SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation

In this paper, we propose a novel model called SGFormer, Semantic Graph TransFormer for point cloud-based 3D scene graph generation. The task aims to parse a point cloud-based scene into a semantic structural graph, with the core challenge of modeling the complex global structure. Existing methods based on graph convolutional networks (GCNs) suffer from the over-smoothing dilemma and can only propagate information from limited neighboring nodes. In contrast, SGFormer uses Transformer layers as the base building block to allow global information passing, with two types of newly-designed layers tailored for the 3D scene graph generation task. Specifically, we introduce the graph embedding layer to best utilize the global information in graph edges while maintaining comparable computation costs. Furthermore, we propose the semantic injection layer to leverage linguistic knowledge from large-scale language model (i.e., ChatGPT), to enhance objects' visual features. We benchmark our SGFormer on the established 3DSSG dataset and achieve a 40.94% absolute improvement in relationship prediction's R@50 and an 88.36% boost on the subset with complex scenes over the state-of-the-art. Our analyses further show SGFormer's superiority in the long-tail and zero-shot scenarios. Our source code is available at https://github.com/Andy20178/SGFormer.Comment: To be published in Thirty-Eighth AAAI Conference on Artificial Intelligenc

Global Transcriptomic Analysis and Function Identification of Malolactic Enzyme Pathway of Lactobacillus paracasei L9 in Response to Bile Stress

Tolerance to bile stress is crucial for Lactobacillus paracasei to survive in the intestinal tract and exert beneficial actions. In this work, global transcriptomic analysis revealed that 104 genes were significantly changed (log2FoldChange > 1.5, P < 0.05) in detected transcripts of L. paracasei L9 when exposed to 0.13% Ox-bile. The different expressed genes involved in various biological processes, including carbon source utilization, amino acids and peptide metabolism processes, transmembrane transport, transcription factors, and membrane proteins. It is noteworthy that gene mleS encoding malolactic enzyme (MLE) was 2.60-fold up-regulated. Meanwhile, L-malic acid was proved to enhance bile tolerance, which could be attributed to the intracellular alkalinization caused by MLE pathway. In addition, membrane vesicles were observed under bile stress, suggesting a disturbance in membrane charge without L-malic acid. Then, genetic and physiological experiments revealed that MLE pathway enhanced the bile tolerance by maintaining a membrane balance in L. paracasei L9, which will provide new insight into the molecular basis of MLE pathway involved in bile stress response in Lactic acid bacteria

Generalized Category Discovery in Semantic Segmentation

This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled set of base classes. The unlabeled images contain pixels of the base class or novel class. In contrast to Novel Category Discovery in Semantic Segmentation (NCDSS), there is no prerequisite for prior knowledge mandating the existence of at least one novel class in each unlabeled image. Besides, we broaden the segmentation scope beyond foreground objects to include the entire image. Existing NCDSS methods rely on the aforementioned priors, making them challenging to truly apply in real-world situations. We propose a straightforward yet effective framework that reinterprets the GCDSS challenge as a task of mask classification. Additionally, we construct a baseline method and introduce the Neighborhood Relations-Guided Mask Clustering Algorithm (NeRG-MaskCA) for mask categorization to address the fragmentation in semantic representation. A benchmark dataset, Cityscapes-GCD, derived from the Cityscapes dataset, is established to evaluate the GCDSS framework. Our method demonstrates the feasibility of the GCDSS problem and the potential for discovering and segmenting novel object classes in unlabeled images. We employ the generated pseudo-labels from our approach as ground truth to supervise the training of other models, thereby enabling them with the ability to segment novel classes. It paves the way for further research in generalized category discovery, broadening the horizons of semantic segmentation and its applications. For details, please visit https://github.com/JethroPeng/GCDS

Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain. As a source domain, redundant features that are not conducive to the task will be extracted. Migration between ultrasound images can avoid this problem, but there are few types of public datasets, and it is difficult to find sufficiently similar source domains. Compared with natural images, ultrasound images have less information, and there are fewer transferable features between different ultrasound images, which may cause negative transfer. To this end, a multi-source adversarial transfer learning network for ultrasound image segmentation is proposed. Specifically, to address the lack of annotations, the idea of adversarial transfer learning is used to adaptively extract common features between a certain pair of source and target domains, which provides the possibility to utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a single source domain, multi-source transfer learning is adopted to fuse knowledge from multiple source domains. In order to ensure the effectiveness of the fusion and maximize the use of precious data, a multi-source domain independent strategy is also proposed to improve the estimation of the target domain data distribution, which further increases the learning ability of the multi-source adversarial migration learning network in multiple domains.Comment: Submitted to Applied Soft Computing Journa

Cox-2 Inhibition Protects against Hypoxia/Reoxygenation-Induced Cardiomyocyte Apoptosis via

The present study explored the potential causal link between ischemia-driven cyclooxygenase-2 (COX-2) expression and enhanced apoptosis during myocardial ischemia/reperfusion (I/R) by using H9C2 cardiomyocytes and primary rat cardiomyocytes subjected to hypoxia/reoxygenation (H/R). The results showed that H/R resulted in higher COX-2 expression than that of controls, which was prevented by pretreatment with Helenalin (NFκB specific inhibitor). Furthermore, pretreatment with NS398 (COX-2 specific inhibitor) significantly attenuated H/R-induced cell injury [lower lactate dehydrogenase (LDH) leakage and enhanced cell viability] and apoptosis (higher Bcl2 expression and lower level of cleaved caspases-3 and TUNEL-positive cells) in cardiomyocytes. The amelioration of posthypoxic apoptotic cell death was paralleled by significant attenuation of H/R-induced increases in proinflammatory cytokines [interleukin 6 (IL6) and tumor necrosis factor (TNFα)] and reactive oxygen species (ROS) production and by higher protein expression of phosphorylated Akt and inducible nitric oxide synthase (iNOS) and enhanced nitric oxide production. Moreover, the application of LY294002 (Akt-specific inhibitor) or 1400W (iNOS-selective inhibitor) cancelled the cellular protective effects of NS398. Findings from the current study suggest that activation of NFκB during cardiomyocyte H/R induces the expression of COX-2 and that higher COX-2 expression during H/R exacerbates cardiomyocyte H/R injury via mechanisms that involve cross talks among inflammation, ROS, and Akt/iNOS/NO signaling

Acceptability and feasibility of smartphone-assisted 24 h recalls in the Chinese population

Abstract Objective To examine the acceptability and feasibility of using smartphone technology to assess beverage intake and evaluate whether the feasibility of smartphone use is greater among key sub-populations. Design An acceptability and feasibility study of recording the video dietary record, the acceptability of the ecological momentary assessment (EMA), wearing smartphones and whether the videos helped participants recall intake after a cross-over validation study. Setting Rural and urban area in Shanghai, China. Subjects Healthy adults ( n 110) aged 20–40 years old. Results Most participants reported that the phone was acceptable in most aspects, including that videos were easy to use (70 %), helped with recalls (77 %), EMA reminders helped them record intake (75 %) and apps were easy to understand (85 %). However, 49 % of the participants reported that they had trouble remembering to take videos of the beverages before consumption or 46 % felt embarrassed taking videos in front of others. Moreover, 72 % reported that the EMA reminders affected their consumption. When assessing overall acceptability of using smartphones, 72 % of the participants were favourable responders. There were no statistically significant differences in overall acceptability for overweight v. normal-weight participants or for rural v. urban residents. However, we did find that the overall acceptability was higher for males (81 %) than females (61 %, P =0·017). Conclusions Our study did not find smartphone technology helped with dietary assessments in a Chinese population. However, simpler approaches, such as using photographs instead of videos, may be more feasible for enhancing 24 h dietary recalls

Comparative transcriptome analyses indicate molecular homology of zebrafish swimbladder and mammalian lung

10.1371/journal.pone.0024019PLoS ONE68