Abnormally misfolded and aggregated α-synuclein (αsyn) is the hallmark of Parkinson's disease (PD). Molecular chaperone protein disulfide isomerase (PDI) has been shown to interact with αsyn and inhibit its aggregation in vitro, but the mechanism for the recognition of αsyn by PDI is not yet clear. Herein, we used nuclear magnetic resonance (NMR) spectroscopy to identify that human PDI b'xa' bound with the N-terminal domain of αsyn, and thioflavin T (ThT) fluorescence assay revealed that b'xa' domain of PDI significantly inhibited αsyn aggregation. Furthermore, by using NMR titration, we observed that PDI bound to αsyn mainly through its hydrophobic cavity of the b' domain. Based on these findings, a docking model of PDI binding with αsyn was established and a possible mechanism of how PDI inhibits αsyn aggregation was proposed. Our work provides experimental evidences for understanding the inhibitory role of PDI in αsyn aggregation.

In this study, ¹H magnetic resonance spectroscopy (¹H MRS) was used to analyze metabolites in the hippocampus of chronic type 1 diabetes mellitus (T1DM) rats induced by streptozocin and T1DM rats treated with long-term insulin. The results showed that, compared with control rats and insulin-treated T1DM rats, the fasting blood glucose of T1DM rats increased significantly, while the body weight of T1DM rats decreased remarkably (p < 0.05). The contents of myo-inositol (Ins), taurine (Tau) and glutamate (Glu) of T1DM rats were significantly higher than those of control rats (p=0.000, p=0.003, p=0.014, respectively), and the contents of Ins and Tau of insulin-treated T1DM rats were significantly decreased than those in the T1DM diabetic rats (p=0.000, p=0.010, respectively). Contents of Glu and Glx (Glu and glutamine) of insulin-treated T1DM rats were significantly higher than those in the control group (p=0.007, p=0.042, respectively). The metabolite changes of Ins and Tau in hippocampus of T1DM rats are sensitive to insulin treatment.

To improve the accuracy of computer aided diagnosis (CAD) based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the differentiation of benign and malignant breast lesions, this study proposed a convolutional neural network model (AC_Ulsam_CNN) that is based on multi-modal feature fusion and the combination of asymmetric convolution (AC) and ultra-lightweight subspace attention module (Ulsam). Firstly, the transfer learning method was used to pre-train the model to screen out the most effective DCE-MRI time phase scan for differentiating benign and malignant breast lesions. Then, a network model based on AC_Ulsam_CNN was constructed based on the optimal time phase scan images to enhance the feature expression ability and robustness of the classification model. Finally, multimodal information such as breast imaging reporting and data system (BI-RADS) classification, apparent diffusion coefficient (ADC) and time-signal intensity curve (TIC) type were incorporated for feature fusion, to further improve the distinguishing performance of benign and malignant breast lesions. The performance of the model was verified by 5-fold cross-validation method, and the accuracy (ACC) of the proposed method was 0.826 and the area under the curve (AUC) was 0.877. The experimental results show that the proposed algorithm performs well in the classification of benign and malignant breast lesions with small sample size, and the fusion model based on multimodal data further enriches the feature information, thus this study improves the detection accuracy of lesions, and provides a new method for automatic differential diagnosis of benign and malignant breast lesions.

The tensor data of subjects are usually averaged linearly over multiple channels to obtain the tensor template. However, linear averaging ignores the vector information in the tensor. Additionally, it will render the interface between the gray matter and white matter too smooth, resulting in resolution reduction. To address the above problems, this paper introduced quaternion and Gaussian weighted average to construct a Gaussian diffusion tensor imaging (DTI) brain template. First, the DTI data of 55 healthy subjects were preprocessed to minimize data artifacts. The obtained data were then subjected to preliminary spatial standardization. Then, the tensor was decomposed to acquire eigenvectors and eigenvalues. Finally, the eigenvalues and quaternion converted from the eigenvectors were followed by Gaussian weighted average to gain the averaged eigenvectors and eigenvalues. The tensor template was obtained by reconstructing the averaged eigenvectors and eigenvalues. The experimental results show that compared with the linear DTI template, the Gaussian DTI template performs better on the DTED, COH, DVED, OVL, and corr_FA evaluation indicators but poorer on the IA indicator. The Gaussian DTI template proposed in this paper has certain advantage on the overall information retention, but is to be further improved on the orientation information.

In this paper, the conversion of methanol and butanol and the differences in product distribution over H-SAPO-34 were investigated. Some important reaction intermediates were captured during the reaction process by using gas chromatography-mass spectrometry (GC-MS) and ¹³C cross polarization magic angle spinning nuclear magnetic resonance (¹³C CP MAS NMR) spectroscopy. In the methanol conversion process, ethene, propene and butene are the main products, while in the butanol conversion process, butanol is mainly dehydrated to form butene, and propene and butene are the main products in the initial stage. Light olefins are generated from both methanol and butanol conversion over H-SAPO-34. Furthermore, the aromatic species were observed in both retained species analysis and ¹³C CP MAS NMR, indicating that similar organic species confined in the H-SAPO-34 during the conversion of alcohols. Starting from different kinds of alcohols, the acid-catalysis environment in the confined space of H-SAPO-34 can catalyze the methanol and butanol conversion to produce light olefins.

