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Herpes simplex virus-2 genital tract shedding is not predictable over months or years in infected persons.

Dhankani V, Kutz JN, Schiffer JT - PLoS Comput. Biol. (2014)

Bottom Line: Empirical and model-derived, time-series data gathered over >30 days consists of multiple complex episodes that could not be reduced to a manageable number of descriptive features with PCA and SVD.We applied linear discriminant analysis (LDA) to simulation output and identified that local immune cell density at the viral reactivation site had a predictive effect on episode duration, though longer term shedding suggested chaotic dynamics and could not be predicted based on spatial patterns of immune cell density.These findings suggest that HSV-2 shedding patterns within an individual are impossible to predict over weeks or months, and that even highly complex single HSV-2 episodes can only be partially predicted based on spatial distribution of immune cell density.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.

ABSTRACT
Herpes simplex virus-2 (HSV-2) is a chronic reactivating infection that leads to recurrent shedding episodes in the genital tract. A minority of episodes are prolonged, and associated with development of painful ulcers. However, currently, available tools poorly predict viral trajectories and timing of reactivations in infected individuals. We employed principal components analysis (PCA) and singular value decomposition (SVD) to interpret HSV-2 genital tract shedding time series data, as well as simulation output from a stochastic spatial mathematical model. Empirical and model-derived, time-series data gathered over >30 days consists of multiple complex episodes that could not be reduced to a manageable number of descriptive features with PCA and SVD. However, single HSV-2 shedding episodes, even those with prolonged duration and complex morphologies consisting of multiple erratic peaks, were consistently described using a maximum of four dominant features. Modeled and clinical episodes had equivalent distributions of dominant features, implying similar dynamics in real and simulated episodes. We applied linear discriminant analysis (LDA) to simulation output and identified that local immune cell density at the viral reactivation site had a predictive effect on episode duration, though longer term shedding suggested chaotic dynamics and could not be predicted based on spatial patterns of immune cell density. These findings suggest that HSV-2 shedding patterns within an individual are impossible to predict over weeks or months, and that even highly complex single HSV-2 episodes can only be partially predicted based on spatial distribution of immune cell density.

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Grouped scatter plots including second ring (T7, T8,…., T18) of T-cell densities for classifying Classes B (3–6 days), C (7–9 days), and D (> = 10 days).(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6) demonstrates no separation between groups, (b) Mean of the first ring versus mean of the second ring ((T7+T8+…+T18)/12) of T-cell densities demonstrates no separation between groups. (c) First 2 canonical features C1 and C2 obtained by projecting second ring of T-cell densities onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class D features clustering on its left side, while Classes B, and C show significant overlap on both sides.
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pcbi-1003922-g008: Grouped scatter plots including second ring (T7, T8,…., T18) of T-cell densities for classifying Classes B (3–6 days), C (7–9 days), and D (> = 10 days).(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6) demonstrates no separation between groups, (b) Mean of the first ring versus mean of the second ring ((T7+T8+…+T18)/12) of T-cell densities demonstrates no separation between groups. (c) First 2 canonical features C1 and C2 obtained by projecting second ring of T-cell densities onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class D features clustering on its left side, while Classes B, and C show significant overlap on both sides.

Mentions: Next, we tried to achieve separation between Classes B, C, and D by using mean of the second ring of T-cell densities ((T7+T8+…+T18)/12) around the region of episode initiation as our features for predicting severity of ensuing episodes. Figure 8a re-emphasizes the overlap amongst Classes B, C, and D when only T0 and the first ring of T-cell densities were used as features. A grouped scatter plot of average first ring T-cell densities versus average second ring T-cell densities also showed similar overlap amongst Classes B, C, and D (Figure 8b). Grouped scatter plot of the first two canonical features obtained by multivariate analysis of variance (MATLAB's manova1) of the second ring of T-cell densities is shown in Figure 8c. Though Class D (circles) lied mostly on one side of the decision boundary, its features overlapped with Classes B and C.


