Digital Commons Network^™

The 9th Annual Lafora Science Symposium: A Rare Epilepsy Community Makes Progress Towards Clinical Readiness, Meredith I. Williams, Katherine J. Donahue, Pascual Sanz, Souad Messahel, Jose M. Serratosa, Jordi Duran, Roberto Michelucci, Lorenzo Muccioli, Antonio Delgado-Escueta, Viet-Hong Nguyen, Berge A. Minassian, Matthew S. Gentry

Pharmacy Faculty Articles and Research

Lafora disease (LD) is a fatal childhood progressive myoclonus epilepsy and glycogen storage disease that is caused by recessive mutations in either EPM2A or EPM2B. The hallmarks of LD are cytoplasmic, aberrant glycogen-like aggregates, called Lafora bodies (LBs), that drive disease progression. The 9th Annual Lafora Science Symposium was held in San Diego, California and brought together over 70 researchers, clinicians, academic trainees, and friends and family members of patients with LD and 80 attendees joined virtually. This symposium focused primarily on international collaborations for therapeutic development and biomarker identification and strategies for preparing the Lafora community for upcoming …

Fs Steering Committee Minutes 2025-08-27, Northern Illinois University

Faculty Senate Archives

No abstract provided.

The Impact Of Monetary Trend On Prices And Nominal Income In The Uk During The Covid-19 Pandemic, Sebastian Meyer

Journal of New Finance

The essay addresses the Bank of England’s (BoE) key role in explaining the development of the UK economy during the Covid-19 pandemic. Focus will be provided on the impact of changes in money growth M4 and M4x on prices and nominal income, respectively. In so doing, the reader will be equipped with some theoretical basis from a neoclassical monetarist and Keynesian point of view to understand the importance of the BoE’s decisions in the explanation of inflation and household spending along the business cycle. The policy conclusions support classical economics with monetarism.

Spartan Daily, August 27, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 3

Modulating Treatment Outcomes Of Patients With Solid Tumors In Immunotherapy Trials: A Drug Interaction Analysis From A Phase 1 Unit, Camila Braganca Xavier, Clark R Andersen, Joann Lim, Julian H Slade, Stacie A Bean, Lei Kang, Hung Le, Apostolia M Tsimberidou, Aung Naing, David S Hong, Ecaterina E Dumbrava, Jordi Rodon Ahnert, Paula R Pohlmann, Sarina A Piha-Paul, Stephane Champiat, Timothy A Yap, Tin-Yun Tang, Funda Meric-Bernstam, Siqing Fu

Faculty, Staff and Student Publications

Concurrent use of medications can modulate the effectiveness of immunotherapy. Although this interaction is well documented for immune checkpoint inhibitors, whether this occurs with new experimental compounds has not been evaluated. A computerized data extraction tool was used to collect clinical data and identify the prescription of a predefined set of medications within 30 days of immunotherapy infusion in the Department of Investigational Cancer Therapeutics at The University of Texas MD Anderson Cancer Center. The primary endpoints were median overall survival (OS) and progression-free survival. Tumor responses were addressed using RECIST. We identified 897 patients. The most prevalent tumor types …

The Bg News August 27, 2025, Bowling Green State University

BG News (Student Newspaper)

The BGSU campus student newspaper. August 27, 2025. Volume 105-Issue 01.

Topological Indices And Quantitative Structure-Property Analysis Of Novel Drugs For Acute Lymphoblastic Leukemia Treatment, P. Nathiya, M. Suresh, Jalal Hatem Hussein Bayati

Baghdad Science Journal

Acute lymphoblastic leukemia in children is a bone marrow and blood cancer. Healthy blood cells begin to change and grow out of control when leukemia initially occurs. Acute lymphoblastic leukemia (ALL) is an immune system-related malignancy of the lymphocytes, a subtype of white blood cells. The most common form of cancer affecting children is ALL. In this work, the topological indices are derived from the structure of a chemical graph, and predict the physical, chemical, and biological characteristics of several anticancer drugs. ALL are usually treated with drugs such as methotrexate, 6-mercaptopurine (6-MP), vincristine, L-asparaginase, and/or prednisone, etc., which are …

2025-05-27 Executive Council Retreat Minutes, Susie Salazar

Executive Council Minutes

No abstract provided.

