The effect of Cr impurity to superconductivity in electron-doped BaFe2-xNixAs2

We use transport and magnetization measurements to study the effect of Cr-doping to the phase diagram of the electron-doped superconducting BaFe2-xNixAs2 iron pnictides. In principle, adding Cr to electron-doped BaFe2-xNixAs2 should be equivalent to the effect of hole-doping. However, we find that Cr doping suppresses superconductivity via impurity effect, while not affecting the normal state resistivity above 100 K. We establish the phase diagram of Cr-doped BaFe2-x-yNixCryAs2 iron pnictides, and demonstrate that Cr-doping near optimal superconductivity restore the long-range antiferromagnetic order suppressed by superconductivity.Comment: 10 pages, 5 figure

Estimation of Stochastic Degradation Models Using Uncertain Inspection Data

Degradation of components and structures is a major threat to the safety and reliability of large engineering systems, such as the railway networks or the nuclear power plants. Periodic inspection and maintenance are thus required to ensure that the system is in good condition for continued service. A key element for the optimal inspection and maintenance is to accurately model and forecast the degradation progress, such that inspection and preventive maintenance can be scheduled accordingly. In recently years, probabilistic models based on stochastic process have become increasingly popular in degradation modelling, due to their flexibility in modelling both the temporal and sample uncertainties of the degradation. However, because of the often complex structure of stochastic degradation models, accurate estimate of the model parameters can be quite difficult, especially when the inspection data are noisy or incomplete. Not considering the effect of uncertain inspection data is likely to result in biased parameter estimates and therefore erroneous predictions of future degradation. The main objective of the thesis is to develop formal methods for the parameter estimation of stochastic degradation models using uncertain inspection data. Three typical stochastic models are considered. They are the random rate model, the gamma process model and the Poisson process model, among which the random rate model and the gamma process model are used to model the flaw growth, and the Poisson process model is used to model the flaw generation. Likelihood functions of the three stochastic models given noisy or incomplete inspection data are derived, from which maximum likelihood estimates can be obtained. The thesis also investigates Bayesian inference of the stochastic degradation models. The most notable advantage of Bayesian inference over classical point estimates is its ability to incorporate background information in the estimation process, which is especially useful when inspection data are scarce. A major obstacle for accurate parameter inference of stochastic models from uncertain inspection data is the computational difficulties of the likelihood evaluation, as it often involves calculation of high dimensional integrals or large number of convolutions. To overcome the computational difficulties, a number of numerical methods are developed in the thesis. For example, for the gamma process model subject to sizing error, an efficient maximum likelihood method is developed using the Genz's transform and quasi-Monte Carlo simulation. A Markov Chain Monte Carlo simulation with sizing error as auxiliary variables is developed for the Poisson flaw generation model, A sequential Bayesian updating using approximate Bayesian computation and weighted samples is also developed for Bayesian inference of the gamma process subject to sizing error. Examples on the degradation of nuclear power plant components are presented to illustrate the use of the stochastic degradation models using practical uncertain inspection data. It is shown from the examples that the proposed methods are very effective in terms of accuracy and computational efficiency

Doping evolution of antiferromagnetism and transport properties in the non-superconducting BaFe2-2xNixCrxAs2

We report elastic neutron scattering and transport measurements on the Ni and Cr equivalently doped iron pnictide BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$. Compared with the electron-doped BaFe$_{2-x}$Ni$_{x}$As$_{2}$, the long-range antiferromagnetic (AF) order in BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$ is gradually suppressed with vanishing ordered moment and N\'{e}el temperature near $x= 0.20$ without the appearance of superconductivity. A detailed analysis on the transport properties of BaFe$_{2-x}$Ni$_{x}$As and BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$ suggests that the non-Fermi-liquid behavior associated with the linear resistivity as a function of temperature may not correspond to the disappearance of the static AF order. From the temperature dependence of the resistivity in overdoped compounds without static AF order, we find that the transport properties are actually affected by Cr impurity scattering, which may induce a metal-to-insulator crossover in highly doped BaFe$_{1.7-y}$Ni$_{0.3}$Cr$_{y}$As$_{2}$.Comment: 10 pages, 12 figure

rac-(S)-2-(1H-Imidazol-1-yl)-3-methyl­butan-1-ol

In the crystal structure of the title compound, C8H14N2O, inter­molecular O—H⋯N hydrogen bonds link mol­ecules related by translation along the a axis into chains. Weak inter­molecular C—H⋯O hydrogen bonds and C—H⋯π inter­actions enhance the crystal packing stability

Structural basis for DNMT3A-mediated de novo DNA methylation.

DNA methylation by de novo DNA methyltransferases 3A (DNMT3A) and 3B (DNMT3B) at cytosines is essential for genome regulation and development. Dysregulation of this process is implicated in various diseases, notably cancer. However, the mechanisms underlying DNMT3 substrate recognition and enzymatic specificity remain elusive. Here we report a 2.65-ångström crystal structure of the DNMT3A-DNMT3L-DNA complex in which two DNMT3A monomers simultaneously attack two cytosine-phosphate-guanine (CpG) dinucleotides, with the target sites separated by 14 base pairs within the same DNA duplex. The DNMT3A-DNA interaction involves a target recognition domain, a catalytic loop, and DNMT3A homodimeric interface. Arg836 of the target recognition domain makes crucial contacts with CpG, ensuring DNMT3A enzymatic preference towards CpG sites in cells. Haematological cancer-associated somatic mutations of the substrate-binding residues decrease DNMT3A activity, induce CpG hypomethylation, and promote transformation of haematopoietic cells. Together, our study reveals the mechanistic basis for DNMT3A-mediated DNA methylation and establishes its aetiological link to human disease

MED19 regulates adipogenesis and maintenance of white adipose tissue mass by mediating PPARγ-dependent gene expression

The Mediator complex relays regulatory signals from gene-specific transcription factors to the basal transcriptional machinery. However, the role of individual Mediator subunits in different tissues remains unclear. Here, we demonstrate that MED19 is essential for adipogenesis and maintenance of white adipose tissue (WAT) by mediating peroxisome proliferator-activated receptor gamma (PPARγ) transcriptional activity. MED19 knockdown blocks white adipogenesis, but not brown adipogenesis or C2C12 myoblast differentiation. Adipose-specific MED19 knockout (KO) in mice results in a striking loss of WAT, whitening of brown fat, hepatic steatosis, and insulin resistance. Inducible adipose-specific MED19 KO in adult animals also results in lipodystrophy, demonstrating its requirement for WAT maintenance. Global gene expression analysis reveals induction of genes involved in apoptosis and inflammation and impaired expression of adipose-specific genes, resulting from decreased PPARγ residency on adipocyte gene promoters and reduced association of PPARγ with RNA polymerase II. These results identify MED19 as a crucial facilitator of PPARγ-mediated gene expression in adipose tissue