SearcharxivSearch

arXiv · 2409.14345

Evaluation of drought tolerance of some almond genotypes by morphological, phytochemical and molecular markers in Sulaymaniyah governorate

Abstract

The study was carried out during 2017 to 2019 growing seasons at four locations in Sulaimani governorate and one location in Halabja governorate, in the Iraqi Kurdistan region including SH, M, Q, B and H. A huge number almond trees were observed for all locations, among them 38 trees were selected with the best morphological characteristics which were chosen 9,3,5,7 and 14 trees depending on the locations, respectively. A simple experiment was conducted using RCBD for this experiment and means was separated by Duncans test. In order to evaluate their tolerances to drought in glass house, an experiment was conducted at Department of Horticulture, College of Agricultural Engineering Sciences, University of Sulaimani, that seeds were taken from those genotype trees and stratified then sown in pots. A factorial RCBD experiment was used with two factors genotypes and irrigation intervals. Therefore, thirty eight seedling genotypes grown in pots under glasshouse condition were exposed to three irrigation intervals 10,20 and 40 days after 10 days from seedling emergence. Therefore, the number of treatment combinations was 114 seedlings for each replicate and with a total 342 seedlings for the whole experiment. Analysis of variance was carried out and the means were compared according to LSD 0.05. As a result of the study, the seedlings showed different levels of adaptation to drought that can be used to future breeding programs as rootstocks. The objectives of this study were to identify morphological, phytochemical and genetic diversity with relatedness among the most important almond genotypes in Sulaimani Region which related to drought tolerance to and relationship between morphological, biochemical and molecular data.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anwar Mohammed Raouf Mahmood. 2024-09-22. Evaluation of drought tolerance of some almond genotypes by morphological, phytochemical and molecular markers in Sulaymaniyah governorate. https://arxiv.org/abs/2409.14345

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

KEEP EXPLORING

Related papers

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

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

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