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https://www.selleckchem.com/products/t-5224.html © 2020 Wiley Periodicals, Inc.Several deep-learning models have been proposed to shorten MRI scan time. Prior deep-learning models that utilize real-valued kernels have limited capability to learn rich representations of complex MRI data. In this work, we utilize a complex-valued convolutional network (ℂNet) for fast reconstruction of highly under-sampled MRI data and evaluate its ability to rapidly reconstruct 3D late gadolinium enhancement (LGE) data. ℂNet preserves the complex nature and optimal combination of real and imaginary components of MRI data throughout the reconstruction process by utilizing complex-valued convolution, novel radial batch normalization, and complex activation function layers in a U-Net architecture. A prospectively under-sampled 3D LGE cardiac MRI dataset of 219 patients (17 003 images) at acceleration rates R = 3 through R = 5 was used to evaluate ℂNet. The dataset was further retrospectively under-sampled to a maximum of R = 8 to simulate higher acceleration rates. We created three reconstructions of the 3D igher acceleration rates. ℂNet enables fast reconstruction of highly accelerated 3D MRI with superior performance to real-valued networks, and achieves faster reconstruction than compressed sensing. © 2020 John Wiley & Sons, Ltd.We report the synthesis of anionic diniobium hydride complexes with a series of alkali metal cations (Li + , Na + , and K + ) and their counterion dependence of the reactivity with N 2 . Exposure of these complexes to N 2 initially produces the corresponding side-on end-on N 2 complexes, the fate of which depends on the nature of counter cations. The lithium derivative undergoes stepwise migratory insertion of the hydride ligands onto the aryloxide units, yielding the end-on bridging N 2 complex. For the potassium derivative, the N-N bond cleavage takes place along with H 2 elimination to form the nitride complex. Treatment of the side-on end-on N 2 complex wi

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