High-field solid-state nuclear magnetic resonance (NMR) technology boasts high sensitivity and multi-nucleus detection capability. However, when studying paramagnetic materials like lithium-ion batteries, the strong paramagnetism of transition metal ions (such as Mn³⁺, Fe³⁺, etc.) leads to issues like magnetic field inhomogeneity, spectral line broadening, signal attenuation, and inability to perform magic-angle spinning (MAS) at high magnetic fields. Under low-field conditions, the magnetic field distortion induced by paramagnetic effects is significantly reduced, offering a potential solution to these problems. This paper presents a theoretical analysis of the advantages of low-field environments for studying paramagnetic substances. A low-field solid-state MAS probe for a 0.5 T Halbach magnet was developed. Furthermore, a complete low-field solid-state MAS spectrometer was constructed. In experiments, ⁷Li NMR signals of various paramagnetic samples were acquired at a spinning speed of 12 kHz, verifying the feasibility of the self-developed low-field solid-state MAS technology for paramagnetic samples. This approach addresses the problems of overlapping spin sidebands and MAS failure at high fields, providing a new pathway for NMR research on paramagnetic materials.

In nuclear magnetic resonance (NMR) systems, the radio frequency (RF) pulse generator critically influences imaging quality. Traditional direct digital synthesis (DDS) techniques rely on large-capacity lookup tables to achieve high precision, which results in excessive consumption of on-chip block random access memory (BRAM) resources and limits flexibility. This study presents a novel RF pulse generator design based on the coordinate rotation digital computer (CORDIC) algorithm. By integrating a custom CORDIC core within a field-programmable gate array (FPGA), the proposed system achieves digital modulation of RF signal’s frequency, phase, and amplitude. Combined with a Zynq-7000 system-on-chip (SoC), this design delivers a highly integrated and low-power hardware architecture. Experimental results demonstrate that the generator can output RF pulses with a frequency resolution of 0.046 Hz and a phase resolution of 0.005 5˚, while reducing BRAM resources occupied by four channels by approximately 21.4% compared to the traditional DDS solution. This design offers a feasible solution for achieving miniaturization and high performance in the RF front-end of NMR instruments.

With the widespread application of nuclear magnetic resonance (NMR) technology in fields such as food safety inspection and petroleum exploration, there is a growing demand for miniaturized and portable NMR spectrometers. In response to this demand, this paper proposes a system design framework for a palm-sized NMR spectrometer console and develops a comprehensive and flexible software architecture. The software system consists of two main components: embedded control software and host computer application software. The embedded control software is responsible for real-time control of spectrometer hardware and communication with the host computer. The host software handles user interface interaction, sequence parameter configuration, and post-processing of acquired data. Furthermore, an open and lightweight NMR data communication protocol is designed to support custom host software development based on specific user requirements, thereby significantly enhancing system flexibility and scalability. Experimental results demonstrate that the proposed palm-sized NMR spectrometer console delivers reliable performance and substantial practical value.

Oxidative modification of cytochrome c (Cyt c) may influence the local conformation of protein, yet the mechanism by which structural alterations of Cyt c affect its degree of oxidative modification remains unclear. In this study, Girard’s reagent T (GRT) was employed as a nuclear magnetic resonance (NMR) probe to investigate the oxidative modification levels of human Cyt c under varying environmental conditions. Experimental results demonstrated that protecting lysine residues through reductive methylation effectively reduced protein oxidation. Partial unfolding of Cyt c was found to enhance its oxidative modification, while binding Cyt c with cardiolipin significantly increased the extent of oxidation. Additionally, other factors such as protein aggregation exhibited inhibitory effects on oxidative modification.

Magnetic resonance fingerprint (MRF) is an efficient multi-parameter quantitative imaging technology. However, traditional methods relying on signal dictionaries for parameter quantization are plagued by significant discretization errors and low matching efficiency. To overcome the limitations of existing supervised learning approaches that depend on pseudo-labels and lack physical interpretability, this study proposes a self-supervised parameter quantization method that integrates imaging physical models and manifold structure modeling. This method establishes reliable unlabeled constraints through Bloch equation-driven self-supervised physical consistency learning. By incorporating manifold structure-driven knowledge distillation, it transfers features of long frames to short frame models, realizing joint optimization of physical constraints and structural priors, thereby improving both accuracy and efficiency under unlabeled conditions. Experiments have verified this method’s superior accuracy and robustness, providing a novel approach for efficient and reliable MRF parameter estimation.

