Digital Commons Network^™

Early Changes In Spatiotemporal Dynamics Of Remapped Circuits And Global Networks Predict Functional Recovery After Stroke In Mice, Ryan M Bowen, Jake Lee, Brendon Wang, Keith R Lohse, Hanyang Miao, Jonah A Padawer-Curry, Asher J Albertson, Eric C Landsness, Adam Q Bauer, Jin-Moo Lee

2020-Current year OA Pubs

SIGNIFICANCE: Stroke is the leading cause of chronic disability in the United States. How stroke size affects post-stroke repair and recovery is poorly understood.

AIM: We aim to investigate the effects of stroke size on early repair patterns and determine how early changes in neuronal circuits and networks predict functional outcomes after stroke.

APPROACH: We used wide-field optical imaging, photothrombosis, and the cylinder-rearing assay to examine changes in neuronal circuit and network activity in the context of functional recovery after stroke.

RESULTS: Larger strokes ablating

CONCLUSIONS: These results suggest that diffuse patterns of remapping, and desynchronization and hyperconnectivity of cortical …

Tourette Syndrome Research Highlights From 2024 [Version 3; Peer Review: 2 Approved, 1 Approved With Reservations], Andreas Hartmann, Per Andrén, Cyril Atkinson-Clement, Virginie Czernecki, Cécile Delorme, Simon Morand-Beaulieu, Nanette Mol Debes, Kirsten Müller-Vahl, Peristera Paschou, Natalia Szejko, Apostolia Topaloudi, Kevin J Black

2020-Current year OA Pubs

We summarize research reports from 2024 relevant to Tourette syndrome, which the authors consider the most important or interesting. This working draft aims to submit this content for publication around the beginning of 2025 in the yearly Tourette Syndrome Research Highlights series on F1000Research. The authors welcome article suggestions and thoughtful feedback from readers, who can add a comment by clicking on the rectangular comment box icon to the left of the LOG IN link at the top of this page. For private comments, you can reach us by email ( [email protected] or [email protected]).

Adaptive Crisis Management At The Operational Level: Responses To Covid-19 In The Australian Resources Sector, Michael M. Ajith, Andrei A. Lux, Tim Bentley, Michelle Striepe

Research outputs 2022 to 2026

The COVID-19 pandemic in 2020 significantly impacted Australia’s resources sector, particularly mining, oil, and gas industries, posing challenges for operational leaders. This study applied Adaptive Crisis Management Theory (ACMT) to understand how these leaders adapted during the crisis. Through interviews with 32 operational leaders, it was found that their roles evolved as crisis demands changed. Initially, they addressed immediate needs, then shifted focus to remote work facilitation and digital transformation, and finally emphasised recovery, trust, and resilience. These adaptations influenced leaders’ behaviours, highlighting the importance of flexibility in supporting employee wellbeing and organisational continuity during crises.

Young Children Moving Through Ecological Anxiety And Grief: Dancing With Demolition, Jane Merewether, Jo Pollitt, Mindy Blaise

Research outputs 2022 to 2026

As the extent of planetary unraveling becomes increasingly apparent, scholars are beginning to document responses to the loss of ecological systems, mass extinctions, and climate change. However, there are few studies documenting children’s eco-anxiety responses. In this study, children’s eco-anxiety and grief became apparent when they expressed dismay at the imminent demolition of a derelict building. Children were concerned for the fate of the building, the land on which it stood, and the myriad others it sheltered. Using participatory and creative methodologies including dance, this project worked with children to tell a more-than-human story of becoming-with children-building-weeds-graffiti-dust-researchers-unhoused-bacteria-rats-and unloved others, which …

The Cross-Sectional Association Between State-Level Public Health Funding Per Capita And Physical Health Among Adults In The United States, Stephen Hunter, Sze Y. Liu, Daniel M. Cook, Kia L. Davis, Brendan T. Smith, Roman Pabayo

2020-Current year OA Pubs

OBJECTIVES: This study examined the association between state-level public health funding per capita and the odds of poor physical health.

STUDY DESIGN: Cross-sectional.

