Digital Commons Network^™

Metabolic Bioactivation Of Antidepressants: Advance And Underlying Hepatotoxicity, Saleh M Khalil, Kevin R Mackenzie, Mirjana Maletic-Savatic, Feng Li

Faculty, Staff and Students Publications

Many drugs that serve as first-line medications for the treatment of depression are associated with severe side effects, including liver injury. Of the 34 antidepressants discussed in this review, four have been withdrawn from the market due to severe hepatotoxicity, and others carry boxed warnings for idiosyncratic liver toxicity. The clinical and economic implications of antidepressant-induced liver injury are substantial, but the underlying mechanisms remain elusive. Drug-induced liver injury may involve the host immune system, the parent drug, or its metabolites, and reactive drug metabolites are one of the most commonly referenced risk factors. Although the precise mechanism by which …

Citrulline And Adi-Peg20 Reduce Inflammation In A Juvenile Porcine Model Of Acute Endotoxemia, Caitlin Vonderohe, Barbara Stoll, Inka Didelija, Trung Nguyen, Mahmoud Mohammad, Yava Jones-Hall, Miguel A Cruz, Juan Marini, Douglas Burrin

Faculty, Staff and Students Publications

BACKGROUND: Arginine is a conditionally essential amino acid that is depleted in critically ill or surgical patients. In pediatric and adult patients, sepsis results in an arginine-deficient state, and the depletion of plasma arginine is associated with greater mortality. However, direct supplementation of arginine can result in the excessive production of nitric oxide (NO), which can contribute to the hypotension and macrovascular hypo-reactivity observed in septic shock. Pegylated arginine deiminase (ADI-PEG20, pegargiminase) reduces plasma arginine and generates citrulline that can be transported intracellularly to generate local arginine and NO, without resulting in hypotension, while maintaining microvascular patency. The objective of …

Modelled Effectiveness Of Nbs In Reducing Disaster Risk: Evidence From The Operandum Project, Paul Bowyer, Silvia Maria Alfieri, Bidroha Basu, Emilie Cremin, Sisay Debele, Prashant Kumar, Veronika Lechner, Michael Loupis, Massimo Menenti, Slobodan Mickovski, Jan Pfeiffer, Francesco Pilla, Beatrice Pulvirenti, Paolo Ruggieri, Arunima Sarkar Basu, Christos Spyrou, Silvia Unguendoli, Thomas Zieher, Silviana Di Sabatino

Publications

The use of nature-based solutions (NbS) to address the risks posed by hydro-meteorological hazards have not yet become part of the mainstream policy response, and one of the main reasons cited for this, is the lack of evidence that they can effectively reduce disaster risk. This paper addresses this issue, by providing model-based evidence from five European case studies which demonstrate the effectiveness of five different NbS in reducing the magnitude of the hazard and thus risk, in present-day and possible future climates. In OAL-Austria, the hazard is a deep-seated landslide, and the NbS analysed is afforestation. Modelling results …

Ispad Clinical Practice Consensus Guidelines 2024: Screening, Staging, And Strategies To Preserve Beta-Cell Function In Children And Adolescents With Type 1 Diabetes, Michael J Haller, Kirstine J Bell, Rachel E J Besser, Kristina Casteels, Jenny J Couper, Maria E Craig, Helena Elding Larsson, Laura Jacobsen, Karin Lange, Tal Oron, Emily K Sims, Cate Speake, Mustafa Tosur, Francesca Ulivi, Anette-G Ziegler, Diane K Wherrett, M Loredana Marcovecchio

Faculty, Staff and Students Publications

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 …

A Study Protocol Testing Pre-Exposure Dose And Compound Pre-Exposure On The Mechanisms Of Latent Inhibition Of Dental Fear, Andrew L. Geers, Laura D. Seligman, Keenan A. Pituch, Ben Colagiuri, Hilary A. Marusak, Christine A. Rabinak, Natalie Turner, Sena L. Al-Ado, Michael Nedley

Psychological Science Faculty Publications

Background: Dental stimuli can evoke fear after being paired - or conditioned - with aversive outcomes (e.g., pain). Pre-exposing the stimuli before conditioning can impair dental fear learning via a phenomenon known as latent inhibition. Theory suggests changes in expected relevance and attention are two mechanisms responsible for latent inhibition. In the proposed research, we test whether pre-exposure dose and degree of pre-exposure novelty potentiate changes in expected relevance and attention to a pre-exposed stimulus. We also assess if the manipulations alter latent inhibition and explore the possible moderating role of individual differences in pain sensitivity.