Herpes simplex virus-2 genital tract shedding is not predictable over months or years in infected persons.

Dhankani V, Kutz JN, Schiffer JT - PLoS Comput. Biol. (2014)

Grouped scatter plots including second ring (T7, T8,…., T18) of T-cell densities for classifying Classes B (3–6 days), C (7–9 days), and D (> = 10 days).(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6) demonstrates no separation between groups, (b) Mean of the first ring versus mean of the second ring ((T7+T8+…+T18)/12) of T-cell densities demonstrates no separation between groups. (c) First 2 canonical features C1 and C2 obtained by projecting second ring of T-cell densities onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class D features clustering on its left side, while Classes B, and C show significant overlap on both sides.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4222642&req=5

pcbi-1003922-g008: Grouped scatter plots including second ring (T7, T8,…., T18) of T-cell densities for classifying Classes B (3–6 days), C (7–9 days), and D (> = 10 days).(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6) demonstrates no separation between groups, (b) Mean of the first ring versus mean of the second ring ((T7+T8+…+T18)/12) of T-cell densities demonstrates no separation between groups. (c) First 2 canonical features C1 and C2 obtained by projecting second ring of T-cell densities onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class D features clustering on its left side, while Classes B, and C show significant overlap on both sides.
Mentions: Next, we tried to achieve separation between Classes B, C, and D by using mean of the second ring of T-cell densities ((T7+T8+…+T18)/12) around the region of episode initiation as our features for predicting severity of ensuing episodes. Figure 8a re-emphasizes the overlap amongst Classes B, C, and D when only T0 and the first ring of T-cell densities were used as features. A grouped scatter plot of average first ring T-cell densities versus average second ring T-cell densities also showed similar overlap amongst Classes B, C, and D (Figure 8b). Grouped scatter plot of the first two canonical features obtained by multivariate analysis of variance (MATLAB's manova1) of the second ring of T-cell densities is shown in Figure 8c. Though Class D (circles) lied mostly on one side of the decision boundary, its features overlapped with Classes B and C.

Bottom Line: Empirical and model-derived, time-series data gathered over >30 days consists of multiple complex episodes that could not be reduced to a manageable number of descriptive features with PCA and SVD.We applied linear discriminant analysis (LDA) to simulation output and identified that local immune cell density at the viral reactivation site had a predictive effect on episode duration, though longer term shedding suggested chaotic dynamics and could not be predicted based on spatial patterns of immune cell density.These findings suggest that HSV-2 shedding patterns within an individual are impossible to predict over weeks or months, and that even highly complex single HSV-2 episodes can only be partially predicted based on spatial distribution of immune cell density.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.

ABSTRACT
Herpes simplex virus-2 (HSV-2) is a chronic reactivating infection that leads to recurrent shedding episodes in the genital tract. A minority of episodes are prolonged, and associated with development of painful ulcers. However, currently, available tools poorly predict viral trajectories and timing of reactivations in infected individuals. We employed principal components analysis (PCA) and singular value decomposition (SVD) to interpret HSV-2 genital tract shedding time series data, as well as simulation output from a stochastic spatial mathematical model. Empirical and model-derived, time-series data gathered over >30 days consists of multiple complex episodes that could not be reduced to a manageable number of descriptive features with PCA and SVD. However, single HSV-2 shedding episodes, even those with prolonged duration and complex morphologies consisting of multiple erratic peaks, were consistently described using a maximum of four dominant features. Modeled and clinical episodes had equivalent distributions of dominant features, implying similar dynamics in real and simulated episodes. We applied linear discriminant analysis (LDA) to simulation output and identified that local immune cell density at the viral reactivation site had a predictive effect on episode duration, though longer term shedding suggested chaotic dynamics and could not be predicted based on spatial patterns of immune cell density. These findings suggest that HSV-2 shedding patterns within an individual are impossible to predict over weeks or months, and that even highly complex single HSV-2 episodes can only be partially predicted based on spatial distribution of immune cell density.

Show MeSH
Related in: MedlinePlus