Alexander C Razavi, Mikaila P Reyes, John T Wilkins
et al.
Abstract Aims To assess the association of traditional risk factor burden and Life’s Simple 7 (LS7) score with incident atherosclerotic cardiovascular disease (ASCVD) across lipoprotein(a) [Lp(a)] levels. Methods and results There were 6676 participants without clinical ASCVD from the Multi-Ethnic Study of Atherosclerosis who underwent Lp(a) testing and were followed for incident ASCVD events (coronary heart disease and stroke). Low, intermediate, and elevated Lp(a) were defined as <30, 30–49, and ≥50 mg/dL, respectively. Cox proportional hazards regression assessed the association of traditional risk factors and LS7 score (poor: 0–8, average: 9–10, and optimal: 11–14) with incident ASCVD across Lp(a) groups during a median follow-up of 17.7 years, adjusting for demographics and time-varying statin and aspirin therapy. The mean age was 62.1 years, 53% were women, and 61% were non-white. The median Lp(a) was 17 (interquartile range 8–41) mg/dL, 13% had Lp(a) 30–49 mg/dL, and 20% had Lp(a) ≥ 50 mg/dL. Individuals with Lp(a) ≥ 50 mg/dL had higher absolute event rates across all LS7 categories. There was no significant interaction between Lp(a) and LS7 score on incident ASCVD (P-interaction = 0.60). Compared to a poor LS7 score, optimal LS7 conferred a lower risk for incident ASCVD among individuals with Lp(a) < 30 [hazard ratio (HR) = 0.45, 95% confidence interval (CI): 0.28–0.71], Lp(a) 30–49 (HR = 0.12, 95% CI: 0.02–0.89), and Lp(a) ≥ 50 mg/dL (HR = 0.35, 95% CI: 0.13–0.99). Conclusion Participants without clinical ASCVD who achieved an optimal LS7 score had ASCVD risk reduction regardless of Lp(a) level. These results emphasize the importance of a healthy lifestyle and ASCVD risk factor control among individuals with elevated Lp(a).
Mohammad R. Ostovaneh, Timothy M. Hughes, Colin O. Wu
et al.
Background Our understanding of the specific aspects of vascular contributions to dementia remains unclear. Objectives We aim to identify the correlates of incident dementia in a multi-ethnic cardiovascular cohort. Methods A total of 6806 participants with follow-up data for incident dementia were included. Probable dementia diagnoses were identified using hospitalization discharge diagnoses according to the International Classification of Diseases Codes (ICD). We used Random Forest analyses to identify the correlates of incident dementia and cognitive function from among 198 variables collected at the baseline MESA exam entailing demographic risk factors, medical history, anthropometry, lab biomarkers, electrocardiograms, cardiovascular magnetic resonance imaging, carotid ultrasonography, coronary artery calcium and liver fat content. Death and stroke were considered competing events. Results Over 14 years of follow-up, 326 dementia events were identified. Beyond age, the top correlates of dementia included coronary artery calcification, high sensitivity troponin, common carotid artery intima to media thickness, NT-proBNP, physical activity, pulse pressure, tumor necrosis factor-α, history of cancer, and liver to spleen attenuation ratio from computed tomography. Correlates of cognitive function included income and physical activity, body size, serum glucose, glomerular filtration rate, measures of carotid artery stiffness, alcohol use, and inflammation indexed as IL-2 and TNF soluble receptors and plasmin-antiplasmin complex. Conclusion In a deeply phenotyped cardiovascular cohort we identified the key correlates of dementia beyond age as subclinical atherosclerosis and myocyte damage, vascular function, inflammation, physical activity, hepatic steatosis, and history of cancer.
With the rapid development of LLMs and AIGC technology, we present a Rhino platform plugin utilizing stable diffusion technology. This plugin enables real-time application deployment from 3D modeling software, integrating stable diffusion models with Rhino's features. It offers intelligent design functions, real-time feedback, and cross-platform linkage, enhancing design efficiency and quality. Our ongoing efforts focus on optimizing the plugin to further advance AI applications in CAD, empowering designers with smarter and more efficient design tools. Our goal is to provide designers with enhanced capabilities for creating exceptional designs in an increasingly AI-driven CAD environment.
This paper examines the complex nature of cyber attacks through an analysis of the LastPass breach. It argues for the integration of human-centric considerations into cybersecurity measures, focusing on mitigating factors such as goal-directed behavior, cognitive overload, human biases (e.g., optimism, anchoring), and risky behaviors. Findings from an analysis of this breach offers support to the perspective that addressing both the human and technical dimensions of cyber defense can significantly enhance the resilience of cyber systems against complex threats. This means maintaining a balanced approach while simultaneously simplifying user interactions, making users aware of biases, and discouraging risky practices are essential for preventing cyber incidents.
