BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks (DNNs), including autoencoders (AEs), convolutional neural networks (CNNs) and vision transformers (ViTs), have shown remarkable performance in this field due to their powerful ability to model the complicated background. However, for reconstruction tasks, DNNs tend to incorporate both background and anomalies into the estimated background, which is referred to as the identical mapping problem (IMP) and leads to significantly decreased performance. To address this limitation, we propose a model-independent binary mask-guided separation training strategy for DNNs, named BiGSeT. Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image. The background is preserved, while the potential anomalies are suppressed by using an efficient second-order Laplacian of Gaussian (LoG) operator, generating a pure background estimate. In order to maintain separability during training, we periodically update the mask using a robust proportion threshold estimated before the training. In our experiments, We adopt a vanilla AE as the network to validate our training strategy on several real-world datasets. Our results show superior performance compared to some state-of-the-art methods. Specifically, we achieved a 90.67% AUC score on the HyMap Cooke City dataset. Additionally, we applied our training strategy to other deep network structures, achieving improved detection performance compared to their original versions, demonstrating its effective transferability. The code of our method will be available at https://github.com/enter-i-username/BiGSeT.Comment: 13 pages, 13 figures, submitted to IEEE TRANSACTIONS ON IMAGE PROCESSIN

Paradoxical phenomenon in urban renewal practices: promotion of sustainable construction versus buildings’ short lifespan

The current urban renewal programs in some developing countries, such as China, are at the expense of demolishing a huge number of existing buildings without distinction. As a consequence, the buildings’ short lifespan due to premature demolition and resultant adverse impacts on environment and society have been criticized for not being in line with sustainable development principles. This study therefore examines impacts of urban renewal practices on buildings’ lifespan by referring to a typical urban renewal region in western China – the Gailanxi region of Chongqing city which is considered representative. Findings show in current practices, little consideration is given to the implications of building demolitions across economic, social and environmental horizons collectively. As a result, premature demolition due to requirements of urban renewal has been a major factor leading to the significantly short lifespan of buildings. This is against the core intention of implementing urban renewal, which is promoting sustainability of the cities. Particularly, buildings’ short lifespan results in consequences against sustainable construction principles, such as energy and resources waste, construction waste generation, environmental pollution, and higher lifecycle costs of buildings. Furthermore, building demolitions without distinction lead to losses of valuable historic buildings. Therefore, the urban renewal process presents a paradoxical phenomenon: the promotion of sustainable construction versus buildings’ short lifespan. The dominance of economic consideration in the decision-making on buildings is considered as the underlying reason to the paradox. The learned experience presented in this study should be built into the decision-making process for carrying out future urban renewal programs

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%

Hyperspectral Endmember Extraction Using Spatially Weighted Simplex Strategy

Spatial information is increasingly becoming a vital factor in the field of hyperspectral endmember extraction, since it takes into consideration the spatial correlation of pixels, which generally involves jointing spectral information for preprocessing and/or endmember extraction in hyperspectral imagery (HSI). Generally, simplex-based endmember extraction algorithms (EEAs) identify endmembers without considering spatial attributes, and the spatial preprocessing strategy is an independently executed module that can provide spatial information for the endmember search process. Despite this interest, to the best of our knowledge, no one has studied the integration framework of the spatial information-embedded simplex for hyperspectral endmember extraction. In this paper, we propose a spatially weighted simplex strategy, called SWSS, for hyperspectral endmember extraction that investigates a novel integration framework of the spatial information-embedded simplex for identifying endmember. Specifically, the SWSS generates the spatial weight scalar of each pixel by determining its corresponding spatial neighborhood correlations for weighting itself within the simplex framework to regularize the selection of the endmembers. The SWSS could be implemented in the traditional simplex-based EEAs, such as vertex component analysis (VCA), to introduce spatial information into the data simplex framework without the computational complexity excessively increasing or endmember extraction accuracy loss. Based on spectral angle distance (SAD) and root-mean-square-error (RMSE) evaluation criteria, experimental results on both synthetic and C u p r i t e real hyperspectral datasets indicate that the simplex-based EEA re-implemented by the SWSS has a significant improvement on endmember extraction performance over the techniques on their own and without re-implementing

