Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Sum rules for e+e−→W+W−e^+e^- \to W^+W^- helicity amplitudes from BRS invariance

The BRS invariance of the electroweak gauge theory leads to relationships between amplitudes with external massive gauge bosons and amplitudes where some of these gauge bosons are replaced with their corresponding Nambu-Goldstone bosons. Unlike the equivalence theorem, these identities are exact at all energies. In this paper we discuss such identities which relate the process $e^+e^- \to W^+W^-$ to $W^\pm\chi^\mp$ and $\chi^+\chi^-$ production. By using a general form-factor decomposition for $e^+e^- \to W^+W^-$, $e^+e^- \to W^\pm \chi^\mp$ and $e^+e^- \to \chi^+\chi^-$ amplitudes, these identities are expressed as sum rules among scalar form factors. Because these sum rules may be applied order by order in perturbation theory, they provide a powerful test of higher order calculations. By using additional Ward-Takahashi identities we find that the various contributions are divided into separately gauge-invariant subsets, the sum rules applying independently to each subset. After a general discussion of the application of the sum rules we consider the one-loop contributions of scalar-fermions in the Minimal Supersymmetric Standard Model as an illustration.Comment: 37 pages, including 16 figure

Prevalence of and factors related to the use of antidepressants and benzodiazepines: results from the Singapore Mental Health Study

BACKGROUND: Prescription and use of antidepressants and benzodiazepines are common in the general population. Prescription of psychotropic drugs is a complex process: patient, physician and healthcare characteristics mediate, interact and influence it. The current study aimed to establish the prevalence and factors associated with the use of antidepressants (ADs) and benzodiazepines (BZDs) in Singapore. METHODS: The Singapore Mental Health Study (SMHS) was a nationally representative survey of Singapore Residents aged 18 years and above. Face-to-face interviews were conducted from December 2009 to December 2010. The diagnoses of mental disorders were established using the Composite International Diagnostic Interview version 3.0 (CIDI-3.0). The pharmacoepidemiology section was used to collect information on medication use. RESULTS: The overall prevalence estimates for ADs and BZDs use during the 12 months prior to the interview were 1.1% and 1.2% respectively. In all, 2.0% had used ADs and/or BZDs. ‘Help seeking for emotional or mental health problems’ was the most important predictor for the use of ADs and BZDs—help seekers were much more likely to use ADs (adjusted OR: 31.62, 95% CI: 13.36–74.83) and more likely to use BZDs than non-help seekers in the previous 12 months (adjusted OR: 34.38, 95% CI: 12.97–91.16). Only 27.6% of those with 12-month major depressive disorder (MDD) had sought formal medical help for their problems and ADs were being used by just over a quarter of this ‘help-seeking group’ (26.3%). CONCLUSIONS: We found that the use of ADs and BZDs in our population was relatively low, and ‘help-seeking’ was the most important predictor of the use of ADs and BZDs. In concordance with research from other Western countries, use of ADs was low among those with 12-month MDD

Deadenylation is prerequisite for P-body formation and mRNA decay in mammalian cells

Deadenylation is the major step triggering mammalian mRNA decay. One consequence of deadenylation is the formation of nontranslatable messenger RNA (mRNA) protein complexes (messenger ribonucleoproteins [mRNPs]). Nontranslatable mRNPs may accumulate in P-bodies, which contain factors involved in translation repression, decapping, and 5′-to-3′ degradation. We demonstrate that deadenylation is required for mammalian P-body formation and mRNA decay. We identify Pan2, Pan3, and Caf1 deadenylases as new P-body components and show that Pan3 helps recruit Pan2, Ccr4, and Caf1 to P-bodies. Pan3 knockdown causes a reduction of P-bodies and has differential effects on mRNA decay. Knocking down Caf1 or overexpressing a Caf1 catalytically inactive mutant impairs deadenylation and mRNA decay. P-bodies are not detected when deadenylation is blocked and are restored when the blockage is released. When deadenylation is impaired, P-body formation is not restorable, even when mRNAs exit the translating pool. These results support a dynamic interplay among deadenylation, mRNP remodeling, and P-body formation in selective decay of mammalian mRNA

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by exploiting information from multiple neuroimaging modalities or views, we are able to obtain an embedding that is more useful than the embedding learned from an individual view. Therefore, multi-view multi-graph embedding becomes a crucial task. Currently, only a few studies have been devoted to this topic, and most of them focus on the vector-based strategy which will cause structural information contained in the original graphs lost. As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks. Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions

The effectiveness of internet-based psychoeducation programs for caregivers of people living with dementia:a systematic review and meta-analysis

OnlinePublObjective: The objectives of this systematic review and meta-analysis were to identify the characteristics of internet-based psychoeducational programs for caregivers of people living with dementia and to synthesise program effectiveness. Method: Five English databases and four Chinese databases were searched in June 2021 with no time limit applied. A narrative summary was performed to describe the characteristics of studies reviewed. Meta-analysis was applied to synthesise the pooled effects where data were available. Results: A total of 14352 articles were identified from the database search and 19 were included in the final review. Interventions comprised educational, psychological, and behavioural training relevant to dementia care. Program duration ranged from 3 weeks to 12 months. Meta-analysis of 13 RCTs showed that internet-based psychoeducational programs had a significant effect on reducing caregivers’ depressive symptoms (SMD −0.19; 95% CI −0.03 − 0.35) and stress (SMD −0.29; 95% CI −0.03 −0.54). However, these programs did not show an effect on quality of life, anxiety, burden or self-efficacy in caregivers. Conclusion: Internet-based psychoeducational programs can improve some aspects of caregivers’ mental health and emotional wellbeing. The effects of programs on self-efficacy, anxiety, burden and quality of life for caregivers remain inconclusive.Ying Yu, Lily Xiao, Shahid Ullah, Claudia Meyer, Jing Wang, Ann Margriet Pot and Jin Jie H