Abstract: Background: Lobar cerebral microbleeds (CMBs), which can impair white matter (WM), are often concomitant with definite Alzheimer’s disease (AD). Objective: To explore the features of cognitive impairments and WM disruptions due to lobar CMBs in patients with AD. Methods: There were 310 participants who underwent Florbetapir F18 (AV45) amyloid PET and susceptibility-weighted imaging. Participants with cognitive impairment and amyloid-β positive (ADCI) were included into three groups: ADCI without CMBs, with strictly lobar CMBs (SL-CMBs), and with mixed CMBs (M-CMBs). Tract-based spatial statistics were performed to detect the group differences in WM integrity. Results: There were 82 patients and 29 healthy…controls finally included. A decreasing tendency in memory and executive performance can be found among HCs > no CMBs (n = 16) >SL-CMBs (n = 41) >M-CMBs (n = 25) group. Compared to no CMBs, M-CMBs group had significantly decreased fractional anisotropy in left anterior thalamic radiation (ATR), forceps major, forceps minor and inferior longitudinal fasciculus, bilateral inferior fronto-occipital fasciculus (IFOF), and superior longitudinal fasciculus. M-CMBs group also had lower fractional anisotropy in left ATR, IFOF, uncinate fasciculus, and forceps minor compared with SL-CMBs. Furthermore, analysis of Pearson correlation indicated damages in discrepant WMs were positively associated with impairment of memory, executive function, and attention. Conclusion: This study showed lobar CMBs had intensively aggravated cognitive impairments associated with extensive WM damages in definite AD. These findings highlight that lobar CMBs play an important role in AD progression and need to be taken into consideration for the early detection of AD.
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Abstract: Process discovery techniques analyze process logs to extract models that characterize the behavior of business processes. In real-life logs, however, noises exist and adversely affect the extraction and thus decrease the understandability of discovered models. In this paper, we propose a novel double granularity filtering method, executed on both the event and trace levels, to detect noises by analyzing the directly-following and parallel relations between events. Based on the probability of an event occurring in a sequence, the infrequent behaviors and redundant events in the logs can be filtered out. In addition, the missing events in parallel blocks are detected…to further improve the performance of filtering. Experiments on synthetic logs and five real-life datasets demonstrate that our method significantly outperforms other state-of-the-art methods.
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Keywords: Process discovery, process mining, event logs, noise filtering, event dependency, parallel relation