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Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer’s Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN. 

Multimodal medical image synthesis is an important task. Previous efforts mainly focus on the task domain of medical image synthesis using the complete source data and have achieved great success. However, data collection with completeness in real life might be prohibitive due to high expenses or other difficulties, particularly in brain imaging studies. In this paper, we address the challenging and important problem of medical image synthesis from incomplete multimodal data sources. We propose to learn the modal-wise representations and synthesize the targets accordingly. Particularly, a surrogate sampler is derived to generate the target representations from incomplete observations, based on which an interpretable attention-redistribution network is designed. The experimental results synthesizing PET images from MRI images demonstrate that the proposed method can solve different missing data scenarios and outperforms related baselines consistently. 

Abstract—There are myriad real-life examples of contagion processes on human social networks, e.g., spread of viruses, information, and social unrest. Also, there are many methods to control or block contagion spread. In this work, we introduce a novel method of blocking contagions that uses nodes from dominating sets (DSs). To our knowledge, this is the first use of DS nodes to block contagions. Finding minimum dominating sets of graphs is an NP-Complete problem, so we generalize a well-known heuristic, enabling us to customize its execution. Our method produces a prioritized list of dominating nodes, which is, in turn, a prioritized list of blocking nodes. Thus, for a given network, we compute this list of blocking nodes and we use it to block contagions for all blocking node budgets, contagion seed sets, and parameter values of the contagion model. We report on computational experiments of the blocking efficacy of our approach using two mined networks. We also demonstrate the effectiveness of our approach by comparing blocking results with those from the high degree heuristic, which is a common standard in blocking studies. Index Terms—contagion blocking, dominating sets, threshold models, social networks, simulation, high degree heuristic 

Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug Aplysia californica’s feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the Aplysia feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot’s normalized translational and rotational kinematics of the odontophore followed profiles observed in vivo despite morphological differences. This brings Aplysia-inspired control in roboto one step closer to multifunctional neural control schema in vivo and in silico. Future additions may improve SLUGBOT’s viability as a neuromechanical research platform. 

During Expedition 386, two Giant Piston Corer (GPC) system deployments in the northern study area (Basin S3) of the southern Japan Trench (Figure F1) resulted in the recovery of cores from four holes at Site M0091 (Figure F2). The water depth was between 7802 and 7812 meters below sea level (mbsl). A breakdown of operational time is reported weekly instead of daily (see OPS in Supplementary material) due to decisions to move between sites based on weather and current conditions. Holes at Site M0091 were cored during Week 6 of the offshore phase. In total, 51.94 m of cores (Table T1) and 53.5 km of hydroacoustic profiles (see Hydroacoustics) were recovered and acquired, respectively, in the focus area. Further operations details, including winch log and inclinometer information, are found for all sites in Coring methodology in the Expedition 386 methods chapter (Strasser et al., 2023a) and associated files (see PALEOMAG and WINCHLOGS in Supplementary material). 

During Expedition 386, one Giant Piston Corer (GPC) system deployment at Basin C/N1 in the boundary area between the central and northern Japan Trench (Figure F1) resulted in the recovery of cores from two holes at Site M0093 (Figure F2). The water depth was 7454 m below sea level (mbsl). A breakdown of operational time is reported weekly instead of daily (see OPS in Supplementary material) due to decisions to move between sites based on weather and current conditions. Holes at Site M0093 were cored during Week 7 of the offshore phase. In total, 26.91 m of cores (Table T1) and 3.89 km of hydroacoustic profiles (see Hydroacoustics) were recovered and acquired, respectively, in this focus area. Further operations details, including winch log and inclinometer information, are found for all sites in Coring methodology in the Expedition 386 methods chapter (Strasser et al., 2023a) and associated files (see PALEOMAG and WINCHLOGS in Supplementary material). 

During Expedition 386, one Giant Piston Corer (GPC) system deployment at the boundary area between the central and northern Japan Trench (Figure F1) resulted in the recovery of cores from two holes at Site M0094 (Figure F2). The water depth was 7469 meters below sea level (mbsl). A breakdown of operational time is reported weekly instead of daily (see OPS in Supplementary materials) due to decisions to move between sites based on weather and current conditions. Holes at Site M0094 were acquired during Week 7 of the offshore phase. In total, 19.065 m of cores (Table T1) and 5.8 km of hydroacoustic profiles (see Hydroacoustics) were recovered and acquired in this focus area. Further operations details, including winch log and inclinometer information, are found for all sites in Coring methodology in the Expedition 386 methods chapter (Strasser et al., 2023a) and associated files (see PALEOMAG and WINCHLOGS in Supplementary materials). 

During Expedition 386, a total of five Giant Piston Corer (GPC) system deployments in the central Japan Trench (Basin C2; Figure F1) resulted in the recovery of cores from six holes at Site M0083 and four at Site M0089 (Figure F2). The water depth ranged 7602–7626 meters below sea level (mbsl). A breakdown of operational time is reported weekly instead of daily (see OPS in Supplementary material) due to decisions to move between sites based on weather and current conditions. Sites M0083 and M0089 were cored during Weeks 2–4 of the offshore phase. In this focus area, a total of 154 m of cores (Table T1) were recovered. In addition, 121 km of hydroacoustic profiles (see Hydroacoustics) were acquired. Further operations details, including winch log and inclinometer information for all sites, are found in Coring methodology in the Expedition 386 methods chapter (Strasser, 2023a) and associated files (see PALEOMAG and WINCHLOGS in Supplementary material).