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Excessive macroautophagy/autophagy leads to pancreatic β-cell failure that contributes to the development of diabetes. Our previous study proved that the occurrence of deleterious hyperactive autophagy attributes to glucolipotoxicity-induced NR3C1 activation. Here, we explored the potential protective effects of (-)-epigallocatechin 3-gallate (EGCG) on β-cell-specific NR3C1 overexpression mice in vivo and NR3C1-enhanced β cells in vitro. We showed that EGCG protects pancreatic β cells against NR3C1 enhancement-induced failure through inhibiting excessive autophagy. RNA demethylase FTO (FTO alpha-ketoglutarate dependent dioxygenase) caused diminished m6A modifications on mRNAs of three pro-oxidant genes (Tlr4, Rela, Src) and, hence, oxidative stress occurs; by contrast, EGCG promotes FTO degradation by the ubiquitin-proteasome system in NR3C1-enhanced β cells, which alleviates oxidative stress, and thereby prevents excessive autophagy. Moreover, FTO overexpression abolishes the beneficial effects of EGCG on β cells against NR3C1 enhancement-induced damage. Collectively, our results demonstrate that EGCG protects pancreatic β cells against NR3C1 enhancement-induced excessive autophagy through suppressing FTO-stimulated oxidative stress, which provides novel insights into the mechanisms for the anti-diabetic effect of EGCG.Abbreviation 3-MA: 3-methyladenine; AAV: adeno-associated virus; Ad: adenovirus; ALD: aldosterone; AUC: area under curve; βNR3C1 mice: pancreatic β-cell-specific NR3C1 overexpression mice; Ctrl: control; CHX: cycloheximide; DEX: dexamethasone; DHE: dihydroethidium; EGCG: (-)-epigallocatechin 3-gallate; FTO: FTO alpha-ketoglutarate dependent dioxygenase; GSIS: glucose-stimulated insulin secretion; HFD: high-fat diet; HG: high glucose; i.p.: intraperitoneal; IOD: immunofluorescence optical density; KSIS: potassium-stimulated insulin secretion; m6A: N6-methyladenosine; MeRIP-seq: methylated RNA immunoprecipitation sequencing; NO: nitric oxide; NR3C1/GR: nuclear receptor subfamily 3, group C, member 1; NR3C1-Enhc.: NR3C1-enhancement; NAC: N-acetylcysteine; NC: negative control; PBS: phosphate-buffered saline; PI: propidium iodide; OCR: oxygen consumption rate; Palm.: palmitate; RELA: v-rel reticuloendotheliosis viral oncogene homolog A (avian); RNA-seq: RNA sequencing; O2.-: superoxide anion; SRC: Rous sarcoma oncogene; ROS: reactive oxygen species; T2D: type 2 diabetes; TEM: transmission electron microscopy; TLR4: toll-like receptor 4; TUNEL: terminal dUTP nick-end labeling; UTR: untranslated region; WT: wild-type. [ABSTRACT FROM AUTHOR]

Multilabel remote sensing image classification (MLRSIC) has received increasing research interest. Taking the co-occurrence relationship of multiple labels as additional information helps to improve the overall performance. However, current methods only focus on using it to constrain the final feature which is output from a convolutional neural network (CNN). On the one hand, these methods need to exploit the potential of label correlation in feature representation fully. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called 'Semantic Interleaving Global chaNnel Attention' (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the training set and fed into a graph neural network (GNN) to generate optimal semantic feature representations of each label. Next, the semantic features are interleaved with visual features which are extracted by CNNs to guide the overall features of the input image transform from the original feature space to the semantic feature space with embedded label relations. Then, global attention triggered by semantic interleaving is used to emphasize visual features in important channels. Finally, to make SIGNA easier to use and more optimized, multihead SIGNA-based feature adaptive weighting networks are proposed as plug-in blocks to plug into any layers of a CNN. For remote sensing images, better classification performance can be achieved by inserting the plug-in blocks into the shallow layers of CNNs. We conducted extensive experimental comparisons on three data sets: UCM, AID and DFC15. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. Notes that the codes of this paper will be open to the community for reproducibility research. [ABSTRACT FROM AUTHOR]

An energy transition programme based on the principles of energy justice is an important way to mitigate climate change; however, empirical studies on energy justice remain scarce. This study explores key aspects of energy transition policy implementation in China, using three dimensions of energy justice – distributional, recognition and procedural justice – to establish an analytical framework and help develop quantification methodologies. We focused on Luquan District, Shijiazhuang City (Hebei Province, China), to assess energy justice levels following the implementation of China's 'coal-to-gas' energy transition policy in rural regions. The findings indicate that China's rural energy policy has not delivered energy justice. Analysis revealed a markedly low procedural justice index, registering a mere 0.37 on a scale of 0–1, attributable to factors such as the lack of involvement of rural households and a lack of transparency in the design and formulation of policy, as well as a lack of access of rural households to policy-related information. Furthermore, insufficient acknowledgment of the needs of specific groups during the energy transition has negatively impacted recognition justice, resulting in a mid-level index value of 0.69. By comparison, this study shows reveals a reasonably high energy distributional justice index value of 0.89. These findings suggest the need for the government to enhance energy policy communication and responsiveness to its stakeholders, to acknowledge and fairly address the energy transition needs of rural consumers, and to implement targeted energy subsidies to augment distributional justice and preclude the waste of limited financial resources. The analytical framework and calculation methods presented here could contribute to quantifying energy justice levels and to informing energy transition policy both in China and more broadly elsewhere in the world. Since 2013, implementation of China's 'coal-to-gas' energy transition policy has engendered impacts on different dimensions of social justice that have influenced the realization of a just transition. A composite index system is established, encompassing distributional justice, recognition justice, and procedural justice, and quantitative exploration of energy justice is undertaken using a reverse deduction method. The greatest losses in justice, following implementation of China's 'coal-to-gas' energy transition policy, are related to procedural justice, whereas recognition justice incurs moderate losses, and distributional justice experiences the lowest level of losses. Acknowledging the disparities among different groups, the ideal allocation of energy subsidies should be actualized through preliminary surveys, consultation and coordination, thereby preventing resource wastage. Recognizing individual rights and guaranteeing that the policy implementation process is open, transparent and participatory, could enhance energy justice and movement toward a just transition in the context of climate change policy. [ABSTRACT FROM AUTHOR]

We demonstrate in this paper a super-hydrophilic and oleophobic green porous photothermal material based on wood fiber as a novel oil-repellent photothermal material for efficient solar vapor generation (WF-SP). WF-SP is an excellent light-absorbing material with the advantages of a porous nanostructure, super-hydrophilic, low thermal conductivity, high-temperature resistance, and oleophobicity. Based on these advantages, WF-SP exhibits good solar photothermal conversion performance with solar vapor generation efficiencies of 85.4%, 75.3%, and 70.2% at 1 kW m−2, 2 kW m−2, and 3 kW m−2 radiation intensities, respectively. The results provide new possibilities for the design and preparation of low-cost, green, and pollution-free photothermal materials with high solar thermal conversion efficiency for solar photothermal conversion vapor generation. [ABSTRACT FROM AUTHOR]