Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Finding Groups in Gene Expression Data

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups

Changepoint detection on a graph of time series

When analysing multiple time series that may be subject to changepoints, it is sometimes possible to specify a priori, by means of a graph G, which pairs of time series are likely to be impacted by simultaneous changepoints. This article proposes a novel Bayesian changepoint model for multiple time series that borrows strength across clusters of connected time series in G to detect weak signals for synchronous changepoints. The graphical changepoint model is further extended to allow dependence between nearby but not necessarily synchronous changepoints across neighbour time series in G. A novel reversible jump MCMC algorithm making use of auxiliary variables is proposed to sample from the graphical changepoint model. The merit of the proposed model is demonstrated via a changepoint analysis of real network authentication data from Los Alamos National Laboratory (LANL), with some success at detecting weak signals for network intrusions across users that are linked by network connectivity, whilst limiting the number of false alerts.Comment: 31 pages, 13 figure

Roundtable debate: Controversies in the management of the septic patient – desperately seeking consensus

Despite continuous advances in technologic and pharmacologic management, the mortality rate from septic shock remains high. Care of patients with sepsis includes measures to support the circulatory system and treat the underlying infection. There is a substantial body of knowledge indicating that fluid resuscitation, vasopressors, and antibiotics accomplish these goals. Recent clinical trials have provided new information on the addition of individual adjuvant therapies. Consensus on how current therapies should be prescribed is lacking. We present the reasoning and preferences of a group of intensivists who met to discuss the management of an actual case. The focus is on management, with emphasis on the criteria by which treatment decisions are made. It is clear from the discussion that there are areas where there is agreement and areas where opinions diverge. This presentation is intended to show how experienced intensivists apply clinical science to their practice of critical care medicine

Roundtable Debate: Controversies in the Management of the Septic Patient – Desperately Seeking Consensus

Despite continuous advances in technologic and pharmacologic management, the mortality rate from septic shock remains high. Care of patients with sepsis includes measures to support the circulatory system and treat the underlying infection. There is a substantial body of knowledge indicating that fluid resuscitation, vasopressors, and antibiotics accomplish these goals. Recent clinical trials have provided new information on the addition of individual adjuvant therapies. Consensus on how current therapies should be prescribed is lacking. We present the reasoning and preferences of a group of intensivists who met to discuss the management of an actual case. The focus is on management, with emphasis on the criteria by which treatment decisions are made. It is clear from the discussion that there are areas where there is agreement and areas where opinions diverge. This presentation is intended to show how experienced intensivists apply clinical science to their practice of critical care medicine

Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

<p>Abstract</p> <p>Background</p> <p>Fission yeast <it>Schizosaccharomyces pombe </it>and budding yeast <it>Saccharomyces cerevisiae </it>are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast.</p> <p>Results</p> <p>By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor <it>SPBC19G7.04</it>. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved <it>cis</it>-regulatory motifs. In particular, we show that <it>SPBC19G7.04 </it>has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor <it>fkh2</it>. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in <it>S. pombe</it>. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs.</p> <p>Conclusion</p> <p>Using a systems-level analysis of the phase-specific nature of the <it>S. pombe </it>cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we demonstrated how one can generate and investigate plausible hypotheses on fission yeast cell cycle regulation which can potentially be explored experimentally.</p

Does Physical Therapy Impact Clinical Outcomes After Lumbar Decompression Surgery?

OBJECTIVES: The objectives of our study were to (1) determine if physical therapy (PT) impacts patient-reported outcomes (PROMs) after lumbar decompression surgery and (2) determine if PT impacts postsurgical readmissions or reoperations after lumbar decompression surgery. METHODS: Patients \u3e18 years of age who underwent primary one‑ or two‑level lumbar decompression at our institution were identified. Patient demographics, surgical characteristics, surgical outcomes (all‑cause 90 days readmissions and 90 days surgical readmissions), and patient‑reported outcomes (PROMs) were compared between the groups. Multivariate linear regression was utilized to determine the individual predictors of 90 days readmissions and PROMs at the 1‑year postoperative point. Alpha was set at P \u3c 0.05. RESULTS: Of the 1003 patients included, 421 attended PT postoperatively. On univariate analysis, PT attendance did not significantly impact 90‑day surgical reoperations (P = 0.225). Although bivariate analysis suggests that attendance of PT is associated with worse improvement in physical function (P = 0.041), increased preoperative Visual Analogue Scale leg pain (0 = 0.004), and disability (P = 0.006), as measured by the Oswestry Disability Index, our multivariate analysis, which accounts for confounding variables found there was no difference in PROM improvement and PT was not an independent predictor of 90‑day all‑cause readmissions (P = 0.06). Instead, Charlson Comorbidity Index (P = 0.025) and discharge to a skilled nursing facility (P = 0.013) independently predicted greater 90‑day all‑cause readmissions. CONCLUSIONS: Postoperative lumbar decompression PT attendance does not significantly affect clinical improvement, as measured by PROMs or surgical outcomes including all-cause 90 days readmissions and 90-day surgical readmissions