Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation

Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of ground-truth labels are required in supervised methods and confusing background structures make neural networks hard to segment vessels in an unsupervised manner. To address this, here we introduce a novel diffusion adversarial representation learning (DARL) model that leverages a denoising diffusion probabilistic model with adversarial learning, and apply it to vessel segmentation. In particular, for self-supervised vessel segmentation, DARL learns the background signal using a diffusion module, which lets a generation module effectively provide vessel representations. Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information. Once the proposed model is trained, the model generates segmentation masks in a single step and can be applied to general vascular structure segmentation of coronary angiography and retinal images. Experimental results on various datasets show that our method significantly outperforms existing unsupervised and self-supervised vessel segmentation methods.Comment: Accepted at ICLR 202

ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts

Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) has led to great promise in zero-shot semantic segmentation by transferring image-text aligned knowledge to pixel-level classification. However, existing methods usually require an additional image encoder or retraining/tuning the CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal Transport (ZegOT) method that matches multiple text prompts with frozen image embeddings through optimal transport. In particular, we introduce a novel Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an optimal mapping between multiple text prompts and visual feature maps of the frozen image encoder hidden layers. This unique mapping method facilitates each of the multiple text prompts to effectively focus on distinct visual semantic attributes. Through extensive experiments on benchmark datasets, we show that our method achieves the state-of-the-art (SOTA) performance over existing Zero-shot Semantic Segmentation (ZS3) approaches.Comment: 18pages, 8 figure

Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models

In an ever-evolving world, the dynamic nature of knowledge presents challenges for language models that are trained on static data, leading to outdated encoded information. However, real-world scenarios require models not only to acquire new knowledge but also to overwrite outdated information into updated ones. To address this under-explored issue, we introduce the temporally evolving question answering benchmark, EvolvingQA - a novel benchmark designed for training and evaluating LMs on an evolving Wikipedia database, where the construction of our benchmark is automated with our pipeline using large language models. Our benchmark incorporates question-answering as a downstream task to emulate real-world applications. Through EvolvingQA, we uncover that existing continual learning baselines have difficulty in updating and forgetting outdated knowledge. Our findings suggest that the models fail to learn updated knowledge due to the small weight gradient. Furthermore, we elucidate that the models struggle mostly on providing numerical or temporal answers to questions asking for updated knowledge. Our work aims to model the dynamic nature of real-world information, offering a robust measure for the evolution-adaptability of language models.Comment: 14 pages, 5 figures, 5 tables; accepted at NeurIPS Syntheticdata4ML workshop, 202

Systemic-Lupus-Erythematosus-Related Acute Pancreatitis: A Cohort from South China

Acute pancreatitis (AP) is a rare but life-threatening complication of SLE. The current study evaluated the clinical characteristics and risk factors for the mortality of patients with SLE-related AP in a cohort of South China. Methods. Inpatient medical records of SLE-related AP were retrospectively reviewed. Results. 27 out of 4053 SLE patients were diagnosed as SLE-related AP, with an overall prevalence of 0.67%, annual incidence of 0.56‰ and mortality of 37.04%. SLE patients with AP presented with higher SLEDAI score (21.70 ± 10.32 versus 16.17 ± 7.51, P = 0.03), more organ systems involvement (5.70 ± 1.56 versus 3.96 ± 1.15, P = 0.001), and higher mortality (37.04% versus 0, P = 0.001), compared to patients without AP. Severe AP (SAP) patients had a significant higher mortality rate compared to mild AP (MAP) (75% versus 21.05%, P = 0.014). 16 SLE-related AP patients received intensive GC treatment, 75% of them exhibited favorable prognosis. Conclusion. SLE-related AP is rare but concomitant with high mortality in South Chinese people, especially in those SAP patients. Activity of SLE, multiple-organ systems involvement may attribute to the severity and mortality of AP. Appropriate glucocorticosteroid (GC) treatment leads to better prognosis in majority of SLE patients with AP

ROMPI-CDSA: Ring-Opening Metathesis Polymerization-Induced Crystallization-Driven Self-Assembly of Metallo-Block Copolymers

Polymerization-induced self-assembly (PISA) and crystallization-driven self-assembly (CDSA) are among the most prevailing methods for block copolymer self-assembly. Taking the merits of scalability of PISA and dimension control of CDSA, we report one-pot synchronous PISA and CDSA ring-opening metathesis polymerization (ROMP) to prepare nano-objects based on a crystalline poly(ruthenocene) motif. We denote this self-assembly methodology as ROMPI-CDSA to enable a simple, yet robust approach for the preparation of functional nanomaterials

Kdm3b haploinsufficiency impairs the consolidation of cerebellum-dependent motor memory in mice

Histone modifications are a key mechanism underlying the epigenetic regulation of gene expression, which is critically involved in the consolidation of multiple forms of memory. However, the roles of histone modifications in cerebellum-dependent motor learning and memory are not well understood. To test whether changes in histone methylation are involved in cerebellar learning, we used heterozygous Kdm3b knockout (Kdm3b+/−) mice, which show reduced lysine 9 on histone 3 (H3K9) demethylase activity. H3K9 di-methylation is significantly increased selectively in the granule cell layer of the cerebellum of Kdm3b+/− mice. In the cerebellum-dependent optokinetic response (OKR) learning, Kdm3b+/− mice show deficits in memory consolidation, whereas they are normal in basal oculomotor performance and OKR acquisition. In addition, RNA-seq analyses revealed that the expression levels of several plasticity-related genes were altered in the mutant cerebellum. Our study suggests that active regulation of histone methylation is critical for the consolidation of cerebellar motor memory.This work was supported by grants to S.B.S. and Y.-S.L. (NRF2019R1A4A2001609), Y.-S.L. (NRF-2017M3C7A1026959), and S.J.K. (NRF2018R1A5A2025964) from the National Research Foundation of Korea

Real-Time Measurement of F-Actin Remodelling during Exocytosis Using Lifeact-EGFP Transgenic Animals

F-actin remodelling is essential for a wide variety of cell processes. It is important in exocytosis, where F-actin coats fusing exocytic granules. The purpose of these F-actin coats is unknown. They may be important in stabilizing the fused granules, they may play a contractile role and promote expulsion of granule content and finally may be important in endocytosis. To elucidate these functions of F-actin remodelling requires a reliable method to visualize F-actin dynamics in living cells. The recent development of Lifeact-EGFP transgenic animals offers such an opportunity. Here, we studied the characteristics of exocytosis in pancreatic acinar cells obtained from the Lifeact-EGFP transgenic mice. We show that the time-course of agonist-evoked exocytic events and the kinetics of each single exocytic event are the same for wild type and Lifeact-EGFP transgenic animals. We conclude that Lifeact-EGFP animals are a good model to study of exocytosis and reveal that F-actin coating is dependent on the de novo synthesis of F-actin and that development of actin polymerization occurs simultaneously in all regions of the granule. Our insights using the Lifeact-EGFP mice demonstrate that F-actin coating occurs after granule fusion and is a granule-wide event