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Background: The involvement of ferroptosis in the pathogenesis and progression of various cancers has been well established. However, limited studies have investigated the role of ferroptosis-mediated tumor microenvironment (TME) in skin cutaneous melanoma (SKCM). Methods: By leveraging single-cell RNA sequencing data, the nonnegative matrix factorization (NMF) approach was employed to comprehensively characterize and identify distinct gene signatures within ferroptosis-associated TME cell clusters. Prognostic and treatment response analyses were conducted using both bulk datasets and external cancer cohort to evaluate the clinical implications of TME clusters. Results: This NMF-based analysis successfully delineated fibroblasts, macrophages, T cells, and B cells into multiple clusters, enabling the identification of unique gene expression patterns and the annotation of distinct TME clusters. Furthermore, pseudotime trajectories, enrichment analysis, cellular communication analysis, and gene regulatory network analysis collectively demonstrated significant intercellular communication between key TME cell clusters, thereby influencing tumor cell development through diverse mechanisms. Importantly, our bulk RNA-seq analysis revealed the prognostic significance of ferroptosis-mediated TME cell clusters in SKCM patients. Moreover, our analysis of immune checkpoint blockade highlighted the crucial role of TME cell clusters in tumor immunotherapy, facilitating the discovery of potential immunotherapeutic targets. Conclusions: In conclusion, this pioneering study employing NMF-based analysis unravels the intricate cellular communication mediated by ferroptosis within the TME and its profound implications for the pathogenesis and progression of SKCM. We provide compelling evidence for the prognostic value of ferroptosis-regulated TME cell clusters in SKCM, as well as their potential as targets for immunotherapy. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

Development of inexpensive and environmentally friendly chemical sensors can help improve the global ecological environment and reduce pollutant emissions. In this work, asphaltene of coal liquefaction by-product was employed as the carbon source to fabricate carbon dots (CDs) by straightforward microwave radiation. The asphaltene-based CDs gave out blue emission under UV light excitation. Interestingly, the CDs are highly lipophilic because of its carbonized structure and insufficient hydrophilic groups. Based on these findings, an outstanding fluorescent sensor for 2,4,6-trinitrophenol (TNP) was developed by loading asphaltene-based CDs into polymeric micelles. The micelle formed by nonionic tri-block copolymers exhibited a hydrophobic inner cavity and hydrophilic surface. Due to the protection of the micelles, the emission of asphaltene-based CDs was enhanced by 2.89 times. But the fluorescence was significantly quenched by TNP through energy transfer and electron transfer mechanism, showing high sensitivity to TNP with a detection limit of 0.21 μM. The findings presented here provide a green method to synthesize fluorescent CDs and a promising strategy to design fluorescent probes. [ABSTRACT FROM AUTHOR]

In recent years, air pollution has become a factor that cannot be ignored, affecting human lives and health. The distribution of high-density populations and high-intensity development and construction have accentuated the problem of air pollution in China. To accelerate air pollution control and effectively improve environmental air quality, the target of our research was cities with serious air pollution problems to establish a model for air pollution prediction. We used the daily monitoring data of air pollution from January 2016 to December 2020 for the respective cities. We used the long short term memory networks (LSTM) algorithm model to solve the problem of gradient explosion in recurrent neural networks, then used the particle swarm optimization algorithm to determine the parameters of the CNN-LSTM model, and finally introduced the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) decomposition to decompose air pollution and improve the accuracy of model prediction. The experimental results show that compared with a single LSTM model, the CEEMDAN-CNN-LSTM model has higher accuracy and lower prediction errors. The CEEMDAN-CNN-LSTM model enables a more precise prediction of air pollution, and may thus be useful for sustainable management and the control of air pollution. [ABSTRACT FROM AUTHOR]

Environmentally persistent free radicals (EPFRs) have attracted increasing research interest in recent years. Herein, the generation of EPFRs during the hydrothermal carbonization of sewage sludge (SS) was studied. First, the surface morphology, functional groups, constituent elements and free radicals were characterized for a holistic description of the raw SS and the selected hydrochar obtained from hydrothermal carbonization of SS (SHC). Then, the impact of hydrothermal temperature, residence time and initial pH on the formation of EPFRs was explored in detail through the investigation of g-factors and intensities of EPFRs identified in SHC. The results have shown that the formation of EPFRs was affected by the factors mentioned above, in which the impact of temperature is the greatest. Two types of EPFRs were spotted in the hydrochar, oxygen-centered (O-centered) and carbon-centered (C-centered) EPFRs, which were caught in 120–150 °C and 260–280 °C, respectively. Moreover, the intensities of Electron Paramagnetic Resonance (EPR) signals enhanced with increasing hydrothermal temperature. Whereas, residence time and initial pH only affected the amount of EPFRs in a manner. Additionally, the half-life of the O-centered EPFRs and the C-centered EPFRs was determined as long as 160.45 days and 401.10 days, respectively, indicating that EPFRs are stable in a long time. [ABSTRACT FROM AUTHOR]