Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable devices. By using energy-efficient accelerometers to predict heart rate, significant energy savings can be made. Further, we are interested in understanding patient recovery after a heart rate intervention, where we expect a variation in heart rate over time. Therefore, we propose an online approach to tackle the concept as time passes. We evaluate the methods on approximately 4 weeks of free living data from three patients over a number of months. We show that our approach can achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).Comment: MLMH 2018: 2018 KDD Workshop on Machine Learning for Medicine and Healthcar

Vesta:A Digital Health Analytics Platform for a Smart Home in a Box

© 2020 This paper presents Vesta, a digital health platform composed of a smart home in a box for data collection and a machine learning based analytic system for deriving health indicators using activity recognition, sleep analysis and indoor localization. This system has been deployed in the homes of 40 patients undergoing a heart valve intervention in the United Kingdom (UK) as part of the EurValve project, measuring patients health and well-being before and after their operation. In this work a cohort of 20 patients are analyzed, and 2 patients are analyzed in detail as example case studies. A quantitative evaluation of the platform is provided using patient collected data, as well as a comparison using standardized Patient Reported Outcome Measures (PROMs) which are commonly used in hospitals, and a custom survey. It is shown how the ubiquitous in-home Vesta platform can increase clinical confidence in self-reported patient feedback. Demonstrating its suitability for digital health studies, Vesta provides deeper insight into the health, well-being and recovery of patients within their home

Metabolomic analyses of Leishmania reveal multiple species differences and large differences in amino acid metabolism

Comparative genomic analyses of Leishmania species have revealed relatively minor heterogeneity amongst recognised housekeeping genes and yet the species cause distinct infections and pathogenesis in their mammalian hosts. To gain greater information on the biochemical variation between species, and insights into possible metabolic mechanisms underpinning visceral and cutaneous leishmaniasis, we have undertaken in this study a comparative analysis of the metabolomes of promastigotes of L. donovani, L. major and L. mexicana. The analysis revealed 64 metabolites with confirmed identity differing 3-fold or more between the cell extracts of species, with 161 putatively identified metabolites differing similarly. Analysis of the media from cultures revealed an at least 3-fold difference in use or excretion of 43 metabolites of confirmed identity and 87 putatively identified metabolites that differed to a similar extent. Strikingly large differences were detected in their extent of amino acid use and metabolism, especially for tryptophan, aspartate, arginine and proline. Major pathways of tryptophan and arginine catabolism were shown to be to indole-3-lactate and arginic acid, respectively, which were excreted. The data presented provide clear evidence on the value of global metabolomic analyses in detecting species-specific metabolic features, thus application of this technology should be a major contributor to gaining greater understanding of how pathogens are adapted to infecting their hosts

Arene <i>cis</i>-Diol Dehydrogenase-Catalysed Regio- and Stereoselective Oxidation of Arene-, Cycloalkane- and Cycloalkene-<i>cis</i>-diols to Yield Catechols and Chiral α-Ketols

Benzene cis-diol dehydrogenase and naphthalene cis-diol dehydrogenase enzymes, expressed in Pseudomonas putida wild-type and Escherichia coli recombinant strains, were used to investigate regioselectivity and stereoselectivity during dehydrogenations of arene, cyclic alkane and cyclic alkene vicinal cis-diols. The dehydrogenase-catalysed production of enantiopure cis-diols, α-ketols and catechols, using benzene cis-diol dehydrogenase and naphthalene cis-diol dehydrogenase, involved both kinetic resolution and asymmetric synthesis methods. The chemoenzymatic production and applications of catechol bioproducts in synthesis were investigated.Fil: Boyd, Derek R.. The Queens University of Belfast; IrlandaFil: Sharma, Narain D.. The Queens University of Belfast; IrlandaFil: Berberian, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. The Queens University of Belfast; IrlandaFil: Cleij, Marcel. The Queens University of Belfast; IrlandaFil: Hardacre, Christopher. The Queens University of Belfast; IrlandaFil: Ljubez, Vera. The Queens University of Belfast; IrlandaFil: McConville, Gareth. The Queens University of Belfast; IrlandaFil: Stevenson, Paul J.. The Queens University of Belfast; IrlandaFil: Kulakov, Leonid A.. The Queens University of Belfast; IrlandaFil: Allen, Christopher C. R.. The Queens University of Belfast; Irland