In this article, we explore the transformative potential of integrating generative AI, particularly Large Language Models (LLMs), into behavioral and experimental economics to enhance internal validity. By leveraging AI tools, researchers can improve adherence to key exclusion restrictions and in particular ensure the internal validity measures of mental models, which often require human intervention in the incentive mechanism. We present a case study demonstrating how LLMs can enhance experimental design, participant engagement, and the validity of measuring mental models.
This demo showcases a simple tool that utilizes AIGC technology, enabling both professional designers and regular users to easily customize clothing for their digital avatars. Customization options include changing clothing colors, textures, logos, and patterns. Compared with traditional 3D modeling processes, our approach significantly enhances efficiency and interactivity and reduces production costs.
Barbara Sienkiewicz, Zuzanna Radosz-Knawa, Bipin Indurkhya
We present our ongoing research on applying a participatory design approach to using social robots for elderly care. Our approach involves four different groups of stakeholders: the elderly, (non-professional) caregivers, medical professionals, and psychologists. We focus on card sorting and storyboarding techniques to elicit the concerns of the stakeholders towards deploying social robots for elderly care. This is followed by semi-structured interviews to assess their attitudes towards social robots individually. Then we are conducting two-stage workshops with different elderly groups to understand how to engage them with the technology and to identify the challenges in this task.
The Prism XR project is a curated exhibition experience in virtual reality (VR) for art and archaeology education with features designed for the enhancement of interactivity and collaborative learning. The project integrates peer annotations and a virtual exhibition guide to augment educational experiences. The peer annotation features are intended for facilitating visitor critiques and comments pivotal in fostering a dialog between the curator and the audience and a dialogue between the visitors in art and archaeology education, which are demonstrated to have positive impacts on the learning motivations and learning outcomes. The virtual exhibition guide is intended to address the issue of isolation in the virtual exhibition space and to increase interactivity in the virtual curatorial experiences.
Disclosing sensitive health conditions offers significant benefits at both individual and societal levels. However, patients often face challenges due to concerns about stigma. The use of social robots and chatbots to support sensitive disclosures is gaining traction, especially with the emergence of LLM models. Yet, numerous technical, ethical, privacy, safety, efficacy, and reporting concerns must be carefully addressed in this context. In this position paper, we focus on the example of HIV status disclosure, examining key opportunities, technical considerations, and risks associated with LLM-backed social robotics.
Matthew M Scarpaci, Jee Won Park, Laura Dionne
et al.
Abstract Few prospective studies examine multilevel resilience resources and psychosocial factors in relation to cardiovascular health and disease. Recent research indicates that resilience resources are associated with a reduction in the incidence of cardiovascular disease-related events, but few studies have examined this relationship across different racial/ethnic populations or in large cohorts. Harmonization may address these limitations because it allows data from several cohorts to be analyzed together, potentially increasing sample size and in turn power overall and in minority populations. This article describes the process involved in combining 3 cardiovascular health-focused cohorts: Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, and Mediators of Atherosclerosis in South Asians Living in America Study. Using a systematic process, we identified appropriate data harmonization techniques to use in harmonizing variables across cohorts. Variables included exposures (eg, resilience resources), outcomes (eg, American Heart Association’s Life’s Simple 7), and covariates (eg, race and ethnicity). Post-harmonization examinations included psychometric analyses of the harmonized variables. A total of 13 284 participants were included in the final harmonized data set. This project provides opportunities for future research in resilience resources and informs future studies that need to harmonize data. Results based on the harmonized data set could inform interventions and policies.
Tianyi Huang, Scott A. Sands, Meir J. Stampfer
et al.
Abstract Rationale Recent prospective studies suggest diabetes as a risk factor for the development of obstructive sleep apnea (OSA). However, the extent to which diabetes-related traits, such as hyperglycemia and insulin resistance, are related to OSA risk remains uncertain. Objectives To examine the risk of developing OSA according to baseline concentrations of fasting insulin and hemoglobin A1c (HbA1c). Methods Participants from four prospective U.S. cohorts were included: NHS (Nurses’ Health Study; 2002–2012), NHSII (Nurses’ Health Study II; 1995–2013), HPFS (Health Professionals Follow-up Study; 1996–2012), and MESA (Multi-Ethnic Study of Atherosclerosis; 2000–2012). OSA was assessed by self-reported clinical diagnosis in NHS/NHSII/HPFS and at-home polysomnography in MESA (defined as Apnea–Hypopnea Index ⩾30). Results Of 9,283 participants with fasting insulin data, 790 (8.5%) developed OSA over 10 to 18 years of follow-up. After adjusting for sociodemographic, lifestyle, and comorbidity factors, the odds ratio for incident OSA comparing the extreme quintiles of fasting insulin was 3.59 (95% confidence interval, 2.67–4.82; P-trend < 0.0001). Of 6,342 participants with HbA1c data, 715 (11.3%) developed OSA. The comparable odds ratio for HbA1c was 2.21 (95% confidence interval, 1.69–2.89; P-trend < 0.0001). Additional adjustment for body mass index and waist circumference attenuated the associations for fasting insulin (P-trend = 0.005) and HbA1c (P-trend = 0.03). In the fully adjusted model simultaneously including both biomarkers, only fasting insulin but not HbA1c was associated with OSA risk. Conclusions Independent of obesity, insulin resistance may play a more important role than hyperglycemia in the pathogenesis of OSA. Given the limitation of using self-reported diagnosis to exclude baseline prevalent OSA cases, additional studies are needed to further establish the temporal relationship and assess whether improving insulin resistance may reduce OSA risk.
