10 research outputs found

    High Spatial and Temporal Resolution Census Data Reveals Communities at Risk Along the Wildland-Urban Interface (WUI) in California, USA

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    We tracked census tract level population change along California\u27s wild land-urban interface (WUI) during the past decade (2010-2019), an ecological sensitive region transitioning from developed land to wilderness. Our results from Mann-Kendall analysis, a method employed for monotonic trend detection showed that about one-third (29.1%) of census tracts in California’s WUI have seen a significant population increase from 2010 to 2019, affecting 12.7% population in California. The population increase along WUI is largely driven by the sixteen counties in the San Francisco Bay Area (10) and Southern California (6). We also found that higher proportion of WUI residents in Bay Area and larger number of WUI residents in Southern California. Bay Area counties in general have a higher proportion of population living in WUI tracts with significant population increase than Southern California counties. However, the lower proportion of residents living in WUI in Southern California counties account for a much larger population. Riverside is the county with the highest number of residents living in WUI tracts that have experienced significant population increase during the past decade. These residents also account for a high proportion (29.2%) of total population in Riverside. Preliminary results showed that the increase of population along WUI is driven by the house affordability and house ownership in 16 counties of Bay Area and Southern California. These factors can still explain a significant amount of the spatial pattern if extended to all counties in California

    The Vehicle, 1963, Vol. 5

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    Vol. 5 Table of Contents Milepostspage 3 Rhyme Conceived At DawnDaun Alan Leggpage 4 NightRoss Kokospage 4 UncrownedOra Blanche T. Kingpage 4 SunfishingL.J.G.page 5 The Man Who Went To New YorkEric Crookspage 7 The DreamPauline B. Smithpage 18 Open WindowsDavid Helmpage 19 SalvationChristine McCollpage 19 The Chess GamePierre Hooverpage 20 CataclysmRaymond Kapraunpage 20 A Microscopic ViewKenneth L. Vadovskypage 21 See How Love ComesLiz Puckettpage 21 A Can Of Beer For AndyKenneth L. Vadovskypage 22 A MonsterDixie Lee Motleypage 28 InconstancyJanice Brookspage 29 DreamerDaun Alan Leggpage 29 The Third WishGlenda Vursellpage 30 The MiracleJanice Brookspage 32 What Lives Where Love Once Dwelt?Vernell Vyvialpage 33 The Most Unforgettable Person I Have Ever KnownJames Flingpage 34 Winter ThoughtsPauline B. Smithpage 35 A Winter NightPeggy Lambertpage 35 The Silver WhaleL.J.G.page 36 RaindropsDixie Lee Motleypage 40 Conflict Of Soul IJean Konzelmanpage 40 JudyChristine McCollpage 41 Sadness No. 3 (Vergessen)Sherry Sue Frypage 41 Lost GoldLarry Pricepage 42 EchoesCharles Cooleypage 48 TruthDaun Alan Leggpage 48 SunsetCarol Bennettpage 48 Cover designTom Windsor Illustration for winning storyJoel E. Hendrickshttps://thekeep.eiu.edu/vehicle/1011/thumbnail.jp

    Polygenic risk scores for prediction of breast cancer and breast cancer subtypes

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    Abstract Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs
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