The formation and reactivity of surface methoxy species in methanol reaction over SSZ-13 zeolite were investigated by using solid-state nuclear magnetic resonance (NMR) technique. The surface methoxy species bound to Brønsted acid sites was identified by two-dimensional ¹³C-²⁷Al heteronuclear multiple quantum coherence (HMQC) NMR experiment. The formation of another surface methoxy species bound to Lewis acid sites was also observed. The combination of ¹³C NMR and gas chromatography-mass spectrometry (GC-MS) showed that the two kinds of surface methoxy species possess high reactivity, contributing to the formation of hydrocarbon pool species and hydrocarbons via the hydrocarbon pool mechanism.

The magnet of permanent magnet magnetic resonance instrument is susceptible to the interference of temperature and other environmental magnetic fields, resulting in fluctuations of the main magnetic field, which affects the repeatability and accuracy of the measurement. In this paper, two locking methods to solve the magnetic field fluctuation are discussed. On the one hand, the transient magnetic field caused by environmental fluctuation is detected by fluxgate sensor with high sensitivity, and then the field programmable gate array is used for real-time processing and calculation of the magnetic field compensation. On the other hand, the time-domain digital frequency discrimination locking method is employed for the slow magnetic field drift caused by changes in ambient temperature. After the locked sample being excited by radio frequency, nuclear magnetic resonance (NMR) signal is converted to a lower frequency range through frequency mixing, converted into a square wave signal, and directly sent to the field programmable gate array for periodic measurement. The magnetic field compensation amount is also obtained by calculation. The magnetic field compensation obtained by the two methods is superimposed, and then converted into current signal to drive B₀ compensation coil mounted on the magnet, thereby a magnetic field locking system is developed to realize the locking of the magnetic field. The test is carried out on a 0.5 T food rapid detection magnetic resonance analyzer. When subjected to transient interference, the magnetic field can be stabilized within the range of ±4 Hz (corresponding to ±0.093 9 μT), and the magnetic field drift caused by temperature can also be accurately measured, which verifies the feasibility of the magnetic field locking method presented in this paper.

Polarimetry is an important part of a polarized ³He system. This work describes a laser detection setup which measures the free induction decay nuclear magnetic resonance (FID NMR) signal in the spin-polarized ³He system. The setup provides a novel approach to the high precision real-time polarization measurement of the polarized ³He systems. This laser detection method is based on Faraday rotation effect, which utilizes a linearly polarized laser beam to detect the FID signal of the magnetic moment of ³He atoms which precess along the direction of the main magnetic field. Compared with traditional pick-up coils, experimental results demonstrate that the laser detection method has a signal-to-noise ratio improvement of 106% and consequently higher precision in ³He polarization measurement. The laser detection method proposed in this paper can replace the pickup coils, and it has various applications in the polarization measurement of ³He systems or in precision measurement experiments.

A miniaturized low noise quartz crystal oscillator for rubidium atomic frequency standard (RAFS) was designed in this paper, with the oscillation circuit applying Colpitts parallel configuration and SC-cut crystal resonator. The phase noise of quartz crystal oscillator was analyzed based on Leeson model, and the oscillation circuit was simulated by using ADS software, which can provide guidance for oscillator design and debugging. Finally, a low noise crystal oscillator with volume of 22 mm×28.5 mm×13 mm has been completed. Test results show that it reached the phase noise of −102.7 dBc/Hz@1 Hz and −164.2 dBc/Hz@10 kHz, and the short-term stability of 1.73×10⁻¹²/s.

The atomic relaxation time of a hydrogen atomic clock is the time required for atomic system after the selected state removing the atoms of the ground state hyperfine energy level (F = 0, m_F = 0) and (F = 1, m_F = −1) state to change from the (F = 1, m_F = 0) state to the (F = 0, m_F = 0) state until the atomic system reaches equilibrium. This parameter reflects the lifetime of atoms and directly affects performance of hydrogen clock. In order to measure the relaxation time of active hydrogen atomic clock and thus evaluate its performance, hydrogen atomic relaxation test system was established, which was composed of a Raspberry Pi (RPI), signal generator, digital attenuation, microwave detection, and data acquisition circuits. RPI generates timing signal to control the relay of digital attenuator and ionization source power supply circuit, so as to control the opening of microwave detection signal and the on-off of atomic beam current. The relaxation time of the hydrogen atomic clock is measured by data fitting of the acquired free induction decay signals. This method is important for optimizing the atomic linewidth and improving the performance of the active hydrogen clock.