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Delta activity on electroencephalogram (EEG) is considered a biomarker of homeostatic sleep drive. Delta power is often associated with sleep duration and intensity. Here, we reviewed the literature to explore how sleep quality was influenced by changes in delta power. However, we found that both the decrease and increase in delta power could indicate a higher sleep quality due to the various factors below. First, the differences in changes in delta power in patients whose sleep quality is lower than that of the healthy controls may be related to the different diseases they suffered from. We found that the patients mainly suffered from borderline personality disorder, and Rett syndrome may have a higher delta power than healthy individuals. Meanwhile, patients who are affected by Asperger syndrome, respiratory failure, chronic fatigue, and post-traumatic stress disorder have lower delta power. Second, if the insomnia patients received the therapy, the difference may be caused by the treatment method. Cognitive or music therapy shows that a better therapeutic effect is associated with decreased delta power, whereas in drug treatment, there is an opposite change in delta power. Last, for healthy people, the difference in delta change may be related to sleep stages. The higher sleep quality is associated with increased delta power during the NREM period, whereas a deceased delta change accompanies higher sleep quality during the REM period. Our work summarizes the effect of changes in delta power on sleep quality and may positively impact the monitoring and intervention of sleep quality.

Objective: Major depressive disorder (MDD) can be characterized as a multidimensional and system-level disorder. The neuropathophysiological abnormalities have been reported to be distributed in emotion regulation system, involving the prefrontal cortex (PFC), limbic and striatum in convergent studies. Decrease of positive affect and increase of negative affect are recognized as a hallmark of MDD. However, the dysfunctions in affective processing in MDD within the emotion regulation system remains largely unclear. In this study, our goals are to characterize the dysconnectivity pattern within this system and explore the relationships between this kind of dysconnectivity pattern and affective symptoms, which might help us better look into the neuropathophysiological mechanisms underlying MDD.Methods: A total of 34 MDD and 34 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rsfMRI). The alterations in functional connectivity (FC) within the emotion regulation system and their relationships with affective symptoms were explored.Results: Compared with HCs, MDD patients showed aberrant FC within this system. Importantly, deceased FC was mainly involved in the prefrontal-limbic system, while elevated FC was observed in the prefrontal-striatum system. In the MDD group, decreased FC of right posterior hippocampus-left dorsolateral prefrontal cortex (dlPFC) was negatively associated with the negative affect scores and Hamilton Depression Rating Scale scores and the FC of left ventral striatum-left dlPFC was significantly negatively related with the positive affect scores. Conclusions:These findings demonstrated that MDD showed characteristic pathological alterations of the emotion regulation system. Dysconnectivity within prefrontal-limbic system might be more related to the dysregulation of negative affect, whereas dysconnectivity within prefrontal-striatum system might influence more on positive affect processing. The decrease in positive affect and increase in negative affect in MDD might have different pathological basis. These results could help better understand the dysconnectivity pattern in the emotion-regulating system underlying depression.

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.