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NSI‐566 is a stable, primary adherent neural stem cell line derived from a single human fetal spinal cord and expanded epigenetically with no genetic modification. This cell line is being tested in clinical trials in the U.S. for treatment of amyotrophic lateral sclerosis and spinal cord injury. In a single‐site, phase I study, we evaluated the feasibility and safety of NSI‐566 transplantation for the treatment of hemiparesis due to chronic motor stroke and determined the maximum tolerated dose for future trials. Three cohorts (n = 3 per cohort) were transplanted with one‐time intracerebral injections of 1.2 × 107, 2.4 × 107, or 7.2 × 107 cells. Immunosuppression therapy with tacrolimus was maintained for 28 days. All subjects had sustained chronic motor strokes, verified by magnetic resonance imaging (MRI), initiated between 5 and 24 months prior to surgery with modified Rankin Scores [MRSs] of 2, 3, or 4 and Fugl‐Meyer Motor Scores of 55 or less. At the 12‐month visit, the mean Fugl‐Meyer Motor Score (FMMS, total score of 100) for the nine participants showed 16 points of improvement (p = .0078), the mean MRS showed 0.8 points of improvement (p = .031), and the mean National Institutes of Health Stroke Scale showed 3.1 points of improvement (p = .020). For six participants who were followed up for 24 months, these mean changes remained stable. The treatment was well tolerated at all doses. Longitudinal MRI studies showed evidence indicating cavity‐filling by new neural tissue formation in all nine patients. Although this was a small, one‐arm study of feasibility, the results are encouraging to warrant further studies. Stem Cells Translational Medicine 2019;8:999–1007

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

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The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.