In moderate stages of amnestic mild cognitive impairment, common cognitive tests provide better predictive accuracy than measures of whole brain, ventricular, entorhinal cortex, or hippocampal volumes for assessing progression to Alzheimer disease.

Background and Aim: Carotid atherosclerosis (CAS) is a common pathogenesis of cerebrovascular disease closely related to stroke and silent cerebrovascular disease (SCD), while the insufficient brain perfusion mechanism cannot quite explain the mechanism. The purpose of this study was to utilize diffusion tensor image analysis along the perivascular space (DTI-ALPS) to evaluate the glymphatic system activity and correlated DTI-ALPS with enlarged perivascular spaces (ePVS), carotid intima-media thickening (CIMT), mini-mental state examination (MMSE), and serological indicator in individuals with carotid plaque.Methods: Routine MRI and diffusion tensor images scan of the brain, carotid ultrasound, and blood examination were conducted on 74 individuals (52 carotid plaque subjects, 22 non-carotid plaque subjects), whose demographic and clinical characteristics were also recorded. DTI-ALPS index between patients with carotid plaque and normal controls were acquired and the correlations with other variables were analyzed.Results: The values of ALPS-index in the carotid plaque group was significantly lower compared to normal controls (2.12 ± 0.39, 1.95 ± 0.28, respectively, p = 0.034). The ALPS-index was negatively correlated with the basal ganglia (BG)-ePVS score (r = −0.242, p = 0.038) while there was no significant difference in the centrum semiovale (CSO)-ePVS score. Further analysis showed that there are more high-grade ePVS in the BG compared to the carotid plaque group than in the non-carotid plaque group (84.6% vs. 40.9%, p = 0.001).Conclusions: ALPS-index reflects the glymphatic system of the brain, which is associated with early high-risk cerebrovascular diseases. There may be damage in the function of the glymphatic system which induces the expansion of the perivascular space (PVS) in the BG in individuals with carotid plaque.

Background As a common mental disease, depression seriously affects people’s physical and mental health. According to the statistics of the World Health Organization, depression is one of the main reasons for suicide and self-harm events in the world. Therefore, strengthening depression detection can effectively reduce the occurrence of suicide or self-harm events so as to save more people and families. With the development of computer technology, some researchers are trying to apply natural language processing techniques to detect people who are depressed automatically. Many existing feature engineering methods for depression detection are based on emotional characteristics, but these methods do not consider high-level emotional semantic information. The current deep learning methods for depression detection cannot accurately extract effective emotional semantic information. Objective In this paper, we propose an emotion-based attention network, including a semantic understanding network and an emotion understanding network, which can capture the high-level emotional semantic information effectively to improve the depression detection task. Methods The semantic understanding network module is used to capture the contextual semantic information. The emotion understanding network module is used to capture the emotional semantic information. There are two units in the emotion understanding network module, including a positive emotion understanding unit and a negative emotion understanding unit, which are used to capture the positive emotional information and the negative emotional information, respectively. We further proposed a dynamic fusion strategy in the emotion understanding network module to fuse the positive emotional information and the negative emotional information. Results We evaluated our method on the Reddit data set. The experimental results showed that the proposed emotion-based attention network model achieved an accuracy, precision, recall, and F-measure of 91.30%, 91.91%, 96.15%, and 93.98%, respectively, which are comparable results compared with state-of-the-art methods. Conclusions The experimental results showed that our model is competitive with the state-of-the-art models. The semantic understanding network module, the emotion understanding network module, and the dynamic fusion strategy are effective modules for depression detection. In addition, the experimental results verified that the emotional semantic information was effective in depression detection.

The scheduling problem in distributed data-intensive computing environments has been an active research topic due to immense practical applications. In this paper, we model the scheduling problem for work-Flow applications in distributed Data-intensive computing environments (FDSP) and make an attempt to formulate and solve the problem using a particle swarm optimization approach. We illustrate the algorithm performance and trace its feasibility and effectiveness with the help of an example.

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