A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent

Identification of the molecular signatures integral to regenerating photoreceptors in the retina of the zebra fish

Investigating neuronal and photoreceptor regeneration in the retina of zebra fish has begun to yield insights into both the cellular and molecular means by which this lower vertebrate is able to repair its central nervous system. However, knowledge about the signaling molecules in the local microenvironment of a retinal injury and the transcriptional events they activate during neuronal death and regeneration is still lacking. To identify genes involved in photoreceptor regeneration, we combined light-induced photoreceptor lesions, laser-capture microdissection of the outer nuclear layer (ONL) and analysis of gene expression to characterize transcriptional changes for cells in the ONL as photoreceptors die and are regenerated. Using this approach, we were able to characterize aspects of the molecular signature of injured and dying photoreceptors, cone photoreceptor progenitors, and microglia within the ONL. We validated changes in gene expression and characterized the cellular expression for three novel, extracellular signaling molecules that we hypothesize are involved in regulating regenerative events in the retina

Target Detection Performance Bounds in Compressive Imaging

This paper describes computationally efficient approaches and associated theoretical performance guarantees for the detection of known targets and anomalies from few projection measurements of the underlying signals. The proposed approaches accommodate signals of different strengths contaminated by a colored Gaussian background, and perform detection without reconstructing the underlying signals from the observations. The theoretical performance bounds of the target detector highlight fundamental tradeoffs among the number of measurements collected, amount of background signal present, signal-to-noise ratio, and similarity among potential targets coming from a known dictionary. The anomaly detector is designed to control the number of false discoveries. The proposed approach does not depend on a known sparse representation of targets; rather, the theoretical performance bounds exploit the structure of a known dictionary of targets and the distance preservation property of the measurement matrix. Simulation experiments illustrate the practicality and effectiveness of the proposed approaches.Comment: Submitted to the EURASIP Journal on Advances in Signal Processin

Data-recursive algorithms for blind channel identification in oversampled communication systems

Data-recursive algorithms are presented for performing blind channel identification in oversampled communication systems. Novel on-line solutions with complexities that are only linear in the oversampling rate are considered, and mean convergence conditions are provided. Numerical results are presented for a binary phase-shift keyed (BPSK) system

An information-based approach to sensor management in large dynamic networks

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Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes

Objective: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. Methods: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to an H1N1 influenza pathogen. Results: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data, the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. Conclusion: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. Significance: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications

Initiating Industrie 4.0 by Implementing Sensor Management – Improving Operational Availability

To stay competitive in the future, industrialists must be prepared to adopt the imminent changes and new technologies associated with Industrie 4.0. These changes apply equally to the field of maintenance, which is also developing quickly. Sensors, along with analyses and competence, are one of the most critical factors for Industrie 4.0 as they are the connectors between the digital and physical world. Utilization of these sensors within maintenance is a relatively unexplored field. Thus, the aim of this paper is to present a novel concept for ways sensor management can be linked to maintenance and thereby improve operational availability. The paper also presents an overview of sensor management and trends within maintenance