Chronology of martian breccia NWA 7034 and the formation of the martian crustal dichotomy

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). The attached file is the published version of the article

Identifying rare variants using a Bayesian regression approach

Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Statistical methods that test variants individually are underpowered to detect rare variants, so it is desirable to perform association analysis of rare variants by combining the information from all variants. In this study, we use a Bayesian regression method to model all variants simultaneously to identify rare variants in a data set from Genetic Analysis Workshop 17. We studied the association between the quantitative risk traits Q1, Q2, and Q4 and the single-nucleotide polymorphisms and identified several positive single-nucleotide polymorphisms for traits Q1 and Q2. However, the model also generated several apparent false positives and missed many true positives, suggesting that there is room for improvement in this model

An innovative quality improvement curriculum for third-year medical students

Background: Competence in quality improvement (QI) is a priority for medical students. We describe a self-directed QI skills curriculum for medical students in a 1-year longitudinal integrated third-year clerkship: an ideal context to learn and practice QI. Methods: Two groups of four students identified a quality gap, described existing efforts to address the gap, made quantifying measures, and proposed a QI intervention. The program was assessed with knowledge and attitude surveys and a validated tool for rating trainee QI proposals. Reaction to the curriculum was assessed by survey and focus group. Results: Knowledge of QI concepts did not improve (mean knowledge score±SD): pre: 5.9±1.5 vs. post: 6.6±1.3, p=0.20. There were significant improvements in attitudes (mean topic attitude score±SD) toward the value of QI (pre: 9.9±1.8 vs. post: 12.6±1.9, p=0.03) and confidence in QI skills (pre: 13.4±2.8 vs. post: 16.1±3.0, p=0.05). Proposals lacked sufficient analysis of interventions and evaluation plans. Reaction was mixed, including appreciation for the experience and frustration with finding appropriate mentorship. Conclusion: Clinical-year students were able to conduct a self-directed QI project. Lack of improvement in QI knowledge suggests that self-directed learning in this domain may be insufficient without targeted didactics. Higher order skills such as developing measurement plans would benefit from explicit instruction and mentorship. Lessons from this experience will allow educators to better target QI curricula to medical students in the clinical years

Biotic carbon feedbacks in a materially-closed soil-vegetation-atmosphere system

The magnitude and direction of the coupled feedbacks between the biotic and abiotic components of the terrestrial carbon cycle is a major source of uncertainty in coupled climate–carbon-cycle models1, 2, 3. Materially closed, energetically open biological systems continuously and simultaneously allow the two-way feedback loop between the biotic and abiotic components to take place4, 5, 6, 7, but so far have not been used to their full potential in ecological research, owing to the challenge of achieving sustainable model systems6, 7. We show that using materially closed soil–vegetation–atmosphere systems with pro rata carbon amounts for the main terrestrial carbon pools enables the establishment of conditions that balance plant carbon assimilation, and autotrophic and heterotrophic respiration fluxes over periods suitable to investigate short-term biotic carbon feedbacks. Using this approach, we tested an alternative way of assessing the impact of increased CO2 and temperature on biotic carbon feedbacks. The results show that without nutrient and water limitations, the short-term biotic responses could potentially buffer a temperature increase of 2.3 °C without significant positive feedbacks to atmospheric CO2. We argue that such closed-system research represents an important test-bed platform for model validation and parameterization of plant and soil biotic responses to environmental changes

Signatures of Star-planet interactions

Planets interact with their host stars through gravity, radiation and magnetic fields, and for those giant planets that orbit their stars within $\sim$10 stellar radii ($\sim$0.1 AU for a sun-like star), star-planet interactions (SPI) are observable with a wide variety of photometric, spectroscopic and spectropolarimetric studies. At such close distances, the planet orbits within the sub-alfv\'enic radius of the star in which the transfer of energy and angular momentum between the two bodies is particularly efficient. The magnetic interactions appear as enhanced stellar activity modulated by the planet as it orbits the star rather than only by stellar rotation. These SPI effects are informative for the study of the internal dynamics and atmospheric evolution of exoplanets. The nature of magnetic SPI is modeled to be strongly affected by both the stellar and planetary magnetic fields, possibly influencing the magnetic activity of both, as well as affecting the irradiation and even the migration of the planet and rotational evolution of the star. As phase-resolved observational techniques are applied to a large statistical sample of hot Jupiter systems, extensions to other tightly orbiting stellar systems, such as smaller planets close to M dwarfs become possible. In these systems, star-planet separations of tens of stellar radii begin to coincide with the radiative habitable zone where planetary magnetic fields are likely a necessary condition for surface habitability.Comment: Accepted for publication in the handbook of exoplanet

Digging into the extremes: a useful approach for the analysis of rare variants with continuous traits?

