Atomic Layer Deposition of ZnO on Multi-walled Carbon Nanotubes and Its Use for Synthesis of CNT-ZnO Heterostructures.

In this article, direct coating of ZnO on PECVD-grown multi-walled carbon nanotubes (MWCNTs) is achieved using atomic layer deposition (ALD). Transmission electron microscopy investigation shows that the deposited ZnO shell is continuous and uniform, in contrast to the previously reported particle morphology. The ZnO layer has a good crystalline quality as indicated by Raman and photoluminescence (PL) measurements. We also show that such ZnO layer can be used as seed layer for subsequent hydrothermal growth of ZnO nanorods, resulting in branched CNT-inorganic hybrid nanostructures. Potentially, this method can also apply to the fabrication of ZnO-based hybrid nanostructures on other carbon nanomaterials.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The spectral variability of FSRQs

The optical variability of 29 flat spectrum radio quasars in SDSS Stripe 82 region are investigated by using DR7 released multi-epoch data. All FSRQs show variations with overall amplitude ranging from 0.24 mag to 3.46 mag in different sources. About half of FSRQs show a bluer-when-brighter trend, which is commonly observed for blazars. However, only one source shows a redder-when-brighter trend, which implies it is rare in FSRQs. In this source, the thermal emission may likely be responsible for the spectral behavior.Comment: 4 pages, 1 figure, to be published in Journal of Astrophysics and Astronomy, as a proceeding paper of the conference "Multiwavelength Variability of Blazars", Guangzhou, China, September 22-24, 201

Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations

Early star-forming galaxies and the reionization of the Universe

Star forming galaxies represent a valuable tracer of cosmic history. Recent observational progress with Hubble Space Telescope has led to the discovery and study of the earliest-known galaxies corresponding to a period when the Universe was only ~800 million years old. Intense ultraviolet radiation from these early galaxies probably induced a major event in cosmic history: the reionization of intergalactic hydrogen. New techniques are being developed to understand the properties of these most distant galaxies and determine their influence on the evolution of the universe.Comment: Review article appearing in Nature. This posting reflects a submitted version of the review formatted by the authors, in accordance with Nature publication policies. For the official, published version of the review, please see http://www.nature.com/nature/archive/index.htm

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Two highly divergent alcohol dehydrogenases of melon exhibit fruit ripening-specific expression and distinct biochemical characteristics

Alcohol dehydrogenases (ADH) participate in the biosynthetic pathway of aroma volatiles in fruit by interconverting aldehydes to alcohols and providing substrates for the formation of esters. Two highly divergent ADH genes (15% identity at the amino acid level) of Cantaloupe Charentais melon (Cucumis melo var. Cantalupensis) have been isolated. Cm-ADH1 belongs to the medium-chain zinc-binding type of ADHs and is highly similar to all ADH genes expressed in fruit isolated so far. Cm-ADH2 belongs to the short-chain type of ADHs. The two encoded proteins are enzymatically active upon expression in yeast. Cm-ADH1 has strong preference for NAPDH as a co-factor, whereas Cm-ADH2 preferentially uses NADH. Both Cm-ADH proteins are much more active as reductases with Kms 10–20 times lower for the conversion of aldehydes to alcohols than for the dehydrogenation of alcohols to aldehydes. They both show strong preference for aliphatic aldehydes but Cm-ADH1 is capable of reducing branched aldehydes such as 3-methylbutyraldehyde, whereas Cm-ADH2 cannot. Both Cm-ADH genes are expressed specifically in fruit and up-regulated during ripening. Gene expression as well as total ADH activity are strongly inhibited in antisense ACC oxidase melons and in melon fruit treated with the ethylene antagonist 1-methylcyclopropene (1-MCP), indicating a positive regulation by ethylene. These data suggest that each of the Cm-ADH protein plays a specific role in the regulation of aroma biosynthesis in melon fruit

The validation of pharmacogenetics for the identification of Fabry patients to be treated with migalastat

PURPOSE: Fabry disease is an X-linked lysosomal storage disorder caused by mutations in the α-galactosidase A gene. Migalastat, a pharmacological chaperone, binds to specific mutant forms of α-galactosidase A to restore lysosomal activity. METHODS: A pharmacogenetic assay was used to identify the α-galactosidase A mutant forms amenable to migalastat. Six hundred Fabry disease-causing mutations were expressed in HEK-293 (HEK) cells; increases in α-galactosidase A activity were measured by a good laboratory practice (GLP)-validated assay (GLP HEK/Migalastat Amenability Assay). The predictive value of the assay was assessed based on pharmacodynamic responses to migalastat in phase II and III clinical studies. RESULTS: Comparison of the GLP HEK assay results in in vivo white blood cell α-galactosidase A responses to migalastat in male patients showed high sensitivity, specificity, and positive and negative predictive values (≥0.875). GLP HEK assay results were also predictive of decreases in kidney globotriaosylceramide in males and plasma globotriaosylsphingosine in males and females. The clinical study subset of amenable mutations (n = 51) was representative of all 268 amenable mutations identified by the GLP HEK assay. CONCLUSION: The GLP HEK assay is a clinically validated method of identifying male and female Fabry patients for treatment with migalastat