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Brain fluid clearance by pathways including the recently described paravascular glymphatic system is a critical homeostatic mechanism by which metabolic products, toxins, and other wastes are removed from the brain. Brain fluid clearance may be especially important after traumatic brain injury (TBI), when blood, neuronal debris, inflammatory cells, and other substances can be released and/or deposited. Using a non-invasive dynamic positron emission tomography (PET) method that models the rate at which an intravenously injected radiolabeled molecule (in this case 11C-flumazenil) is cleared from ventricular cerebrospinal fluid (CSF), we estimated the overall efficiency of brain fluid clearance in humans who had experienced complicated-mild or moderate TBI 3?6 months before neuroimaging (n?=?7) as compared to healthy controls (n?=?9). While there was no significant difference in ventricular clearance between TBI subjects and controls, there was a significant group difference in dependence of ventricular clearance upon tracer delivery/blood flow to the ventricles. Specifically, in controls, ventricular clearance was highly, linearly dependent upon blood flow to the ventricle, but this relation was disrupted in TBI subjects. When accounting for blood flow and group-specific alterations in blood flow, ventricular clearance was slightly (non-significantly) increased in TBI subjects as compared to controls. Current results contrast with past studies showing reduced glymphatic function after TBI and are consistent with possible differential effects of TBI on glymphatic versus non-glymphatic clearance mechanisms. Further study using multi-modal methods capable of assessing and disentangling blood flow and different aspects of fluid clearance is needed to clarify clearance alterations after TBI.

Background: Recent studies have shown that choroid plexuses (CP) may be involved in the neuro-immune axes, playing a role in the interaction between the central and peripheral inflammation. Here we aimed to investigate CP volume alterations in depression and their associations with inflammation. Methods: 51 depressed participants (HDRS score > 13) and 25 age- and sex-matched healthy controls (HCs) from the Wellcome Trust NIMA consortium were re-analysed for the study. All the participants underwent full peripheral cytokine profiling and simultaneous [11C]PK11195 PET/structural MRI imaging for measuring neuroinflammation and CP volume respectively. Results: We found a significantly greater CP volume in depressed subjects compared to HCs (t(76) = +2.17) that was positively correlated with [11C]PK11195 PET binding in the anterior cingulate cortex (r = 0.28, p = 0.02), prefrontal cortex (r = 0.24, p = 0.04), and insular cortex (r = 0.24, p = 0.04), but not with the peripheral inflammatory markers: CRP levels (r = 0.07, p = 0.53), IL-6 (r = -0.08, p = 0.61), and TNF-α (r = -0.06, p = 0.70). The CP volume correlated with the [11C]PK11195 PET binding in CP (r = 0.34, p = 0.005). Integration of transcriptomic data from the Allen Human Brain Atlas with the brain map depicting the correlations between CP volume and PET imaging found significant gene enrichment for several pathways involved in neuroinflammatory response. Conclusion: This result supports the hypothesis that changes in brain barriers may cause reduction in solute exchanges between blood and CSF, disturbing the brain homeostasis and ultimately contributing to inflammation in depression. Given that CP anomalies have been recently detected in other brain disorders, these results may not be specific to depression and might extend to other conditions with a peripheral inflammatory component.

This work explores the use of three deep learning methods for gesture recognition: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) using Fast Dynamic Time Warping (FastDTW). The gestures were captured by Kinect sensors, two skeleton-based databases are used: Microsoft Research Cambridge-12 (MSRC-12) and NTU RGB+D. Also, the FastDTW technique was also employed to standardize the input size of the data. The MSRC-12 database achieved an accuracy rate of 82,36% in the test set with the CNN, the LSTM achieved an accuracy rate of 87,30% also in the test set, and in GRU the accuracy achieved in the test set was 89,34%. With the NTU RGB+D database, two evaluation methods were used: Cross-View and Cross-Subject. In the test set with Cross-View evaluation was obtained an accuracy rate of 63,53%, 55,14%, and 61,00%, with CNN, LSTM, and GRU respectively; and with the Cross-Subject evaluation method, it was achieved an accuracy rate of 66,19%, 64,43% and 60,17% in the test set on CNN, LSTM and GRU, respectively. [ABSTRACT FROM AUTHOR]

Research indicates that a major factor influencing retention in university is the activities or processes that occur before matriculation. Based on a three-stage student retention model of inviting, involving, and integrating prospective students, this paper will describe the value of student partnerships as a tool for facilitating students' transition from high school to post-secondary education. First-year university students were trained in a generative model of student leadership to function as Student Ambassadors. Their roles and responsibilities as caring connectors were designed to educate prospective university students about the survival skills necessary for making a successful transition to the university and to enhance feelings of belonging by building personal connections between diverse groups of students. Information on the educational outreach approaches they developed will be presented. Data on the impact of this program on enrolments in the Introduction to University course provide evidence for the value of student partnerships as an effective means of helping students to make the transition from high school to post-secondary education. [ABSTRACT FROM AUTHOR]