METHODS: Data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) were used. Participants' self-reported physical health was reported using the CDC Healthy Days Core Module. State-level public health funding per capita was obtained from the State Health Access Data Assistance Center website. Multilevel logistic regression was used to adjust for self-reported individual-level characteristics and state-level characteristics from the 2018 American Community Survey. We also tested whether household income or education attainment moderated any observed associations.

RESULTS: …

Sociodemographic Factors Predict Outcomes And Reveal Spatial Tumor Patterns In Glioblastoma, Patrick H Luckett, Michael Olufawo, Noah Naddaff-Slocum, Ki Yun Park, Bidhan Lamichhane, Donna Dierker, Gabriel Trevino Verastegui, John J Lee, Peter Yang, Albert Kim, Omar H Butt, Milan G Chheda, Abraham Z Snyder, Joshua S Shimony, Eric C Leuthardt

2020-Current year OA Pubs

BACKGROUND: Glioblastoma (GBM) is the deadliest malignant glioma of the central nervous system. Postsurgical functional impairment correlates with survival and is estimated using metrics such as extent and location of resection. This study uses machine learning to evaluate the predictive ability of baseline sociodemographic and lifestyle factors for forecasting postoperative functional outcomes in GBM patients.

METHODS: Glioblastoma patients (

RESULTS: Utilizing decision trees with age, SVI/SES, sex, tobacco use, alcohol use, obesity, and race as predictors, we achieved 88% accuracy in classifying median KPS and 85% accuracy in classifying KPS slope. Socioeconomic factors were the strongest predictors. Age, sex, and …

Thalamic And Visual Network Dysfunction Relates To Tremor Response In Thalamic Deep Brain Stimulation, Aimee E Morris, Babatunde Adeyemo, Meghan C Campbell, Abraham Z Snyder, Joel S Perlmutter, Jonathan W Mink, Scott A Norris

2020-Current year OA Pubs

BACKGROUND: Essential tremor (ET) is the most common movement disorder in adults, but its pathogenesis is incompletely understood. Deep brain stimulation of the ventral intermediate thalamic nucleus (VIM DBS) provides effective treatment for medically-refractory cases. We aimed to determine how pre-surgical resting-state functional connectivity (FC) in medically-refractory ET relates to VIM DBS clinical response.

METHODS: We analyzed resting-state FC MRI in 21 participants with medically-refractory ET who subsequently underwent VIM DBS and 34 matched controls. We applied rigorous quality assurance to minimize motion artifact. Whole-brain correlation matrices were computed across 300 cortical, subcortical, and cerebellar regions and compared across groups …

Pediatric Cervical Spine Clearance: A 10-Year Evaluation Of X-Ray In Evaluable Patients At A Level 1 Pediatric Trauma Center, William Starr, Stephanie Iantorno, Jing Wang, Richard Eldredge, Rajiv Iyer, Karch Smith, Natalya Mcnamara, Kezlyn Larsen, Stephen Fenton, Robert Swendiman, Katie Russell

2020-Current year OA Pubs

OBJECTIVES: Cervical spine (c-spine) X-ray (XR) remains an important tool for pediatric trauma patients. XR is a low radiation alternative to multidetector CT (MDCT). Our primary aims were to analyze the sensitivity of a negative c-spine XR and to analyze what factors lead to additional imaging after a negative c-spine XR.

METHODS: A retrospective cohort study was conducted at a level 1 pediatric trauma center from 2012 to 2021. We compared the group with negative c-spine XR and no additional imaging to the group with negative c-spine XR followed by additional c-spine MDCT and/or MRI.

RESULTS: There were 2,081 negative …

A Clinical Practice Review Of Optimizing The Approach For Correction Of Thoracolumbar Deformity— When And How To Perform Anterior Releases And Fusion, Brian M. Phelps, Munish C. Gupta

2020-Current year OA Pubs

No abstract provided.

