Purpose: The EphA2 receptor tyrosine kinase is believed to play a role in tumor growth and metastasis. The clinical significance of the expression of EphA2 was observed in breast, prostate, colon, skin, cervical, ovarian, and lung cancers. The purpose of this work was to determine the expression of EphA2 and its ligand, Ephrin A-1, and E-cadherin in carcinoma of the urinary bladder, and determine EphA2 as a new target for therapy in bladder cancer.

This study addresses the urgent need for comprehensive climate education amid a climate emergency. Human (energy) behaviors are developed from childhood and early adulthood. This study hypothesizes that transcending a nation’s net-zero energy ambition can be accomplished through experiential education. An Urban Governance Lab plus nEt-Zero Energy league model is introduced. Various behavioral interventions are designed based on the principles of serious games. Discussions provide rich narratives on how a nation with so many diverse communities can forge a rapid net-zero transition. The blended multi-disciplinary STEM education can drive energy citizenship in campus-like communities. A scenarios-based analysis demonstrating the potential of the proposed model in shaping energy behavior in young citizens leading to net zero is presented. The results from the scenario analysis present optimistic evidence underlining how campus-like communities driven by bottom-up initiatives can realize net-zero ambition beyond hope.

Low-cost sensors (LCSs) for particulate matter (PM) monitoring have gained popularity due to their affordability, compact size, and low power requirements. These sensors typically offer the capability to collect data at sampling rates that can be adjusted according to the application. However, the effect of varying the sampling frequency on sensor performance has not been thoroughly examined. This study explores how variations in sampling frequency influence the performance of low-cost PM sensors and identifies some possible use cases for different sampling rates. During this study conducted over a 1-month period, data from five SPS30 sensors were collected at 15 s intervals and then aggregated into 5, 10, 15, 30, and 60 min intervals. During the study, hourly PM_2.5 levels ranged from 117 to 303.3 µg m⁻³, with significant diurnal variations, influenced by temperature and humidity. It was found that changes in sampling frequency had minimal impact on sensor performance, as evidenced by comparable linearity and error metrics across different sampling intervals. However, the study also revealed that short-lived plume events could be missed at lower sampling frequencies. This suggests that for monitoring gradual changes in PM_2.5 levels, higher sampling frequencies do not necessarily improve measurement accuracy but are crucial for capturing transient events. This study underscores the importance of optimizing sampling frequency based on specific monitoring objectives and the need to balance power consumption with data resolution, particularly in remote or battery-based deployments.