Large scale fabrication of nitrogen vacancy-embedded diamond nanostructures for single-photon source applications

Some color centers in diamond can serve as quantum bits which can be manipulated with microwave pulses and read out with laser, even at room temperature. However, the photon collection efficiency of bulk diamond is greatly reduced by refraction at the diamond/air interface. To address this issue, we fabricated arrays of diamond nanostructures, differing in both diameter and top end shape, with HSQ and Cr as the etching mask materials, aiming toward large scale fabrication of single-photon sources with enhanced collection efficiency made of nitrogen vacancy (NV) embedded diamond. With a mixture of O2 and CHF3 gas plasma, diamond pillars with diameters down to 45 nm were obtained. The top end shape evolution has been represented with a simple model. The tests of size dependent single-photon properties confirmed an improved single-photon collection efficiency enhancement, larger than tenfold, and a mild decrease of decoherence time with decreasing pillar diameter was observed as expected. These results provide useful information for future applications of nanostructured diamond as a single-photon source

Modelling Survival Events with Longitudinal Covariates Measured with Error

In survival analysis, time-dependent covariates are usually present as longitudinal data collected periodically and measured with error. The longitudinal data can be assumed to follow a linear mixed effect model and Cox regression models may be used for modelling of survival events. The hazard rate of survival times depends on the underlying time-dependent covariate measured with error, which may be described by random effects. Most existing methods proposed for such models assume a parametric distribution assumption on the random effects and specify a normally distributed error term for the linear mixed effect model. These assumptions may not be always valid in practice. In this article, we propose a new likelihood method for Cox regression models with error-contaminated time-dependent covariates. The proposed method does not require any parametric distribution assumption on random effects and random errors. Asymptotic properties for parameter estimators are provided. Simulation results show that under certain situations the proposed methods are more efficient than the existing methods. © 2013 Copyright Taylor and Francis Group, LLC

Inhibition of DNA methyltransferases blocks mutant huntingtin-induced neurotoxicity

Although epigenetic abnormalities have been described in Huntington’s disease (HD), the causal epigenetic mechanisms driving neurodegeneration in HD cortex and striatum remain undefined. Using an epigenetic pathway-targeted drug screen, we report that inhibitors of DNA methyltransferases (DNMTs), decitabine and FdCyd, block mutant huntingtin (Htt)-induced toxicity in primary cortical and striatal neurons. In addition, knockdown of DNMT3A or DNMT1 protected neurons against mutant Htt-induced toxicity, together demonstrating a requirement for DNMTs in mutant Htt-triggered neuronal death and suggesting a neurodegenerative mechanism based on DNA methylation-mediated transcriptional repression. Inhibition of DNMTs in HD model primary cortical or striatal neurons restored the expression of several key genes, including Bdnf, an important neurotrophic factor implicated in HD. Accordingly, the Bdnf promoter exhibited aberrant cytosine methylation in mutant Htt-expressing cortical neurons. In vivo, pharmacological inhibition of DNMTs in HD mouse brains restored the mRNA levels of key striatal genes known to be downregulated in HD. Thus, disturbances in DNA methylation play a critical role in mutant Htt-induced neuronal dysfunction and death, raising the possibility that epigenetic strategies targeting abnormal DNA methylation may have therapeutic utility in HD

A CDC20-APC/SOX2 Signaling Axis Regulates Human Glioblastoma Stem-like Cells

SummaryGlioblastoma harbors a dynamic subpopulation of glioblastoma stem-like cells (GSCs) that can propagate tumors in vivo and is resistant to standard chemoradiation. Identification of the cell-intrinsic mechanisms governing this clinically important cell state may lead to the discovery of therapeutic strategies for this challenging malignancy. Here, we demonstrate that the mitotic E3 ubiquitin ligase CDC20-anaphase-promoting complex (CDC20-APC) drives invasiveness and self-renewal in patient tumor-derived GSCs. Moreover, CDC20 knockdown inhibited and CDC20 overexpression increased the ability of human GSCs to generate brain tumors in an orthotopic xenograft model in vivo. CDC20-APC control of GSC invasion and self-renewal operates through pluripotency-related transcription factor SOX2. Our results identify a CDC20-APC/SOX2 signaling axis that controls key biological properties of GSCs, with implications for CDC20-APC-targeted strategies in the treatment of glioblastoma

Non-Parametric Change-Point Method for Differential Gene Expression Detection

We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short), by using a single equation for detecting differential gene expression (DGE) in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability.NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods.Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

SIMULATION-BASED OPTIMIZATION FOR RESOURCE ALLOCATION AT TPL SYSTEMS

Allocating resource at TPL systems differs significantly from traditional private logistics systems. The resources are considered commodities sold to customers of different types. Total yield suffers when over-allocate to lower-rate or price-sensitive customers; but the resource become &ldquo;spoiled&rdquo; when reserve too much for full-rate or time-sensitive customers that do not arrive as expected. Uncertain order characteristics make the optimization of such decisions very hard, if not impossible. In this paper we proposed a simulation-based optimization to address related issues. A genetic algorithm based optimization module is developed to generate/search good solutions; and a discrete-event simulation model is created to evaluate the solutions generated. The two modules are integrated to work in evolutionary cycles to achieve the optimization. The study also compared GA/Simulation model with more traditional approach such as response surface methodology via designed experiments. The models were validated through experimental analysis

Versatile Medium Entropy Ti-Based Bulk Metallic Glass Composites

An ultra-strong Ti-based bulk metallic glass composite was developed via the transformation-induced plasticity (TRIP) effect to enhance both the ductility and work-hardening capability of the amorphous matrix. The functionally graded composites with a continuous gradient microstructure were obtained. It was found that the austenitic center possesses good plasticity and toughness. Furthermore, the amorphous surface exhibited high strength and hardness, as well as excellent wear corrosion resistance. Compared with the Ti-6Al-4V alloy, bulk metallic glass composites (BMGCs) exhibit better spontaneous passivation behavior during the potential dynamic polarization. No crystallization was observed on the friction surface, indicating their good friction-reduction and anti-wear properties