Structural Similarity Research Articles

Investigating the Visual Utility of Differentially Private Scatterplots.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting

Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

Individualism and bank financial structure similarity

We use a sample of 75 countries to conduct the first international study of national culture on bank financial structure similarity. We construct a novel and comprehensive measure of bank financial structure similarity using banks’ financial statement data. Our empirical results show that individualism is robustly and positively associated with bank financial structure similarity, holding bank-pair levels, bank levels, and country levels equal. We find that other cultural dimensions, such as power distance, uncertainty avoidance, masculinity, and long-term orientation, have little effect on similarity. Our work also shows that financial structure similarity is correlated with higher bank risk.

One-Stage Shifted Laplacian Refining for Multiple Kernel Clustering.

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a "three-stage" scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the "one-stage" scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to O(n) and O(n2) , respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.

Bis-benzylisoquinoline alkaloids inhibit African swine fever virus internalization and replication by impairing late endosomal/lysosomal function.

African swine fever (ASF), caused by the African swine fever virus (ASFV), is a highly infectious disease afflicting domestic pigs and wild boars. It exhibits an alarming acute infection fatality rate of up to 100%. Regrettably, no commercial vaccines or specific drugs for combating this disease are currently available. This study evaluated the anti-ASFV activities in porcine alveolar macrophages, 3D4/21 cells, and PK-15 cells of four bis-benzylisoquinoline alkaloids (BBAs): cepharanthine (CEP), tetrandrine, fangchinoline, and iso-tetrandrine. Furthermore, we demonstrated that CEP, which exhibited the highest selectivity index (SI = 81.31), alkalized late endosomes/lysosomes, hindered ASFV endosomal transport, disrupted virus uncoating signals, and thereby inhibited ASFV internalization. Additionally, CEP disrupted ASFV DNA synthesis, leading to the inhibition of viral replication. Moreover, berbamine was labeled with NBD to synthesize a fluorescent probe to study the cellular location of these BBAs. By co-staining with Lyso-Tracker and lysosome-associated membrane protein 1, we demonstrated that BBAs target the endolysosomal compartments for the first time. Our data together indicated that BBAs are a class of natural products with significant inhibitory effects against ASFV infection. These findings suggest their potential efficacy as agents for the prevention and control of ASF, offering valuable references for the identification of potential drug targets.IMPORTANCEThe urgency and severity of African swine fever (ASF) underscore the critical need for effective interventions against this highly infectious disease, which poses a grave threat to domestic pigs and wild boars. Our study reveals the potent anti-African swine fever virus (ASFV) efficacy of bis-benzylisoquinoline alkaloids (BBAs), particularly evident in the absence of progeny virus production under a 5 µM concentration treatment. The structural similarity among cepharanthine, tetrandrine, fangchinoline, and iso-tetrandrine, coupled with their analogous inhibitory stages and comparable selectivity indexes, strongly suggests a shared antiviral mechanism within this drug category. Further investigation revealed that BBAs localize to lysosomes and inhibit the internalization and replication of ASFV by disrupting the endosomal/lysosomal function. These collective results have profound implications for ASF prevention and control, suggesting the potential of the investigated agents as prophylactic and therapeutic measures. Furthermore, our study offers crucial insights into identifying drug targets and laying the groundwork for innovative interventions.

Computational underwater ghost imaging based on scattering-and-absorption degradation.

In underwater computational ghost imaging, the presence of scattering and absorption introduces significant degradation, leading to blurring and distortion of illuminating patterns. This work proposes an anti-degradation underwater computational ghost imaging (AUGI) method based on the physical degradation model of underwater forward degradation caused by scattering and absorption. Through AUGI, we can enhance the quality of a reconstructed image by about 10% compared to differential ghost imaging (DGI) as measured by peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as a result of simulations. We experimentally demonstrate the superior effectiveness of this method in the artificial submarine environment. Additionally, benefitting from its simplicity, this method is expected to be applied across a wide range of underwater ghost imaging applications.

In silico analysis of Naringenin and its O-alkyl derivatives for its potential binding with Estrogen Receptor- α

Breast cancer remains the second-most significant cause of female mortality worldwide. The emergence of naturally occurring plant-derived compounds called phytoestrogens has aroused substantial interest in their potential to mitigate breast cancer. Phytoestrogens demonstrate structural similarities to human estrogen and can therefore interact with the estrogen receptors. Naringenin, an important flavonoid and phytoestrogen, has gathered significant attention in the field of cancer due to its potential anti-inflammatory and antioxidant properties while its O-alkyl derivatives can enhance its bioavailability and target specificity. In this study, we have screened Naringenin and four of its O-alkyl derivatives, namely Isosakuranetin, 5-O-Methylnaringenin, Sakuranetin and 4',7-Di-O-methylnaringenin for their binding affinity with estrogen receptor-α (PDB ID: 6VJD). Molecular docking studies targeting estrogen receptor-α (PDB ID: 6VJD) revealed favorable binding energies and interaction patterns for naringenin and its derivatives, suggesting potential as therapeutic agents against breast cancer. Additionally, analysis with SwissADME highlighted the compounds' permeability across the blood-brain barrier (BBB), high absorption rates and water solubility, emphasizing their potential for therapeutic applications beyond breast cancer. Furthermore, the compounds exhibited promising pharmacokinetic profiles, synthetic accessibility and adherence to Lipinski's rule of five, supporting their candidacy for further development as therapeutic agents.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Bidirectional brain image translation using transfer learning from generic pre-trained models

Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the understanding of brain anatomy, abnormalities, and functional connectivity. However, cost and radiation dose may limit the acquisition of specific image modalities, so medical image synthesis can be used to generate required medical images without actual addition. CycleGAN and other GANs are valuable tools for generating synthetic images across various fields. In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge. Recent studies propose using transfer learning to overcome this issue. This involves adapting pre-trained CycleGAN models, initially trained on non-medical data, to generate realistic medical images. In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models, and the models were fine-tuned to have the best result. The models’ performance was evaluated using four widely used image quality metrics: Peak-signal-to-noise-ratio, Structural Similarity Index, Universal Quality Index, and Visual Information Fidelity. Quantitative evaluation and qualitative perceptual analysis by radiologists demonstrate the potential of transfer learning in medical imaging and the effectiveness of the generic pre-trained model. The results provide compelling evidence of the model’s exceptional performance, which can be attributed to the high quality and similarity of the training images to actual human brain images. These results underscore the significance of carefully selecting appropriate and representative training images to optimize performance in brain image analysis tasks.

Performance comparison of decision-based median filtering for brain MR images with high multiplicative noise

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.

ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering

Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.

LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning

Low-dose X-CT scanning method effectively reduces radiation hazards, however, reducing the radiation dose will introduce noise and artifacts during the projection process, resulting in a decrease in the quality of the reconstructed image. To address this problem, we combined 2D variational modal decomposition and dictionary learning. We proposed a low-dose CT (LDCT) image denoising algorithm based on an improved K-SVD algorithm with image decomposition. The dictionary obtained by K-SVD training lacks consideration of image structure information. To address this problem, we employ the two-dimensional variational mode decomposition (2D-VMD) method to decompose the image into distinct modal components. Through the adaptive learning of dictionaries based on the characteristics of each modal component, independent denoising processing is applied to each component, avoiding the loss of structural and detailed information in the image. In addition, we introduce the regularized orthogonal matching pursuit algorithm (ROMP) and dictionary atom optimization method to improve the sparse representation ability of the dictionary and reduce the impact of noise atoms on denoising performance. The experiments show that the proposed method outperforms other denoising methods regarding peak signal-to-noise ratio and structural similarity. The proposed method maintains the denoised image details and structural information while removing LDCT image noise and artifacts. The image quality after denoising is significantly improved and facilitates more accurate detection and analysis of lesion areas.

A potential treatment with phytoestrogens: A literature review of experimental models on female rat’s reproductive health

Introduction: Phytoestrogens are nonsteroidal substances produced from plants with biological properties such as estrogen. The two main types of phytoestrogens are flavonoids and non-flavonoids. Flavonoids, including isoflavones, are among the most estrogenic compounds, and due to their structural similarities, they can bind to estrogen receptors. Isoflavones have been found in various foods, including fruits, vegetables, sprouts, seeds, and oilseeds (including flaxseeds). This review focuses on plants phytoestrogens that contain flavonoids, isoflavones, daidzein, and genistein. In addition, isoflavones can also be considered endocrine disruptors with the possibility of negative effects on the state of health in certain parts of the population or environment. This literature review will include phytoestrogen-rich crop yields on the reproductive health of mice. Method Systematic review: Literature searches are conducted on databases such as PubMed, ScienceDirect, Google Scholar, and Cochrane, with time limits. From 2014 to 2024, searches were carried out on this database, considering studies involving herbs, phytoestrogens, isoflavones, and genistein in ovariectomy rat’s model (OVX). The study found that plants rich in phytoestrogens can overcome post-ovariectomy complaints, increase estrogen levels, and increase normal cell proliferation. This herb is used as an effective alternative therapy in treating post-ovariectomy complaints. Result: The search results found 2,401 studies on phytoestrogens and hypoestrogenics in the reproductive organs. Conclusion: Phytoestrogens have active compounds that can improve the reproductive system after ovariectomy in female mice.

Research on deep learning restoration algorithm of X-ray backscatter imaging based on virtual training dataset

The X-ray lobster eye lens, an innovative technique for focusing high-energy radiation, enables wide-field X-ray imaging. However, its inherent cross point spread function introduces noise and degradation into the resultant images. Conventional image restoration methods are inadequate for suppressing such noise. This paper introduces a backscatter image restoration technique utilizing a virtual training dataset. By convolving the point spread function (PSF) with an object to simulate the image degradation process, the method generates a multitude of convolved images for deep learning training, eliminating the need for manual annotation. Given the high structural similarity between the synthetic convolved images and actual backscatter images, the trained model effectively restores real backscatter images. The restoration process yields a structural similarity index (SSIM) of 0.86 and a mean intersection over union (MIoU) of 0.83 when compared to the reference images. This approach mitigates the limitations of sparse real backscatter datasets, substantially reducing image acquisition time, decreasing radiation flux, and enhancing system safety.

IMPACT OF POLITICAL CULTURE ON THE POLICY-MAKING PROCESS: COMPARATIVE ANALYSIS OF THE UNITED KINGDOM AND TÜRKIYE

Questioning the codes, sociological tendencies, fields of social conflict/ harmony of a country’s political culture is essential in identifying the problems of that country’s political, social, and human life. All these parameters, such as determining values, symbols, and tendencies in political culture, directly or indirectly affect the policy-making process of countries. This study focuses on the effects of political culture on policy making in the United Kingdom (UK) and Türkiye, trying to understand the differences and similarities in this area and includes extensive comparative analysis. The main problem of this research is whether phenomena can be transferred from the UK to Türkiye in terms of political culture to solve political and social issues in Türkiye. The hypothesis derived from this research question is that Türkiye’s political culture and institutions have been politicized as a historical tradition, especially in recent years. In this context, the study aims to contribute to an under-researched area of comparative political science and literature by providing insights into the political dynamics of Türkiye and the UK. The study’s methodology and explanations of the literature on political culture, integrated with analyses of selected cases, provide a framework for understanding and comparing Türkiye’s political culture with that of the UK. These efforts can be seen as an insufficient but essential step towards solving the problems of Türkiye’s political culture.

Electronic structure evolution upon lithiation: A Li K-edge study of silicon oxide anode through X-ray Raman spectroscopy

The comprehensive understanding of the local structural changes surrounding lithium in lithium silicate (LixSiOy) and silicide (LixSi) within Li/SiOx batteries during the reversible structural transformations has been hindered by the limitations of current methodologies. In this work, the evolution of electronic structure at various lithiation stages has been addressed well by examining the Li K-edge spectra through X-ray Raman spectroscopy (XRS). The features observed in the Li K-edge XRS spectra provide insights into the development and alteration of LixSiOy, which emerges in the initial phases and may be accompanied by a reduction in the ionicity of Li–O bonding during lithiation. These features also agree well with the accompanying FDMNES code simulation. The correlation between electrochemical mechanisms and spectral characteristics is further explored by applying pseudo-Voigt peaks and cumulative pseudo-Voigt functions for fitting purposes. The absence of a significant edge shift indicates a similarity in the electronic structure of LixSi throughout lithiation, and no evidence of Li2O formation has also been observed. The Li K-edge XRS spectra exhibit strong agreement with the electrochemical behavior, establishing it as a valuable tool for investigating the evolution of electronic structure in Li/SiOx batteries.

AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.

Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accuracy. To achieve this goal, we developed AutoCorNN, an unsupervised physics-aware deep-learning model for correcting geometric distortions. Two publicly available datasets, the MPI-Leipzig Mind-Brain-Body with 318 subjects, and the Vestibular Schwannoma-SEG dataset, encompassing 242 patients were utilized. AutoCorNN integrates two 2D convolutional encoder-decoder neural networks with the forward physical model of MRI signal generation to predict undistorted MR and field map images from distorted MR input. The network is trained in an unsupervised manner by minimizing the mean absolute error between the measured and estimated k-space data, without requiring ground truth images during training or deployment. The model was evaluated on vestibular schwannoma cases. AutoCorNN achieved a peak signal-to-noise ratio (PSNR) of 41.35 ± 0.02dB, a root mean square error (RMSE) of 0.02 ± 0.003, and a structural similarity index (SSIM) of 0.99 ± 0.02 outperforming uncorrected and B0-mapping correction methods. Geometric distortions of about 1.6mm were observed at the air-tissue interfaces at the air canal and nasal cavity borders. Geometrically, distortion correction increased the target volume from 3.12 ± 0.52cc to 3.84 ± 0.54cc. Dosimetrically, AutoCorNN improved target coverage (0.96 ± 0.01 to 0.97 ± 0.02), conformity index (0.92 ± 0.03 to 0.94 ± 0.03), and reduced dose gradients outside the target. AutoCorNN achieves accurate geometric distortion correction comparable to conventional iterative methods while offering substantial computational acceleration, enabling precise target delineation and conformal dose delivery for improved radiation therapy outcomes.

An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm

Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority.

A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion

Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer from poor image quality and loss of crucial details due to inadequate handling of semantic information and limited feature extraction capabilities. This paper introduces a novel medical image fusion technique leveraging unsupervised image segmentation to enhance the semantic understanding of the fusion process. The proposed method, named DUSMIF, employs a multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms to refine the feature extraction and fusion processes. An innovative approach that utilizes unsupervised image segmentation to extract semantic information is introduced, which is then integrated into the fusion process. This not only enhances the semantic relevance of the fused images but also improves the overall fusion quality. The paper proposes a sophisticated network structure that extracts and fuses features at multiple scales and across multiple branches. This structure is designed to capture a comprehensive range of image details and contextual information, significantly improving the fusion outcomes. Multiple attention mechanisms are incorporated to selectively emphasize important features and integrate them effectively across different modalities and scales. This approach ensures that the fused images maintain high quality and detail fidelity. A joint loss function combining content loss, structural similarity loss, and semantic loss is formulated. This function not only guides the network in preserving image brightness and texture but also ensures that the fused image closely resembles the source images in both content and structure. The proposed method demonstrates superior performance over existing fusion techniques in objective assessments and subjective evaluations, confirming its effectiveness in enhancing the diagnostic utility of fused medical images.

Dynamic development of microglia and macrophages after spinal cord injury.

Secondary injury following spinal cord injury is primarily characterized by a complex inflammatory response, with resident microglia and infiltrating macrophages playing pivotal roles. While previous studies have grouped these two cell types together based on similarities in structure and function, an increasing number of studies have demonstrated that microglia and macrophages exhibit differences in structure and function and have different effects on disease processes. In this study, we used single-cell RNA sequencing and spatial transcriptomics to identify the distinct evolutionary paths of microglia and macrophages following spinal cord injury. Our results showed that microglia were activated to a pro-inflammatory phenotype immediately after spinal cord injury, gradually transforming to an anti-inflammatory steady state phenotype as the disease progressed. Regarding macrophages, our findings highlighted abundant communication with other cells, including fibroblasts and neurons. Both pro-inflammatory and neuroprotective effects of macrophages were also identified; the pro-inflammatory effect may be related to integrin β2 (Itgb2) and the neuroprotective effect may be related to the oncostatin M pathway. These findings were validated by in vivo experiments. This research underscores differences in the cellular dynamics of microglia and macrophages following spinal cord injury, and may offer new perspectives on inflammatory mechanisms and potential therapeutic targets.