Longitudinal Optogenetic Motor Mapping Revealed Structural and Functional Impairments and Enhanced Corticorubral Projection after Contusive Spinal Cord Injury in Mice

Current evaluation of impairment and repair after spinal cord injury (SCI) is largely dependent on behavioral assessment and histological analysis of injured tissue and pathways. Here, we evaluated whether transcranial optogenetic mapping of motor cortex could reflect longitudinal structural and functional damage and recovery after SCI. In Thy1-Channelrhodopsin2 transgenic mice, repeated motor mappings were made by recording optogenetically evoked electromyograms (EMGs) of a hindlimb at baseline and 1 day and 2, 4, and 6 weeks after mild, moderate, and severe spinal cord contusion. Injuries caused initial decreases in EMG amplitude, losses of motor map, and subsequent partial recoveries, all of which corresponded to injury severity. Reductions in map size were positively correlated with motor performance, as measured by Basso Mouse Scale, rota-rod, and grid walk tests, at different time points, as well as with lesion area at spinal cord epicenter at 6 weeks post-SCI. Retrograde tracing with Fluoro-Gold showed decreased numbers of cortico- and rubrospinal neurons, with the latter being negatively correlated with motor map size. Combined retro- and anterograde tracing and immunostaining revealed more neurons activated in red nucleus by cortical stimulation and enhanced corticorubral axons and synapses in red nucleus after SCI. Electrophysiological recordings showed lower threshold and higher amplitude of corticorubral synaptic response after SCI. We conclude that transcranial optogenetic motor mapping is sensitive and efficient for longitudinal evaluation of impairment and plasticity of SCI, and that spinal cord contusion induces stronger anatomical and functional corticorubral connection that may contribute to spontaneous recovery of motor function

An Efficient Method for Fractured Shale Reservoir Simulation & History Matching: The cEDFM Approach

The intricate physics of unconventional reservoir fluid transportation has posed great challenges to traditional simulation approaches. Resources such as shale are usually associated with complex fracture networks generated either naturally as a result of the geo-stress evolution, or artificially during hydraulic fracturing to improve well productivity. In either case, fractures greatly influence the underground fluid transportation, which highlights the importance to accurately simulate the flux with fractures. In this work, a novel discrete fracture model, compartmental EDFM (cEDFM) is developed based on the original EDFM framework. By assuming a linearly distributed pressure near fractures, EDFM can provide a sub-grid resolution that lifts the requirement to perform local refinement. Although efficient, considerable error is reported when applying this method to simulate flow barriers, especially when dominant flux direction is across instead of along the fractures. Therefore, different from the original method, the fracture would split matrix grid blocks when intersecting them in the proposed approach, resulting in a model more similar to an explicit fracture model. The proposed method maintained the high efficiency of the original EDFM, while overcame some of the limitations. The new model is benchmarked for single-phase and multi-phase problems, and the accuracy is evaluated by comparing to multiple reference cases. Results indicate the new model yields much better accuracy even for multi-phase flow simulation with flow barriers. A major part of the uncertainty for shale reservoirs comes from the distribution and properties of the fracture network. However, explicit fracture models are rarely used in uncertainty quantification due to the high computational cost. The later part of this work explored several workflows to match the history of reservoirs with fractures. By taking advantage of the efficiency of cEDFM, fractures can be explicitly characterized, and the corresponding uncertainty about the distribution and properties of fractures can be evaluated. No upscaling of the fracture properties is necessary, which is usually a required step in a traditional workflow. A modified two-stage MCMC algorithm as well as the Ensemble Kalman Filter (EnKF) are implemented as the data assimilation algorithms, with the latter preferred for more complex cases with larger parameter space

Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

Mixed-precision quantization (MPQ) suffers from time-consuming policy search process (i.e., the bit-width assignment for each layer) on large-scale datasets (e.g., ISLVRC-2012), which heavily limits its practicability in real-world deployment scenarios. In this paper, we propose to search the effective MPQ policy by using a small proxy dataset for the model trained on a large-scale one. It breaks the routine that requires a consistent dataset at model training and MPQ policy search time, which can improve the MPQ searching efficiency significantly. However, the discrepant data distributions bring difficulties in searching for such a transferable MPQ policy. Motivated by the observation that quantization narrows the class margin and blurs the decision boundary, we search the policy that guarantees a general and dataset-independent property: discriminability of feature representations. Namely, we seek the policy that can robustly keep the intra-class compactness and inter-class separation. Our method offers several advantages, i.e., high proxy data utilization, no extra hyper-parameter tuning for approximating the relationship between full-precision and quantized model and high searching efficiency. We search high-quality MPQ policies with the proxy dataset that has only 4% of the data scale compared to the large-scale target dataset, achieving the same accuracy as searching directly on the latter, and improving the MPQ searching efficiency by up to 300 times

Active RIS Versus Passive RIS: Which Is Superior with the Same Power Budget?

This letter theoretically compares the active reconfigurable intelligent surface (RIS)-aided system with the passive RIS-aided system. For a fair comparison, we consider that these two systems have the same overall power budget that can be used at both the base station (BS) and the RIS. For active RIS, we first derive the optimal power splitting between the BS’s transmit signal power and RIS’s output signal power. We also analyze the impact of various system parameters on the optimal power splitting ratio. Then, we theoretically and numerically compare the performance between the active RIS and the passive RIS, which demonstrates that the active RIS would be superior if the power budget is not very small and the number of RIS elements is not very large

Current-voltage characteristics of NdFeAsO0.85F0.15 and NdFeAsO0.85 superconductors

The vortex phase diagrams of NdFeAsO0.85F0.15 and NdFeAsO0.85 superconductors are determined from the analysis of resistivity and current-voltage (I-V) measurements in magnetic fields up to 9 T. A clear vortex glass to liquid transition is identified only in the oxygen deficient NdFeAsO0.85, in which I-V curves can be well scaled onto liquid and glass branches consistent with the vortex glass theory. With increasing magnetic field, the activation energy U0, deduced from the Arrhenius plots of resistivity based on the thermally activated flux-flow model (TAFF), decays more quickly for NdFeAsO0.85F0.15 than for NdFeAsO0.85. Moreover, the irreversibility field Hirr of NdFeAsO0.85 increases more rapidly than that of NdFeAsO0.85F0.15 with decreasing temperature. These observations evidence the strong vortex pinning effects, presumably caused by the enhanced defects and disorders in the oxygen deficient NdFeAsO0.85. It is inferred that the enhanced defects and disorder can be also responsible for the vortex glass to liquid transition in the NdFeAsO0.85.Comment: 19 pages, 5 figure