Doing The Nutbush: How Australia Got Its Very Own Line Dance, Panizza Allmark, Jon Stratton

Research outputs 2022 to 2026

The Nutbush dance is unique to Australia. It is danced to the Ike and Tina Turner track Nutbush City Limits released in 1973. It is a line dance. Anybody can join the line. This article explores the history and reception of the Nutbush. The Nutbush seems have been developed around 1975 in Sydney as a part of modernizing the physical education and creative arts curricula for state primary and secondary schools. The Nutbush is relatively simple and is danced on the beat, a characteristic of dancing to rock music. Nutbush City Limits has a driving beat. This is no doubt …

Gans And Synthetic Financial Data: Calculating Var*, David E. Allen, Leonard Mushunje, Shelton Peiris

Research outputs 2022 to 2026

Generative Adversarial Neural nets (GANs) are a new branch of machine learning techniques. A GAN learns to generate new data from the training data set. We examine the characteristics of the fake financial data using GANs trained on samples of daily S&P 500 and FTSE 100 index values. GANs feature two competing neural networks in a game theoretic context. The Generator net generates pseudo data that is presented to the discriminator net which then attempts to distinguish between the real and the fake data. This facilitates unsupervised learning on the dataset. The generative network generates data sets, while the discriminative …

S-Methyl Cysteine Sulfoxide And Its Potential Role In Human Health: A Scoping Review, Caroline R. Hill, Alex Haoci Liu, Lyn Mccahon, Liezhou Zhong, Armaghan Shafaei, Lois Balmer, Joshua R. Lewis, Jonathan M. Hodgson, Lauren C. Blekkenhorst

Research outputs 2022 to 2026

Higher intakes of cruciferous and allium vegetables are associated with a lower risk of cardiometabolic-related outcomes in observational studies. Whilst acknowledging the many healthy compounds within these vegetables, animal studies indicate that some of these beneficial effects may be partially mediated by S-methyl cysteine sulfoxide (SMCSO), a sulfur-rich, non-protein, amino acid found almost exclusively within cruciferous and alliums. This scoping review explores evidence for SMCSO, its potential roles in human health and possible mechanistic action. After systematically searching several databases (EMBASE, MEDLINE, SCOPUS, CINAHL Plus Full Text, Agricultural Science), we identified 21 original research articles meeting our inclusion criteria. These …

Measuring Stigma In Pediatric Oncology: A Cross-Sectional Analysis Of Three Global Sites, Lara E Counts, Sara M Malone, Et Al.

2020-Current year OA Pubs

PURPOSE: Stigma contributes to fear and shame, resulting in delays in care-seeking behavior among individuals with cancer. As a social construct, stigma is affected by language, religion, culture, and local norms. This study explored pediatric cancer stigma at the time of diagnosis across diverse settings through the adaptation of two stigma measures.

METHODS: This study was conducted with adolescents and caregivers of children with osteosarcoma and retinoblastoma at three centers in Jordan, Guatemala, and Zimbabwe. The Stigma-related Social Problems (SSP) and the eight-item Stigma Scale for Chronic Illness (SSCI-8) measures were translated into Arabic, Spanish, and Shona and contextually adapted …

Proteomic Characterization And Comparison Of The Infective And Adult Life Stage Secretomes From Necator Americanus And Ancylostoma Ceylanicum, Yide Wong, Bruce A. Rosa, Makedonka Mitreva, Et Al.

2020-Current year OA Pubs

More than 470 million people globally are infected with the hookworms Ancylostoma ceylanicum and Necator americanus, resulting in an annual loss of 2.1 to 4 million disability-adjusted-life-years. Current infection management approaches are limited by modest drug efficacy, the costs associated with frequent mass drug administration campaigns, and the risk of reinfection and burgeoning drug resistance. Subunit vaccines based on proteins excreted and secreted (ES) by hookworms that reduce worm numbers and associated disease burden are a promising management strategy to overcome these limitations. However, studies on the ES proteomes of hookworms have mainly described proteins from the adult life stage …

Utilizing Graph Theory Algorithms For The Modeling And Analysis Of Covid-19 Infection Dynamics, Ioannis Adamopoulos, Antonios Valamontes, Niki Syrou, Jovanna Adamopoulou, Antonios Bardavouras

