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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.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.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.(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6). Class A (0–2 days) was characterized by higher T-cell densities than Classes B (3–6 days), C (7–9 days), and D (> = 10 days). Class A features were well separated in a cluster while Classes B, C, and D had overlapping feature clusters, (b) 2 randomly selected regions from the first ring reveal less separation among the 4 classes. (c) First 2 canonical features C1 and C2 obtained by projecting original features <T1; T2; T3; T4; T5; T6> onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class A features mostly clustering on its right side, while Classes B, C, and D showing significant overlap on the left side.
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pcbi-1003922-g007: Grouped scatter plots.(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6). Class A (0–2 days) was characterized by higher T-cell densities than Classes B (3–6 days), C (7–9 days), and D (> = 10 days). Class A features were well separated in a cluster while Classes B, C, and D had overlapping feature clusters, (b) 2 randomly selected regions from the first ring reveal less separation among the 4 classes. (c) First 2 canonical features C1 and C2 obtained by projecting original features <T1; T2; T3; T4; T5; T6> onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class A features mostly clustering on its right side, while Classes B, C, and D showing significant overlap on the left side.

Mentions: Scatter plots of the features (T0, T1, T2, T3, T4, T5, and T6) explain LDA results. Figure 7a shows grouped scatter plot of T0 vs. average first ring T-cell densities ((T1+T2+…+T6)/6) and demonstrates that higher T-cell densities in T0, and to a lesser extent in the surrounding spatial ring (T1, T2….T6), are predictive of Class A episodes more so than Classes B, C, and D. Shorter duration episodes (Class A) appeared in a cohesive cluster relative to longer duration episodes (Classes B, C, and D) which had overlapping features. We used MATLAB's gplotmatrix routine to generate a matrix of grouped scatter plots of pairs of these 7 features (T0, T1,…, T6). All 21 pairwise scatter plots showed similar patterns of minimal separation between episode classes (Figure 7b), indicating that T-cell density within single surrounding regions did not predict episode duration.


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.(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6). Class A (0–2 days) was characterized by higher T-cell densities than Classes B (3–6 days), C (7–9 days), and D (> = 10 days). Class A features were well separated in a cluster while Classes B, C, and D had overlapping feature clusters, (b) 2 randomly selected regions from the first ring reveal less separation among the 4 classes. (c) First 2 canonical features C1 and C2 obtained by projecting original features <T1; T2; T3; T4; T5; T6> onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class A features mostly clustering on its right side, while Classes B, C, and D showing significant overlap on the left side.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003922-g007: Grouped scatter plots.(a) T-cell density in region of episode initiation (T0) versus mean of the first ring of T-cell densities ((T1+T2+…+T6)/6). Class A (0–2 days) was characterized by higher T-cell densities than Classes B (3–6 days), C (7–9 days), and D (> = 10 days). Class A features were well separated in a cluster while Classes B, C, and D had overlapping feature clusters, (b) 2 randomly selected regions from the first ring reveal less separation among the 4 classes. (c) First 2 canonical features C1 and C2 obtained by projecting original features <T1; T2; T3; T4; T5; T6> onto the eigen vector given by MATLAB's manova1 routine to maximize separation between classes. Black line shows a possible decision boundary with Class A features mostly clustering on its right side, while Classes B, C, and D showing significant overlap on the left side.
Mentions: Scatter plots of the features (T0, T1, T2, T3, T4, T5, and T6) explain LDA results. Figure 7a shows grouped scatter plot of T0 vs. average first ring T-cell densities ((T1+T2+…+T6)/6) and demonstrates that higher T-cell densities in T0, and to a lesser extent in the surrounding spatial ring (T1, T2….T6), are predictive of Class A episodes more so than Classes B, C, and D. Shorter duration episodes (Class A) appeared in a cohesive cluster relative to longer duration episodes (Classes B, C, and D) which had overlapping features. We used MATLAB's gplotmatrix routine to generate a matrix of grouped scatter plots of pairs of these 7 features (T0, T1,…, T6). All 21 pairwise scatter plots showed similar patterns of minimal separation between episode classes (Figure 7b), indicating that T-cell density within single surrounding regions did not predict episode duration.

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.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.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