A Clustering Technique Based On The Hard K-Means (H.Km.) Method To Determine The Governorate That Have More Influence For Spreading Covid-19 In The Kingdom Of Saudi Arabia, Rand Muhaned Fawzi, Wurood R. Abd Al-Hussein, Iden Hassan Alkanani

Baghdad Science Journal

The Kingdom of Saudi Arabia is the gathering place of most of the nationalities of the Islamic world. When a certain disease spreads, it will be important to know which governorate has the greatest influence on the spread of the disease and take the necessary precautions to limit its spread, and this is the goal of this study. COVID-19, The SRS-COV-2 coronavirus that caused the most recent pandemic is known as the Corona pandemic. To determine which Saudi governorates had the greatest influence on the epidemics spread, data was gathered for thirteen governorates over two months (July and August). The …

A New Approach For Selecting Optimal Neuron Number In Input Layer Of Generalized Regression Neural Network Model, Muhammad Muhajir, Hermansah Hermansah, Aditya Pandu Wicaksono, Lathifah Aliya Pratiwi

Baghdad Science Journal

This study aimed to determine the input layer neurons for the Generalized Regression Neural Network (GRNN) model by using various classical methods, namely 1) Partial Autocorrelation Function (PACF), 2) frequency-based method, 3) frequency and Forward Selection (FS), 4) frequency and Backward Elimination (BE), 5) frequency and step-based methods, and 6) frequency method combined with the Least Absolute Shrinkage and Selection Operator (LASSO). These classical methods were combined with various parameters within GRNN, including smoothing parameters, forecasting strategies, and transformations. The most accurate model, with the lowest RMSE, MAE, MAPE, and SMAPE values, resulted from the combination of frequency and BE, …

A New Efficient Method To Solve Bi-Objective Transportation Problems Under Fuzzy Parameters, Ramakant Sharma, Sohan Lal Tyagi

Baghdad Science Journal

The transportation problem (TP) is a classical optimization problem in operations research and logistics. Due to several factors, real-life situations may have inconsistent supply, demand, and unit transportation costs. The fuzzy numbers represent these inaccurate data. In the present scenario, the decision-maker handles several objectives simultaneously. This paper presents a simple method to solve linear Bi-Objective Transportation Problems (BOTP) using (η, ξ) interval-valued fuzzy numbers (IVFN), offering a more realistic way to model uncertainty than normal fuzzy numbers. Using the signed distance ranking, the Fuzzy BOTP was transformed into the equivalent crisp BOTP. In this paper, A method is developed …

Emerging Challenges In Adversarial Deep Learning For Computer Vision And Cybersecurity, Younis Al-Arbo, Asmaa Alqassab

Baghdad Science Journal

The emergence of "Deep Learning" DL has revolutionized the scope of cybersecurity and computer vision. However, this technology is not immune to emerging challenges that can affect its performance and security. One major challenge is the availability of large datasets for training DL algorithms. Furthermore, there is a need for improved algorithms and architectures that can effectively process such datasets. Another challenge is the constant evolution of cyber threats, which require the development of new DL models to defend against them. Additionally, the interpretability and explain ability of DL models in cybersecurity pose a significant challenge, as their black-box nature …

A Comparative Of Detecting Physical And Cyber Attacks On Drones Using Machine Learning And Deep Learning Techniques, Khattab M Ali Alheeti, Sara Abbas Rafa, Maha Mahmood, Aythem Khairi Kareem, Mohammad Aljanabi, Ahmed Adil Nafea

Baghdad Science Journal

Multitudes of Unmanned Aerial Vehicles (UAVs) are generally embraced in military and civilian applications. Yet, this physical-cyber method is intimidated by cyber-attacks. Recently, Machine Learning (ML) based attacks detection approaches have been effectively embraced to detect cyber-attacks. This paper presented the Intrusion Detection Security (IDS) approach. The proposed approach investigates UAVs' cyber and physical attributes under normal process and attack circumstances. Two types of cyber-attacks have been classified: Denial-of-service (DoS) and replay. This study developed IDS approaches established on ML and Deep Learning (DL) prototypes, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and One Dimensional Convolutional …

An Efficient Glaucoma Classification For Fundus Images By Pre-Trained Deep Cnns And Accuracy-Weighted Voting Classifier Fusion, Salam Jabbar Edan, Mohammad-Reza Feizi-Derakhshi

Baghdad Science Journal

Glaucoma, a chronic eye disease characterized by progressive optic nerve damage, remains a significant public health concern worldwide. Early and accurate diagnosis is crucial for timely intervention and preventing irreversible vision loss. In this paper, developed an approach for glaucoma classification utilizing pre-trained convolutional neural networks (CNNs), specifically ResNet-101, ResNet-18, and DarkNet-19. Also employed accuracy-based weighted voting as a classifier fusion technique. Leveraging the capabilities of these CNN architectures, extract high-level features from retinal fundus images and apply transfer learning to adapt the networks for glaucoma classification. Leveraging the capabilities of these CNN architectures, extract high-level features from retinal fundus …

