Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction

While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data which is difficult to acquire in real clinical scenarios. Especially in the federated setting, the common situation is that several medical institutions only have single-modal data, termed the modality missing issue. Therefore, it is infeasible to deploy a standard federated learning framework in such conditions. In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction. Specifically, we utilize a pseudo modality generation mechanism to recover the missing modality for each single-modal client by sharing the distribution information of the amplitude spectrum in frequency space. However, the step of sharing the original amplitude spectrum leads to heavy communication costs. To reduce the communication cost, we introduce a clustering scheme to project the set of amplitude spectrum into finite cluster centroids, and share them among the clients. With such an elaborate design, our approach can effectively complete the missing modality within an acceptable communication cost. Extensive experiments demonstrate that our proposed method can attain similar performance with the ideal scenario, i.e., all clients have the full set of modalities. The source code will be released.Comment: 10 pages, 5 figures

CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification

In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. To overcome this challenge, mixing-based methods have emerged as effective alternatives to traditional preprocessing data augmentation techniques. Nonetheless, these methods fail to fully consider the unique features of pathology images, such as local specificity, global distribution, and inner/outer-sample instance relationships. To better comprehend these characteristics and create valuable pseudo samples, we propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. By dividing images into patches based on the granularity of pathology instances and shuffling them within the same batch, the absolute relationships between instances can be effectively preserved when generating new samples. Moreover, we develop a curriculum learning-inspired, loss-driven strategy to handle perturbations and distribution-related noise during training, enabling the model to adaptively fit the augmented data. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets. This innovative instance relationship-centered method has the potential to inform general data augmentation approaches for pathology image classification. The associated codes are available at https://github.com/sagizty/CellMix

Individual phosphorylation sites at the C-terminus of the apelin receptor play different roles in signal transduction

The apelin and Elabela proteins constitute a spatiotemporal double-ligand system that controls apelin receptor (APJ) signal transduction. Phosphorylation of multiple sites within the C-terminus of APJ is essential for the recruitment of β-arrestins. We sought to determine the precise mechanisms by which apelin and Elabela promote APJ phosphorylation, and to elucidate the influence of β-arrestin phosphorylation on G-protein-coupled receptor (GPCR)/β-arrestin-dependent signaling. We used techniques including mass spectrometry (MS), mutation analysis, and bioluminescence resonance energy transfer (BRET) to evaluate the role of phosphorylation sites in APJ-mediated G-protein-dependent and β-dependent signaling. Phosphorylation of APJ occurred at five serine residues in the C-terminal region (Ser335, Ser339, Ser345, Ser348 and Ser369). We also identified two phosphorylation sites in β-arrestin1 and three in β-arrestin2, including three previously identified residues (Ser412, Ser361, and Thr383) and two new sites, Tyr47 in β-arrestin1 and Tyr48 in β-arrestin2. APJ mutations did not affect the phosphorylation of β-arrestins, but it affects the β-arrestin signaling pathway, specifically Ser335 and Ser339. Mutation of Ser335 decreased the ability of the receptor to interact with β-arrestin1/2 and AP2, indicating that APJ affects the β-arrestin signaling pathway by stimulating Elabela. Mutation of Ser339 abolished the capability of the receptor to interact with GRK2 and β-arrestin1/2 upon stimulation with apelin-36, and disrupted receptor internalization and β-arrestin-dependent ERK1/2 activation. Five peptides act on distinct phosphorylation sites at the APJ C-terminus, differentially regulating APJ signal transduction and causing different biological effects. These findings may facilitate screening for drugs to treat cardiovascular and metabolic diseases

Rethinking Client Drift in Federated Learning: A Logit Perspective

Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to client drift which degrades the performance of FL. Interestingly, we find that the difference in logits between the local and global models increases as the model is continuously updated, thus seriously deteriorating FL performance. This is mainly due to catastrophic forgetting caused by data heterogeneity between clients. To alleviate this problem, we propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models. FedCSD does not simply transfer global knowledge to local clients, as an undertrained global model cannot provide reliable knowledge, i.e., class similarity information, and its wrong soft labels will mislead the optimization of local models. Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype. To enhance the quality of global logits, FedCSD adopts an adaptive mask to filter out the terrible soft labels of the global models, thereby preventing them to mislead local optimization. Extensive experiments demonstrate the superiority of our method over the state-of-the-art federated learning approaches in various heterogeneous settings. The source code will be released.Comment: 11 pages, 7 figure

