Digital Commons Network^™

What Are The Effects Of Time-Restricted Eating Upon Metabolic Health Outcomes In Individuals With Metabolic Syndrome: A Scoping Review, Rory J Heath, Jessie Welbourne, Daniel Martin

Peninsula Medical School

The primary objective of this scoping review (ScR) was to assess the breadth and type of evidence related to time-restricted eating (TRE) as an intervention to modify metabolic health outcomes in individuals with diagnosed metabolic syndrome (MetS), a major health challenge due to increasing prevalence and association with other chronic diseases. MetS comprises three or more of hypertension, hypercholesterolaemia, dyslipidaemia, dysregulated glucose homeostasis, and abdominal obesity. TRE, also known as time-restricted feeding (TRF), restricts food intake to specific time windows within a day, for example, a 10-h eating period between 10:00 and 20:00. Via multiple mechanisms, TRE interventions may provide …

Defining The Enemy Within: Examining The Case For A Federal Mechanism To Designate Domestic Terrorist Organizations In The United States, Evelyn Schneider

College Honors Program

Domestic terrorist incidents are on the rise within the United States. Activities of domestic terrorism increased by 357% between 2013 and 2021. While the United States has experienced a dramatic growth in domestic terrorism from violent organizations, other countries have faced this similar problem with a different approach as they have created mechanisms to officially designate domestic terrorist organizations. In this thesis, I will answer the question: Could and should the United States create a federal proscription mechanism for domestic terrorist organizations? Chapter 2 explores whether the Proud Boys, a domestic organization within the United States, could be considered a …

Proteogenomic Analysis To Inform Causal Gene Prioritization For Human Disease, Daniel Western

Arts & Sciences Graduate Student Theses and Dissertations

Genome-wide association studies (GWAS) have been key in expanding our understanding of the genetic contributions to common diseases. However, these genetic associations frequently fail to clarify causal disease mechanisms, as they often fall in non-coding regions and are difficult to interpret. One solution is to perform a GWAS for the levels of a cellular trait, known as quantitative trait locus (QTL) mapping. Through methods such as colocalization, Mendelian Randomization, and transcriptome/proteome-wide association studies, the QTL variants can be compared to disease GWAS, identifying shared variation between cellular and disease traits. We can then prioritize cellular traits as potential causative, targetable …

Gut Dysbiosis Patterns In Cvid Patients With Noninfectious Complications Observed In A Germ-Free Mouse Model Through Fecal Microbiota Transplantation, Joud Hajjar, Anita Y Voigt, Margaret E Conner, Alton G Swennes, Stephanie Fowler, Chadi Calarge, Danielle D Mendonca, Dominique Armstrong, Chen-Yen Chang, Jolan E Walter, Manish J Butte, Tor Savidge, Julia Oh, Farrah Kheradmand, Joseph F Petrosino

Faculty, Staff and Students Publications

Patients with common variable immunodeficiency (CVID) who develop noninfectious complications (NIC) have worse clinical outcomes than those with infections only (INF). While gut microbiome aberrations have been linked to NIC, reductionist animal models that accurately recapitulate CVID are lacking. Our aim in this study was to uncover potential microbiome roles in the development of NIC in CVID. We performed whole-genome shotgun sequencing on fecal samples from CVID patients with NIC, INF, and their household controls. We also performed fecal microbiota transplants from CVID patients to germ-free mice. We found potentially pathogenic microbes

Mitochondrial Network Expansion And Loss During Oligodendrocyte Life And Death, Xhoela Bame

Dartmouth College Ph.D Dissertations

Oligodendrocytes are the myelinating cells of the central nervous system, known for modulating signal transmission, refining neural circuits, and providing metabolic support to axons. Oligodendrocytes are generated throughout life from oligodendrocyte precursor cells (OPCs) and are damaged or lost in demyelinating and neurodegenerative diseases and age-related pathologies. Thus, understanding the cellular checkpoints that occur during the generation and degeneration of oligodendrocytes is crucial for maintaining their population in health and recovering it in disease and aging.