Mesopotamian Journal of Artificial Intelligence in Healthcare

The COVID-19 outbreak has shown how urgently good models are needed to understand and project the dissemination of infectious diseases. Graph theory's excellent basis for presenting and analyzing complex networks helps one to grasp COVID-19 transmission dynamics. This work investigates the simulation and evaluation of the COVID-19 spread using multiple graph theory approaches. We utilize algorithms, including centrality assessments, community detection, and epidemic spreading models, to identify significant transmission channels and viable intervention sites; we also study the usage of network-building strategies to show relationships between individuals and communities. By incorporating real-world data with graph-based models, we demonstrate how these …

A Systematic Review Of Artificial Intelligence's Function In The Diagnosis Of Lung Cancer (2018–2024), Rokan Hazim Hamad

Mesopotamian Journal of Artificial Intelligence in Healthcare

Lung cancer is a leading cause of cancer-related mortality, often diagnosed at advanced stages. This systematic review explores AI applications in lung cancer diagnosis, focusing on medical imaging, pathology, and genetic analysis. A systematic literature review methodology was employed, analyzing studies from databases such as PubMed, IEEE Xplore, and Scopus (2018–2024). Findings indicate that AI-powered diagnostic models, particularly deep learning techniques, outperform conventional methods in accuracy, sensitivity, and early detection capabilities. However, integration into clinical practice presents challenges, including data privacy concerns, model biases, and regulatory limitations. This review highlights the potential of AI in lung cancer screening and provides …

Models Of Machine Learning To Diagnose Chronic Kidney Disease Using A  Weka-Based Classifier, Shaymaa Adnan Abdulrahman, Sameerah Faris Khlebis

Mesopotamian Journal of Artificial Intelligence in Healthcare

In the present day, humans are confronted with a variety of diseases as a result of their lifestyle and the current environmental conditions. Therefore, it is crucial to identify and predict these diseases in their early phases in order to prevent their severe manifestations. Manually identifying maladies is a challenging task for physicians on a regular basis. Predicting chronic illnesses is the aim of this article. This goal is applicable through a state-of-the-art approach to classification correctly identifies people with chronic illnesses. Predicting maladies is also a difficult endeavor. Therefore, disease prediction is significantly influenced by data mining. To get …

Optimizing Decision Tree Classifiers For Healthcare Predictions: A Comparative Analysis Of Model Depth, Pruning, And Performance, Hussein Alkattan, Ali Subhi Alhumaima, Mohammed Shakir Mohmood, Ghassan Dhahir Mohammed Al-Thabhawee

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study presents of Decision Tree classifiers for predictive modeling in medicine, focusing on model depth optimization, pruning techniques, and performance evaluation. On the basis of a synthetic healthcare dataset containing over 55,000 records, each with features such as age, gender, blood type, bill amount, and medical condition, we investigate the impact of varying tree depth from 1 to 5 on predictive accuracy, interpretability, and generalizability. Shallow models have strong transparency but poor classification strength, and deep models obtain stronger interactions but suffer from overfitting. With pruning, we find a balance between model simplicity and precision and yield strong classifiers …

Meta-Learning Approaches For Causal Discovery In Dynamic Healthcare And Robotics Environments, Hayder Abbood Md Facp

Mesopotamian Journal of Artificial Intelligence in Healthcare

Healthcare and robotics dynamic task domains are also known to be both complex and time-varying systems where reasoning about causal links are important for making reliable decisions. But causal structures in these domains are difficult to ascertain with limited data, distribution shifts, and changing environments. Meta-learning (or learning to learn) is a promising approach to combating these two obstacles by learning from experience across tasks to quickly adapt to new task environments. In this paper we provide a new framework that combines ideas from meta-learning and causal discovery, whose objective is to discover the underlying cause effect relationships in sequential, …

Α Novel Ai-Based Modeling With Bias Classification Hybrid Risk Evaluation System For Confidence Enhanced Network Meta-Analysis Of Occupational Hazards And Burnout Risk Among Public Health Inspectors, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Public Health Inspectors (PHIs) serve a critical role in enforcing health and safety regulations, particularly under the growing pressures of climate change. With rising exposure to occupational hazards such as heat waves, air pollution, and vector-borne diseases, PHIs now face escalating stress and burnout. Geographical variability, limited resources, and institutional gaps in training and support further shape their complex risk profile. Despite growing concern, systematic, confidence-based evaluations of these occupational risks—especially tailored to PHIs—remain rare. This study addresses that gap using a Confidence in Network Meta-Analysis (CINeMA)-enhanced framework to assess domain-specific occupational risk profiles of PHIs working in climate-stressed environments. …

