Given its exceptional durability and high information density, deoxyribonucleic acid (DNA) has the potential to meet the escalating global demand for data storage if it can be stored efficiently and accessed randomly in exabyte-to-yottabyte-scale databases. Here, this work introduces the Engineered Living Memory Microspheroid (ELMM) as a novel material for DNA data storage, retrieval, and management. This work engineers a plasmid library and devises a random access strategy pairing plasmid function with DNA data in a key-value format. Each DNA segment is integrated with its corresponding plasmid, introduced into bacteria, and encapsulated within matrix material via droplet microfluidics within 5 min. ELMMs can be stored at room temperature following lyophilization and, upon rehydration, each type of ELMM exhibits specific functions expressed by the plasmids, allowing for physical differentiation based on these characteristics. This work demonstrates fluorescent expression as the plasmid function and employs fluorescence-based sorting access image files in a prototype database. By utilizing N optical channels, to retrieve 2 file types, each with a minimum of 10 copies. ELMM offers a digital-to-biological information solution, ensuring the preservation, access, replication, and management of files within large-scale DNA databases.
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http://dx.doi.org/10.1002/adma.202415358 | DOI Listing |
R Soc Open Sci
March 2025
School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia.
The landscape of artificial intelligence (AI) research is witnessing a transformative shift with the emergence of the Kolmogorov-Arnold network (KAN), presenting a novel architectural paradigm aimed to redefine the structural foundations of AI models, which are based on multilayer perceptron (MLP). Through rigorous experimentation and evaluation, we introduce the KAN-electroencephalogram (EEG) model, a tailored design for efficient seizure detection. Our proposed network is tested and successfully generalized on three different datasets, one from the USA, one from Europe, and one from Oceania, recorded with different front-end hardware.
View Article and Find Full Text PDFFront Microbiol
February 2025
School of Resources Environment and Safety Engineering, University of South China, Hengyang, China.
Antimony pollution poses a significant threat to both the ecological environment and the health of people living in mining regions. Using organic fertilizers is an efficient approach for the remediation of heavy metal contamination in soil. This study aimed to explore how food waste organic fertilizer (FF) can remediate antimony-contaminated soil and the associated rhizosphere microbial response mechanism.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
March 2025
Injury, Recovery and Inflammation Sciences Academic Unit, School of Medicine and National Institute for Health and Care Research, Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK.
In this opinion piece, we examine the pivotal role that uncertainty quantification (UQ) plays in informing clinical decision-making processes. We explore challenges associated with healthcare data and the potential barriers to the widespread adoption of UQ methodologies. In doing so, we highlight how these techniques can improve the precision and reliability of medical evaluations.
View Article and Find Full Text PDFHealthcare (Basel)
February 2025
Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients using various supervised, unsupervised, and deep learning approaches.
View Article and Find Full Text PDFArch Public Health
March 2025
School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China.
Background: Studying the spatial pattern of out-of-hospital cardiac arrest (OHCA) and its environmental impactors is crucial for both providing timely medical assistance and implementing preventative measures. Existing researches have mainly focused on natural and sociodemographic environments, usually at a macro- or meso-scale, while giving less attention to understanding the association between environment and OHCA risk from the perspective of daily-life contexts.
Methods: In this study, we utilized 1843 eligible OHCA cases from core districts of Beijing in 2020 and employed modified Besag-York-Mollié (BYM2) Bayesian models to investigate the association between living environment (consisting of food environment, physical activity environment, healthcare environment and leisure environment) and adult OHCA risk, as well as its age disparities, at a 1 × 1 km cell resolution.
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