Toward Adaptive Trust Calibration for Level 2 Driving Automation

Properly calibrated human trust is essential for successful interaction between humans and automation. However, while human trust calibration can be improved by increased automation transparency, too much transparency can overwhelm human workload. To address this tradeoff, we present a probabilistic framework using a partially observable Markov decision process (POMDP) for modeling the coupled trust-workload dynamics of human behavior in an action-automation context. We specifically consider hands-off Level 2 driving automation in a city environment involving multiple intersections where the human chooses whether or not to rely on the automation. We consider automation reliability, automation transparency, and scene complexity, along with human reliance and eye-gaze behavior, to model the dynamics of human trust and workload. We demonstrate that our model framework can appropriately vary automation transparency based on real-time human trust and workload belief estimates to achieve trust calibration.Comment: 10 pages, 8 figure

Xanthine oxidase inhibition and white matter hyperintensity progression following ischaemic stroke and transient ischaemic attack (XILO-FIST): a multicentre, double-blinded, randomised, placebo-controlled trial

Acknowledgments This work was supported by the Stroke Association and British Heart Foundation [grant number TSA BHF 2013/01]. The work of Dr David Dickie and Dr Terry Quinn is funded by the Stroke Association. We would like to thank Christine McAlpine, Ruth Graham, Glasgow Royal Infirmary, UK; Lauren Pearce, Royal United Hospital, UK; Caroline Fornolles, Louise Tate, Frances Justin, Luton and Dunstable University Hospital, UK; Dean Waugh, Leeds Teaching Hospitals NHS Trust, UK; Donal Concannon, Altnagelvin Hospital, UK; Sharon Tysoe, Nina Francia, Nisha Menon, Raji Prabakaran, Southend University Hospital, UK; Amy Ashton, Caroline Watchurst, Marilena Marinescu, Sabaa Obarey, Scheherazade Feerick, University College London NHS Foundation Trust, UK; and Janice Irvine, Sandra Williams, and German Guzman Gutierrez, Aberdeen Royal Infirmary, UK; Caroline Fox and Joanne Topliffe, Broomfield Hospital, Essex, UK.Peer reviewedPublisher PD

Toward Identifying the Next Generation of Superfund and Hazardous Waste Site Contaminants

Reproduced with permission from Environmental Health Perspectives."This commentary evolved from a workshop sponsored by the National Institute of Environmental Health Sciences titled "Superfund Contaminants: The Next Generation" held in Tucson, Arizona, in August 2009. All the authors were workshop participants." doi:10.1289/ehp.1002497Our aim was to initiate a dynamic, adaptable process for identifying contaminants of emerging concern (CECs) that are likely to be found in future hazardous waste sites, and to identify the gaps in primary research that cause uncertainty in determining future hazardous waste site contaminants. Superfund-relevant CECs can be characterized by specific attributes: they are persistent, bioaccumulative, toxic, occur in large quantities, and have localized accumulation with a likelihood of exposure. Although still under development and incompletely applied, methods to quantify these attributes can assist in winnowing down the list of candidates from the universe of potential CECs. Unfortunately, significant research gaps exist in detection and quantification, environmental fate and transport, health and risk assessment, and site exploration and remediation for CECs. Addressing these gaps is prerequisite to a preventive approach to generating and managing hazardous waste sites.Support for the workshop, from which this article evolved, was provided by the National Institute of Environmental Health Sciences Superfund Research Program (P42-ES04940)

One-year outcomes after low-dose intracoronary alteplase during primary percutaneous coronary intervention. The T-TIME randomized trial

No abstract available

Toward Identifying the Next Generation of Superfund and Hazardous Waste Site Contaminants

Reproduced with permission from Environmental Health Perspectives."This commentary evolved from a workshop sponsored by the National Institute of Environmental Health Sciences titled "Superfund Contaminants: The Next Generation" held in Tucson, Arizona, in August 2009. All the authors were workshop participants." doi:10.1289/ehp.1002497Our aim was to initiate a dynamic, adaptable process for identifying contaminants of emerging concern (CECs) that are likely to be found in future hazardous waste sites, and to identify the gaps in primary research that cause uncertainty in determining future hazardous waste site contaminants. Superfund-relevant CECs can be characterized by specific attributes: they are persistent, bioaccumulative, toxic, occur in large quantities, and have localized accumulation with a likelihood of exposure. Although still under development and incompletely applied, methods to quantify these attributes can assist in winnowing down the list of candidates from the universe of potential CECs. Unfortunately, significant research gaps exist in detection and quantification, environmental fate and transport, health and risk assessment, and site exploration and remediation for CECs. Addressing these gaps is prerequisite to a preventive approach to generating and managing hazardous waste sites.Support for the workshop, from which this article evolved, was provided by the National Institute of Environmental Health Sciences Superfund Research Program (P42-ES04940)

Persistency of lactation using random regression models and different fixed regression modeling approaches

Milk yield test-day records on the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters and predict breeding values for nine measures of persistency and 305-d milk yield in a random regression animal model using two criteria to define the fixed regression. Legendre polynomials of fourth and fifth orders were used to model the fixed and random regressions of lactation curves. The fixed regressions were adjusted for average milk yield on populations (single) or subpopulations (multiple) formed by cows that calved at the same age and in the same season. Akaike Information (AIC) and Bayesian Information (BIC) criteria indicated that models with multiple regression lactation curves had the best fit to test-day milk records of first lactations, while models with a single regression curve had the best fit for the second and third lactations. Heritability and genetic correlation estimates between persistency and milk yield differed significantly depending on the lactation order and the measures of persistency used. These parameters did not differ significantly depending on the criteria used for defining the fixed regressions for lactation curves. In general, the heritability estimates were higher for first (0.07 to 0.43), followed by the second (0.08 to 0.21) and third (0.04 to 0.10) lactation. The rank of sires resulting from the processes of genetic evaluation for milk yield or persistency using random regression models differed according to the criteria used for determining the fixed regression of lactation curve

Linkage to chromosome 2q32.2-q33.3 in familial serrated neoplasia (Jass syndrome)

Causative genetic variants have to date been identified for only a small proportion of familial colorectal cancer (CRC). While conditions such as Familial Adenomatous Polyposis and Lynch syndrome have well defined genetic causes, the search for variants underlying the remainder of familial CRC is plagued by genetic heterogeneity. The recent identification of families with a heritable predisposition to malignancies arising through the serrated pathway (familial serrated neoplasia or Jass syndrome) provides an opportunity to study a subset of familial CRC in which heterogeneity may be greatly reduced. A genome-wide linkage screen was performed on a large family displaying a dominantly-inherited predisposition to serrated neoplasia genotyped using the Affymetrix GeneChip Human Mapping 10 K SNP Array. Parametric and nonparametric analyses were performed and resulting regions of interest, as well as previously reported CRC susceptibility loci at 3q22, 7q31 and 9q22, were followed up by finemapping in 10 serrated neoplasia families. Genome-wide linkage analysis revealed regions of interest at 2p25.2-p25.1, 2q24.3-q37.1 and 8p21.2-q12.1. Finemapping linkage and haplotype analyses identified 2q32.2-q33.3 as the region most likely to harbour linkage, with heterogeneity logarithm of the odds (HLOD) 2.09 and nonparametric linkage (NPL) score 2.36 (P = 0.004). Five primary candidate genes (CFLAR, CASP10, CASP8, FZD7 and BMPR2) were sequenced and no segregating variants identified. There was no evidence of linkage to previously reported loci on chromosomes 3, 7 and 9

Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups