Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models

Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential of time-series S-2 data still remains unclear. To fill this gap, this study introduced an innovative approach for mining time-series data. Using 200 top soil organic carbon samples as an example, we revealed temporal variation patterns in the correlation between SOC and time-series S-2 data and subsequently identified the optimal monitoring time window for SOC. The integration of environmental covariates with multiple ensemble models enabled precise mapping of SOC in the arid region of southern Xinjiang, China (6109 km2). Our results indicated the following: (a) the correlation between SOC and time-series S-2 data exhibited both interannual and monthly variations, while July to August is the optimal monitoring time window for SOC; (b) adding soil properties and S-2 texture information could greatly improve the accuracy of SOC prediction models. Soil properties and S-2 texture information contribute 8.85% and 61.78% to the best model, respectively; (c) among different ensemble models, the stacking ensemble model outperformed both the weight averaging and sample averaging ensemble models in terms of prediction performance. Therefore, our study proved that mining spectral and texture information from the optimal monitoring time window, integrated with environmental covariates and ensemble models, has a high potential for accurate SOC mapping.

Salidroside sensitizes Triple-negative breast cancer to ferroptosis by SCD1-mediated lipogenesis and NCOA4-mediated ferritinophagy

INTRODUCTION

Triple-negative breast cancer (TNBC) is the primary cause of breast cancer-induced death in women. Literature has confirmed the benefits of Salidroside (Sal) in treating TNBC. However, the study about potential therapeutic targets and mechanisms of Sal-anchored TNBC remains limited.

OBJECTIVE

This study was designed to explore the main targets and potential mechanisms of Sal against TNBC.

METHODS

Network pharmacology, bioinformatics, and machine learning algorithm strategies were integrated to examine the role, potential targets, and mechanisms of the Sal act in TNBC. MDA-MB-231 cells and tumor-bearing nude mice were chosen for in vitro and in vivo experimentation. Cell viability and cytotoxicity were determined using CCK-8, LDH test, and Calcein-AM/PI staining. Antioxidant defense, lipid peroxidation, and iron metabolism were explored using glutathione, glutathione peroxidase, malondialdehyde (MDA), C11-BODIPY 581/591 probe, and FerroOrange dye. Glutathione peroxidase 4 (GPX4) or stearoyl-CoA desaturase 1 (SCD1) overexpression or nuclear receptor co-activator 4 (NCOA4) deficiency was performed to demonstrate the mechanism of Sal on TNBC.

RESULTS

The prediction results confirmed that 22 ferroptosis-related genes were identified in Sal and TNBC, revealing that the potential mechanism of the Sal act on TNBC was linked with ferroptosis. Besides, these genes were mainly involved in the mTOR, PI3K/AKT, and autophagy signaling pathway by functional enrichment analysis. The in vitro validation results confirmed that Sal inhibited TNBC cell proliferation by modulating ferroptosis via elevation of intracellular Fe2+ and lipid peroxidation. Mechanistically, Sal sensitized TNBC cells to ferroptosis by inhibiting the PI3K/AKT/mTOR axis, thereby suppressing SCD1-mediated lipogenesis of monounsaturated fatty acids to induce lipid peroxidation, additionally facilitating NCOA4-mediated ferritinophagy to increase intracellular Fe2+ content. The GPX4 or SCD1 overexpression or NCOA4 deficiency results further supported our mechanistic studies. In vivo experimentation confirmed that Sal is vital for slowing down tumor growth by inducing ferroptosis.

CONCLUSIONS

Overall, this study elucidates TNBC pathogenesis closely linked to ferroptosis and identifies potential biomarkers in TNBC. Meanwhile, the study elucidates that Sal sensitizes TNBC to ferroptosis by SCD1-mediated lipogenesis and NCOA4-mediated ferritinophagy, regulated by PI3K/AKT/mTOR signaling pathways. Our findings provide a theoretical basis for applying Sal to treat TNBC.

A critical systematic review on spectral-based soil nutrient prediction using machine learning

The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability.

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha ^-1), 6.64 (Mg C ha^-1), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha^-1, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha^-1 and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha^-1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha^-1). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.