Methods: Participants will be …

Bioconcentration Of Per- And Polyfluoroalkyl Substances And Precursors In Fathead Minnow Tissues Environmentally Exposed To Aqueous Film-Forming Foam–Contaminated Waters, Nicholas I. Hill, Jitka Becanova, Simon Vojta, Larry A. Barber, Denis R. Leblanc, Alan M. Vajda, Heidi M. Pickard, Rainer Lohmann

Graduate School of Oceanography Faculty Publications

Exposure to per- and polyfluoroalkyl substances (PFAS) has been associated with toxicity in wildlife and negative health effects in humans. Decades of fire training activity at Joint Base Cape Cod (MA, USA) incorporated the use of aqueous film-forming foam (AFFF), which resulted in long-term PFAS contamination of sediments, groundwater, and hydrologically connected surface waters. To explore the bioconcentration potential of PFAS in complex environmental mixtures, a mobile laboratory was established to evaluate the bioconcentration of PFAS from AFFF-impacted groundwater by flow-through design. Fathead minnows (n = 24) were exposed to PFAS in groundwater over a 21-day period and tissue-specific …

Emerging Contaminants: A One Health Perspective, Fang Wang, Rainer Lohmann, Et Al

Graduate School of Oceanography Faculty Publications

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing …

Effect Of Nutrient Reductions On Dissolved Oxygen And Ph: A Case Study Of Narragansett Bay, Hongjie Wang, Daniel L. Codiga, Heather Stoffel, Candace A. Oviatt, Kristin Huizenga, Jason Grear

Graduate School of Oceanography Faculty Publications

To assess the consequences of nutrient reduction strategies on water quality under climate change, we investigated the long-term dynamics of dissolved oxygen (DO) and pH in Narragansett Bay (NB), a warming urbanized estuary in Rhode Island, where nitrogen loads have declined due to extensive wastewater treatment plant upgrades. We use 15 years (January 2005-December 2019) of measurements from the Narragansett Bay Fixed Site Monitoring network. Nutrient-enhanced phytoplankton growth can increase DO in the upper water column while subsequent respiration can reduce water column DO and enhance bottom water acidification, and vice-versa. We observed significant decreases in surface DO levels, concurrent …

Cross-Cutting Studies Of Per- And Polyfluorinated Alkyl Substances (Pfas) In Arctic Wildlife And Humans, Rainer Lohmann, Khaled Abass, Eva Cecilie Bonefeld-Jørgensen, Rossana Bossi, Rune Dietz, Steve Ferguson, Kim J. Fernie, Philippe Grandjean, Dorte Herzke, Magali Houde, Mélanie Lemire, Robert J. Letcher, Derek Muir, Amila O. De Silva, Sonja K. Ostertag, Amy A. Rand, Jens Søndergaard, Christian Sonne, Elsie M. Sunderland, Katrin Vorkamp, Simon Wilson, Pal Weihe

Graduate School of Oceanography Faculty Publications

This cross-cutting review focuses on the presence and impacts of per- and polyfluoroalkyl substances (PFAS) in the Arctic. Several PFAS undergo long-range transport via atmospheric (volatile polyfluorinated compounds) and oceanic pathways (perfluorinated alkyl acids, PFAAs), causing widespread contamination of the Arctic. Beyond targeting a few well-known PFAS, applying sum parameters, suspect and non-targeted screening are promising approaches to elucidate predominant sources, transport, and pathways of PFAS in the Arctic environment, wildlife, and humans, and establish their time-trends. Across wildlife species, concentrations were dominated by perfluorooctane sulfonic acid (PFOS), followed by perfluorononanoic acid (PFNA); highest concentrations were present in mammalian livers …

