Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer

The ability of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model to predict BRCA1 and BRCA2 mutations and breast cancer incidence in women with a family history of breast cancer was evaluated. Observed mutations in 263 screened families were compared to retrospective predictions. Similarly, observed breast cancers in 640 women were compared to retrospective predictions of breast cancer incidence. The ratios of observed to expected number of BRCA1- , BRCA2- and BRCA(1 or 2) mutations were 1.43 (95% CI 1.05–1.90), 0.63 (95% CI 0.34–1.08), and 1.12 (95% CI 0.86–1.44), showing a significant underestimation of BRCA1 mutations. Discrimination between carriers and non-carriers as measured by area under the receiver operating characteristic (ROC) curve was 0.83 (95% CI 0.76–0.88). The ratio of observed to expected number of invasive breast cancers was 1.41 (0.91–2.08). The corresponding area under the ROC curve for prediction of invasive breast cancer at individual level was 0.62 (95% CI 0.52–0.73). In conclusion, the BOADICEA model can predict the total prevalence of BRCA(1 or 2) mutations and the incidence of invasive breast cancers. The mutation probability as generated by BOADICEA can be used clinically as a guideline for screening, and thus decrease the proportion of negative mutation analyses. Likewise, individual breast cancer risks can be used for selecting women whose risk of breast cancer indicates follow-up. Application of local mutation frequencies of BRCA1 and BRCA2 could improve the ability to distinguish between the two genes

The Road to Quantum Computational Supremacy

We present an idiosyncratic view of the race for quantum computational supremacy. Google's approach and IBM challenge are examined. An unexpected side-effect of the race is the significant progress in designing fast classical algorithms. Quantum supremacy, if achieved, won't make classical computing obsolete.Comment: 15 pages, 1 figur

Drawing Boundaries

In “On Drawing Lines on a Map” (1995), I suggested that the different ways we have of drawing lines on maps open up a new perspective on ontology, resting on a distinction between two sorts of boundaries: fiat and bona fide. “Fiat” means, roughly: human-demarcation-induced. “Bona fide” means, again roughly: a boundary constituted by some real physical discontinuity. I presented a general typology of boundaries based on this opposition and showed how it generates a corresponding typology of the different sorts of objects which boundaries determine or demarcate. In this paper, I describe how the theory of fiat boundaries has evolved since 1995, how it has been applied in areas such as property law and political geography, and how it is being used in contemporary work in formal and applied ontology, especially within the framework of Basic Formal Ontology

The effects of weather and climate change on dengue

There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors

Determining Peptide Partitioning Properties via Computer Simulation

The transfer of polypeptide segments into lipid bilayers to form transmembrane helices represents the crucial first step in cellular membrane protein folding and assembly. This process is driven by complex and poorly understood atomic interactions of peptides with the lipid bilayer environment. The lack of suitable experimental techniques that can resolve these processes both at atomic resolution and nanosecond timescales has spurred the development of computational techniques. In this review, we summarize the significant progress achieved in the last few years in elucidating the partitioning of peptides into lipid bilayer membranes using atomic detail molecular dynamics simulations. Indeed, partitioning simulations can now provide a wealth of structural and dynamic information. Furthermore, we show that peptide-induced bilayer distortions, insertion pathways, transfer free energies, and kinetic insertion barriers are now accurate enough to complement experiments. Further advances in simulation methods and force field parameter accuracy promise to turn molecular dynamics simulations into a powerful tool for investigating a wide range of membrane active peptide phenomena

