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Molecular data storage offers the intriguing possibility of higher theoretical density and longer lifetimes than today's electronic memory devices. Some demonstrations have used deoxyribonucleic acid (DNA), but bottlenecks in nucleic acid synthesis continue to make DNA data storage orders of magnitude more expensive than electronic storage media. Additionally, despite its potential for long-term storage, DNA faces durability challenges from environmental degradation. In this work, we demonstrate nongenomic molecular data storage using molecular libraries redirected from chemical waste streams. This approach requires no synthetic effort and can be implemented by using molecules that have a minimal associated cost. While the technique is agnostic about the exact molecular content of its inputs, we confirmed that some sources contained poly fluoroalkyl substances (PFAS), which persist for long periods in the natural environment and could offer extremely durable information storage as well as environmental benefits. These demonstrations provide a perspective on some of the valuable possibilities for nongenomic molecular information systems.
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http://dx.doi.org/10.1021/acsomega.3c09234 | DOI Listing |
Cereb Cortex
August 2025
Research Imaging Institute, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229, United States.
Statistical Parametric Mapping (SPM) adheres to rigorous methodological standards, including: spatial normalization, inter-subject averaging, voxel-wise contrasts, and coordinate reporting. This rigor ensures that a thematically diverse literature is amenable to meta-analysis. BrainMap is a community database (www.
View Article and Find Full Text PDFInt Rev Psychiatry
September 2025
Institute of Psychiatry, Emeritus Mental Health & Cultural Diversity, IoPPN, Kings College, London, United Kingdom.
With increasing use of AI in public life, various technological tools like ChatGPT are being used for psychotherapy. Psychiatrists face unique ethical challenges in understanding their use. Clinicians have an obligation to advise policymakers and to update scope of practice laws related to the mental health workforce and technology.
View Article and Find Full Text PDFInorg Chem
September 2025
Institute of Inorganic and Analytical Chemistry, University of Münster, Corrensstraße 28/30, 48149 Münster, Germany.
Isovalent anion substitution has been shown to have a tremendous effect on the transport properties in lithium halide solid ionic conductors. Although sodium-ion solid state batteries based on chloride ionic conductors have recently gathered significant attention, investigations of anion substitution in sodium containing chlorides remain scarce. Here, we investigate the role of Br isoelectronic anion substitution in a perovskite-related compound with nominal composition of NaTaCl.
View Article and Find Full Text PDFAlzheimers Dement
September 2025
Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Introduction: We compared and measured alignment between the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard used by electronic health records (EHRs), the Clinical Data Interchange Standards Consortium (CDISC) standards used by industry, and the Uniform Data Set (UDS) used by the Alzheimer's Disease Research Centers (ADRCs).
Methods: The ADRC UDS, consisting of 5959 data elements across eleven packets, was mapped to FHIR and CDISC standards by two independent mappers, with discrepancies adjudicated by experts.
Results: Forty-five percent of the 5959 UDS data elements mapped to the FHIR standard, indicating possible electronic obtainment from EHRs.
Front Comput Neurosci
August 2025
Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters.
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