GIS applications to maritime boundary definitions: diplomacy on and under the sea

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Associations between self-referral and health behavior responses to genetic risk information

Background: Studies examining whether genetic risk information about common, complex diseases can motivate individuals to improve health behaviors and advance planning have shown mixed results. Examining the influence of different study recruitment strategies may help reconcile inconsistencies. Methods: Secondary analyses were conducted on data from the REVEAL study, a series of randomized clinical trials examining the impact of genetic susceptibility testing for Alzheimer’s disease (AD). We tested whether self-referred participants (SRPs) were more likely than actively recruited participants (ARPs) to report health behavior and advance planning changes after AD risk and APOE genotype disclosure. Results: Of 795 participants with known recruitment status, 546 (69%) were self-referred and 249 (31%) had been actively recruited. SRPs were younger, less likely to identify as African American, had higher household incomes, and were more attentive to AD than ARPs (all P < 0.01). They also dropped out of the study before genetic risk disclosure less frequently (26% versus 41%, P < 0.001). Cohorts did not differ in their likelihood of reporting a change to at least one health behavior 6 weeks and 12 months after genetic risk disclosure, nor in intentions to change at least one behavior in the future. However, interaction effects were observed where ε4-positive SRPs were more likely than ε4-negative SRPs to report changes specifically to mental activities (38% vs 19%, p < 0.001) and diets (21% vs 12%, p = 0.016) six weeks post-disclosure, whereas differences between ε4-positive and ε4-negative ARPs were not evident for mental activities (15% vs 21%, p = 0.413) or diets (8% versus 16%, P = 0.190). Similarly, ε4-positive participants were more likely than ε4-negative participants to report intentions to change long-term care insurance among SRPs (20% vs 5%, p < 0.001), but not ARPs (5% versus 9%, P = 0.365). Conclusions: Individuals who proactively seek AD genetic risk assessment are more likely to undergo testing and use results to inform behavior changes than those who respond to genetic testing offers. These results demonstrate how the behavioral impact of genetic risk information may vary according to the models by which services are provided, and suggest that how participants are recruited into translational genomics research can influence findings. Trial registration ClinicalTrials.gov NCT00089882 and NCT00462917 Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0124-0) contains supplementary material, which is available to authorized users

Erratum to: How Can Psychological Science Inform Research About Genetic Counseling for Clinical Genomic Sequencing?

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147141/1/jgc40372.pd

How Can Psychological Science Inform Research About Genetic Counseling for Clinical Genomic Sequencing?

Next generation genomic sequencing technologies (including whole genome or whole exome sequencing) are being increasingly applied to clinical care. Yet, the breadth and complexity of sequencing information raise questions about how best to communicate and return sequencing information to patients and families in ways that facilitate comprehension and optimal health decisions. Obtaining answers to such questions will require multidisciplinary research. In this paper, we focus on how psychological science research can address questions related to clinical genomic sequencing by explaining emotional, cognitive, and behavioral processes in response to different types of genomic sequencing information (e.g., diagnostic results and incidental findings). We highlight examples of psychological science that can be applied to genetic counseling research to inform the following questions: (1) What factors influence patients’ and providers’ informational needs for developing an accurate understanding of what genomic sequencing results do and do not mean?; (2) How and by whom should genomic sequencing results be communicated to patients and their family members?; and (3) How do patients and their families respond to uncertainties related to genomic information?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147034/1/jgc40193.pd

Incidence of HACEK bacteraemia in Denmark:A 6-year population-based study

Objectives: Bacteria with common microbiological and clinical characteristics are often recognized as a particular group. The acronym HACEK stands for five fastidious genera associated with infective endocarditis (Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, and Kingella). Data on the epidemiology of HACEK are sparse. This article reports a 6-year nationwide study of HACEK bacteraemia in Denmark. Methods: Cases of HACEK bacteraemia occurring during the years 2010–2015 were retrieved from the national Danish microbiology database, covering an average surveillance population of 5.6 million per year. Results: A total of 147 cases of HACEK bacteraemia were identified, corresponding to an annual incidence of 0.44 per 100 000 population. The annual incidence for males was 0.56 per 100 000 and for females was 0.31 per 100 000. The median age was 56 years (range 0–97 years), with variation among the genera. One hundred and forty-three isolates were identified to the species level and six to the genus level: Haemophilus spp, n = 55; Aggregatibacter spp, n = 37; Cardiobacterium spp, n = 9; Eikenella corrodens n = 21; and Kingella spp, n = 27. Conclusions: This is the first study on the incidence of HACEK bacteraemia in a large surveillance population and may inspire further studies on the HACEK group. Haemophilus spp other than Haemophilus influenzae accounted for most cases of HACEK bacteraemia in Denmark, with Aggregatibacter spp in second place. Keywords: Epidemiology, Incidence, Age, Sex, Haemophilus, Aggregatibacte

Paving the path for implementation of clinical genomic sequencing globally:Are we ready?

Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.</p

Paving the path for implementation of clinical genomic sequencing globally:Are we ready?

Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.</p

Frequency dependent specific heat of viscous silica

We apply the Mori-Zwanzig projection operator formalism to obtain an expression for the frequency dependent specific heat c(z) of a liquid. By using an exact transformation formula due to Lebowitz et al., we derive a relation between c(z) and K(t), the autocorrelation function of temperature fluctuations in the microcanonical ensemble. This connection thus allows to determine c(z) from computer simulations in equilibrium, i.e. without an external perturbation. By considering the generalization of K(t) to finite wave-vectors, we derive an expression to determine the thermal conductivity \lambda from such simulations. We present the results of extensive computer simulations in which we use the derived relations to determine c(z) over eight decades in frequency, as well as \lambda. The system investigated is a simple but realistic model for amorphous silica. We find that at high frequencies the real part of c(z) has the value of an ideal gas. c'(\omega) increases quickly at those frequencies which correspond to the vibrational excitations of the system. At low temperatures c'(\omega) shows a second step. The frequency at which this step is observed is comparable to the one at which the \alpha-relaxation peak is observed in the intermediate scattering function. Also the temperature dependence of the location of this second step is the same as the one of the $\alpha-$peak, thus showing that these quantities are intimately connected to each other. From c'(\omega) we estimate the temperature dependence of the vibrational and configurational part of the specific heat. We find that the static value of c(z) as well as \lambda are in good agreement with experimental data.Comment: 27 pages of Latex, 8 figure

Disclosing genetic risk for Alzheimer’s dementia to individuals with mild cognitive impairment

IntroductionThe safety of predicting conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia using apolipoprotein E (APOE) genotyping is unknown.MethodsWe randomized 114 individuals with MCI to receive estimates of 3‐year risk of conversion to AD dementia informed by APOE genotyping (disclosure arm) or not (non‐disclosure arm) in a non‐inferiority clinical trial. Primary outcomes were anxiety and depression scores. Secondary outcomes included other psychological measures.ResultsUpper confidence limits for randomization arm differences were 2.3 on the State Trait Anxiety Index and 0.5 on the Geriatric Depression Scale, below non‐inferiority margins of 3.3 and 1.0. Moreover, mean scores were lower in the disclosure arm than non‐disclosure arm for test‐related positive impact (difference: ‐1.9, indicating more positive feelings) and AD concern (difference: ‐0.3).DiscussionProviding genetic information to individuals with MCI about imminent risk for AD does not increase risks of anxiety or depression and may provide psychological benefits.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154645/1/trc212002_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154645/2/trc212002.pd

Social and behavioral research in genomic sequencing: approaches from the Clinical Sequencing Exploratory Research Consortium Outcomes and Measures Working Group

The routine use of genomic sequencing in clinical medicine has the potential to dramatically alter patient care and medical outcomes. To fully understand the psychosocial and behavioral impact of sequencing integration into clinical practice, it is imperative that we identify the factors that influence sequencing-related decision making and patient outcomes. In an effort to develop a collaborative and conceptually grounded approach to studying sequencing adoption, members of the National Human Genome Research Institute's Clinical Sequencing Exploratory Research Consortium formed the Outcomes and Measures Working Group. Here we highlight the priority areas of investigation and psychosocial and behavioral outcomes identified by the Working Group. We also review some of the anticipated challenges to measurement in social and behavioral research related to genomic sequencing; opportunities for instrument development; and the importance of qualitative, quantitative, and mixed-method approaches. This work represents the early, shared efforts of multiple research teams as we strive to understand individuals' experiences with genomic sequencing. The resulting body of knowledge will guide recommendations for the optimal use of sequencing in clinical practice