Digital Commons Network^™

Staphylococcus Aureus Strain Heterogeneity Modulates Virulence Traits Associated To Dual-Species Biofilms With Candida Albicans, Laure Nicolas Annick Ries, Beata Adrienn Brezniczky, Binduja Srelatha Pradeep, Esther Olabamiji, Lee Hutt, Glenn M. Harper, Alexander Strachan, Gyorgy Fejer

School of Biomedical Sciences

Staphylococcus aureus is a commensal Gram-positive bacterium and opportunistic pathogen that exhibits substantial variation in antimicrobial resistance and virulence traits. S. aureus is frequently co-isolated with the fungus Candida albicans, and together they represent the most common microorganisms recovered from biofilm-associated infections originating from both biotic and abiotic surfaces. Despite this clinical relevance, the impact of bacterial intra-species variation on interkingdom interactions remains poorly understood. This study therefore investigated how S. aureus strain heterogeneity influences C. albicans within dual-species biofilms. The majority of the 60 S. aureus strains tested suppressed fungal growth in a contact-dependent manner across different nutrient conditions …

Streamlining Literacy Assessment, Sheila Mulder, Katie Anderson, Samuel W. Flint

Annual Research Symposium

Reading proficiency scores are low throughout the nation. Reading proficiency is a foundational skill that supports academic success across all content areas. Skilled reading involves multiple interrelated components. Assessment can support teachers to identify skill deficits and inform instruction; however, rural teachers lack the time, resources, and specialized support to translate the data into effective instruction and interventions. To better contextualize this need, we received 59 surveys from South Dakota teachers with 23 of the surveys being valid responses, finding that teachers feel prepared to teach reading and struggling readers. Teachers reported that they spend relatively little time in assessment, …

Secure-Home: Detect And Redact Pii, Hajar Niroomand, Omar F. El-Gayar

Annual Research Symposium

Personally Identifiable Information (PII) leakage from home environments poses significant identity theft risks. While enterprise networks employ robust security measures, firewalls, intrusion detection systems, and access controls, these protections rarely extend to home settings, creating a critical security gap. Current firewall technologies lack the capability to detect and scrub PII from outbound traffic, leaving vulnerable populations such as children, elderly users, and remote workers exposed. This design science research proposes Secure-Home, a prototype outbound inspection tool that detects and redacts clear-text PII before data leaves home networks. Using rule-based detection and selective redaction, Secure-Home provides a practical last line of …

Comparative Analysis Of Shipping Costs Across Multiple Routes Using Fedex Api Data, Valerija Curikova, Andrew Kramer

Annual Research Symposium

Shipping costs are an important factor for both individuals and companies, especially in industries like healthcare where medical material should be delivered quickly and reliably. While working as an intern at Avera Health in the Supply Chain logistics team, I was introduced to the challenge of the high cost of delivering medical supplies to different patient locations. Shipping prices vary depending on distance, route direction, and service type. Using real-time data from the FedEx API allows for more accurate analysis of shipping costs and helps identify opportunities to optimize delivery decisions and reduce logistics expenses.

Post-Quantum Cryptography Secure Communication, Iot, And Blockchain, Nidhish Bhanse, Mark Spanier

Annual Research Symposium

Modern public-key cryptography, such as RSA and Elliptic Curve Cryptography (ECC), plays a crucial role in securing data. However, the development of quantum computing threatens the security of data encrypted with these methods. Data encrypted today might be decrypted in the future due to the increased power of quantum computers. To combat this, the National Institute of Standards and Technology (NIST) has developed new standards for post-quantum cryptography. The following research aims to provide an analysis of these NIST post-quantum cryptographic algorithms and their potential for use in various secure communication protocols and platforms.

Evaluating The Reliability And Equity Implication Of Acute Hospital Readmission Metrics In New Zealand, Christopher D. Elce, Martinson Ofori, Andrew Behrens

Annual Research Symposium

Acute 28-day hospital readmission rates are widely used in New Zealand to monitor hospital quality and health system performance. However, the policy value of readmission metrics depends on whether observed variation reflects real clinical differences or is driven by data-quality artifacts and population structure. This study evaluates the reliability of published benchmarked readmission rates for all Districts of Service.

Postmortem Analysis Of Israel's 7 October 2023 Intelligence Failure, Emily Helgeson, William Bendix

Annual Research Symposium

On October 7, 2023, Hamas launched an attack from the Gaza Strip into Israel, resulting in the deaths of 1,200 people and triggering a larger conflict. Despite possessing impressive intelligence capabilities, Israel failed to anticipate the attack. This project provides one of the first postmortem analyses of the October 7 intelligence failure and demonstrates methods for assessing the role and significance of key contributing factors.

Interactions Of Bad Actors With Honeypots, Maryam Aliyeva, Andrew Kramer

Annual Research Symposium

Throughout daily life, computer users encounter different forms of malware, such as ransomware, adware, viruses, trojan horses, and spyware. To protect networks, individuals need to be able to comprehensively analyze a malicious actor’s behavior and tactics. These observations can be made through the usage of honeypots – a computer security mechanism set to track and deflect unauthorized activity. Honeypots frequently operate like decoys of legitimate websites that perfectly mimic an existing database, which makes them a valuable tool for researching cyber criminals’ behavior.

Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To …

Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients 'selfish'. This work proposes a Fair and Robust strategy for aggregation in the Federated Learning (FL) server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel …

Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …

Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes …

Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma

Computer Science Faculty Research & Creative Works

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das

Computer Science Faculty Research & Creative Works

Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these networks are often large and dynamic, introducing significant computational challenges. Due to the absence of specialized software packages and data structures, the analysis of large dynamic hypergraphs remains largely unexplored. Motivated by this gap, we propose ESCHER, a GPU-centric parallel data structure for Efficient and Scalable Hypergraph Evolution Representation, designed to manage largescale hypergraph dynamics efficiently. We also design …

Surgical Treatment Of Osteoradionecrosis Of The Jaw Following Radiotherapy For Oropharyngeal Cancer: A Retrospective Clinical Analysis In A Single-Center, Yu Nakai, Ken-Ichiro Sakata, Kazuhito Yoshikawa, Taku Maeda, Tougo Tanabe, Masayuki Shinohara, Kenji Imamachi, Hiroshi Hikasa, Jun Sato, Ikuya Miyamoto

Journal of Dental Sciences

Background/purpose: Osteoradionecrosis of the jaw (ORNJ) is an intractable disease that develops within the radiation field in patients with head and neck cancer. This study aimed to investigate differences in healing rates among surgical treatments for mandibular ORN that developed after radiotherapy for oropharyngeal cancer.

Materials and methods: We retrospectively evaluated 318 patients who underwent radiotherapy or chemoradiotherapy for oropharyngeal cancer at our institution to determine the incidence of ORNJ. Furthermore, 16 patients (15 males and 1 female) with a mean age of 63.3 years were included in the surgical analysis. Healing was defined as the absence of …

Effects Of 850-Nm Light-Emitting Diode Photobiomodulation On Pain Intensity And Dentin Sialoprotein Levels During Canine Distalization: A Randomized Controlled Trial., Thanh Thuy-Nhat Cao, Anh Ho-Quynh Nguyen, Hung Trong Hoang, Tham Dong Khac, Phuc Hoai Le, Tu Trinh Hk, Kieu-Minh Le, Diem Truong

Journal of Dental Sciences

Background/purpose

Light-emitting diode-based photobiomodulation (LED PBM) potentially reduces pain and root resorption risk in orthodontics. This study evaluated 850-nm LED PBM effects on pain and dentin sialoprotein (DSP) levels during canine distalization.

Materials and methods

This single-blind, parallel-group randomized controlled trial included 16 orthodontic patients (8 per group). Participants were randomly allocated in a 1:1 ratio to either an active LED group or a sham control group. Pain intensity was assessed using a visual analog scale (VAS) at 0, 24, and 48 hours following canine distalization at four consecutive monthly visits (T₁-T₄) and expressed as the …

An Artificial Intelligence Assisted Framework For Automated Quantified Root Canal Curvature Measurement On Periapical Radiographs, Ming-Yi Chen, Shih-Lun Chen, Yu-Jen Chang, Yuan-Jin Lin, Chiung-An Chen, Tsung-Yi Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R. Abu

Journal of Dental Sciences

Background/purpose: Root canal curvature is a complex anatomical variation that complicates endodontic treatments. Traditionally, dentists rely on two-dimensional periapical radiographs (PA) and manual methods to estimate curvature angles. However, this conventional approach is highly time-consuming and subjective, often leading to inconsistent measurements.

