FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, personalized FL methods have a tendency to overfit to local data, leading to a sharp valley in the local model and inhibiting its ability to generalize to out-of-distribution data. In this paper, we propose a novel federated model soup method (i.e., selective interpolation of model parameters) to optimize the trade-off between local and global performance. Specifically, during the federated training phase, each client maintains its own global model pool by monitoring the performance of the interpolated model between the local and global models. This allows us to alleviate overfitting and seek flat minima, which can significantly improve the model's generalization performance. We evaluate our method on retinal and pathological image classification tasks, and our proposed method achieves significant improvements for out-of-distribution generalization. Our code is available at https://github.com/ubc-tea/FedSoup.Comment: Accepted by MICCAI202

Controllable Path of Destruction

Path of Destruction (PoD) is a self-supervised method for learning iterative generators. The core idea is to produce a training set by destroying a set of artifacts, and for each destructive step create a training instance based on the corresponding repair action. A generator trained on this dataset can then generate new artifacts by ``repairing'' from arbitrary states. The PoD method is very data-efficient in terms of original training examples and well-suited to functional artifacts composed of categorical data, such as game levels and discrete 3D structures. In this paper, we extend the Path of Destruction method to allow designer control over aspects of the generated artifacts. Controllability is introduced by adding conditional inputs to the state-action pairs that make up the repair trajectories. We test the controllable PoD method in a 2D dungeon setting, as well as in the domain of small 3D Lego cars.Comment: 8 pages, 6 figures, and 2 tables. Published at CoG Conference 202

A novel prognostic 7-methylguanosine signature reflects immune microenvironment and alternative splicing in glioma based on multi-omics analysis

Glioma is the most common type of central nervous system tumor with increasing incidence. 7-methylguanosine (m7G) is one of the diverse RNA modifications that is known to regulate RNA metabolism and its dysregulation was associated with various cancers. However, the expression pattern of m7G regulators and their roles in regulating tumor immune microenvironments (TIMEs) as well as alternative splicing events (ASEs) in glioma has not been reported. In this study, we showed that m7G regulators displayed a close correlation with each other and most of them were differentially expressed between normal and glioma tissues. Two m7G signatures were then constructed to predict the overall survival of both GBM and LGG patients with moderate predictive performance. The risk score calculated from the regression coefficient and expression level of signature genes was proved to be an independent prognostic factor for patients with LGG, thus, a nomogram was established on the risk score and other independent clinical parameters to predict the survival probability of LGG patients. We also investigated the correlation of m7G signatures with TIMEs in terms of immune scores, expression levels of HLA and immune checkpoint genes, immune cell composition, and immune-related functions. While exploring the correlation between signature genes and the ASEs in glioma, we found that EIF4E1B was a key regulator and might play dual roles depending on glioma grade. By incorporating spatial transcriptomic data, we found a cluster of cells featured by high expression of PTN exhibited the highest m7G score and may communicate with adjacent cancer cells via SPP1 and PTN signaling pathways. In conclusion, our work brought novel insights into the roles of m7G modification in TIMEs and ASEs in glioma, suggesting that evaluation of m7G in glioma could predict prognosis. Moreover, our data suggested that blocking SPP1 and PTN pathways might be a strategy for combating glioma

Transcriptome profile analysis in spinal cord injury rats with transplantation of menstrual blood-derived stem cells

IntroductionMenstrual blood-derived stem cells (MenSCs) are vital in treating many degenerative and traumatic disorders. However, the underlying molecular mechanisms remain obscure in MenSCs-treating spinal cord injury (SCI) rats.MethodsMenSCs were adopted into the injured sites of rat spinal cords at day 7 post surgery and the tissues were harvested for total RNA sequencing analysis at day 21 after surgery to investigate the expression patterns of RNAs. The differentially expressed genes (DEGs) were analyzed with volcano and heatmap plot. DEGs were sequentially analyzed by weighted gene co-expression network, functional enrichment, and competitive endogenous RNAs (ceRNA) network analysis. Next, expression of selected miRNAs, lncRNAs, circRNAs and mRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Bioinformatics packages and extra databases were enrolled to scoop the genes functions and their interaction relationships.ResultsA total of 89 lncRNAs, 65 circRNAs, 120 miRNAs and 422 mRNAs were significantly upregulated and 65 lncRNAs, 72 circRNAs, 74 miRNAs, and 190 mRNAs were significantly downregulated in the MenSCs treated rats compared to SCI ones. Current investigation revealed that MenSCs treatment improve the recovery of the injured rats and the most significantly involved pathways in SCI regeneration were cell adhesion molecules, nature killer cell mediated cytotoxicity, primary immunodeficiency, chemokine signaling pathway, T cell receptor signaling pathway and B cell receptor signaling pathway. Moreover, the lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA ceRNA network of SCI was constructed. Finally, the protein-protein interaction (PPI) network was constructed using the top 100 DE mRNAs. The constructed PPI network included 47 nodes and 70 edges.DiscussionIn summary, the above results revealed the expression profile and potential functions of differentially expressed (DE) RNAs in the injured spinal cords of rats in the MenSCs-treated and SCI groups, and this study may provide new clues to understand the mechanisms of MenSCs in treating SCI

Autologous Skin Fibroblast-Based PLGA Nanoparticles for Treating Multiorgan Fibrosis

Fibrotic diseases remain a substantial health burden with few therapeutic approaches. A hallmark of fibrosis is the aberrant activation and accumulation of myofibroblasts, which is caused by excessive profibrotic cytokines. Conventional anticytokine therapies fail to undergo clinical trials, as simply blocking a single or several antifibrotic cytokines cannot abrogate the profibrotic microenvironment. Here, biomimetic nanoparticles based on autologous skin fibroblasts are customized as decoys to neutralize multiple fibroblast-targeted cytokines. By fusing the skin fibroblast membrane onto poly(lactic-co-glycolic) acid cores, these nanoparticles, termed fibroblast membrane-camouflaged nanoparticles (FNPs), are shown to effectively scavenge various profibrotic cytokines, including transforming growth factor-beta, interleukin (IL)-11, IL-13, and IL-17, thereby modulating the profibrotic microenvironment. FNPs are sequentially prepared into multiple formulations for different administration routines. As a proof-of-concept, in three independent animal models with various organ fibrosis (lung fibrosis, liver fibrosis, and heart fibrosis), FNPs effectively reduce the accumulation of myofibroblasts, and the formation of fibrotic tissue, concomitantly restoring organ function and indicating that FNPs are a potential broad-spectrum therapy for fibrosis management.Peer reviewe

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the 18F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).Conclusions: The RM based on 18F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions