the dynamics is highlighted through its dependence on the density gradient. The steepness of the density profile is varied, therefore, assessing the impact of the linear term on the overall dynamics. Then, numerical simulations of the HM model are conducted using FreeFEM++. The simulation results are then analyzed in light of the analytical results. The re sults show that an increase in the amplitude of the electrostatic potential occurs in the gradient region, and there is no spreading of the turbulence beyond that regio

Arab Art Audiences – Case Study at Mathaf: Arab Museum of Modern Art

This thesis explores the dynamics between Arab art and audiences, focusing on Mathaf: Arab Museum of Modern Art in Qatar and its temporary exhibitions. Beginning with a personal narrative that encapsulates my deep-rooted passion for Arab art, the work reflects on the intersection of identity and culture in the context of Arab artistic expressions within Arab art institutions. My initial outreach to acquaintances in Qatar revealed a surprising disconnect between the local Arab community and their awareness of Arab art history, a phenomenon I attribute to broader educational and cultural gaps. Through a phenomenological lens, the research investigates how personal, cultural, and institutional factors contribute to the perceived disinterest in local art. Despite the rich tapestry of Arab art as validated by scholars, many in the community remain largely unaware of significant artists and movements, often favoring familiar Western counterparts. Through repeated visits to Mathaf, I document my evolving understanding and appreciation of the relationship between visitors and the museum’s exhibition processes, culminating in a critical reflection on the role of museums as cultural intermediaries. The thesis posits that the process of engaging with Arab art is not simply about fostering a sense of Arab identity but involves navigating complex layers of history, locality, and personal experience. Ultimately, this project serves as both an inquiry into audience engagement and a call to action for deeper cultural recognition of audiences in Arab art institutions

Exploring the Triad: Mental Health, Substance Use, and Injury Occurrences among Young Adults in Lebanon

Background: Injuries remain a major public health concern globally, accounting for over 4.3 million annual deaths, with low- and middle- income countries bearing the greatest burden. In Lebanon, injuries account for 3.4% of total deaths. This study investigated the relationship between injuries and two key risk factors, mental distress and substance use disorders, particularly their co-occurrence, among a sample of young adults from Lebanon. Methods: An online survey was conducted among 401 Lebanese adults aged 18–35, recruited via multiple social media platforms. Validated tools were used to assess mental distress (General Health Questionnaire-12) and potential substance use disorders (CAGE for alcohol; Drug Abuse Screening Test-10 for drugs). Data on injury occurrence, types, severity, and contexts were collected using an adaptation of the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) form. Bivariate analyses and multiple logistic regression models examined associations between injuries and the predictors, adjusting for demographic variables. Results: Approximately 34% of participants reported at least one injury in the past 12 months, with falls (28.3%) and burns (22.9%) as the most common. Mental distress was detected in 66.6% of participants, and 16% exhibited potential substance use disorders (alcohol and/or drug use). Controlling for sociodemographic, severe mental distress was associated with two-fold odds of injury, while potential alcohol use disorder was associated with a 2.5 increase in odds (p < 0.05). Our main finding highlights that the co-occurrence (vs. not) of mental distress and potential substance use disorders (alcohol and/or drug use) was associated with more than triple the odds of injury. Conclusion: Both mental distress and potential substance disorders significantly contribute to injury among young Lebanese adults, particularly when co-occurring. These findings underscore the need for integrated prevention strategies, including routine mental health screening and substance use interventions, as well as strengthened safety policies and public health infrastructure. Future research should employ longitudinal designs to establish causal pathways and refine targeted prevention programs

Design, Synthesis, and applications of New Metal Organic Frameworks, and Metal Organic Frameworks-Polymer System

Metal Organic Frameworks (MOFs) are emerging porous crystalline materials which have many applications ranging from adsorption, catalysis, to drug delivery and fluorescence. Previous work from the literature showed that combining MOFs and polymers resulted in enhancement of the properties and performance of the developed systems in specific applications. Herein, we investigate the effect of combining both MOF and polymer chemistry to develop fluorescent nanocrystals based on a well-known fluorescent conjugated polymer namely poly(phenyl ethylene), PPE-CO2 and integrated with zirconium cluster that exhibits high chemical and thermal stability. Also, the obtained nanocrystals are fully characterized using a set of spectrometric and microscopic techniques. The optical characteristics (fluorescent intensities and photostability) of these crystals are further investigated. In addition, the physio-chemical properties of our synthesized nanocrystals are compared with another composite assembled by mixing PPECO2 with UiO-66 MOF. Finally, novel stable MOFs are synthesized from new metalo-linkers. The produced MOFs crystals are promising photocatalysts since they incorporate a catalytic center in their backbones. Therefore, they will be tested for carbon dioxide conversion under visible light illumination. To demonstrate the heterogeneity of the system, control experiments will be performed by using the free metallo-complex as photocatalyst. Finally, the recyclability and the stability of the MOFs will be assessed. In brief, this work paves the way towards the development of new MOF systems and composites for a multitude of promising applications

Biaxial Ellipsoid in Discrete Gravity

We study the biaxial ellipsoid manifold in the context of discrete gravity. The metric of the ellipsoid is transformed into that of a lattice, labeling the cells using integers which become coordinates in the continuous limit. The scalar curvature in the discrete gravity framework is computed using derived explicit solutions of spin connections for this setting and is shown to successfully recover the scalar curvature in the continuous case as the number of cells is increased. Likewise, the spin connections are numerically computed in the discrete framework by solving a nonlinear system of equations obtained through the torsion-free condition. They are shown to converge to the continuous spin connections as the number of cells is increased

Stacking Large Language Models is All You Need: A Case Study on Phishing URL Detection

Prompt-engineered Large Language Models (LLMs) have gained widespread adoption across various applications due to their ability to perform complex tasks without requiring additional training. Despite their impressive performance, there is considerable scope for improvement, particularly in addressing the limitations of individual models. One promising avenue is the use of ensemble learning strategies, which combine the strengths of multiple models to enhance overall performance. In this study, we investigate the effectiveness of stacking ensemble techniques for chat-based LLMs in text classification tasks, with a focus on phishing URL detection. Notably, we introduce and evaluate three stacking methods: (1) prompt-based stacking, which uses multiple prompts to generate diverse responses from a single LLM; (2) modelbased stacking, which combines responses from multiple LLMs using a unified prompt; (3) hybrid stacking, which integrates the first two approaches by employing multiple prompts across different LLMs to generate responses. For each of these methods, we explore meta-learners of varying complexities, ranging from Logistic Regression to BERT. Additionally, we investigate the impact of including the input text as a feature for the meta-learner. Our results demonstrate that stacking ensembles consistently outperform individual models, achieving superior performance with minimal training and computational overhead