Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

From buds to shoots: insights into grapevine development from the Witch's Broom bud sport

BACKGROUND

Bud sports occur spontaneously in plants when new growth exhibits a distinct phenotype from the rest of the parent plant. The Witch's Broom bud sport occurs occasionally in various grapevine (Vitis vinifera) varieties and displays a suite of developmental defects, including dwarf features and reduced fertility. While it is highly detrimental for grapevine growers, it also serves as a useful tool for studying grapevine development. We used the Witch's Broom bud sport in grapevine to understand the developmental trajectories of the bud sports, as well as the potential genetic basis. We analyzed the phenotypes of two independent cases of the Witch's Broom bud sport, in the Dakapo and Merlot varieties of grapevine, alongside wild type counterparts. To do so, we quantified various shoot traits, performed 3D X-ray Computed Tomography on dormant buds, and landmarked leaves from the samples. We also performed Illumina and Oxford Nanopore sequencing on the samples and called genetic variants using these sequencing datasets.

RESULTS

The Dakapo and Merlot cases of Witch's Broom displayed severe developmental defects, with no fruit/clusters formed and dwarf vegetative features. However, the Dakapo and Merlot cases of Witch's Broom studied were also phenotypically different from one another, with distinct differences in bud and leaf development. We identified 968-974 unique genetic mutations in our two Witch's Broom cases that are potential causal variants of the bud sports. Examining gene function and validating these genetic candidates through PCR and Sanger-sequencing revealed one strong candidate mutation in Merlot Witch's Broom impacting the gene GSVIVG01008260001.

CONCLUSIONS

The Witch's Broom bud sports in both varieties studied had dwarf phenotypes, but the two instances studied were also vastly different from one another and likely have distinct genetic bases. Future work on Witch's Broom bud sports in grapevine could provide more insight into development and the genetic pathways involved in grapevine.

Topological data analysis reveals core heteroblastic and ontogenetic programs embedded in leaves of grapevine (Vitaceae) and maracuyá (Passifloraceae)

Leaves are often described in language that evokes a single shape. However, embedded in that descriptor is a multitude of latent shapes arising from evolutionary, developmental, environmental, and other effects. These confounded effects manifest at distinct developmental time points and evolve at different tempos. Here, revisiting datasets comprised of thousands of leaves of vining grapevine (Vitaceae) and maracuyá (Passifloraceae) species, we apply a technique from the mathematical field of topological data analysis to comparatively visualize the structure of heteroblastic and ontogenetic effects on leaf shape in each group. Consistent with a morphologically closer relationship, members of the grapevine dataset possess strong core heteroblasty and ontogenetic programs with little deviation between species. Remarkably, we found that most members of the maracuyá family also share core heteroblasty and ontogenetic programs despite dramatic species-to-species leaf shape differences. This conservation was not initially detected using traditional analyses such as principal component analysis or linear discriminant analysis. We also identify two morphotypes of maracuyá that deviate from the core structure, suggesting the evolution of new developmental properties in this phylogenetically distinct sub-group. Our findings illustrate how topological data analysis can be used to disentangle previously confounded developmental and evolutionary effects to visualize latent shapes and hidden relationships, even ones embedded in complex, high-dimensional datasets.

Computational reproducibility of Jupyter notebooks from biomedical publications

BACKGROUND

Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows, including for research publications. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications.

APPROACH

We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article's full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion.

RESULTS

Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions.

CONCLUSIONS

We zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.

Species relationships within the genus Vitis based on molecular and morphological data

The grape genus Vitis L. includes the domesticated V. vinifera, which is one of the most important fruit crop, and also close relatives recognized as valuable germplasm resources for improving cultivars. To resolve some standing problems in the species relationships within the Vitis genus we analyzed diversity in a set of 90 accessions comprising most of Vitis species and some putative hybrids. We discovered single nucleotide polymorphisms (SNPs) in SANGER sequences of twelve loci and genotyped accessions at a larger number of SNPs using a previously developed SNP array. Our phylogenic analyses consistently identified: three clades in North America, one in East Asia, and one in Europe corresponding to V. vinifera. Using heterozygosity measurement, haplotype reconstruction and chloroplast markers, we identified the hybrids existing within and between clades. The species relationships were better assessed after discarding these hybrids from analyses. We also studied the relationships between phylogeny and morphological traits and found that several traits significantly correlated with the phylogeny. The American clade that includes important species such as V. riparia and V. rupestris showed a major divergence with all other clades based on both DNA polymorphisms and morphological traits.

