Search

Introduction: Escherichia coli (E. coli) is a significant commensal gram-negative bacterium that can give rise to various diseases. The roles of Toll-like receptor 2 (TLR2) and the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome in sepsis induced by E. coli infection remain unclear. Methods: In vivo, we investigated differences in mortality, production of inflammatory mediators, organ damage, neutrophil count, and bacterial load during E. coli infection in C57BL/6J mice, as well as in mice deficient in TLR2 or NLRP3. In vitro, we investigated the impact of E. coli on the activation of TLR2 and NLRP3 in macrophages and the influence of TLR2 and NLRP3 on the activation of inflammatory signaling pathways and the secretion of inflammatory mediators in macrophages induced by E. coli infection. Results: TLR2-deficient (TLR2−/−) and NLRP3-deficient (NLRP3−/−) mice exhibit significantly increased mortality and organ damage after E. coli infection. These mice also show elevated levels of TNF-α and IL-10 in serum and peritoneal lavage fluid. Additionally, TLR2−/− and NLRP3−/− mice display heightened neutrophil recruitment and increased bacterial load in the blood. Furthermore, macrophages from these mice demonstrate a significant reduction in the activation of the MAPK signaling pathway. Conclusion: TLR2 and NLRP3 play crucial roles in modulating inflammatory mediator expression, immune cell recruitment, and bactericidal activity, thereby preventing excessive tissue damage and reducing mortality in E. coli-induced sepsis.

Summary: Background: Previous conventional epidemiological studies found a J-shape relationship between alcohol consumption and dementia, but this result was subject to confounding biases and reverse causation. Therefore, we aimed to investigate the potential linear or non-linear causal association between alcohol consumption and the incident risk of dementia in current drinkers. Methods: This study used data from the UK Biobank to investigate the relationship between alcohol consumption and dementia risk. 313,958 White British current drinkers, who were free of dementia during 2006–2010, were followed up until 2021. Alcohol consumption was self-reported and calculated according to the National Health Service guideline. The primary outcome was all-cause dementia identified through hospital and mortality records. We used multivariable Cox models with restricted cubic splines for conventional analysis and both non-linear and linear Mendelian Randomization (MR) analyses to assess causal relationships, employing a genetic score based on 95 SNPs identified from a meta-genome-wide association study of 941,280 people from Europe. Findings: 313,958 current drinkers consumed an average of 13.6 [IQR: 7.1–25.2] units/week alcohol (men averaged 20.2 [11.1–33.9] units/week and women 9.5 [5.3–16.7] units/week). During a mean follow-up of 13.2 years, 5394 (1.7%) developed dementia. Multivariable Cox model with restricted cubic spline functions identified a J-shaped relationship between alcohol consumption and dementia risk, with the lowest risk at 12.2 units/week. The non-linear MR failed to identify a significant non-linear causal relationship (p = 0.45). Both individual-level (HR: 2.22 95%CI [1.06–4.66]) and summary-level (1.89 [1.53–2.32]) linear MR analyses indicated that higher genetically predicted alcohol consumption increased dementia risk. Interpretation: This study identified a positive linear causal relationship between alcohol consumption and dementia among current drinkers. The J-shaped association found in conventional epidemiological analysis was not supported by non-linear MR analyses. Our findings suggested that there was no safe level of alcohol consumption for dementia. Funding: The Shenzhen Science and Technology Program and the Strategic Priority Research Program of Chinese Academy of Sciences.

Abstract Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of ‘stroke‐radical‐part‐character’. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

Abstract User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information for recommendation tasks. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a neural inference method (NIGraphNet) by mining user social interest for item recommendation. It can unearth user latent topics combined with user relation learning. Specifically, we exploit a neural variational inference approach to learn the distributions between user interests and hidden topics. (We denote it as interest-topic distributions in the following.) Then, we adopt a unified graph-based training loss that jointly learns the hidden topics and user relations for item recommendation. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations is conducive to the user’s automatic language understanding.

Abstract Objective To compare the spinal stability with different fixation methods after thoracic TES using finite element analysis Methods The spinal finite element model was established from a healthy volunteer, and the validity was verified. The models of T8 thoracic total en bloc spondylectomy (TES) with and without artificial vertebral body were established combination with different fixation methods: the first was long segment fixation with fixed segments T5–7, T9–11; the second was short segment fixation with fixed segments T6–7, T9–10; the third was modified short segment with a pair of vertebral body screws on T7 and T9 added on the basis of short segment fixation. The motions of each model in standing state were simulated in software. The range of motion (ROM) and internal fixation stress changes were analyzed. Results When anterior support was effective, the three fixation methods could effectively maintain the stability of the spine. However, when anterior support failed, the ROM of the long segment fixation group and the short segment fixation group in the flexion-extension directions was significantly higher than that of when the anterior support existed, while the modified short segment fixation group had no significant changes. Meanwhile, the stress of internal fixation in the long segment fixation group and the short segment fixation group were greatly increased. However, there were no significant changes in modified short segment fixation group. Conclusion After TES, the presence of the thoracic cage gives partial anterior stabilization. When the anterior support failed, the modified short segment fixation method can provide better stability.

Macao Guangdong-Macau Joint Laboratory for Advanced and Intelligent Computing The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-specific predictions may be contradictory for unverified edge samples with uncertain types. This challenge is termed edge-type disturbance in link prediction in heterogeneous networks. To address this challenge, we develop a disturbance-resilient prediction method (DRPM) comprising a structural characterizer, a type differentiator, and a resilient predictor. The structural characterizer is responsible for learning edge representations for link prediction. Concurrently, the type differentiator distinguishes type-specific edge representations to generate diverse type experts while maximizing their link prediction performances on specific types. Furthermore, the resilient predictor evaluates the reliability weights of different type experts to develop a resilient prediction mechanism to aggregate discriminable predictions. Extensive experiments conducted on various real-world datasets demonstrate the importance of the explainable introduction of the edge-type disturbance and the superiority of DRPM over state-of-the-art methods. [ABSTRACT FROM AUTHOR]