1,615 research outputs found
Exclusion of Stellar Companions to Exoplanet Host Stars
Given the frequency of stellar multiplicity in the solar neighborhood, it is
important to study the impacts this can have on exoplanet properties and
orbital dynamics. There have been numerous imaging survey projects established
to detect possible low-mass stellar companions to exoplanet host stars. Here we
provide the results from a systematic speckle imaging survey of known exoplanet
host stars. In total, 71 stars were observed at 692~nm and 880~nm bands using
the Differential Speckle Survey Instrument (DSSI) at the Gemini-North
Observatory. Our results show that all but 2 of the stars included in this
sample have no evidence of stellar companions with luminosities down to the
detection and projected separation limits of our instrumentation. The
mass-luminosity relationship is used to estimate the maximum mass a stellar
companion can have without being detected. These results are used to discuss
the potential for further radial velocity follow-up and interpretation of
companion signals.Comment: 11 pages, 4 figures, 3 tables, accepted for publication in A
Gender and ethnic differences in chronic myelogenous leukemia prognosis and treatment response: a single-institution retrospective study
<p>Abstract</p> <p>Background</p> <p>In the last decade the importance of ethnicity, socio-economic and gender differences in relation to disease incidence, diagnosis, and prognosis has been realized. Differences in these areas have become a major health policy focus in the United States. Our study was undertaken to examine the demographic and clinical features of chronic myelogenous leukemia (CML) patients presenting initially at the LAC+USC Medical Center, which serves an ethnically diverse population.</p> <p>Results</p> <p>Patients were evenly split by gender, overwhelmingly Hispanic (60.9%), and quite young (median age 39, range 17–65) compared with previously reported CML patient populations. Previous CML studies identified significant anemia (Hgb <12 g/dl), significant thrombocytosis (platelets >450 × 10<sup>9</sup>/l), and significant leukocytosis (WBC >50 × 10<sup>9</sup>/l) as significant adverse pretreatment prognostic factors. Using these indicators, in addition to the validated Hasford and Sokal scores, patients were stratified and analyzed via gender and ethnicity. A significantly greater proportion of women presented with significant anemia (p = 0.019, Fisher's exact test) and significant thrombocytosis (p = 0.041, Fisher's exact test) compared to men, although no differences were found in risk stratification or treatment response. MCV values for women were significantly (p = 0.02, 2-sample t-test) lower than those for men, suggesting iron deficiency anemia. Focusing on ethnicity, Hispanics as a whole had significantly lower Hasford risk stratification (p = 0.046, Fisher's exact test), and significantly greater likelihood (p = 0.016, Fisher's exact test) of achieving 3-month complete haematological remission (CHR) compared with non-Hispanics at LAC+USC Medical Center, though differences in treatment outcome were no longer significant with analysis limited to patients treated with first-line imatinib.</p> <p>Conclusion</p> <p>Female CML patients at LAC+USC Medical Center present with more significant adverse pre-treatment prognostic factors compared to men, but achieve comparable outcomes. Hispanic patients present with lower risk profile CML and achieve better treatment responses compared to non-Hispanic patients as a whole; these ethnic differences are no longer significant when statistical analysis is limited to patients given imatinib as first-line therapy. Our patients achieve response rates inferior to those of large-scale national studies. This constellation of findings has not been reported in previous studies, and is likely reflective of a unique patient population.</p
A Comparison of Spectroscopic versus Imaging Techniques for Detecting Close Companions to Kepler Objects of Interest
(Abbreviated) Kepler planet candidates require both spectroscopic and imaging
follow-up observations to rule out false positives and detect blended stars.
[...] In this paper, we examine a sample of 11 Kepler host stars with
companions detected by two techniques -- near-infrared adaptive optics and/or
optical speckle interferometry imaging, and a new spectroscopic deblending
method. We compare the companion Teff and flux ratios (F_B/F_A, where A is the
primary and B is the companion) derived from each technique, and find no cases
where both companion parameters agree within 1sigma errors. In 3/11 cases the
companion Teff values agree within 1sigma errors, and in 2/11 cases the
companion F_B/F_A values agree within 1sigma errors. Examining each Kepler
system individually considering multiple avenues (isochrone mapping, contrast
curves, probability of being bound), we suggest two cases for which the
techniques most likely agree in their companion detections (detect the same
companion star). Overall, our results support the advantage the spectroscopic
deblending technique has for finding very close-in companions (0.02-0.05") that are not easily detectable with imaging. However, we
also specifically show how high-contrast AO and speckle imaging observations
detect companions at larger separations (0.02-0.05") that are
missed by the spectroscopic technique, provide additional information for
characterizing the companion and its potential contamination (e.g., PA,
separation, m), and cover a wider range of primary star effective
temperatures. The investigation presented here illustrates the utility of
combining the two techniques to reveal higher-order multiples in known
planet-hosting systems.Comment: Accepted to AJ. 40 pages, 12 figure
High-resolution Multi-band Imaging for Validation and Characterization of Small Kepler Planets
High-resolution ground-based optical speckle and near-infrared adaptive
optics images are taken to search for stars in close angular proximity to host
stars of candidate planets identified by the NASA Kepler Mission. Neighboring
stars are a potential source of false positive signals. These stars also blend
into Kepler light curves, affecting estimated planet properties, and are
important for an understanding of planets in multiple star systems. Deep images
with high angular resolution help to validate candidate planets by excluding
potential background eclipsing binaries as the source of the transit signals. A
study of 18 Kepler Object of Interest stars hosting a total of 28 candidate and
validated planets is presented. Validation levels are determined for 18 planets
against the likelihood of a false positive from a background eclipsing binary.
