Nonparametric Bayesian Mixed-effect Model: a Sparse Gaussian Process Approach

Multi-task learning models using Gaussian processes (GP) have been developed and successfully applied in various applications. The main difficulty with this approach is the computational cost of inference using the union of examples from all tasks. Therefore sparse solutions, that avoid using the entire data directly and instead use a set of informative "representatives" are desirable. The paper investigates this problem for the grouped mixed-effect GP model where each individual response is given by a fixed-effect, taken from one of a set of unknown groups, plus a random individual effect function that captures variations among individuals. Such models have been widely used in previous work but no sparse solutions have been developed. The paper presents the first sparse solution for such problems, showing how the sparse approximation can be obtained by maximizing a variational lower bound on the marginal likelihood, generalizing ideas from single-task Gaussian processes to handle the mixed-effect model as well as grouping. Experiments using artificial and real data validate the approach showing that it can recover the performance of inference with the full sample, that it outperforms baseline methods, and that it outperforms state of the art sparse solutions for other multi-task GP formulations.Comment: Preliminary version appeared in ECML201

Forecasting and Granger Modelling with Non-linear Dynamical Dependencies

Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed of a set of input kernels and a set of output kernels learned in the cone of positive semi-definite matrices. In addition to superior predictive performance in the presence of strong non-linearities, our method also recovers the hidden dynamic relationships between the series and thus is a new alternative to existing graphical Granger techniques.Comment: Accepted for ECML-PKDD 201

Molecular investigation of isolates from a multistate polymicrobial outbreak associated with contaminated total parenteral nutrition in Brazil

Background: Between November 2013 and June 2014, 56 cases of bacteremia (15 deaths) associated with the use of Total Parenteral Nutrition (TPN) and/or calcium gluconate (CG) were reported in four Brazilian states. Methods: We analyzed 73 bacterial isolates from four states: 45 from blood, 25 from TPN and three from CG, originally identified as Acinetobacter baumannii, Rhizobium radiobacter, Pantoea sp. or Enterobacteriaceae using molecular methods. Results: The first two bacterial species were confirmed while the third group of species could not be identified using standard identification protocols. These isolates were subsequently identified by Multi-Locus Sequence Analysis as Phytobacter diazotrophicus, a species related to strains from similar outbreaks in the United States in the 1970’s. Within each species, TPN and blood isolates proved to be clonal, whereas the R. radiobacter isolates retrieved from CG were found to be unrelated. Conclusion: This is the first report of a three-species outbreak caused by TPN contaminated with A. baumannii, R. radiobacter and P. diazotrophicus. The concomitant presence of clonal A. baumannii and P. diazotrophicus isolates in several TPN and blood samples, as well as the case of one patient, where all three different species were isolated simultaneously, suggest that the outbreak may be ascribed to a discrete contamination of TPN. In addition, this study highlights the clinical relevance of P. diazotrophicus, which has been involved in outbreaks in the past, but was often misidentified as P. agglomerans

Advantages of the single delay model for the assessment of insulin sensitivity from the intravenous glucose tolerance test

The Minimal Model, (MM), used to assess insulin sensitivity (IS) from Intra-Venous Glucose-Tolerance Test (IVGTT) data, suffers from frequent lack of identifiability (parameter estimates with Coefficients of Variation (CV) less than 52%). The recently proposed Single Delay Model (SDM) is evaluated as a practical alternative

Molecular investigation of isolates from a multistate polymicrobial outbreak associated with contaminated total parenteral nutrition in Brazil

Background: Between November 2013 and June 2014, 56 cases of bacteremia (15 deaths) associated with the use of Total Parenteral Nutrition (TPN) and/or calcium gluconate (CG) were reported in four Brazilian states. Methods: We analyzed 73 bacterial isolates from four states: 45 from blood, 25 from TPN and three from CG, originally identified as Acinetobacter baumannii, Rhizobium radiobacter, Pantoea sp. or Enterobacteriaceae using molecular methods. Results: The first two bacterial species were confirmed while the third group of species could not be identified using standard identification protocols. These isolates were subsequently identified by Multi-Locus Sequence Analysis as Phytobacter diazotrophicus, a species related to strains from similar outbreaks in the United States in the 1970’s. Within each species, TPN and blood isolates proved to be clonal, whereas the R. radiobacter isolates retrieved from CG were found to be unrelated. Conclusion: This is the first report of a three-species outbreak caused by TPN contaminated with A. baumannii, R. radiobacter and P. diazotrophicus. The concomitant presence of clonal A. baumannii and P. diazotrophicus isolates in several TPN and blood samples, as well as the case of one patient, where all three different species were isolated simultaneously, suggest that the outbreak may be ascribed to a discrete contamination of TPN. In addition, this study highlights the clinical relevance of P. diazotrophicus, which has been involved in outbreaks in the past, but was often misidentified as P. agglomerans

