Digital Commons Network^™

Calcium Carbonate Microparticles As Acid Responsive Targeted Drug Delivery Carriers For Oral Diseases And Its Anti-Bacterial Activity, Hem Raj Joshi

Dissertations and Theses

Dental diseases are mostly caused by bacterial infections in the teeth, forming biofilms that release acids and decay the enamel of the teeth. More than half of the world's population were affected by oral diseases, including periodontitis (gum disease), dental caries (cavities), and oral cancer. Chlorhexidine (CHX) is the mostly used antimicrobial drug for dental diseases, but its overexposure to the healthy teeth causes many side effects, such as tooth staining, taste alteration, and many more. Taking advantage of the acidic environment of infected teeth, we designed a pH responsive targeted drug delivery system using calcium carbonate microparticles (CCMPs) as …

Convergence In Cancer: Integrative Multi-Modal Modeling For Pan-Cancer Drug Response Prediction, Jessica Zylla

Dissertations and Theses

Heterogeneity and rarity can make cancer difficult to study and treat. Cancer's heterogeneity, evident in tumor locations, tumor cell types, subtypes, and microbiota within the tumor microenvironment, complicates diagnosis, prognosis, and treatment. Research into the tumor microenvironment and its microbiota is an evolving area of oncology that may advance cancer drug-patient response. Cancer-omics signatures are crucial to drug response, yet there is no tool that physicians use as a gold standard. Research indicates that microbial presence can alter drug metabolism and immune response, affecting cancer-drug efficacy on a patient-specific basis. This work highlights the need for training readiness in transdisciplinary …

A Computational Approach To The Game Of Cycles, Jocelyn Garcia, Mike Janssen, Eliza Kautz

Faculty Work Comprehensive List

In Mathematics for Human Flourishing, Francis E. Su introduced The Game of Cycles, a game played on a finite simple planar graph. In game play, opponents alternate adding direction to the edges of the graph with the goal of creating a cycle or making the last legal move. Recent work has sought to determine winning strategies on certain classes of graphs. We introduce a tabular representation of a game state and provide a computer program that determines which player has a winning strategy on any legal game board. The program builds a directed graph of all possible game states, utilizing …

Graduate Catalog 2025-2026, Southwestern Oklahoma State University

Graduate Catalogs

No abstract provided.

Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …

Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.

A Comprehensive Exploration Of 6g Wireless Communication Technologies, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md. Shahriar Uzzal, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into …

Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping …

Détermination De La Concentration Des Métaux Dans L'Air Par Icp-Ms Avec Prélèvement Effectué Sur Filtre Encapsulé Digérable : Solu-Sert, Sébastien Gagné, Luminita Damian, Saphos Vlavianos

Environnementales

No abstract provided.

Détermination De La Concentration Des Métaux De La Faction Inhalable Dans L'Air Par Icp-Ms Avec Prélèvement Effectué Sur Dis, Sébastien Gagné, Luminita Damian, Isabelle Madore

Environnementales

No abstract provided.

Ml Model To Better Identify Instances Of Bullying Faced By Members Of The Lgbtq+ Community, Arslan Bisharat

Master's Theses

Cyberbullying poses a significant threat to online communities, with the LGBTQ+ community facing disproportionately higher rates of harassment. While existing cyberbullying detection systems have made progress in identifying general instances of online harassment, they often fail to capture the nuanced and context-dependent nature of LGBTQ+-targeted bullying. This thesis presents a novel approach to this challenge by developing SpectrumNet, an LGBTQ+-centric transformer-based model for cyberbullying detection. Our research was conducted in two phases. In Phase 1, we evaluated the effectiveness of pre-trained transformer models (RoBERTa, BERT, and GPT-2) in identifying LGBTQ+-related cyberbullying. Building on these findings, Phase 2 introduced SpectrumNet which …

A Qualitative Analysis Of Open Banking In Poland: The Perspective Of Market Experts, Sylwia Ciechanowska

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: The purpose of this article is to present an in-depth analysis of the development of open banking in Poland based on interviews conducted with market experts representing financial institutions and regulatory authorities. The article identifies both the benefits and implementation barriers of open banking, with particular emphasis on the quality of APIs (application programming interfaces), system interoperability, and the impact of regulatory frameworks on service innovation and security.

