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Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various remote sensing problems. To fully leverage the power of AI and ML to tackle impactful remote sensing problems, it is essential to enable researchers and practitioners to understand how AI and ML models actually work and thus to improve the model performance strategically. Accurate and timely land cover maps are essential components for informed land management decision making. To address the ever-increasing need for high spatial and temporal resolution maps, this paper developed an interactive and open-source online tool, in Python, to help interpret and improve the ML models used for land cover mapping with Google Earth Engine (GEE). The tool integrates the workflow of both land cover classification and land cover change dynamics, which requires the generation of a time series of land cover maps. Three feature importance metrics are reported, including impurity-based, permutation-based, and SHAP (Shapley additive explanations) value-based feature importance. Two case studies are presented to showcase the tool’s capability and ease of use, enabling a globally accessible and free convergent application of remote sensing technologies. This tool may inspire researchers to facilitate explainable AI (XAI)-empowered remote sensing applications with GEE.

Hydraulic excavators are among the most energy-intensive machines in construction and mining, with conventional hydraulic systems often operating under fixed pressure and flow settings that lead to significant energy loss. Improving energy efficiency while ensuring safety and adaptability under uncertain operating conditions remains a critical challenge. This study proposes a novel adaptive control framework that integrates Bayesian inference with reinforcement learning (RL) to enhance energy recuperation in hydraulic excavator arms. The framework explicitly models system dynamics, including hydraulic cylinders, pumps, valves, and accumulators, while accounting for uncertainties from soil resistance, temperature-dependent viscosity, component wear, and sensor noise. A Bayesian particle filter is employed to continuously estimate latent states such as soil resistance multipliers and accumulator pre-charge offsets, enabling belief-space reinforcement learning to make informed control decisions. The learned control policy adjusts pump pressure and valve commands in real time, while a safety-projection layer enforces strict operational constraints (5–35 MPa hydraulic pressure, 12–28 MPa accumulator window, valve rate limits, and section-level relief protections).

Polylactic acid (PLA) scaffolds with triply periodic minimal surface (TPMS) structures have become ideal scaffolds in the field of bone defect repair due to their good designability, connectivity, biocompatibility, and degradability. However, it is currently difficult to obtain the scaffold degradation rate and osteogenic efficacy from in vivo experiments, making it challenging to provide recommendations for scaffold design. In this study, an algorithm to construct a TPMS scaffold–interfacial layer–tissue three-phase composite model was developed using polylactic acid hydrolysis and bone remodeling as the governing equations to simulate scaffold degradation and tissue osteogenesis behavior under an external mechanical stimulus. This method is based on a numerical calculation framework that can more closely simulate the in vivo environment, characterizing the changes in the overall macroscopic mechanical properties of tissue under the influence of scaffold degradation and tissue osteogenesis. The results confirmed the accelerating effect of mechanical stimulation on scaffold degradation and its promoting effect on new bone formation. Under 10% compressive loading, the Schwarz P representative volume element (RVE) lost 33% of its apparent modulus within initial days, while the lidinoid RVE, despite showing a much higher initial modulus, dropped to only 20% of its initial value over the same period. In addition, the mechanical performance of the fused TPMS RVE was not simply linear, even though the surface equations are combined linearly. These results provide a new method for pre-designing scaffold structures based on numerical simulation results using the finite element simulation.