Lightweight Deep Learning For Iot-Based Medical Diagnosis: A Survey, Balqees Talal Hasan, Ali Mohsin Ahmed Al-Sabaawi, Zaid J. Al-Araji

Mesopotamian Journal of Artificial Intelligence in Healthcare

The rapid expansion of Internet of Things (IoT) technologies in healthcare has enabled continuous patient monitoring and data-driven clinical decision support. Deep learning models excel at extracting intricate patterns from high-dimensional medical data but face significant deployment challenges on resource-constrained IoT devices due to high computational and memory demands. This survey systematically analyzes four core strategies to adapt deep learning for edge based medical diagnosis: model pruning, quantization, knowledge distillation, and inherently lightweight architectures. For each approach, we dissect the underlying methodologies, evaluate the trade-offs between model efficiency and predictive performance, and present relevant medical case studies. We further review …

Analysing The Behaviour Change Techniques In An Effective Food Literacy Program To Inform Future Program Design, Lucy M. Butcher, Caylah Batt, Sophie Royce, Eamon Barron, Roslyn Giglia, Andrea Begley

Research outputs 2022 to 2026

Aim: Food literacy programs aim to improve food related skills and knowledge and are selected Governments as a strategy to address dietary intakes at a community level. The purpose of this research is to identify behaviour change techniques in a food literacy program, which were assessed by the achievement of participant goals related to food literacy and dietary behaviour changes. Methods: Consensus methods were applied to map behaviour change techniques to an adult food literacy program. A second phase investigation applied content analysis of participant process evaluation data (collected between 2016 and June 2021) to align target behaviours and behaviour …

Impact Of Rurality On Health Care Utilization Among Australian Residents From 2009 To 2021, Engida Yisma, Martin Jones, Lee San Pauh, Sandra Walsh, Sara Jones, Esther May, Marianne Gillam

Research outputs 2022 to 2026

Purpose: In Australia, there is limited research regarding the effect of rurality on health care utilization using longitudinal data.

Methods: We analyzed data from four annual waves (2009, 2013, 2017, and 2021) of the longitudinal Household, Income and Labour Dynamics in Australia (HILDA) Survey to examine changes in the health care utilization over time among urban and rural residents. Poisson regression models estimated adjusted incidence rate ratios (aIRR) and 95% confidence intervals (CIs) for rural versus urban residents, accounting for a range of health-related and sociodemographic characteristics. Health care utilization was measured using four key indicators: visits to family doctor …

A New Interpolated Pseudodifferential Preconditioner For The Helmholtz Equation In Heterogeneous Media

Faculty, Staff and Students Publications

This paper introduces a new pseudodifferential preconditioner for the Helmholtz equation in variable media with absorption. The pseudodifferential operator is associated with the multiplicative inverse to the symbol of the Helmholtz operator. This approach is well suited for the intermediate and high-frequency regimes. The main novel idea for the fast evaluation of the preconditioner is to interpolate its symbol, not as a function of the (high-dimensional) phase-space variables, but as a function of the wave speed itself. Since the wave speed is a real-valued function, this approach allows us to interpolate in a univariate setting even when the original problem …

Developing A True North For Regulating At: The Importance Of Protecting Consumers And Promoting Innovation, Phil Weiser

Colorado Technology Law Journal

No abstract provided.

Code As Content: Open Source Software And Platform Liability, Jessica Cheng

Colorado Technology Law Journal

No abstract provided.

