The density functional theory (DFT) and experimental data presented in this paper refer to the research article "Computational and experimental study on undoped and Er-doped lithium tantalate nano fluorescent probes". The DFT data contain electronic and optical properties for both LiTaO and LiTaO:Er, with Er occupying either Li or Ta sites at 4.167 mol. %. All these properties were calculated at the generalized gradient approximation (GGA) limit. Additionally, electronic information was calculated using the hybrid functional by Heyd, Scuseria, and Ernzerho (HSE06), which accurately predicts the location in energy for all Er-4f orbitals. We also include simulated X-ray near edge (XANES) and emission spectra (XES) for the host and the doped configurations using the FEFF10 code, which provide information similar to the DFT calculated optical properties. Experimentally, we synthesized LiTaO:Ernanoparticles, and validated them through X-ray diffraction and Scanning Electron Microscopy. We used differential scanning calorimetry and thermogravimetric analysis to confirm increases in the activation energy and the lowering of the reaction temperature due to Er doping. We collected photoluminescence data, which confirms strong f-f emission in the visible and near-infrared regions and correlates well with the HSE06 electronic information.
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http://dx.doi.org/10.1016/j.dib.2024.110771 | DOI Listing |
Biomed Phys Eng Express
January 2025
Shandong University of Traditional Chinese Medicine, Qingdao Academy of Chinese Medical Sciences, Jinan, Shandong, 250355, CHINA.
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFAnnu Rev Public Health
January 2025
2Ross School of Business, University of Michigan, Ann Arbor, Michigan, USA.
A 2008 review in the considered the question of whether health insurance improves health. The answer was a cautious yes because few studies provided convincing causal evidence. We revisit this question by focusing on a single outcome: mortality.
View Article and Find Full Text PDFACS Nano
January 2025
NOVA Medical School|Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisbon 1169-056, Portugal.
The "" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science and technology, medicine, bioengineering, and computational approaches, in bridging the gap between research, manufacturing, and clinical applications. Effective communication is key to bridging team gaps, enhancing trust, and resolving conflicts, thereby fostering teamwork and individual growth toward shared goals. Drawing from the success of the COVID-19 vaccine development, we advocate the application of similar collaborative models in other complex health areas such as nanomedicine and biomedical engineering.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain.
Background: Motor imagery is the mental representation of a movement without physical execution. When motor imagery is performed to enhance motor learning and performance, participants must reach a temporal congruence between the imagined and actual movement execution. Identifying factors that can influence this capacity could enhance the effectiveness of motor imagery programs.
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