In the field of agriculture, automated harvesting of fruit has become an important research area. However, accurately detecting fruit in a natural environment is a challenging task. The task of accurately detecting fruit in natural environments is complex due to factors such as shadows, which can impede the performance of traditional detection techniques, highlighting the need for more robust methods. To overcome these challenges, we propose an efficient deep learning method called YOLO-CFruit, which is specifically designed to accurately detect Camellia oleifera fruits in challenging natural environments. First, we collected images of fruits and created a dataset, and then used a data enhancement method to further enhance the diversity of the dataset. Our YOLO-CFruit model combines a CBAM module for identifying regions of interest in landscapes with Camellia oleifera fruit and a CSP module with Transformer for capturing global information. In addition, we improve YOLOCFruit by replacing the CIoU Loss with the EIoU Loss in the original YOLOv5. By testing the training network, we find that the method performs well, achieving an average precision of 98.2%, a recall of 94.5%, an accuracy of 98%, an F1 score of 96.2, and a frame rate of 19.02 ms. The experimental results show that our method improves the average precision by 1.2% and achieves the highest accuracy and higher F1 score among all state-of-the-art networks compared to the conventional YOLOv5s network. The robust performance of YOLO-CFruit under different real-world conditions, including different light and shading scenarios, signifies its high reliability and lays a solid foundation for the development of automated picking devices.

Background Glycolysis under normoxic conditions, known as the Warburg effect, confers a selective advantage for the survival and proliferation of many tumors. In this study, we investigated the role of estrogen-related receptor gamma (ESRRG) in metabolic reprogramming in esophageal squamous cell carcinoma (ESCC). Methods Bioinformatics analysis indicated that ESRRG expression was decreased in ESCC tissue and associated with poor clinical outcomes. We also examined the effects of altered ESRRG expression on the proliferation and metabolic reprogramming of ESCC cells. We explored the impact of ESRRG on Pyruvate kinase M2 (PKM2) expression and malignant behavior in ESCC. Results Our study revealed the inhibitory effects of ESRRG on the growth, tumorigenesis, and glycolysis activity of ESCC cells, which were mediated by the downregulation of PKM2 expression. We further demonstrated that ESRRG directly interacts with the PKM2 promoter to inhibit its activity in ESCC. Notably, the ESRRG-specific agonist, DY131, inhibited ESCC cell proliferation and glycolysis activity by modulating genes in the glycolysis pathway. Moreover, we verified that DY131 exhibits enhanced activity as an immune checkpoint inhibitor, considering the significance of the ESRRG-PKM2 axis in the lactate regulation of ESCC cells. Conclusion Our findings provide novel insights into the role of ESRRG-PKM2 signaling in regulating ESCC cell metabolism and immune checkpoint regulation. Additionally, we suggest that DY131 holds promise as a promising therapeutic agent for ESCC treatment.

Background The efficacy of anti-PD-1 therapy is primarily hindered by the limited T-cell immune response rate and immune evasion capacity of tumor cells. Autophagy-related protein 7 (ATG7) plays an important role in autophagy and it has been linked to cancer. However, the role of ATG7 in the effect of immune checkpoint blockade (ICB) treatment on high microsatellite instability (MSI-H)/mismatch repair deficiency (dMMR) CRC is still poorly understood. Methods In this study, patients from the cancer genome altas (TCGA) COAD/READ cohorts were used to investigate the biological mechanism driving ATG7 development. Several assays were conducted including the colony formation, cell viability, qRT-PCR, western blot, immunofluorescence, flow cytometry, ELISA, immunohistochemistry staining and in vivo tumorigenicity tests. Results We found that ATG7 plays a crucial role in MSI-H CRC. Its knockdown decreased tumor growth and caused an infiltration of CD8 + T effector cells in vivo. ATG7 inhibition restored surface major histocompatibility complex I (MHC-I) levels, causing improved antigen presentation and anti-tumor T cell response by activating reactive oxygen species (ROS)/NF-κB pathway. Meanwhile, ATG7 inhibition also suppressed cholesterol accumulation and augmentation of anti-tumor immune responses. Combining ATG7 inhibition and statins improved the therapeutic benefit of anti-PD-1 in MSI-H CRC. Importantly, CRC patients with high expression of both ATG7 and recombinant 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) experienced worse prognosis compared to those with low ATG7 and HMGCR expression. Conclusions Inhibition of ATG7 leads to upregulation of MHC-I expression, augments immune response and suppresses cholesterol accumulation. These findings demonstrate that ATG7 inhibition has therapeutic potential and application of statins can increase the sensitivity to immune checkpoint inhibitors.

