Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response

Analysing and Improving Robustness of Predictive Energy Harvesting Systems

Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even to render batteries obsolete. Such systems employ an energy scheduler to optimize their behavior and thus performance by adapting the node operation. Predictive models of harvesting sources, which are inherently non-deterministic and consequently challenging to predict, are often necessary for the scheduler to optimize performance. Therefore the accuracy of the predictive model inevitably impacts the scheduler and system performance. This fact has been largely overlooked in the vast amount of available results on energy management systems. We define a novel robustness metric for energy-harvesting systems that describes the effect prediction errors have on the system performance. Furthermore, we show that if a scheduler is optimal when predictions are accurate, it is not very robust. Thus there is a tradeoff between robustness and performance. We propose a prediction scaling method to improve a system's robustness and demonstrate the results using energy harvesting data sets from both outdoor and indoor scenarios. The method improves a non-robust system's performance by up to 75 times in a real-world setting

Accurate Onboard Predictions for Indoor Energy Harvesting using Random Forests

Indoor energy harvesting has recently enabled long-term deployments of sustainable IoT sensor nodes. The performance of such systems operating in an energy-neutral manner can be optimized by exploiting energy prediction models. Numerous prediction algorithms have been developed, yet they are primarily intended for outdoor (solar) energy harvesting. Indoor environments are much more challenging to predict since the primary energy is very variable. We propose a prediction method based on random forests that is capable of capturing and predicting this variability. It estimates the harvested energy for various locations in different scenarios with high accuracy while only requiring limited resources. We deploy the predictor on a dual processor platform powered by indoor lighting with various sensors including indoor air quality sensors. The predictor executes in 22.2 mu s and requires 2.60 mu J to generate a prediction. Furthermore, the predictor continuously learns from the system's local environment. The proposed online learning is resource-efficient and requires only limited data, enabling it to run on the harvesting-based system. Over time, online learning reduces the energy required to generate a prediction by up to 77% while maintaining its high prediction accuracy.ISSN:2377-547

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non-deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting-based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non-robust system's performance by up to 13.8 times in a real-world setting.ISSN:1751-8601ISSN:1751-861

Energy-Efficient Bootstrapping in Multi-hop Harvesting-Based Networks

Short-range multi-hop communication is an energy-efficient way to collect, share, and distribute large amounts of data with Internet of Things (IoT) systems. Nevertheless, the resource demands of wireless communication impose a burden on battery-operated IoT nodes, limiting their lifetime. Energy harvesting can address the energy limitation but introduces significant power variability, which affects reliable operation causing nodes to disconnect and join the network repeatedly. To mitigate this, we present Dual-Range Bootstrapping (DRB), a new mechanism for bootstrapping harvesting-based IoT nodes in multi-hop networks. DRB has both a very low and predictable energy cost, key properties for efficient energy management of harvesting-based nodes. We demonstrate DRB's effectiveness in numerous scenarios using LoRa and FSK modulations, leveraging the former's long range and the latter's high data rate. We experimentally evaluate DRB with energy measurements performed on a communication testbed. Lastly, we use real-world energy harvesting traces to simulate the long-term behavior of a harvesting-based network

Thermoelectric energy harvesting from gradients in the earth surface

ISSN:0278-0046ISSN:1557-994

Robust Resource-Aware Self-Triggered Model Predictive Control

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This letter proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems.ISSN:2475-145

Self-triggered Control with Energy Harvesting Sensor Nodes

Distributed embedded systems are pervasive components jointly operating in a wide range of applications. Moving toward energy harvesting powered systems enables their long-term, sustainable, scalable, and maintenance-free operation. When these systems are used as components of an automatic control system to sense a control plant, energy availability limits when and how often sensed data are obtainable and therefore when and how often control updates can be performed. The time-varying and non-deterministic availability of harvested energy and the necessity to plan the energy usage of the energy harvesting sensor nodes ahead of time, on the one hand, have to be balanced with the dynamically changing and complex demand for control updates from the automatic control plant and thus energy usage, on the other hand. We propose a hierarchical approach with which the resources of the energy harvesting sensor nodes are managed on a long time horizon and on a faster timescale, self-triggered model predictive control controls the plant. The controller of the harvesting-based nodes' resources schedules the future energy usage ahead of time and the self-triggered model predictive control incorporates these time-varying energy constraints. For this novel combination of energy harvesting and automatic control systems, we derive provable properties in terms of correctness, feasibility, and performance. We evaluate the approach on a double integrator and demonstrate its usability and performance in a room temperature and air quality control case study.ISSN:2378-9638ISSN:2378-962