Methodology: This qualitative study was conducted using structured interviews with market experts representing banks, fintech companies, regulatory authorities, and independent industry consultants. The selection of experts was purposive, with …

Exploring The Factors That Influence K-12 Teachers’ Use Of Open Educational Resources, Yu-Chun Kuo, Yu-Tung Kuo, Hungwei Tseng

Research, Publications & Creative Work

The features of open educational resources (OERs) have the potential of enhancing teaching effectiveness and student learning experiences in K-12 education. Encouraging K-12 teachers to use OERs requires an understanding of the factors that have an influence on teachers’ decisions to adopt or use OERs in their teaching. In this regard, this study explored the factors related to how teachers perceived their use of OERs, including the perceived ease of use of OERs, perceived usefulness of OERs, intention to adopt OERs, attitudes, OER self-efficacy, and so on. The relationships of several proposed factors were explored. The participants were teachers from …

The Impact Of International Baccalaureate (Ib) Courses: Experience And Outcome Differences For Full Ib Diploma Earners Vs. Non-Diploma Earners In An Urban School In The Us, Christopher Jennens, Nilufer Guler

Research, Publications & Creative Work

This study examines differences in educational experiences and outcomes for high school(HS) students who participated in the International Baccalaureate (IB) Diploma Program and earned full diplomas compared to those who participated in the IB Diploma Program but did not earn the diploma. ACT scores, after-high school placement, and HS graduation rates of 256 participants were collected. 122 participants completed an online survey, and 12 participated in interviews. The results revealed that the educational outcomes and experiences for diploma and non-diploma earners were vastly similar. Diploma earners had significantly higher ACT scores, but their college entrance and other academic outcomes were …

Digital Twins In Healthcare: A Comprehensive Review And Future Directions, Hamid Khoshfekr Rudsari, Becky Tseng, Hongxu Zhu, Lulu Song, Chunhui Gu, Abhishikta Roy, Ehsan Irajizad, Joseph Butner, James Long, Kim-Anh Do

Faculty, Staff and Student Publications

Digital Twin (DT) technology has emerged as a transformative force in healthcare, offering unprecedented opportunities for personalized medicine, treatment optimization, and disease prevention. This comprehensive review examines the current state of DTs in healthcare, analyzing their implementation across different physiological levels-from cellular to whole-body systems. We systematically review the latest developments, methodologies, and applications while identifying challenges and opportunities. Our analysis encompasses technical frameworks for cardiovascular, neurological, respiratory, metabolic, hepatic, oncological, and cellular DTs, highlighting significant achievements such as population-scale cardiac modeling (3,461 patient cohort), reduced atrial fibrillation recurrence rates through patient-specific cardiac models, improved brain tumor radiotherapy planning, advanced …

Combining Biology-Based And Mri Data-Driven Modeling To Predict Response To Neoadjuvant Chemotherapy In Patients With Triple-Negative Breast Cancer, Casey E Stowers, Chengyue Wu, Zhan Xu, Sidharth Kumar, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I Tamir, Gaiane M Rauch, Thomas E Yankeelov

Faculty, Staff and Student Publications

Purpose

To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC).

Materials and Methods

In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN …

The Comparison Of Human And Machine Performance In Object Recognition, Gokcek Kul

School of Psychology Theses

Deep learning algorithms have advanced significantly due to better hardware and large training sets, with some claiming they now match or exceed human performance. Recent findings indicate improvements in robustness against Out-Of-Distribution benchmark sets and alignment with human categorisation behaviour. This thesis evaluates various machine learning models to ensure a fair comparison with human performance using principles from comparative psychology. Chapter 2 introduces a psychologically grounded framework for comparing human and machine object categorisation performance, focusing on different presentation times and task complexities. The results indicate that while recent machine learning models have reached or exceeded human performance under optimised …

“Fish In Different Water, To Survive Or To Thrive?” Understanding Direct-Entry Pre-Sessional Chinese Students’ Learning Experiences During Covid-19 And Its Aftermath, Lingyuan Osgathorpe

School of Law, Humanities and Social Sciences Theses

This thesis explores the learning experiences of direct-entry pre-sessional Chinese students in the UK during the COVID-19 pandemic and its aftermath, using Pierre Bourdieu’s theoretical framework. Through the metaphor of students as "fish in different water", the research examines how these learners navigate and adapt to new educational environments that often starkly contrast with their prior experiences.The study employs a two-stage semi-structured interview process with student participants, first in China during their online pre-sessional courses and later in the UK during their degree programmes. All interviews were conducted in Mandarin Chinese to ensure linguistic and cultural accessibility. Additionally, expert interviews …

Unraveling Complex Temporal Patterns In Ehrs Via Robust Irregular Tensor Factorization, Ren Yifei, Linghui Zeng, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium V Bhavani

Faculty, Staff and Student Publications

Electronic health records (EHRs) contain diverse patient data with varying visit frequencies. While irregular tensor factorization techniques such as PARAFAC2 have been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose REPAR, an RNN REgularized Robust PARAFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs Recurrent Neural Networks (RNNs) for temporal regularization and a low-rank constraint for robustness, enabling precise patient subgroup identification and improved clinical decision-making in noisy EHR data. …

Explainable Diagnosis Prediction Through Neuro-Symbolic Integration, Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu

