Biology subjects

Ray, I.

Publications and source records attributed to Ray, I..

5 recordsLinked to original sources

bioRxiv2026

Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials

Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.

genetics↗

bioRxiv2024

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa

Multi-spectral imaging (MSI) collection by unoccupied aerial vehicles (UAV) is an important tool to measure growth of forage crops. Information from estimated growth curves can be used to infer harvest biomass and to gain insights in the relationship of growth dynamics and harvest biomass stability across cuttings and years. In this study, we used MSI to evaluate Alfalfa (Medicago sativa L. subsp. sativa) to understand the longitudinal relationship between vegetative indices (VIs) and forage/biomass, as well as evaluation of irrigation treatments and genotype by environment interactions (GEI) of different alfalfa cultivars. Alfalfa is a widely cultivated perennial forage crop grown for high yield, nutritious forage quality for feed rations, tolerance to abiotic stress, and nitrogen fixation properties in crop rotations. The direct relationship between biomass and VIs such as Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red edge normalized difference vegetation index (NDRE), and Near infrared (NIR) provide a non-destructive and high throughput approach to measure biomass accumulation over subsequent alfalfa harvests. In this study, we aimed to estimate the genetic parameters of alfalfa VIs and utilize longitudinal modeling of VIs over growing seasons to identify potential relationships between stability in growth parameters and cultivar stability for alfalfa biomass yield across cuttings and years. We found VIs of GNDVI, NDRE, NDVI, NIR and simple ratios to be moderately heritable with median values for the field trial in Ithaca, NY to be 0.64, 0.56, 0.45, 0.45 and 0.40 respectively, Normal Irrigation (NI) trial in Leyendecker, NM to be 0.3967, 0.3813, 0.3751, 0.3239 and 0.3019 respectively, and Summer Irrigation Termination (SIT) trial in Leyendecker, NM to be of 0.11225, 0.1389, 0.1375, 0.2539 and 0.1343, respectively. Genetic correlations between NDVI and harvest biomass ranged from 0.52 - .99 in 2020 and 0.08 - .99 in 2021 in the NY trial. Genetic correlations for NI trial in NM for NDVI ranged from 0.72 - .98 in 2021 and SIT ranged from 0.34-1.0 in 2021. Genotype by genotype by interaction (GGE) biplots were used to differentiate between stable and unstable cultivars for locations NY and NM, and Random regression modeling approaches were used to estimate growth parameters for each cutting. Results showed high correspondence between stability in growth parameters and stability, or persistency, in harvest biomass across cuttings and years. In NM, the SIT trial showed more variation in growth curves due to stress conditions. The temporal growth curves derived from NDVI, NIR and Simple ratio were found to be the best phenotypic indices on studying the stability of growth parameters across different harvests. The strong correlation between VIs and biomass present opportunities for more efficient screening of cultivars, and the correlation between estimated growth parameters and harvest biomass suggest longitudinal modeling of VIs can provide insights into temporal factors influencing cultivar stability.

genetics↗

bioRxiv2023

Reproductive biology of the mudskippers Boleophthalmus boddarti and Periophthalmus novemradiatus in the Indian Sundarbans

Reproductive biology of mudskippers Boleophthalmus boddarti and Periophthalmus novemradiatus are investigated along the mudflats of the Indian Sundarbans. We investigated a total of 794 sampes for both male and female Boleophthalmus boddarti and Periophthalmus novemradiatus fish species to explore properties such as the size-at-50% maturity, fecundity, spawning, gona-dosomatic and hepatosomatic indices along with the characterization of the macroscopic and histological attributes of the female gonads. Our work reports for the first time the weight-length relationships for these fish species in the Sundarbans region depicting a negative allometric growth pattern. The size-at-50% maturity which is often a crucial parameter for stock and reserve management is found for the female and male Boleophthalmus boddarti as 10.2 cm and 9.9 cm respectively, whereas, for the Periophthalmus novemradiatus, the same were 4.0 cm and 4.4 cm, respectively. Fully matured female Boleophthalmus boddarti were found to be multiple spawners in comparison to Periophthalmus novemradiatus with monthly variation of Le Crens relative condition [≤]1, indicating good physiological conditions of the fish. The relationships between batch fecundity (F), body weight (i.e., BW, gm) and total length (i.e., TL, cm) were Log10F = 1.12 Log10BW + 2.41 and Log10F = 1.93 Log10TL + 1.82, respectively for Boleoph-thalmus boddarti and for the Periophthalmus novemradiatus, it was Log10F = 2.15 Log10BW + 2.57 and Log10F = 5.43 Log10TL - 0.81. The reproductive parameters estimated in this study are important information for studying population dynamics and stock assessments of these mudskippers in the Indian Sundarbans and will contribute to the management, conservation and prediction of environmental changes as these mudskippers are often studied as bio-indicators for climate-change effects.

zoology↗

bioRxiv2019

Securing the exchange of synthetic genetic constructs using digital signatures

Synthetic biology relies on an ever-growing supply chain of synthetic genetic material. Technologies to secure the exchange of this material are still in their infancy. Solutions proposed thus far have focused on watermarks, a dated security approach that can be used to claim authorship, but is subject to counterfeit, and does not provide any information about the integrity of the genetic material itself. We describe how data encryption and digital signature algorithms can be used to ensure the integrity and authenticity of synthetic genetic constructs. Using a pilot software that generates digital signatures and other encrypted data for plasmids, we demonstrate that we can predictably extract information about the author, the identity, and the integrity of plasmid sequences from sequencing data alone without a reference sequence, all without compromising the function of the plasmids. We discuss how this technology can be improved, applied, and expanded to support the new bioeconomy.

synthetic biology↗

bioRxiv2019

Catestatin improves insulin sensitivity in diet-induced obese mice: in vivo and in silico validation

Obesity is characterized by a state of chronic, unresolved inflammation in insulin-targeted tissues. Obesity-induced inflammation accumulates proinflammatory macrophages in adipose tissue and liver. Proinflammatory cytokines released from tissue macrophages inhibits insulin sensitivity. Chromogranin A (CgA) peptide catestatin (CST: hCgA352-372) improves obesity-induced hepatic insulin resistance by reducing inflammation and inhibiting proinflammatory macrophage infiltration. Obesity leads to inflammation-induced endoplasmic reticulum (ER) stress and insulin resistance. We reasoned that the anti-inflammatory effects of CST would alleviate ER stress. CST decreased obesity-induced ER dilation in hepatocytes and macrophages. CST reduced phosphorylation of UPR signaling molecules and increased phosphorylation of insulin signaling molecules. We developed an in silico state space model mimicking dynamics of integrated ER stress and insulin pathways. Proportional-Integral-Derivative (PID) controllers helped in checking whether the reduction of phosphorylated PERK resulting in attenuation of ER stress, resembling CST effect, could enhance insulin sensitivity. The simulation results showed CST not only decreased ER stress but also enhanced insulin sensitivity. Simulation results also revealed that enhancement of AKT phosphorylation overcame effects of high ER stress to achieve insulin sensitivity.

systems biology↗