Multidimensional nuclear magnetic resonance (NMR) spectroscopy is frequently used to analyze molecular structures, and widely applied in researches in the fields of chemistry, biology and medicine. However, data acquisition time increases rapidly with increasing spectral dimension and number of sampling points. Non-uniformly sampling (NUS) can speed up data acquisition by reducing the amount of sampling data in the indirect dimensions, while obtaining a complete spectrum with proper reconstruction methods. How to achieve faster sampling and better reconstruction of a high-quality spectrum are important problems in multidimensional NMR. This article reviews the recent progresses on the low-rank NMR spectra reconstruction methods. First, the related mathematical basics of low-rank matrices are introduced. Then, the spectra reconstruction models are discussed from two perspectives:general low-rank matrix and structured low-rank Hankel matrix. Finally, the limitations and future trends of these methods are discussed.

Based on the principle of birdcage coil and the decoupling theory of array coil, a design scheme for dual-nuclear magnetic resonance imaging (MRI) coil is proposed. A LC parallel trap is used to improve the adaptability of the inductive decoupling scheme. According to the design scheme and simulation, a ¹H/³¹P dual-nuclear parallel MRI coil is fabricated for a 4.7 T system. Dual-nuclear parallel imaging experiments are carried out on a custom-built MRI system to verify the feasibility of the coil design.

High-field clinical magnetic resonance imaging (MRI) system often requires multiple-channel (i.e., 16 or 32) receivers that are capable of transmitting data at a high speed, especially during fast imaging. In this work, a data transmission module based on PowerPC processor is developed and integrated in a custom-built MRI spectrometer for high-speed data transmission between the spectrometer and the host computer. The module uses the Freescale's high-performance PowerPC processor MPC8270 as its core, and runs on the embedded Linux operating system. The processor and the computer are connected through a 100 M Ethernet network, while the sequence running the data acquisition modules (quantity scalability) is connected to the processor using a local bus. Responding to the interruption requests from the data acquisition module, the processor reads and uploads the data quickly. The speed and reliability of the response are guaranteed through the design of the driver program. The results of imaging experiments demonstrate that the design proposed can meet the high-speed data transmission requirements of multiple receiving channels.

The nucleating agent acts as a crystal nucleus during polypropylene (PP) crystallization, affecting the structure and size distribution of the crystal, and in turn the properties of PP products. It is a hot research topic to study the mechanisms by which the nucleating agent promotes polymer crystallization. Currently it is still unclear how the structure of nucleating agent affects polymer crystallization. In addition, a nucleating agent may have different chemical structures due to isomerization, further complicating the nucleation mechanism study. Structural studies of nucleating agents are therefore the foundation of the nucleation mechanism studies. In this work, the structure and configuration of a β nucleating agent used in PP crystallization were studied by the combination of nuclear magnetic resonance (NMR) spectroscopy, Fourier transform-infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS). The FT-IR data suggested that the chelating way of calcium salt of cis-Δ4-tetrahydrophthalic acid in the nucleating agent belongs to the bridge type, while the cis-Δ4-tetrahydrophthalic acid is a mesomer.

Meningioma and auditory neuroma are two types of brain tumors frequently found in the cerebellopontine angle. Misdiagnosis of the two is common due to their similarity in clinical manifestations and imaging manifestations. In this work, the deep learning algorithm was developed to classify the meningioma and auditory neuroma from the magnetic resonance images, assisting the timely and accurate diagnosis to the two brain tumors. T₁-weighted spin-echo (T₁W-SE) images were collected from 307 patients with brain tumors. Contrast limited adaptive histogram equalization (CLAHE) pre-processing was used to improve the quality of the dataset. The image features were learnt in the deep learning framework of 3-dimensional convolutional neural network (3D CNN). By optimizing the image enhancement parameters and the network structure parameters, the accuracy of the meningioma/acoustic neuroma classification model reached 0.918 0, and the area under curve (AUC) was found to be 0.913 4.

In this study, an olfactory inhalation instrument was devised to allow the rats to inhale volatile citral or odorless air. Manganese-enhanced magnetic resonance imaging (MEMRI) was used to reveal changes of accumulative brain activities after a 24 h inhalation of volatile citral. Compared with the control group, the rats in the citral group showed increased functional activities in the core of nucleus accumbens (AcbC) and olfactory glomerular layer (GL), and decreased Mn²⁺ accumulation in the brain regions of visual cortex (VC), auditory cortex (AC) and retrosplenial cortex (RSC). Functional correlations between the GL and associated brain regions increased after citral inhalation. These results suggested that MEMRI might be used to detect brain activation associated with sustained olfactory stimulation.

Alzheimer's disease (AD) is one of the most common forms of dementia and a degenerative mental disorder that seriously affects people's daily lives. Rapid and effective diagnosis is essential for the treatment of patients with Alzheimer's disease. To solve this problem, this paper proposes a deep convolutional neural network structure with multiple semantic levels to classify AD patients and healthy controls from magnetic resonance imaging (MRI) data. Firstly, the deep supervision integration algorithm and the classification model of Alzheimer's disease based on the traditional UNet++ network were improved. Then, a new feature fusion structure was constructed, which further refined the different semantic levels. Lastly, the proposed protocol was applied to different tissue regions (e.g., white matter, gray matter and cerebrospinal fluid), and the effects of different tissue information combinations on the classification outcome were explored. The method proposed was applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to classify the AD patients. The results demonstrated that the highest accuracy of 98.74%, and an average accuracy of 98.47%.

