Uric acid (UA) is the final product of purine metabolism in the human body, and impaired purine metabolism can increase the uric acid in serum, finally resulting in hyperuricemia (HUA). Current evidences suggest that urates might have antioxidant properties under certain circumstances, but most evidences suggest that urates promote inflammation. Hyperuricemia leads to the formation of urate crystals, which might be recognized as a red flag by the immune system. Such a response stimulates macrophage activation, leads to the activation of NOD-like receptor protein 3 (NLRP3) inflammasome vesicles, and ultimately the production and liberation of interleukin-1b (IL-1b) and interleukin-18 (IL-18), which can mediate inflammation, apoptosis and necroinflammation and cause an inflammatory cascade response. The kidney is one of the most commonly affected organs in HUA, which promotes the development of chronic kidney disease (CKD) by damaging endothelial cells, activating the renin-angiotensin system (RAS), and promoting inflammatory responses. Pharmacological interventions and lifestyle modifications are the primary means for controlling gout and lowering UA. The febuxostat is safe for CKD patients in the UA lowering therapy. Although dialysis can reduce UA levels, the application of drug is also necessary for dialysis patients. This article reviews the synthesis and metabolism of UA, etiology of HUA, the relationship between HUA and kidney disease, the treatment of gout and gouty nephropathy (GN).

Detecting similar code fragments, usually referred to as code clones, is an important task. In particular, code clone detection can have significant uses in the context of vulnerability discovery, refactoring and plagiarism detection. However, false positives are inevitable and always require manual reviews. In this paper, we propose Twin-Finder+, a novel closed-loop approach for pointerrelated code clone detection that integrates machine learning and symbolic execution techniques to achieve precision. Twin-Finder+ introduces a formal verification mechanism to automate such manual reviews process. Our experimental results show Twin-Finder+ that can remove 91.69% false positives in average. We further conduct security analysis for memory safety using realworld applications, Links version 2.14 and libreOffice-6.0.0.1. Twin-Finder+ is able to find 6 unreported bugs in Links version 2.14 and one public patched bug in libreOffice-6.0.0.1.

Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay significantly improves the training efficiency and stability of RL algorithms. Many decisionmaking problems in practice naturally involve multiple agents and require multi-agent reinforcement learning (MARL) under centralized training decentralized execution paradigm. Nevertheless, existing MARL algorithms often adopt standard experience replay where the transitions are uniformly sampled regardless of their importance. Finding prioritized sampling weights that are optimized for MARL experience replay has yet to be explored. To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions. Such optimization is relaxed and solved using the Lagrangian multiplier approach to obtain the close-form optimal sampling weights. By minimizing the resulting policy regret, we can narrow the gap between the current policy and a nominal optimal policy, thus acquiring an improved prioritization scheme for multi-agent tasks. Our experimental results on Predator-Prey and StarCraft Multi-Agent Challenge environments demonstrate the effectiveness of our method, having a better ability to replay important transitions and outperforming other state-of-the-art baselines.

Learning with multiple modalities is crucial for automated brain tumor segmentation from magnetic resonance imaging data. Explicitly optimizing the common information shared among all modalities (e.g., by maximizing the total correlation) has been shown to achieve better feature representations and thus enhance the segmentation performance. However, existing approaches are oblivious to partial common information shared by subsets of the modalities. In this paper, we show that identifying such partial common information can significantly boost the discriminative power of image segmentation models. In particular, we introduce a novel concept of partial common information mask (PCI-mask) to provide a fine-grained characterization of what partial common information is shared by which subsets of the modalities. By solving a masked correlation maximization and simultaneously learning an optimal PCI-mask, we identify the latent microstructure of partial common information and leverage it in a self-attention module to selectively weight different feature representations in multi-modal data. We implement our proposed framework on the standard U-Net. Our experimental results on the Multi-modal Brain Tumor Segmentation Challenge (BraTS) datasets consistently outperform those of state-of-the-art segmentation baselines, with validation Dice similarity coefficients of 0.920, 0.897, 0.837 for the whole tumor, tumor core, and enhancing tumor on BraTS-2020.

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