Introduction: Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images. Methods: OCT images of 468 eyes were retrospectively collected from a total of 404 ERM patients. One expert manually annotated the ERM regions for all images. A total of 422 images (90%) and the remainig 46 images (10%) were used as the training dataset and validation dataset for deep learning algorithm training and validation, respectively. One senior and one junior clinician read the images. The diagnostic results were compared. Results: The algorithm accurately segmented and located the ERM regions in OCT images. The image-level accuracy was 95.65%, and the ERM region-level accuracy was 90.14%, respectively. In comparison experiments, the accuracies of the junior clinician improved from 85.00% to 61.29% without the assistance of the algorithm to 100.00% and 90.32% with the assistance of the algorithm. The corresponding results of the senior clinician were 96.15%, 95.00% without the assistance of the algorithm, and 96.15%, 97.50% with the assistance of the algorithm. Conclusions: The developed deep learning algorithm can accurately segment ERM regions in OCT images. This deep learning approach may help clinicians in clinical diagnosis with better accuracy and efficiency.

In many industries, partners are interconnected in project alliances that have limited lifespans and clearly-defined boundaries. The transparency of the movie industry provides a unique opportunity to study how alliance network topologies impact the performance of project alliances from the perspectives of social networks and organization theories. In this work, we compiled a massive movie dataset and constructed alliance networks for both movie production and distribution companies. Using the box office as the proxy for the financial performance of a movie project alliance, this research investigates how the two alliance networks impact the box office. We introduce the social network properties of degrees, centralities, and structural holes as alliance network variables into empirical regression models. The results show that alliance networks have a significant influence on the box office. The degrees of production companies and the structural holes of distribution companies are especially important to achieve success in the box office. The results add new evidence for the study of the movie economy and alliance networks. Meanwhile, this work also provides implications for the movie industry by revealing that it is essential to wisely choose partners that are appropriately embedded in alliance networks for the success of a movie project.

Alliance networks are the underlying structures of social systems in business, management, and society. The sustainability and dynamics of a social system rely on the structural evolutions of the topologies. Understanding the evolution sheds light on the dynamics and sustainability of a social system. Minority game models have been successfully applied across social science, economy, management, and engineering. They provide simple yet applicable modeling to articulate the evolutionary cooperation dynamics of competitive players in binary decision situations. By extending the minority games played in alliance networks, the cooperation in structured systems of different network topologies is analyzed. In this model, local and global score strategies are considered with and without cooperation rewiring options. The cooperation level, the score, and the topological properties are investigated. The research uses a numerical simulation approach on random networks, scale-free networks, and small-world networks. The results suggest that the network rewiring strategy leads to higher systemic performance with a higher score and a higher level of stability in decision-making. Competitive decision-making can lead to a higher level of cooperation from a poor initial start. However, stubbornness in decision-making can lead to a poor situation when cooperation is discouraged. Players with local or global information adopt local and global score strategies. The results show that local strategies might lead to imbalance, while a global strategy might achieve a relatively stable outcome. This work contributes to bridge minority games in structured networks to study the cooperation between formation and evolution, and calls for future minority game modeling on social networks.

Although there is a large body of literature linking atrial fibrillation (AF) to adverse clinical outcomes, little is known with regards to the potential links between health-related quality of life (HRQoL) and AF related clinical events. To address this gap, individuals with AF aged ≥ 65 years were recruited from clinics in Massachusetts and Georgia between 2016-18 and followed until 2020. HRQoL was assessed via the Atrial Fibrillation Effect on Quality of Life (AFEQT). The AFEQT is a validated HRQoL measure that quantifies the effect of AF on patients’ daily life on a scale of 0 to 100. AFEQT scores < 80 indicated poor HRQoL and ≥ 80 indicated good HRQoL. The clinical events included major bleeding, stroke, and death. Of the 1,244 participants, the mean age was 75.5 (SD: 7.1), 49% identified as female, 13% were non-White, and 57% were married. After 2 years, 105 major bleeding events (49% poor HRQoL), 19 strokes (42% poor HRQoL) and 108 deaths (59% poor HRQoL) occurred. Our composite outcome included 200 participants (53% poor HRQoL). After adjusting for key covariates, poor HRQoL was associated with an increased risk of our composite outcome (adjusted hazard ratio [AHR]= 1.34, 95% CI: 1.00, 1.80) and death (AHR= 1.58, 95% CI: 1.05, 2.38). We found no significant differences between poor and good HRQoL in major bleeding and strokes. HRQoL is important when assessing health of AF patients. Health providers who treat patients with AF should consider HRQoL when evaluating the risk of death and other adverse outcomes.