Development of Non-Traditional Detection Algorithms for Undersea Warfare

This study addresses the problem of hydrodynamically-based detection of the surface and subsurface wakes generated by transiting submersibles. Our primary objective is to investigate the wake intensity, its thermal signatures and detection potential. Research activities involved numerical, laboratory and field experiments. Our work was aimed at providing a comprehensive and systematic analysis of stratified wakes in a realistic oceanic environment and will offer valuable operational guidance in this regard. This project is most timely since numerical modelling capabilities, and understanding of environmental influences, have only recently reached the level at which all key physical components can be fully represented. The identification of detection vulnerabilities will affect the tactics of undersea warfare by narrowing search areas for USW. These techniques became particularly appealing in view of continuous technological advances in remote sensing methods, which have dramatically improved the accuracy of measurements in the submarine wake.Naval Research ProgramPrepared for Topic Sponsor: N9; Research Sponsor OPNAV, N97; Research POC Name: Jennifer S. RobertsNPS-N16-N155-

Ariel - Volume 8 Number 3

Executive Editor James W. Lockard, Jr. Business Manager Neeraj K. Kanwal University News Richard J . Perry World News Doug Hiller Opinions Elizabeth A. McGuire Features Patrick P. Sokas Sports Desk Shahab S. Minassian Managing Editor Edward H. Jasper Managing Associate Brenda Peterson Photography Editor Robert D. Lehman. Jr. Graphics Christine M. Kuhnl

Effect of log10(ratio) on Proportion of “Disagreeing” Probe Sets at a p-value of 0.05 in the Source Set.

<p>The data sets are the same as for the data shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052242#pone-0052242-g001" target="_blank">Figure 1</a>. Either NCT00867100 (A, C) or Gudjonsson Low (B, D) were chosen as the source set. The proportion of probe sets disagreeing (out of all the probe sets) is shown for different log10(ratio) cutoffs. A and B: p-value of 0.05 in the source set and no cut-offs in the target sets; C and D: p-value cut-off of 0.05 in source and target set.</p

Effect of log10(ratio) and p-value Cut-offs on Overlap of Differentially Expressed Probe Sets.

<p>Effects are shown with source sets (A,C) NCT00867100 and (B,D) Gudjonsson low. (A, B): Number of probe sets agreeing and disagreeing for log10(ratios) from 0 to 0.5 in increments of 0.05 and p-values up to 0.2 in increments of 0.01 in the source set without cut-offs in the target set, (C, D): Number of probe sets agreeing and disagreeing from an analysis using log10(ratios) from 0 to 0.5 in increments of 0.5 and p≤0.05 in the source set and p-values up to 0.2 in increments of 0.01 in the target set are shown. Note: X-axis scales in (A, C) differ from those in (B, D).</p

Comparison of Fold-changes in Psoriasis PP/PN Pairs by Microarray and qRT-PCR.

<p>Fold-changes for a selection of mostly immune system transcripts were assessed by qRT-PCR and microarray in a subset of eight psoriasis PP/PN skin biopsies from the Asterand set. Transcripts were selected based on relevance to psoriasis, range of expression level and range of fold-changes; patient biopsies were selected based on microarray data so that the range of differential expression was large. The black line indicates complete concordance.</p

Effect of Implementing Fold-Change and p-value Cut-offs on a Comparison Between Two Experiments.

<p>Panels A-D show a hexbin plot comparison of the average log10(ratio) values between the Gudjonsson Low and the NCT00867100 data sets with (A) no cut-offs, (B) a p-value≤0.05 cut-off in the source set only, (C) p-value≤0.05 and log10(ratio)≥0.1 cut-offs only in the source set, and (D) p-value≤0.05 and log10(ratio)≥0.1 cut-offs in the source set and a p-value≤0.05 cut-off in the target set. The numbers in the panel corners indicate the number of data points in those quadrants. Panel E shows average log10(ratio) distributions in the Gudjonsson Low data set (target set) for sequences with log10(ratio) values of 0.100±0.005 (blue), 0.200±0.005 (pink), and 0.300±0.005 (green) in the source set (NCT00867100).</p

Comparison of Differential Expression Across Data Sets.

<p>For each data set a list of probe sets with differential expression at p≤0.05 was generated and compared to all the other data sets. The probe sets were then categorized into four different groups according to the extent of agreement between the source data set and the other data sets: i) “consistent” meant that there was at least one other data set in which the probe set showed differential expression in the same direction with p≤0.05 and no data sets with differential expression in the opposite direction with p≤0.05; ii) “inconsistent between platforms” indicated that there was at least one data set from the other platform with differential expression at p≤0.05 in the opposite direction; iii) the “inconsistent within platform” group contained probe sets with differential expression at p≤0.05 in different directions within the same platform; and iv) the “p>0.05 in all other” group contained probe sets where the source set was the only one with significant differential expression. The number of probe sets with differential expression in the Zaba (GSE11903) and the Reischl sets were smaller because samples were run on U133A arrays, which contain only 22,215 probe sets.</p

Proportion of Probe Sets Agreeing in the Direction of Expression Change (Log10(ratio)) between PN and PP Skin.

*<p>labeled cDNA target.</p>#<p>labeled cRNA target.</p