Using high-resolution optical imaging, I have discovered a dynamic redistribution and subcellular partitioning of mitochondria during oligodendrogenesis. Mitochondria transiently expanded towards the differentiating OPC …

Examing The Role Of Complement Signaling In Viral Neuroinflammatory Responses, Marlene Kanmogne

Arts & Sciences Graduate Student Theses and Dissertations

The complement system is an ancient pathway that serves dual roles as a key regulator of neuroinflammation and a critical regulator of neural networks. Few studies have addressed how these two pathways may interact during infection of the central nervous system. While some components of complement are expressed in the healthy developing brain, complement component 3a receptor (C3aR) is a G protein coupled receptor that is expressed by astrocytes, microglia and infiltrating immune cells during inflammatory states. We previously found that C3aR is required for engulfment of synaptic material by activated microglia during neuroinvasive viral infection. Here, we used a …

First-Principles Studies Of Excited-State Properties In Large-Scale Systems, Du Li

Arts & Sciences Graduate Student Theses and Dissertations

Density Functional Theory (DFT) is widely used as a powerful tool for studying the electronic structure of materials. However, due to the local or semilocal nature of exchange-correlation functionals, such as the local density approximation and generalized gradient approximation functional, DFT often underestimates the electronic band gap. Many-body perturbation theory (MBPT) within the GW approximation provides more accurate quasiparticle energies calculation by incorporating many-electron screening effects. Additionally, solving the Bethe-Salpeter Equation (BSE) allows for a detailed analysis of excitonic effects in optical spectra. Despite their accuracy, GW-BSE calculations are computationally expensive, particularly for large-scale systems such as defects and substrate-supported …

Cumulative Lifespan Stress And Inflammation Are Associated With Black-White Racial Disparities In Mortality Among Americans, Isaiah Spears

Arts & Sciences Graduate Student Theses and Dissertations

Black Americans disproportionately experience higher rates of health challenges and mortality compared to White Americans, yet the mechanisms underlying these disparities remain inadequately understood. Prominent theoretical models highlight stress and resulting allostatic load as putative mechanisms through which these Black-White racial disparities emerge; however, empirical data supporting such models with regard to mortality remains sparse. The current study examined the potential role of cumulative stress exposure across the life span and elevated levels of C-Reactive Protein, a biomarker of inflammation, in contributing to the longstanding increased mortality risk among Black relative to White Americans. Data were drawn from the Saint …

Intouch Week Of May 5, 2025, New York Medical College

InTouch

• Showcasing Innovation: 21st Annual Doctoral Project Presentation Day Highlights Student Research Shaping the Future of Physical Therapy
• Sameh Said, M.D., Leads First Mid-Delivery Open Heart Surgery
• Student Spotlight: First-Time Parents Navigate the Demands of Medical School at NYMC

Ndls Communicator: Week Of 05.05.25, Notre Dame Law School

NDLS Communicator

The Latest News

• Notre Dame Military and Veterans Law Society hosts Symposium:"What Do We Owe Our Veterans?"
• Catholic educators urge Supreme Court to uphold their freedom to serve communities in need
• Notre Dame Law School students help prepare religious charter school case for U.S. Supreme Court
• Faith and freedom: Dean Marcus Cole on religious liberty
• Notre Dame Law School Hosts Private Law Workshop Featuring Leading Legal Scholars
• Program on Law and Economics holds End of the Academic Year breakfast

Faculty Briefs

• Last week, the Supreme Court heard oral arguments in the St. Isidore case.
• Nicole Garnett wrote about the St. …

Novel Combination Therapies For Estrogen Receptor-Positive Breast Cancer Driven By Rational Molecular Mechanisms, Anneka Lila Johnson

Dartmouth College Ph.D Dissertations

Breast cancer (BC) is the most common non-keratinocyte cancer diagnosed in women in the United States with approximately 300,000 new cases diagnosed each year. Despite a myriad of treatment options, BC remains the second-most deadly cancer. Estrogen receptor-positive (ER+) BC comprises 60-70% of BC diagnoses and is treated with endocrine therapies that limit ER signaling. Despite endocrine therapy options, ~1/3 of patients experience recurrence within 10-20 years of diagnosis. Novel therapeutic strategies are required to limit BC recurrence-related morbidity and mortality.

Radiotherapy is used as an adjuvant treatment for ER+ BC patients prior to the use of endocrine therapy. Radiation …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

Identifying Genetic Errors Of Immunity Due To Mosaicism, Elizabeth G Schmitz, Malachi Griffith, Obi L Griffith, Megan A Cooper

2020-Current year OA Pubs

Inborn errors of immunity are monogenic disorders of the immune system that lead to immune deficiency and/or dysregulation in patients. Identification of precise genetic causes of disease aids diagnosis and advances our understanding of the human immune system; however, a significant portion of patients lack a molecular diagnosis. Somatic mosaicism, genetic changes in a subset of cells, is emerging as an important mechanism of immune disease in both young and older patients. Here, we review the current landscape of somatic genetic errors of immunity and methods for the detection and validation of somatic variants.

