Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

Multimedia on the Mountaintop: Using public snow images to improve water systems operation

This paper merges multimedia and environmental research to verify the utility of public web images for improving water management in periods of water scarcity, an increasingly critical event due to climate change. A multimedia processing pipeline fetches mountain images from multiple sources and extracts virtual snow indexes correlated to the amount of water accumulated in the snow pack. Such indexes are used to predict water availability and design the operating policy of Lake Como, Italy. The performance of this informed policy is contrasted, via simulation, with the current operation, which depends only on lake water level and day of the year, and with a policy that exploits official Snow Water Equivalent (SWE) estimated from ground stations data and satellite imagery. Virtual snow indexes allow improving the system performance by 11.6% w.r.t. The baseline operation, and yield further improvement when coupled with official SWE information, showing that the two data sources are complementary. The proposed approach exemplifies the opportunities and challenges of applying multimedia content analysis methods to complex environmental problems

Pible: Battery-Free Mote for Perpetual Indoor BLE Applications

Smart building applications require a large-scale deployment of sensors distributed across the environment. Recent innovations in smart environments are driven by wireless networked sensors as they are easy to deploy. However, replacing these batteries at scale is a non-trivial, labor-intensive task. Energy harvesting has emerged as a potential solution to avoid battery replacement but requires compromises such as application specific design, simplified communication protocol or reduced quality of service. We explore the design space of battery-free sensor nodes using commercial off the shelf components, and present Pible: a Perpetual Indoor BLE sensor node that leverages ambient light and can support numerous smart building applications. We analyze node-lifetime, quality of service and light availability trade-offs and present a predictive algorithm that adapts to changing lighting conditions to maximize node lifetime and application quality of service. Using a 20 node, 15-day deployment in a real building under varying lighting conditions, we show feasible applications that can be implemented using Pible and the boundary conditions under which they can fail.Comment: 4 pages, 4 figures, BuildSys '18: Conference on Systems for Built Environments, November 7--8, 2018, Shenzen, Chin

BCR-ABL residues interacting with ponatinib are critical to preserve the tumorigenic potential of the oncoprotein

Patients with chronic myeloid leukemia in whom tyrosine kinase inhibitors (TKIs) fail often present mutations in the BCR-ABL catalytic domain. We noticed a lack of substitutions involving 4 amino acids (E286, M318, I360, and D381) that form hydrogen bonds with ponatinib. We therefore introduced mutations in each of these residues, either preserving or altering their physicochemical properties. We found that E286, M318, I360, and D381 are dispensable for ABL and BCR-ABL protein stability but are critical for preserving catalytic activity. Indeed, only a "conservative" I360T substitution retained kinase proficiency and transforming potential. Molecular dynamics simulations of BCR-ABLI360T revealed differences in both helix αC dynamics and protein-correlated motions, consistent with a modified ATP-binding pocket. Nevertheless, this mutant remained sensitive to ponatinib, imatinib, and dasatinib. These results suggest that changes in the 4 BCR-ABL residues described here would be selected against by a lack of kinase activity or by maintained responsiveness to TKIs. Notably, amino acids equivalent to those identified in BCR-ABL are conserved in 51% of human tyrosine kinases. Hence, these residues may represent an appealing target for the design of pharmacological compounds that would inhibit additional oncogenic tyrosine kinases while avoiding the emergence of resistance due to point mutations.This work was supported by an investigator grant to P.V. from Associazione Italiana per la Ricerca sul Cancro (AIRC) and by funding from the Biotechnology and Biological Sciences Research Council (BB/I023291/1 and BB/H018409/1 to AP and FF). P.B. is the recipient of an AIRC - Marie Curie fellowship

Fatal metformin overdose: case report and postmortem biochemistry contribution

Metformin is an oral antihyperglycemic agent used in the management of type 2 diabetes mellitus. Lactic acidosis from metformin overdose is a rare complication of metformin therapy and occurs infrequently with therapeutic use. Fatal cases, both accidental and intentional, are extremely rare in clinical practice. Metformin is eliminated by the kidneys, and impaired renal function can result in an increased plasma concentration of the drug. In this report, we describe an autopsy case involving a 70-year-old woman suffering from diabetes mellitus and impaired renal function who received metformin treatment. Metformin concentrations in the peripheral blood collected during hospitalization and femoral blood collected during autopsy were 42 and 47.3µg/ml, respectively. Lactic acidosis (29.10mmol/l) was objectified during hospitalization. Furthermore, postmortem biochemistry allowed ketoacidosis to be diagnosed (blood β-hydroxybutyrate, 10,500µmol/l). Death was attributed to lactic acidosis due to metformin intoxication. Increased plasma concentrations of the drug were attributed to severely impaired renal function. The case emphasizes the usefulness of performing exhaustive toxicology and postmortem biochemistry towards the more complete understanding of the pathophysiological mechanisms that may be involved in the death process