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Cardiovascular disease is a leading cause of death worldwide. The differentiation of human pluripotent stem cells (hPSCs) into functional cardiomyocytes offers significant potential for disease modeling and cell-based cardiac therapies. However, hPSC-derived cardiomyocytes (hPSC-CMs) remain largely immature, limiting their experimental and clinical applications. A critical challenge in current in vitro culture systems is the absence of standardized metrics to quantify maturity. This study presents a data-driven pipeline to quantify hPSC-CM maturity using gene expression data across various stages of cardiac development. We determined that culture time serves as a feasible proxy for maturity. To improve prediction accuracy, machine learning algorithms were employed to identify heart-related genes whose expression strongly correlates with culture time. Our results reduced the average discrepancy between predicted and observed culture time to 4.461 days and CASQ2 (Calsequestrin 2), a gene involved in calcium ion storage and transport, was identified as the most critical cardiac gene associated with culture duration. This novel framework for maturity assessment moves beyond traditional qualitative methods, providing deeper insights into hPSC-CM maturation dynamics. It establishes a foundation for developing advanced lab-on-chip devices capable of real-time maturity monitoring and adaptive stimulus selection, paving the way for improved maturation strategies and broader experimental/clinical applications. 

This paper presents an innovative system to monitor the physical soil conditions needed for modern agriculture. The current technique to measure soil properties relies on taking samples from place to place and takes them for laboratory testing. To build up and monitor a data-based system for a large area, such a method is costly and time-consuming. This paper reported our recent work on the development of a passive impedance-loaded surface acoustic wave (SAW) sensor for a low-cost soil condition monitoring system. The SAW sensor will eventually be connected to an antenna and a impedance-based sensor for autonomous soil nutrient sensing. In this research, first, the coupling-of-modes (COM) analysis was performed to simulate the SAW device. The sensors were fabricated with E-beam lithography techniques and tested with different external load resistances. We investigated how the sensor signal changed with the external resistance loading. The experimental results were verified by comparing them with simulation results.