Online Informative Path Planning for Active Information Gathering of a 3D Surface

This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the surface information is uniformly distributed hence ignoring potential spatial correlations of the information field. In this paper, we utilize manifold Gaussian processes (mGPs) with geodesic kernel functions for mapping surface information fields and plan informative paths online in a receding horizon manner. Our approach actively plans information-gathering paths based on recent observations that respect dynamic constraints of the vehicle and a total flight time budget. We provide planning results for simulated temperature modeling for simple and complex 3D surface geometries (a cylinder and an aircraft model). We demonstrate that our informative planning method outperforms traditional approaches such as 3D coverage planning and random exploration, both in reconstruction error and information-theoretic metrics. We also show that by taking spatial correlations of the information field into planning using mGPs, the information gathering efficiency is significantly improved.Comment: 7 pages, 7 figures, to be published in 2021 IEEE International Conference on Robotics and Automation (ICRA

Inherited determinants of Crohn's disease and ulcerative colitis phenotypes: a genetic association study

Crohn's disease and ulcerative colitis are the two major forms of inflammatory bowel disease; treatment strategies have historically been determined by this binary categorisation. Genetic studies have identified 163 susceptibility loci for inflammatory bowel disease, mostly shared between Crohn's disease and ulcerative colitis. We undertook the largest genotype association study, to date, in widely used clinical subphenotypes of inflammatory bowel disease with the goal of further understanding the biological relations between diseases

Autonomous soaring flight for unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) provide unique capabilities in a range of industrial, scientific and defence applications. A small UAV could extend flight duration without requiring additional propulsive power through the use of soaring. This thesis examines the aerodynamic mechanisms of soaring flight and proposes planning and control algorithms for a UAV to autonomously sense and utilise the wind environment to extend flight duration. In order to utilise soaring a thorough understanding of the energy interaction between an aircraft and the surrounding atmosphere is required. This thesis presents a mathematical model for a gliding aircraft and examines how wind contributes to the energy change of an aircraft. Conditions for optimal energy efficiency are identified for gliding and soaring flight in linear wind shear. The proposed path planner takes advantage of the energy equations for a gliding aircraft to plan energy efficient paths over a known wind field. Previous soaring planners have focused on a single type of energy gain such as static soaring. By using the energy equations directly the planner can exploit all energy gain conditions rather than relying on specialised controllers. The planner requires an adequate estimate of the wind field to plan reliable energy gain paths. A small UAV would typically only have access to direct wind observations taken during flight. Gaussian Process (GP) regression is proposed to generate a wind map from direct wind observations. This model-free approach can account for static and dynamic wind fields and does not restrict the planner to particular types of wind structure. Maintaining an accurate map requires the planner to ensure efficient map sampling and maintain sufficient energy to continue flight. The path planning algorithm exploits the variance estimate from the GP map to identify regions of the map which require improvement. The planner assesses the aircraft’s energy state and current map to determine target regions of the wind field for further exploration or energy exploitation. Results demonstrate that this architecture is capable of generating energy-gain paths in both static and dynamic wind fields. The mapping algorithm records direct samples of the wind to generate a wind map that is used by the planning algorithm to simultaneously explore and exploit the wind field to extend flight duration without propulsive power

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47.

Genome-wide association studies and candidate gene studies in ulcerative colitis have identified 18 susceptibility loci. We conducted a meta-analysis of six ulcerative colitis genome-wide association study datasets, comprising 6,687 cases and 19,718 controls, and followed up the top association signals in 9,628 cases and 12,917 controls. We identified 29 additional risk loci (P < 5 × 10(-8)), increasing the number of ulcerative colitis-associated loci to 47. After annotating associated regions using GRAIL, expression quantitative trait loci data and correlations with non-synonymous SNPs, we identified many candidate genes that provide potentially important insights into disease pathogenesis, including IL1R2, IL8RA-IL8RB, IL7R, IL12B, DAP, PRDM1, JAK2, IRF5, GNA12 and LSP1. The total number of confirmed inflammatory bowel disease risk loci is now 99, including a minimum of 28 shared association signals between Crohn's disease and ulcerative colitis