1,307 research outputs found
A Superwind from Early Post-Red Giant Stars?
We suggest that the gap observed at 20,000 K in the horizontal branches of
several Galactic globular clusters is caused by a small amount of extra mass
loss which occurs when stars start to "peel off" the red giant branch (RGB),
i.e., when their effective temperature starts to increase, even though they may
still be on the RGB. We show that the envelope structure of RGB stars which
start to peel off is similar to that of late asymptotic giant branch stars
known to have a super-wind phase. An analogous super-wind in the RGB peel-off
stars could easily lead to the observed gap in the distribution of the hottest
HB stars.Comment: 9 pages; Accepted by ApJ Letters; Available also at
http://www.astro.puc.cl/~mcatelan
A model of an expanding giant that swallowed planets for the eruption of V838 Monocerotis
In early 2002 V838 Monocerotis had an extraordinary outburst whose nature is
still unclear. The optical light curve showed at least three peaks and imaging
revealed a light echo around the object - evidence for a dust shell which was
emitted several thousand years ago and now reflecting light from the eruption.
Spectral analysis suggests that the object was relatively cold throughout the
event, which was characterized by an expansion to extremely large radii. We
show that the three peaks in the light curve have a similar shape and thus it
seems likely that a certain phenomenon was three times repeated. Our suggestion
that the outburst was caused by the expansion of a red giant, followed by the
successive swallowing of three relatively massive planets in close orbits,
supplies a simple explanation to all observed peculiarities of this intriguing
object.Comment: 5 pages, 1 LaTex file, 2 .eps figures, accepted for publication in
MNRA
Can Planets Influence the Horizontal Branch Morphology?
As stars which have planetary systems evolve along the red giant branch and
expand, they interact with the close planets. The planets deposit angular
momentum and energy into the red giant stars' envelopes, both of which are
likely to enhance mass loss on the red giant branch. The enhanced mass loss
causes the star to become bluer as it turns to the horizontal branch. I propose
that the presence of planetary systems, through this mechanism, can explain
some anomalies in horizontal branch morphologies. In particular, planetary
systems may be related to the ``second parameter'', which determines the
distribution of horizontal branch stars on the Hertzsprung-Russel diagram. The
proposed scenario predicts that surviving massive planets or brown dwarfs orbit
many of the extreme blue horizontal branch stars, at orbital periods of tens
days.Comment: 21 pages, preprint, uses aasms4.st
Detection of a period decrease in NN Ser with ULTRACAM: evidence for strong magnetic braking or an unseen companion?
We present results of high time resolution photometry of the eclipsing
pre-cataclysmic variable NN Ser. We observed 13 primary eclipses of NN Ser
using the high-speed CCD camera ULTRACAM and derived times of mid-eclipse, from
fitting of light curve models, with uncertainties as low as 0.06 s. The
observed rates of period change appear difficult to reconcile with any models
of orbital period change. If the observed period change reflects an angular
momentum loss, the average loss rate is consistent with the loss rates (via
magnetic stellar wind braking) used in standard models of close binary
evolution, which were derived from observations of much more massive cool
stars. Observations of low-mass stars such as NN Ser's secondary predict rates
of ~100 times lower than we observe. We show that magnetic activity-driven
changes in the quadrupole moment of the secondary star (Applegate, 1992) fail
to explain the period change by an order of magnitude on energetic grounds, but
that a light travel time effect caused by the presence of a third body in a
long (~ decades) orbit around the binary could account for the observed changes
in the timings of NN Ser's mid-eclipses. We conclude that we have either
observed a genuine angular momentum loss for NN Ser, in which case our
observations pose serious difficulties for the theory of close binary
evolution, or we have detected a previously unseen low-mass companion to the
binary.Comment: 10 pages, 6 figures. Accepted for publication in MNRA
Prognostic Significance of Lymph Node Metastases in Small Intestinal Neuroendocrine Tumors