A Review of Ampelometry: Morphometric Characterization of the Grape (Vitis spp.) Leaf

Grape (Vitis spp.) is one of the most important horticultural crops, cultivated worldwide on more than 7.3 million hectares for various purposes such as winemaking, fresh fruit consumption, rootstock, and ornamental plants. Based on the inter- and intraspecific morphological variability, several descriptor lists, manuals and ampelographic studies are available for identification. Among the organs, leaves have the most traits, while the young shoot, bunch and berry are also important in the characterization of the genotypes. Vitis species and cultivars are described by leaf morphological characterization developed in many ways for the identification of genotypes, to clarify synonymies and distinct clones or evaluate the diversity of wild Vitis taxa. Morphometric-also known as ampelometric-evaluation has an extensive background in the literature. However, for some reasons, only a part of the literature is cited, despite its significant scientific value. In this paper, we summarize the efforts of metric characterization of the grapevine leaf with the introduction of the scientific objectives and reviewing the studies showing the innovations in phenotyping during the past 120 years.

Increases in vein length compensate for leaf area lost to lobing in grapevine

PREMISE

Leaf lobing and leaf size vary considerably across and within species, including among grapevines (Vitis spp.), some of the best-studied leaves. We examined the relationship between leaf lobing and leaf area across grapevine populations that varied in extent of leaf lobing.

METHODS

We used homologous landmarking techniques to measure 2632 leaves across 2 years in 476 unique, genetically distinct grapevines from five biparental crosses that vary primarily in the extent of lobing. We determined to what extent leaf area explained variation in lobing, vein length, and vein to blade ratio.

RESULTS

Although lobing was the primary source of variation in shape across the leaves we measured, leaf area varied only slightly as a function of lobing. Rather, leaf area increases as a function of total major vein length, total branching vein length, and vein to blade ratio. These relationships are stronger for more highly lobed leaves, with the residuals for each model differing as a function of distal lobing.

CONCLUSIONS

For leaves with different extents of lobing but the same area, the more highly lobed leaves have longer veins and higher vein to blade ratios, allowing them to maintain similar leaf areas despite increased lobing. These findings show how more highly lobed leaves may compensate for what would otherwise result in a reduced leaf area, allowing for increased photosynthetic capacity through similar leaf size.

Decoding family-level features for modern and fossil leaves from computer-vision heat maps

PREMISE

Angiosperm leaves present a classic identification problem due to their morphological complexity. Computer-vision algorithms can identify diagnostic regions in images, and heat map outputs illustrate those regions for identification, providing novel insights through visual feedback. We investigate the potential of analyzing leaf heat maps to reveal novel, human-friendly botanical information with applications for extant- and fossil-leaf identification.

METHODS

We developed a manual scoring system for hotspot locations on published computer-vision heat maps of cleared leaves that showed diagnostic regions for family identification. Heat maps of 3114 cleared leaves of 930 genera in 14 angiosperm families were analyzed. The top-5 and top-1 hotspot regions of highest diagnostic value were scored for 21 leaf locations. The resulting data were viewed using box plots and analyzed using cluster and principal component analyses. We manually identified similar features in fossil leaves to informally demonstrate potential fossil applications.

RESULTS

The method successfully mapped machine strategy using standard botanical language, and distinctive patterns emerged for each family. Hotspots were concentrated on secondary veins (Salicaceae, Myrtaceae, Anacardiaceae), tooth apices (Betulaceae, Rosaceae), and on the little-studied margins of untoothed leaves (Rubiaceae, Annonaceae, Ericaceae). Similar features drove the results from multivariate analyses. The results echo many traditional observations, while also showing that most diagnostic leaf features remain undescribed.

CONCLUSIONS

Machine-derived heat maps that initially appear to be dominated by noise can be translated into human-interpretable knowledge, highlighting paths forward for botanists and paleobotanists to discover new diagnostic botanical characters.

A predicted developmental and evolutionary morphospace for grapevine leaves

Using conventional statistical approaches there exist powerful methods to classify shapes. Embedded in morphospaces is information that allows us to visualise theoretical leaves. These unmeasured leaves are never considered nor how the negative morphospace can inform us about the forces responsible for shaping leaf morphology. Here, we model leaf shape using an allometric indicator of leaf size, the ratio of vein to blade areas. The borders of the observable morphospace are restricted by constraints and define an orthogonal grid of developmental and evolutionary effects which can predict the shapes of possible grapevine leaves. Leaves in the genus Vitis are found to fully occupy morphospace available to them. From this morphospace, we predict the developmental and evolutionary shapes of grapevine leaves that are not only possible, but exist, and argue that rather than explaining leaf shape in terms of discrete nodes or species, that a continuous model is more appropriate.

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online .