Most of these are validated at the 99% level or higher, including 5
newly-validated planets in two systems: Kepler-430 and Kepler-431. The stellar
properties of the candidate host stars are determined by supplementing existing
literature values with new spectroscopic characterizations. Close neighbors of
7 of these stars are examined using multi-wavelength photometry to determine
their nature and influence on the candidate planet properties. Most of the
close neighbors appear to be gravitationally-bound secondaries, while a few are
best explained as closely co-aligned field stars. Revised planet properties are
derived for each candidate and validated planet, including cases where the
close neighbors are the potential host stars.Comment: in press at AJ, 44 pages, 8 figures; The only changes made relative
to version 1 are updates to the list of reference
Lines, Circles, Planes and Spheres
Let be a set of points in , no three collinear and not
all coplanar. If at most are coplanar and is sufficiently large, the
total number of planes determined is at least . For similar conditions and
sufficiently large , (inspired by the work of P. D. T. A. Elliott in
\cite{Ell67}) we also show that the number of spheres determined by points
is at least , and this bound is best
possible under its hypothesis. (By , we are denoting the
maximum number of three-point lines attainable by a configuration of
points, no four collinear, in the plane, i.e., the classic Orchard Problem.)
New lower bounds are also given for both lines and circles.Comment: 37 page
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
Quantifying uncertainty of predictions has been identified as one way to
develop more trustworthy artificial intelligence (AI) models beyond
conventional reporting of performance metrics. When considering their role in a
clinical decision support setting, AI classification models should ideally
avoid confident wrong predictions and maximise the confidence of correct
predictions. Models that do this are said to be well-calibrated with regard to
confidence. However, relatively little attention has been paid to how to
improve calibration when training these models, i.e., to make the training
strategy uncertainty-aware. In this work we evaluate three novel
uncertainty-aware training strategies comparing against two state-of-the-art
approaches. We analyse performance on two different clinical applications:
cardiac resynchronisation therapy (CRT) response prediction and coronary artery
disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The
best-performing model in terms of both classification accuracy and the most
common calibration measure, expected calibration error (ECE) was the Confidence
Weight method, a novel approach that weights the loss of samples to explicitly
penalise confident incorrect predictions. The method reduced the ECE by 17% for
CRT response prediction and by 22% for CAD diagnosis when compared to a
baseline classifier in which no uncertainty-aware strategy was included. In
both applications, as well as reducing the ECE there was a slight increase in
accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD
diagnosis respectively. However, our analysis showed a lack of consistency in
terms of optimal models when using different calibration measures. This
indicates the need for careful consideration of performance metrics when
training and selecting models for complex high-risk applications in healthcare
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Advances in deep learning (DL) have resulted in impressive accuracy in some
medical image classification tasks, but often deep models lack
interpretability. The ability of these models to explain their decisions is
important for fostering clinical trust and facilitating clinical translation.
Furthermore, for many problems in medicine there is a wealth of existing
clinical knowledge to draw upon, which may be useful in generating
explanations, but it is not obvious how this knowledge can be encoded into DL
models - most models are learnt either from scratch or using transfer learning
from a different domain. In this paper we address both of these issues. We
propose a novel DL framework for image-based classification based on a
variational autoencoder (VAE). The framework allows prediction of the output of
interest from the latent space of the autoencoder, as well as visualisation (in
the image domain) of the effects of crossing the decision boundary, thus
enhancing the interpretability of the classifier. Our key contribution is that
the VAE disentangles the latent space based on `explanations' drawn from
existing clinical knowledge. The framework can predict outputs as well as
explanations for these outputs, and also raises the possibility of discovering
new biomarkers that are separate (or disentangled) from the existing knowledge.
We demonstrate our framework on the problem of predicting response of patients
with cardiomyopathy to cardiac resynchronization therapy (CRT) from cine
cardiac magnetic resonance images. The sensitivity and specificity of the
proposed model on the task of CRT response prediction are 88.43% and 84.39%
respectively, and we showcase the potential of our model in enhancing
understanding of the factors contributing to CRT response.Comment: MICCAI 2020 conferenc
Validation of Twelve Small Kepler Transiting Planets in the Habitable Zone
We present an investigation of twelve candidate transiting planets from
Kepler with orbital periods ranging from 34 to 207 days, selected from initial
indications that they are small and potentially in the habitable zone (HZ) of
their parent stars. Few of these objects are known. The expected Doppler
signals are too small to confirm them by demonstrating that their masses are in
the planetary regime. Here we verify their planetary nature by validating them
statistically using the BLENDER technique, which simulates large numbers of
false positives and compares the resulting light curves with the Kepler
photometry. This analysis was supplemented with new follow-up observations
(high-resolution optical and near-infrared spectroscopy, adaptive optics
imaging, and speckle interferometry), as well as an analysis of the flux
centroids. For eleven of them (KOI-0571.05, 1422.04, 1422.05, 2529.02, 3255.01,
3284.01, 4005.01, 4087.01, 4622.01, 4742.01, and 4745.01) we show that the
likelihood they are true planets is far greater than that of a false positive,
to a confidence level of 99.73% (3 sigma) or higher. For KOI-4427.01 the
confidence level is about 99.2% (2.6 sigma). With our accurate characterization
of the GKM host stars, the derived planetary radii range from 1.1 to 2.7
R_Earth. All twelve objects are confirmed to be in the HZ, and nine are small
enough to be rocky. Excluding three of them that have been previously validated
by others, our study doubles the number of known rocky planets in the HZ.
KOI-3284.01 (Kepler-438b) and KOI-4742.01 (Kepler-442b) are the planets most
similar to the Earth discovered to date when considering their size and
incident flux jointly.Comment: 27 pages in emulateapj format, including tables and figures. To
appear in The Astrophysical Journa
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