Accurate Skin Lesion Segmentation Through Feature Fusion In Dermoscopy Images, Fallah H. Najjar, Farhan Mohamed, Asniyani Nur Haidar Abdullah, Mohd Shafry Mohd Rahim, Karrar A. Kadhim

Baghdad Science Journal

In the diagnosis of skin cancer, dermoscopy images have to be evaluated for feature extraction. In this paper, the authors design a new method to enhance the skin dermoscopy image segmentation accuracy by eliminating artifacts and hair using photometric quasi-invariants, followed by a unique approach for skin lesion segmentation using histogram-based feature fusion. Histogram features are used to extract data range and mean images from the histogram, and these are fused with the free artifacts and hair image to generate the final image. the authors used the PH2 dataset for the proposed segmentation method because it contains the ground truth …

Trends In Public Interest In Digital Musculoskeletal Care: A Google Trends Analysis Of Hinge Health From 2020 To 2025, Nikhil Maddina, Albert D. Mousad, Fawaz Ibikunle, Minna Hassaballa, Shantanu Amin, Nicole Yeroushalmi

Student Publications

Introduction

Hinge Health (Hinge Health, Inc., San Francisco, CA) and similar musculoskeletal (MSK) telehealth platforms have grown rapidly in recent years, offering a new avenue for accessible, at-home care. As digital health further integrates into routine clinical practice, understanding public interest in these services can provide insight into shifting care-seeking behaviors and potential disparities in healthcare access. This study aims to analyze Google Trends (Google LLC, Mountain View, CA) data to evaluate temporal and geographic patterns of public search interest in Hinge Health in the United States, comparing upper and lower extremity musculoskeletal injuries, with the hypothesis that interest has …

It Takes A Village: Addressing Community Needs To Implement Firearm Prohibitions For Domestic Violence Protective Order Respondents, Kellie R Lynch, Michael K Gusmano, Jeff R Temple, Sarah Lane

Faculty, Staff and Student Publications

Prohibiting domestic violence protective order (DVPO) respondents from firearms is an effective strategy to prevent intimate partner homicide. However, DVPO gun laws vary considerably across states, and the implementation of such laws is inconsistent across localities. Local context, such as resource availability, priorities, and politics, differs across types of communities and impacts the implementation of laws. We conducted in-depth key informant interviews with victim service and criminal justice professionals (N = 27) working in urban communities, rural communities, and statewide organizations to better understand: (a) what communities need to effectively prohibit DVPO respondents from firearms (i.e., the DVPO gun …

Educator, Community And Student Reflections On Community-Engaged Research And Learning For Mathematics, Gareth A. Tribello, Jason Donaghy, Jyoutir Raj, Joshua Cunningham, Daniel Pyper, David Heaney, Kai Q. Lian, Sarah Conway, Judy Williams, Emma Mckenna

Irish Journal of Academic Practice

Community-engaged research and learning (CERL) is a constructivist pedagogy that involves students working on projects in collaboration with community partners. This approach was introduced to universities in the late 1960s, and although some disciplines have wholeheartedly embraced it, it is still rarely used in mathematics. This paper consists of a series of reflections written by educators, students, community partners and university leaders about a module in a mathematics degree that includes an element of CERL. These reflections demonstrate that community partners find value in the work done by mathematics students and that university leaders are broadly supportive of efforts to …

Explaining Time Series Classifiers Through Post-Hoc Xai Methods Capturing Temporal Dependencies, Ephrem Tibebe Mekonnen

Conference papers

Time series classification is essential in domains such as healthcare and finance, where accurate predictions can have significant real-world consequences. However, in many high-stakes applications, understanding why a model makes a certain decision is just as important as the prediction itself. While deep learning models excel at capturing complex temporal patterns, their black-box nature limits transparency, making it difficult to trust and interpret their decisions. Although eXplainable AI (XAI) methods have advanced considerably for image and tabular data, applying them to time series remains challenging due to the intricate temporal dependencies and high dimensionality of the data. Post-hoc model-agnostic XAI …

Preparing Educators To Meet The Unique Needs Of Multilingual Learners, Anna Adamo