The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility

Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40–69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5–10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1–T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4–28.7) μg/m3; NOx, 41.3 (36.2–46.7) μg/m3; PM10, 15.9 (15.4–16.4) μg/m3; PM2.5, 9.9 (9.5–10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0–1.2) per metre; and PM2.5–10, 6.1 (5.9–6.3) μg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5–10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5–10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants

Disruption of 5-hydroxytryptamine 1A receptor and orexin receptor 1 heterodimer formation affects novel G protein-dependent signaling pathways and has antidepressant effects in vivo

G protein-coupled receptor (GPCR) heterodimers are new targets for the treatment of depression. Increasing evidence supports the importance of serotonergic and orexin-producing neurons in numerous physiological processes, possibly via a crucial interaction between 5-hydroxytryptamine 1A receptor (5-HT1AR) and orexin receptor 1 (OX1R). However, little is known about the function of 5-HT1AR/OX1R heterodimers. It is unclear how the transmembrane domains (TMs) of the dimer affect its function and whether its modulation mediates antidepressant-like effects. Here, we examined the mechanism of 5-HT1AR/OX1R dimerization and downstream G protein-dependent signaling. We found that 5-HT1AR and OX1R form constitutive heterodimers that induce novel G protein-dependent signaling, and that this heterodimerization does not affect recruitment of β-arrestins to the complex. In addition, we found that the structural interface of the active 5-HT1AR/OX1R dimer transforms from TM4/TM5 in the basal state to TM6 in the active conformation. We also used mutation analyses to identify key residues at the interface (5-HT1AR R1514.40, 5-HT1AR Y1985.41, and OX1R L2305.54). Injection of chronic unpredictable mild stress (CUMS) rats with TM4/TM5 peptides improved their depression-like emotional status and decreased the number of endogenous 5-HT1AR/OX1R heterodimers in the rat brain. These antidepressant effects may be mediated by upregulation of BDNF levels and enhanced phosphorylation and activation of CREB in the hippocampus and medial prefrontal cortex. This study provides evidence that 5-HT1AR/OX1R heterodimers are involved in the pathological process of depression. Peptides including TMs of the 5-HT1AR/OX1R heterodimer interface are candidates for the development of compounds with fast-acting antidepressant-like effects

A Conceptual Design of Residual Stress Reduction with Multiple Shape Laser Beams In Direct Laser Deposition

Residual stress is a major problem in metal parts fabrication with the direct laser deposition (DLD) process due to severe temperature gradient around a molten pool. A three-dimensional finite element analysis (FEA) model with a simplified substrate clamping fixture modeling method is proposed, validated, and then implemented with a novel DLD heat input strategy in Ti-6Al-4V thin-wall structure fabrication, which was applied with multiple beam shapes, including a super-Gaussian beam, Gaussian beam, and inverse-Gaussian beam, to reduce residual stress in the final part. A regression model of the heat input and final part residual stress was obtained via a three-factor two-level full factorial design. An optimized heat input strategy was achieved based on response surface contour plots of the regression model

Bisection Searching based Reference Frame Update Strategy for Digital Image Correlation

The strategy to update the reference frames in the digital image correlation analysis is an essential but often overlooked problem. A good reference frame update strategy should be able to adjust the frame step in respect of varying practical circumstances including the different loading rate, speckle pattern, plastic deformation, imaging system, lighting condition, etc. In this work, a simple but effective bisection searching (BS) strategy is presented to solve this problem. The frame step is reduced into one half for the unconverged locations, and the intermediate frame is utilized to assist the correlating process. This process is iteratively conducted to adjust the frame step in different regions automatically. The performance of the BS strategy is evaluated against the constant step (CS) update strategy on simulated experiments. The results indicate that the BS strategy can automatically adjust the frame step for changing speckle pattern and loading rate. The accuracy and robustness of the BS strategy are better than the CS strategy with the same pixel level and subpixel level searching algorithms. The BS strategy also successfully tracked all POIs and adjusted the frame step in the real world experiment with changing loading rate and large plastic deformation (over 70% engineering strain)