Giant defect emission enhancement from ZnO nanowires through desulfurization process.

Zinc oxide (ZnO) is a stable, direct bandgap semiconductor emitting in the UV with a multitude of technical applications. It is well known that ZnO emission can be shifted into the green for visible light applications through the introduction of defects. However, generating consistent and efficient green emission through this process is challenging, particularly given that the chemical or atomic origin of the green emission in ZnO is still under debate. In this work we present a new method, for which we coin term desulfurization, for creating green emitting ZnO with significantly enhanced quantum efficiency. Solution grown ZnO nanowires are partially converted to ZnS, then desulfurized back to ZnO, resulting in a highly controlled concentration of oxygen defects as determined by X-ray photoelectron spectroscopy and electron paramagnetic resonance. Using this controlled placement of oxygen vacancies we observe a greater than 40-fold enhancement of integrated emission intensity and explore the nature of this enhancement through low temperature photoluminescence experiments

Accelerated Policy Gradient: On the Nesterov Momentum for Reinforcement Learning

Policy gradient methods have recently been shown to enjoy global convergence at a $\Theta(1/t)$ rate in the non-regularized tabular softmax setting. Accordingly, one important research question is whether this convergence rate can be further improved, with only first-order updates. In this paper, we answer the above question from the perspective of momentum by adapting the celebrated Nesterov's accelerated gradient (NAG) method to reinforcement learning (RL), termed \textit{Accelerated Policy Gradient} (APG). To demonstrate the potential of APG in achieving faster global convergence, we formally show that with the true gradient, APG with softmax policy parametrization converges to an optimal policy at a $\tilde{O}(1/t^2)$ rate. To the best of our knowledge, this is the first characterization of the global convergence rate of NAG in the context of RL. Notably, our analysis relies on one interesting finding: Regardless of the initialization, APG could end up reaching a locally nearly-concave regime, where APG could benefit significantly from the momentum, within finite iterations. By means of numerical validation, we confirm that APG exhibits $\tilde{O}(1/t^2)$ rate as well as show that APG could significantly improve the convergence behavior over the standard policy gradient.Comment: 51 pages, 8 figure

Effects of different ceramic and dentin thicknesses on the temperature rise during photocuring

AbstractBackground/purposeThe aims of this investigation were to describe the effect of different ceramic and remaining dentin thicknesses on substrate temperature during photocuring, and investigate whether the temperature increased by >5.5°C for different dentin/ceramic combinations.Materials and methodsThree groups of dentin thicknesses of 1.0 (D1.0), 1.5 (D1.5), and 2.0 mm (D2.0), and three groups of ceramic thicknesses of 1.5 (C1.5), 2.5 (C2.5), and 3.5 mm (C3.5) were examined. Temperature changes and the maximum temperature were observed under a high-intensity halogen light (QTH-Atralis 10 ECS program at 1200mW/cm2 for 30 seconds, Ivoclar Vivadent AG, Schaan, Liechtenstein). Four groups, D1.0–C1.5 (+11°C), D1.5–C1.5 (+7.2°C), D1.0–C2.5 (+6.7°C), and D2–0C1.5 (+5.8°C), demonstrated temperature changes of >5.5°C.Results and ConclusionsA statistical analysis showed that separate individual thicknesses and combinations of dentin and ceramic had significant effects on temperature changes (P<0.01). It was observed that the ceramic exhibited a smaller temperature shielding effect than dentin. Clinically, it would be optimal to preserve the dentin to avoid damaging pulp tissues. Where there is insufficient overall thickness (≤3.5mm), continuous high-energy output photocuring should be avoided to protect pulp tissues from thermal injury

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Rainfall Simulations of Typhoon Morakot with Controlled Translation Speed Based on EnKF Data Assimilation