Materials and methods: This study aims to develop a fully automated, objective framework for precise angular measurement and severity grading of root canal curvature. The YOLO26n-seg model was employed to segment roots and classify single- or multi-rooted teeth from PA. Furthermore, we proposed an image processing algorithm that standardized orientation, extracted centerlines, and applied three-point circle fitting …

Very Early Interceptive Management Of Class Iii Malocclusion In Primary Dentition Using A Prefabricated Functional Education Appliance: Preliminary Clinical Observations, Thi Ngoc Anh Do, Thi Huong Le, I-Ta Lee, Tong-Hsien Chow, Pao-Chang Chiang, Thi Thuy Tien Vo

Journal of Dental Sciences

Background/purpose: Class III malocclusion with anterior crossbite in primary dentition may progress and complicate future orthodontic treatment. This short communication presents preliminary clinical observations of very early interception using a prefabricated functional education appliance.

Materials and methods: Three children aged 4 to 6 years with dental, functional, or mild skeletal class III malocclusion were treated using a prefabricated removable functional education appliance worn at night, with regular follow-up.

Results: In the dental class III case, anterior crossbite was corrected within 9 months and remained stable at the 2-year follow-up. In the functional class III case, anterior crossbite and mandibular shift …

Artificial Intelligence In Dentistry: A Cross-Domain Bibliometric Analysis Of Technologies, Functional Applications, And Dental Specialties, Yinghui Yang, Cheng-Chia Yu, Min Yee Ng

Journal of Dental Sciences

Background/purpose: Artificial intelligence is increasingly applied in dentistry across diagnosis, prediction, treatment planning, education, and patient communication. This study aimed to provide a structured cross-domain overview of dental AI research by integrating AI technologies, functional applications, and dental specialties within one bibliometric framework.

Materials and methods: AI-related dental publications from 2016 to 2026 were retrieved from the Web of Science Core Collection. Original and review articles were screened and classified by AI technology, functional application, and dental specialty. Bibliometric mapping, collaboration analysis, keyword co-occurrence, and heatmap-based cross-domain visualization were performed.

Results: A total of 920 publications were included. Publication …

Oral Health Knowledge Across Cultural Boundaries: A Physiology And Hygiene Textbook From The Tamsui Middle School During The Japanese Colonial Period, Hung-Lun Pan, Feng-Chou Cheng, Chun-Pin Chiang

Journal of Dental Sciences

No abstract provided.

Ruptured Silicone Cheek Implant Mimicking Odontogenic Infection: A Diagnostic Pitfall, Kimiko Ohgi, Tomoko Shiraishi, Toyohiro Kagawa, Yasunori Yoshinaga

Journal of Dental Sciences

No abstract provided.

Corrigendum To "Human Umbilical Cord Mesenchymal Stem Cells Secretome And Nanoemulsion Propolis Combination Ameliorate Osteoclastogenesis In Lipopolysaccharide-Induced Osteolysis In Hyperglycemia Rats"

Journal of Dental Sciences

No abstract provided.

Evaluating The Use Of Machine Learning For Road Maintenance Cost Estimation, Shahla Shirinzad, David Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Accurate estimation of highway maintenance costs is essential for efficient resource allocation and long-term infrastructure sustainability. This study evaluates the effectiveness of advanced machine learning models, including ResNet, Transformer, and other neural network architectures, for forecasting maintenance costs using historical data from the Highway Maintenance Improvement Program (HMIP) provided by the North Carolina Department of Transportation (NC DOT). A comprehensive comparative analysis is conducted across multiple models using standard performance metrics, including R², MAE, RMSE, and MSE. The results demonstrate that advanced architectures, particularly ResNet and Transformer, consistently outperform traditional statistical approaches and baseline machine learning models, achieving …

Dental Treatment And Oral Care For Patients With Severe Mental Illness Receiving Psychiatric Home-Visit Care: A Narrative Review With An Illustrative Case, Takayuki Suga, Trang Thi Huyen Tu, Yuji Gamo, Takafumi Asakura, Shigeru Iida, Akira Toyofuku

Journal of Dental Sciences

Patients with severe mental illness (SMI) often have untreated caries, periodontal inflammation, tooth loss, xerostomia-related risk, dental anxiety, and delayed dental attendance. This narrative review aimed to synthesize evidence relevant to dental treatment planning for patients with SMI receiving psychiatric home-visit care and to clarify when domiciliary dental assessment and repeated professional oral care may be indicated. Targeted literature searches and citation tracking were used to identify reviews, epidemiological studies, intervention studies, qualitative syntheses, and special care dentistry guidance. The evidence indicates that oral disease in SMI reflects not only limited knowledge but also avolition, cognitive impairment, psychosis, sedation, medication-induced …

