The site-specific signal enhancement provided by parahydrogen induced polarization (PHIP) may be combined with magnetic resonance imaging (MRI) to study chemical and biomolecular processes. However, imaging of hydrogen nuclei (H) is hampered by background signals arising from the presence of thermally polarized nuclei. Additionally, fast imaging sequences are commonly based on multiple radio-frequency pulses, where the signals resulting from PHIP oscillate due to the evolution with a J-coupling Hamiltonian. In this article, an innovative imaging scheme for single-scan MRI is presented that effectively detects hyperpolarized components while simultaneously canceling out thermal contributions. This method is based on the quenching of inherent oscillations of PHIP-originated signals due to J-couplings during the multipulse sequence and the suppression of thermal signals by spin dynamics and a tailored restructuring of the k-space. A series of numerical simulations on specific two- and three-spin systems serve to support the feasibility of the approach. Furthermore, this theoretical study demonstrates the potential of combining hyperpolarization and long-lived states (PHIP and LLS) in the selected molecules, which could be seen as a preliminary step towards the development of fast imaging techniques, for example in the field of biomolecular research.
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http://dx.doi.org/10.1016/j.jmr.2024.107740 | DOI Listing |
Pharmaceutics
January 2025
Laboratory of Nuclear Medicine (LIM-43), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo 05403-911, SP, Brazil.
Background/objectives: Dithiocarbazates (DTCs) and their metal complexes have been studied regarding their property as anticancer activities. In this work, using S-benzyl-5-hydroxy-3-methyl-5-phenyl-4,5-dihydro-1H-pirazol-1-carbodithionate (Hbdtc), we prepared [ReO(bdtc)(Hbdtc)] and [[Tc]TcO(bdtc)(Hbdtc)] complexes for tumor uptake and animal biodistribution studies.
Methods: Re complex was prepared by a reaction of H2bdtc and (NBu)[ReOCl], the final product was characterized by IR, H NMR, CHN, and MS-ESI.
Sensors (Basel)
January 2025
Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.
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January 2025
Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany.
This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.
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January 2025
Peking University Yangtze River Delta Institute of Optoelectronics, Nantong 100871, China.
To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM-Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference.
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January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
A method of bridge structure seismic response identification combining signal processing technology and deep learning technology is proposed. The short-time energy method is used to intelligently extract the non-smooth segments in the sensor acquired signals, and the short-time Fourier transform, continuous wavelet transform, and Meier frequency cestrum coefficients are used to analyze the spectrum of the non-smooth segments of the response of the bridge structure, and the response feature matrix is extracted and used to classify sequences or images in the LSTM network and the Resnet50 network. The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency.
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