Typhoon Morakot produced record-breaking accumulated rainfall over southern Taiwan in August 2009. The combination of several factors resulted in this extreme weather event: the steep terrain in Taiwan, the prevailing south-westerly flow in the monsoon trough, Typhoon Goni over the northern South China Sea, and the slow translation speed of Morakot itself over Taiwan. In this study, the influence of the translation speed is particularly emphasized. Based on the EnKF data assimilation, an innovative method is applied to perform ensemble simulations with several designated translation speeds of Morakot using the WRF model. Thus the influence of the translation speed on the amount of accumulated rainfall over Taiwan can be quantitatively evaluated. In the control simulation with observed translation speed, the maximum amount and geographic pattern of accumulated rainfall during the landfall period of Morakot are generally consistent with the observations, though the detailed overall distributions of accumulated rainfall is mostly underestimated, resulting in the low bias of the frequency distribution of the accumulated rainfall. In a simulation with nearly-doubled translation speed of Morakot, the maximum accumulated rainfall is decreased by 33% than that in the control simulation, while the rainfall distribution over Taiwan remains similar. In addition, the 28 ensemble members can further provide additional information in terms of their spread and other statistics. The results from ensemble members reveal the usefulness of ensemble simulations for the quantitative precipitation forecast

MetaSquare: An integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification

MOTIVATION: Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. RESULTS: MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. AVAILABILITY AND IMPLEMENTATION: Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Fluoroquinolones are associated with delayed treatment and resistance in tuberculosis: a systematic review and meta-analysis

SummaryBackgroundCurrent guidelines for treating community-acquired pneumonia recommend the use of fluoroquinolones for high-risk patients. Previous studies have reported controversial results as to whether fluoroquinolones are associated with delayed diagnosis and treatment of pulmonary tuberculosis (TB) and the development of fluoroquinolone-resistant Mycobacterium tuberculosis. We performed a systematic review and meta-analysis to clarify these issues.MethodsThe following databases were searched through September 30, 2010: PubMed, EMBASE, CINAHL, Cochrane Library, Web of Science, BIOSIS Previews, and the ACP Journal Club. We considered studies that addressed the issues of delay in diagnosis and treatment of TB and the development of resistance.ResultsNine eligible studies (four for delays and five for resistance issues) were included in the meta-analysis from the 770 articles originally identified in the database search. The mean duration of delayed diagnosis and treatment of pulmonary TB in the fluoroquinolone prescription group was 19.03 days, significantly longer than that in the non-fluoroquinolone group (95% confidence interval (CI) 10.87 to 27.18, p<0.001). The pooled odds ratio of developing a fluoroquinolone-resistant M. tuberculosis strain was 2.70 (95% CI 1.30 to 5.60, p=0.008). No significant heterogeneity was found among studies in the meta-analysis.ConclusionsEmpirical fluoroquinolone prescriptions for pneumonia are associated with longer delays in diagnosis and treatment of pulmonary TB and a higher risk of developing fluoroquinolone-resistant M. tuberculosis

Expression and immunogenicity of secreted forms of bovine ephemeral fever virus glycoproteins applied to subunit vaccine development

This study was support by grants from the Taiwan Ministry of Science and Technology (MOST-106-2911-I-020-501; MOST-107-2313-B-020-011-MY3) and the UK Biotechnology and Biological Sciences Research Council (BB/P025080/1).Aims  Vaccines for bovine ephemeral fever virus (BEFV) are available but are difficult to produce, expensive, or suffer from genetic instability. Therefore, we designed constructs encoding C-terminally truncated forms (transmembrane anchoring region deleted) of glycoproteins G and GNS such that they were secreted from the cell into the media to achieve high-level antigen expression, correct glycosylation pattern, and enable further simple purification with the V5 epitope tag. Methods and Results  In this study, synthetic biology was employed to create membrane-bound and secreted forms of G and GNS glycoprotein. Mammalian cell culture was employed as an antigen expression platform, and the secreted forms of G and GNS protein were easily purified from media by using a highly effective, single-step method. The V5 epitope tag was genetically fused to the C-termini of the proteins, enabling detection of the antigen through immunoblotting and immunomicroscopy. Our data demonstrated that the C-terminally truncated form of the G glycoprotein was efficiently secreted from cells into the cell media. Moreover, the immunogenicity was confirmed in mice test. Conclusions  The immuno-dot blots showed that the truncated G glycoprotein was present in the total cell extract, and was clearly secreted into the media, consistent with the western blotting data and live-cell images. Our strategy presented the expression of secreted, epitope-tagged, forms of the BEFV glycoproteins such that appropriately glycosylated forms of BEFV G protein was secreted from the BHK-21 cells. This indicates that high-level expression of secreted G glycoprotein is a feasible strategy for large-scale production of vaccines and improving vaccine efficacy. Significance and Impact of the Study The antigen expression strategy designed in this study can produce high-quality recombinant protein and reduce the amount of antigen used in the vaccine.PostprintPeer reviewe