The common disease/rare variant hypothesis predicts that rare variants with large effects will have a strong impact on corresponding phenotypes. Therefore it is assumed that rare functional variants are enriched in the extremes of the phenotype distribution. In this analysis of the Genetic Analysis Workshop 17 data set, my aim is to detect genes with rare variants that are associated with quantitative traits using two general approaches: analyzing the association with the complete distribution of values by means of linear regression and using statistical tests based on the tails of the distribution (bottom 10% of values versus top 10%). Three methods are used for this extreme phenotype approach: Fisher’s exact test, weighted-sum method, and beta method. Rare variants were collapsed on the gene level. Linear regression including all values provided the highest power to detect rare variants. Of the three methods used in the extreme phenotype approach, the beta method performed best. Furthermore, the sample size was enriched in this approach by adding additional samples with extreme phenotype values. Doubling the sample size using this approach, which corresponds to only 40% of sample size of the original continuous trait, yielded a comparable or even higher power than linear regression. If samples are selected primarily for sequencing, enriching the analysis by gathering a greater proportion of individuals with extreme values in the phenotype of interest rather than in the general population leads to a higher power to detect rare variants compared to analyzing a population-based sample with equivalent sample size

Stellar Coronal and Wind Models: Impact on Exoplanets

Surface magnetism is believed to be the main driver of coronal heating and stellar wind acceleration. Coronae are believed to be formed by plasma confined in closed magnetic coronal loops of the stars, with winds mainly originating in open magnetic field line regions. In this Chapter, we review some basic properties of stellar coronae and winds and present some existing models. In the last part of this Chapter, we discuss the effects of coronal winds on exoplanets.Comment: Chapter published in the "Handbook of Exoplanets", Editors in Chief: Juan Antonio Belmonte and Hans Deeg, Section Editor: Nuccio Lanza. Springer Reference Work

Capability of common SNPs to tag rare variants

Genome-wide association studies are based on the linkage disequilibrium pattern between common tagging single-nucleotide polymorphisms (SNPs) (i.e., SNPs having only common alleles) and true causal variants, and association studies with rare SNP alleles aim to detect rare causal variants. To better understand and explain the findings from both types of studies and to provide clues to improve the power of an association study with only common SNPs genotyped, we study the correlation between common SNPs and the presence of rare alleles within a region in the genome and look at the capability of common SNPs in strong linkage disequilibrium with each other to capture single rare alleles. Our results indicate that common SNPs can, to some extent, tag the presence of rare alleles and that including SNPs in strong linkage disequilibrium with each other among the tagging SNPs helps to detect rare alleles

Trends in prenatal cares settings: association with medical liability

<p>Abstract</p> <p>Background</p> <p>Medical liability concerns centered around maternity care have widespread public health implications, as restrictions in physician scope of practice may threaten quality of and access to care in the current climate. The purpose of this study was to examine national trends in prenatal care settings based on medical liability climate.</p> <p>Methods</p> <p>Analysis of prenatal visits in the National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, 1997 to 2004 (N = 21,454). To assess changes in rates of prenatal visits over time, we used the linear trend test. Multivariate logistic regression modeling was developed to determine characteristics associated with visits made to hospital outpatient departments.</p> <p>Results</p> <p>In regions of the country with high medical liability (N = 11,673), the relative number, or proportion, of all prenatal visits occurring in hospital outpatient departments increased from 11.8% in 1997–1998 to 19.4% in 2003–2004 (p < .001 for trend); the trend for complicated obstetrical visits (N = 3,275) was more pronounced, where the proportion of prenatal visits occurring in hospital outpatient departments almost doubled from 22.7% in 1997–1998 to 41.6% in 2003–2004 (p = .004 for trend). This increase did not occur in regions of the country with low medical liability (N = 9,781) where the proportion of visits occurring in hospital outpatient departments decreased from 13.3% in 1997–1998 to 9.0% in 2003–2004.</p> <p>Conclusion</p> <p>There has been a shift in prenatal care from obstetrician's offices to safety net settings in regions of the country with high medical liability. These findings provide strong indirect evidence that the medical liability crisis is affecting patterns of obstetric practice and ultimately patient access to care.</p

Gene-based multiple trait analysis for exome sequencing data

The common genetic variants identified through genome-wide association studies explain only a small proportion of the genetic risk for complex diseases. The advancement of next-generation sequencing technologies has enabled the detection of rare variants that are expected to contribute significantly to the missing heritability. Some genetic association studies provide multiple correlated traits for analysis. Multiple trait analysis has the potential to improve the power to detect pleiotropic genetic variants that influence multiple traits. We propose a gene-level association test for multiple traits that accounts for correlation among the traits. Gene- or region-level testing for association involves both common and rare variants. Statistical tests for common variants may have limited power for individual rare variants because of their low frequency and multiple testing issues. To address these concerns, we use the weighted-sum pooling method to test the joint association of multiple rare and common variants within a gene. The proposed method is applied to the Genetic Association Workshop 17 (GAW17) simulated mini-exome data to analyze multiple traits. Because of the nature of the GAW17 simulation model, increased power was not observed for multiple-trait analysis compared to single-trait analysis. However, multiple-trait analysis did not result in a substantial loss of power because of the testing of multiple traits. We conclude that this method would be useful for identifying pleiotropic genes