Domestic heating behaviour and room temperatures: Empirical evidence from Scottish homes

In this paper, we describe patterns of residential heating based on data from 255 homes in and around Edinburgh, Scotland, UK, spanning August 2016 to June 2018. We describe: (i) the room temperatures achieved, (ii) the diurnal durations of heating use, and (iii) common diurnal patterns of heating behaviour. We investigate how these factors vary between weekdays and weekends, over the course of the year, by external temperature, and by room type. We compare these empirical findings with the simplifying assumptions about heating patterns found in the UK’s Standard Assessment Procedure (SAP), a widely-used building energy performance model. There are areas of concurrence and others of substantial difference with these model assumptions. Indoor achieved temperatures are substantially lower than SAP assumptions. The duration and timings of heating use vary substantially between homes and along lines of season and outdoor temperature, whereas the SAP model assumes no such variation. Little variation is found along the lines of weekday vs. weekend, whereas the SAP model assumes differences, or between living space and other rooms, consistent with the SAP. The results are relevant for those interested in how SAP assumptions regarding household heating behaviours and achieved indoor temperatures concur with empirical data

The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

This paper is based on research funded by the UK Engineering and Physical Sciences Research Council and undertaking in the projects Intelligent Domestic Energy Advice Loop (grant reference EP/K002732/1) and Data-Driven Methods for a New National Household Energy Survey (grant reference EP/M008223/1). The authors are grateful to the research funding agency and to all those who participated directly in these projects alongside the authors: Prof. D.K. Arvind, Cillian Brewitt, Edmund Farrow, Elaine Farrow, Prof. Johanna Moore, Dr. Evan Morgan and Prof. David Shipworth. Thanks also to the participants in the project, whose participation made this dataset and other work in the projects possible, and to Changeworks (https://www.changeworks.org.uk/) for identifying and recruiting potential participants, managing participant interactions, and installing and maintaining homes’ sensor and app systemsPeer reviewedPublisher PD

kmdouglass/bstore: Astigmatic 3D Analysis Capabilities

Added Two new processors, CalibrateAstigmatism and DefaultAstigmatismComputer were created for computing astigmatism-based 3D calibration curves to localize molecules in three dimensions. There is a new processor called ComputeZPosition that takes the calibration curve from CalibrateAstigmatism and computes the z-positions of localizations with x and y PSF widths. A 3D astigmatic imaging example notebook was added to the examples folder to explain how to use the new functionality. Wobble curves in 3D astigmatic imaging are also taken accounted for using the CalibrateAstigmatism and ComputeZPosition processors. Changed The version number contained in the bstore.__version__ string is now formatted to contain only the major/minor/patch numbers: for example, 1.1.1 instead of v1.1.1-f7129fe. The code for visualing local densities of localizations and selecting regions of interest has been separated from FiducialDriftCorrect and moved into a new processor class called SelectLocalizations. FiducialDriftCorrect inherits from this new class and will work exactly as before. This change will allow other processors to reuse the visualization code. The code for visualizing the fiducial trajectories in time and bead trajectories in z was consolidated into the ComputeTrajectories metaclass. This makes the drift correction and astigmatism calibration more in line with the DRY principle (Don't Repeat Yourself). The visualizations in the OverlayClusters multiprocessor are now improved to better facilitate the visual comparison between the localizations and widefield images. A showAll property was also added for making a scatter plot of all the localizations in the display, rather than just the current cluster. Fixed The FiducialDriftCorrect processor no longer raises an error when the removeFiducials parameter is set to False. Clusters with too few data points were causing computation of some cluster statistics with the ComputeClusterStats processor to fail. In particular, the convex hull and eccentricity were susceptible to these errors. NaN's are now returned instead when the computation for a cluster fails

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

<div><p>Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.</p></div

ELASPIC methodology.

<p>Schematic view of the strategy used to derive predictive features and train and validate ELASPIC for the prediction of stability effects in domain core and domain-domain interfaces upon mutation.</p

Summary of stability prediction of nsSNP mutations.

<p>Predicted absolute ΔΔG_DT box plots (right) are shown for (A) core and (B) interface mutations and the three types of mutations (Hapmap, OMIM and COSMIC driver/passenger).</p

Disease mutations calculated for domain cores and interfaces.

<p>Disease mutations calculated for domain cores and interfaces.</p

Summary of the results.

<p>Correlation between predicted and experimental ΔΔG values for our curated ProTherm core dataset (A) and SKEMPI interface dataset (B). (C) Comparative histograms of the Pearson correlation among several state-of-the-art methods using three versions of ProTherm datasets for the core predictions, and SKEMPI dataset for the interface prediction.</p

Feature importance for core and interface predictions.

<p>Histogram representing the relative importance of the different features for core predictions (A) and interface prediction (B). To avoid cluttering, only features with a relative importance of 10% or larger were considered and coloured according to the three categories. Abbreviations: t: torsional, diS: disulfide, E: electrostatics, ion: ionization, dS: entropy, Hdipole: helix dipole, cb: covalent bond, sb: salt bridge, hb: hydrogen bond, cisb: cysteine bond, wb: water bridge, vdW: wan der Waals, mc: main chain, sc: side chain, if: interface, dm: domain, sasa: solvent accessibility, solv: solvation, ap: apolar, po: polar (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107353#pone.0107353.s005" target="_blank">Table S1</a> for feature description).</p

Centre for Research in Digital Education Annual Report 2018

The annual report of the Centre for Research in Digital Education, based with the College of Arts, Humanities and Social Sciences at the University of Edinburgh