Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

Novel applications of shotgun phage display

In a shotgun phage display library, theoretically, the entire proteome of a bacterium is represented. Phages displaying specific polypeptides can be isolated by affinity selection, while the corresponding gene remains physically linked to the gene product. The overall objective of the study in this thesis was to explore the shotgun phage display technique in new areas. Initially, it was used to study interactions between Staphylococcus aureus and an in vivo coated biomaterial. It was shown to be well suited for the identification of bacterial proteins that bind to ex vivo central venous catheters. Several known interactions were detected, but it was also found that β2-glycoprotein I (β2-GPI) is deposited on this type of biomaterial – a finding that is of interest both for the adherence of S. aureus, but perhaps also in view of the occurrence of autoantibodies in certain autoimmune diseases. Further, it is of interest to identify the subset of extracellular proteins in a bacterium since they are involved in important functions like pathogenesis and symbiosis. A method that allows for the rapid and general isolation of extracellular proteins is desirable, and may prove particularly useful when applied to bacteria for which the genome sequences are not known. For this purpose, a specialised phage display method was developed to isolate extracellular proteins by virtue of the presence of signal peptides (SS phage display). It was successfully applied to S. aureus and, on a larger scale, to the symbiotic bacterium Bradyrhizobium japonicum. In elaboration of the SS phage display method, an inducible antisense RNA system was incorporated to enable gene silencing of the isolated genes. A tetracycline-regulated promoter was inserted in such a way, that an antisense RNA covering the cloned gene could be expressed. The new element was shown to be compatible with the properties of SS phage display, and to promote gene expression upon induction on both the transcriptional and translational level. However, screening for clones affected by the induction of antisense RNA transcription was unsuccessful, and further developments of the system are required to improve the efficiency of this attractive application

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy

Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).Comment: Accepted by IJCAI 201