The K2 Galactic Archaeology Program Data Release 2: Asteroseismic Results from Campaigns 4, 6, and 7

Studies of Galactic structure and evolution have benefited enormously from Gaia kinematic information, though additional, intrinsic stellar parameters like age are required to best constrain Galactic models. Asteroseismology is the most precise method of providing such information for field star populations en masse, but existing samples for the most part have been limited to a few narrow fields of view by the CoRoT and Kepler missions. In an effort to provide well-characterized stellar parameters across a wide range in Galactic position, we present the second data release of red giant asteroseismic parameters for the K2 Galactic Archaeology Program (GAP). We provide V_{max} and Delta_{v} based on six independent pipeline analyses; first-ascent red giant branch (RGB) and red clump (RC) evolutionary state classifications from machine learning; and ready-to-use radius and mass coefficients, K_{R} and K_{M}, which, when appropriately multiplied by a solar-scaled effective temperature factor, yield physical stellar radii and masses. In total, we report 4395 radius and mass coefficients, with typical uncertainties of 3.3% (stat.) ± 1% (syst.) for K_{R} and 7.7% (stat.) ± 2% (syst.) for κM among RGB stars, and 5.0% (stat.) ± 1% (syst.) for K_{R} nd 10.5% (stat.) ± 2% (syst.) for κM among RC stars. We verify that the sample is nearly complete—except for a dearth of stars with V_{max} \leqslant 10-20 mHz-by comparing to Galactic models and visual inspection. Our asteroseismic radii agree with radii derived from Gaia Data Release 2 parallaxes to within 2.2% ± 0.3% for RGB stars and 2.0% ± 0.6% for RC stars

A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait

Confirming chemical clocks: asteroseismic age dissection of the Milky Way disk(s)

Investigations of the origin and evolution of the Milky Way disk have long relied on chemical and kinematic identification of its components to reconstruct our Galactic past. Difficulties in determining precise stellar ages have restricted most studies to small samples, normally confined to the solar neighbourhood. Here we break this impasse with the help of asteroseismic inference and perform a chronology of the evolution of the disk throughout the age of the Galaxy. We chemically dissect the Milky Way disk population using a sample of red giant stars spanning out to 2~kpc in the solar annulus observed by the {\it Kepler} satellite, with the added dimension of asteroseismic ages. Our results reveal a clear difference in age between the low- and high-$\alpha$ populations, which also show distinct velocity dispersions in the $V$ and $W$ components. We find no tight correlation between age and metallicity nor [$\alpha$/Fe] for the high-$\alpha$ disk stars. Our results indicate that this component formed over a period of more than 2~Gyr with a wide range of [M/H] and [$\alpha$/Fe] independent of time. Our findings show that the kinematic properties of young $\alpha$-rich stars are consistent with the rest of the high-$\alpha$ population and different from the low-$\alpha$ stars of similar age, rendering support to their origin being old stars that went through a mass transfer or stellar merger event, making them appear younger, instead of migration of truly young stars formed close to the Galactic bar

Confirming chemical clocks: asteroseismic age dissection of the Milky Way disc(s)

Investigations of the origin and evolution of the Milky Way disc have long relied on chemical and kinematic identifications of its components to reconstruct our Galactic past. Difficulties in determining precise stellar ages have restricted most studies to small samples, normally confined to the solar neighbourhood. Here, we break this impasse with the help of asteroseismic inference and perform a chronology of the evolution of the disc throughout the age of the Galaxy. We chemically dissect the Milky Way disc population using a sample of red giant stars spanning out to 2 kpc in the solar annulus observed by the Kepler satellite, with the added dimension of asteroseismic ages. Our results reveal a clear difference in age between the low- and high-α populations, which also show distinct velocity dispersions in the V and W components. We find no tight correlation between age and metallicity nor [α/Fe] for the high-α disc stars. Our results indicate that this component formed over a period of more than 2 Gyr with a wide range of [M/H] and [α/Fe] independent of time. Our findings show that the kinematic properties of young α-rich stars are consistent with the rest of the high-α population and different from the low-α stars of similar age, rendering support to their origin being old stars that went through a mass transfer or stellar merger event, making them appear younger, instead of migration of truly young stars formed close to the Galactic bar

Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data

Population Attributable Risk of Unintentional Childhood Poisoning in Karachi Pakistan

Background: The percentage of unintentional childhood poisoning cases in a given population attributable to specific risk factors (i.e., the population attributable risk) which can be calculated, determination of such risk factors associated with potentially modifiable risk factors, are necessary to focus on the prevention strategies. Methods: We calculated PARs, using 120 cases with unintentional poisoning and 360 controls in a hospital based matched case- control study. The risk factors were accessibility to hazardous chemicals and medicines due to unsafe storage, child behavior reported as hyperactive, storage of kerosene and petroleum in soft drink bottles, low socioeconomic class, less education of the mother and the history of previous poisoning. Results: The Following Attrubuted Risks Were Observed: 12% (95% confidence interval [CI] = 8%-16%) for both chemicals and medicines stored unsafe, 19% (15%-23%) for child reported as hyperactive, 40% (38%-42%) for storage of kerosene and petroleum in soft drink bottles, 48% (42%-54%) for low socioeconomic status, 38% (32%-42%) for no formal mothers education and 5.8% (2%-10%) for history of previous poisoning. 48% of cases for overall study population which could be attributed to at least one of the six risk factors. Among girls, this proportion was 23% and 43% among boys. About half of the unintentional childhood poisoning cases in this Pakistani population could be avoided. Conclusion: Exposure to potentially modifiable risk indicators explained about half of the cases of unintentional poisoning among children under five years of age in this Pakistani population, indicating the theoretical scope for prevention of the disease

