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Change detection (CD) aims to explore surface changes in coaligned image pairs. However, many existing networks primarily focus on learning deep features, without considering the impact of attention and fusion strategies on detection performance. Therefore, a new frequency-temporal-aware network (FTA-Net) is proposed, it recognizes changes by means of a frequency-domain temporal fusion module and supervised attention to multilevel time-difference features, while reducing the model size. The frequency temporal fusion module is designed to introduce the frequency attention mechanism into the fusion process. First, it has a two-branch Transformer-INN feature extractor using a Lite-Transformer that utilizes remote attention for low-frequency global features, and a invertible neural network that focuses on extracting high-frequency local information. The semantic information and details of the object in both high-frequency and low-frequency feature maps are further strengthened by fusing the high-frequency local features and low-frequency global representations. Then, a stepwise modification detection module is proposed to better extract temporal difference information from bitemporal features. In addition, a supervised learning module is constructed to reweight features to efficiently aggregate multilevel features from high-level to low-level. FTA-Net outperforms state-of-the-art methods on three challenging CD datasets, and it have fewer parameters (4.93M) and lower computational cost (6.71 G).

Introduction and Objectives: Prostate apoptosis response protein-4 (PAR-4) is considered a tumor suppressor. However, the role of PAR-4 in hepatocellular carcinoma (HCC) has rarely been reported. The study explores the role of PAR-4 in the malignant behaviors of HCC cells. Materials and Methods: TCGA database was applied to analyze the expression of PAR-4 in HCC. Evaluated PAR-4 relationship with clinical parameters and prognosis by tissue microarray; expression of STAT3, p-STAT3, Src and Ras was detected by Western blotting or laser confocal microscopy. Cell scratch and flow cytometry assays were used to observe IL-6 regulation of the malignant behaviors of HCC cells. The tumorigenic potential of HCC cells in vivo was evaluated in a nude mouse tumor model. Results: Analysis indicated that the expression of PAR-4 in HCC tissues was significantly higher than that in normal liver tissues; and PAR-4 interacted with STAT3. KEGG analysis showed that PAR-4 plays a role in the Janus kinase (JAK)/STAT signaling pathway. The positive expression rate of PAR-4 in HCC tissues was significantly higher than that in adjacent tissues. Positive correlation between IL-6 and PAR-4 expression in the HCC tissues. Exogenous IL-6 significantly promoted the proliferation and migration of HCC cells and up-regulated the expression of PAR-4 and p-STAT3 in HCC cells. Interference of the expression of PAR-4 could reduce the malignant behaviors of HCC cells and inhibit tumorigenesis in a nude mouse tumor model. Conclusions: PAR-4 expression is positively correlated with HCC; PAR-4 promotes malignant behavior of HCC cells mediated by the IL-6/STAT3 signaling pathway.

Abstract Penpulimab is an anti-programmed cell death-1 (PD-1) IgG1 antibody with no Fc gamma receptor (FcγR) binding activity, and thus theoretically reduced immune-related adverse events (irAEs) while maintaining efficacy. This single-arm, phase II trial conducted across 20 tertiary care centers in China enrolled adult patients with metastatic nasopharyngeal carcinoma (NPC) who had failed two or more lines of previous systemic chemotherapy. Patients received 200-mg penpulimab intravenously every 2 weeks (4 weeks per cycle) until disease progression or intolerable toxicities. The primary endpoint was objective response rate (ORR) per RECIST (version 1.1), as assessed by an independent radiological review committee. The secondary endpoints included progression-free survival (PFS) and overall survival (OS). One hundred thirty patients were enrolled and 125 were efficacy evaluable. At the data cutoff date (September 28, 2022), 1 patient achieved complete response and 34 patients attained partial response. The ORR was 28.0% (95% CI 20.3–36.7%). The response was durable, with 66.8% still in response at 9 months. Thirty-three patients (26.4%) were still on treatment. The median PFS and OS were 3.6 months (95% CI = 1.9–7.3 months) and 22.8 months (95% CI = 17.1 months to not reached), respectively. Ten (7.6%) patients experienced grade 3 or higher irAEs. Penpulimab has promising anti-tumor activities and acceptable toxicities in heavily pretreated metastatic NPC patients, supporting further clinical development as third-line treatment of metastatic NPC.