Background/Aims: Current staging guidelines for small intestinal neuroendocrine tumors (SI-NETs) differentiate between the presence (N1) and absence (N0) of lymph node (LN) metastases. However, the prognostic significance of the extent of LN involvement remains unknown. In this study, we used data from a population-based cancer registry to examine whether involvement of a higher number of LNs is associated with worse survival. Methods: We used the Surveillance, Epidemiology, and End Results (SEER) database to identify patients with histologically confirmed, surgically resected SI-NETS diagnosed between 1988 and 2010. Patients were classified into three groups by the LN ratio (number of positive LNs/number of total LNs examined, LNR):0.2-0.5, and \u3e0.5. We used the Kaplan-Meier method and Cox models to assess NET cancer-specific survival differences (up to 10 years from diagnosis) according to LNR status. Results: We identified 2,984 surgically resected patients with stage IIIb (N1, M0) SI-NETs with detailed LN data. More than half of the NETs were located in the ileum. A higher LNR was significantly associated with worse NET cancer-specific survival (p \u3c 0.0001). Ten-year NET-specific survival was 85, 77, and 74% for patients in the0.2-0.5, and \u3e0.5 LNR groups, respectively. In stratified analyses, higher LNR groups had worse survival only in early tumor (T1, T2) disease (p \u3c 0.0001). Conclusions: The extent of LN involvement provides independent prognostic information on patients with LN-positive SI-NETs. This information may be used to identify patients at high risk of recurrence and inform decisions about the use of adjuvant therapy. (C) 2015 S. Karger AG, Base
The Galactic Population of Low- and Intermediate-Mass X-ray Binaries
(abridged) We present the first study that combines binary population
synthesis in the Galactic disk and detailed evolutionary calculations of low-
and intermediate-mass X-ray binaries (L/IMXBs). We show that the formation
probability of IMXBs with initial donor masses of 1.5--4 Msun is typically >~5
times higher than that of standard LMXBs, and suggest that the majority of the
observed systems may have descended from IMXBs. Distributions at the current
epoch of the orbital periods, donor masses, and mass accretion rates have been
computed, as have orbital-period distributions of BMPs. Several significant
discrepancies between the theoretical and observed distributions are discussed.
The orbital-period distribution of observed BMPs strongly favors cases where
the envelope of the neutron-star progenitor is more easily ejected during the
common-envelope phase. However, this leads to a >~100-fold overproduction of
the theoretical number of luminous X-ray sources relative to the total observed
number of LMXBs. X-ray irradiation of the donor star may result in a dramatic
reduction in the X-ray active lifetime of L/IMXBs, thus possibly resolving the
overproduction problem, as well as the long-standing BMP/LMXB birthrate
problem.Comment: 12 pages, emulateapj, submitted to Ap
Annotation analysis for testing drug safety signals using unstructured clinical notes
BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records
Changes in mental health among U.S. military veterans during the COVID-19 pandemic: A network analysis
Increases of symptoms of posttraumatic stress disorder (PTSD), anxiety and depression have been observed among individuals exposed to potentially traumatic events in the first months of the COVID-19 pandemic. Similarly, associations among different aspects of mental health, such as symptoms of PTSD and suicidal ideation, have also been documented. However, studies including an assessment prior to the onset and during the height of the pandemic are lacking. We investigated changes in symptoms of PTSD, depression, anxiety, suicidal ideation, and posttraumatic growth in a population-based sample of 1232 U.S. military veterans who experienced a potentially traumatic event during the first year of the pandemic. Symptoms were assessed prior to (fall/winter 2019) and one year into the pandemic (fall/winter 2020). We compared changes in symptom interrelations using network analysis, and assessed their associations with pandemic-related PTSD and posttraumatic growth symptoms. A subtle increase in psychopathological symptoms and a decrease in posttraumatic growth was observed one year into the pandemic. The peripandemic network was more densely connected, and pandemic-related PTSD symptoms were positively associated with age, anxiety, worst-event PTSD symptoms, and pandemic-related posttraumatic growth. Our findings highlight the resilience of veterans exposed to a potentially traumatic event during the first year of a pandemic. Similarly, the networks did not fundamentally change from prepandemic to one year into the pandemic. Despite this relative stability on a group level, individual reactions to potentially traumatic events could have varied substantially. Clinicians should individualize their assessments but be aware of the general resilience of most veterans
Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study
Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice
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