Low Serum miR-607 Level as a Potential Diagnostic and Prognostic Biomarker in Patients of Pancreatic Ductal Adenocarcinoma: A Preliminary Study

Background. One of the microRNAs (miRNAs) known to be associated with cancer development is miR-607. The aim of this study is to investigate the clinical significance and diagnostic and prognostic value of miR-607 and to explore its potential role in pancreatic ductal adenocarcinoma (PDAC). Methods. The expression levels of miR-607 were assessed by quantitative real-time polymerase chain reaction (qRT-PCR). The correlation between miR-607 expression and clinical characteristics was analyzed by the Chi-square test. Overall survival (OS) and progression-free survival (PFS) were evaluated via the Kaplan–Meier method, and the association between miR-607 expression and OS was investigated by the Cox proportional hazard analysis. The diagnostic value was estimated via receiver operating characteristic (ROC) curve analysis. The effect of miR-607 overexpression on cell migration, invasion, and epithelial-mesenchymal transition (EMT) was determined by wound-healing, Transwell invasion, and Western blotting assays. Results. miR-607 levels were downregulated in PDAC tumor tissues compared with normal tissues. Also, low miR-607 levels were observed in serum samples from PDAC patients than that in healthy controls. The miR-607 level was found to be closely correlated with lymphatic metastasis and liver metastasis, perineural invasion, and OS and PFS, and the low miR-607 level was an independent prognostic factor for the poor OS of PDAC patients. Furthermore, the area under the curve (AUC) of serum miR-607 for discriminating PDAC patients was 0.785 with a sensitivity of 0.647 and a specificity of 0.772, which was better than those for CA19-9 (AUC: 0.702, sensitivity: 0.607, specificity: 0.736) and CEA (AUC: 0.648, sensitivity: 0.542, specificity: 0.670). The AUC (0.863), sensitivity (0.766), and specificity (0.831) of their combination in the diagnosis of PDAC were better than those for alone. Moreover, ectopic overexpression of miR-607 could inhibit cell migration and invasion of BxPc-3 and PANC-1 cells by decreasing EMT ability. Conclusions. Low serum miR-607 level may serve as a potential diagnostic and prognostic biomarker through regulation of tumor metastasis in PDAC patients

Imatinib induces ferroptosis in gastrointestinal stromal tumors by promoting STUB1-mediated GPX4 ubiquitination

Abstract Imatinib (IM) has significantly improved the prognosis of gastrointestinal stromal tumor (GIST) patients, but some patients still have primary resistance to IM, and approximately half of patients develop acquired drug resistance within 2 years of treatment, necessitating exploration of new treatment strategies. Targeting ferroptosis as a novel approach to tumor treatment has gained attention. Yet, there is limited research on ferroptosis in GIST, and the underlying mechanism remains unclear. In this study, we revealed that IM increased lipid reactive oxygen species and intracellular Fe2+ levels, and decreased glutathione levels in GIST. This effect could be partially inhibited by Ferrostatin-1. Additionally, knocking down STUB1 and overexpressing GPX4 reversed the IM-induced ferroptosis effect. Moreover, STUB1 was identified as a novel E3 ubiquitin ligase of GPX4, promoting the ubiquitination at site K191 of GPX4. The combination of the GPX4 inhibitor RSL3 and IM synergistically induces ferroptosis, inhibiting GIST proliferation both in vivo and in vitro. Furthermore, STUB1 and GPX4 expression serve as independent prognostic factors for GIST. In conclusion, This study is the first to demonstrate that IM induces ferroptosis by promoting STUB1-mediated GPX4 ubiquitination in GIST, and the combination of RSL3 and IM emerges as a promising therapeutic strategy for GIST

Additional file 2 of THZ1 targeting CDK7 suppresses c-KIT transcriptional activity in gastrointestinal stromal tumours

Additional file 1. The siRNA sequences and qRT-PCR primer sequences used in this research.