CRISPR-dCAS9 genomic engineering for boosting the therapeutic potential of Extracellular Vesicles

Degenerative disc disease is a major contributor to low back pain, characterized by inflammation of the intervertebral disc (IVD), degradation of the extracellular matrix, loss of hydration, and cell death. Current therapies fail to address these underlying mechanisms, underscoring the need for regenerative strategies. Mesenchymal stem cells (MSCs) exhibit immunomodulatory and regenerative potential, but their efficacy is hampered by the harsh microenvironment of the degenerated IVD. Acellular MSC-derived extracellular vesicles (EVs) have shown therapeutic potential and offer a promising alternative for IVD regeneration. Here, we explore CRISPR-dCas9 mediated activation of TSG6 and STEAP3 to boost the therapeutic potency and biogenesis of MSC-derived EVs, respectively. This project seeks to determine the feasibility of using CRISPR-dCas9 activation to enhance the regenerative capacity of MSC-EVs in an IVD (Aim 1) and a macrophage treatment model (Aim 2), and to determine the feasibility of using CRISPR multiplexing for advancing EV therapy (Aim 3). CRISPRa-mediated activation of TSG-6 in MSCs produced EVs that exerted anti-inflammatory effects on both human IVD cells and macrophages, partially exceeding those of control MSCs. Small RNA-seq and proteomic analyses of these EVs confirmed enrichment of anti-inflammatory microRNAs and proteins. Proof-of-concept multiplexed activation of TSG-6 and STEAP3 in pluripotent stem cells was successfully achieved, yielding a modest increase in EV production upon differentiation into MSCs. These findings validate CRISPR-dCas9 activation as a robust strategy to improve the therapeutic efficacy and production of MSC-derived EVs, and they establish the feasibility of gene-multiplexing approaches for future regenerative-medicine applications

Domain-Specific Customization for Improving Speech to Text

The advent of transformer-based models has revolutionized natural language processing, bringing remarkable improvements in tasks like automatic speech recognition (ASR). Inspired by these advancements, this thesis explores the optimization of a transformer-based ASR model to improve transcription accuracy in educational settings, particularly for lecture content. The goal of this research is to provide real-time, high-accuracy captions that enhance accessibility for all students, while offering a cost-effective solution for educators. To assess the potential of domain-specific fine-tuning, Whisper-small underwent two phases of fine-tuning. In the first phase, it was finetuned on care- fully selected, publicly available datasets: SpeechColab’s Gigaspeech-XS [39], AMI Meeting corpus [14]. In the second phase, fine-tuned model was optimized on a self-curated dataset [16] consisting of roughly 10 hours of live lecture recordings collected and assembled by me. Finally, a real-time captioning assistant application was developed to leverage the finetuned model and transcribe speech in real time with live editing capabilities. The optimized Whisper-small model was evaluated against Whisper’s retrained small, medium and large(version 2) counterparts. The evaluation was performed on a clean unseen data [15] prepared by me. The fine-tuned model achieved lower Word Error Rates (WER) of 4.53%, compared to 5.51% and 5.78% for Whisper-Medium and Whisper-Large-V2 respectively. These results demonstrate that fine-tuning a transformer-based ASR model on domain- specific data can significantly enhance its performance in a targeted context, such as live lecture transcription. The findings of this experiment highlight the promise of transformer-based models for improving educational accessibility. From thereon, building an application tailored to live lecture settings, this research contributes to the development of adaptable, low-cost technologies that support inclusive learning environments. The success of this experiment lays the groundwork for future breakthroughs in speech recognition, aiming to make education more accessible for everyone

The DX7 of Literature? Creative Writing in the Age of GenAI

Abstract: This paper examines the impact of artificial intelligence on contemporary literature, focusing on how writers are incorporating AI into their creative processes. Through case studies of avant-garde artists, mainstream literary authors, and genre fiction writers, the study explores various approaches to AI collaboration in writing. The analysis reveals that while AI offers new possibilities for artistic expression, it also presents ethical challenges and potential threats to traditional notions of authorship. The paper concludes by discussing the implications of AI in creative writing education, emphasizing the need for a balanced approach that embraces technological advancements while preserving the human element in literary creation