Faculty, Staff and Student Publications

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models …

Rank Aggregation Methods And Tools In Genomic Data Analysis, Wenping Zou, Savannah Mwesigwa, Sayed-Rzgar Hosseini, Zhongming Zhao

Faculty, Staff and Student Publications

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. …

Global Research Trends And Focus On Immunotherapy For Endometrial Cancer: A Comprehensive Bibliometric Insight And Visualization Analysis (2012–2024), Yachen Xu, Tao Wang, Xiaojing Liang, Jie Yang, Yuxiang Zhang, Shan Bao

Faculty, Staff and Student Publications

Background: This study conducted a novel systematic bibliometric and visualization analysis of global literature on immunotherapy for endometrial cancer (EC) to explore dynamic trends, research hotspots, and emerging topics, providing valuable references for future research.

Methods: Articles and reviews on EC immunotherapy published between 2012 and August 2024 were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric tools, CiteSpace and VOSviewer, were used to analyze clustering patterns and research dynamics.

Results: A total of 861 articles were contributed by 5,331 authors from 1,392 institutions across 58 countries or regions, involving 1,823 keywords. China demonstrated outstanding performance in this …

Characterization Of Additive Gene-Environment Interactions For Colorectal Cancer Risk, Claire E Thomas, Yi Lin, Michelle Kim, Eric S Kawaguchi, Conghui Qu, Caroline Y Um, Brigid M Lynch, Bethany Van Guelpen, Kostas Tsilidis, Robert Carreras-Torres, Franzel J B Van Duijnhoven, Lori C Sakoda, Peter T Campbell, Yu Tian, Jenny Chang-Claude, Stéphane Bézieau, Arif Budiarto, Julie R Palmer, Polly A Newcomb, Graham Casey, Loic Le Marchandz, Marios Giannakis, Christopher I Li, Andrea Gsur, Christina Newton, Mireia Obón-Santacana, Victor Moreno, Pavel Vodicka, Hermann Brenner, Michael Hoffmeister, Andrew J Pellatt, Robert E Schoen, Niki Dimou, Neil Murphy, Marc J Gunter, Sergi Castellví-Bel, Jane C Figueiredo, Andrew T Chan, Mingyang Song, Li Li, D Timothy Bishop, Stephen B Gruber, James W Baurley, Stephanie A Bien, David V Conti, Jeroen R Huyghe, Anshul Kundaje, Yu-Ru Su, Jun Wang, Temitope O Keku, Michael O Woods, Sonja I Berndt, Stephen J Chanock, Catherine M Tangen, Alicja Wolk, Andrea Burnett-Hartman, Anna H Wu, Emily White, Matthew A Devall, Virginia Díez-Obrero, David A Drew, Edward Giovannucci, Akihisa Hidaka, Andre E Kim, Juan Pablo Lewinger, John Morrison, Jennifer Ose, Nikos Papadimitriou, Bens Pardamean, Anita R Peoples, Edward A Ruiz-Narvaez, Anna Shcherbina, Mariana C Stern, Xuechen Chen, Duncan C Thomas, Elizabeth A Platz, W James Gauderman, Ulrike Peters, Li Hsu

Faculty, Staff and Student Publications

Background: Colorectal cancer (CRC) is a common, fatal cancer. Identifying subgroups who may benefit more from intervention is of critical public health importance. Previous studies have assessed multiplicative interaction between genetic risk scores and environmental factors, but few have assessed additive interaction, the relevant public health measure.

Methods: Using resources from CRC consortia, including 45,247 CRC cases and 52,671 controls, we assessed multiplicative and additive interaction (relative excess risk due to interaction, RERI) using logistic regression between 13 harmonized environmental factors and genetic risk score, including 141 variants associated with CRC risk.

Results: There was no evidence of multiplicative interaction …

The Tripod-Llm Reporting Guideline For Studies Using Large Language Models, Jack Gallifant, Majid Afshar, Saleem Ameen, Yindalon Aphinyanaphongs, Shan Chen, Giovanni Cacciamani, Dina Demner-Fushman, Dmitriy Dligach, Roxana Daneshjou, Chrystinne Fernandes, Lasse Hyldig Hansen, Adam Landman, Lisa Lehmann, Liam G Mccoy, Timothy Miller, Amy Moreno, Nikolaj Munch, David Restrepo, Guergana Savova, Renato Umeton, Judy Wawira Gichoya, Gary S Collins, Karel G M Moons, Leo A Celi, Danielle S Bitterman

