SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection

Among the current mainstream change detection networks, transformer is deficient in the ability to capture accurate low-level details, while convolutional neural network (CNN) is wanting in the capacity to understand global information and establish remote spatial relationships. Meanwhile, both of the widely used early fusion and late fusion frameworks are not able to well learn complete change features. Therefore, based on swin transformer V2 (Swin V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to inherit the advantages of both transformer and CNN and overcome the shortcomings of existing networks in feature learning. Firstly, it captures the change relationship features through the densely connected Swin V2 backbone, and provides the low-level pre-changed and post-changed features through a CNN branch. Based on these three change features, we accomplish accurate change detection results. Secondly, combined with transformer and CNN, we propose mixed feature pyramid (MFP) which provides inter-layer interaction information and intra-layer multi-scale information for complete feature learning. MFP is a plug and play module which is experimentally proven to be also effective in other change detection networks. Further more, we impose a self-supervision strategy to guide a new CNN branch, which solves the untrainable problem of the CNN branch and provides the semantic change information for the features of encoder. The state-of-the-art (SOTA) change detection scores and fine-grained change maps were obtained compared with other advanced methods on four commonly used public remote sensing datasets. The code is available at https://github.com/DalongZ/SwinV2DNet

Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images

Change detection has always been a concerned task in the interpretation of remote sensing images. It is essentially a unique binary classification task with two inputs, and there is a change relationship between these two inputs. At present, the mining of change relationship features is usually implicit in the network architectures that contain single-branch or two-branch encoders. However, due to the lack of artificial prior design for change relationship features, these networks cannot learn enough change semantic information and lose more accurate change detection performance. So we propose a network architecture NAME for the explicit mining of change relation features. In our opinion, the change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively. In addition, we design the continuous change relation (CCR) branch to further obtain the continuous and detail change relation features to improve the change discrimination capability of the model. The experimental results show that our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection on four public very high-resolution (VHR) remote sensing datasets. Our source code is available at https://github.com/DalongZ/NAME

Association of interleukin 17 / angiotensin II with refractory hypertension risk in hemodialysis patients.

Objective: The study was performed to investigate the association of interleukin 17 (IL 17) or angiotensin II (Ang II) with refractory hypertension risk in hemodialysis patients. Methods: Ninety hemodialysis patients were enrolled into this study, and those with hypertension were divided into two groups. The Easy-to-Control Hypertension group (ECHG) had fifty patients, while the refractory hypertension group (RHG) had forty patients. Twenty healthy individuals were recruited as the control group. IL17 and Ang II were determined using a human IL 17 / Ang II enzyme-linked immunosorbent assay kit. Serum IL 17 and Ang II concentrations in RHG patients were higher than those in ECHG patients. Results: Serum IL 17 and Ang II concentrations in both patient groups were higher than those in the control group. Linear regression analysis showed a positive correlation between IL 17 and Ang II. In multivariate regression analysis, we found that IL17 and Ang II were associated with refractory hypertension risk in hemodialysis patients. Conclusion: IL17 and Ang II were associated with refractory hypertension risk in hemodialysis patients. There was also a positive correlation between IL 17and Ang II

Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark

Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.</jats:p

Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture

We proposed and implemented a disease detection and semantic segmentation pipeline using a modified mask-RCNN infrastructure model on the EDD2020 dataset1. On the images provided for the phase-I test dataset, for ’BE’, we achieved an average precision of 51.14%, for ’HGD’ and ’polyp’ it is 50%. However, the detection score for ’suspicious’ and ’cancer’ were low. For phase-I, we achieved a dice coefficient of 0.4562 and an F2 score of 0.4508. We noticed the missed and mis-classification was due to the imbalance between classes. Hence, we applied a selective and balanced augmentation stage in our architecture to provide more accurate detection and segmentation. We observed an increase in detection score to 0.29 on phase-II images after balancing the dataset from our phase-I detection score of 0.24. We achieved an improved semantic segmentation score of 0.62 from our phase-I score of 0.52

EFTUD2 is a promising diagnostic and prognostic indicator involved in the tumor immune microenvironment and glycolysis of lung adenocarcinoma

BackgroundElongation Factor Tu GTP Binding Domain Containing 2 (EFTUD2), a conserved spliceosomal GTPase, is involved in craniofacial development and various cancers, but its role in lung adenocarcinoma (LUAD) remains unclear.MethodsEFTUD2 expression in LUAD tissues was analyzed using data from TCGA and GEO, and validated by immunohistochemistry, RT-qPCR, and Western blotting. The relationship between EFTUD2 expression and clinical features was examined using Fisher’s exact test. Diagnostic and prognostic analyses were performed in R. Hub genes related to EFTUD2 were identified through topological algorithms, and immune infiltration was assessed using CIBERSORT. The cGAS-STING pathway and m6A modification were also analyzed in the TCGA LUAD cohort. Functional assays were conducted to assess EFTUD2’s impact on LUAD cell proliferation, cell cycle, invasion, and metastasis, while glycolytic enzyme levels were measured by Western blotting.ResultsEFTUD2 was upregulated in LUAD tissues and cells, correlating with N classification, visceral pleural invasion, intravascular tumor embolism, and cytokeratin-19 fragment antigen 21-1. Sixteen EFTUD2-related hub genes were identified. Higher EFTUD2 expression was linked to altered immune cell infiltration, with increased TumorPurity scores and decreased StromalScore, ImmuneScore, and ESTIMATEScore values. Gene enrichment analyses highlighted EFTUD2’s involvement in cell adhesion, immune response. EFTUD2 was strongly associated with the cGAS-STING pathway and m6A modification. EFTUD2 knockdown inhibited LUAD cell proliferation, migration, and tumorigenicity, causing G0/G1 phase cell cycle arrest, and altered glycolytic enzyme expression. These findings may suggest that EFTUD2 positively regulates the progression of LUAD and modulates the glycolytic activity of tumor cells, making it valuable for LUAD treatment and prognosis.ConclusionsEFTUD2 is a potential diagnostic and prognostic marker for LUAD, associated with immune infiltration, the tumor microenvironment, the cGAS-STING pathway, m6A modification, and glycolysis

How the carbon emissions trading system affects green total factor productivity? A quasi-natural experiment from 281 Chinese cities

China’s emissions trading system is often cited as a model for developing countries using market-based means to solve pollution problems, but few have objectively assessed the solution from a productivity perspective. Therefore, in this study, the green total factor productivity (GTFP) of 281 prefecture-level cities was calculated by using the DEA–Malmquist method, and the policy effects were evaluated by setting up quasi-natural experiments. The results show that the carbon emissions trading system has a positive contribution to GTFP; when facing a more compatible carbon trading system, enterprises will choose two paths: innovation compensation and industrial upgrading to improve GTFP, so as to get rid of the cost constraints caused by carbon emission control; the policy effect of the carbon emissions trading system varies significantly in different regions. In the economically developed eastern region, the effect of policy implementation is relatively significant, while the effect of policy implementation in the western region is not significant. Further analysis shows that as a market-based environmental policy, the incentive effect of the carbon trading system relies on a perfect market system. This study provides empirical evidence and policy enlightenment for developing countries to build and improve the emissions trading system.</jats:p