Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models

This is the final version. Available on open access from International Society for Bayesian Analysis (ISBA) via the DOI in this record. With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayesian inference scheme for a flexible class of latent Gaussian models (LGMs) where one or more of the likelihood parameters are modeled by latent additive Gaussian processes. Max-and-Smooth consists of two-steps. In the first step (Max), the likelihood function is approximated by a Gaussian density with mean and covariance equal to either (a) the maximum likelihood estimate and the inverse observed information, respectively, or (b) the mean and covariance of the normalized likelihood function. In the second step (Smooth), the latent parameters and hyperparameters are inferred and smoothed with the approximated likelihood function. The proposed method ensures that the uncertainty from the first step is correctly propagated to the second step. Since the approximated likelihood function is Gaussian, the approximate posterior density of the latent parameters of the LGM (conditional on the hyperparameters) is also Gaussian, thus facilitating efficient posterior inference in high dimensions. Furthermore, the approximate marginal posterior distribution of the hyperparameters is tractable, and as a result, the hyperparameters can be sampled independently of the latent parameters. In the case of a large number of independent data replicates, sparse precision matrices, and high-dimensional latent vectors, the speedup is substantial in comparison to an MCMC scheme that infers the posterior density from the exact likelihood function. The proposed inference scheme is demonstrated on one spatially referenced real dataset and on simulated data mimicking spatial, temporal, and spatio-temporal inference problems. Our results show that Max-and-Smooth is accurate and fast.NER

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

Incidence of Arrhythmias and Myocardial Ischaemia During Haemodialysis and Haemofiltration

Thirty-two patients (10 male, 22 female; age 37-82 years) undergoing maintenance haemodialysis or haemofiltration were studied by means of Holter device capable of simultaneously analysing rhythm and ST changes in three leads. Twenty-five patients were on haemodialysis, seven on haemofiltration, mean duration of haemodialysis/haemofiltration being 3.4±3 years. Incidence of ventricular tachycardia was low, being detected only in 1 of 32 patients. Ventricular premature beats in excess of 10/h during a period of 2 h were found in 8 of 32 patients and 100 supraventricular premature beats for 2 h or more in 4 of 32 patients. Both ventricular premature beats and supraventricular premature beats were most frequently recorded during the last hour of haemodialysis/haemofiltration. ECG signs of ischaemia were detected in eight patients, four of whom were asymptomatic. Ischaemia also occurred predominantly during the last hour of haemodialysis/haemofiltration. Two symptomatic patients displayed neither arrhythmias nor ST-changes while being monitored. The study shows that silent ischaemia and arrhythmias in patients under going chronic haemodialysis/haemofiltration may not be infrequent. Recognition of these events could be of importance in the management of these patient

Bayesian Networks for Max-linear Models

We study Bayesian networks based on max-linear structural equations as introduced in Gissibl and Kl\"uppelberg [16] and provide a summary of their independence properties. In particular we emphasize that distributions for such networks are generally not faithful to the independence model determined by their associated directed acyclic graph. In addition, we consider some of the basic issues of estimation and discuss generalized maximum likelihood estimation of the coefficients, using the concept of a generalized likelihood ratio for non-dominated families as introduced by Kiefer and Wolfowitz [21]. Finally we argue that the structure of a minimal network asymptotically can be identified completely from observational data.Comment: 18 page

Approximate Bayesian inference for analysis of spatiotemporal flood frequency data

This is the final version. Available from the Institute of Mathematical Statistics via the DOI in this recordExtreme floods cause casualties and widespread damage to property and vital civil infrastructure. Predictions of extreme floods, within gauged and ungauged catchments, is crucial to mitigate these disasters. In this paper a Bayesian framework is proposed for predicting extreme floods, using the generalized extreme-value (GEV) distribution. A major methodological challenge is to find a suitable parametrization for the GEV distribution when multiple covariates and/or latent spatial effects are involved and a time trend is present. Other challenges involve balancing model complexity and parsimony, using an appropriate model selection procedure and making inference based on a reliable and computationally efficient approach. We here propose a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models, which include catchment descriptors as covariates and spatially correlated model components, are proposed for the four parameters at the latent level. To achieve computational efficiency with large datasets and richly parametrized models, we exploit a highly accurate and fast approximate Bayesian inference approach which can also be used to efficiently select models separately for each of the four regression models at the latent level. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom. The framework performed well in terms of flood predictions for both ungauged catchments and future observations at gauged catchments. The results show that the spatial model components for the transformed location and scale parameters as well as the time trend are all important, and none of these should be ignored. Posterior estimates of the time trend parameters correspond to an average increase of about 1.5% per decade with range 0.1% to 2.8% and reveal a spatial structure across the United Kingdom. When the interest lies in estimating return levels for spatial aggregates, we further develop a novel copula-based postprocessing approach of posterior predictive samples in order to mitigate the effect of the conditional independence assumption at the data level, and we demonstrate that our approach indeed provides accurate results.University of Iceland Research Fun

