30 research outputs found

    Comparison of the Oxidation State of Fe in Comet 81P/Wild 2 and Chondritic-Porous Interplanetary Dust Particles

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    The fragile structure of chondritic-porous interplanetary dust particles (CP- IDPs) and their minimal parent-body alteration have led researchers to believe these particles originate in comets rather than asteroids where aqueous and thermal alteration have occurred. The solar elemental abundances and atmospheric entry speed of CP-IDPs also suggest a cometary origin. With the return of the Stardust samples from Jupiter-family comet 81P/Wild 2, this hypothesis can be tested. We have measured the Fe oxidation state of 15 CP-IDPs and 194 Stardust fragments using a synchrotron-based x-ray microprobe. We analyzed ~300 nanograms of Wild 2 material - three orders of magnitude more material than other analyses comparing Wild 2 and CP-IDPs. The Fe oxidation state of these two samples of material are >2{\sigma} different: the CP-IDPs are more oxidized than the Wild 2 grains. We conclude that comet Wild 2 contains material that formed at a lower oxygen fugacity than the parent body, or parent bodies, of CP-IDPs. If all Jupiter-family comets are similar, they do not appear to be consistent with the origin of CP-IDPs. However, comets that formed from a different mix of nebular material and are more oxidized than Wild 2 could be the source of CP-IDPs.Comment: Earth and Planetary Science Letters, in pres

    Short-lived Nuclei in the Early Solar System: Possible AGB Sources

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    (Abridged) We review abundances of short-lived nuclides in the early solar system (ESS) and the methods used to determine them. We compare them to the inventory for a uniform galactic production model. Within a factor of two, observed abundances of several isotopes are compatible with this model. I-129 is an exception, with an ESS inventory much lower than expected. The isotopes Pd-107, Fe-60, Ca-41, Cl-36, Al-26, and Be-10 require late addition to the solar nebula. Be-10 is the product of particle irradiation of the solar system as probably is Cl-36. Late injection by a supernova (SN) cannot be responsible for most short-lived nuclei without excessively producing Mn-53; it can be the source of Mn-53 and maybe Fe-60. If a late SN is responsible for these two nuclei, it still cannot make Pd-107 and other isotopes. We emphasize an AGB star as a source of nuclei, including Fe-60 and explore this possibility with new stellar models. A dilution factor of about 4e-3 gives reasonable amounts of many nuclei. We discuss the role of irradiation for Al-26, Cl-36 and Ca-41. Conflict between scenarios is emphasized as well as the absence of a global interpretation for the existing data. Abundances of actinides indicate a quiescent interval of about 1e8 years for actinide group production in order to explain the data on Pu-244 and new bounds on Cm-247. This interval is not compatible with Hf-182 data, so a separate type of r-process is needed for at least the actinides, distinct from the two types previously identified. The apparent coincidence of the I-129 and trans-actinide time scales suggests that the last actinide contribution was from an r-process that produced actinides without fission recycling so that the yields at Ba and below were governed by fission.Comment: 92 pages, 14 figure files, in press at Nuclear Physics

    Association between implantable cardioverter-defibrillator and survival in patients awaiting heart transplantation: A meta-analysis and systematic review

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    BackgroundPatients with end-stage heart failure are at high risk for sudden cardiac death. However, implantable cardioverter-defibrillator (ICD) is not routinely implanted given the high competing risk of pump failure. A unique population worth separate consideration are patients with end-stage heart failure awaiting heart transplantation, as prolonged survival improves the chances of receiving transplant.ObjectiveTo compare clinical outcomes of heart failure patients with and without an ICD awaiting heart transplant.MethodsWe performed an extensive literature search and systematic review of studies that compared end-stage heart failure patients with and without an ICD awaiting heart transplantation. We separately assessed the rates of total mortality, sudden cardiac death, nonsudden cardiac death, and heart transplantation. Risk ratio (RR) and 95% confidence intervals were measured using the Mantel-Haenszel method. The random effects model was used owing to heterogeneity across study cohorts.ResultsTen studies with a total of 36,112 patients were included. A total of 62.5% of patients had an ICD implanted. Patients with an ICD had decreased total mortality (RR 0.60, 95% CI 0.51-0.71, P < .00001) and sudden cardiac death (RR 0.27, 95% CI 0.11-0.66, P = .004) and increased rates of heart transplantation (RR 1.09, 95% CI 1.05-1.14, P < .0001). There was no difference in prevalence of nonsudden cardiac death (RR 0.68, 95% CI 0.44-1.04, P = .07).ConclusionICD implantation is associated with improved outcomes in patients awaiting heart transplant, characterized by decreased total mortality and sudden cardiac death as well as higher rates of heart transplantation

    Improving risk prediction in heart failure using machine learning

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    Background: Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi-dimensional interactions. Methods and results: We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high-risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations. Conclusions: Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging
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