Background/Aims: To evaluate the psychometric properties of the Hong Kong Montreal Cognitive Assessment (HK-MoCA) in patients with cerebral small vessel disease (SVD). Methods: 40 SVD patients and 40 matched controls were recruited. Concurrent and criterion validity, inter-rater and test-retest reliability, internal consistency of the HK-MoCA were examined and clinical observations were made. Results: Performance on the HK-MoCA was significantly predicted by both executive (β = 0.23, p = 0.013) and non-executive (β = 0.64, p < 0.001) composite scores. It differentiated SVD patients from controls (area under the curve = 0.81, p < 0.001) with an optimal cutoff at 21/22. Reliability, internal consistency and clinical utility were good. Conclusion: The HK-MoCA is a useful cognitive screening instrument for use in SVD patients.

Investigation of transcription factors (TFs) and their downstream regulated genes (targets) is a significant issue in post-genome era, which can provide a brand new vision for some vital biological process. However, information of TFs and their targets in mammalian is far from sufficient. Here, we developed an integrated TF platform (ITFP), which included abundant TFs and their targets of mammalian. In current release, ITFP includes 4105 putative TFs and 69 496 potential TF-target pairs for human, 3134 putative TFs and 37 040 potential TF-target pairs for mouse, and 1114 putative TFs and 18 055 potential TF-target pairs for rat. In short, ITFP will serve as an important resource for the research community of transcription and provide strong support for regulatory network study.

Age-related white matter changes (WMC) are considered manifestation of arteriolosclerotic small vessel disease and are related to age and vascular risk factors. Most recent studies have shown that WMC are associated with a host of poor outcomes, including cognitive impairment, dementia, urinary incontinence, gait disturbances, depression, and increased risk of stroke and death. Although the clinical relevance of WMC has been extensively studied, to date, only very few clinical trials have evaluated potential symptomatic or preventive treatments for WMC. In this paper, we reviewed the current understanding in the pathophysiology, epidemiology, clinical importance, chemical biomarkers, and treatments of age-related WMC.

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In order to model the evolution of user preference, we should learn user/item embeddings based on time-ordered item purchasing sequences, which is defined as Sequential Recommendation (SR) problem. Existing methods leverage sequential patterns to model item transitions. However, most of them ignore crucial temporal collaborative signals, which are latent in evolving user-item interactions and coexist with sequential patterns. Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging. Firstly, it is hard to simultaneously encode sequential patterns and collaborative signals. Secondly, it is non-trivial to express the temporal effects of collaborative signals.Hence, we design a new framework Temporal Graph Sequential Recommender (TGSRec) upon our defined continuous-time bipartite graph. We propose a novel Temporal Collaborative Transformer (TCT) layer in TGSRec, which advances the self-attention mechanism by adopting a novel collaborative attention. TCT layer can simultaneously capture collaborative signals from both users and items, as well as considering temporal dynamics inside sequential patterns. We propagate the information learned from TCT layer over the temporal graph to unify sequential patterns and temporal collaborative signals. Empirical results on five datasets show that TGSRec significantly outperforms other baselines, in average up to 22.5% and 22.1% absolute improvements in Recall@10 and MRR, respectively. Our code is available online in https://github.com/ DyGRec/TGSRec.

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1358-1366.