Spatio-Temporal Dynamics of Yeast Mitochondrial Biogenesis: Transcriptional and Post-Transcriptional mRNA Oscillatory Modules

Examples of metabolic rhythms have recently emerged from studies of budding yeast. High density microarray analyses have produced a remarkably detailed picture of cycling gene expression that could be clustered according to metabolic functions. We developed a model-based approach for the decomposition of expression to analyze these data and to identify functional modules which, expressed sequentially and periodically, contribute to the complex and intricate mitochondrial architecture. This approach revealed that mitochondrial spatio-temporal modules are expressed during periodic spikes and specific cellular localizations, which cover the entire oscillatory period. For instance, assembly factors (32 genes) and translation regulators (47 genes) are expressed earlier than the components of the amino-acid synthesis pathways (31 genes). In addition, we could correlate the expression modules identified with particular post-transcriptional properties. Thus, mRNAs of modules expressed “early” are mostly translated in the vicinity of mitochondria under the control of the Puf3p mRNA-binding protein. This last spatio-temporal module concerns mostly mRNAs coding for basic elements of mitochondrial construction: assembly and regulatory factors. Prediction that unknown genes from this module code for important elements of mitochondrial biogenesis is supported by experimental evidence. More generally, these observations underscore the importance of post-transcriptional processes in mitochondrial biogenesis, highlighting close connections between nuclear transcription and cytoplasmic site-specific translation

Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements

<p>Abstract</p> <p>Background</p> <p>Transcription factor binding to DNA requires both an appropriate binding element and suitably open chromatin, which together help to define regulatory elements within the genome. Current methods of identifying regulatory elements, such as promoters or enhancers, typically rely on sequence conservation, existing gene annotations or specific marks, such as histone modifications and p300 binding methods, each of which has its own biases.</p> <p>Results</p> <p>Herein we show that an approach based on clustering of transcription factor peaks from high-throughput sequencing coupled with chromatin immunoprecipitation (Chip-Seq) can be used to evaluate markers for regulatory elements. We used 67 data sets for 54 unique transcription factors distributed over two cell lines to create regulatory element clusters. By integrating the clusters from our approach with histone modifications and data for open chromatin, we identified general methylation of lysine 4 on histone H3 (H3K4me) as the most specific marker for transcription factor clusters. Clusters mapping to annotated genes showed distinct patterns in cluster composition related to gene expression and histone modifications. Clusters mapping to intergenic regions fall into two groups either directly involved in transcription, including miRNAs and long noncoding RNAs, or facilitating transcription by long-range interactions. The latter clusters were specifically enriched with H3K4me1, but less with acetylation of lysine 27 on histone 3 or p300 binding.</p> <p>Conclusion</p> <p>By integrating genomewide data of transcription factor binding and chromatin structure and using our data-driven approach, we pinpointed the chromatin marks that best explain transcription factor association with different regulatory elements. Our results also indicate that a modest selection of transcription factors may be sufficient to map most regulatory elements in the human genome.</p

Explosive volcanism on the ultraslow-spreading Gakkel ridge, Arctic Ocean

Author Posting. © Nature Publishing Group, 2008. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 453 (2008): 1236-1238, doi:10.1038/nature07075.Roughly 60% of the Earth’s outer surface is comprised of oceanic crust formed by volcanic processes at mid-ocean ridges (MORs). Although only a small fraction of this vast volcanic terrain has been visually surveyed and/or sampled, the available evidence suggests that explosive eruptions are rare on MORs, particularly at depths below the critical point for steam (3000 m). A pyroclastic deposit has never been observed on the seafloor below 3000 m, presumably because the volatile content of mid-ocean ridge basalts is generally too low to produce the gas fractions required to fragment a magma at such high hydrostatic pressure. We employed new deep submergence technologies during an International Polar Year expedition to the Gakkel Ridge in the Arctic Basin at 85°E, to acquire the first-ever photographic images of ‘zero-age’ volcanic terrain on this remote, ice-covered MOR. Our imagery reveals that the axial valley at 4000 m water depth is blanketed with unconsolidated pyroclastic deposits, including bubble wall fragments (limu o Pele), covering a large area greater than 10 km2. At least 13.5 wt% CO2 is required to fragment magma at these depths, which is ~10x greater than the highest values measured to-date in a MOR basalt. These observations raise important questions regarding the accumulation and discharge of magmatic volatiles at ultra-slow spreading rates on the Gakkel Ridge (6- 14 mm yr-1, full-rate), and demonstrate that large-scale pyroclastic activity is possible along even the deepest portions of the global MOR volcanic system.This research was funded by the National Aeronautics and Space Administration, the National Science Foundation, and the Woods Hole Oceanographic Institution