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Butte Treatment Lagoons (Btl) Groundwater Treatment System Quarterly Operation And Maintenance Report – Quarter 4 2024 (Dated March 28, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

The Rising Returns To R&D: Ideas Are Not Getting Harder To Find, Yoshiki Ando, James Bessen, Xiupeng Wang

Faculty Scholarship

R&D investment has grown robustly, yet aggregate productivity growth has stagnated. Is this because “ideas are getting harder to find”? This paper uses micro-data from the US Census Bureau to explore the relationship between R&D and productivity in the manufacturing sector from 1976 to 2018. We find that both the elasticity of output (TFP) with respect to R&D and the marginal returns to R&D have risen sharply. Exploring factors affecting returns, we conclude that R&D obsolescence rates must have risen. Using a novel estimation approach, we find consistent evidence of sharply rising technological rivalry. These findings suggest that R&D has …

Message From The Dean: Law Day, Anthony W. Crowell

NYLS Community News

No abstract provided.

Personalizing Ai Models Using Low-Rank Adaptation And Direct Preference Optimization, Saravanan Ganesh, Yi Li, Yunfei Hu, Krystal Kallarackal, Kelvin Nguyen, Chu-Cheng Lin

Defensive Publications Series

Aligning a large language model (LLM) to individual preferences is difficult to perform at scale. This disclosure describes techniques that leverage direct preference optimization (DPO) and low-rank adaptation (LoRA) to enable scalable alignment of artificial intelligence (AI) models. With user permission, user’s edits to suggestions from the model are obtained. The context of the user’s written data or interaction with the LLM is obtained. The user’s edits serve as training data to contextually fine-tune the model using LoRA and DPO. Training data is created as a side product of the user's tasks assisted by the LLM. The training data is …

Remote Cryptographic Device Verification For Theft Deterrence, Siddarth Pandit, Max Bires

Defensive Publications Series

This disclosure describes techniques that leverage remotely provisioned attestation certificates (RPAC) to actively deter device theft. Devices that are reported as lost are identified, and a robust access restriction mechanism is activated that denies both device-local and backend services to the device. To deny service, a bipartite mechanism is deployed that includes enabling a user to declare that their device is stolen and conveying to various services that the device is stolen.

Enhanced Deixis In Video Conferencing With Floating Video And Synthesized Gestures, Xun Qian, David Kim, Ruofei Du

Defensive Publications Series

In traditional video conferencing with screensharing, presenters struggle to reference specific on-screen elements effectively due to the limitations of small video feeds and basic pointer tools. This disclosure describes video conferencing (VC) techniques for enhancing the ability to point (deixis) in a videoconference using synthesized hand gestures. The video feed of the presenter is integrated into the shared screen, while synthesized hand gestures are overlaid to align with speech and pointer movements. With user permission, a multimodal machine learning pipeline accepts as input shared screen content, pointer/controller data, and ongoing speech to generate gestures and to determine optimal video placement. …

Dualprompt Gnn: Tackling Graph Heterogeneity Via Bi- Perspective Prompting

Defensive Publications Series

The challenges tied to unstructured graph data are manifold, primarily falling into node, edge, and graph-level problem categories. Graph Neural Networks (GNNs) serve as effective tools to tackle these issues. However, individual tasks often demand distinct model architectures, and training these models typically requires abundant labeled data, a luxury often unavailable in practical settings. Recently, various "prompt tuning" methodologies have emerged to empower GNNs to adapt to multitask learning with limited labels. The crux of these methods lies in bridging the gap between pretraining tasks and downstream objectives. Nonetheless, a prevalent oversight in existing studies is the homophily-centric nature of …

Heterophily-Aware Personalized Graph Masked Autoencoder

Defensive Publications Series

Graph Masked AutoEncoder (GMAE) has recently attracted vast interest in handling graph-related tasks by adopting the 'masking-reconstruction' learning paradigm. Most existing GMAE-based methods adhere to the homophily assumption, i.e., connected nodes share the same attributes or labels. However, this assumption is not always right because most graphs from real-world applications are mixed by both homophilic and heterophilic edges. Therefore, it is necessary to distinguish them to improve the representative ability of GMAE. In this paper, we propose a heterophily-aware personalized graph masked autoencoder (HAP-GMAE). Specifically, we design a teacher-guided edge discriminator that distinguishes homophilic and heterophilic edges by leveraging the …

