Poor self-reported sleep is associated with risk factors for cardiovascular disease: A cross-sectional analysis in half a million adults.

BACKGROUND: Sleep is known to affect cardiovascular health, but some controversy exists on the independent association between different sleep characteristics (duration, restfulness, difficulties falling asleep) and specific risk factors for cardiovascular disease (CVD). We aimed to assess the association between self-reported sleep characteristics and the likelihood of major CVD risk factors. METHODS: Totally, 521,364 Spanish workers (32% female, 44 ± 9 years [18-64]) insured by an occupational risk prevention company participated in this nationwide cross-sectional study. Participants' sleep was considered 'poor' if they reported having ≥1 of the following conditions: excessively short (<6 h/d) or long (>9 h/d) sleep, unrestful sleep, or difficulties to fall asleep. We assessed the independent association between aforementioned sleep characteristics and the prevalence of hypertension, diabetes, hypercholesterolaemia, obesity and physical inactivity. RESULTS: Poor sleep (reported by 33% of participants) was associated with a higher likelihood of presenting all CVD risk factors individually, particularly physical inactivity (which prevalence was ~3-fold higher in the poor sleep group compared with participants reporting no sleep abnormality). In separate analyses, all the different sleep characteristics were associated with the likelihood of ≥2 CVD risk factors. Participants with optimal sleep, normal sleep duration, no difficulties falling sleep and restful sleep showed a lower total CVD risk score than their peers with poor sleep, short sleep duration, difficulties falling sleep and unrestful sleep, respectively (all p < .001). CONCLUSIONS: Poor sleep was associated with a higher likelihood of presenting major CVD risk factors. These findings might support the importance of monitoring and improving sleep patterns for primary CVD prevention.

AI in drug development: a multidisciplinary perspective.

The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this process using innovative technologies such as artificial intelligence (AI). Different AI tools are being applied to support all four steps of the drug development process (basic research for drug discovery; pre-clinical phase; clinical phase; and postmarketing). Some of the main tasks where AI has proven useful include identifying molecular targets, searching for hit and lead compounds, synthesising drug-like compounds and predicting ADME-Tox. This review, on the one hand, brings in a mathematical vision of some of the key AI methods used in drug development closer to medicinal chemists and, on the other hand, brings the drug development process and the use of different models closer to mathematicians. Emphasis is placed on two aspects not mentioned in similar surveys, namely, Bayesian approaches and their applications to molecular modelling and the eventual final use of the methods to actually support decisions. Promoting a perfect synergy.

Variationally Inferred Sampling through a Refined Bound.

In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation. We call this framework "refined variational approximation". Its strengths are its ease of implementation and the automatic tuning of sampler parameters, leading to a faster mixing time through automatic differentiation. Several strategies to approximate evidence lower bound (ELBO) computation are also introduced. Its efficient performance is showcased experimentally using state-space models for time-series data, a variational encoder for density estimation and a conditional variational autoencoder as a deep Bayes classifier.

An Adversarial Risk Analysis Framework for Cybersecurity.

Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.

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Adversarial Risk Analysis for Urban Security Resource Allocation.

Adversarial risk analysis (ARA) provides a framework to deal with risks originating from intentional actions of adversaries. We show how ARA may be used to allocate security resources in the protection of urban spaces. We take into account the spatial structure and consider both proactive and reactive measures, in that we aim at both trying to reduce criminality as well as recovering as best as possible from it, should it happen. We deal with the problem by deploying an ARA model over each spatial unit, coordinating the models through resource constraints, value aggregation, and proximity. We illustrate our approach with an example that uncovers several relevant policy issues.

Modeling Opponents in Adversarial Risk Analysis.

Adversarial risk analysis has been introduced as a framework to deal with risks derived from intentional actions of adversaries. The analysis supports one of the decisionmakers, who must forecast the actions of the other agents. Typically, this forecast must take account of random consequences resulting from the set of selected actions. The solution requires one to model the behavior of the opponents, which entails strategic thinking. The supported agent may face different kinds of opponents, who may use different rationality paradigms, for example, the opponent may behave randomly, or seek a Nash equilibrium, or perform level-k thinking, or use mirroring, or employ prospect theory, among many other possibilities. We describe the appropriate analysis for these situations, and also show how to model the uncertainty about the rationality paradigm used by the opponent through a Bayesian model averaging approach, enabling a fully decision-theoretic solution. We also show how as we observe an opponent's decision behavior, this approach allows learning about the validity of each of the rationality models used to predict his decision by computing the models' (posterior) probabilities, which can be understood as a measure of their validity. We focus on simultaneous decision making by two agents.

Adversarial risk analysis for counterterrorism modeling.

Recent large-scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the intentional actions of intelligent adversaries. Most approaches to these problems have a game-theoretic flavor although there are also several interesting decision-analytic-based proposals. One of them is the recently introduced framework for adversarial risk analysis, which deals with decision-making problems that involve intelligent opponents and uncertain outcomes. We explore how adversarial risk analysis addresses some standard counterterrorism models: simultaneous defend-attack models, sequential defend-attack-defend models, and sequential defend-attack models with private information. For each model, we first assess critically what would be a typical game-theoretic approach and then provide the corresponding solution proposed by the adversarial risk analysis framework, emphasizing how to coherently assess a predictive probability model of the adversary's actions, in a context in which we aim at supporting decisions of a defender versus an attacker. This illustrates the application of adversarial risk analysis to basic counterterrorism models that may be used as basic building blocks for more complex risk analysis of counterterrorism problems.

Modeling external risks in project management.

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.