Biology subjects

Noshita, K.

Publications and source records attributed to Noshita, K..

4 recordsLinked to original sources

bioRxiv2025

Haplotype Bias Detection Using Pedigree-Based Transmission Simulation: Traces of Selection That Occurred in Apple Breeding

With the increasing ability to integrate pedigree and genomic data, it is essential to evaluate their potential to uncover valuable genetic insights that can drive the advancement of crop breeding and conservation of genetic diversity. Pedigree analysis remains a fundamental approach for investigating the inheritance of phenotypic traits, exploring evolutionary history, and understanding hybridization processes in crop plants. Among these approaches, gene drop simulations using pedigree and allele origin data enable the construction of genetic maps and provide insights into complex genetic backgrounds. In this study, we developed a new method to identify useful genetic regions associated with single-nucleotide polymorphism (SNP) markers based on gene drop simulations, focusing on 185 Japanese domestic apple cultivars. By performing 10 million gene drop simulations, we generated null distributions for each founder haplotype, which revealed SNP markers with significant frequency biases, which is a potential signal for selection. Frequency biases were identified in eight founder haplotypes that were particularly consistent with genome-wide association studies peaks associated with key fruit traits such as malic acid and fructose content. Gene Ontology enrichment analysis suggested that these SNPs are not only associated with fruit traits but may also play a role in critical biological functions, including stress tolerance and reproductive processes, highlighting their broader relevance to crop resilience. Our integrative approach, which combines founder haplotype analysis with extensive gene drop simulations, effectively detects selection pressure, provides new insights into the genetic basis of apple breeding, and identifies SNP markers with strong potential to improve breeding programs.

genetics↗

bioRxiv2025

Continuous and widespread population interactions in the Jomon society via geometric morphometrics on 3D data of human crania

This study examines the morphological variations within and between regional populations via geometric morphometrics of larger samples of three-dimensional data of J[o]mon human crania (N = 363, including 146 females, 215 males and two unknown-sex individuals) from 97 sites to investigate the population interactions in the period, which Japanese anthropologists and archaeologists have extensively discussed. The results show that morphological variations are more pronounced within individual populations especially in the principal component one, relating to the facial width, degree of prognathism, and location of occipital areas, in contrast to the relatively smaller variations observed between different phases and geographical regions. This observation is consistent with the possibility that the population interactions of the J[o]mon people had been widespread and continuous, and which has an important implication for their resilience against severe climate changes at that time: The relative stability of the J[o]mon society might be sustained by their frequent interactions with various populations, as suggested by insights from relevant archaeological, ethnographic, and genetic research.

evolutionary biology↗

bioRxiv2024

A comparative study of plant phenotyping workflows based on three-dimensional reconstruction from multi-view images

With the world facing escalating food demand, limited agricultural land, and environmental change, there is a growing need for data-driven sustainable agricultural management. Advances in sequencing and sensor networks have reduced costs of acquiring genomic and environmental data; however, collecting phenotypic data, crucial for monitoring plant growth and detecting pests and diseases, remains labor-intensive. Technological advances have enabled efficient collection of three-dimensional (3D) data, yet this process currently involves intricate steps. Therefore, developing effective phenotyping methods is essential. In this study, we developed a phenotyping process based on 3D data, including mask image generation using deep neural network models, 3D reconstruction using the Structure from Motion/Multi-View Stereo (SfM/MVS) pipeline, and surface reconstruction for leaf area estimation. Using soybean datasets, we found that a 1/5.4x magnification effectively generated mask images. Among four mask image usage scenarios in SfM/MVS, applying soybean-and-stage masks before SfM and only soybean masks after SfM yielded the highest-quality point cloud data with the second shortest processing time. Finally, we compared Poisson reconstruction and B-spline surface fitting in leaf area estimation; B-spline fitting showed greater correlation with destructive measurements. We propose an optimal workflow for estimating leaf area and provide tools and datasets for future phenotyping research.

plant biology↗

bioRxiv2022

Network feature-based phenotyping of leaf venation robustly reconstructs the latent space

Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements (e.g., length, diameter, branching angles, and areole area) and their derived statistics to quantify phenotypes. In this study, we developed a simple, high-throughput phenotyping workflow for the quantification of vein networks and identified leaf venation-specific morphospace patterns. The proposed method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. To demonstrate the proposed method, we applied it to images of non-chemically treated leaves of five species for classification based on network features alone, with an accuracy of 90.6%. By dimensionality reduction, a one-dimensional morphospace, along which venation shows variation in loopiness, was identified for both untreated and cleared leaf images, suggesting that patterns of venation are determined by a functional trade-off. The proposed network feature-based method is a useful morphological descriptor, providing a quantitative representation of the topological aspects of venation and enabling inverse mapping to leaf vein structures. Accordingly, our approach is promising for analyses of the functional and structural properties of veins. Author SummaryLeaf venation exhibits diverse network structures among taxa and conservation within taxa, reflecting complex evolutionary processes involving functional, developmental, and structural constraints. We used network features to characterize hierarchical and complex venation patterns. We analyzed 479 non-chemically treated leaves of five species and demonstrated that network features contain sufficient information for species classification. Furthermore, we identified biased distribution patterns in the leaf venation morphospace by characterizing leaf samples from both untreated and cleared leaf images. These results improve our understanding of morphological constraints and functional trade-offs shaping divergence in leaf venation. Our approach provides a basis for similar analyses in various fields targeting reticulate networks, which are ubiquitous in nature, including biomimetics, generative design, and microfluidics.

plant biology↗