Background and aims Nitrogen (N) deficiency and drought are two key limiting factors for rice production worldwide, but the relationship of drought stress with N homeostasis in rice is rarely advanced. The aim of this study was to dissect the physiological effects of drought stress on rice growth that coupled unbalanced N metabolism. Results Water-deficient stress (WD) limited stomatal aperture function and activity of Rubisco carboxylase to photosynthesis. The rate of total electron transport (Jt) and the electron to carboxylation (Jc) were considerably decreased, whereas the proportion of e− flow to photorespiration was stimulated by WD, especially at 1600 μmol m−2 s−1 PPFD. Concurrently, the expressions of glycolate oxidase genes (GOX1, GOX5) and glycine decarboxylase complex (GDCH, GDCP and GDCT) were significantly induced in leaves of WD treatment, which led to the accumulation of reactive oxygen species in leaves. With the photosynthetic change, nitrate uptake and reduction were suppressed. Moreover, the enhanced photorespiration generated excess NH3 accumulation in leaves and stimulated the expressions of GS1;1, GS1;2 and GS2, which were tightly coupled with the expressions of PEPC1 and PEPC2 under WD stress. Conclusions Our results suggest that the inhibited nitrate reduction associated with diminished electron transport rate, and the photorespiration-associated accumulation of hydrogen peroxide and NH3 were critical in the drought-induced rice growth inhibition.

pH is an important factor regulating plant growth. Here, we found that rice was better adapted to low pH than alkaline conditions, as its growth was severely inhibited at high pH, with shorter root length and an extreme biomass reduction. Under alkaline stress, the expression of genes for ethylene biosynthesis enzymes in rice roots was strongly induced by high pH and exogenous ethylene precursor ACC and ethylene overproduction in mutant aggravated the alkaline stress-mediated inhibition of rice growth, especially for the root elongation with decreased cell length in root apical regions. Conversely, the ethylene perception antagonist silver (Ag ) and mutants could partly alleviate the alkaline-induced root elongation inhibition. The H -ATPase activity was extremely inhibited by alkaline stress and exogenous ACC. However, the H -ATPase-mediated rhizosphere acidification was enhanced by exogenous Ag , while H efflux on the root surface was extremely inhibited by exogenous ACC, suggesting that ethylene negatively regulated H -ATPase activity under high-pH stress. Our results demonstrate that H -ATPase is involved in ethylene-mediated inhibition of rice growth under alkaline stress.

Several mechanisms have been proposed to explain NH 4 + toxicity. However, the core information about the biochemical regulation of plants in response to NH 4 + toxicity is still lacking. In this study, the tissue NH 4 + concentration is an important factor contributing to variations in plant growth even under nitrate nutrition and NH 4 + tolerance under ammonium nutrition. Furthermore, NH 4 + led to the reprogramming of the transcriptional profile, as genes related to trehalose-6-phosphate and zeatin biosynthesis were downregulated, whereas genes related to nitrogen metabolism, camalexin, stilbenoid and phenylpropanoid biosynthesis were upregulated. Further analysis revealed that a large number of genes, which enriched in phenylpropanoid and stilbenoid biosynthesis, were uniquely upregulated in the NH 4 + - tolerant ecotype Or-1. These results suggested that the NH 4 + -tolerant ecotype showed a more intense response to NH 4 + by activating defense processes and pathways. Importantly, the tolerant ecotype had a higher 15 NH 4 + uptake and nitrogen utilization efficiency, but lower NH 4 + , indicating the tolerant ecotype maintained a low NH 4 + level, mainly by promoting NH 4 + assimilation rather than inhibiting NH 4 + uptake. The carbon and nitrogen metabolism analysis revealed that the tolerant ecotype had a stronger carbon skeleton production capacity with higher levels of hexokinase, pyruvate kinase, and glutamate dehydrogenase activity to assimilate free NH 4 + , Taken together, the results revealed the core mechanisms utilized by plants in response to NH 4 + , which are consequently of ecological and agricultural importance.