Dimensional analysis of MINMOD leads to definition of the disposition index of glucose regulation and improved simulation algorithm

BACKGROUND: Frequently Sampled Intravenous Glucose Tolerance Test (FSIVGTT) together with its mathematical model, the minimal model (MINMOD), have become important clinical tools to evaluate the metabolic control of glucose in humans. Dimensional analysis of the model is up to now not available. METHODS: A formal dimensional analysis of MINMOD was carried out and the degree of freedom of MINMOD was examined. Through re-expressing all state variable and parameters in terms of their reference scales, MINMOD was transformed into a dimensionless format. Previously defined physiological indices including insulin sensitivity, glucose effectiveness, and first and second phase insulin responses were re-examined in this new formulation. Further, the parameter estimation from FSIVGTT was implemented using both the dimensional and the dimensionless formulations of MINMOD, and the performances were compared utilizing Monte Carlo simulation as well as real human FSIVGTT data. RESULTS: The degree of freedom (DOF) of MINMOD was found to be 7. The model was maximally simplified in the dimensionless formulation that normalizes the variation in glucose and insulin during FSIVGTT. In the new formulation, the disposition index (Dl), a composite parameter known to be important in diabetes pathology, was naturally defined as one of the dimensionless parameters in the system. The numerical simulation using the dimensionless formulation led to a 1.5–5 fold gain in speed, and significantly improved accuracy and robustness in parameter estimation compared to the dimensional implementation. CONCLUSION: Dimensional analysis of MINMOD led to simplification of the model, direct identification of the important composite factors in the dynamics of glucose metabolic control, and better simulations algorithms

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Deep brain stimulation (DBS) of Subthalamic nucleus (STN) is the most successful treatment for advanced Parkinson’s disease. Localization of the STN through Microelectrode recordings (MER) is a key step during the surgery. However, it is a complex task even for a skilled neurosurgeon. Different researchers have developed methodologies for processing and classification of MER signals to locate the STN. Previous works employ the classical paradigm of supervised classification, assuming independence between patients. The aim of this paper is to introduce a patient-dependent learning scenario, where the predictive ability for STN identification at the level of a particular patient, can be used to improve the accuracy for STN identification in other patients. Our inspiration is the multi-task learning framework, that has been receiving increasing interest within the machine learning community in the last few years. To this end, we employ the multi-task Gaussian processes framework that exhibits state of the art performance in multi-task learning problems. In our context, we assume that each patient undergoing DBS is a different task, and we refer to the method as multi-patient learning. We show that the multi-patient learning framework improves the accuracy in the identification of STN in a range from 4.1 to 7.7%, compared to the usual patient-independent setup, for two different datasets. Given that MER are non stationary and noisy signals. Traditional approaches in machine learning fail to recognize accurately the STN during DBS. By contrast in our proposed method, we properly exploit correlations between patients with similar diseases, obtaining an additional information. This information allows to improve the accuracy not only for locating STN for DBS but also for other biomedical signal classification problems

Resolving taxonomic confusion : establishing the genus Phytobacter on the list of clinically relevant Enterobacteriaceae

Although many clinically significant strains belonging to the family Enterobacteriaceae fall into a restricted number of genera and species, there is still a substantial number of isolates that elude this classification and for which proper identification remains challenging. With the current improvements in the field of genomics, it is not only possible to generate high-quality data to accurately identify individual nosocomial isolates at the species level and understand their pathogenic potential but also to analyse retrospectively the genome sequence databases to identify past recurrences of a specific organism, particularly those originally published under an incorrect or outdated taxonomy. We propose a general use of this approach to classify further clinically relevant taxa, i.e., Phytobacter spp., that have so far gone unrecognised due to unsatisfactory identification procedures in clinical diagnostics. Here, we present a genomics and literature-based approach to establish the importance of the genus Phytobacter as a clinically relevant member of the Enterobacteriaceae family