Biology subjects

Gavish, M.

Publications and source records attributed to Gavish, M..

6 recordsLinked to original sources

bioRxiv2026

Designed nutrient-gradient phenotyping enables predictive ionomics of tropical leafy greens

Accurate prediction of edible-leaf mineral composition is challenging because tissue ionomes integrate nutrient supply, plant identity, growth dilution, water-balance regulation and cumulative environmental exposure. We combined a designed nutrient-gradient experimental framework with machine learning to predict leaf ionome using 1,163 Chinese spinach (Amaranthus dubius) and Chinese broccoli (Brassica oleracea Alboglabra Group) plants grown across 16 designed nutrient-gradient experiments in a semi-controlled tropical greenhouse. Targeted perturbations of N, P, K, Ca, Mg, Fe and Mo were combined with cumulative transpiration, integrated microclimate, shoot dry weight and laboratory quantification of 12 harvested-leaf mineral targets. The resulting ionome was broad, non-Gaussian and species dependent, with coordinated off-target shifts also evident for minerals whose supplied concentrations were held constant. We benchmarked AutoGluon and TabPFN using five-fold leakage-free, distribution-balanced grouped cross-validation, in which all biological replicates from each dosing condition were withheld together. Across previously unseen nutrient conditions, pooled out-of-fold R2 ranged from 0.72 to 0.92, with broadly comparable performance between architectures. Learning curves showed rapid early gains for several targets but persistent condition-level generalization gaps for total reduced nitrogen, K, Mn and Zn. Model-agnostic Shapley additive explanations identified target-specific combinations of nutrient inputs, plant type, substrate, cumulative environment and physiological traits, while cross-model attribution agreement varied among minerals. Finally, reduced-feature models were tested on an independent grower-operated cohort of 99 plants. Calibration was closest for NO3, Mg and B, whereas Mn and Na showed substantial external bias. These results establish leakage-aware predictive ionomics as a robust framework for post-harvest lab-free crop mineral estimation while defining the calibration and sensing requirements for commercial deployment.

plant biology↗

bioRxiv2025

Liposomal iron bisglycinate hydrochloride, but not free iron bisglycinate, enhances serum iron restoration in a mouse model of inflammation-induced anemia

Background/ObjectivesIron deficiency is a major global health concern, particularly in inflammation-related anemia (IRA), where iron absorption and mobilization are impaired. Liposomal encapsulation offers a promising strategy to enhance the bioavailability and efficacy of iron supplements. This study aimed to evaluate the therapeutic efficacy of orally administered liposomal iron bisglycinate hydrochloride (LIBH), compared to free iron bisglycinate hydrochloride (FIBH), in restoring serum iron levels in a mouse model of lipopolysaccharide (LPS)-induced anemia of inflammation (LIAI). MethodsMale C57BL/6 mice received intraperitoneal LPS (5 mg/kg) to induce LIAI and were simultaneously treated with either FIBH or LIBH (1 mg/kg, oral). Liposomes (150 - 200 nm) exhibited high encapsulation efficiency (93%) and stability (-38 to -45 mV zeta potential). Serum iron levels were measured 24 hours post-treatment. ResultsLPS administration significantly reduced serum iron levels. LIBH restored serum iron levels by 35 - 65% compared to baseline (p < 0.005), outperforming FIBH (0 - 16%, p = NS). Electron microscopy confirmed the structural integrity of LIBH liposomes. ConclusionLIBH supplementation significantly improves serum iron levels in LIAI and may represent a superior alternative to traditional free iron therapy, particularly in inflammatory conditions. Further studies are warranted to assess its efficacy in other models of anemia.

pharmacology and toxicology↗

bioRxiv2024

Harnessing Smartphone RGB Imagery and LiDAR Point Cloud for Enhanced Leaf Nitrogen and Shoot Biomass Assessment - Chinese Spinach as a Case Study

