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Time-Synchronized Sentiment Labeling Via Autonomous Online Comments Data Mining: A Multimodal Information Fusion on LargeScale Multimedia Data
No full textWhile temporal sentiment labels prove invaluable for video tagging, segmentation, and labeling tasks in multimedia studies, large-scale manual annotation remains cost and time-prohibitive. Emerging Online Time-Sync Comment (TSC) datasets offer promising alternatives for generating sentiment maps. However, limitations in existing TSC scope and a lack of resource-constrained data creation guidelines hinder broader use. This study addresses these challenges by proposing a novel system for automated TSC generation utilizing recent YouTube comments as a readily accessible source of time-synchronized data. The efficacy of our multi-platform data mining system is evaluated through extensive long-term trials, leading to the development and analysis of two large-scale TSC datasets. Benchmarking against original temporal Automatic Speech Recognition (ASR) sentiment annotations validates the accuracy of our generated data. This work establishes a promising method for automatic TSC generation, laying the groundwork for further advancements in multimedia research and paving the way for novel sentiment analysis applications
COVID-19 and the Impact of Physical Activity on Persistent Symptoms
No full textIntroduction: Physical activity is protective against chronic disease but whether activity is associated with persistent symptoms in non-hospitalized coronavirus disease 2019 (COVID-19) survivors is unknown. The purpose of the study was to determine the impact of the COVID-19 pandemic on physical activity levels and the influence of physical activity on acute COVID-19 and long COVID symptoms in non-hospitalized COVID-19 survivors.
Methods: In total, 64 non-hospitalized COVID-19 survivors (45 female participants, 40 ± 18 years) were assessed for activity levels, body composition, and symptoms of COVID-19 8.5 ± 4.7 months post-infection and categorized into two groups: (1) persistent symptoms and (2) no symptoms at the time of testing. Furthermore, 43 of the 64 participants (28 female participants, 46 ± 18 years) completed a follow-up questionnaire online 51.0 ± 39.7 months (4.25 years) post-infection. A subset of 22 COVID-19 survivors (16 female participants, 35 ± 16 years) were matched for age, sex, and body mass index with healthy controls. Physical activity was quantified using (1) self-reported questionnaire (International Physical Activity Questionnaire; IPAQ-SF) at three time periods; prior to COVID-19 infection, at the time of laboratory testing (8.5 ± 4.7 months after infection), and during an online follow-up (51.0 ± 39.7 months, i.e., 4.25 years after infection); and (2) 7 days of wearing an ActiGraph accelerometer following laboratory testing.
Results: Physical activity (IPAQ-SF) declined in COVID-19 survivors from pre-COVID-19 infection to 8.5 ± 4.7 months after infection [3,656 vs. 2,656 metabolic equivalent of task (MET) min/week, 27% decrease, p \u3c 0.001, n = 64] and rebounded to levels similar to pre-COVID-19 infection at 4.25 years after infection (p = 0.068, n = 43). Activity levels quantified with accelerometry did not differ between COVID-19 survivors and controls. However, COVID-19 survivors who reported persistent symptoms 8.5 months after infection (n = 29) engaged in less moderate-vigorous physical activity and steps/day than those without persistent symptoms (n = 27) (37 vs. 49 MET min/day, p = 0.014 and 7,915 vs. 9,540 steps/day, p = 0.014).
