Genetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease

Obesity has been posited as an independent risk factor for diabetic kidney disease (DKD), but establishing causality from observational data is problematic. We aimed to test whether obesity is causally related to DKD using Mendelian randomization, which exploits the random assortment of genes during meiosis. In 6,049 subjects with type 1 diabetes, we used a weighted genetic risk score (GRS) comprised of 32 validated BMI loci as an instrument to test the relationship of BMI with macroalbuminuria, end-stage renal disease (ESRD), or DKD defined as presence of macroalbuminuria or ESRD. We compared these results with cross-sectional and longitudinal observational associations. Longitudinal analysis demonstrated a U-shaped relationship of BMI with development of macroalbuminuria, ESRD, or DKD over time. Cross-sectional observational analysis showed no association with overall DKD, higher odds of macroalbuminuria (for every 1 kg/m(2) higher BMI, odds ratio [OR] 1.05, 95% CI 1.03-1.07, P < 0.001), and lower odds of ESRD (OR 0.95, 95% CI 0.93-0.97, P < 0.001). Mendelian randomization analysis showed a 1 kg/m(2) higher BMI conferring an increased risk in macroalbuminuria (OR 1.28, 95% CI 1.11-1.45, P = 0.001), ESRD (OR 1.43, 95% CI 1.20-1.72, P < 0.001), and DKD (OR 1.33, 95% CI 1.17-1.51, P < 0.001). Our results provide genetic evidence for a causal link between obesity and DKD in type 1 diabetes. As obesity prevalence rises, this finding predicts an increase in DKD prevalence unless intervention should occur.

Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.

Children's use of interventions to learn causal structure

Children aged between 5 and 8 years freely intervened on a three-variable causal system, with their task being to discover whether it was a common-cause structure or one of two causal chains. From 6-7 years, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children’s interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children’s correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task in which children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children’s performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves.

A survey of location inference techniques on Twitter

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author’s location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.

Evidential event inference in transport video surveillance

This paper presents a new framework for multi-subject event inference in surveillance video, where measurements produced by low-level vision analytics usually are noisy, incomplete or incorrect. Our goal is to infer the composite events undertaken by each subject from noise observations. To achieve this, we consider the temporal characteristics of event relations and propose a method to correctly associate the detected events with individual subjects. The Dempster–Shafer (DS) theory of belief functions is used to infer events of interest from the results of our vision analytics and to measure conflicts occurring during the event association. Our system is evaluated against a number of videos that present passenger behaviours on a public transport platform namely buses at different levels of complexity. The experimental results demonstrate that by reasoning with spatio-temporal correlations, the proposed method achieves a satisfying performance when associating atomic events and recognising composite events involving multiple subjects in dynamic environments.

Moral asymmetries in judgments of agency withstand ludicrous causal deviance

Americans have been shown to attribute greater intentionality to immoral than to amoral actions in cases of causal deviance, that is, cases where a goal is satisfied in a way that deviates from initially planned means (e.g., a gunman wants to hit a target and his hand slips, but the bullet ricochets off a rock into the target). However, past research has yet to assess whether this asymmetry persists in cases of extreme causal deviance. Here, we manipulated the level of mild to extreme causal deviance of an immoral versus amoral act. The asymmetry in attributions of intentionality was observed at all but the
most extreme level of causal deviance, and, as we hypothesized, was mediated by attributions of Blame/credit and judgments of action performance. These findings are discussed as they support a multiple-concepts interpretation of the asymmetry, wherein blame renders a naïve concept of intentional action (the outcome matches the intention) more salient than a composite concept (the outcome matches the intention and was brought about by planned means), and in terms of their implications for cross-cultural research on judgments of agency.

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages

We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas. This approach leads to a fine grained study in which local information such as context-sensitive independence and determinism can be considered. It also allows us to characterize more precisely the jump from tractability to NP-hardness and beyond, and to consider the complexity introduced by evidence alone.

Inference in Polytrees with Sets of Probabilities

Inferences in directed acyclic graphs associated with probability intervals and sets of probabilities are NP-hard, even for polytrees. We propose: 1) an improvement on Tessem’s A/R algorithm for inferences on polytrees associated with probability intervals; 2) a new algorithm for approximate inferences based on local search; 3) branch-and-bound algorithms that combine the previous techniques. The first two algorithms produce complementary approximate solutions, while branch-and-bound procedures can generate either exact or approximate solutions. We report improvements on existing techniques for inference with probability sets and intervals, in some cases reducing computational effort by several orders of magnitude.

Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.

The vertical dimension of social relations and accurate interpersonal perception: A meta-analysis

There is little consensus regarding how verticality (social power, dominance, and status) is related to accurate interpersonal perception. The relation could be either positive or negative, and there could be many causal processes at play. The present article discusses the theoretical possibilities and presents a meta-analysis of this question. In studies using a standard test of interpersonal accuracy, higher socioeconomic status (SES) predicted higher accuracy defined as accurate inference about the meanings of cues; also, higher experimentally manipulated vertical position predicted higher accuracy defined as accurate recall of others’ words. In addition, although personality dominance did not predict accurate inference overall, the type of personality dominance did, such that empathic/responsible dominance had a positive relation and egoistic/aggressive dominance had a negative relation to accuracy. In studies involving live interaction, higher experimentally manipulated vertical position produced lower accuracy defined as accurate inference about cues; however, methodological problems place this result in doubt.