Langevin dynamics has become a popular tool to simulate the Boltzmann equilibrium distribution. When the repartition of the Langevin equation involves the exact realization of the Ornstein-Uhlenbeck noise, in addition to the conventional density evolution, there exists another type of discrete evolution that may not correspond to a continuous, real dynamical counterpart. This virtual dynamics case is also able to produce the desired stationary distribution. Different types of repartition lead to different numerical schemes, of which the accuracy and efficiency are investigated through studying the harmonic oscillator potential, an analytical solvable model. By analyzing the asymptotic distribution and characteristic correlation time that are derived by either directly solving the discrete equations of motion or using the related phase space propagators, it is shown that the optimal friction coefficient resulting in the minimum characteristic correlation time depends on the time interval chosen in the numerical implementation. When the recommended "middle" scheme is employed, both analytical and numerical results demonstrate that, for good numerical performance in efficiency as well as accuracy, one may choose a friction coefficient in a wide range from around the optimal value to the high friction limit.
Background: Long-term maintenance of neural stem cells in vitro is crucial for their stage specific roles in neurogenesis. To have an in-depth understanding of optimal conditional microenvironmental niche for long-term maintenance of neural stem cells (NSCs), we imposed different combinatorial treatment of growth factors to EGF/FGF-responsive cells. We hypothesized, that IGF-1-treatment can provide an optimal niche for long-term maintenance and proliferation of EGF/FGF-responsive NSCs.Objective: This study was performed to investigate the cellular morphology and growth of rat embryonic striatal tissue derived-NSCs in long-term culture under the influence of different combinatorial effects of certain growth factors, such as EGF, bFGF, LIF and IGF-1.Methods: The NSCs were harvested and cultured from striatal tissue of 18 days old rat embryos. We have generated neurospheres from these NSCs and cultured them till passage 7 (28 days in vitro) under four different conditional microenvironments: (A) without growth factor, (B) EGF/bFGF, (C) EGF/bFGF/LIF, (D) EGF/bFGF/IGF-1 and (E) EGF/bFGF/LIF/IGF-1. Isolated NSCs were characterised by Immunoflouroscence for nestin expression. The cell growth and proliferation was evaluated at different time intervals (P1, P3, P5 & P7), assessing the metabolic activity based cell proliferation. Apoptosis was studied in each of these groups by In situ cell death assay.Results: Our results demonstrated certain important findings relevant to long-term culture and maintenance of striatal NSC-derived neurospheres. This suggested that IGF-1 can induce enhanced cell proliferation during early stages of neurogenesis, impose long-term maintenance (up to passage 7) to cultured NSCs and enhance survival efficiency in vitro, in the presence of EGF and FGF.Conclusions: Our findings support the hypothesis that the enforcement of IGF-1 treatment to the EGF/FGF-responsive NSCs, can lead to enhanced cell proliferation during early stages of neurogenesis, and an extended life span in vitro. This information will be beneficial for improving future therapeutic implication of NSCs, by addressing improved in vitro production of NSCs.
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models; instead, we introduce various useful concepts and ideas brought by modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we give an outlook for promising directions which may help address the existing issues in the current framework of deep molecular modeling.
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