19,392 research outputs found

    Study of the cytological features of bone marrow mesenchymal stem cells from patients with neuromyelitis optica.

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    Neuromyelitis optica (NMO) is a refractory autoimmune inflammatory disease of the central nervous system without an effective cure. Autologous bone marrow‑derived mesenchymal stem cells (BM‑MSCs) are considered to be promising therapeutic agents for this disease due to their potential regenerative, immune regulatory and neurotrophic effects. However, little is known about the cytological features of BM‑MSCs from patients with NMO, which may influence any therapeutic effects. The present study aimed to compare the proliferation, differentiation and senescence of BM‑MSCs from patients with NMO with that of age‑ and sex‑matched healthy subjects. It was revealed that there were no significant differences in terms of cell morphology or differentiation capacities in the BM‑MSCs from the patients with NMO. However, in comparison with healthy controls, BM‑MSCs derived from the Patients with NMO exhibited a decreased proliferation rate, in addition to a decreased expression of several cell cycle‑promoting and proliferation‑associated genes. Furthermore, the cell death rate increased in BM‑MSCs from patients under normal culture conditions and an assessment of the gene expression profile further confirmed that the BM‑MSCs from patients with NMO were more vulnerable to senescence. Platelet‑derived growth factor (PDGF), as a major mitotic stimulatory factor for MSCs and a potent therapeutic cytokine in demyelinating disease, was able to overcome the decreased proliferation rate and increased senescence defects in BM‑MSCs from the patients with NMO. Taken together, the results from the present study have enabled the proposition of the possibility of combining the application of autologous BM‑MSCs and PDGF for refractory and severe patients with NMO in order to elicit improved therapeutic effects, or, at the least, to include PDGF as a necessary and standard growth factor in the current in vitro formula for the culture of NMO patient‑derived BM‑MSCs

    Artificial Gauge Field and Quantum Spin Hall States in a Conventional Two-dimensional Electron Gas

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    Based on the Born-Oppemheimer approximation, we divide total electron Hamiltonian in a spinorbit coupled system into slow orbital motion and fast interband transition process. We find that the fast motion induces a gauge field on slow orbital motion, perpendicular to electron momentum, inducing a topological phase. From this general designing principle, we present a theory for generating artificial gauge field and topological phase in a conventional two-dimensional electron gas embedded in parabolically graded GaAs/Inx_{x}Ga1−x_{1-x}As/GaAs quantum wells with antidot lattices. By tuning the etching depth and period of antidot lattices, the band folding caused by superimposed potential leads to formation of minibands and band inversions between the neighboring subbands. The intersubband spin-orbit interaction opens considerably large nontrivial minigaps and leads to many pairs of helical edge states in these gaps.Comment: 9 pages and 4 figure

    Does share pledging promote or impede corporate social responsibility? An examination of Chinese listed firms

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    By employing the Chinese listed firm’s data from 2010 to 2017, this study explores the impact of share pledging on firms’ corporate social responsibility (CSR) performance. Empirical results indicate a negative relationship between share pledging and CSR performance. This effect is robust after using alternative measures and different regression methods, and also consistent after tackling the endogenous issues. Furthermore, we find that risk-taking and agency cost are two possible underlying mechanisms through which share pledging reduces CSR. In addition, CSR reduction caused by share pledging leads to poorer economic performance and lower market value of firms

    On the Optimal Batch Size for Byzantine-Robust Distributed Learning

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    Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer from a significant drop on model accuracy due to the large variance of stochastic gradients. Increasing batch sizes is a simple yet effective way to reduce the variance. However, when the total number of gradient computation is fixed, a too-large batch size will lead to a too-small iteration number (update number), which may also degrade the model accuracy. In view of this challenge, we mainly study the optimal batch size when the total number of gradient computation is fixed in this work. In particular, we theoretically and empirically show that when the total number of gradient computation is fixed, the optimal batch size in BRDL increases with the fraction of Byzantine workers. Therefore, compared to the case without attacks, the batch size should be set larger when under Byzantine attacks. However, for existing BRDL methods, large batch sizes will lead to a drop on model accuracy, even if there is no Byzantine attack. To deal with this problem, we propose a novel BRDL method, called Byzantine-robust stochastic gradient descent with normalized momentum (ByzSGDnm), which can alleviate the drop on model accuracy in large-batch cases. Moreover, we theoretically prove the convergence of ByzSGDnm for general non-convex cases under Byzantine attacks. Empirical results show that ByzSGDnm has a comparable performance to existing BRDL methods under bit-flipping failure, but can outperform existing BRDL methods under deliberately crafted attacks
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