Discussion: Both COVID-19 survivors and matched controls reported reductions in physical activity indicating that lower levels of activity were likely due to the pandemic rather than COVID-19 infection alone. However, those who were most affected by COVID-19 infection with persistent symptoms had the greatest reductions in physical activity, even at ∼8 months and ∼4 years post-infection
Emergence of Multivariate Extremes in Multilayer Inhomogeneous Random Graphs
No full textIn this paper we develop a multilayer inhomogeneous random graph model (MIRG). Layers of the MIRG may consist of both single-edge and multi-edge graphs. In the single layer case, it has been shown that the regular variation of the weight distribution underlying the inhomogeneous random graph implies the regular variation of the typical degree distribution. We extend this correspondence to the multilayer case by showing that multivariate regular variation of the weight distribution implies multivariate regular variation of the asymptotic degree distribution. Furthermore, under suitable assumptions, the extremal dependence structure present in the weight distribution will be adopted by the asymptotic degree distribution. By considering the asymptotic degree distribution, a wider class of Chung–Lu and Norros–Reittu graphs may be incorporated into the MIRG layers. Additionally, we prove consistency of the Hill estimator when applied to degrees of the MIRG that have a tail index greater than 1. Simulation results indicate that, in practice, hidden regular variation may be consistently detected from an observed MIRG. Finally, we analyze user interactions on Reddit and observe that they exhibit properties of the MIRG
A Developmental Texture Framework for Food Texture Progression: Implications for Feeding Development, Oral Motor Skills, and Pediatric Feeding Disorder
No full textThe introduction of food textures in a child\u27s first 2 years of life plays a vital role in growth, nutrition, and feeding development. However, the absence of a standardized texture framework for studying texture progression limits both pediatric feeding research and the ability to diagnose pediatric feeding disorder (PFD) based on age-appropriate expectations. To address this gap, authors Delaney and Goday proposed data sharing from the Nestlé Feeding Infant-Toddler Study (FITS) to explore texture progression and define age-appropriate texture expectations. In response, Nestlé assembled a multidisciplinary panel of feeding experts to create standardized textures but did not provide financial or nonfinancial assistance for this study. This panel integrated literature on global guidelines, texture classification systems, the International Dysphagia Diet Standardization Initiative, food properties, and developmental research. Through an iterative process, they developed a framework with standardized definitions based on food properties. The framework categorizes food textures into five main groups: liquids, purees, mashed solids, chewable solids, and combination foods. These categories are based on food properties (flow rate/cohesiveness, moisture content, firmness, particle size, and particle distribution) and oral motor skills (biting, chewing, tongue force, and tongue control). Each category is further divided into three subcategories. The texture category is determined by how the food is prepared and presented, rather than its original form. This property-based framework offers flexibility in classifying foods based on preparation and presentation, making it ideal for coding existing data and supporting at-home data collection. By establishing a standardized language for food textures, the framework will help fill gaps in normative data, assist in PFD diagnostics, and support future research and clinical applications
Development and Validation of an Artificial Intelligence System for Surgical Case Length Prediction
No full textBackground Accurate case length estimation is a vital part of optimizing operating room use; however, significant inaccuracies exist with current solutions. The purpose of this study was to develop and validate an artificial intelligence system for improved surgical case length prediction by applying natural language processing and machine-learning methods. Methods All inpatient elective surgical cases longer than 30 minutes completed between 2017 and 2023 at a single, quaternary care hospital were considered. Data were split into training, test, and hold-out validation for model training and testing. Linear regression, CategoricalBoost, and feed-forward neural network each were trained and used embeddings created by bidirectional encoder representations from transformers or a bidirectional encoder representations from transformers model pretrained on clinical text. The average root mean squared error and mean absolute error were calculated for each model. Results A total of 125,493 cases were included. The highest performing model was the CategoricalBoost Regressor with bidirectional encoder representations from transformers model pretrained on clinical text embeddings (mean absolute error, 46.4 minutes), which was lower than the existing electronic health record estimates (120.0 minutes, P \u3c 0.001). Accurate estimation of case length was defined as within ±20% of the actual case length with our model having 48% accuracy vs 17% accuracy for electronic health record–generated estimates. Conclusion An artificial intelligence model for surgical case length estimation outperforms existing institutional electronic health record predictions. On average, the estimate improved by 62% and approximately 2.8× the number of cases were correctly estimated. This study shows the successful development of machine learning models using advanced natural language processing techniques for improved surgical case length prediction
Factors Associated With Health Decision-Making Autonomy on Own Healthcare Among Tanzanian Women: A 2022–2023 Demographic Health Survey Study
No full textBackground