Masters Projects

Effectively educating multilingual learners requires knowledge of culturally and linguistically responsive teaching, as well as an understanding of translanguaging techniques. Research shows that the population of multilingual learners continues to grow in the United States, however, qualified educators with knowledge of the specific needs of this population are lacking. This project explores the need for ongoing support for secondary novice and non-ESL certified educators who teach multilingual students. Included in this project is a complete full year of professional development sessions ranging from culturally and linguistically responsive teaching practices, guidelines for accommodations and modifications, opportunities for classroom observations, and technology …

Conversations With The Ancestors: Pursuing An Understanding Of Klamath Basin Rock Art Through The Use Of Myth, The Ethnographic Record, And Local Artistic Conventions, Robert David

Anthropology Faculty Publications and Presentations

Past archaeological practices have resulted in a distorted history of Native American cultures based upon western-biased research. This has been especially apparent in the rock art of the Klamath Basin in southern Oregon and northern California. In response to this, Native and non-Native scholars are striving to develop a counter-discourse that both challenges and replaces western constructs in research on Native American communities. The result of this approach is a growing trend within the discipline that has come to be called “Indigenous Archaeology.” Critical to this approach is that Native voices are transported from the margins of the research to …

Enhance Telecom-Related International Indicators Using Machine Learning, Omar Alnemer

Theses

This thesis investigates the enhancement of the Network Readiness Index (NRI) through the application of machine learning methodologies to refine indicator weighting, clustering, and dimensionality. The study builds on research promoting data-driven methods to address the limitations of equal-weighted indices in capturing indicator importance and interdependence. The research aims to evaluate how unsupervised and supervised learning methods can optimize the interpretation of NRI data. The study focuses on three primary research questions: (1) How can PCA be used to reduce the dimensionality of the NRI without significant loss of information? (2) Can k-means clustering reveal meaningful groups of countries with …

Free-Running Ring Oscillators For Crystal-Free Communication Systems: Design, Simulation Challenges, And Frequency Stability Improvements, Haziq Rohail

Dissertations and Theses

This thesis presents techniques for enhancing the frequency stability of ring oscillators (ROs) for crystal-free wireless communication systems. The first major contribution is a tutorial-style study of frequency stability metrics, providing clear definitions, conversions methods, and comparative analysis of commonly used figures of merit such as phase noise, Allan deviation, and jitter. The second contribution addresses the challenges of simulating phase noise in free-running ROs. Key techniques including Periodic Steady-State Noise (PNoise), Harmonic Balance Noise (HBNoise), and transient noise analysis are evaluated in terms of accuracy, convergence behavior, and simulation runtime. Based on these results, practical guidelines are offered for …

Nf1-Depleted Er+ Breast Cancers Are Differentially Sensitive To Cdk4/6 Inhibitors, Ze-Yi Zheng, Anran Chen, Eric J Jaehnig, Meenakshi Anurag, Jonathan T Lei, Long Feng, Chenwei Wang, Diana Fandino, Purba Singh, Hilda Kennedy, Ghazal Yadav, Craig T Vollert, Jill Tsai, Xi Chen, Yi Li, Bora Lim, Alastair Thompson, Shunqiang Li, Charles E Foulds, Bing Zhang, Matthew J Ellis, Eric C Chang

Faculty, Staff and Students Publications

Neurofibromin/NF1 is a RAS (rat sarcoma virus) GTPase activating protein and estrogen receptor (ER) transcriptional corepressor. NF1low status, identified by copy number loss or low mRNA/protein expression is associated with endocrine therapy resistance in approximately 20% of ER+/HER2− (human epidermal growth factor receptor 2) early-stage breast cancers. The identification of targeted treatments for NF1low ER+/HER2− breast cancer is therefore a priority. In this study proteogenomic analysis of ER+/HER2− breast cancer demonstrated that NF1low tumors exhibited elevated cyclin-dependent kinase 4/6 (CDK4/6) activity. In cell lines, NF1-deletion had a dual effect on CDK4 activity. First, by promoting ER recruitment to CCND1 …

Heterozygous Variants In Plcg1 Affect Hearing, Vision, Cardiac, And Immune Function, Mengqi Ma, Yiming Zheng, Mingxi Deng, Shenzhao Lu, Xueyang Pan, Xi Luo, Michelle Etoundi, David Li-Kroeger, Kim C Worley, Lindsay C Burrage, Lauren S Blieden, Aimee Allworth, Wei-Liang Chen, Giuseppe Merla, Barbara Mandriani, Catherine E Otten, Pierre Blanc, Jill A Rosenfeld, Debdeep Dutta, Shinya Yamamoto, Michael F Wangler, Ian A Glass, Jingheng Chen, Elizabeth Blue, Paolo Prontera, Jeremie Rosain, Sandrine Marlin, Seema R Lalani, Hugo J Bellen, Undiagnosed Diseases Network