Early Cerebrospinal Fluid Elevations Of Ptau-217 In Severe Traumatic Brain Injury Subjects, Spyridoula Tsetsou, John B Williamson, Damon G Lamb, Amy K Wagner, Todd Kibaugh, Shih-Han Kao, Zhifeng Kou, Robert D Welch, Jose-Miguel Yamal, Luis Leon-Novelo, Richard Rubenstein, Kevin K W Wang

Faculty, Staff and Students Publications

Introduction: Tauopathies, including Alzheimer's disease (AD), feature abnormal accumulations of hyperphosphorylated Tau protein; however, their biomarker potential in traumatic brain injury (TBI) is not well-defined. This study investigated whether cerebrospinal fluid (CSF) phosphorylated Tau at threonine-217 (pTau-217) could serve as an early biomarker for severe TBI (sTBI).

Methods: CSF samples from 26 sTBI patients, collected between 6 and 240 h post-injury, and 19 healthy controls were analyzed using an optimized direct enzyme-linked immunosorbent assay (ELISA; sensitivity < 4.7 pg/mL) for pTau-217 detection, complemented by Western blot validation. Temporal analysis, ROC curves, and trajectory clustering were used for interpretation.

Results: CSF pTau-217 levels were significantly elevated in sTBI patients at 6, 12, 18, 24, and 48 h post-injury compared to controls (p …

Assessing The Cardioprotective Effects Of Exercise In Apoe Mouse Models Using Deep Learning And Photon-Counting Micro-Ct, Darin P Clark, Ketan B Ghaghada, Alexandra Badea, Cristian T Badea

Faculty, Staff and Students Publications

Background: The allelic variations of the apolipoprotein E (APOE) gene play a critical role in regulating lipid metabolism and significantly impact cardiovascular disease risk (CVD). This study aimed to evaluate the impact of exercise on cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT) and deep learning-based segmentation.

Methods: A total of 140 mice were grouped based on APOE genotype (APOE2, APOE3, APOE4), sex, and exercise regimen. All mice were maintained on a controlled diet to isolate the effects of exercise. Low dose cardiac photon counting micro-CT imaging with intrinsic gating was performed …

Defining Dysphoric Milk Ejection Reflex: Using Premenstrual Dysphoric Disorder As A Framework For Proposing Preliminary Diagnostic Criteria

Faculty, Staff and Students Publications

Dysphoric Milk Ejection Reflex (D-MER) is a distinct neurobiological condition characterized by negative alterations in mental state in response to milk letdown during lactation. Symptoms vary by patient and can include feelings of sadness, anxiety or agitation. Importantly, the symptoms are brief, typically lasting no more than 5 minutes. Prevalence has been found between 6 and 27% of lactating women, but studies show heterogeneity, due in part to inconsistent definition. D-MER is not currently classified in the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD), which presents a challenge for researchers of the …

Prevalence Of Abuse-Specific Injuries In Infants With Prior Injury Evaluated For Abuse, Krista K Wheeler, Rachel M Stanley, Megan M Letson, Kristine A Campbell, Angela N Bachim, Colleen J Bressler, Carmen M Coombs, Lori Frasier, Nicole Johnson, Sara Kilbride, Jan Leonard, Daniel M Lindberg, Noelle T Tran, Joanne N Wood, Farah W Brink

Faculty, Staff and Students Publications

Background and objectives: Physical abuse is a common cause of morbidity in infants but is often missed in those with medically minor injuries, increasing the risk for escalating violence. Our objective was to compare the prevalence of abuse-specific injuries among infants with and without prior injuries.

Methods: This is a retrospective, multicenter study of infants (aged < 12 months) undergoing child abuse pediatrics provider (CAP) consultation for suspected physical abuse between 2/2021 and 12/2022 at 10 participating US centers. Encounters were dichotomized into those with and without history of prior injury. The groups were compared for rates of diagnostic screening and prevalence of abuse-specific injuries. The association of prior injuries with identification of abuse-specific injury was assessed using logistic regression controlling for demographics and presence of prior injury.

Results: We identified 2352 eligible encounters (median age: 4.2 months), of which 13% had a history of prior injury. In infants who underwent diagnostic testing, injury identification was significantly higher in those with prior injury (45% vs 22%, P < .05). Adjusting for patient characteristics, the presence of prior injury in infants was associated with higher odds of at least 1 type of abuse-specific injury being identified, compared with those without prior injury (adjusted odds ratio = 2.81, 95% CI = 2.10-3.76).

Conclusion: Infants with prior injury …