The Impacts of Water Lily Invasion and Removal on Wetland Ecosystem Function

Colonization by native and non-native invasive plants is considered a primary reason for the failure of wetland restoration and creation projects. We investigated the impact of invasion and subsequent removal of Nymphaea odorata (white water lily) in two permanently flooded wetlands in Western New York State. Long-term grazer exclusion experiments at these sites demonstrated that selective grazing by herbivores, such as waterfowl, reduced emergent vegetation and overall plant diversity, simultaneously facilitating N. odorata expansion. This interaction ultimately promoted a negative feedback to waterfowl use of the wetlands because of the lack of open water space. To evaluate potential remediation options, we experimentally removed N. odorata in both small and large-scale plots and assessed impacts on methane emission, plant diversity, soil characteristics, potential denitrification, and waterbird use. In small plots, N. odorata removal was crossed with grazer exclusion to evaluate interactive effects. In smaller plots, removal resulted in a marginal decrease in N. odorata cover, but only where grazers were excluded. There were no persistent effects among years. However, plant diversity increased in grazed plots with N. odorata removal, trending towards diversity measured in exclusion plots. Soil characteristics, methane flux, and potential denitrification were not impacted by removal efforts. In large zones, bird use increased significantly with removal in spite of the lack of reduction in N. odorata cover. These results highlight the importance of considering multiple drivers of ecosystem functions, including invasive plants and herbivory, during efforts to improve wetland restoration outcomes

AI-Driven Prediction of Flight Cancellations: A Machine Learning Approach in Minimizing Airline Disruption

This thesis investigates the use of machine learning to predict flight cancellations, aiming to reduce operational disruption and improve airline decision-making. The research is motivated by the need for more proactive strategies in aviation, where flight cancellations often result in financial losses and customer dissatisfaction. The study is framed within the CRISP-DM methodology and demonstrates how historical flight data can be transformed into actionable insights using structured analytics. The research addresses three core questions: how effectively machine learning can predict cancellations, which evaluation metrics are most suitable in an imbalanced context, and how model outputs can support airline operations. A dataset of U.S. domestic flights from 2019 to 2023 was used, containing features such as delays, carriers, routes, and cancellation indicators. Through data preprocessing, irrelevant variables were removed, categorical features encoded, and outliers retained to preserve meaningful variation. Stratified sampling was applied to handle class imbalance and ensure fair evaluation. Several classification models were trained using stratified cross-validation and tested on a holdout set. Evaluation metrics such as precision, recall, F1-score, and AUC-ROC were used to compare models. Emphasis was placed on recall to reduce the chance of missing true cancellations. The results show that machine learning models, when carefully developed, can predict cancellations with strong reliability and practical relevance. The study concludes that predictive analytics has strong potential to enhance airline disruption management. The structured approach used in this research, which includes business understanding, data preparation, and performance evaluation, provides a replicable framework for practical implementation. Practical implications include integrating predictive tools into airline scheduling systems and training operational staff to interpret model outputs. Future research should consider incorporating real-time data and explainable AI to improve model responsiveness and transparency

What’s Coco Eating Today: Using Fragmented Time to Reduce Stress

In modern society, electronic mobile devices have become an integral part of life and entertainment. From a developmental perspective, the abundance of virtual content has brought significant changes in emotional, cognitive, and social aspects. On this background, the rise of the gaming industry has made \u27digital games\u27 a common form of entertainment. As games become a normalized part of daily life, the variety of game types has expanded greatly. This led me to consider: through interaction design and using games as a medium, is it possible to reduce users\u27 stress in a short time and provide positive emotional feedback? Based on this idea, I designed a collection game that can be played during fragmented moments of free time. In this game, the designer needs to balance the length of gameplay with how the game is played, looking for a simple way to interact without losing the fun. The main gameplay focuses on randomness and collecting elements, allowing players to enjoy the game experience. At the same time, visual elements like animations and character interactions help keep the game lively. These visuals attract and entertain users, helping them relax and reduce stress. I believe that the virtual reward system used in digital games can meet some of the users’ emotional needs, leading to positive emotional feedback

IntelliMAD: a Framework for Secure Machine Learning Models Evaluation and Fine-tuning in Federated Setting

