A review of heterogeneous interpretations of emotional reactivity

‘Emotional reactivity’ (ER) is an important construct in the analysis of individual temperamental differences, and has accounted for significant variance in studies with respect to its definition. Between 1920 and 2015, the meaning of ER has varied from physiology of emotional reactions, to stress, depression, and as a subtype of empathy. This paper highlights the confusion in the literature about the meaning of ER and raises questions about the current use of the term ER as a valid construct. It clarifies heterogeneity within ER through the creation of a framework to explain different subtypes of ER and suggests new labels designed to help researchers specify the constructs underpinning the term ER.peer-reviewe

Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation

Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. The importance of automated and accurate brain tumor segmentation cannot be overstated. It enables medical professionals to precisely delineate tumor regions, assess tumor growth or regression, and plan targeted treatments. Various deep learning-based techniques proposed in the literature have made significant progress in this field, however, they still face limitations in terms of accuracy due to the complex and variable nature of brain tumor morphology. In this research paper, we propose a novel Hybrid Multihead Attentive U-Net architecture, to address the challenges in accurate brain tumor segmentation, and to capture complex spatial relationships and subtle tumor boundaries. The U-Net architecture has proven effective in capturing contextual information and feature representations, while attention mechanisms enhance the model's ability to focus on informative regions and refine the segmentation boundaries. By integrating these two components, our proposed architecture improves accuracy in brain tumor segmentation. We test our proposed model on the BraTS 2020 benchmark dataset and compare its performance with the state-of-the-art well-known SegNet, FCN-8s, and Dense121 U-Net architectures. The results show that our proposed model outperforms the others in terms of the evaluated performance metrics

Service and its association with matching into a primary care residency

BACKGROUND AND OBJECTIVES: There is a shortfall in the primary care workforce, and an effort is needed in learning more about what motivates students to work as generalists. There is enthusiasm about service as a potential motivator. The objective is to determine whether there is an association between high participation in service and selection of a primary care residency. METHODS: This is a retrospective cohort analysis. The service award was used to delineate two groups, recipients and non-recipients, with the recipients considered high service participators. This was associated with residency match data using test of proportions to examine relationships between service and selection of a primary care residency and other secondary factors. RESULTS: Of award recipients, 57.3% matched in primary care, compared to 52.8%, though this did not reach statistical significance. Service was linked with induction into Alpha Omega Alpha honor society (23.3% versus 14.6%) and induction into the Gold Humanism Honor Society (22.6%. versus 10.4%), with statistical significance. CONCLUSION: This was an unsuccessful attempt to find a link between service and a primary care career choice, though there is a trend in the direction. The association with induction into the humanism honor society suggests that service is linked with development and/or retention of positively viewed qualities in medical students