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