An {\alpha}-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image is to fuse several images focusing at different depths. However, existing multi-focus image fusion methods cannot obtain clear results for areas near the focused/defocused boundary (FDB). In this paper, a novel {\alpha}-matte boundary defocus model is proposed to generate realistic training data with the defocus spread effect precisely modeled, especially for areas near the FDB. Based on this {\alpha}-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB. More specifically, the MMF-Net consists of two cascaded sub-nets for initial fusion and boundary fusion, respectively; these two sub-nets are designed to first obtain a guidance map of FDB and then refine the fusion near the FDB. Experiments demonstrate that with the help of the new {\alpha}-matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.Comment: 10 pages, 8 figures, journal Unfortunately, I cannot spell one of the authors' name coorectl

Exploit the Best of Both End-to-End and Map-Based Methods for Multi-Focus Image Fusion

Multi-focus image fusion is a technique to fuse the images focused on different depth ranges to generate an all-in-focus image. Existing deep learning approaches to multi-focus image fusion can be categorized as end-to-end methods and decision map based methods. End-to-end methods can generate natural fusion near the focus-defocus boundaries (FDB), but the output is often inconsistent with the input in the areas far from the boundaries (FFB). On the contrary, decision map based methods can preserve original images in the FFB areas, but often generate artifacts near the FDB. In this paper, we propose a dual-branch network for multi-focus image fusion (DB-MFIF) to exploit the best of both worlds, achieving better results in both FDB and FFB areas, i.e. with naturally sharper FDB areas and more consistent FFB areas with the inputs. In our DB-MFIF, an end-to-end branch and a decision map based branch are proposed to mutually assist each other. In addition, to this end, two map-based loss functions are also proposed. Experiments show that our method surpasses existing algorithms on multiple datasets, both qualitatively and quantitatively, and achieves the state-of-the-art performance. The code and model is available on GitHub: https://github.com/Zancelot/DB-MFIF

Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth

Multi-focus image fusion, a technique to generate an all-in-focus image from two or more partially-focused source images, can benefit many computer vision tasks. However, currently there is no large and realistic dataset to perform convincing evaluation and comparison of algorithms in multi-focus image fusion. Moreover, it is difficult to train a deep neural network for multi-focus image fusion without a suitable dataset. In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images. The dataset is generated by light field images, and both the source images and the ground truth images are realistic. To serve as both a well-established benchmark for existing multi-focus image fusion algorithms and an appropriate training dataset for future development of deep-learning-based methods, the dataset contains a variety of scenes, including buildings, plants, humans, shopping malls, squares and so on. We also evaluate 10 typical multi-focus algorithms on this dataset for the purpose of illustration

The efficacy of mobile phone-based text message interventions (‘Happy Quit’) for smoking cessation in China

Background Considering the extreme shortage of smoking cessation services in China, and the acceptability, feasibility and efficacy of mobile phone-based text message interventions for quitting smoking in other countries, here we propose a study of “the efficacy of mobile phone-based text message interventions (‘Happy Quit’) for smoking cessation in China”. The primary objective of this proposed project is to assess whether a program of widely accessed mobile phone-based text message interventions (‘Happy Quit’) will be effective at helping people in China who smoke, to quit. Based on the efficacy of previous studies in smoking cessation, we hypothesize that ‘Happy Quit’ will be an effective, feasible and affordable smoking cessation program in China. Methods/Design In this single-blind, randomized trial, undertaken in China, about 2000 smokers willing to make a quit attempt will be randomly allocated, using an independent telephone randomization system that includes a minimization algorithm balancing for sex (male, female), age (19–34 or \u3e34 years), educational level (≤ or \u3e12 years), and Fagerstrom score for nicotine addiction (≤5, \u3e5), to ‘Happy Quit’, comprising motivational messages and behavioral-change support, or to a control group that receives text messages unrelated to quitting. Messages will be developed to be suitable for Chinese. A pilot study will be conducted before the intervention to modify the library of messages and interventions. The primary outcome will be self-reported continuous smoking abstinence. A secondary outcome will be point prevalence of abstinence. Abstinence will be assessed at six time points (4, 8, 12, 16, 20 and 24 weeks post-intervention). A third outcome will be reductions in number of cigarettes smoked per day. Discussion/Implications The results will provide valuable insights into bridging the gap between need and services received for smoking cessation interventions and tobacco use prevention in China. It will also serve as mHealth model for extending the public health significance of other interventions, such as mental health interventions

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies

Chemically programmed metabolism drives a superior cell fitness for cartilage regeneration

The rapid advancement of cell therapies underscores the importance of understanding fundamental cellular attributes. Among these, cell fitness—how transplanted cells adapt to new microenvironments and maintain functional stability in vivo—is crucial. This study identifies a chemical compound, FPH2, that enhances the fitness of human chondrocytes and the repair of articular cartilage, which is typically nonregenerative. Through drug screening, FPH2 was shown to broadly improve cell performance, especially in maintaining chondrocyte phenotype and enhancing migration. Single-cell transcriptomics indicated that FPH2 induced a super-fit cell state. The mechanism primarily involves the inhibition of carnitine palmitoyl transferase I and the optimization of metabolic homeostasis. In animal models, FPH2-treated human chondrocytes substantially improved cartilage regeneration, demonstrating well-integrated tissue interfaces in rats. In addition, an acellular FPH2-loaded hydrogel proved effective in preventing the onset of osteoarthritis. This research provides a viable and safe method to enhance chondrocyte fitness, offering insights into the self-regulatory mechanisms of cell fitness

Parametric study of density wave instability in parallel channels of a water-cooled blanket in a fusion reactor

In fusion reactors, many blanket concepts are designed with water as a coolant to transfer high-density heat from the fusion reaction out of the reactor core. The coolant temperature and pressure are maintained as the validated use in water-cooled fission reactors. However, the flow channel in a water-cooled blanket is independent of each other, and there is no flow mixing between coolant channels. Therefore, flow instability may occur in the independent parallel channels in a water-cooled blanket due to its unique structure and heat distribution, especially under the high heat flux caused by plasma rupture. In this study, the parametric analysis of density wave instability is performed using a thermal-hydraulic code developed for independent parallel channels based on the homogeneous model for the two-phase flow. The parallel-channel system in a water-cooled ceramic breeder (WCCB) blanket of the China Fusion Engineering Experimental Reactor (CFETR) is established for its first wall structure. A small disturbance is introduced into the system to determine if it is stable under different conditions. It is found that the channel number has no obvious influence on the prediction of the flow instability boundary. Therefore, the two-channel system is adopted to investigate the influence of different parameters, such as the pressure, resistance, flow rate, and inclination, on the flow instability boundary of the parallel-channel system in the CFETR WCCB blanket. The results show that flow instability occurs more easily in this study compared to the traditional instability analysis, especially under high-pressure conditions. In general, conditions of high pressure, large flow rate, and no inclination can stabilize the system, while the influence of resistance is quite different under different conditions of resistance and pressure. The research work indicates that more attention should be paid to the joint influence of different parameters for the water-cooled blanket during its design and operation

SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation

Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts in incoorporating global aggregation for heterophilous GNNs usually require iteratively maintaining and updating full-graph information, which entails $\mathcal{O}(n^2)$ computation efficiency for a graph with $n$ nodes, leading to weak scalability to large graphs. In this paper, we propose SIMGA, a GNN structure integrating SimRank structural similarity measurement as global aggregation. The design of SIMGA is simple, yet it leads to promising results in both efficiency and effectiveness. The simplicity of SIMGA makes it the first heterophilous GNN model that can achieve a propagation efficiency near-linear to $n$. We theoretically demonstrate its effectiveness by treating SimRank as a new interpretation of GNN and prove that the aggregated node representation matrix has expected grouping effect. The performances of SIMGA are evaluated with 11 baselines on 12 benchmark datasets, usually achieving superior accuracy compared with the state-of-the-art models. Efficiency study reveals that SIMGA is up to 5$\times$ faster than the state-of-the-art method on the largest heterophily dataset pokec with over 30 million edges