Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion still exist in their fusion results due to the insufficiency in details learning and the frequency mismatch between MSand PAN. Therefore, the improvement of spatial details at the premise of reducing spectral distortion is still a challenge. In this paper, we propose a frequency-aware fusion network (FAFNet) together with a novel high-frequency feature similarity loss to address above mentioned problems. FAFNet is mainly composed of two kinds of blocks, where the frequency aware blocks aim to extract features in the frequency domain with the help of discrete wavelet transform (DWT) layers, and the frequency fusion blocks reconstruct and transform the features from frequency domain to spatial domain with the assistance of inverse DWT (IDWT) layers. Finally, the fusion results are obtained through a convolutional block. In order to learn the correspondence, we also propose a high-frequency feature similarity loss to constrain the HF features derived from PAN and MS branches, so that HF features of PAN can reasonably be used to supplement that of MS. Experimental results on three datasets at both reduced- and full-resolution demonstrate the superiority of the proposed method compared with several state-of-the-art pansharpening models.Comment: 14 page

A Competitive Study on Effective Use of Human Resources in China’s Provinces

The principal component analysis is a practical method for multivariate statistical analyses. It can eliminate the correlation between sample indexes, and on the premise of keeping the main information of samples, extract a few representative principal components. This article adopts the input—output method and principal component analysis. It carries on the transverse comparison research on the effective utilization situation of human resources in China in 2008 and reveals the actual situation of efficient use of human resources in the provinces in China. The degree of effective use of human resources in Beijing is the highest, while in Ningxia is the lowest. It is closely related to the economic development. Finally, it puts forward the thoughts and suggestions of improving the effective use of human resources in China

Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra

Low-rank approximation of images via singular value decomposition is well-received in the era of big data. However, singular value decomposition (SVD) is only for order-two data, i.e., matrices. It is necessary to flatten a higher order input into a matrix or break it into a series of order-two slices to tackle higher order data such as multispectral images and videos with the SVD. Higher order singular value decomposition (HOSVD) extends the SVD and can approximate higher order data using sums of a few rank-one components. We consider the problem of generalizing HOSVD over a finite dimensional commutative algebra. This algebra, referred to as a t-algebra, generalizes the field of complex numbers. The elements of the algebra, called t-scalars, are fix-sized arrays of complex numbers. One can generalize matrices and tensors over t-scalars and then extend many canonical matrix and tensor algorithms, including HOSVD, to obtain higher-performance versions. The generalization of HOSVD is called THOSVD. Its performance of approximating multi-way data can be further improved by an alternating algorithm. THOSVD also unifies a wide range of principal component analysis algorithms. To exploit the potential of generalized algorithms using t-scalars for approximating images, we use a pixel neighborhood strategy to convert each pixel to "deeper-order" t-scalar. Experiments on publicly available images show that the generalized algorithm over t-scalars, namely THOSVD, compares favorably with its canonical counterparts.Comment: 20 pages, several typos corrected, one appendix adde

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

BackgroundCancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs’ progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.MethodsWe combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.ResultsWe identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.ConclusionOur study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients

Stromal protein CCN family contributes to the poor prognosis in lower-grade gioma by modulating immunity, matrix, stemness, and metabolism

Background: The CCN family of stromal proteins is involved in the regulation of many important biological functions. However, the role of dysregulated CCN proteins in lower-grade glioma (LGG) remain less understand.Methods: The clinical significance of the CCN proteins was explored based on RNA-seq profiles from multiple cohorts. A CCNScore was constructed using LASSO regression analysis. The PanCanAtlas data and MEXPRESS database were employed to elucidate molecular underpinnings.Results: The expression of CCN4 was associated with poor prognosis in LGG. The CCNScore (CCN1 = 0.06, CCN4 = 0.86) showed implication in prognosis prediction, subtype assessment and therapy selection. The gene mutation pattern of the high-CCNScore group was similar with glioblastoma, including EGFR, PTEN, and NF1 mutation frequently. Besides, the high-CCNScore group was comprised of samples mainly classic-like and mesenchymal-like, had lower methylation levels, higher stemness, higher inflammation, higher levels of extracellular matrix remodel and dysfunction of metabolic pathways. On the other hand, the low-CCNScore group consisted mainly of IDH-mutation LGG, and was characterized by TP53, CIC, and ATRX gene mutations, hyper-methylation status, lower stemness, lower proliferation, immune quietness and low extracellular matrix stiffness.Conclusion: In summary, these results outlined the role of CCN family in LGG and provided a potential and promising therapeutic target

