Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods

Non-Parametric Learning for Monocular Visual Odometry

This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results

Loops under Strategies ... Continued

While there are many approaches for automatically proving termination of term rewrite systems, up to now there exist only few techniques to disprove their termination automatically. Almost all of these techniques try to find loops, where the existence of a loop implies non-termination of the rewrite system. However, most programming languages use specific evaluation strategies, whereas loop detection techniques usually do not take strategies into account. So even if a rewrite system has a loop, it may still be terminating under certain strategies. Therefore, our goal is to develop decision procedures which can determine whether a given loop is also a loop under the respective evaluation strategy. In earlier work, such procedures were presented for the strategies of innermost, outermost, and context-sensitive evaluation. In the current paper, we build upon this work and develop such decision procedures for important strategies like leftmost-innermost, leftmost-outermost, (max-)parallel-innermost, (max-)parallel-outermost, and forbidden patterns (which generalize innermost, outermost, and context-sensitive strategies). In this way, we obtain the first approach to disprove termination under these strategies automatically.Comment: In Proceedings IWS 2010, arXiv:1012.533

Causal Discovery with Missing Data in a Multicentric Clinical Study

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways

Reduced Tie2 in Microvascular Endothelial Cells Is Associated with Organ-Specific Adhesion Molecule Expression in Murine Health and Endotoxemia

Endothelial cells (ECs) in the microvasculature in organs are active participants in the pathophysiology of sepsis. Tyrosine protein kinase receptor Tie2 (Tek; Tunica interna Endothelial cell Kinase) is thought to play a role in their inflammatory response, yet data are inconclusive. We investigated acute endotoxemia-induced changes in the expression of Tie2 and inflammation-associated endothelial adhesion molecules E-selectin and VCAM-1 (vascular cell adhesion molecule-1) in kidneys and lungs in inducible, EC-specific Tie2 knockout mice. The extent of Tie2 knockout in healthy mice differed between microvascular beds, with low to absent expression in arterioles in kidneys and in capillaries in lungs. In kidneys, Tie2 mRNA dropped more than 70% upon challenge with lipopolysaccharide (LPS) in both genotypes, with no change in protein. In renal arterioles, tamoxifen-induced Tie2 knockout was associated with higher VCAM-1 protein expression in healthy conditions. This did not increase further upon challenge of mice with LPS, in contrast to the increased expression occurring in control mice. Also, in lungs, Tie2 mRNA levels dropped within 4 h after LPS challenge in both genotypes, while Tie2 protein levels did not change. In alveolar capillaries, where tamoxifen-induced Tie2 knockout did not affect the basal expression of either adhesion molecule, a 4-fold higher E-selectin protein expression was observed after exposure to LPS compared to controls. The here-revealed heterogeneous effects of absence of Tie2 in ECs in kidney and lung microvasculature in health and in response to acute inflammatory activation calls for further in vivo investigations into the role of Tie2 in EC behavior. </p

Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

The HST Key Project on the Extragalactic Distance Scale. XXII. The Discovery of Cepheids in NGC 1326-A

We report on the detection of Cepheids and the first distance measurement to the spiral galaxy NGC 1326-A, a member of the Fornax cluster of galaxies. We have employed data obtained with the Wide Field and Planetary Camera 2 on board the Hubble Space Telescope. Over a 49 day interval, a total of twelve V-band (F555W) and eight I-band (F814W) epochs of observation were obtained. Two photometric reduction packages, ALLFRAME and DoPHOT, have been employed to obtain photometry measures from the three Wide Field CCDs. Variability analysis yields a total of 17 Cepheids in common with both photometry datasets, with periods ranging between 10 and 50 days. Of these 14 Cepheids with high-quality lightcurves are used to fit the V and I period-luminosity relations and derive apparent distance moduli, assuming a Large Magellanic Cloud distance modulus (m-M) (LMC) = 18.50 +- 0.10 mag and color excess E(B-V) = 0.10 mag. Assuming A(V)/E(V-I) = 2.45, the DoPHOT data yield a true distance modulus to NGC 1326-A of (m-M)_0 = 31.36 +- 0.17 (random) +- 0.13 (systematic) mag, corresponding to a distance of 18.7 \pm 1.5 (random) \pm 1.2 (systematic) Mpc. The derived distance to NGC 1326-A is in good agreement with the distance derived previously to NGC 1365, another spiral galaxy member of the Fornax cluster. However the distances to both galaxies are significantly lower than to NGC 1425, a third Cepheid calibrator in the outer parts of the cluster.Comment: 33 pages A gzipped tar file containing 12 figures can be obtained from http://www.ipac.caltech.edu/H0kp/n1326a/n1326a.htm

The lower mass function of the young open cluster Blanco 1: from 30 Mjup to 3 Mo

We performed a deep wide field optical survey of the young (~100-150 Myr) open cluster Blanco1 to study its low mass population well down into the brown dwarf regime and estimate its mass function over the whole cluster mass range.The survey covers 2.3 square degrees in the I and z-bands down to I ~ z ~ 24 with the CFH12K camera. Considering two different cluster ages (100 and 150 Myr), we selected cluster member candidates on the basis of their location in the (I,I-z) CMD relative to the isochrones, and estimated the contamination by foreground late-type field dwarfs using statistical arguments, infrared photometry and low-resolution optical spectroscopy. We find that our survey should contain about 57% of the cluster members in the 0.03-0.6 Mo mass range, including 30-40 brown dwarfs. The candidate's radial distribution presents evidence that mass segregation has already occured in the cluster. We took it into account to estimate the cluster mass function across the stellar/substellar boundary. We find that, between 0.03Mo and 0.6Mo, the cluster mass distribution does not depend much on its exact age, and is well represented by a single power-law, with an index alpha=0.69 +/- 0.15. Over the whole mass domain, from 0.03Mo to 3Mo, the mass function is better fitted by a log-normal function with m0=0.36 +/- 0.07Mo and sigma=0.58 +/- 0.06. Comparison between the Blanco1 mass function, other young open clusters' MF, and the galactic disc MF suggests that the IMF, from the substellar domain to the higher mass part, does not depend much on initial conditions. We discuss the implications of this result on theories developed to date to explain the origin of the mass distribution.Comment: 18 pages, 15 figures and 5 tables accepted in A&

The Spitzer c2d Survey of Large, Nearby, Interstellar Clouds. VII. Ophiuchus Observed with MIPS

We present maps of 14.4 deg^2 of the Ophiuchus dark clouds observed by the Spitzer Space Telescope Multiband Imaging Photometer for Spitzer (MIPS). These high-quality maps depict both numerous point sources and extended dust emission within the star-forming and non–star-forming portions of these clouds. Using PSF-fitting photometry, we detect 5779 sources at 24 μm and 81 sources at 70 μm at the 10 σ level of significance. Three hundred twenty-three candidate young stellar objects (YSOs) were identified according to their positions on the MIPS/2MASS K versus color-magnitude diagrams, as compared to 24 μm detections in the SWIRE extragalactic survey. We find that more than half of the YSO candidates, and almost all those with protostellar Class I spectral energy distributions, are confined to the known cluster and aggregates