6,030 research outputs found
Vertically aligned ZnO nanostructures grown on graphene layers
We report the vertical growth of ZnO nanostructures on graphene layers and their photoluminescence (PL) characteristics. ZnO nanostructures were grown vertically on the graphene layers using catalyst-free metal-organic vapor-phase epitaxy. The surface morphology of the ZnO nanostructures on the graphene layers depended strongly on the growth temperature. Further, interesting growth behavior leading to the formation of aligned ZnO nanoneedles in a row and vertically aligned nanowalls was also observed and explained in terms of enhanced nucleation on graphene step edges and kinks. Additionally, the optical characteristics and carbon incorporation into ZnO were investigated using variable-temperature PL spectroscopy.open11121134sciescopu
Enhanced light output of GaN-based light-emitting diodes with ZnO nanorod arrays
We report enhanced light output of GaN-based light-emitting diodes (LEDs) with vertically aligned ZnO nanorod arrays. The ZnO nanorod arrays were prepared on the top layer of GaN LEDs using catalyst-free metalorganic vapor phase epitaxy. Compared to conventional GaN LEDs, light output of GaN LEDs with the ZnO nanorod arrays increased up to 50% and 100% at applied currents of 20 and 50 mA, respectively. The source of the enhanced light output is also discussed. (C) 2008 American Institute of Physics.open11132146sciescopu
Possible scale invariant linear magnetoresistance in pyrochlore iridates Bi2Ir2O7
We report the observation of a linear magnetoresistance in single crystals and epitaxial thin films of the pyrochlore iridate Bi2Ir2O7. The linear magnetoresistance is positive and isotropic at low temperatures, without any sign of saturation up to 35 T. As temperature increases, the linear field dependence gradually evolves to a quadratic field dependence. The temperature and field dependence of magnetoresistance of Bi2Ir2O7 bears strikingly resemblance to the scale invariant magnetoresistance observed in the strange metal phase in high Tc cuprates. However, the residual resistivity of Bi2Ir2O7 is more than two orders of magnitude higher than the curpates. Our results suggest that the correlation between linear magnetoresistance and quantum fluctuations may exist beyond high temperature superconductors
Development and testing of a database of NIH research funding of AAPM members: A report from the AAPM Working Group for the Development of a Research Database (WGDRD).
PURPOSE: To produce and maintain a database of National Institutes of Health (NIH) funding of the American Association of Physicists in Medicine (AAPM) members, to perform a top-level analysis of these data, and to make these data (hereafter referred to as the AAPM research database) available for the use of the AAPM and its members. METHODS: NIH-funded research dating back to 1985 is available for public download through the NIH exporter website, and AAPM membership information dating back to 2002 was supplied by the AAPM. To link these two sources of data, a data mining algorithm was developed in Matlab. The false-positive rate was manually estimated based on a random sample of 100 records, and the false-negative rate was assessed by comparing against 99 member-supplied PI_ID numbers. The AAPM research database was queried to produce an analysis of trends and demographics in research funding dating from 2002 to 2015. RESULTS: A total of 566 PI_ID numbers were matched to AAPM members. False-positive and -negative rates were respectively 4% (95% CI: 1-10%, N = 100) and 10% (95% CI: 5-18%, N = 99). Based on analysis of the AAPM research database, in 2015 the NIH awarded 116M, which is lower than the historic mean of $120M (in 2015 USD). CONCLUSIONS: A database of NIH-funded research awarded to AAPM members has been developed and tested using a data mining approach, and a top-level analysis of funding trends has been performed. Current funding of AAPM members is lower than the historic mean. The database will be maintained by members of the Working group for the development of a research database (WGDRD) on an annual basis, and is available to the AAPM, its committees, working groups, and members for download through the AAPM electronic content website. A wide range of questions regarding financial and demographic funding trends can be addressed by these data. This report has been approved for publication by the AAPM Science Council
D-brane anomaly inflow revisited
Axial and gravitational anomaly of field theories, when embedded in string
theory, must be accompanied by canceling inflow. We give a self-contained
overview for various world-volume theories, and clarify the role of smeared
magnetic sources in I-brane/D-brane cases. The proper anomaly descent of the
source, as demanded by regularity of RR field strengths H's, turns out to be an
essential ingredient. We show how this allows correct inflow to be generated
for all such theories, including self-dual cases, and also that the mechanism
is now insensitive to the choice between the two related but inequivalent forms
of D-brane Chern-Simons couplings. In particular, SO(6)_R axial anomaly of d=4
maximal SYM is canceled by the inflow onto D3-branes via the standard minimal
coupling to C_4. We also propose how, for the anomaly cancelation, the four
types of Orientifold planes should be coupled to the spacetime curvatures, of
which conflicting claims existed previously.Comment: 41 pages, references updated; version to appear in JHE
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions
The field of algorithmic self-assembly is concerned with the design and
analysis of self-assembly systems from a computational perspective, that is,
from the perspective of mathematical problems whose study may give insight into
the natural processes through which elementary objects self-assemble into more
complex ones. One of the main problems of algorithmic self-assembly is the
minimum tile set problem (MTSP), which asks for a collection of types of
elementary objects (called tiles) to be found for the self-assembly of an
object having a pre-established shape. Such a collection is to be as concise as
possible, thus minimizing supply diversity, while satisfying a set of stringent
constraints having to do with the termination and other properties of the
self-assembly process from its tile types. We present a study of what we think
is the first practical approach to MTSP. Our study starts with the introduction
of an evolutionary heuristic to tackle MTSP and includes results from extensive
experimentation with the heuristic on the self-assembly of simple objects in
two and three dimensions. The heuristic we introduce combines classic elements
from the field of evolutionary computation with a problem-specific variant of
Pareto dominance into a multi-objective approach to MTSP.Comment: Minor typos correcte
Signal transducer and activator of transcription 3-mediated CD133 up-regulation contributes to promotion of hepatocellular carcinoma
published_or_final_versio
Seroprevalence of Mycoplasma bovis infection in dairy cows in subtropical southern China
The seroprevalence of Mycoplasma bovis infection in dairy cows in Guangxi Zhuang Autonomous Region (GZAR) in subtropical southern China was surveyed between June 2009 and March 2010. A total of 455 serum samples of dairy cows were collected from 6 districts in 4 different cities, and examined for M. bovis antibodies with the indirect enzyme-linked immunosorbent assay (ELISA) using a commercially available kit. The overall seroprevalence of M. bovis infection in dairy cows was 7.69% (35/455). Three year-old dairy cows had the highest seroprevalence (15.0%), followed by dairy cows of 4 year-old (11.1%). Dairy cows with the history of 5 pregnancies had the highest seroprevalence (33.3%). However, no statistically significant association was found between M. bovis infection and age or number of pregnancies (p > 0.05). All the aborting dairy cows were negative for M. bovis antibodies, suggesting that bovine abortion may have no association with M. bovis infection in GZAR. These results indicate that M. bovis infection in dairy cows was widespread in GZAR, and integrated strategies and measures should be performed to control and prevent M. bovis infection and disease outbreak.Key words: Mycoplasma bovis, seroprevalence, dairy cows, Guangxi Zhuang Autonomous Region (GZAR), China, enzyme-linked immunosorbent assay (ELISA)
Listen to genes : dealing with microarray data in the frequency domain
Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes
normalization, clustering and network analysis of genes.
Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and
estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The
normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger
causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger
causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting
networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000
genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new
global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.
Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum
analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency
domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray
data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by
step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of
potential interest to Arabidopsis researchers
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