Rapid progression of subcutaneous glioblastoma: A case report and literature review

Extra-neural spread of glioblastoma (GBM) is extremely rare. We report a case of postoperative intracranial GBM spreading to the subcutaneous tissue via the channel of craniotomy defect in a 73-year-old woman. Radiological images and histopathology indicate that the tumor microenvironment of the subcutaneous tumor is clearly different from the intracranial tumor. We also model the invasion of GBM cells through the dura-skull defect in mouse. The retrospective analysis of GBM with scalp metastases suggests that craniectomy is a direct cause of subcutaneous metastasis in patients with GBM. Imaging examinations of other sites for systemic screening is also recommended to look for metastases outside the brain when GBM invades the scalp or metastasizes to it

Systematic analysis of the necroptosis index in pan-cancer and classification in discriminating the prognosis and immunotherapy responses of 1716 glioma patients

Necroptosis is a programmed form of necrotic cell death that serves as a host gatekeeper for defense against invasion by certain pathogens. Previous studies have uncovered the essential role of necroptosis in tumor progression and implied the potential for novel therapies targeting necroptosis. However, no comprehensive analysis of multi-omics data has been conducted to better understand the relationship between necroptosis and tumor. We developed the necroptosis index (NI) to uncover the effect of necroptosis in most cancers. NI not only correlated with clinical characteristics of multiple tumors, but also could influence drug sensitivity in glioma. Based on necroptosis-related differentially expressed genes, the consensus clustering was used to classify glioma patients into two NI subgroups. Then, we revealed NI subgroup I were more sensitive to immunotherapy, particularly anti-PD1 therapy. This new NI-based classification may have prospective predictive factors for prognosis and guide physicians in prioritizing immunotherapy for potential responders

Loss of Hairless Confers Susceptibility to UVB-Induced Tumorigenesis via Disruption of NF-kappaB Signaling

In order to model squamous cell carcinoma development in vivo, researchers have long preferred hairless mouse models such as SKH-1 mice that have traditionally been classified as ‘wild-type’ mice irrespective of the genetic factors underlying their hairless phenotype. The work presented here shows that mutations in the Hairless (Hr) gene not only result in the hairless phenotype of the SKH-1 and Hr−/− mouse lines but also cause aberrant activation of NFκB and its downstream effectors. We show that in the epidermis, Hr is an early UVB response gene that regulates NFκB activation and thereby controls cellular responses to irradiation. Therefore, when Hr expression is decreased in Hr mutant animals there is a corresponding increase in NFκB activity that is augmented by UVB irradiation. This constitutive activation of NFκB in the Hr mutant epidermis leads to the stimulation a large variety of downstream effectors including the cell cycle regulators cyclin D1 and cyclin E, the anti-apoptosis protein Bcl-2, and the pro-inflammatory protein Cox-2. Therefore, Hr loss results in a state of uncontrolled epidermal proliferation that promotes tumor development, and Hr mutant mice should no longer be considered merely hairless 'wild-type' mice. Instead, Hr is a crucial UVB response gene and its loss creates a permissive environment that potentiates increased tumorigenesis

GMAW welding procedure expert system based on machine learning

In order to simplify the robot preparation before welding and improve the automation of the whole welding process, an intelligent expert system for Gas Metal Arc Welding is designed in this paper. In the system, the user inputs the initial welding information and the output interface displays suitable welding procedure parameter schemes. The user can choose the schemes according to the actual requirements or directly generate the welding procedure specification required by the enterprise format for direct use. In addition, the system also combines the database technology and XGBoost algorithm in the field of machine learning, migrates the model trained on the data set to predict the welding raw data, accumulates more data for daily use to optimize the model, which makes the whole system more systematic and intelligent, and achieves the goal of more accurate use