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As a valuable resource in coastal areas, coastlines are not only vulnerable to natural processes such as erosion, siltation, and disasters, but are also subjected to strong pressures from human processes such as urban growth, resource development, and pollution discharge. This is especially true for reef nations with rich coastline resources and a large population, like Indonesia. The technical joint of remote sensing (RS) and geographic information system (GIS) has significant advantages for monitoring coastline changes on a large scale and for quantitatively analyzing their change mechanisms. Indonesia was taken as an example in this study because of its abundant coastline resources and large population. First, Landsat images from 1990 to 2018 were used to obtain coastline information. Then, the index of coastline utilization degree (ICUD) method, the changes in land and sea patterns method, and the ICUD at different scales method were used to reveal the spatiotemporal change pattern for the coastline. The results found that: (1) Indonesia’s total coastline length has increased by 777.40 km in the past 28 years, of which the natural coastline decreased by 5995.52 km and the artificial coastline increased by 6771.92 km. (2) From the analysis of the island scale, it was known that the island with the largest increase in ICUD was Kalimantan, at the expense of the mangrove coastline. (3) On the provincial scale, the province with the largest change of ICUD was Sumatera Selatan Province, which increased from 100 in 1900 to 266.43 in 2018. (4) The change trend of the land and sea pattern for the Indonesian coastline was mainly expanded to the sea. The part that eroded to the land was relatively small; among which, Riau Province had the most significant expansion of land area, about 177.73 km2, accounting for 23.08% of the increased national land area. The worst seawater erosion was in the Jawa Barat Province. Based on the analysis of population and economic data during the same period, it was found that the main driving mechanism behind Indonesia’s coastline change was population growth, which outweighed the impact of economic development. However, the main constraint on the Indonesian coastline was the topographic factor. The RS and GIS scheme used in this study can not only provide support for coastline resource development and policy formulation in Indonesia, but also provide a valuable reference for the evolution of coastline resources and environments in other regions around the world.

Perylene tetracarboxylic diimide (PDI) and its derivatives exhibit excellent thermal, chemical and optical stability, strong electron affinity, strong visible-light absorption and unique fluorescence on/off features. The combination of these features makes PDIs ideal molecular frameworks for development in a broad range of sensors for detecting environmental pollutants such as heavy metal ions (e.g., Cu2+, Cd2+, Hg2+, Pd2+, etc.), inorganic anions (e.g., F−, ClO4−, PO4−, etc.), as well as poisonous organic compounds such as nitriles, amines, nitroaromatics, benzene homologues, etc. In this review, we provide a comprehensive overview of the recent advance in research and development of PDI-based fluorescent sensors, as well as related colorimetric and multi-mode sensor systems, for environmental detection in aqueous, organic or mixed solutions. The molecular design of PDIs and structural optimization of the sensor system (regarding both sensitivity and selectivity) in response to varying analytes are discussed in detail. At the end, a perspective summary is provided covering both the key challenges and potential solutions for the future development of PDI-based optical sensors.

This paper explores different interactions and processes involved in the transport of microplastics from agricultural systems to surrounding environments. We conducted an exhaustive review of the most recent scientific papers on microplastic transport in terrestrial systems, with an emphasis on agricultural systems. In the following sections, several aspects of this problem are discussed, namely (i) direct and indirect sources of microplastics, (ii) biotic and abiotic transportation of microplastics in and from the terrestrial environment, (iii) modelling of microplastics in the terrestrial environment and (iv) facilitated chemicals and pathogens in combination with plastic particles. There is very little information available concerning microplastic transport in the terrestrial environment; therefore, more research is needed to gain a better understanding of how these processes take place. The novelty of this review lies in assessing how microplastic transport occurs from the plastisphere (cellular) to the landscape level and from agricultural systems to the surrounding areas.

Background Lysyl oxidase-like 4 (LOXL4) has been found to be dysregulated in several human malignancies, including hepatocellular carcinoma (HCC). However, the role of LOXL4 in HCC progression remains largely unclear. In this study, we investigated the clinical significance and biological involvement of LOXL4 in the progression of HCC. Methods LOXL4 expression was measured in HCC tissues and cell lines. Overexpression, shRNA-mediated knockdown, recombinant human LOXL4 (rhLOXL4), and deletion mutants were applied to study the function of LOXL4 in HCC. Exosomes derived from HCC cell lines were assessed for the ability to promote cancer progression in standard assays. The effects of LOXL4 on the FAK/Src pathway were examined by western blotting. Results LOXL4 was commonly upregulated in HCC tissues and predicted a poor prognosis. Elevated LOXL4 was associated with tumor differentiation, vascular invasion, and tumor-node-metastasis (TNM) stage. Overexpression of LOXL4 promoted, whereas knockdown of LOXL4 inhibited cell migration and invasion of HCC in vitro, and overexpressed LOXL4 promoted intrahepatic and pulmonary metastases of HCC in vivo. Most interestingly, we found that HCC-derived exosomes transferred LOXL4 between HCC cells, and intracellular but not extracellular LOXL4 promoted cell migration by activating the FAK/Src pathway dependent on its amine oxidase activity through a hydrogen peroxide-mediated mechanism. In addition, HCC-derived exosomes transferred LOXL4 to human umbilical vein endothelial cells (HUVECs) though a paracrine mechanism to promote angiogenesis. Conclusions Taken together, our data demonstrate a novel function of LOXL4 in tumor metastasis mediated by exosomes through regulation of the FAK/Src pathway and angiogenesis in HCC.

