Biology subjects

Bheemanahalli, R.

Publications and source records attributed to Bheemanahalli, R..

3 recordsLinked to original sources

bioRxiv2024

Negative Synergistic Effects of Drought and Heat During Flowering and Seed Setting in Soybean

Rising temperatures and intense heatwaves combined with lower precipitations are the new norms of current global scenarios. These altered climatic conditions negatively impact soybean yield potential and quality. Ten soybean cultivars were subjected to four different growing conditions: control, drought, heat, and combined heat and drought to understand the physiological, yield, and molecular changes. Stomatal conductance was reduced by 62% and 10% under drought and heat, respectively. This reduction was further exacerbated to 93% when exposed to both stresses simultaneously. The highest canopy temperature was recorded at +8 {degrees}C with combinatorial treatments, whereas heat and drought exhibited +5.4 {degrees}C and +2 {degrees}C, respectively. Furthermore, combined stress displayed a more pronounced negative impact on greenness-associated vegetative index; the gene expression analysis further corroborated these findings. Particularly, each {degrees}C increase in temperature during flowering-seed filling reduced seed weight by [~]7% and [~]4% with and without drought, respectively. The seed protein increased under drought, whereas the oil showed a converse trend under drought and combined stresses. Most physiology and yield traits showed no significant correlations between control or individual and combined stress. This suggests that selection for combinatorial stress may not be appropriate based on nonstress or individual stress performance. Thus, incorporating stress-resilient traits into elite soybean cultivars could significantly boost soybean production under hot and dry climatic conditions.

plant biology↗

bioRxiv2021

Evidence for microbiome-dependent chilling tolerance in Sorghum

Early season chilling stress is a major constraint on sorghum production in temperate climates. Chilling-tolerant sorghum is an active area of development, but the potential for early season microbial-enhanced chilling tolerance in sorghum has not yet been explored. In this study, we characterized traits of field-grown sorghum accessions in response to chilling and non-chilling temperatures and the corresponding cohorts of phyllosphere fungal and bacterial taxa. Further, we characterized the effects of chilling temperatures and microbial inoculation on sorghum accession traits in a growth chamber experiment. By comparing sorghum trait responses under chilling stress with and without soil microbial inoculation, we were able to detect a potential microbe-dependent sorghum response to chilling stress. Four sorghum genotypes showed a negative response to chilling stress with vs. without microbial inoculation, while five sorghum accessions show increased shoot biomass or leaf area under chilling stress when inoculated with a soil microbiome. These differential responses provides opportunities to exploit beneficial microbial taxa for enhancing early-stage chilling tolerance in sorghum, with a potential to be extended to other crops.

plant biology↗

bioRxiv2018

Root Anatomy based on Root Cross-Section Image Analysis with Deep Learning

Aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has potential to further increase food production. However, belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. To address this limitation, we use a fully automated approach, specifically, the Faster Region-based Convolutional Neural Network (Faster R-CNN), to identify anatomical traits in root cross-section images. By training Faster R-CNN models on root cross-section images, we can detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes can be used to estimate the root diameter, stele diameter, late metaxylem number, and average diameter. Experimental evaluation using standard object detection metrics, such as intersection-over-union and mean average precision, has shown that the Faster R-CNN models trained on rice root cross-section images can accurately detect root, stele and late metaxylem objects. Furthermore, the results have shown that the measurements estimated based on predicted bounding boxes have small root mean square error when compared with the corresponding ground truth values, suggesting that Faster R-CNN can be used to accurately detect anatomical features. A webserver for performing root anatomy using the Faster R-CNN models trained on rice images is available at https://rootanatomy.org, together with a link to a GitHub repository that contains a copy of the Faster R-CNN code. The labeled images used for training and evaluating the Faster R-CNN models are also available from the GitHub repository.

bioinformatics↗