555 research outputs found
Significance of chemokine receptor expression in aggressive NK cell leukemia
ArticleLEUKEMIA. 19(7): 1169-1174 (2005)journal articl
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data
With the availability of big medical image data, the selection of an adequate
training set is becoming more important to address the heterogeneity of
different datasets. Simply including all the data does not only incur high
processing costs but can even harm the prediction. We formulate the smart and
efficient selection of a training dataset from big medical image data as a
multi-armed bandit problem, solved by Thompson sampling. Our method assumes
that image features are not available at the time of the selection of the
samples, and therefore relies only on meta information associated with the
images. Our strategy simultaneously exploits data sources with high chances of
yielding useful samples and explores new data regions. For our evaluation, we
focus on the application of estimating the age from a brain MRI. Our results on
7,250 subjects from 10 datasets show that our approach leads to higher accuracy
while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding
Smoking influences the yield of dendritic cells for cancer immunotherapy
Background: Dendritic cell (DC)-based vaccination is considered to be a potentially effective therapeutic strategy against advanced cancer. The aim of this study was to address the smoking history that might affect the preparation of DC vaccines in validated instructional manufacture. Materials and Methods: Data on mature DCs generated from 102 sessions of leukapheresis performed on 92 patients with advanced cancer or sarcoma were retrospectively evaluated and compared in relation to the data between their smoking history and the generation of DCs from these patients. 61 patients with adenocarcinoma, including 7 with lung, 10 with breast, 8 with stomach, 12 with colorectal, and 23 with pancreatic adenocarcinoma were enrolled. Results: The average yield of autologous DCs (15.5 ± 8.3x107) was thought to be dependent on the number of monocytes (124.2 ± 74.1x107) collected by leukapheresis. The average ratio of DCs/apheresed monocytes (DC/aM ratio) was lower in the smoker group (11.1 ± 7.2%) than that in the non-smoker group (17.2 ± 9.3%, p=0.001). The number of DCs and the DC/aM ratio were lower in the patients with gastric and pancreatic cancer than in those with adenocarcinoma of other sites. Conclusions: As cancer therapy moves forward into the field of personaArticlePharmaceutical Regulatory Affairs. 4(1):133 (2015)journal articl
Performance of a Dry Low-NO x Gas Turbine Combustor Designed With a New Fuel Supply Concept
This paper describes the performance of a dry low-N
The reliability of the AIC method in Cosmological Model Selection
The Akaike information criterion (AIC) has been used as a statistical
criterion to compare the appropriateness of different dark energy candidate
models underlying a particular data set. Under suitable conditions, the AIC is
an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate
model A with respect to the truth T. Thus, a dark energy model with a smaller
AIC is ranked as a better model, since it has a smaller Kullback-Leibler
discrepancy with T. In this paper, we explore the impact of statistical errors
in estimating the AIC during model comparison. Using a parametric bootstrap
technique, we study the distribution of AIC differences between a set of
candidate models due to different realizations of noise in the data and show
that the shape and spread of this distribution can be quite varied. We also
study the rate of success of the AIC procedure for different values of a
threshold parameter popularly used in the literature. For plausible choices of
true dark energy models, our studies suggest that investigating such
distributions of AIC differences in addition to the threshold is useful in
correctly interpreting comparisons of dark energy models using the AIC
technique.Comment: Figures and further discussions of the results were added, and the
version matches the version published in MNRA
Lineage Divergence and Historical Gene Flow in the Chinese Horseshoe Bat (Rhinolophus sinicus)
PMCID: PMC3581519This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Search for Anisotropy of Ultra-High Energy Cosmic Rays with the Telescope Array Experiment
We study the anisotropy of Ultra-High Energy Cosmic Ray (UHECR) events
collected by the Telescope Array (TA) detector in the first 40 months of
operation. Following earlier studies, we examine event sets with energy
thresholds of 10 EeV, 40 EeV, and 57 EeV. We find that the distributions of the
events in right ascension and declination are compatible with an isotropic
distribution in all three sets. We then compare with previously reported
clustering of the UHECR events at small angular scales. No significant
clustering is found in the TA data. We then check the events with E>57 EeV for
correlations with nearby active galactic nuclei. No significant correlation is
found. Finally, we examine all three sets for correlations with the large-scale
structure of the Universe. We find that the two higher-energy sets are
compatible with both an isotropic distribution and the hypothesis that UHECR
sources follow the matter distribution of the Universe (the LSS hypothesis),
while the event set with E>10 EeV is compatible with isotropy and is not
compatible with the LSS hypothesis at 95% CL unless large deflection angles are
also assumed. We show that accounting for UHECR deflections in a realistic
model of the Galactic magnetic field can make this set compatible with the LSS
hypothesis.Comment: 10 pages, 9 figure
- …