Effects of NaB on HDAC1/2 expression and recruitment in MSCs.

<p>(A) Immunofluorescence analysis of HDAC1 and HDAC2 expression in MSCs treated with NaB (1 mM for 48 h). (B) Western blot assay to determine the expression of HDAC1 and HDAC2 in MSCs treated with 1 mM NaB for 48 h. (C) ChIP-qPCR assay to determine the recruitment of HDAC1 and HDAC2 to the α-SMA, calponin and SM-MHC promoters in MSCs treated with 1 mM NaB for 48 h. The ChIP assay was conducted with ChIP grade anti-HDAC1, HDAC2 and normal rat IgG antibodies, which were incubated with the sonicated supernatants of MSCs treated with 1 mM NaB. The isolated DNA fragments were analyzed by qPCR to determine the presence of the promoter regions of the α-SMA, calponin and SM-MHC genes. Values were given as fold changes normalized with normal rat IgG control. **, <i>P</i><0.01 compared to the untreated MSCs.</p

Histone acetylation modifications in co-cultured MSCs treated with NaB.

<p>MSCs were co-cultured with SMCs in a transwell chamber for 48 h. The MSCs were pretreated with 1.0 mM NaB before co-culturing. The MSCs in the co-culture system were harvested, and the genomic DNA was isolated and sonicated for a ChIP assay with antibodies against acetyl-histone H3K9, acetyl-histone H4 and normal rat IgG. The specific DNA fragments retrieved in the pull-down were further used for qPCR assays. The qPCR primers were designed to target the promoter of each gene. Values were given as folds of enrichment relative to the IgG control. Data are expressed as the mean ± SD of three biological replicates.*, <i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001 compared to the untreated MSCs. ^#, <i>P</i><0.05, ^##<i>P</i><0.01, ^###<i>P</i><0.001 compared to the co-cultured MSCs.</p

Immunofluorescence analysis of MSC specific protein expression in NaB-treated MSCs.

<p>MSCs were treated with 1 mmol/L NaB for 48 h, stained with FITC-conjugated anti-α-SMA, calponin or SM-MHC antibodies, and observed under a fluorescence microscope. The untreated MSCs were used as negative control and the primary SMCs were used as positive control. The isotype antibody was used as a background control. DAPI was used to stain the nuclei. Scale bar = 25 µm.</p

Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations

Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that a simulation cell up to 64000 atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to 1011 K s−1 are required for almost convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair-correlation function, angular-distribution function, coordination number, ring statistics, and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum-correction method based on the spectral thermal conductivity. </p

NaB induces SMC specific gene expression in MSCs co-cultured with SMCs.

<p>MSCs were co-cultured with SMCs in a transwell chamber with SMCs in the insert chamber and MSCs in the lower chamber. The MSCs were pretreated with 0, 0.5, 1.0 and 1.5 mM NaB before co-culturing. The MSCs in the co-culture system were harvested, and the expression of the SMC specific genes α-SMA, calponin and SM-MHC was determined by quantitative real-time RT-PCR (A) and Western blot (B). *, <i>P</i><0.05, **, <i>P</i><0.01 <i>vs</i>. all other time points in the same NaB concentration group; ▴, <i>P</i><0.01 <i>vs</i>. all other time points in all NaB concentration groups. (C) The co-cultured MSCs were stimulated with 1 mmol/L NaB for 48 h, stained with FITC-conjugated anti-α-SMA, calponin or SM-MHC antibodies, and observed under a fluorescence microscope. DAPI was used to stain the cell nuclei. The isotype antibody was used as a background control. Scale bar = 25 µm.</p

Structure and pore size distribution in nanoporous carbon

We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csányi adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm3) with structures containing 131072 atoms. Our results are in good agreement with experimental data for the available observables and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials.</p

A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties

Silicon is an important material and many empirical interatomic potentials have been developed for atomistic simulations of it. Among them, the Tersoff potential and its variants are the most popular ones. However, all the existing Tersoff-like potentials fail to reproduce the experimentally measured thermal conductivity of diamond silicon. Here we propose a modified Tersoff potential and develop an efficient open source code called GPUGA (graphics processing units genetic algorithm) based on the genetic algorithm and use it to fit the potential parameters against energy, virial and force data from quantum density functional theory calculations. This potential, which is implemented in the efficient open source GPUMD (graphics processing units molecular dynamics) code, gives significantly improved descriptions of the thermal conductivity and phonon dispersion of diamond silicon as compared to previous Tersoff potentials and at the same time well reproduces the elastic constants. Furthermore, we find that quantum effects on the thermal conductivity of diamond silicon at room temperature are non-negligible but small: Using classical statistics underestimates the thermal conductivity by about 10% as compared to using quantum statistics

Data_Sheet_2_Genetic insights into the crude protein and fiber content of ramie leaves.xlsx

Ramie (Boehmeria nivea L.) is a perennial plant with vigorously vegetative growth and high nutritive value that is an excellent source of green feed in China. Crude protein and fiber content are the most important traits associated with ramie forage quality; however, their genetic basis remains largely unknown. In this study, we investigated the genetic architecture of these two traits using an F2 population derived from cultivated Zhongsizhu 1 (ZSZ1) and wild Boehmeria nivea var. tenacissima (tenacissima). Linkage mapping identified eight quantitative trait loci (QTLs) in crude fiber and one QTL in crude protein. Of these, five were further validated by association analysis. Then, two major QTLs for crude fiber content, CF7 and CF13, were further identified using bulked segregant analysis (BSA) sequencing, and their exact physical intervals were determined via genotype analysis of F2 progenies with extremely low crude fiber content. In total, 10 genes in the CF7 and CF13 regions showed differential expression in ZSZ1 and tenacissima leaves, including an MYB gene whole_GLEAN_10016511 from the CF13 region. Wide variation was observed in the promoter regions of whole_GLEAN_10016511, likely responsible for its downregulated expression in tenacissima. Interestingly, more fiber cells were observed in Arabidopsis with overexpression of whole_GLEAN_10016511, indicating that the downregulated expression of this gene could have an association with the relatively low fiber content in wild tenacissima. These results provided evidence that whole_GLEAN_10016511 is a logical candidate for CF13. This study provides important insights into the genetic basis underlying ramie crude protein and fiber content, and it presents genetic loci for improving the forage quality of ramie using marker-assisted selection.</p

Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

We develop a neuroevolution-potential (NEP) framework for generating neural network-based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source gpumd package, which can attain a computational speed over atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods

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