In contemporary artificial intelligence (AI) appli- cations, Machine Learning (ML) models are core components enabling AI-driven functionalities, yet selecting and fine-tuning a model and its hyperparameters remains challenging. ML model architecture, as well as key model training parameters, such as the number of training epochs, batch size, and learning rate, are highly dependent on both the dataset modalities and the specific task resolved in a particular application. More sophisticated execution setups may require determination of additional environment-related parameters, such as identifica- tion of computational capabilities required for execution of a particular AI-driven task, or discovery and establishment of desired security-related parameters. The accurate configuration of these parameters is crucial for a robust and secure end-user application, as their selection influences the performance and reliability of the resulting Foundation Model (FM). In order to investigate the set of the most suitable parameters, the iterative and systematic experimentation is required. IntelliMAD is a comprehensive framework that enables FM evaluation and fine- tuning in a Federated Learning manner. The framework facil- itates the investigation and determination of execution environ- ment parameters and security mechanisms. It provides a unified entry point for experiment settings, where each aspect of model training and aggregation is handled as a configurable parameter. In this work, the application of IntelliMAD is demonstrated in the case of implementation of the Model Anomaly Detection mechanism in Federated Learning

Under the Mask: Labeling and Self-Perception in Augmented Reality

Under the Mask is a speculative AR experience that critically explores how social labels shape both perception and self-perception in everyday life. The project reimagines augmented reality not as a technological novelty, but as a conceptual mirror—a way to visualize how identity is continuously negotiated in social space. Users interact with two distinct labeling functions: when they assign a tag to themselves, it becomes visible to everyone, symbolizing the ways in which self-identification enters public discourse. In contrast, when users label others, those tags remain private—visible only to the individual user—highlighting how our assumptions primarily influence our own vision, not objective reality. The project draws attention to the emotional and psychological tension between internal identity and external judgment. By simulating this interaction in a playful and reflective way, Under the Mask encourages users to question both the power and the fragility of labeling systems. Rather than rejecting labels altogether, it reframes them as tools for social navigation—useful but incomplete, meaningful but mutable. This experience ultimately advocates for self-awareness, openness, and the recognition that while we live within a network of mutual influence, the choice of how to interpret and internalize labels remains our own

Determining Sustainable Urban Renewal: A case of quantifying sustainability for Adaptive Reuse within Mumbai’s Textile Mill Sector

This study explores adaptive reuse as a sustainable approach to urban development in Mumbai, India, focusing on the revitalization of a heritage textile mill site. It investigates how historical significance and evolving community needs can shape design decisions, while assessing sustainability through Life Cycle Analysis (LCA) and energy efficiency metrics. Mumbai’s defunct textile mills, once central to its industrial growth, now pose challenges to urban vitality. Through a literature review, the paper examines their historical, social, and cultural relevance and the decline of the mill industry. Adaptive reuse emerges as a key strategy to reimagine these structures, offering an environmentally responsible alternative to demolition and new construction. Case studies are used to analyze factors influencing adaptive reuse—community engagement, environmental impact, and regulatory frameworks. In the context of Mumbai’s rapid urbanization and resource scarcity, adaptive reuse is positioned as a crucial solution for sustainable redevelopment. The paper evaluates one mill building through three reuse scenarios—residential, commercial, and mixed-use—assessing each for LCA and energy performance against the existing condition. The findings inform design and policy recommendations for integrating adaptive reuse into Mumbai’s broader urban planning framework

Customer Flow Prediction at Emirates ID Centers

Emirates ID centers face significant resource management challenges due to fluctuating customer traffic, leading to long wait times, customer dissatisfaction, and inefficient resource use. This thesis explores the application of time series analysis to predict customer traffic at Emirates ID centers, focusing on the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The primary research question is: “Can historical queue data from the Qmatic system be effectively used to forecast customer traffic at Emirates ID centers?” To answer this question, 800,000 observations of historical ticket issuance data from the Qmatic queue management system were analyzed. The study employs ARIMA and LSTM modeling techniques to uncover daily patterns, trends, and seasonality in customer traffic. The ARIMA model was optimized to capture long-term trends and weekly seasonality, while the LSTM model was designed to handle complex, non-linear dynamics. The findings reveal that both models can predict customer traffic, but the LSTM model significantly outperforms ARIMA in terms of accuracy. The baseline LSTM model achieved a Mean Absolute Error (MAE) of 162.96 and a Root Mean Squared Error (RMSE) of 239.21, reducing forecast errors by approximately 56% compared to ARIMA. However, ARIMA remains valuable for its simplicity and ability to capture overall trends. These results demonstrate the potential of predictive analytics to enhance resource allocation, optimize staff scheduling, and reduce customer wait times at Emirates ID centers. Future research should explore hybrid models that combine the interpretability of ARIMA with the adaptive capabilities of LSTM, incorporate external variables, and investigate real-time implementation to further improve forecasting accuracy and operational efficiency