Additional file 2 of Disease-induced changes in plant microbiome assembly and functional adaptation
Notice bibliographique
Résumé
Additional file 1. Fig. S1 Pathogen isolation, identification, and pathogenicity test. Fig. S2 Samples were divided into different compartments for preparing of DNA extraction. Fig. S3 NMDS of bacterial communities in soil, root, stem (3 sections), and fruit. Fig. S4 NMDS of fungal communities in soil, root, stem (3 sections), and fruit. Fig. S5 Changes of alpha diversity indices and taxonomic composition of bacterial and fungal communities. Fig. S6 Relative abundance of differentially abundant taxa between healthy and diseased plant. Fig. S7 The volcano plots illustrating the enrichment and depletion patterns of the bacterial and fungal microbiomes in FWD plant compartments compared with the healthy. Fig. S8 The volcano plots illustrating the enrichment and depletion patterns of the bacterial class in FWD plants compartments in Guiyang (top) and Huishui (bottom), when the healthy plants were used as a baseline. Fig. S9 The volcano plots illustrating the enrichment and depletion patterns of the fungal phylum in FWD plants compartments in Guiyang (top) and Huishui (bottom), when the healthy plants were used as a baseline. Fig. S10 The volcano plots illustrating the enrichment and depletion patterns of microbiome in FWD plants all compartments in Guiyang (left) and Huishui (right), when the healthy plants were used as a baseline. Fig. S11 Intra- and interkingdom co-occurrence networks at Guiyang and Huishui. Fig. S12 Interkingdom co-occurrence networks in soil, root, stem (3 sections), and fruit. Fig. S13 Taxonomic composition and differentially abundant taxa of bacterial and fungal communities between healthy and diseased root endosphere and upper stem epidermis from metagenomic sequencing data. Fig. S14 Changes of microbiome functional profiles between healthy and diseased root endosphere and upper stem epidermis. Table S1. Primers information used in this study. Table S2. PERMANOVA by adonis of all bacterial 16S and fungal ITS samples. Table S3. PERMANOVA by adonis of bacterial 16S conducted separately for each compartment. Table S4. PERMANOVA by adonis of fungal ITS conducted separately for each compartment. Table S5. Distance to centroid was calculated by analysis of beta-dispersion using Bray–Curtis dissimilarity. Table S6. Linear-mixed model (LMM) for alpha diversity indices. Table S7. Linear-mixed model for bacterial phylum and fungal class composition. Table S8. Differentially abundant analysis showing the enrichment and depletion patterns of bacterial microbiomes in diseased organs compared with healthy organs. Table S9. Differentially abundant analysis showing the enrichment and depletion patterns of fungal microbiomes in diseased organs compared with healthy organs. Table S10. Topology properties of the intra- and interkingdom networks. Table S11. The taxonomic composition of bacterial phylum and fungal class between healthy and diseased intra- and interkingdom networks. Table S12. The taxonomic position of top 10 hubs in intra- and interkingdom networks. Table S13. Numbers of enriched and depleted functions in diseased plant compared with the healthy plant. Table S14. Functional annotation of differentially abundant genes (top 20) between healthy and diseased plant calculated by LEfSe difference analysis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,813 | 0,144 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».