Faculty, Staff and Student Publications

Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes …

Phase 1 Trial Of Hypofractionated Stereotactic Re-Irradiation In Combination With Nivolumab, Ipilimumab, And Bevacizumab For Recurrent High-Grade Gliomas, Solmaz Sahebjam, Raju R Raval, Peter A Forsyth, Heiko Enderling, Nam D Tran, John A Arrington, Robert Macaulay, Haley K Perlow, Joshua D Palmer, Jayeeta Ghose, Prajwal Rajappa, Pierre Giglio, Zihai Li, Arnold B Etame, Sepideh Mokhtari, Ruben J Cruz-Chamorro, Menal Bhandari, Ram Thapa, Timothy J Robinson, Dung-Tsa Chen, Hsiang-Hsuan Michael Yu

Faculty, Staff and Student Publications

Background: Our previous clinical investigation suggested that hypofractionated stereotactic re-irradiation (HFSRT) and PD-1 blockade may act synergistically to enhance the immune response against glioma. This subsequent trial investigated the dual blockade of CTLA4 and PD-1 in combination with HFSRT and bevacizumab.

Methods: This phase I study enrolled eligible patients with bevacizumab-naïve recurrent glioblastoma or anaplastic astrocytoma. Participants received nivolumab, ipilimumab, and bevacizumab concurrently with HFSRT (3000 cGy in 5 fractions). Subsequently, nivolumab, ipilimumab, and bevacizumab were administered for a total of 4 cycles followed by nivolumab and bevacizumab until progression. The primary end point of this study was the safety …

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Evaluation Of Quantum Contouring Algorithms For Treatment Planning On Mr Abdominal Images, Rachel Glenn, Tucker Netherton, Adrian Celaya, Mohamed Eltaher, Beatrice Riviere, Eugene J Koay, David Fuentes

Faculty, Staff and Student Publications

Introduction: Quantum computing is increasingly being investigated for integration into medical radiology and healthcare applications worldwide. Given its potential to enhance clinical care and medical research, there is growing interest in evaluating its practical applications in clinical workflows.

Methods: We developed an evaluation of quantum computing-based auto-contouring methods to introduce medical physicists to this emerging technology. We implemented existing quantum algorithms as prototypes tailored for specific quantum hardware, focusing on their application to auto-contouring in medical imaging. The evaluation was performed using a medical resonance imaging (MRI) abdominal dataset, comprising 102 patient scans.

Results: The quantum algorithms were applied to …

Quantile Regression Based Method For Characterizing Risk-Specific Behavioral Patterns In Relation To Longitudinal Left-Censored Biomarker Data Collected From Heterogeneous Populations, Minjae Lee, Belinda M Reininger, Kelley Pettee Gabriel, Nalini Ranjit, Larkin L Strong

Faculty, Staff and Student Publications

There are many studies aimed at promoting positive lifestyle behaviors to reduce lifetime risk of cancer and related diseases. However, assessing these modifiable behaviors through statistical modeling is challenging because of the multidimensionality of interrelated measurements that may dramatically differ between at-risk individuals. Taking into account this heterogeneity while considering the multidimensionality of behavior changes is fundamental to tailoring interventions to their needs. Biomarkers that identify high-risk individuals may help validate proximal measures, but the number of validated methods that link biomarkers to multiple behavioral measurements by determining their dynamic relations with disease risks is limited to just a few, …

Characterizing Spatial Immune Architecture In Metastatic Melanoma Using High-Dimensional Multiplex Imaging, Joel Eliason, Santhoshi Krishnan, Yasunari Fukuda, Matias A Bustos, Dan Winkowski, Sungnam Cho, Akshay Basi, Regan Baird, Elizabeth A Grimm, Michael A Davies, Dave S B Hoon, Arvind Rao, Jared K Burks, Suhendan Ekmekcioglu

Faculty, Staff and Student Publications

Introduction: Immune checkpoint inhibitors (ICIs) have significantly improved survival for patients with metastatic melanoma, yet many experienceresistance due to immunosuppressive mechanisms within the tumor immune microenvironment (TIME). Understanding how the spatial architecture of immune and inflammatory components changes across disease stages may reveal novel prognostic biomarkers and therapeutic targets.

Methods: We performed high-dimensional spatial profiling of two melanoma tissue microarrays (TMAs), representing Stage III (n = 157) and Stage IV (n = 248) metastatic tumors. Using imaging mass cytometry (IMC) and multiplex immunofluorescence (mIF), we characterized the phenotypic, functional, and spatial properties of the TIME. Cellular neighborhoods were …

Challenging Assumptions: Ai And Teacher Assessment Identity Formation, Nadia Ollington

Australian Journal of Teacher Education

This article explores the ways in which teacher educators can strengthen the developing teacher assessment identity of preservice teachers who are at the precipice of Industry 4.0, where work with Artificial Intelligence (AI) and assessment will require flexible thinking. This reflective case study narrative outlines a teacher educator’s experiences in a novel assessment task using Generative AI for technology enhanced learning in an Australian postgraduate teaching program. In the task, those who held positive beliefs and emotions about AI were more likely to engage in flexible thinking skills than those who were more critical of the AI. To ensure that …