Huazhong University of Science and Technology

About: Huazhong University of Science and Technology is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Computer science. The organization has 120339 authors who have published 122521 publications receiving 2168040 citations. The organization is also known as: Central China University of Science and Technology.

Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping.

Abstract: Quantitative susceptibility mapping (QSM) opens the door for measuring tissue magnetic susceptibility properties that may be important biomarkers, and QSM is becoming an increasingly active area of scientific and clinical investigations. In practical applications, there are sources of errors for QSM including noise, phase unwrapping failures, and signal model inaccuracy. To improve the robustness of QSM quality, we propose a nonlinear data fidelity term for frequency map estimation and dipole inversion to reduce noise and effects of phase unwrapping failures, and a method for model error reduction through iterative tuning. Compared with the previous phase based linear QSM method, this nonlinear QSM method reduced salt and pepper noise or checkerboard pattern in high susceptibility regions in healthy subjects and markedly reduced artifacts in patients with intracerebral hemorrhages.

Differentiation of rat marrow mesenchymal stem cells into pancreatic islet beta-cells.

Abstract: AIM: To explore the possibility of marrow mesenchymal stem cells (MSC) in vitro differentiating into functional islet-like cells and to test the diabetes therapeutic potency of Islet-like cells. METHODS: Rat MSCs were isolated from Wistar rats and cultured. Passaged MSCs were induced to differentiate into islet-like cells under following conditions: pre-induction with L-DMEM including 10 mmol/L nicotinamide + 1 mmol/L β –mercaptoethanol + 200 mL/L fetal calf serum (FSC) for 24 h, followed by induction with serum free H-DMEM solution including 10 mmol/L nicotinamide + 1 mmol/L,β -mercaptoethanol for 10 h. Differentiated cells were observed under inverse microscopy, insulin and nestin expressed in differentiated cells were detected with immunocytochemistry. Insulin excreted from differentiated cells was tested with radioimmunoassay. Rat diabetic models were made to test in vitro function of differentiated MSCs. RESULTS: Typical islet –like clustered cells were observed. Insulin mRNA and protein expressions were positive in differentiated cells, and nestin could be detected in pre-differentiated cells. Insulin excreted from differentiated MSCs (446.93 ± 102.28 IU/L) was much higher than that from pre-differentiated MSCs (2.45 ± 0.81 IU/L (P < 0.01). Injected differentiated MSCs cells could down-regulate glucose level in diabetic rats. CONCLUSION: Islet-like functional cells can be differentiated from marrow mesenchymal stem cells, which may be a new procedure for clinical diabetes stem -cell therapy, these cells can control blood glucose level in diabetic rats. MSCs may play an important role in diabetes therapy by islet differentiation and transplantation.

A transfer convolutional neural network for fault diagnosis based on ResNet-50

Abstract: With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.

Experimental demonstration of anomalous Floquet topological insulator for sound.

Abstract: Time-reversal invariant topological insulator is widely recognized as one of the fundamental discoveries in condensed matter physics, for which the most fascinating hallmark is perhaps a spin-based topological protection, the absence of scattering of conduction electrons with certain spins on matter surface. Recently, it has created a paradigm shift for topological insulators, from electronics to photonics, phononics and mechanics as well, bringing about not only involved new physics but also potential applications in robust wave transport. Despite the growing interests in topologically protected acoustic wave transport, T-invariant acoustic topological insulator has not yet been achieved. Here we report experimental demonstration of anomalous Floquet topological insulator for sound: a strongly coupled metamaterial ring lattice that supports one-way propagation of pseudo-spin-dependent edge states under T-symmetry. We also demonstrate the formation of pseudo-spin-dependent interface states due to lattice dislocations and investigate the properties of pass band and band gap states.

Caution on Kidney Dysfunctions of COVID-19 Patients

Abstract: Background: To date, large amounts of epidemiological and case study data have been available for the Coronavirus Disease 2019 (COVID-19), which suggested that