Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2507.00069

Genetic Diversity Assessment of German Chamomile Populations Based on Phenotypic Detection and SSR Molecular Markers in Bushehr Area

Seyyedeh Mahdiyeh Hashempour·Mohammad Modarresi·Mojtaba Ghasemi·Ivan Salamon
View original source ↗Read source document ↗Export metadata ↓

Abstract

The present study aimed to investigate the genetic diversity in 42 German chamomile populations using morphological, biochemical, and ssr molecular markers, in both field and laboratory sections. Different populations of German chamomile collected from different regions were as statistical populations. Phenological, physiological and phytochemical traits, were measured. DNA extraction was done by CTAB method and DNA quantity and quality were checked by spectrophotometer and gel electrophoresis. 5 target populations were genetically evaluated with the help of 6 SSR markers. According to the obtained results, Jam 1 and Shahijan populations had the most effective substances (essential oil and chamazulene). The correlation analysis revealed a positive and significant correlation of 59% between the percentage of chamazulene and the percentage of essential oil. Chamazulene percentage and fresh weight as the most important traits were entered into the regression model step by step. These traits were found to explain 0.43% of the changes in the data. These findings have significant implications for future research aimed at identifying the most effective populations for essential oil and chamazulene production. The cluster analysis divided the genotypes into five groups. The second group was the most important, and Jam1 and Shahijan genotypes were in this group. The results of the molecular analysis showed that the Seho Sermak-Dashti population had the most effective allele, and this population was superior to other populations in terms of Shannon's index and nei diversity coefficient. According to the diagram, analysis into main coordinates showed that the genotypes are scattered on the surface of the diagram and this indicates the appropriate diversity of the studied genotypes. As a result, it can be stated that the grouping of phenotypic and molecular data was very consistent with each other.

Explore related subjects

q-bio.OT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Seyyedeh Mahdiyeh Hashempour, Mohammad Modarresi, Mojtaba Ghasemi, Ivan Salamon. 2025-06-28. Genetic Diversity Assessment of German Chamomile Populations Based on Phenotypic Detection and SSR Molecular Markers in Bushehr Area. https://arxiv.org/abs/2507.00069

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models

We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).

q-bio.OT↗
arXiv2026

Enhancing Clinical Decision Support and Differential Diagnosis with Knowledge Graphs, and Retrieval Augmented Generation in Generative AI

Diagnostic error carries a burden, while unconstrained large language models (LLMs) remain vulnerable to hallucination and weak integration of quantitative laboratory dynamics. We developed a decision-support pipeline combining disease-specific biomarker correlation graphs, ordinary differential equations (ODEs), deep sequence classification, and retrieval-augmented generation (RAG). For 103 disease classes from a full blood count (FBC) repository, biomarker networks were used as coupling matrices to generate 30 trajectories per disease (3,090 total). A one-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) network classified disease trajectories and six dynamical clusters. A constrained GPT-4o-mini RAG layer used a 19-pattern BMJ Best Practice/NICE corpus to generate differential diagnoses evaluated for diagnostic suitability, evidential grounding, and clinical plausibility. Across five random-seed runs, disease-level accuracy was $0.940 \pm 0.006$ for the CNN (95\% CI 0.933--0.948) and $0.852 \pm 0.019$ for the LSTM (95\% CI 0.828--0.875); the CNN advantage was 8.87 percentage points (95\% CI 6.47--11.27; $t(4)=10.26$, $p=5.1\times10^{-4}$; Hedges' $g=3.67$). Among 100 sampled RAG cases, 96 parsed successfully; evidence was cited in 97.9\%, the true diagnosis was mentioned in 71.9\%, and the composite score was 3.82/5 with a 47.9\% strict pass rate. The central finding was a decoupling between grounding and diagnostic correctness: classifier-correct versus classifier-wrong outputs differed in diagnostic suitability but not evidential grounding. Post-hoc analysis confirmed a 1.02-point diagnostic-score difference (Mann--Whitney $p=0.0024$; Hedges' $g=0.72$), whereas grounding differed by only $-0.02$ points ($p=0.839$; $g=-0.04$).

q-bio.OT↗
arXiv2026

Expanding the Human Ancestry Ontology to include under-represented populations and ethnicities for broader utility in annotations

Successful discovery, integration and reuse of data relies on the availability of rich, well-structured and machine-readable metadata to describe every aspect of the data, from sample sources to collection processes to experimental protocols. The use of standardised terminologies to express concepts in a harmonised fashion lies at the core of high-quality data annotation, increasing the FAIRness of the data, facilitating data integration and promoting reproducibility. Here, we describe the Human Ancestry Ontology (HANCESTRO), originally developed to improve standardised reporting of genetic ancestry genomic resources such as the NHGRI-EBI GWAS Catalog and the Human Cell Atlas through high-level population descriptors, and more recently expanded to include diverse and previously under-represented populations in genomics and genetics research. HANCESTRO provides a framework for population descriptors that includes both ancestry based on the analysis of genetic information and self-reported ethnicity, which is based on social and cultural factors that don't necessarily align with genetic populations. By enabling the accurate and interoperable representation of population-related data, it promotes inclusive, representative and reproducible science.

q-bio.OT↗