Accurate estimation of leaf nitrogen concentration and shoot dry-weight biomass in leafy vegetables is crucial for crop yield management, stress assessment, and nutrient optimization in precision agriculture. However, obtaining this information often requires access to reliable plant physiological and biophysical data, which typically involves sophisticated equipment, such as high-resolution in-situ sensors and cameras. In contrast, smartphone-based sensing provides a cost-effective, manual alternative for gathering accurate plant data. In this study, we propose an innovative approach for estimating leaf nitrogen concentration and shoot biomass by integrating smartphone RGB imagery with Light Detection and Ranging (LiDAR) data, using Amaranthus dubius (Chinese spinach) as a case study. The influence of varying nitrogen dosages on individual spectral and structural features derived from smartphone RGB imagery and LiDAR data was modeled. Additionally, the spectral indices from RGB imagery and structural indices from LiDAR data were combined to model both leaf nitrogen concentration and shoot biomass. The performance of crop parameter modeling was evaluated using support vector regression, random forest regression, and lasso regression. Results demonstrate that the combined use of smartphone RGB imagery and LiDAR data can accurately estimate leaf total reduced nitrogen concentration, leaf nitrate concentration, and shoot dry-weight biomass, with average relative root mean square errors as low as 0.06, 0.16, and 0.05, respectively. Furthermore, the optimal nitrogen dosage for maximizing biomass yield in Chinese spinach was also estimated using the smartphone data. This study lays the groundwork for smartphone-based estimate leaf nitrogen concentration and shoot biomass, supporting accessible precision agriculture practices.

bioinformatics↗

bioRxiv2024

Nitrogen ionome dynamics on leafy vegetables in tropical climate

Nitrogen is known to be a critical macro-nutrient influencing plant physiology, growth, and mineral composition. In tropical conditions, which are challenging for leafy vegetable farming, the nitrogen delivery effect is unclear. In this study, we aimed to investigate the effect of nitrogen application on key physiological traits and the mineral composition of the plants, the plant ionome. Experiments were conducted under tropical conditions greenhouse with varying levels of nitrogen supply to examine the effect on plant transpiration, yield, use efficiency of water and nitrogen, and nutrient uptake dynamics followed by cross-correlation analysis, trying to understand the physiological behavior-uptake dynamics relationships. The results demonstrated that transpiration, yield and WUE theoretic optimum curve, which peaking in nitrogen concentration of 120 mg/L for Chinese spinach and 200 mg/L for Chinese broccoli. Conversely, NUE reduce significantly with increasing nitrogen delivery which reflected on antagonistic increase of excess nitrogen. In terms of mineral composition, nitrogen application resulted in an increase I nitrogen content in the plant leaf tissue, while concentration of certain macronutrients and micronutrients were affected, including potassium, phosphorus, calcium, magnesium, iron, zinc, and molybdenum. Part of the minerals exhibited decreasing pattern due to potential competitive uptake mechanism, iron revealed increasing pattern that correlated with nitrogen delivery, and some minerals correlated with the measured physiological parameters. These results underscore the importance of optimizing nitrogen fertilization to balance plant growth, physiological processes, and plant nutrient homeostasis. The study offers valuable insights for sustainable nitrogen management in agricultural systems aimed at maximizing crop yield while maintaining nutritional quality.

plant biology↗

bioRxiv2023

From Selfies to Science - Precise 3D Leaf Measurement with iPhone 13 and Its Implications for Plant Development and Transpiration

Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R2=0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R2=0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R2=0.83) with actual canopy measurements. The iPhones surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R2=0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This TemplateThe template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the "Styles" menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com.

plant biology↗

bioRxiv2021

Evaluating the heterogeneous effect of extended incubation to blastocyst transfer on the implantation outcome via causal inference

In IVF treatments, extended culture to single blastocyst-transfer is the recommended protocol over cleavage-stage transfer. However, evidence-based criteria for assessing the heterogeneous implications on implantation outcome are lacking. To estimate the causal effect of blastocyst-transfer on implantation outcome, we assembled a multicenter dataset of embryo time-lapse imaging. The data includes a natural source of randomness and has a strong claim for satisfying the assumptions needed for valid causal inference. By fitting a causal forest model, we assessed the Transfer Lift, which quantifies the probability difference in embryo implantation if transferred as a blastocyst versus cleavage-stage. Blastocyst transfer increased the average implantation rate, however we revealed a subpopulation of negative Transfer Lift embryos whose implantation potential is predicted to increase via cleavage-stage transfer. We provide day-of-transfer decision-support tools that are retrospectively estimated to improve implantation rate by 32%, thus demonstrating the efficacy of embryo-level causal inference in reproductive medicine. One Sentence SummaryA causal inference model predicts the heterogeneous effect of prolonged incubation to blastocyst transfer on embryo implantation, thus providing means for optimizing pregnancy rates in IVF treatments.

bioengineering↗