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Data Readout Techniques for DNA-Based Information Storage

DNA is a natural chemical substrate that carries genetic information, which also serves as a powerful toolkit for storing digital data. Compared to traditional storage media, DNA molecules offer higher storage density, longer lifespan, and lower maintenance energy consumption. In DNA storage process, data readout is a critical step that bridges the gap between DNA molecular/structures with stored digital information. With the continued development of strategies in DNA data storage technology, the readout techniques have evolved. However, there is a lack of systematic introduction and discussion on the readout techniques for reported DNA data storage systems, especially the correlation between the design of the data storage system and the corresponding selection of readout techniques. This review first introduces two main categories of DNA data storage units (i.e., sequence and structure) and their corresponding readout techniques (i.e., sequencing and nonsequencing methods), and then reviewed representative examples of notable advancements in DNA data storage technology, focusing on data storage unit design, and readout technique selection. It also introduces emerging approaches to assist data readout techniques, such as implementation of microfluidic and fluorescent probes. Finally, the paper discusses the limitations, challenges, and potential of DNA data readout approaches.

Machine learning methods for histopathological image analysis: Updates in 2024

The combination of artificial intelligence and digital pathology has emerged as a transformative force in healthcare and biomedical research. As an update to our 2018 review, this review presents comprehensive analysis of machine learning applications in histopathological image analysis, with focus on the developments since 2018. We highlight significant advances that have expanded the technical capabilities and practical applications of computational pathology. The review examines progress in addressing key challenges in the field as follows: processing of gigapixel whole slide images, insufficient labeled data, multidimensional analysis, domain shifts across institutions, and interpretability of machine learning models. We evaluate emerging trends, such as foundation models and multimodal integration, that are reshaping the field. Overall, our review highlights the potential of machine learning in enhancing both routine pathological analysis and scientific discovery in pathology. By providing this comprehensive overview, this review aims to guide researchers and clinicians in understanding the current state of the pathology image analysis field and its future trajectory.