Fake Malware Generation Using Gans As Api Calls, Saieswar Reddy Vaka

Master's Projects

With malware threats on the rise, they have also grown more complicated and subtle. Consequently, incorporating cutting-edge machine learning into cybersecurity defenses has never been more crucial. Nevertheless, building resilient machine-learning models is a significant challenge due to the need for existing diversified and complete malware datasets. This project will relieve this difficulty by employing a Generative

Adversarial Network (GAN) to develop artificial malware samples featuring an Appli- cation Programming Interface (API) call series. While traditional generative modeling

has primarily been limited to image-based fields, we offer an “outside the box” domain – malware signature generation – as an API …

Base Station Selection Based On The Predicted Dwell Time In 5g-V2x Handover, Anushree Jayesh Shah

Master's Projects

Vehicle-to-Everything (V2X) networks have facilitated smooth communication in vehicles and their surroundings via 5G technology. As vehicles move into different coverage areas, they tend to switch between cellular base stations in order to talk to these "things". Handovers are crucial to maintaining these networks’ effectiveness. To ensure uninterrupted connectivity and seamless handovers, it is extremely important to select a base station that is most appropriate. A vehicle’s dwell time is the duration it stays connected to a base station before handoff. This research focuses on predicting dwell time using regression techniques to proactively choose the most suitable base station. We …

Ai-Driven Credit Card Fraud Detection With Enhanced User Interaction, Toshi Bhat

Master's Projects

This thesis describes the development and testing of a unique system for detecting credit card fraud. The system employs graph neural networks (GNNs) and a real-time user interaction platform. The primary goal of this study is to use advanced machine learning methods and interactive technologies to improve fraud detection accuracy and the speed with which users can receive assistance. GraphSAGE, a type of GNN, was trained on a simulated set of credit card transactions, allowing the system to detect and predict fraud very accurately. Simulating a real-world transaction scenario is an important aspect of the project. In this case, the …

Nft Price Prediction Using Machine Learning, Akhil Patil Bagili

Master's Projects

In the evolving cryptocurrency marketplace, Non Fungible Tokens (NFT) pose a unique challenge when it comes to predicting the prices due to their high volatility and fluctuating nature. This project aims to create a model that utilizes deep learning techniques to accurately forecast NFT prices. Based on the real time data and the transaction history, the model uses Convolutional Neural Networks (CNNs) and Long Term Short Memory Networks (LSTM) to analyze and make effective predictions about future prices. The methodology involves gathering data from two online marketplaces, Dune and Opensea to create a dataset that enhances the model’s predictive capabilities. …

Optimized Community Detection Across Distributed Heterogeneous Servers, Akash Narang

Master's Projects

The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, …

Detecting Fake Reviews Using Aspect Based Sentiment Analysis And Graph Convolutional Networks, Prathana Phukon

Master's Projects

Online reviews significantly influence consumer behavior and business reputa- tions. Detecting fake reviews is important for maintaining trust and integrity in

these platforms. In this project, an application of Aspect-Based Sentiment Analy- sis (ABSA) called FakeDetectionGCN is introduced to distinguish genuine feedback

from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph Convolutional Networks (GCNs) are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated as …

Emotion Detection Using Ensemble Learning, Priya Harika Yerapothu

Master's Projects

Emotion detection is gaining exponential necessity in today’s technological age. This research seeks to delve into ways conversational AI could be enhanced by integrating emotional intelligence using an ensemble learning approach. Traditional machine learning along with advanced neural network architectures are implemented to improve the understanding and intricacies of emotion detection from textual data. The dataset we use is GoEmotions dataset, annotated with 27 emotional labels, to conduct a detailed analysis of emotion recognition. Various machine learning models, such as HistGradientBoosting, LightGBM, CatBoost, and MLP, will be evaluated side by side with advanced models of Bidirectional Long Short-Term Memory (BiLSTM) …

