An age-of-allele test of neutrality for transposable element insertions

How natural selection acts to limit the proliferation of transposable elements (TEs) in genomes has been of interest to evolutionary biologists for many years. To describe TE dynamics in populations, many previous studies have used models of transposition-selection equilibrium that rely on the assumption of a constant rate of transposition. However, since TE invasions are known to happen in bursts through time, this assumption may not be reasonable in natural populations. Here we propose a test of neutrality for TE insertions that does not rely on the assumption of a constant transposition rate. We consider the case of TE insertions that have been ascertained from a single haploid reference genome sequence and have subsequently had their allele frequency estimated in a population sample. By conditioning on the age of an individual TE insertion (using information contained in the number of substitutions that have occurred within the TE sequence since insertion), we determine the probability distribution for the insertion allele frequency in a population sample under neutrality. Taking models of varying population size into account, we then evaluate predictions of our model against allele frequency data from 190 retrotransposon insertions sampled from North American and African populations of Drosophila melanogaster. Using this non-equilibrium model, we are able to explain about 80% of the variance in TE insertion allele frequencies based on age alone. Controlling both for nonequilibrium dynamics of transposition and host demography, we provide evidence for negative selection acting against most TEs as well as for positive selection acting on a small subset of TEs. Our work establishes a new framework for the analysis of the evolutionary forces governing large insertion mutations like TEs, gene duplications or other copy number variants.Comment: 40 pages, 6 figures, Supplemental Data available: [email protected]

Numerical modelling of geothermal borehole heat exchanger systems

The large proportion of energy used in the built environment has made improving energy efficiency in buildings, in particular their heating, ventilation, and air conditioning (HVAC) systems, a policy objective for reducing energy consumption and CO2 emissions nationally and internationally. Ground source heat pump (GSHP) systems, due to their high coefficient of performance (COP) and low CO2 emissions are consequently, receiving increasing attention. This work is concerned with the modelling of borehole heat exchangers (BHEs), the commonest form of ground heat exchangers found in GSHP systems. Their careful design is critical to both the short timescale and long timescale performance of geothermal heat pump systems. Unlike conventional components of HVAC systems, BHEs cannot be designed on the basis of peak load data but require 3 application of dynamic thermal models that are able to take account of the heat transfer inside the borehole as well as the surrounding ground. The finite volume method has been applied to develop a dynamic three-dimensional (3D) model for a single BHE and BHE arrays. The multi-block boundary fitted structured mesh used in this model allows the complex geometries around the pipes in BHEs and the surrounding ground around the borehole to be represented exactly. The transport of the fluid circulating along the pipe loop has been simulated explicitly in this model. The ground underneath the borehole has also been represented in this model. Validation of the 3D model has been carried out by reference to analytical models of borehole thermal resistance and fluid transport in pipes, as well as experimental data. In this work, the 3D numerical model has been applied to investigate the three-dimensional characteristics of heat transfer in and around a BHE at both short and long timescales. By implementing a two-dimensional (2D) model using the same numerical method and comparing the simulation results from the 3D and 2D models, the most significant three-dimensional effects have been identified and quantified. The findings have highlighted some of the limitations of 2D models, and based on the findings, methods to improve the accuracy of a 2D model have been suggested and validated. Furthermore, the 3D and 2D finite volume models have been applied to simulate an integrated GSHP system and their effects on overall system performance predictions have been investigated. The 3D numerical model has also been applied to examine thermal interactions within BHE arrays and to evaluate the assumptions of the line source model and their implications in the analysis of thermal response test data

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bound- ing boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth- based 3D proposal generation.Chinese Scholarship Council; CSIRO-Data61; The Program of Shanghai Subject Chief Scientist (A type) (No.15XD1502900)

Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object instances, others aim at inferring the semantic labels of given regions, or their support relationships. These different tasks are typically treated as separate ones. However, they bear strong connections: good regions should respect the semantic labels; support can only be defined for meaningful regions; support relationships strongly depend on semantics. In this paper, we, therefore, introduce an approach to jointly segment the object instances and infer their semantic labels and support relationships from a single input image. By exploiting a hierarchical segmentation, we formulate our problem as that of jointly finding the regions in the hierarchy that correspond to instances and estimating their class labels and pairwise support relationships. We express this via a Markov Random Field, which allows us to further encode links between the different types of variables. Inference in this model can be done exactly via integer linear programming, and we learn its parameters in a structural SVM framework. Our experiments on NYUv2 demonstrate the benefits of reasoning jointly about all these subtasks of indoor scene parsing.Chinese Scholarship Council; CSIRO-Data61

Multi-dwelling Refurbishment Optimization: Problem Decomposition, Solution, and Trade-off Analysis

A methodology has been developed for the multiobjective optimization of the refurbishment of domestic building stock on a regional scale. The approach is based on the decomposition of the problem into two stages: first to find the energy-cost trade-off for individual houses, and then to apply it tomultiple houses. The approach has been applied to 759 dwellings using buildings data from a survey of the UK housing stock. The energy use of each building and their refurbished variants were simulated using EnergyPlus using automatically-generated input files. The variation in the contributing refurbishment options from least to highest cost along the Pareto front shows loft and cavity wall insulation to be optimal intially, and solid wall insulation and double glazing appearing later

Seasonal variation in household electricity demand: A comparison of monitored and synthetic daily load profiles

Abstract This paper examines seasonal variation in household electricity demand through analysis of two sets of half-hourly electricity demand data: a monitored dataset gathered from 58 English households between July and December 2011; and a synthetic dataset generated using a time-of-use-based load modelling tool. Analysis of variance (ANOVA) tests were used to identify statistically significant between-months differences in four metrics describing the shape of household-level daily load profiles: mean electrical load; peak load; load factor; and timing of peak load. For the monitored dataset, all four metrics exhibited significant monthly variation. With the exception of peak load time, significant between-months differences were also present for all metrics calculated for the synthetic dataset. However, monthly variability was generally under-represented in the synthetic data, and the predicted between-months differences in load factors and peak load timing were inconsistent with those exhibited by the monitored data. The study demonstrates that the shapes of household daily electrical load profiles can vary significantly between months, and that limited treatment of seasonal variation in load modelling can lead to inaccurate predictions of its effects

Evaluating energy savings retrofits for residential buildings in China

Building retrofit plays an important role in reducing energy consumption and carbon dioxide emissions whilst increasing occupant thermal comfort. This study used DesignBuilder to predict the energy saved by retrofitting a typical flat in Chongqing, a city in the hot summer, cold winter region of China. To increase the reliability of predictions, the model was verified by measured indoor air temperature for a one-week period in April. Five retrofit measures were evaluated, external wall insulation, new windows, increased air tightness, external shading, and higher efficiency of air conditioning. Three types of households with different AC operating schedule were assumed, high, medium and low. The variance in the model predictions due to the uncertainty in the model input parameters was calculated. The results showed that the energy saved depended on the use that was made of the AC system. For high energy users, 40 to 68% of the annual space-conditioning energy could be saved depending on the retrofit, whereas for low energy users the savings were 30 to 58%. Thermal comfort has improved in winter for low and medium energy users, but no improvement in summer