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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. 

Nitrate (NO3) pollution in groundwater, caused by various factors both natural and synthetic, contributes to the decline of human health and well-being. Current techniques used for nitrate detection include spectroscopic, electrochemical, chromatography, and capillary electrophoresis. It is highly desired to develop a simple cost-effective alternative to these complex methods for nitrate detection. Therefore, a real-time poly (3,4-ethylenedioxythiophene) (PEDOT)-based sensor for nitrate ion detection via electrical property change is introduced in this study. Vapor phase polymerization (VPP) is used to create a polymer thin film. Variations in specific parameters during the process are tested and compared to develop new insights into PEDOT sensitivity towards nitrate ions. Through this study, the optimal fabrication parameters that produce a sensor with the highest sensitivity toward nitrate ions are determined. With the optimized parameters, the electrical resistance response of the sensor to 1000 ppm nitrate solution is 41.79%. Furthermore, the sensors can detect nitrate ranging from 1 ppm to 1000 ppm. The proposed sensor demonstrates excellent potential to detect the overabundance of nitrate ions in aqueous solutions in real time. 

Recently, deep learning has shown to be effective for Electroencephalography (EEG) decoding tasks. Yet, its performance can be negatively influenced by two key factors: 1) the high variance and different types of corruption that are inherent in the signal, 2) the EEG datasets are usually relatively small given the acquisition cost, annotation cost and amount of effort needed. Data augmentation approaches for alleviation of this problem have been empirically studied, with augmentation operations on spatial domain, time domain or frequency domain handcrafted based on expertise of domain knowledge. In this work, we propose a principled approach to perform dynamic evolution on the data for improvement of decoding robustness. The approach is based on distributionally robust optimization and achieves robustness by optimizing on a family of evolved data distributions instead of the single training data distribution. We derived a general data evolution framework based on Wasserstein gradient flow (WGF) and provides two different forms of evolution within the framework. Intuitively, the evolution process helps the EEG decoder to learn more robust and diverse features. It is worth mentioning that the proposed approach can be readily integrated with other data augmentation approaches for further improvements. We performed extensive experiments on the proposed approach and tested its performance on different types of corrupted EEG signals. The model significantly outperforms competitive baselines on challenging decoding scenarios. 

Abstract Suncus etruscusis one of the world’s smallest mammals, with an average body mass of about 2 grams. The Etruscan shrew’s small body is accompanied by a very high energy demand and numerous metabolic adaptations. Here we report a chromosome-level genome assembly using PacBio long read sequencing, 10X Genomics linked short reads, optical mapping, and Hi-C linked reads. The assembly is partially phased, with the 2.472 Gbp primary pseudohaplotype and 1.515 Gbp alternate. We manually curated the primary assembly and identified 22 chromosomes, including X and Y sex chromosomes. The NCBI genome annotation pipeline identified 39,091 genes, 19,819 of them protein-coding. We also identified segmental duplications, inferred GO term annotations, and computed orthologs of human and mouse genes. This reference-quality genome will be an important resource for research on mammalian development, metabolism, and body size control.