Duncan NRI Faculty and Staff Publications

Phospholipase C isozymes (PLCs) hydrolyze phosphatidylinositol 4,5-bisphosphate (PIP₂) into inositol 1,4,5-trisphosphate (IP₃) and diacylglycerol (DAG), important signaling molecules involved in many cellular processes including Ca²⁺ release from the endoplasmic reticulum (ER). PLCG1 encodes the PLCγ1 isozyme that is broadly expressed. Hyperactive somatic mutations of PLCG1 are observed in multiple cancers, but only one germline variant has been reported. Here, we describe seven individuals with heterozygous missense variants in PLCG1 [p.(Asp1019Gly), p.(His380Arg), p.(Asp1165Gly), and p.(Leu597Phe)] who present with hearing impairment (5/7), ocular pathology (4/7), cardiac septal defects (3/6), and various immunological issues (5/7). To model these …

Context-Aware Natural Language Processing For Malware Detection, Helen Liu, Summer Mccune, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

As malware continues to evolve and cyber attacks become increasingly prevalent, it is critical to develop effective malware classification techniques for the detection and prevention of such malicious attacks. In our research, we approach malware classification from a natural language processing perspective to explore how various tokenization techniques on malware opcode features can enhance classification accuracy and combat malware obfuscation and evolution techniques with a deep learning approach, previously unaddressed by existing research. We bridge this gap by conducting extensive hyperparameter tuning experiments that examine the effects of five common tokenization methods, that is, White Space Separation, Top Single Words, …

Documents From The August 27, 2025 Meeting Of The Associated Students Of The University Of Montana (Asum), Kate Austin Payne, Associated Students Of The University Of Montana

Senate Meeting Agendas and Minutes, 2007-Present

Description: Agenda and meeting minutes from the August 27, 2025 meeting of the Associated Students of the University of Montana (ASUM).

Accelerating The Fusion Workforce In The Usa, Carlos Paz-Soldan, Eva Belonohy, Troy Carter, Laleh E. Coté, Evdokiya Kostadinova, Calvin Lowe, Subash L. Sharma, Sybil De Clark, Jaydeep Deshpande, Kate Kelly, Bobbi Makani, David A. Schaffner, Kathreen E. Thome

Faculty Research, Scholarly, and Creative Activity

The fusion energy research and development landscape has seen significant advances in recent years, with important scientific and technological breakthroughs and a rapid rise of investment in the private sector. The workforce needs of the nascent fusion industry are growing at a rate that academic workforce development programs are not currently able to match. This paper presents the findings of the Workforce Accelerator for Fusion Energy Development Conference held in Hampton, Virginia, United States of America (USA), on 29-30 May 2024, which was funded by the National Science Foundation of the USA. A major goal of the conference was to …

Epidemiology Of Shoulder Dislocations In The United States From 1990 To 2019: A Temporal Study Using The Global Burden Of Disease Database, Gabrielle Dykhouse, Ambrose Loc T. Ngo, Phillip C. Mckegg, Peter B. Spencer, Arsalaan Sayyed, Taylor J. Manes, Morgan Turnow, Nathaniel Long

Student Publications

Introduction

The shoulder joint is a common site for joint dislocation, with many individuals suffering from recurrent dislocations following treatment. The purpose of this study was to evaluate the epidemiology of shoulder dislocations in the United States from 1990 to 2019.

Methods

The Global Burden of Disease database was utilized to collect epidemiological data on shoulder dislocations in the United States (U.S.) from 1990 to 2019. These data included age-standardized rates of years lived with disability (YLDs), prevalence rates, and incidence rates per 100,000 people. Using the U.S. Census Bureau definitions, the data were stratified into four regions: the Northeast, …

Loss Of Dihydroceramide Desaturase Drives Neurodegeneration By Disrupting Endoplasmic Reticulum And Lipid Droplet Homeostasis In Glial Cells, Yuqing Zhu, Kevin Cho, Haluk Lacin, Yi Zhu, Jose T Dipaola, Beth A Wilson, Gary Patti, James B Skeath

2020-Current year OA Pubs

Dihydroceramide desaturases convert dihydroceramides to ceramides, the precursors of all complex sphingolipids. Reduction of DEGS1 dihydroceramide desaturase function causes pediatric neurodegenerative disorder hypomyelinating leukodystrophy-18 (HLD-18). We discovered that