EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection

Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration. In this paper, we have proposed an original Efficient Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change detection. The designed EMS module cuts redundancy of those similar and containing-no-changes feature maps, computing efficient multi-temporal change information for precise binary change map. Besides, to explore the clustering characteristics of the change detection, a novel supervised contrastive loss is provided to enhance the compactness of the unchanged. Experiments implemented on two hyperspectral change detection datasets manifests the out-standing performance and validity of proposed method.Comment: 4 pages, 5 figures, submitted to IGARSS202

GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection

High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a global perspective, which limits their ability to capture interactions between global features, thus usually resulting in incomplete change regions. In this paper, we propose a Global Multi-head INteractive self-attention change Detection network (GlobalMind) to explore the implicit correlation between different surface objects and variant land cover transformations, acquiring a comprehensive understanding of the data and accurate change detection result. Firstly, a simple but effective Global Axial Segmentation (GAS) strategy is designed to expand the self-attention computation along the row space or column space of hyperspectral images, allowing the global connection with high efficiency. Secondly, with GAS, the global spatial multi-head interactive self-attention (Global-M) module is crafted to mine the abundant spatial-spectral feature involving potential correlations between the ground objects from the entire rich and complex hyperspectral space. Moreover, to acquire the accurate and complete cross-temporal changes, we devise a global temporal interactive multi-head self-attention (GlobalD) module which incorporates the relevance and variation of bi-temporal spatial-spectral features, deriving the integrate potential same kind of changes in the local and global range with the combination of GAS. We perform extensive experiments on five mostly used hyperspectral datasets, and our method outperforms the state-of-the-art algorithms with high accuracy and efficiency.Comment: 14 page, 18 figure

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

As the COVID-19 epidemic began to worsen in the first months of 2020, stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread. Although transportation density reduction inside the city was felt subjectively, there has thus far been no objective and quantitative study of its variation to reflect the intracity population flows and their corresponding relationship with lockdown policy stringency from the view of remote sensing images with the high resolution under 1m. Accordingly, we here provide a quantitative investigation of the transportation density reduction before and after lockdown was implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York, and London) around the world during the COVID-19 epidemic, which is accomplished by extracting vehicles from the multi-temporal high-resolution remote sensing images. A novel vehicle detection model combining unsupervised vehicle candidate extraction and deep learning identification was specifically proposed for the images with the resolution of 0.5m. Our results indicate that transportation densities were reduced by an average of approximately 50% (and as much as 75.96%) in these six cities following lockdown. The influences on transportation density reduction rates are also highly correlated with policy stringency, with an R^2 value exceeding 0.83. Even within a specific city, the transportation density changes differed and tended to be distributed in accordance with the city's land-use patterns. Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.Comment: 14 pages, 7 figures, submitted to IEEE JSTAR

The emerging role of m6A modification of non-coding RNA in gastrointestinal cancers: a comprehensive review

Gastrointestinal (GI) cancer is a series of malignant tumors with a high incidence globally. Although approaches for tumor diagnosis and therapy have advanced substantially, the mechanisms underlying the occurrence and progression of GI cancer are still unclear. Increasing evidence supports an important role for N6-methyladenosine (m6A) modification in many biological processes, including cancer-related processes via splicing, export, degradation, and translation of mRNAs. Under distinct cancer contexts, m6A regulators have different expression patterns and can regulate or be regulated by mRNAs and non-coding RNAs, especially long non-coding RNAs. The roles of m6A in cancer development have attracted increasing attention in epigenetics research. In this review, we synthesize progress in our understanding of m6A and its roles in GI cancer, especially esophageal, gastric, and colorectal cancers. Furthermore, we clarify the mechanism by which m6A contributes to GI cancer, providing a basis for the development of diagnostic, prognostic, and therapeutic targets

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BackgroundAlthough the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated.MethodsThe genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways.ResultsOf the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified.ConclusionsSixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby.Plain language summaryThe epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer

