Significantly lower PSw and Ew were observed in NAWM compared to WM (ΔPSw -11.9 mL/100 g/min, p  less then  .05; ΔEw -4.3%, p  less then  .01). Significantly lower Ew was observed in NAGM compared to GM (ΔEw -12.1%, p  less then  .01). Significantly lower PSw and CBF were observed in non-GBCA contrast enhancing lesions compared to NAWM (ΔPSw = -11.5 mL/100 g/min, p  less then  .05; ΔCBF = -8.1 mL/100 g/min, p  less then  .05). Ew was significantly higher in non-GBCA enhancing chronic MS lesions compared to NAWM (ΔEw = 1.6%, p  less then  .05). The lower BBB water exchange in chronic MS lesions is consistent with previously reported observations and may demonstrate metabolic changes associated with MS. PURPOSE To enable fast reconstruction of undersampled motion-compensated whole-heart 3D coronary magnetic resonance angiography (CMRA) by learning a multi-scale variational neural network (MS-VNN) which allows the acquisition of high-quality 1.2 × 1.2 × 1.2 mm isotropic volumes in a short and predictable scan time. METHODS Eighteen healthy subjects and one patient underwent free-breathing 3D CMRA acquisition with variable density spiral-like Cartesian sampling, combined with 2D image navigators for translational motion estimation/compensation. The proposed MS-VNN learns two sets of kernels and activation functions for the magnitude and phase images of the complex-valued data. For the magnitude, a multi-scale approach is applied to better capture the small calibre of the coronaries. Ten subjects were considered for training and validation. SN-011 chemical structure Prospectively undersampled motion-compensated data with 5-fold and 9-fold accelerations, from the remaining 9 subjects, were used to evaluate the framework. The proposed approach was compared to Wavelet-based compressed-sensing (CS), conventional VNN, and to an additional fully-sampled (FS) scan. RESULTS The average acquisition time (ms) was 411 for 5-fold, 234 for 9-fold acceleration and 1855 for fully-sampled. Reconstruction time with the proposed MS-VNN was ~14 s. The proposed MS-VNN achieves higher image quality than CS and VNN reconstructions, with quantitative right coronary artery sharpness (CS43.0%, VNN43.9%, MS-VNN47.0%, FS50.67%) and vessel length (CS7.4 cm, VNN7.7 cm, MS-VNN8.8 cm, FS9.1 cm) comparable to the FS scan. CONCLUSION The proposed MS-VNN enables 5-fold and 9-fold undersampled CMRA acquisitions with comparable image quality that the corresponding fully-sampled scan. The proposed framework achieves extremely fast reconstruction time and does not require tuning of regularization parameters, offering easy integration into clinical workflow. Various animal models have been employed to understand the pathogenic mechanism of neuropathic pain. Nitric oxide (NO) is an important molecule in nociceptive transmission and is involved in neuropathic pain. However, its mechanistic actions remain unclear. The aim of this study was to better understand the involvement of neuronal and inducible isoforms of nitric oxide synthase (nNOS and iNOS) in neuropathic pain induced by chronic constriction injury (CCI) of the sciatic nerve in rats. We evaluated pain sensitivity (mechanical withdrawal thresholds using Randall and Selitto, and von Frey tests, and thermal withdrawal thresholds using Hargreaves test) prior to CCI surgery, 14 days post CCI and after intrathecal injections of selective nNOS or iNOS inhibitors. We also evaluated the distribution of NOS isozymes in the spinal cord and dorsal root ganglia (DRG) by immunohistochemistry, synthesis of iNOS and nNOS by Western blot, and NO production using fluorescent probe DAF-2 DA (DA). Our results showed higher number of nNOS and iNOS-positive neurons in the spinal cord and DRG of CCI compared to sham rats, and their reduction in CCI rats after treatment with selective inhibitors compared to non-treated groups. Western blot results also indicated reduced expression of nNOS and iNOS after treatment with selective inhibitors. Furthermore, both inhibitors reduced CCI-evoked mechanical and thermal withdrawal thresholds but only nNOS inhibitor was able to efficiently lower mechanical withdrawal thresholds using von Frey test. In addition, we observed higher NO production in the spinal cord and DRG of injured rats compared to control group. Our study innovatively shows that nNOS may strongly modulate nociceptive transmission in rats with neuropathic pain, while iNOS may partially participate in the development of nociceptive responses. Thus, drugs targeting nNOS for neuropathic pain may represent a potential therapeutic strategy. OBJECTIVES Bacillus thuringiensis (BT) is distributed widely in the environment and utilized frequently for its highly specific toxins to target insect. However, BT is potentially pathogenic due to the high similarity between BT and Bacillus anthracis (BA). Meanwhile, there are reports that heavy metal pressure can promote the proliferation of antibiotic resistance in microorganisms through the co-selection of metal resistance genes (MRGs) and antibiotic resistance genes (ARGs). The aim of this work was revealed the MRGs and ARGs in a novel heavy metal tolerant and drug-resistant strain-B. thuringiensis HM-311, which was isolated from radiation and heavy metal-contaminated soil in Xinjiang (China). METHODS The genome of B. thuringiensis HM-311 was sequenced using a PacBio RS II platform and Illumina HiSeq 4000 platform at the Beijing Genomics Institute (BGI, Shenzhen, China). RESULTS The total size of B. thuringiensis HM-311 genome was 6,019,481 bp with a GC content of 35.85%. 134 genes related to antibiotics resistance and 75 genes related to heavy metal resistance were predicted in the B. thuringiensis HM-311 genome, the main ARGs and MRGs were discussed. Moreover, 30 verified virulence factor genes and 297 predicted virulence factor genes were annotated in the B. thuringiensis HM-311 genome. CONCLUSIONS This genome can be used as a reference sequence for comparative genomic studies, elucidating antibiotic resistance development and the relationship between antibiotic resistance genes and heavy metal resistance genes in B. thuringiensis.