A Diffusion Model for Event Skeleton Generation

Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task. Existing methods effectively address this task from a graph generation perspective but suffer from noise-sensitive and error accumulation, e.g., the inability to correct errors while generating schema. We, therefore, propose a novel Diffusion Event Graph Model~(DEGM) to address these issues. Our DEGM is the first workable diffusion model for event skeleton generation, where the embedding and rounding techniques with a custom edge-based loss are introduced to transform a discrete event graph into learnable latent representation. Furthermore, we propose a denoising training process to maintain the model's robustness. Consequently, DEGM derives the final schema, where error correction is guaranteed by iteratively refining the latent representation during the schema generation process. Experimental results on three IED bombing datasets demonstrate that our DEGM achieves better results than other state-of-the-art baselines. Our code and data are available at https://github.com/zhufq00/EventSkeletonGeneration

AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus

Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient’s ventricles.This study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083), in part by the National Natural Science Foundation of China (grant number 82171913), in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology), in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combat ting Coronavirus Infections Award ‘‘DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics’’ [H2020-JTI-IMI2 101005122], in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management’’ [H2020-SC1-FA-DTS-2019-1 952172], in part by the MRC (MC/PC/21013), and in part by the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1

Bacterial conjugated linoleic acid production and their applications

peer-reviewedConjugated linoleic acid (CLA) has been shown to exert various potential physiological properties including anti-carcinogenic, anti-obesity, anti-cardiovascular and anti-diabetic activities, and consequently has been considered as a promising food supplement. Bacterial biosynthesis of CLA is an attractive approach for commercial production due to its high isomer-selectivity and convenient purification process. Many bacterial species have been reported to convert free linoleic acid (LA) to CLA, hitherto only the precise CLA-producing mechanisms in Propionibacterium acnes and Lactobacillus plantarum have been illustrated completely, prompting the development of recombinant technology used in CLA production. The purpose of the article is to review the bacterial CLA producers as well as the recent progress on describing the mechanism of microbial CLA-production. Furthermore, the advances and potential in the heterologous expression of CLA genetic determinants will be presented