Zinc portioning and allocation patterns among various tissues confers variations in Zn use efficiency and bioavailability in lentil genotypes
Bibliographic record
Abstract
Zinc (Zn) is essential for plants and animals as it plays significant roles in several physiological and biological processes. Its deficiency in soil results in low Zn content food and is one of the major reasons for Zn malnutrition in humans. Biofortification of crops with zinc (Zn) is a viable approach to combat malnutrition, especially in developing countries. A hydroponic study was executed to study response and Zn partitioning in various lentil genotypes. Eight preselected lentil genotypes (Line-11504, Mansehra-89, Masoor-2006, Masoor-85, Line-10502, Markaz-09, Masoor-2004, and Shiraz-96) were grown in solution culture with two Zn levels (control and adequate Zn). Plants were sown in polythene lined iron trays with a two inch layer of prewashed riverbed sand. After 10 days of germination, seedlings were transplanted to a 25L capacity container with nutrient solution for 15 days, and afterward, these plants were divided into two groups, receiving either 2.0 mM Zn or no Zn levels. Three plants of each genotype were harvested at the vegetative growth stage (60 DAT) and the remaining three at physiological maturity (117 DAT). Plants were partitioned into roots, shoots, and grains at harvest. Significant variations in root and shoot dry matter production, grain output, partitioning of Zn in plant parts (root, shoot, and grain), grain phytate reduction, and Zn bioavailability were observed among genotypes. Lentil root accumulated more Zn (54 mg kg-1) with respect to shoot Zn (51 mg kg-1) under Zn supply. The Zn efficient genotypes (Line-11504 and Mansehra-89) produced more root and shoot dry weights at both harvests. There was a positive correlation between the relative growth rate of root and grain phytate concentration (r = 0.55) and [phytate]:[Zn] ratio (r = 0.67). Zn-efficient genotype Mansehra-89 had a maximum root shoot ratio (0.57) and higher grain Zn (60 mg kg-1) with a respectively reduced grain phytate (17 µg g-1) and thus, had more Zn bioavailability (3.01 mg d-1). The genotypic ability for Zn uptake and accumulation within different plant tissues may be incorporated into future crop breeding to improve the nutrition of undernourished consumers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".