Background: Tumor genomic profiling has a significant impact on the selection of targeted therapy. Circulating tumor DNA (ctDNA) has emerged as a noninvasive, and reproducible assay compared with tissue biopsy. We aimed to evaluate its utility in identifying mutations and guiding targeted therapy for lung cancer. Methods: A total of 173 lung cancer patients underwent next-generation sequencing (NGS) using a targeted enrichment panel covering 20 lung cancer-related genes. The performance of the ctDNA NGS assay in identifying genetic mutations or alterations was compared with tissue biopsy and droplet digital PCR (ddPCR). The treatment response to epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) therapies based on the ctDNA assay results was also assessed. Results: The ctDNA was detected in 61.85% of patients. Tissue mutations were detected in paired ctDNA in 38.57% of cases, while ctDNA mutations were detected in paired tissues in 89.1% of cases. The ctDNA increased the number of advanced non-small cell lung cancer (NSCLC) patients who received NCCN-recommended genetic testing by 12%. The concordance between ddPCR and ctDNA was relatively high reaching 99.43%. EGFR T790M/C797S c.G2390C and EGFR T790M/C797S c.T2389A were detected in tissue and ctDNA, respectively, in patient 01015. Moreover, ctDNA assay identified the EGFR T790M mutation, which was missed by tissue biopsy in patient 01149, who developed drug resistance after 1 year of EGFR-TKI therapy. Of the 17 patients who received EGFR-TKI targeted therapies based on the ctDNA NGS results, 12 patients achieved a partial response and two patients had stable disease. Conclusions: The results demonstrated that the ctDNA assay could partially overcome tumor heterogeneity in detecting mutations and provide complementary information on tumor genomic profiles. Moreover, the presence of EGFR mutations in ctDNA could offer valuable guidance for selecting appropriate EGFR-TKI treatment for advanced lung cancer patients. However, it is important to note that the ctDNA NGS assay has certain limitations in fully identifying all genomic alterations present in the tumor.

Abstract Alpha-fetoprotein (AFP), a serum glycoprotein, is expressed during embryonic development and the pathogenesis of liver cancer. It serves as a clinical tumor marker, function as a carcinogen, immune suppressor, and transport vehicle; but the detailed AFP structural information has not yet been reported. In this study, we used single-particle cryo-electron microscopy(cryo-EM) to analyze the structure of the recombinant AFP obtained a 3.31 Å cryo-EM structure and built an atomic model of AFP. We observed and identified certain structural features of AFP, including N-glycosylation at Asn251, four natural fatty acids bound to distinct domains, and the coordination of metal ions by residues His22, His264, His268, and Asp280. Furthermore, we compared the structural similarities and differences between AFP and human serum albumin. The elucidation of AFP’s structural characteristics not only contributes to a deeper understanding of its functional mechanisms, but also provides a structural basis for developing AFP-based drug vehicles.

Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD.

Semantic segmentation is primarily employed to generate accurate prediction labels for each pixel of the input image, and then classify the images according to the generated labels. Semantic segmentation of building and water in remote sensing images helps us to conduct reasonable land planning for a city. However, many current mature networks face challenges in simultaneously attending to both contextual and spatial information when performing semantic segmentation on remote sensing imagery. This often leads to misclassifications and omissions. Therefore, this paper proposes a Dual-Branch Network with Spatial Supplementary Information (SPNet) to address the aforementioned issues. We introduce a Context-aware Spatial Feature-Extractor Unit (CSF) to extract contextual and spatial information, followed by the Feature-Interaction Module (FIM) to supplement contextual semantic information with spatial details. Additionally, incorporating the Goal-Oriented Attention Mechanism helps in handling noise. Finally, to obtain more detailed branches, a Multichannel Deep Feature-Extraction Module (MFM) is introduced to extract features from shallow-level network layers. This branch guides the fusion of low-level semantic information with high-level semantic information. Experiments were conducted on building and water datasets, respectively. The results indicate that the segmentation accuracy of the model proposed in this paper surpasses that of other existing mature models. On the building dataset, the mIoU reaches 87.57, while on the water dataset, the mIoU achieves 96.8, which means that the model introduced in this paper demonstrates strong generalization capabilities.

Hepatocellular carcinoma (HCC) is a complex process that plays an important role in its progression. Abnormal glucose metabolism in HCC cells can meet the nutrients required for the occurrence and development of liver cancer, better adapt to changes in the surrounding microenvironment, and escape the attack of the immune system on the tumor. There is a close relationship between reprogramming of glucose metabolism and immune escape. This article reviews the current status and progress of glucose metabolism reprogramming in promoting immune escape in liver cancer, aiming to provide new strategies for clinical immunotherapy of liver cancer.

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation.

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network’s focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques.

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities.

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.

Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models.

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model’s learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model’s ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities.

Objective: This retrospective cohort study aimed to investigate the effects of parity on gestational weight gain (GWG) and its association with maternal and neonatal outcomes in women with gestational diabetes mellitus (GDM). Methods: This retrospective cohort study data from 2,909 pregnant women with GDM who delivered between 2021 and 2023 at Fujian Maternity and Child Health hospital, were analyzed. Participants were categorized into nulliparous (no previous births), primiparous (one previous birth), and multiparous (two or more previous births) groups. Women aged 18–45 years with singleton pregnancies were included, while those with multiple pregnancies, pre-existing diabetes, hypertension, thyroid disorders, or incomplete BMI data were excluded. Data extracted from hospital records included demographic information, BMI, GWG at various gestational stages, and pregnancy outcomes. Statistical analysis involved logistic regression models, adjusting for confounders such as maternal age, BMI, and gestational age at delivery. P-values < 0.05 were considered significant. Results: Primiparous and multiparous women exhibited significantly higher GWG than nulliparous women (p < 0.01). Primiparous women had reduced risks of pregnancy-induced hypertension (adjusted OR, 0.64; 95% CI, 0.45–0.92) and premature rupture of membranes (adjusted OR, 0.58; 95% CI, 0.48–0.70). Both primiparous and multiparous women had lower risks of delivering large-for-gestational-age newborns or those with neonatal jaundice. Conclusion: Parity significantly influences GWG and maternal/neonatal outcomes in GDM pregnancies, highlighting the need for parity-specific guidelines. [ABSTRACT FROM AUTHOR]

Understanding surface changes requires the ability to identify changes in high resolution remote sensing images. Because current deep learning-based change detection algorithms are not able to accurately discriminate between altered and unmodified areas, which leads to the problem of edge uncertainty and small target missing in the detection process. To identify changes in high resolution remote sensing images, this research proposes an unique Attention-Guided Siamese Network (SAGNet). In this network, bitemporal images’ highly representative deep semantic features are retrieved using a fully convolutional dual-stream architecture, and the extracted deep semantic features are then used to extract semantic variation data from the Global Semantic Aggregation Module (GSAM). In the feature decoding stage, the extracted features are refined layer by layer through the Attention Fusion Module (AFM) for change map reconstruction. In addition, we propose two other auxiliary modules: Cross-scale Fusion Module (CFM) and Bilateral Feature Fusion Module (BFFM), which enable the network to remove background noise while improving the recognition accuracy of changing object boundaries and small-changing targets in the output change map. A public dataset called LEVIR-CD and a challenging dataset called BICD made up of bitemporal images from Google Earth covering various parts of China are used to experimentally test SAGNet. Finally, experimental evidence shows that our approach outperforms current cutting-edge change detection techniques.

In Remote Sensing(RS) data analysis, Remote Sensing Change Detection(CD) is an important technology. The existing Remote Sensing Change Detection(RS-CD) methods do not fully consider the advantages and disadvantages of Convolution and Transformer in feature extraction, which will restrict the overall performance of the network to a certain extent. Therefore, this paper proposes a Dual Branch Feature Guided Aggregation Network composed of convolutional neural network(CNN) and Transformer. In the encoding stage, based on the respective characteristics of Convolution and Transformer, a dual-branch backbone network is constructed to extract the spatial information and semantic information of the image respectively; And through the Feature Guidance Aggregation Module, the two branches can guide each other for feature mining, so as to avoid the occurrence of false detection and missed detection of change areas due to insufficient fusion to the greatest extent. Finally, in the decoding stage, the different levels of features extracted by the two branches are fully used for fusion and decoding. And the experiment shows that compared with the existing methods, the mean intersection over union(MIoU) index on the four public datasets are improved by 1.25%, 1.55%, 1.38% and 1.71%.

Alpha-fetoprotein(AFP) is a cancer biomarker for the diagnosis of hepatocellular carcinoma(HCC); however, its role in macrophage polarization and phagocytosis remains unclear. In the present study, we explored the correlation between AFP regulation of macrophage function and the possible regulatory mechanisms. Human mononuclear leukemia cells (THP-1) and monocytes from healthy donors were used to analyze the effect of AFP on the macrophages’ phenotype and phagocytosis. THP-1 cells and healthy human donor-derived monocytes were polarized into M0 macrophages induced by phorbol ester (PMA), and M0 macrophages were polarized into M1 macrophages induced by lipopolysaccharide(LPS) and interferon-γ(IFN-γ). Interleukin-4(IL-4) and interleukin-13(IL-13) were used to induce M0 macrophage polarization into M2 macrophages. Tumor-derived AFP(tAFP) stimulated M0 macrophage polarization into M2 macrophages and inhibited M1 macrophages to phagocytize HCC cells. The role of AFP in promoting macrophage polarization into M2 macrophages and inhibiting the M1 macrophages to phagocytize HCC cells may be involved in activating the PI3K/Akt signaling pathway. AFP could also enhanced the migration ability of macrophages and inhibited the apoptosis of HCC cells when co-cultured with M1-like macrophages. AFP is a pivotal cytokine that inhibits macrophages to phagocytize HCC cells.

Spiraea thunbergii (S. thunbergia) is a very common ornamental shrub in the garden, with important horticultural and economic value. In this study, we assembled the complete chloroplast (cp) genome of S. thunbergia into a typical quadripartite structure. The genome size and GC content of the S. thunbergia cp genome are 155,922 bp and 36.76%, respectively. It contains 84 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The phylogenetic tree supported that S. thunbergii is closely related to Spiraea mongolica in the Rosaceae family. The study will provide significant genomic resource for elucidating the phylogenetic relationship of Spiraea.

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research.

Change detection is an important component in the field of remote sensing. At present, deep-learning-based change-detection methods have acquired many breakthrough results. However, current algorithms still present issues such as target misdetection, false alarms, and blurry edges. To alleviate these problems, this work proposes a network based on feature differences and attention mechanisms. This network includes a Siamese architecture-encoding network that encodes images at different times, a Difference Feature-Extraction Module (DFEM) for extracting difference features from bitemporal images, an Attention-Regulation Module (ARM) for optimizing the extracted difference features through attention, and a Cross-Scale Feature-Fusion Module (CSFM) for merging features from different encoding stages. Experimental results demonstrate that this method effectively alleviates issues of target misdetection, false alarms, and blurry edges.

Among the most difficult difficulties in contemporary satellite image-processing subjects is cloud and cloud shade segmentation. Due to substantial background noise interference, existing cloud and cloud shadow segmentation techniques would result in false detection and missing detection. We propose a Location Pooling Multi-Scale Network (LPMSNet) in this study. The residual network is utilised as the backbone in this method to acquire semantic info on various levels. Simultaneously, the Location Attention Multi-Scale Aggregation Module (LAMA) is introduced to obtain the image’s multi-scale info. The Channel Spatial Attention Module (CSA) is introduced to boost the network’s focus on segmentation goals. Finally, in view of the problem that the edge details of cloud as well as cloud shade are easily lost, this work designs the Scale Fusion Restoration Module (SFR). SFR can perform picture upsampling as well as the acquisition of edge detail information from cloud as well as cloud shade. The mean intersection over union (MIoU) accuracy of this network reached 94.36% and 81.60% on the Cloud and Cloud Shadow Dataset and the five-category dataset L8SPARCS, respectively. On the two-category HRC-WHU Dataset, the accuracy of the network on the intersection over union (IoU) reached 90.51%. In addition, in the Cloud and Cloud Shadow Dataset, our network achieves 97.17%, 96.83%, and 97.00% in precision (P), recall (R), and F1 score (F1) in cloud segmentation tasks, respectively. In the cloud shadow segmentation task, precision (P), recall (R), and F1 score (F1) reached 95.70%, 96.38%, and 96.04%, respectively. Therefore, this method has a significant advantage over the current cloud and cloud shade segmentation methods.