Adaptive Node-Subgraph Contrastive Learning For Heterophilic Graph Fraud Detection

Defensive Publications Series

Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the aforementioned issues, we present a novel adaptive node-subgraph contrastive learning approach for graph-based fraud detection, dubbed ANS-GFD. …

Consistent And Homophily-Aware Representation Learning For Multiplex Graphs

Defensive Publications Series

Although unsupervised multiplex graph representation learning (UMGRL) has been a hot research topic, existing UMGRL methods still has limitations to be addressed. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency. To address these issues, in this paper, we propose a new UMGRL method, CH-MGRL (Consistency and Homophily-Aware Multiplex Graph Representation Learning), to explore both homophily and consistency in the multiplex graph. Specifically, we propose to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to …

Federated Ai Learning In Healthcare: Comprehensive Framework For Managing Urgent Care Denials, Kush Sharma

Defensive Publications Series

Federated learning (FL) represents a paradigm shift in artificial intelligence (AI) by enabling collaborative model training across decentralized entities—such as hospitals, insurance providers, and clinics—without requiring the exchange of raw patient data. This approach is particularly transformative in healthcare, where data privacy regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and impose strict limitations on data sharing. In the context of urgent care denials—such as rejected pre-authorizations, triage prioritization errors, or resource allocation decisions—FL offers a way to improve AI-driven decision-making while preserving patient confidentiality.

This elaborates on a federated AI framework designed to address …

Aggn: Adaptive Granularity Graph Networks For Heterophilic Environments

Defensive Publications Series

Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Existing methods addressing this issue often overlook the importance of information granularity and rarely consider implicit relationships between distant nodes. To overcome these limitations, we propose the Adaptive Granularity Graph Network (AGGN), a novel GNN model specifically designed for heterophilous graphs. AGGN enhances node embeddings by aggregating multi-view information at various granularity levels and incorporating implicit data from distant, …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

Learning Resilient Graph Structures In Heterophilic Settings

Defensive Publications Series

Graphs provide a fundamental way to model relationships between entities and are central to numerous machine learning tasks. Standard graph-based methods often assume the provided graph structure is both accurate and complete. However, real-world graphs frequently suffer from noise and sparsity, negatively impacting downstream tasks like node classification and clustering. While graph representation learning has advanced significantly, many methods implicitly assume graph homophily (connections predominantly between nodes of the same class), struggling when faced with heterophily (connections predominantly between different classes). This paper introduces a novel method, Resilient Graph Learning for Heterophily (RGLH), designed to learn high-quality graph structures directly …

Robust Graph Learning Through Spatial-Spectral Synergy Against Structural Attacks

Defensive Publications Series

Graph Convolutional Networks (GCNs) are powerful tools for learning from graph data but exhibit significant vulnerability to adversarial structural attacks that manipulate node connections. While various defense strategies focusing independently on the spatial or spectral domains exist, they often fail to leverage the complementary strengths of both perspectives. This paper introduces the Spatial- Spectral Graph Convolutional Network (S²-GCN), a novel framework designed to enhance GCN robustness against structural attacks by synergistically combining spatial and spectral defense mechanisms. S²-GCN comprises two core GCN-based modules operating in parallel. The spectral module utilizes a graph structure derived from learnable low-frequency spectral components, adaptively …

Balancedgraphformer: Enhancing Graph Transformers With Localized Training Against Over-Globalization

Defensive Publications Series

As Transformers gain traction in graph machine learning, the issue of "over-globalization" has emerged, where their global attention mechanisms excessively emphasize distant vertices, potentially diluting vital local information. This is particularly detrimental in graphs where local neighborhoods hold significant predictive value. Existing methods often lack flexibility in local processing or fail to effectively integrate local and global contexts. This paper introduces BalancedGraphFormer, a novel framework designed to localize graph transformer training. It integrates a dedicated local module with a complementary global module. The local module captures fine-grained neighborhood patterns, while the global module integrates broader context without overshadowing local details. …

Quantum-Coherent Graphene Via Entangled Plasma Deposition (Coherium™) Room Temp Superconductor, Peter Branton

Defensive Publications Series

Twisted bilayer graphene (TBG) at a magic angle (~1.1°) has demonstrated superconductivity below ~1.7 K due to emergent flat-band behaviour. However, its mechanical assembly limitations, thermal fragility, and scalability issues hinder real-world applications. The Coherium™ process addresses these limitations by synthesizing entangled carbon plasma into a structurally coherent graphene lattice with integrated AI stabilization, targeting room-temperature Superconducting properties.