File:Veins of Resilience.jpg

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Original file (4,888 × 4,944 pixels, file size: 25.03 MB, MIME type: image/jpeg)

Captions

Captions

Transverse section of a sorghum stem stained with FASGA fused with machine-learning-segmented vascular bundles, highlighting water transport anatomy, crucial for resilience in semi-arid climates.

Summary

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Description
English: Transverse section of a sorghum stem observed with a NanoZoomer after FASGA staining, fused with the machine-learning-based segmentation of the vascular bundles.

This image reveals the internal structure of the sorghum stem, a strategic cereal for hot regions such as the Sahel. The FASGA staining allows differentiation of the lignified structures, which appear in shades of red, from the cellulosic structures, in shades of blue. Each small cluster represents a vascular bundle, a key component of the plant's internal transport system. These vascular bundles contain metaxylems, responsible for transporting raw sap from the roots, as well as phloem, which conducts elaborated sap.

To enhance this image, automatic segmentation via machine learning was employed to delineate each vascular bundle, complemented by a Voronoi diagram. This diagram highlights the regions of influence around each bundle using a color gradient that transitions from cool tones at the center of the stem to warm tones at the periphery. This color coding illustrates the spatial distribution of the bundles based on their position within the stem.

This image is an extract from an anatomical phenotyping experiment conducted for the Biomass For The Future project. This research aims to understand better the internal anatomy of sorghum and its water conduction capabilities, to breed the most resilient varieties for frequent water stress in semi-arid regions. Modeling the vascular bundles could thus contribute to developing new sorghum varieties that are better adapted to harsh climatic conditions.
Français : Coupe transversale de tige de Sorgho observée au NanoZoomer après coloration au FASGA, fusionnée avec la segmentation des structures vasculaires par machine learning.

Cette image révèle la structure interne de la tige de sorgho, une céréale d'importance stratégique dans les régions chaudes comme le Sahel. La coloration au FASGA permet de distinguer les structures ligneuses, apparaissant en nuances de rouge, des structures cellulosiques, en nuances de bleu. La coupe laisse apparaître des structures en forme de visage, les faisceaux vasculaires. Les faisceaux sont un élément clé du système de transport interne de la plante. Chaque faisceau contient une paire de métaxylèmes, responsables du transport de la sève brute depuis les racines, ainsi qu'un phloème, dédié au transport de la sève élaborée.

Pour enrichir cette image, une segmentation automatique par apprentissage machine a été utilisée afin de délimiter chaque faisceau vasculaire, et d'afficher les régions d'influence (proximité géométrique) autour de chaque faisceau, en utilisant un gradient de couleur allant des tons froids au centre de la tige aux tons chauds vers la périphérie. Ces codes couleurs illustrent ainsi la distribution spatiale des faisceaux selon leur position dans la tige.

Cette image est extraite d'une campagne de phénotypage anatomique réalisée pour le projet Biomass For The Future. L'analyse des faisceaux vise à mieux comprendre l'anatomie interne du sorgho et ses capacités de conduction hydrique, dans le but de sélectionner les variétés les plus résilientes face aux stress hydriques fréquents dans les zones semi-arides. La modélisation des faisceaux vasculaires pourrait ainsi contribuer au développement de nouvelles variétés de sorgho plus adaptées aux conditions climatiques difficiles.
Date
Source Own work
Author RomainFernandez06
Camera location43° 38′ 58.15″ N, 3° 52′ 08.72″ E Kartographer map based on OpenStreetMap.View this and other nearby images on: OpenStreetMapinfo

This photo could compete in both "microscopy" and "non photographic media", as it is a fusion of a microscopy image and a computed result.

Licensing

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Date/TimeThumbnailDimensionsUserComment
current12:33, 15 November 2024Thumbnail for version as of 12:33, 15 November 20244,888 × 4,944 (25.03 MB)RomainFernandez06 (talk | contribs)Uploaded own work with UploadWizard

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