The proportion of new energy sources, such as wind, photovoltaic and hydropower, in the power grid is increasing year by year. In addition, a large number of nonlinear loads are connected to the grid, resulting in frequent power quality disturbances (PQDs), which pose challenges to the stability and reliability of the power system. Accurate identification of these disturbances is crucial for effective grid management and protection. Although deep learning methods have high accuracy, their lack of interpretability can limit their acceptance in engineering applications. Traditional signal analysis has a good physical foundation, but it is not integrated with deep learning to a sufficient degree. To address these issues, we propose the DWT-1DCNN-LSTM network as an interpretable model for PQD classification. This method effectively decomposes the time-domain signals into sub-signals in different frequency bands by employing the Discrete Wavelet Transform (DWT), which enhances the anti-interference capability of the classification model. This approach enhances the interference resilience of the classification model through the incorporation of the Discrete Wavelet Transform (DWT), which effectively decomposes time-domain signals into sub-signals across different frequency bands. The one-dimensional Convolutional Neural Network (1DCNN) then extracts local features, while the Long Short-Term Memory network (LSTM) analyzes temporal dependencies of the transformed sub-signals. Experimental validation with simulated datasets demonstrates that the DWT-1DCNN-LSTM model achieves an accuracy of 99.27%, outperforming the DWT-1DCNN, 1DCNN-LSTM, LSTM, and CNN models by 1.59%, 1.13%, 1.44%, and 6.48%, respectively. The robustness provided by the DWT module makes the model well suited for PQDs in environments with large disturbances, helping to detect and mitigate PQDs in a timely manner and ultimately contributing to improved power quality and system reliability.

Photothermal catalysis is a promising strategy to combine the advantages of both thermal-catalysis and photocatalysis. Herein we achieve the protolignin conversion to aromatics via the photothermal catalytic transfer hydrogenolysis process intensified by the in-situ protection strategy. The Pd/TiO 2 at 140 °C with UV irradiation can catalyze the reforming of primary alcohols to aldehydes and active H* species, which further participate in the acetalation protection of the 1,3-diol group of β-O-4 linkage and mediate the hydrogenolysis of C β –OAr bonds, respectively. The conversion of birch sawdust with ethanol as the hydrogen donor provides a 40 wt% yield of phenolic monomers, compared with an 11 wt% monomer yield obtained from the conversion of extracted 1,3-diol-protected lignin under the same conditions. The synergistic effect of photocatalysis and thermal-catalysis contributes to the prior cleavage of the C β –OAr bond before other C–O bonds. The feasibility of solar-light-driven photothermal catalytic system is demonstrated. Photothermal catalysis has emerged as a promising strategy to combine the advantages of single thermal catalysis with that of photocatalysis. Herein, the authors report the protolignin direct conversion to aromatics via the photothermal catalytic transfer hydrogenolysis process intensified by the in-situ protection strategy.

Carbon storage (CS) of terrestrial ecosystems is strongly associated with space utilization changes. However, effects of urban spatial planning on the spatio-temporal heterogeneity of production–living–ecological land (PLEL) and CS remain unclear. To bridge this gap, we developed a new PGIP framework that combines PLEL theory, gradient analysis, the InVEST model, and the PLUS model. This integrated approach offers a comprehensive tool for assessing and simulating how urban planning influences CS. We applied this framework in Jinan, China, where we defined four urban gradient zones to examine the gradient differentiation of PLEL and CS. By integrating dynamic driving factors and urban spatial planning, the PLEL and CS in 2030 were simulated under different scenarios. Results revealed significant gradient-based variations in PLEL between 1980 and 2020. The urban living land (ULL) within 10 km of the city center increased by 117.60%. The CS has decreased by 8.14 × 10⁶ t, and the spatial heterogeneity of CS in the southeastern gradient zone was the strongest. More than 50% of the rise in CS resulted from the shift of rural living land to cultivated production land (CPL), while over 41% of the decrease in CS was caused by the conversion of CPL to ULL. By 2030, urban spatial planning is projected to significantly affect CS pattern, especially in downtown, far suburban, and southeastern gradient zone. Compared with unconstrained development scenario, under spatial planning-guided scenario, high-value clusters of CS will increase by 14.86 km 2 , while low-value clusters will decrease by 3.99 km 2 . This study provides a spatially explicit, policy-relevant framework for understanding and planning CS across urban–rural gradients, integrating planning scenarios and mechanistic insights to support low-carbon land use strategies.

