2,821 research outputs found
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
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