Multirelational Twitter Bot Detection Using Graph Neural Networks, Ketan Jadhav

Master's Projects

Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation …

Adaptive Bounded-Confidence Model For Opinion Maximization, Jacob Ortiz

Master's Projects

Social networks have become a significant source of information due to their easy accessibility, low cost, and ability to spread information quickly. Opinions are crucial in shaping our communication and decision-making processes, and our social connections significantly influence them. We model opinions as continuous values from 0 to 1, i.e., 0 means strong disagree and 1 means strong agree. Each agent has an initial opinion as well as a confidence about her opinion and through interactions with the other agents both are updated. Opinion maximization has gained popularity due to social media’s growing impact on our daily lives, where we …

Ensemble Model With Meta-Learning For Ddos Attack Classification In Sdn, Ankith Indrakumar

Master's Projects

In response to the security threats posed by Distributed Denial of Service (DDoS) attacks, this paper presents an intrusion detection framework with a high-accuracy multi-class classification model. In addition to detecting the existence of DDoS attacks, our framework aims to identify the type of attack (e.g., protocol or message type) so that the system can select the most appropriate countermeasure against the DDoS type. We leverage a meta-learner to build an ensemble model of multiple machine learning models such as LSTM, RF, and KNN to enhance detection and classification accuracy. Tested on the CIC-DDoS 2019 dataset, the proposed model achieves …

Community Detection Using Deep Learning: Variational Graph Autoencoder Enhanced With Leiden And K-Truss Techniques, Jyotika Hariom Patil

Master's Projects

Community detection in networks is essential for understanding the complex structures of connected systems. Traditional deep learning (DL) methods such as Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs) have shown promised results in supervised tasks, like classification, but often fail in unsupervised tasks like community detection because of the lack of labels. Self- supervised approaches where we integrate crucial community information offer a solution. This project seeks to explore DL methods for community detection, focusing specifically on using Graph Variational Autoencoders (VGAEs). While classical approaches can efficiently handle small to medium-sized networks, they typically struggle with larger-sized structures. …

Opinion Graphs Construction For Reviews Using Transfer Learning And Large Language Models, Yichen Lin

Master's Projects

With the rapid development of the Internet, reading online reviews before making a purchase, booking a hotel, or making a restaurant reservation has become a part of daily life. Customers often consider reviews as crucial supplementary information before making decisions on how to spend their money. However, reading many reviews to gain helpful information takes time and effort. This project proposes a new method OpinionGraphGenerator that aims to create opinion graphs from hotel reviews to reduce the high volume of text in reviews while preserving essential insights. In an opinion graph, vertices are semantically similar opinions, where each opinion consists …

Skin Cancer Detection Using Reinforcement Learning, Vikas Chercadu

Master's Projects

Advances in medical diagnostics have increasingly harnessed the power of artificial

intelligence, offering substantial improvements in early and accurate disease identifi- cation. This paper elaborates on a novel integration of Multi-Agent Reinforcement

Learning (MARL) with Deep Learning for the early detection of skin cancer, one of the most prevalent and lethal forms of cancer when left unchecked. Our project capitalizes on the sophisticated VGG16 Network for the extraction of detailed features from the widely-utilized HAM10000 dermatoscopic dataset, enhancing these features with additional color, texture, and shape analysis. Utilizing a custom-designed MARL environment, we facilitate a collaboration among multiple intelligent agents, …

Influence Maximization Using Triadic Closures, Communities, And Quotas, Matthew Fu

Master's Projects

Online social networks have exploded in popularity in the last decade. In addition, traditional advertising methods such as television advertising have greatly decreased. This allows companies to utilize viral marketing more effectively. With viral marketing, companies can spread information on a product to a social network by reaching out to a small group of early adopters, who will go on to inform the people around them of the product. The problem is selecting the early adopters that can maximize the spread of influence. The Influence Maximization (IM) problem is finding a social network’s most influential (early adopters) starting nodes, called …

Robustness Of Learning Models To Label Flipping Attacks, Sarvagya Bhargava

Master's Projects

In this paper we compare traditional machine learning and deep learning models

trained on a malware dataset when subjected to adversarial attack based on label- flipping. Specifically, we investigate the robustness of different models when faced

with varying percentages of misleading labels, assessing their ability to maintain their accuracy in the face of such adversarial manipulations of the training data. This research aims to provide insights into which models are more robust, in the sense of being better able to resist intentional disruptions to the training data. We find that traditional machine learning models and boosting techniques are more robust …