Women’s health decision-making autonomy is fundamental for the health and well-being of women and their children. It empowers women to make health decisions and exercise their rights and choices surrounding their health. Like most parts of Africa, women’s autonomy in Tanzania remains contentious, with an estimated 19% prevalence of health decision-making autonomy in 2015. Given the impact of women’s health decision-making autonomy on women’s health outcomes and the fact that women’s health decision-making autonomy is an ongoing process affected by advancements in technology, economic growth, and social and cultural shifts, understanding the sociodemographic correlates of women’s autonomy is imperative. Objective
To examine the factors associated with health decision-making autonomy on their own health among Tanzanian women aged 15–49. Methods
A non-experimental cross-sectional study using secondary data from the current Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) 2022–2023. The R statistical programming language was used to run the analysis. Chi-square and Ordinal Logistic Regression were fitted to identify the sociodemographic characteristics associated with women’s health decision-making autonomy on their own health. The odds ratio with its 95% confidence interval was used to determine the significance level at p-value \u3c 0.05. All estimates were adjusted for sample design (sample weight, strata, and sampling units). Results
A total of 9,249 women were included in the analysis. A large proportion (20%) of women aged 25–29. Only 1,908 (21%) of women had complete autonomy, 4,933 (53%) had joint autonomy, and 2,408 (26%) had no autonomy. Women aged 40–44 years (AOR = 2.15; 95% CI: 1.70, 2.71), a higher education level (AOR = 2.07; 95% CI: 1.39, 3.08), richest household wealth index (AOR = 1.80; 95% CI: 1.39, 2.33), currently working (AOR = 1.61; 95% CI: 1.43, 1.83), and living in the Southwest Highlands zone (AOR = 5.86; 95% CI: 4.47, 7.67) were independently associated with higher odds of complete autonomy in their own healthcare as opposed to no autonomy. Rural residence (AOR = 0.59; 95% CI: 0.46, 0.75) was associated with decreased odds of complete autonomy compared to no autonomy. Conclusion
These results show that health decision-making autonomy among Tanzanian women remains very low. Efforts to empower women through better education and means to improve their economic status are needed to increase complete health decision-making autonomy on their health. Recommendation
Accelerated and concerted efforts to increase health decision-making autonomy among married women will eventually improve their health and well-being and that of society. Future implications to practice, policy, and research
The findings can serve as a basis for exploratory qualitative research to further understand the process of health decision-making autonomy among Tanzanian women. Stakeholders can create focused interventions to improve women’s health decision autonomy, emphasizing education and initiatives that generate income, especially in rural regions. Policymakers are encouraged to continue creating policies that promote women’s education and economic empowerment, as these factors are linked to increased autonomy in healthcare decisions
Engaging a Community-Academic Partnership to Implement Community-Driven Solutions
No full textCommunity engagement is a pivotal public health tool for addressing population health challenges and advancing health equity. Community–academic partnerships that use community-engaged approaches can prioritize community strengths and ensure that resources and interventions match local needs. In 2021–2022, a community-academic partnership, guided by the principles of community engagement, collaborated with residents of Milwaukee’s Near West Side (NWS) to identify strengths and assets and prioritize actions to improve health and quality of life. To inform the development of a planned community resource center, residents were invited for group concept mapping (GCM)
Preoperative and Post-Rehabilitation Predictors of Gait Biomechanics Six Months After Total Knee Arthroplasty
No full textThe primary purpose of this study was to determine the preoperative predictors of gait biomechanics 6 months after unilateral total knee arthroplasty (TKA). There were 126 participants (age 64.4 ± 7.1 years, 75 females) who underwent instrumented biomechanical assessments while walking at a self-selected pace preoperatively, 10 weeks after (post-rehabilitation), and 6 months after unilateral TKA. Outcomes were peak knee extension moment (pKEM), knee angle excursion, and vertical ground reaction force (vGRF) ratio (surgical/contralateral). Potential clinical, demographic, and biomechanical predictors were tested univariately and considered a candidate for the final model if p \u3c 0.15. Each multivariate model initially contained all candidates, and backward selection was used to determine the final model. Greater 6-month surgical limb pKEM was predicted (r2 = 0.31) by greater preoperative pKEM (β = 0.44, p \u3c 0.0001), better quadriceps activation (β = 0.23, p = 0.004), and male sex (β = −0.21, p = 0.009). Greater 6-month surgical knee excursion was predicted (r2 = 0.34) by greater preoperative excursion (β = 0.39, p \u3c 0.0001), male sex (β = −0.28, p = 0.0007), and preoperative quadriceps strength (β = 0.16, p = 0.047). Six-month vGRF ratio was predicted (r2 = 0.16) by preoperative vGRF ratio (β = 0.37, p \u3c 0.0001) and study treatment group (β = 0.18, p = 0.03). Preoperative biomechanical variables at post-rehabilitation were also the strongest predictors of 6-month biomechanics.
Statement of Clinical Significance: The strongest and most consistent predictor of gait biomechanics 6 months after TKA was the respective preoperative gait biomechanics variable, which may have important clinical implications for surgical decision making and prehabilitation/rehabilitation strategies. Biofeedback targeting vGRF predicted vGRF symmetry, but no other gait parameters, suggesting targeted interventions are needed. Improving quadriceps strength and activation may also facilitate knee biomechanics
Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis
No full textThe increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings