The Role Of Biomaterial Substrate In Stem Cell Fate Determination

The physical cues, which included topography, stiffness, and mechanical forces, can influence the stem cell renewal, differentiation, and maturation in vivo and in vitro. The nano-topography of the ECM can stimulate the neural differentiation of the stem cells, while the micro-topography of the ECM can guide the neurite outgrowth. However, the role and functional size of the micro- and nano-topography in the stem cell fate determination is not clear yet. To study this aim, two biomaterial based aligned fiber platforms (ACMFP and ASMFP) were designed, fabrication and evaluated to cover the micro-, submicro-, and nano-fiber topography, which used to study the neural differentiation and maturation of the ESCs. All these platforms were showed good alignment, contiguous, and biocompatibility via the physical factor assay, biocompatibility assay evaluation. The results showed three different gradient sizes of the platforms were fabricated in two type of platforms, which are 60 μm, 90 μm, 120 μm in ACMFPs and 0.7 μm, 1.5 μm, 3 μm in ASFMPs. The ESCs derived NBs were cultured on all platforms and their control flat membranes for six days, which followed with immunofluorescence staining. The result showed that all the ACMFPs and ASMFPs could stimulate the neural lineage cell differentiation. The affection of the ACMFPs induced the neural differentiation stimulate may cause by limit the cell proliferation. Part of the ACMFPs and ASMFPs can guide the neurite outgrowth direction. Part of the ASMFPs can stimulate the synaptogenesis. Overall, the micro-, submicro-, and nano-fiber pattern platforms seem to play a key role in the stem cell determination with different stimulation levels and types. The study of the topography induced stem cell differentiation may contribute the stem cell research and open a new way for clinical therapy in the future

Existence of Periodic Solutions for a Delayed Ratio-Dependent Three-Species Predator-Prey Diffusion System on Time Scales

This paper investigates the existence of periodic solutions of a ratio-dependent predator-prey diffusion system with Michaelis-Menten functional responses and time delays in a two-patch environment on time scales. By using a continuation theorem based on coincidence degree theory, we obtain suffcient criteria for the existence of periodic solutions for the system. Moreover, when the time scale 𝕋 is chosen as ℝ or ℤ, the existence of the periodic solutions of the corresponding continuous and discrete models follows. Therefore, the methods are unified to provide the existence of the desired solutions for the continuous differential equations and discrete difference equations

First-principles investigation of effect of pressure on BaFe2_2As2_2

On experimental side, BaFe$_2$As$_2$ without doping has been made superconducting by applying appropriate pressure (2-6 GPa). Here, we use a full-potential linearized augmented plane wave method within the density-functional theory to investigate the effect of pressure on its crystal structure, magnetic order, and electronic structure. Our calculations show that the striped antiferromagnetic order observed in experiment is stable against pressure up to 13 GPa. Calculated antiferromagnetic lattice parameters are in good agreements with experimental data, while calculations with nonmagnetic state underestimate Fe-As bond length and c-axis lattice constant. The effects of pressure on crystal structure and electronic structure are investigated for both the antiferromagnetic state and the nonmagnetic one. We find that the compressibility of the antiferromagnetic state is quite isotropic up to about 6.4 GPa. With increasing pressure, the FeAs$_4$ tetrahedra is hardly distorted. We observe a transition of Fermi surface topology in the striped antiferromagnetic state when the compression of volume is beyond 8% (or pressure 6 GPa), which corresponds to a large change of $c/a$ ratio. These first-principles results should be useful to understanding the antiferromagnetism and electronic states in the FeAs-based materials, and may have some useful implications to the superconductivity.Comment: 7 pages, 5 figure

Toxicity of metal-based nanoparticles: Challenges in the nano era

With the rapid progress of nanotechnology, various nanoparticles (NPs) have been applicated in our daily life. In the field of nanotechnology, metal-based NPs are an important component of engineered NPs, including metal and metal oxide NPs, with a variety of biomedical applications. However, the unique physicochemical properties of metal-based NPs confer not only promising biological effects but also pose unexpected toxic threats to human body at the same time. For safer application of metal-based NPs in humans, we should have a comprehensive understanding of NP toxicity. In this review, we summarize our current knowledge about metal-based NPs, including the physicochemical properties affecting their toxicity, mechanisms of their toxicity, their toxicological assessment, the potential strategies to mitigate their toxicity and current status of regulatory movement on their toxicity. Hopefully, in the near future, through the convergence of related disciplines, the development of nanotoxicity research will be significantly promoted, thereby making the application of metal-based NPs in humans much safer

SRDA-Net: Super-Resolution Domain Adaptation Networks for Semantic Segmentation

Recently, Unsupervised Domain Adaptation was proposed to address the domain shift problem in semantic segmentation task, but it may perform poor when source and target domains belong to different resolutions. In this work, we design a novel end-to-end semantic segmentation network, Super-Resolution Domain Adaptation Network (SRDA-Net), which could simultaneously complete super-resolution and domain adaptation. Such characteristics exactly meet the requirement of semantic segmentation for remote sensing images which usually involve various resolutions. Generally, SRDA-Net includes three deep neural networks: a Super-Resolution and Segmentation (SRS) model focuses on recovering high-resolution image and predicting segmentation map; a pixel-level domain classifier (PDC) tries to distinguish the images from which domains; and output-space domain classifier (ODC) discriminates pixel label distributions from which domains. PDC and ODC are considered as the discriminators, and SRS is treated as the generator. By the adversarial learning, SRS tries to align the source with target domains on pixel-level visual appearance and output-space. Experiments are conducted on the two remote sensing datasets with different resolutions. SRDA-Net performs favorably against the state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net