The detection of forced oscillations, especially distinguishing them from natural oscillations, has emerged as a major concern in power system stability monitoring. Deep learning (DL) holds significant potential for detecting forced oscillations correctly. However, existing artificial neural networks (ANNs) face challenges when employed in edge devices for timely detection due to their inherent complex computations and high power consumption. This paper proposes a novel hybrid network that integrates a spiking recurrent neural network (SRNN) with long short-term memory (LSTM). The SRNN achieves computational and energy efficiency, while the integration with LSTM is conducive to effectively capturing temporal dependencies in time-series oscillation data. The proposed hybrid network is trained using the backpropagation-through-time (BPTT) optimization algorithm, with adjustments made to address the discontinuous gradient in the SRNN. We evaluate our proposed model on both simulated and real-world oscillation datasets. Overall, the experimental results demonstrate that the proposed model can achieve higher accuracy and superior performance in distinguishing forced oscillations from natural oscillations, even in the presence of strong noise, compared to pure LSTM and other SRNN-related models.

The molecular mechanism underlying the protective role of propofol against myocardial ischemia/reperfusion (I/R) injury remains poorly understood. Previous studies have shown that ferroptosis is an imperative pathological process in myocardial I/R injury. We hypothesized that propofol prevents myocardial I/R injury by inhibiting ferroptosis via the AKT/p53 signaling pathway. The ferroptosis-inducing agent erastin (E) and AKT inhibitor MK2206 (MK) were used to investigate the role of propofol in myocardial I/R injury. H9C2 cells treated without any reagents, erastin for 24 h, propofol for 1 h before adding erastin were assigned as the control (C), E, and E + P group, respectively. Cell viability, reactive oxygen species (ROS), and the expression of antioxidant enzymes, including ferritin heavy chain 1 (FTH1), cysteine/glutamate transporter (XCT), and glutathione peroxidase 4 (GPX4) in H9C2 cells. Rat hearts from the I/R + P or I/R groups were treated with or without propofol for 20 min before stopping perfusion for 30 min and reperfusion for 60 min. Rat hearts from the I/R + P + MK or I/R + MK groups were treated with or without propofol for 20 min, with a 10-min treatment of MK2206 before stopping perfusion. Myocardial histopathology, mitochondrial structure, iron levels, and antioxidant enzymes expression were assessed. Our results demonstrated that erastin increased H9C2 cell mortality and reduced the expression of antioxidant enzymes. I/R, which reduced the expression of antioxidant enzymes and increased iron or p53 ( p < 0.05), boosted myocardium pathological and mitochondrion damage. Propofol inhibited these changes; however, the effects of propofol on I/R injury were antagonized by MK ( p < 0.05). In addition, AKT siRNA inhibited the propofol-induced expression of antioxidant enzymes ( p < 0.05). Our findings confirm that propofol protects myocardium from I/R injury by inhibiting ferroptosis via the AKT/p53 signal pathway.

Aquaculture coasts have become widely distributed in coastal zones as human activities are intensified. Due to the complexity in this type of coast, it is difficult to extract the coastline with traditional automated mapping approaches. In this paper, we present an automated method—object-based region growing integrating edge detection (OBRGIE) for the extraction of this type of coastline. In this method, a new object feature named OMI (object merging index) is proposed to separate land and sea. The OBRGIE method was applied to Landsat Thematic Mapper (TM) (pixel size 30m) and Satellite Pour l’Observation de la Terre (SPOT-5) (pixel size 10 m) images of two coastal segments with lengths of 272.7 km and 35.5 km respectively, and the accuracy of the extracted coastlines was assessed in comparison with the manually delineated coastlines. The mean and RMSE (root mean square error) are 16.0 m and 16.4 m respectively for the TM images, and 8.0 m and 8.6 m, respectively, for the SPOT-5 images, indicating that the proposed method derives coastlines with pixel accuracy. The OBRGIE method is also found to be robust to the segmentation scale parameter, and the OMI feature is much more effective than the spectral attribute in separating land and sea in aquaculture coasts. This method may provide an inexpensive means of fast coastline mapping from remotely sensed imagery with relatively fine-to-moderate spatial resolution in coastal sectors with intense human interference.