Evaluation Of Condylar Position Displacement Using Various Centric Relation Recording Techniques Based On Maximum Intercuspation Position: A Systematic Review And Meta-Analysis, Ling-Shiuan Huang, Yen-Chang Huang, I-Ting Wu, Shinn-Jyh Ding

Journal of Dental Sciences

Background/ purpose: Restoring tooth defects is a key clinical task for dentists, aimed at either maintaining or reconstructing a functional occlusal dentition. This study conducted a systematic review and meta-analysis of clinical trials comparing centric relation (CR) and maximum intercuspal position (MIP) condylar positions obtained using various CR techniques, and to verify the reproducibility of these methods.

Materials and methods: The PubMed, Embase, Google Scholar, and Cochrane Library databases were searched for articles published before January 2026. The single-arm meta-analysis used a random-effects model to calculate the overall effect size.

Results: This study included 12 articles published …

The Leeuwenhoek Paradox In Dental Artificial Intelligence: A Narrative Review Of Artificial Intelligence-Detected Subclinical Periapical Changes And Human-Labeled False Positives, Etyene Schnurr, Mario Parra, David M. Kim

Journal of Dental Sciences

This narrative review explores the “Leeuwenhoek Paradox,” a metaphorical and conceptual challenge that arises when artificial intelligence (AI) detects subtle periapical radiological changes that are not visible to human observers. Drawing on evidence from studies using two-dimensional and cone-beam computed tomography (CBCT) imaging, this review critically examined the limitations of human-centered reference standards and recurrent discrepancies in AI-based periapical detection. The literature shows that AI systems frequently detect more periapical alterations than human experts. These findings may reflect early bone changes, as indicated by CBCT-based analyses of bone density and longitudinal observations. The paradox arises because AI identifies subclinical radiographic …

Oral Pathogens Reprogram Endothelial Barrier And Angiotensin-Converting Enzyme2/Endothelial Nitric Oxide Synthase-Nitric Oxide Signaling To Promote Blood Pressure Dysregulation, Ming-Shou Hsieh, Tung-Nien Hsu, Chin-Sheng Huang, Ting-Yi Renn, Kuang-Tai Kuo, Kuo-Yao Lin, Wei Jen Chang

Journal of Dental Sciences

Background/purpose: Although periodontal disease is associated with an increased risk of hypertension, the endothelial mechanisms linking oral inflammation to vascular dysfunction remain unclear. We investigated whether Porphyromonas gingivalis lipopolysaccharide (Pg-LPS) can directly disturb endothelial homeostasis and whether macrophage–endothelial crosstalk further worsens endothelial injury.

Materials and methods: Human umbilical vein endothelial cells (HUVECs) were exposed to Pg-LPS alone or co-cultured (transwell system) with Pg-LPS-primed human monocytic cells -derived macrophages. Endothelial dysfunction was assessed through nitric oxide (NO) production, endothelial nitric oxide synthase (eNOS) phosphorylation, inflammatory and adhesion molecule expression, as well as junctional and cytoskeletal changes. Macrophage …

C-X-C Motif Chemokine Ligand 13 And The C-X-C Motif Chemokine Receptor 5–C-Jun N-Terminal Kinase–Nuclear Factor Kappa B–Matrix Metalloproteinase 9 Axis In Oral Squamous Cell Carcinoma Metastasis, Ju-Fang Liu, Kuan-Chou Lin, Po-Chih Hsu, Tsung-Ming Chang, Peng Chen, Ying-Sui Sun

Journal of Dental Sciences

Background/purpose: Metastasis is the leading cause of treatment failure in oral squamous cell carcinoma (OSCC). This study investigated the role of C-X-C motif chemokine ligand 13 (CXCL13) in OSCC metastasis and the underlying signaling mechanism.

Materials and methods: Integrative transcriptomic analysis was performed using three independent OSCC datasets to identify metastasis-associated genes. CXCL13 expression was examined in OSCC tissues and highly migratory sublines. The effects of CXCL13 on cell migration were evaluated in vitro. Pathway analysis, pharmacological inhibitors, and receptor silencing were used to investigate the downstream signaling pathway. An orthotopic tongue xenograft model was used to evaluate the effect …