Genetic Variance in the Adiponutrin Gene Family and Childhood Obesity

AIM: The adiponutrin gene family consists of five genes (PNPLA1-5) coding for proteins with both lipolytic and lipogenic properties. PNPLA3 has previously been associated with adult obesity. Here we investigated the possible association between genetic variants in these genes and childhood and adolescent obesity. METHODS/RESULTS: Polymorphisms in the five genes of the adiponutrin gene family were selected and genotyped using the Sequenom platform in a childhood and adolescent obesity case-control study. Six variants in PNPLA1 showed association with obesity (rs9380559, rs12212459, rs1467912, rs4713951, rs10947600, and rs12199580, p0.05). When analyzing these SNPs in relation to phenotypes, two SNPs in the PNPLA3 gene showed association with insulin sensitivity (rs12483959: beta = -0.053, p = 0.016, and rs2072907: beta = -0.049, p = 0.024). No associations were seen for PNPLA2, PNPLA4, and PNPLA5. CONCLUSIONS: Genetic variation in the adiponutrin gene family does not seem to contribute strongly to obesity in children and adolescents. PNPLA1 exhibited a modest effect on obesity and PNPLA3 on insulin sensitivity. These data, however, require confirmation in other cohorts and ethnic groups

A Contributing Role for Anti-Neuraminidase Antibodies on Immunity to Pandemic H1N1 2009 Influenza A Virus

Exposure to contemporary seasonal influenza A viruses affords partial immunity to pandemic H1N1 2009 influenza A virus (pH1N1) infection. The impact of antibodies to the neuraminidase (NA) of seasonal influenza A viruses to cross-immunity against pH1N1 infection is unknown.Antibodies to the NA of different seasonal H1N1 influenza strains were tested for cross-reactivity against A/California/04/09 (pH1N1). A panel of reverse genetic (rg) recombinant viruses was generated containing 7 genes of the H1N1 influenza strain A/Puerto Rico/08/34 (PR8) and the NA gene of either the pandemic H1N1 2009 strain (pH1N1) or one of the following contemporary seasonal H1N1 strains: A/Solomon/03/06 (rg Solomon) or A/Brisbane/59/07 (rg Brisbane). Convalescent sera collected from mice infected with recombinant viruses were measured for cross-reactive antibodies to pH1N1 via Hemagglutinin Inhibition (HI) or Enzyme-Linked Immunosorbent Assay (ELISA). The ectodomain of a recombinant NA protein from the pH1N1 strain (pNA-ecto) was expressed, purified and used in ELISA to measure cross-reactive antibodies. Analysis of sera from elderly humans immunized with trivalent split-inactivated influenza (TIV) seasonal vaccines prior to 2009 revealed considerable cross-reactivity to pNA-ecto. High titers of cross-reactive antibodies were detected in mice inoculated with either rg Solomon or rg Brisbane. Convalescent sera from mice inoculated with recombinant viruses were used to immunize naïve recipient Balb/c mice by passive transfer prior to challenge with pH1N1. Mice receiving rg California sera were better protected than animals receiving rg Solomon or rg Brisbane sera.The NA of contemporary seasonal H1N1 influenza strains induces a cross-reactive antibody response to pH1N1 that correlates with reduced lethality from pH1N1 challenge, albeit less efficiently than anti-pH1N1 NA antibodies. These findings demonstrate that seasonal NA antibodies contribute to but are not sufficient for cross-reactive immunity to pH1N1

Priority for the Worse Off and the Social Cost of Carbon

The social cost of carbon (SCC) is a monetary measure of the harms from carbon emission. Specifically, it is the reduction in current consumption that produces a loss in social welfare equivalent to that caused by the emission of a ton of CO2. The standard approach is to calculate the SCC using a discounted-utilitarian social welfare function (SWF)—one that simply adds up the well-being numbers (utilities) of individuals, as discounted by a weighting factor that decreases with time. The discounted-utilitarian SWF has been criticized both for ignoring the distribution of well-being, and for including an arbitrary preference for earlier generations. Here, we use a prioritarian SWF, with no time-discount factor, to calculate the SCC in the integrated assessment model RICE. Prioritarianism is a well-developed concept in ethics and theoretical welfare economics, but has been, thus far, little used in climate scholarship. The core idea is to give greater weight to well-being changes affecting worse off individuals. We find substantial differences between the discounted-utilitarian and non-discounted prioritarian SCC