Parametrized modified gravity constraints after Planck

We constrain $f(R)$ and chameleon-type modified gravity in the framework of the Berstchinger-Zukin parametrization using the recent released Planck data, including both CMB temperature power spectrum and lensing potential power spectrum. Some other external data sets are included, such as BAO measurements from the 6dFGS, SDSS DR7 and BOSS DR9 surveys, HST $H_0$ measurement and supernovae from Union2.1 compilation. We also use WMAP9yr data for consistency check and comparison. For $f(R)$ gravity, WMAP9yr results can only give quite a loose constraint on the modified gravity parameter $B_0$, which is related to the present value of the Compton wavelength of the extra scalar degree of freedom, $B_0<3.37$ at $95\% {\rm C.L.}$ We demonstrate that this constraint mainly comes from the late ISW effect. With only Planck CMB temperature power-spectrum data, we can improve the WMAP9yr result by a factor $3.7$ ($B_0<0.91$ at $95\% {\rm C.L.}$). If the Planck lensing potential power-spectrum data are also taken into account, the constraint can be further strenghtened by a factor $5.1$ ($B_0<0.18$ at $95\% {\rm C.L.}$). This major improvement mainly comes from the small-scale lensing signal. Furthermore, BAO, HST and supernovae data could slightly improve the $B_0$ bound ($B_0<0.12$ at $95\% {\rm C.L.}$).For the chameleon-type model, we find that the data set which we used cannot constrain the Compton wavelength $B_0$ and the potential index $s$ of chameleon field, but can give a tight constraint on the parameter $\beta_1=1.043^{+0.163}_{-0.104}$ at $95\% {\rm C.L.}$ ($\beta_1=1$ in general relativity), which accounts for the non-minimal coupling between the chameleon field and the matter component. In addition, we find that both modified gravity models we considered favor a relatively higher Hubble parameter than the concordance LCDM model in general relativity.Comment: Match to the published version. Several numerical bugs about modified gravity parameters removed, updated results are based on the analysis of new chains. $B_0$ constraint become loose, other parameter bounds do not change. One more figure added in order to explain the degeneracy of parameters between $\beta_1$ and $B_0$ in the chameleon-type model

Bayesian Analysis of Consumer Choices with Taste, Context, Reference Point and Individual Scale Effects

This paper adopts an approach based on the concepts of random utility maximization and builds on the general theoretical framework of Lancaster and on the conceptual and econometric innovations of McFadden. Recent research in this area explores models that account for context effects, as well as methods for characterizing heterogeneity, response variability and decision strategy selection by consumers. This makes it possible to construct much richer empirical models of individual consumer behavior. A Bayesian approach provides a useful way to estimate and interpret models that are difficult to accomplish by conventional maximization/minimization algorithms. The application reported in the paper involves analysis of reference dependence and product labeling as context effects and the assessment of heterogeneity and response variability.Consumer/Household Economics,

Risk Perceptions, Social Interactions and the Influence of Information on Social Attitudes to Agricultural Biotechnology

We assess Canadian’s risk perceptions for genetically modified (GM) food and probe influences of socio-economic, demographic and other factors impinging on these perceptions. An internet-administered questionnaire with two stated choice split-sample experiments that approximate market choices of individual grocery shoppers is applied to elicit purchase behavior from 882 respondents across Canada. Data are collected to assess the influence on respondents’ choices for a specific food product (bread) of 1) product information which varies in content and by source and 2) information provided through labeling. These data also enable: a) analysis of trade-offs made by consumers between possible risks associated with GM ingredients and potential health or environment benefits in food and b) assessment of influences on respondents’ search for/access of product information. We rigorously document the extent and type of variation in Canadian consumers’ attitudes and risk perceptions for a selected GM food. This is pursued in analysis of experiment 1) data using a latent class model to analyze 445 consumers’ choices for bread products. We identify four distinct groups of Canadian consumers: 51% (value seekers) valued additional health or environmental benefits and were indifferent to GM content; traditional consumers (14 %) preferred their normally-purchased food; fringe consumers (4%) valued the health attribute and could defer consumption. Another 32 % (anti-GM) strongly opposed GM ingredients in food irrespective of introduced attributes. Thus there is a dichotomy in Canadian attitudes to GM content in food: a small majority of the sample (55 per cent) perceive little or no risk from GM food, but this is strongly opposed by 46% of respondents. Differences in gender, number of children in the household, education, and age are associated with the likelihood of segment membership. We also report on the search for information on characteristics of the GM food by a sample of 445 respondents with opportunity for voluntary access to related information through hyperlinks in the survey. Slightly less than half actually sought such information. Gender, employment status, rural or urban residency and the number of children in the household all affected the probability that respondents would access information. A further research component examines product choices made in the context of two common GM labeling policies: mandatory and voluntary labeling. We find these two types of strategies to have distinctive impacts on consumers and on measures of social welfare. Knowledge of these may help policy makers to make more informed analyses of the alternative labeling policies. Specific findings also provide base-line measures of Canadians’ attitudes to risks of GM technology in the context of food and environmental risks, as well as documenting the importance of context influences and reference points on consumers’ preferences for GM food. We also develop methodological improvements for accurately estimating the value of information on a negative attribute. The project built upon initial findings from a previous AARI project (#AARI Project #2000D037) and is complemented by research supported through a Genome Prairie GE3LS (Genetics, Ethics, Environment, Economics, Law and Society) project: “Commercialization and society: its policy and strategic implications.”Research and Development/Tech Change/Emerging Technologies,

Consumers' Preferences for GM Food and Voluntary Information Acquisition: A Simultaneous Choice Analysis

Previous research studies directed at the influence of information on consumers' preferences and choices of food in the context of genetically modified (GM) food assume that information is exogenous, in that this is provided to consumers from external sources. Information made available to consumers is also typically treated as being received and processed. Other literature and observation suggests that these two features tend not to apply in practice. Using data from a choice experiment on consumers' choices for genetically modified food in which respondents were able to voluntarily access information, this study allows information to be endogenous; consumers' product choices and information access decisions are examined within a simultaneous choice framework. We find that these two types of decisions are related, but not entirely as might be expected from the existing agricultural economics literature since those with more negative attitudes toward GM food were most likely to access information made available. Our results are consistent with research findings in the social psychology literature. There is heterogeneity across consumers in the relationship between information access and consumer choices which may reflect differentiation in attitudes to GM food.Genetically modified food, information search, multinomial logit models, simultaneous modeling., Food Consumption/Nutrition/Food Safety, Research and Development/Tech Change/Emerging Technologies, Q13, Q18, C8,

A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure