An Attention-based Graph Neural Network for Heterogeneous Structural Learning

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions

Interactions Study of Hydrodynamic-Morphology-Vegetation for Dam-Break Flows

This study models a dam-break flow over a bed by using a depth-averaged numerical model based on finite-volume method and computes the dam-break flow and bed morphology characteristics. The generalized shallow water equations considering the sediment transport and bed change on dam-break flow are adopted in the numerical model, and the vegetation effects on the flow and morphological changes are considered. The model is verified against three cases from the laboratory and field data documented in the literature. The numerical results are consistent with the measured results, which show that the model could accurately simulate the evolution of the dam-break flows and the morphology evolution of bed within a computational domain with complex plant distribution. The results show that the riparian vegetation in the waterway narrows the channel and reduces the conveyance capacity of river. The flood flow is diverted away from the vegetation community toward two sides and forms a weak flow region behind the vegetation domain. The resistance of plants markedly reduces the flow velocity, which directly alters the fluvial processes and influences the waterway morphology

The Mutual Beneficial Effect between Medical Imaging and Nanomedicine

The reports on medical imaging and nanomedicine are getting more and more prevalent. Many nanoparticles entering into the body act as contrast agents, or probes in medical imaging, which are parts of nanomedicines. The application extent and the quality of imaging have been improved by nanotechnique. On one hand, nanomedicines advance the sensitivity and specificity of molecular imaging. On the other hand, the biodistribution of nanomedicine can also be studied in vivo by medical imaging, which is necessary in the toxicological research. The toxicity of nanomedicine is a concern which may slow down the application of nanomedical. The quantitative description of the kinetic process is significant. Based on metabolic study on radioactivity tracer, a scheme of pharmacokinetic research of nanomedicine is proposed. In this review, we will discuss the potential advantage of medical imaging in toxicology of nanomedicine, as well as the advancement of medical imaging prompted by nanomedicine

The combined impact of social networks and connectedness on anxiety, stress, and depression during COVID-19 quarantine: a retrospective observational study

IntroductionThe COVID-19 pandemic and associated quarantine measures have precipitated a surge in mental health disorders, particularly depression and anxiety. Government policies and restrictions on physical activity have contributed to this phenomenon, as well as diminished subjective social connectedness and exacerbated objective social isolation. As two dimensions of social isolation, it is worth noting that subjectively perceived social connectedness serves as a protective factor for mental health, whereas the decline in the size of objectively evaluated social networks poses a significant risk. However, research investigating the combined influence of these two dimensions remains limited.MethodsThis study used an online survey to collect data to investigate the effects of objective social connectedness and objective social networks on anxiety, stress, and depression during COVID-19 quarantine. A total of 485 participants were analyzed using statistical methods, including paired t-test, Pearson correlation analysis, linear regression, cluster analysis, ANOVA, and moderated mediated.ResultsThe study found that anxiety and depression scores increased during the quarantine, with age, education, and social connectedness scores associated with the increase. Pre-quarantine anxiety and depression levels were strongly correlated with mental health status during quarantine. Cluster analysis, respectively, revealed three clusters for those without increasing anxiety and depression scores. The study also found that objective social network influences the impact of subjective social connectedness on pre-quarantine mental health, which in turn affects anxiety and depression levels during quarantine.ConclusionThe study identified that quarantine increased anxiety and depression, with age being protective, and education and subjective social connectedness as risk factors. The study also emphasizes the comprehensive impact of objective and subjective social isolation. Although individuals perceive the same degree of social connectedness, those with smaller social networks are more prone to developing symptoms of anxiety and depression, which are also more likely to worsen during quarantine

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen for training compared to the end-to-end deep learning method. Results: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. Conclusion: This study provides an intelligent, reliable and robust MRS quantification. Significance: QNet is the first LLS quantification aided by deep learning