This study explored the effects of Panax quinquefolius L.-Acorus Tatarinowii (X-S) on learning and memory behaviors and related brain regions in rats with diabetes-associated cognitive decline (DACD). Type 1 diabetes mellitus (DM) was induced by intraperitoneal injection of streptozotocin (STZ). The DM rats were randomly divided into the DM group and X-S group. Starting at day 7 after the STZ induction, X-S was administered once a day for 113 consecutive days. Morris water maze test was performed to screen the DM rats with and without cognitive impairment at 80 days after the STZ induction. The volume and concentration of gray matter and white matter in the whole brain were analyzed from magnetic resonance imaging (MRI) data acquired at day 120 after the STZ induction, with a region of interest (ROI)-based method and voxel-based morphometry (VBM). Hippocampal neurons were stained with hematoxylin and eosin (HE). The results of ROI-based analysis showed that:the volume of left temporal association cortex of the X-S rats decreased significantly (p<0.05), compared with the DACD rats. The VBM results showed that:the volume or concentration of CA1, CA3 and other brain regions increased or decreased in the X-S rats (p<0.005), compared with the DACD rats. The HE staining results showed that:X-S treatment alleviated cell pyknosis of the CA1 and CA3 neurons. It was concluded that X-S treatment has both positive and negative effects on the hippocampus and other brain areas related to learning and memory in the DACD rats.

Well logging evaluation of the low-porosity and low-permeability carbonate reservoir is difficult because of its complex mineral composition, difficult to determine rock skeleton parameters, diverse types of reservoir space, complex pore structure and pore-permeability relationship, and unobvious response characteristics of conventional logging curves. In order to quantitatively evaluate the pore structure and calculate the reservoir parameters by nuclear magnetic resonance (NMR) logging, NMR experiments on the core samples from low-porosity and low-permeability reservoir were performed. The pore structure was analyzed from the NMR data, and a T₂ cutoff value was calculated using the T₂ spectrum distribution curve. With the NMR method, the total porosity, effective porosity, bound water porosity, permeability and other reservoir parameters of the core samples were determined and compared with the results obtained with the conventional core experiments. The advantages and limitations of NMR methods in evaluating the low-porosity and low-permeability reservoirs were summarized.

The dolomite reservoirs in the fourth member of Leikoupo formation of the western Sichuan gas field have good prospects for natural gas exploration and development. However, the conventional and one-dimensional nuclear magnetic resonance (1D NMR) logging data are not sufficient for a definitive classification of the reservoir fluid type, likely because of heterogeneity of the reservoirs. Through geological characteristics analysis, log response analysis and two-dimensional (2D) NMR experiment, the environmental factors and technical parameters leading to the differences between the laboratory results and actual logging data were determined. According to the analysis of T₂-T₁ and T₁/T₂ (R) differences, the distribution range of drilling fluid, bound fluid, movable water and natural gas signals in the 2D NMR spectra was delineated to build a gas-water 2D NMR identification chart. The results obtained using the identification chart were verified with the actual drilling test results. Complementary to conventional and 1D NMR logging, the 2D NMR identification method was proposed, and can be used to determine the fluid properties in the dolomite reservoirs effectively.

The porosity of reservoir bed and core in the Shahejie formation of the J oilfield of Bohai bay measured by nuclear magnetic resonance (NMR) logging were found to be lower than those measured by the reference helium method, affecting the application of NMR logging. NMR experiments on the core samples were performed to study the contributions of potential affecting factors, including instrument acquisition parameters, wellbore environment and reservoir fluid properties. The main reason for the lower porosity measured by NMR logging was found to be the intrusion of high salinity mud filtrate. The effects of brines with different salinity on NMR T₂ spectrum were analyzed, and the results were used to determine the lower limit of salinity required for morphological correction of the NMR T₂ spectrum. In view of that the intrusion of high salinity mud filtrate could affect the NMR T₂ spectrum during actual logging, a model for morphological correction of the NMR T₂ spectrum and a method to correct the NMR porosity at different salinity were established. The experimental results demonstrated that the average relative error of the NMR porosity reduced from 13.56% to 2.81% after correction.

Dendrobine-type sesquiterpenoid alkaloids from the Dendrobium plants have attracted many research interests due to their unique structures and remarkable physiological activities. This paper reviews the structural features and spectral (i.e., ultraviolet, infrared, mass and NMR) characteristics of dendrobines, and how their structures could be elucidated from the spectral data. The present work may provide a reference for further research and development of dendrobines.

6-(4-chlorophenoxy)-tetrazolo[5,1-a]phthalazine is a national class I innovative antiepileptic drug candidate in clinical trials with independent intellectual property rights. The ¹H, ¹³C and ¹⁵N NMR spectra of the compound were acquired at 500 MHz. Aided by GIAO (Gauge-Independent Atomic Orbital)-NMR quantum chemistry calculation, the chemical shifts of the compound were assigned, providing elaborate structural information of the compound. Linear regression comparison results showed good consistency between the theoretical predictions and the experimental data for structural elucidations.

Electron paramagnetic resonance (EPR) parameters (i.e., g factor and hyperfine structure constant A) of the Cu²⁺ centers in Cu_1-xH_xZr₂(PO₄)₃ were simulated theoretically using high-order perturbation formulas for Cu²⁺ in rhombically elongated octahedra. The Cu-O bond-lengths of the[CuO₆]^10- cluster in the Cu_1-xH_xZr₂(PO₄)₃ crystal were found to be R_|| ≈ 0.241 nm and R_⊥ ≈ 0.215 nm. The plane bond angle was τ ≈ 80.1°. Because of reduced symmetry, the ground state wave function exhibited admixtures between ²A_1g(θ) and ²A_1g(ε) with a mixing coefficient α≈0.995. The calculated EPR parameters showed good agreement with the experimental data.