Synergy effect of silicate fertilizer and iron slag: A sustainable approach for mitigating methane emission in rice farming.

Although silicate fertilizer has been recently recognized for its ability to suppress methane (CH4) emissions in paddy fields, the effects of its consecutive application during the rice farming period are still a subject of debate. Moreover, while it was known that silicate fertilizer can mitigate CH4 emissions through several electron acceptors, the effect of additional application of electron acceptors have not been extensively studied. This study evaluated the effect of silicate fertilizer with varying concentrations of iron slag on CH4 emissions and rice yield over the 3 years rice farming period. Seasonal CH4 fluxes exhibited a significant decrease with the application of silicate fertilizer, with the treatment containing 2.5 % iron slag showing the maximum reduction of 35 % in 2020. Additionally, in 2021 and 2022, the application of silicate fertilizer with 2.5 % iron slag resulted in a decrease of total seasonal CH4 emission by 22 % and 23 %, respectively. Rice grain yield exhibited a significant increase with the inclusion of iron slag in the silicate fertilizer, which resulted in a 37 % and 16 % higher yield compared to no-silicate fertilization and no­iron slag silicate fertilization, respectively. Therefore, iron slag-based silicate fertilizer could be a beneficial soil amendment to mitigate CH4 emissions in rice paddy fields and improve rice productivity without negative effects on the atmospheric and soil ecosystem.

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip.

Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics. Conventionally, most deep-learning applications require task specific large scale expertly annotated datasets. Therefore, these algorithms are oftentimes limited only to applications that have large retrospective datasets available for network development. Here, we report the possibility of utilizing adversarial neural networks to overcome this challenge by expanding the utility of non-specific data for the development of deep learning models. As a clinical model, we report the detection of fentanyl, a small molecular weight drug that is a type of opioid, at the point-of-care using a deep-learning empowered smartphone assay. We used the catalytic property of platinum nanoparticles (PtNPs) in a smartphone-enabled microchip bubbling assay to achieve high analytical sensitivity (detecting fentanyl at concentrations as low as 0.23 ng mL-1 in phosphate buffered saline (PBS), 0.43 ng mL-1 in human serum and 0.64 ng mL-1 in artificial human urine). Image-based inferences were made by our adversarial-based SPyDERMAN network that was developed using a limited dataset of 104 smartphone images of microchips with bubble signals from tests performed with known fentanyl concentrations and using our retrospective library of 17 573 non-specific bubbling-microchip images. The accuracy (± standard error of mean) of the developed system in determining the presence of fentanyl, when using a cutoff concentration of 1 ng mL-1, was 93 ± 0% in human serum (n = 100) and 95.3 ± 1.5% in artificial human urine (n = 100).

Study of Quantitative Trait Loci (QTLs) Associated with Allelopathic Trait in Rice.

In rice there are few genetic studies reported for allelopathy traits, which signify the ability of plants to inhibit or stimulate growth of other plants in the environment, by exuding chemicals. QTL analysis for allelopathic traits were conducted with 98 F8 RILs developed from the cross between the high allelopathic parents of 'Sathi' and non-allelopathic parents of 'Nong-an'. The performance of allelopathic traits were evaluated with inhibition rate on root length, shoot length, total length, root weight, shoot weight, and total weight of lettuce as a receiver plant. With 785 polymorphic DNA markers, we constructed a linkage map showing a total of 2489.75 cM genetic length and 3.17 cM of average genetic distance between each adjacent marker. QTL analysis detected on QTL regions on chromosome 8 responsible for the inhibition of shoot length and inhibition of total length. The qISL-8 explained 20.38% of the phenotypic variation for the inhibition on the shoot length. The qITL-8 explained 14.93% of the phenotypic variation for the inhibition on total length. The physical distance of the detected QTL region was 194 Kbp where 31 genes are located.