Proteomics analysis reveals multiple regulatory mechanisms in response to selenium in rice.

Mixotrophic culture of high selenium-enriched Spirulina platensis on acetate and the enhanced production of photosynthetic pigments

Pigments absorb light, transform it into energy, and provide reaction sites for photosynthesis; thus, the quantification of pigment distribution is vital to plant research. Traditional methods for the quantification of pigments are time-consuming and not suitable for the high-throughput digitization of rice pigment distribution. In this study, using a hyperspectral imaging system, we developed an integrated image analysis pipeline for automatically processing enormous amounts of hyperspectral data. We also built models for accurately quantifying 4 pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoid) from rice leaves and determined the important bands (700-760 nm) associated with these pigments. At the tillering stage, the R² values and mean absolute percentage errors of the models were 0.827-0.928 and 6.94%-12.84%, respectively. The hyperspectral data and these models can be combined for digitizing the distribution of the chlorophyll with high resolution (0.11 mm/pixel). In summary, the integrated hyperspectral image analysis pipeline and selected models can be used to quantify the chlorophyll distribution in rice leaves. The use of this technique will benefit rice functional genomics and rice breeding.

/pdf/accurate-digitization-of-the-chlorophyll-distribution-of-pe4j3bze2n.pdf

Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline


 A rapid multiple biomolecules based life detection protocol (MBLDP-R) from soil samples is proposed to embed in a scientific rover subsystem targeted for planetary analysis missions complying the guidelines of Science Mission of University Rover Challenge 2021 (URC 2021). The proposed protocol selects suitable biomolecules from a preliminary list through a requirement analysis driven filtration process emphasizing two factors: a) rules of URC 2021 and b) compatibility of the biomolecule test equipment to be embedded in a rover subsystem. To sort out the best test methods for finally selected biomolecules, a weighted qualitative test scoring methodology is applied. A rover subsystem that implements the protocol was built to perform onboard sample analysis. Evaluation results show that: 1) ) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro) and the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% Extant, 88% Extinct and 92 % in case of NPL) and 2) the protocol is time-efficient with an average completion time of 17.60 minutes that demonstrates the rapid nature of the protocol to detect bio signatures in soil samples.

MBLDP-R: A Multiple Biomolecules Based Rapid Life Detection Protocol Embedded in a Rover Scientific Subsystem for Soil Sample Analysis

Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand’s unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm−2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

Simultaneous determination of carotenoids and chlorophylls in algae by high performance liquid chromatography

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Simultaneous determination of carotenoids and chlorophylls in algae by high performance liquid chromatography