Michael P Bancks, Alain G Bertoni, Mercedes Carnethon
et al.
Abstract Introduction There are known disparities in diabetes complications by race and ethnicity. Although diabetes subgroups may contribute to differential risk, little is known about how subgroups vary by race/ethnicity. Methods Data were pooled from 1293 (46% female) participants of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) and the Multi-Ethnic Study of Atherosclerosis (MESA) who had diabetes (determined by diabetes medication use, fasting glucose, and glycated hemoglobin [HbA1c]), including 217 South Asian, 240 non-Hispanic white, 125 Chinese, 387 African American, and 324 Hispanic patients. We applied k-means clustering using data for age at diabetes diagnosis, body mass index, HbA1c, and homeostatic model assessment measures of insulin resistance and beta cell function. We assessed whether diabetes subgroups were associated with race/ethnicity, concurrent cardiovascular disease risk factors, and incident diabetes complications. Results Five diabetes subgroups were characterized by older age at diabetes onset (43%), severe hyperglycemia (26%), severe obesity (20%), younger age at onset (1%), and requiring insulin medication use (9%). The most common subgroup assignment was older onset for all race/ethnicities with the exception of South Asians where the severe hyperglycemia subgroup was most likely. Risk for renal complications and subclinical coronary disease differed by diabetes subgroup and, separately, race/ethnicity. Conclusions Racial/ethnic differences were present across diabetes subgroups, and diabetes subgroups differed in risk for complications. Strategies to eliminate racial/ethnic disparities in complications may need to consider approaches targeted to diabetes subgroup.
Ricardo Ladeiras-Lopes, Henrique T. Moreira, Nuno Bettencourt
et al.
The relationship of metabolic syndrome (MetS) and insulin resistance (one of its key pathophysiological mediators) with diastolic dysfunction and myocardial fibrosis is not well understood. This study aimed to evaluate the association of MetS with diastolic function and myocardial extracellular matrix (ECM) using cardiac MRI (CMRI) in a large community-based population. This cross-sectional analysis included 1,582 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) with left ventricular ejection fraction ≥50% and no history of cardiac events. Diastolic function was assessed using tagged CMRI parameters including end-diastolic strain rate (EDSR) and strain relaxation index (SRI). ECM was evaluated using extracellular volume (ECV) quantification. Participants’ mean age was 67.4 ± 8.6 years, and 48.1% were males. MetS was present in 533 individuals (33.7%), and type 2 diabetes in 250 (15.8%). In the multivariable analyses, MetS (irrespective of the presence of type 2 diabetes) and higher insulin resistance were associated with impaired diastolic function (higher SRI and lower EDSR), independent of ECV. In conclusion, MetS, irrespective of the presence of type 2 diabetes, was independently associated with impaired diastole. These functional myocardial changes seem to result from intrinsic cardiomyocyte alterations, irrespective of the myocardial interstitium (including fibrosis).
The work is devoted to a modern state, methods and tools of monitoring, assessment and prediction of the indicators showing physical condition of a person and his/her capabilities to perform work duties. The work contains an analysis of existing gadgets and software that allow tracking physical condition of personnel at the working place. The analysis showing significant interconnections and factors that determine a necessary level of working capacity and productivity of personnel allows organizing Work & Rest Schedule of employees in an effective manner.
The insight and experience gained by a researcher are often lost because the current productive and analytics software are inherently data-centric, disconnected, and scattered. The connected nature of insight and experience can be captured if the applications themselves are connected. How connected applications concept is implemented in COnstruct cheMical and BIological NEtwork (COMBINE), a novel user-centric drug discovery platform, is described. Using publicly available data, how COMBINE users capture insight and experience is explained, and how COMBINE users perform data organization, data sharing, data analysis, and data visualization is illustrated.
In this study, we aim to identify moments of rudeness between two individuals. In particular, we segment all occurrences of rudeness in conversations into three broad, distinct categories and try to identify each. We show how machine learning algorithms can be used to identify rudeness based on acoustic and semantic signals extracted from conversations. Furthermore, we make note of our shortcomings in this task and highlight what makes this problem inherently difficult. Finally, we provide next steps which are needed to ensure further success in identifying rudeness in conversations.