To investigate the dynamic properties of resting-state functional connectivity associated with antiretroviral therapy (ART), functional magnetic resonance imaging data from 45 treated people with HIV (PWH), 56 untreated PWH, and 68 healthy controls were collected. Group independent component analysis and sliding window analysis were conducted to obtain window-functional connectivity matrices, and their dynamic properties were quantified. The results showed that the baseline state and the weakly activated state reflect HIV-related abnormal dynamics and ART-related recovery. The weakly activated state reflected the recovery of cerebellum-related connections and putamen-related functional compensation. The baseline state reflected the recovery of extensive connections except for the visual network. Visual-related connections reflected ART-related adverse reactions in both states. These findings suggest that the cerebellum and putamen may be sensitive biomarkers for ART-related recovery, and the visual network can serve as a target for adjuvant therapy.

Intracranial tumors represent a serious neurological disorder, and early detection is critical for improving patient survival rates. However, current deep learning models for intracranial tumor image classification often suffer from insufficient feature extraction, high model complexity, and class imbalance. To address these challenges, this study proposes a lightweight deep learning architecture, the adaptive dynamic network (AD-Net). The network innovatively incorporates a dynamic convolution mechanism that adaptively adjusts filter responses, thereby enhancing the representation of complex and imbalanced tumor features. Additionally, the integration of a channel attention mechanism enables the model to focus on critical channel information, further improving classification accuracy and interpretability. This study also introduces a combined binary and ternary classification training strategy, which significantly reduces training time and computational resource requirements, making the model more suitable for resource-constrained medical settings. Experimental results demonstrate that AD-Net outperforms existing mainstream deep learning models in accuracy, precision, recall, F1 score, and Cohen’s Kappa coefficient, confirming its effectiveness and practical value for intracranial tumor classification.

To explore the correlation between the imaging anatomical characteristics based on magnetic resonance diffusion-weighted imaging (MR-DWI) and recurrence in patients with acute cerebral infarction, this study retrospectively analyzed clinical and MR data of 211 patients clinically confirmed with acute cerebral infarction and meeting the inclusion and exclusion criteria. The number of lesions and involved cerebral blood supply areas were counted, and the total area of infarction and the area of subcutaneous fat were measured. The correlation between the recurrence and non-recurrence of patients within one year and the above data was analyzed. The results showed that there was a statistically significant difference in the number of lesions and the number of involved blood supply areas between the recurrence and non-recurrence groups of acute cerebral infarction (p<0.05), there was no statistically significant difference in the total area of lesions on axial images and the area of subcutaneous fat between the two groups (p > 0.05). Therefore, it is concluded that the number of lesions and the number of involved cerebral blood supply areas on MR-DWI images of acute cerebral infarction patients are the main influencing factors for recurrence within one year. This study provides imaging evidence for clinicians to conduct individualized treatment and prognostic evaluation for patients.

In the evaluation of low-porosity and low-permeability light oil reservoirs in the eastern South China Sea using nuclear magnetic resonance(NMR) logging data, the permeability values tend to be overestimated. To improve the accuracy of NMR-derived permeability calculations, it is essential to thoroughly analyze the causes of this overestimation and establish corresponding correction methods. This paper systematically conducted NMR transverse relaxation time (T₂) spectroscopy experiments under different saturation states, including water-saturated, bound water, oil-saturated, and residual oil states. By comparing the T₂ spectra of the same core sample under water-saturated and oil-containing states, it was observed that light crude oil causes a significant rightward shift in the distribution of macropores within the T₂ spectrum, while the distribution of micropores remains largely unchanged. This indicates that light oil is the primary cause of T₂ spectrum anomalies and NMR permeability deviations. Based on this finding, T₂ cutoff values for different reservoir types were determined through core classification, and a T₂ spectrum correction method calibrated for macropore components was established. Results demonstrate that this method effectively corrects T₂ spectra from light oil-bearing reservoirs to fully water-saturated state, significantly enhancing the accuracy of NMR-derived permeability measurements.

Due to their millimeter-scale size and low pump power threshold, Kerr optical soliton frequency combs have emerged as a key technology for chip-scale optical atomic clock research. However, the abrupt intracavity power drop during Kerr optical soliton formation leads to cavity frequency drift, which significantly shortens the lifetime of Kerr optical soliton frequency combs. Some active control methods have been reported for long-term stabilization of Kerr solitons, such as soliton power control, Pound-Drever-Hall frequency locking, and auxiliary laser mode. However, the electronic control systems used for these methods are rarely reported. This work presents an active control system for stabilizing Kerr optical soliton frequency combs based on Field-Programmable Gate Array (FPGA). It achieves long-term stable operation of Kerr optical soliton combs in both MgF₂ and CaF₂ microresonators by power control and PDH frequency locking. Furthermore, the system can be extended to other microresonator platforms (e.g., Si₃N₄, AlN, SiO₂) for Kerr optical soliton frequency generation and stabilization.