A deeper understanding of parameters affecting Magic Angle Spinning Dynamic Nuclear Polarization (MAS-DNP), an emerging nuclear magnetic resonance hyperpolarization method, is crucial for the development of new polarizing agents and the successful implementation of the technique at higher magnetic fields (>10 T). Such progress is currently impeded by computational limitation which prevents the simulation of large spin ensembles (electron as well as nuclear spins) and to accurately describe the interplay between all the multiple key parameters at play. In this work, we present an alternative approach to existing cross-effect and solid-effect MAS-DNP codes that yields fast and accurate simulations. More specifically we describe the model, the associated Liouville-based formalism (Bloch-type derivation and/or Landau-Zener approximations) and the linear time algorithm that allows computing MAS-DNP mechanisms with unprecedented time savings. As a result, one can easily scan through multiple parameters and disentangle their mutual influences. In addition, the simulation code is able to handle multiple electrons and protons, which allows probing the effect of (hyper)polarizing agents concentration, as well as fully revealing the interplay between the polarizing agent structure and the hyperfine couplings, nuclear dipolar couplings, nuclear relaxation times, both in terms of depolarization effect, but also of polarization gain and buildup times.
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http://dx.doi.org/10.1039/c6cp07881h | DOI Listing |
Am J Pathol
December 2024
Department of Computer Science, Faculty of Engineering Sciences, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of AI in computational histopathology to evaluate hypoxia in breast cancer. Weakly Supervised Deep Learning (WSDL) models can accurately detect morphological changes associated with hypoxia in routine Hematoxylin and Eosin (H&E) whole slide images (WSI).
View Article and Find Full Text PDFJ Food Sci
December 2024
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
As consumers increasingly prioritize food safety and nutritional value, the dairy industry faces a pressing need for rapid and accurate methods to detect essential nutritional components in milk, such as fat, protein, and lactose. Hyperspectral imaging (HSI) technology, known for its non-destructive, fast, and precise nature, shows great promise in food quality assessment. However, the high dimensionality of HSI data poses challenges for effective band selection and model optimization.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Ossification of the ligamentum flavum (OLF) is the main causative factor of spinal stenosis, but how to accurately and efficiently identify the ossification region is a clinical pain point and an urgent problem to be solved. Currently, we can only rely on the doctor's subjective experience for identification, with low efficiency and large error. In this study, a deep learning method is introduced for the first time into the diagnosis of ligamentum flavum ossificans, we proposed a lightweight, automatic and efficient method for identifying ossified regions, called CDUNeXt.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran.
Numerous algorithms have been proposed to infer the underlying structure of the social networks via observed information propagation. The previously proposed algorithms concentrate on inferring accurate links and neglect preserving the essential topological properties of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties.
View Article and Find Full Text PDFAppl Microbiol Biotechnol
December 2024
Laboratório de Pesquisa em Malária, Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro, Brazil.
Malaria, a parasitic disease caused by Plasmodium spp. and transmitted by Anopheles mosquitoes, remains a major global health issue, with an estimated 249 million cases and 608,000 deaths in 2022. Rapid and accurate diagnosis and treatment are crucial for malaria control and elimination.
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