Personalized modeling for prediction with decision-path models

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Natural variation in immune responses to neonatal mycobacterium bovis bacillus calmette-guerin (BCG) vaccination in a cohort of Gambian infants

Background There is a need for new vaccines for tuberculosis (TB) that protect against adult pulmonary disease in regions where BCG is not effective. However, BCG could remain integral to TB control programmes because neonatal BCG protects against disseminated forms of childhood TB and many new vaccines rely on BCG to prime immunity or are recombinant strains of BCG. Interferon-gamma (IFN-) is required for immunity to mycobacteria and used as a marker of immunity when new vaccines are tested. Although BCG is widely given to neonates IFN- responses to BCG in this age group are poorly described. Characterisation of IFN- responses to BCG is required for interpretation of vaccine immunogenicity study data where BCG is part of the vaccination strategy. Methodology/Principal Findings 236 healthy Gambian babies were vaccinated with M. bovis BCG at birth. IFN-, interleukin (IL)-5 and IL-13 responses to purified protein derivative (PPD), killed Mycobacterium tuberculosis (KMTB), M. tuberculosis short term culture filtrate (STCF) and M. bovis BCG antigen 85 complex (Ag85) were measured in a whole blood assay two months after vaccination. Cytokine responses varied up to 10 log-fold within this population. The majority of infants (89-98% depending on the antigen) made IFN- responses and there was significant correlation between IFN- responses to the different mycobacterial antigens (Spearman’s coefficient ranged from 0.340 to 0.675, p=10-6-10-22). IL-13 and IL-5 responses were generally low and there were more non-responders (33-75%) for these cytokines. Nonetheless, significant correlations were observed for IL-13 and IL-5 responses to different mycobacterial antigens Conclusions/Significance Cytokine responses to mycobacterial antigens in BCG-vaccinated infants are heterogeneous and there is significant inter-individual variation. Further studies in large populations of infants are required to identify the factors that determine variation in IFN- responses

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Modeling concept drift: A probabilistic graphical model based approach

An often used approach for detecting and adapting to concept drift when doing classi cation is to treat the data as i.i.d. and use changes in classi cation accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure effcient inference and learning, we resort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real fi nancial data set from a Spanish bank

MPrime: efficient large scale multiple primer and oligonucleotide design for customized gene microarrays

BACKGROUND: Enhancements in sequencing technology have recently yielded assemblies of large genomes including rat, mouse, human, fruit fly, and zebrafish. The availability of large-scale genomic and genic sequence data coupled with advances in microarray technology have made it possible to study the expression of large numbers of sequence products under several different conditions in days where traditional molecular biology techniques might have taken months, or even years. Therefore, to efficiently study a number of gene products associated with a disease, pathway, or other biological process, it is necessary to be able to design primer pairs or oligonucleotides en masse rather than using a time consuming and laborious gene-by-gene method. RESULTS: We have developed an integrated system, MPrime, in order to efficiently calculate primer pairs or specific oligonucleotides for multiple genic regions based on a keyword, gene name, accession number, or sequence fasta format within the rat, mouse, human, fruit fly, and zebrafish genomes. A set of products created for mouse housekeeping genes from MPrime-designed primer pairs has been validated using both PCR-amplification and DNA sequencing. CONCLUSION: These results indicate MPrime accurately incorporates standard PCR primer design characteristics to produce high scoring primer pairs for genes of interest. In addition, sequence similarity for a set of oligonucleotides constructed for the same set of genes indicates high specificity in oligo design

Legal framework for small autonomous agricultural robots

Legal structures may form barriers to, or enablers of, adoption of precision agriculture management with small autonomous agricultural robots. This article develops a conceptual regulatory framework for small autonomous agricultural robots, from a practical, self-contained engineering guide perspective, sufficient to get working research and commercial agricultural roboticists quickly and easily up and running within the law. The article examines the liability framework, or rather lack of it, for agricultural robotics in EU, and their transpositions to UK law, as a case study illustrating general international legal concepts and issues. It examines how the law may provide mitigating effects on the liability regime, and how contracts can be developed between agents within it to enable smooth operation. It covers other legal aspects of operation such as the use of shared communications resources and privacy in the reuse of robot-collected data. Where there are some grey areas in current law, it argues that new proposals could be developed to reform these to promote further innovation and investment in agricultural robots

Vortex nucleation as a case study of symmetry breaking in quantum systems

Mean-field methods are a very powerful tool for investigating weakly interacting many-body systems in many branches of physics. In particular, they describe with excellent accuracy trapped Bose-Einstein condensates. A generic, but difficult question concerns the relation between the symmetry properties of the true many-body state and its mean-field approximation. Here, we address this question by considering, theoretically, vortex nucleation in a rotating Bose-Einstein condensate. A slow sweep of the rotation frequency changes the state of the system from being at rest to the one containing one vortex. Within the mean-field framework, the jump in symmetry occurs through a turbulent phase around a certain critical frequency. The exact many-body ground state at the critical frequency exhibits strong correlations and entanglement. We believe that this constitutes a paradigm example of symmetry breaking in - or change of the order parameter of - quantum many-body systems in the course of adiabatic evolution.Comment: Minor change

Deficiency of Leishmania phosphoglycans influences the magnitude but does not affect the quality of secondary (memory) anti-Leishmania immunity

Despite inducing very low IFN-γ response and highly attenuated in vivo, infection of mice with phosphoglycan (PG) deficient Leishmania major (lpg2-) induces protection against virulent L. major challenge. Here, we show that mice infected with lpg2- L. major generate Leishmania-specific memory T cells. However, in vitro and in vivo proliferation, IL-10 and IFN-γ production by lpg2- induced memory cells were impaired in comparison to those induced by wild type (WT) parasites. Interestingly, TNF recall response was comparable to WT infected mice. Despite the impaired proliferation and IFN-γ response, lpg2- infected mice were protected against virulent L. major challenge and their T cells mediated efficient infection-induced immunity. In vivo depletion and neutralization studies with mAbs demonstrated that lpg2- L. major-induced resistance was strongly dependent on IFN-γ, but independent of TNF and CD8(+) T cells. Collectively, these data show that the effectiveness of secondary anti-Leishmania immunity depends on the quality (and not the magnitude) of IFN-γ response. These observations provide further support for consideration of lpg2- L. major as a live-attenuated candidate for leishmanization in humans since it protects strongly against virulent challenge, without inducing pathology in infected animals