Search Results (3)

Pimpled eggs have defective shells, which severely impacts hatching rates and transportation safety. In this study, we constructed single-cell resolution transcriptomic and chromatin accessibility maps from uterine tissues of chickens using single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq). We identified 11 major cell types and characterized their marker genes, along with specific transcription factors (TFs) that determine cell fate. CellChat analysis showed that fibroblasts had the most extensive intercellular communication network and that the chickens laying pimpled eggs had amplified immune-related signaling pathways. Differential expression and enrichment analyses indicated that inflammation in pimpled egg-laying chickens may lead to disruptions in their circadian rhythm and changes in the expression of ion transport-related genes, which negatively impacts eggshell quality. We then integrated TF analysis to construct a regulatory network involving TF–target gene–Gene Ontology associations related to pimpled eggs. We found that the transcription factors ATF3, ATF4, JUN, and FOS regulate uterine activities upstream, while the downregulation of ion pumps and genes associated with metal ion binding directly promotes the formation of pimpled eggs. Finally, by integrating the results of scRNA-seq and scATAC-seq, we identified a rare cell type—ionocytes. Our study constructed single-cell resolution transcriptomic and chromatin accessibility maps of chicken uterine tissue and explored the molecular regulatory mechanisms underlying pimpled egg formation. Our findings provide deeper insights into the structure and function of the chicken uterus, as well as the molecular mechanisms of eggshell formation. Full article

Group chat socialization is increasingly central to online activities, yet design strategies to enhance this experience remain underexplored. This study builds on the Stimuli–Organism–Response (SOR) framework to examine how usability, chat rhythm, and user behavior influence emotions and participation in group chats. Using data from 546 users in China, a relevant demographic given the dominance of platforms like WeChat in both social and professional settings, we uncover insights that are particularly applicable to highly connected digital environments. Our analysis shows significant relationships between usability (γ = 0.236, p < 0.001), chat rhythm (γ = 0.172, p < 0.001), user behavior (γ = 0.214, p < 0.001), and emotions, which directly impact participation. Positive emotions (γ = 0.128, p < 0.05) boost participation, while negative emotions (γ = −0.144, p < 0.01), particularly when linked to user behaviors, reduce it. Additionally, we discussed the mediating effects, notably that usability significantly impacts participation through positive emotions, while user behavior exerts a significant influence on participation through negative emotions. This research offers actionable design strategies, such as tailoring sensory inputs to reduce cognitive load and implementing reward systems to motivate participation. Positive feedback mechanisms enhance engagement by leveraging the brain’s reward systems, while optimized error messages can minimize frustration. These insights, which are particularly relevant for China’s active group chat culture, provide a framework to improve platform design and contribute valuable findings to the broader HCI field. Full article

The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload of existing clinicians is consequently increasing. However, the number of cardiovascular clinicians is declining. In this paper, we aim to design a mobile and automatic system to improve the abilities of patients’ cardiovascular health management while also reducing clinicians’ workload. Our system includes both hardware and cloud software devices based on recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies. A small hardware device was designed to collect high-quality Electrocardiogram (ECG) data from the human body. A novel deep-learning-based cloud service was developed and deployed to achieve automatic and accurate cardiovascular disease detection. Twenty types of diagnostic items including sinus rhythm, tachyarrhythmia, and bradyarrhythmia are supported. Experimental results show the effectiveness of our system. Our hardware device can guarantee high-quality ECG data by removing high-/low-frequency distortion and reverse lead detection with 0.9011 Area Under the Receiver Operating Characteristic Curve (ROC–AUC) score. Our deep-learning-based cloud service supports 20 types of diagnostic items, 17 of them have more than 0.98 ROC–AUC score. For a real world application, the system has been used by around 20,000 users in twenty provinces throughout China. As a consequence, using this service, we could achieve both active and passive health management through a lightweight mobile application on the WeChat Mini Program platform. We believe that it can have a broader impact on cardiovascular health management in the world. Full article