TSC22 in mammary gland development and breast cancer

Mammary gland involution is characterised by a high degree of apoptosis. By identifying genes that are upregulated at this developmental stage, we aimed to discover key factors that are involved in the induction of mammary epithelial cell death and therefore present potential tumour suppressors for breast cancer. Among 96 genes recently identified as specifically upregulated early during involution were the transforming growth factor beta (TGFβ)-stimulated clone 22 homologue (TSC-22/TGFβ1-induced transcript 4) and TGFβ3 [1]. TGFβ3 has recently been shown to be necessary for induction of apoptosis during mammary gland involution, while TSC-22 overexpression can lead to cell death. We have therefore tested whether TSC-22 mRNA expression can be induced by TGFβ3 and whether it is involved in or necessary for TGFβ-induced apoptosis. We further show that TSC-22 can enhance TGFβ3-induced Smad response and epithelial cell death. In addition, overexpression of TSC-22 alone can induce a Smad response and apoptosis in mammary epithelial cell cultures, which is independent of p53. Further, we have performed tests to study the necessity for Smad proteins during TSC-22-induced apoptosis, and to establish the intracellular localisation of TSC-22. A pilot study on a small cohort of archival breast cancer cases, representing all stages of malignant progression, shows that TSC-22 protein was reduced or undetectable in 60% of breast carcinomas when compared with adjacent normal breast tissue, suggesting that TSC-22 could indeed be a potential novel tumour suppressor gene. We shall present data showing that methylation of the TSC-22 promoter is not involved in the reduction of TSC-22 protein in breast cancer

Алкогольные виртуальные реальности. Девиртуализация синдрома зависимости от алкоголя

Представлен новый взгляд на синдром зависимости от алкоголя с позиций виртуалистики как на параллельную виртуальную реальность. Подробно освещена рассматриваемая проблема, описан разработанный автором метод лечения алкоголизма ФорсажТМ и показана его высокая эффективность.A new idea about syndrome of alcohol addiction as a parallel virtual reality is presented. The problem is discussed in detail, the original method of treatment of alcoholism Forsazh(tm) is described, its high efficacy is shown

Hepatitis C virus infection upregulates CD55 expression on the hepatocyte surface and promotes association with virus particles

CD55 limits excessive complement activation on the host cell surface by accelerating the decay of C3 convertases. In this study, we observed that hepatitis C virus (HCV) infection of hepatocytes or HCV core protein expression in transfected hepatocytes upregulated CD55 expression at the mRNA and protein levels. Further analysis suggested that the HCV core protein or full-length (FL) genome enhanced CD55 promoter activity in a luciferase-based assay, which was further augmented in the presence of interleukin-6. Mutation of the CREB or SP-1 binding site on the CD55 promoter impaired HCV core protein-mediated upregulation of CD55. HCV-infected or core protein-transfected Huh7.5 cells displayed greater viability in the presence of CD81 and CD55 antibodies and complement. Biochemical analysis revealed that CD55 was associated with cell culture-grown HCV after purification by sucrose density gradient ultracentrifugation. Consistent with this, a polyclonal antibody to CD55 captured cell culture-grown HCV. Blocking antibodies against CD55 or virus envelope glycoproteins in the presence of normal human serum as a source of complement inhibited HCV infection. The inhibition was enhanced in the presence of both the antibodies and serum complement. Collectively, these results suggest that HCV induces and associates with a negative regulator of the complement pathway, a likely mechanism for immune evasion

The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally