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A robust light storage and retrieval (LSR) in high dimensions is highly desirable for light and quantum information processing. However, most schemes on LSR realized up to now encounter problems due to not only dissipation, but also dispersion and diffraction, which make LSR with a very low fidelity. Here we propose a scheme to achieve a robust storage and retrieval of weak nonlinear high-dimensional light pulses in a coherent atomic gas via electromagnetically induced transparency. We show that it is available to produce stable (3 + 1)-dimensional light bullets and vortices, which have very attractive physical property and are suitable to obtain a robust LSR in high dimensions.
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http://dx.doi.org/10.1038/srep08211 | DOI Listing |
Sci Adv
September 2025
Shenzhen Key Laboratory of Smart Healthcare Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering. Southern University of Science and Technology, No. 1088 Xueyuan Rd., Nanshan District, Shenzhen, Guangdong 518055, P. R. China.
DNA with high storage density can serve as an alternative storage medium to respond to the global explosion of data growth and become a powerful personal storage memory if an integrated compact device can store and handle large-scale data. Here, we incorporate a DNA cassette tape with 5.5 × 10 addressable data partitions (addressing rate up to 1570 partitions per second), a DNA loading capacity of 28.
View Article and Find Full Text PDFActa Anaesthesiol Scand
October 2025
Copenhagen Trial Unit, Centre for Clinical Intervention Research, The Capital Region, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
Introduction: Electronic health records can be used to create high-quality databases if data are structured and well-registered, which is the case for most perioperative data in the Capital and Zealand Regions of Denmark. We present the purpose and development of the AI and Automation in Anaesthesia (TRIPLE-A) database-a platform designed for epidemiology, prediction, quality control, and automated research data collection.
Methods: Data collection from the electronic medical record (EPIC Systems Corporation, WI, USA) was approved by the Capital Region, Denmark, and ethical approval was waived.
J Microbiol Methods
September 2025
Dynamics of Respiratory Infections Group, Helmholtz Centre for Infection Research-HZI Braunschweig, Braunschweig, Germany; Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, German Center for Lung Research (DZL), BREATH, Hannover, Germany.
Purpose: The accuracy of oral microbiome research depends significantly on specimen sampling protocols, as well as their storage and preservation. Traditional methods, such as freezing, may not only involve logistical hurdles but can also impact the quality of microbial data, leading to difficulties in the comparability between different studies. This study evaluates the effectiveness of the room temperature nucleic acid preservation protocol using DNA/RNA Shield buffer as compared to standard freezing in preserving oral microbial communities over the course of 7 days.
View Article and Find Full Text PDFJCO Clin Cancer Inform
September 2025
Department of Applied AI and Data Science, City of Hope, Duarte, CA.
Purpose: The recent advancements of retrieval-augmented generation (RAG) and large language models (LLMs) have revolutionized the extraction of real-world evidence from unstructured electronic health records (EHRs) in oncology. This study aims to enhance RAG's effectiveness by implementing a retriever encoder specifically designed for oncology EHRs, with the goal of improving the precision and relevance of retrieved clinical notes for oncology-related queries.
Methods: Our model was pretrained with more than six million oncology notes from 209,135 patients at City of Hope.
Med Phys
August 2025
The University of Texas MD Anderson Cancer Houston, Houston, Texas, USA.
Background: To guarantee high-quality patient scans, thorough quality assurance (QA) of SPECT or gamma cameras, including performance, review, and documentation, is essential.
Purpose: We developed a novel Nuclear Medicine Quality Assurance server (NMQA) with an AI deep learning (AIDL) optical character recognition (OCR) system to automate QA data retrieval and review from SPECT and gamma cameras. The system extracts and compares daily and weekly QA data against specifications.