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Bachelor'sBACHELOR OF SCIENCE (HONOURS

Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

With the development of cloud computing, the advantages of low cost and high computation ability meet the demands of complicated computation of multimedia processing. Outsourcing computation of cloud could enable users with limited computing resources to store and process distributed multimedia application data without installing multimedia application software in local computer terminals, but the main problem is how to protect the security of user data in untrusted public cloud services. In recent years, the privacy-preserving outsourcing computation is one of the most common methods to solve the security problems of cloud computing. However, the existing computation cannot meet the needs for the large number of nodes and the dynamic topologies. In this paper, we introduce a novel privacy-preserving outsourcing computation method which combines GM homomorphic encryption scheme and Bloom filter together to solve this problem and propose a new privacy-preserving outsourcing set intersection computation protocol. Results show that the new protocol resolves the privacy-preserving outsourcing set intersection computation problem without increasing the complexity and the false positive probability. Besides, the number of participants, the size of input secret sets, and the online time of participants are not limited

Polyphenol-Rich Extract of Fermented Chili Pepper Alleviates Insulin Resistance in HepG2 Cells via Regulating INSR, PTP1B, PPAR-γ, and AMPK Pathways

Fermented Capsicum frutescens L. is a well-known traditional food ingredient in China with a variety of potential nutritional functions due to the increased content of polyphenolic compounds during the fermentation process. This study aimed to investigate the ameliorative effect of fermented chili peppers (FCP) on insulin resistance and the potential mechanism of action. HepG2 cells were treated with 5 × 10−6 mol/L insulin for 12 h to establish the insulin resistance model. The results showed that the ethanol extract of FCP (1 mg/mL), rather than non-FCP extract, significantly increased glucose consumption in insulin-resistant HepG2 cells, which was at least partly attributed to an increase in polyphenolic compounds after fermentation, including kaempferol-3-O-rutinoside, caffeic acid, kaempferol-3-O-glucoside, luteolin, and apigenin. Molecular docking analysis suggested that these five significantly increased polyphenolic compounds in FCP could partially and effectively interact with the key amino acid residues of four key insulin resistance-related receptors (INSR, PTP1B, PPAR-γ, and AMPK). In conclusion, the fermentation process enhanced or even conferred a pronounced anti-insulin resistance effect on chili peppers, and the increased polyphenolic compounds in chili pepper had synergistic effects in modulating the INSR, PTP1B, PPAR-γ, and AMPK pathways to regulate the destruction of glucose consumption

Polyphenol-Rich Extract of Fermented Chili Pepper Alleviates Insulin Resistance in HepG2 Cells via Regulating INSR, PTP1B, PPAR-γ, and AMPK Pathways

Fermented Capsicum frutescens L. is a well-known traditional food ingredient in China with a variety of potential nutritional functions due to the increased content of polyphenolic compounds during the fermentation process. This study aimed to investigate the ameliorative effect of fermented chili peppers (FCP) on insulin resistance and the potential mechanism of action. HepG2 cells were treated with 5 × 10−6 mol/L insulin for 12 h to establish the insulin resistance model. The results showed that the ethanol extract of FCP (1 mg/mL), rather than non-FCP extract, significantly increased glucose consumption in insulin-resistant HepG2 cells, which was at least partly attributed to an increase in polyphenolic compounds after fermentation, including kaempferol-3-O-rutinoside, caffeic acid, kaempferol-3-O-glucoside, luteolin, and apigenin. Molecular docking analysis suggested that these five significantly increased polyphenolic compounds in FCP could partially and effectively interact with the key amino acid residues of four key insulin resistance-related receptors (INSR, PTP1B, PPAR-γ, and AMPK). In conclusion, the fermentation process enhanced or even conferred a pronounced anti-insulin resistance effect on chili peppers, and the increased polyphenolic compounds in chili pepper had synergistic effects in modulating the INSR, PTP1B, PPAR-γ, and AMPK pathways to regulate the destruction of glucose consumption