Mitigating Learning Bias In Healthcare Datasets, Samyak Jagdish Kumbhalwar

Master's Projects

CVDs have been a major cause of deaths worldwide with WHO reporting 17.9 million deaths annually. Although there are advancements in the treatment of these diseases, most of the fatalities are a result of untimely diagnosis. Active research is going on to collect data points and risk factors related to these diseases, which can enable early diagnosis. Of the datasets available, many researchers have employed different ML models to predict/detect the prevalence of heart diseases. Many employed Tree based, regression models [3, 6]. Few also tried ensemble approaches [1, 2, 4]. These healthcare datasets are generally found to be imbalanced. …

Frame Rate Enhancement Using Gans: A Deep Learning Approach, Shanmukah Sri Harsha Anivilla

Master's Projects

Videos are sequences of frames that are displayed continuously within a time frame, which creates the illusion. FPS is defined as the number of frames per second, and is crucial to determine the smoothness of motion or scene changes in the video. To improve the appearance of the videos, we can a technique called Frame Rate Enhancement. This is an approach to augment generated frames between pairs of frames using Generative Adversarial Networks. There are a few traditional techniques using Convolution Neural Networks and Optical Flow based methods, but they create unwanted artifacts such as blurring or ghosting and might …

Gesture Recognition Dynamics: Unveiling Video Patterns With Deep Learning, Nithish Reddy Agumamidi

Master's Projects

This paper, Gesture Recognition Dynamics: Revealing Video Patterns with Deep Learning, explores the combination of Long Short-Term Memory(LSTM) with Convolutional Neural Network(CNN) in the identification of convoluted human activities. The study assesses LSTM’s capability to capture temporal dependencies and CNN’s potential to apprehend and extract spatial characteristics to detect the gestures from UCF50. It further evaluates the architecture linkage of LSTM and CNN, which will improve the analytical capacity to interpret and validate dynamic gesture trends. The paper utilizes Mediapipe, an open-source framework created by Google specifically designed for extracting poses. The Mediapipe tool is well-designed to track important body …

Unveiling Gender Bias: An Eye-Tracking Analysis Of Scene Perception, Naga Srija Gopisetty

Master's Projects

Gender bias deeply affects how we perceive and interact with the world around us. This study examined how gender bias affects scene perception by using eye-tracking technology. The relation between gender bias and pupil dilation is examined utilizing art as a stimuli and also studied the underlying cognitive processes. This experiment combines image presentations along with input from participants, drawing on previous research in gender bias, cognitive psychology, and eye-tracking methodologies. The experiment design showcases a series of images to participants, subtly replacing one image during the trial, and then asks participants whether the

replacement took place. This study uses …

Exploring Gender Bias In Large Language Models: Cross-Linguistic Comparisons And Evaluation Letters Analysis, Athira Kumar

Master's Projects

Large language models (LLMs) play a significant role in modern human-computer interaction. They have exploded in popularity recently, becoming widely used for various tasks. However, concerns persist regarding potential biases within these models. This project investigates gender bias in the popular LLMs - GPT-3.5, GPT-4, Gemini, and LLAMA. The first part of our study focuses on analyzing biases using ambiguous sentences across three languages - English, Malayalam, and Tamil. We evaluate the LLMs to see if they associate occupations with commonly held gender stereotypes, by using specific professions within our test sentences. Through the use of two low-resource languages, this …

Understanding Distracted Driving And Gaze Patterns In A Driving Simulator: Simple Vs. Complex Challenges, Rishi Prabhat Narayana Sannala

Master's Projects

Distracted driving has grown in criticality over the recent years, given the numerous distractions that drivers face today, and which have further been magnified by the proliferation of in-vehicle technologies and mobile devices. Such distractions can seriously compromise a driver's ability to be fully focused on the road and to carry out timely responses and informed decisions that are key in minimizing the risks of a crash and maximizing road safety. The general aim of the research is to observe how simple versus complex distractors affect driving performance and gaze patterns. The eyetracker used is the Tobii Pro Fusion, synchronized …