Perivascular waste metabolites clearance in central nervous system (cns)

Efficient clearance of interstitial waste metabolites is essential for normal brain homeostasis. Such effective clearance is hampered by the lack of a lymphatic system in the brain, and the cerebrospinal fluid (CSF) is unable to clear large size waste metabolites in the brain. Here, a novel idea that brain arterial endothelium and smooth muscle cells reactivity regulates the clearance of these water-insoluble large size waste metabolites through the perivascular dynamic exchange, and that low dose ethanol promotes this perivascular clearance is proposed. In Aim 1, the biodistribution of a large size waste metabolite (Amyloid-β protein mimic) in rat perivascular space as a proof-of-concept is examined. Then the effects of low dose alcohol (ethanol) for promoting perivascular clearance path are evaluated. The result shows that ethanol increases perivascular clearance by enhancing the dilative reactivity of arterial endothelial cells (ECs) and alpha-smooth muscle cells (α-SMCs) via the activation of endothelial specific nitric oxide synthase (eNOS) and nitric oxide (NO) production. In Aim 2, the underlying molecular mechanisms of low dose ethanol on the perivascular clearance of waste metabolites is examined. The result shows that low dose ethanol specifically activates eNOS in arterial wall and generates physiological favorable level of NO without affecting the integrity of the Blood-Brain Barrier (BBB). This vasodilator NO stimulates the dilative reactivity of ECs- α-SMCs, which promotes the diffusive movement of waste metabolites from interstitial space/CSF to perivascular-perivenous drainage path. Decrease in phosphorylation of myosin light chain in α-SMCs and increase in arterial vessel diameter validates α-SMCs reactivity and movement of waste metabolites towards perivascular space. In Aim 3, the contrast effects of chronic moderate alcohol intake on perivascular clearance of waste metabolites is assessed. The result reveals that chronic alcohol intake switches the induction of eNOS to inducible nitric oxide synthase (iNOS), thereby generating high level of NO. This continuous production of NO by iNOS in chronic alcohol exposure causes oxidative damage of the arterial endothelial-smooth muscle layers, and reduces dilative reactivity. Decrease in tight junction protein levels validates the BBB dysfunction, and increase in phosphorylation of myosin light chain in α-SMCs validates the impairment of α-SMCs reactivity, that are closely correlated with decrease in waste metabolites movement towards perivascular clearance path. The current work affords huge clinical relevance since aggregation of large size waste metabolites like Ab protein around the perivascular space is a hallmark of Alzheimer\u27s disease. As such, the findings suggest new strategies for prevention and treatment of neurological diseases that are associated with clearance of entangled proteins

Prediction of mRNA polyadenylation sites in the human genome and Mathematical modeling of alternative polyadenylation

Messenger RNA (mRNA) polyadenylation plays many important roles in the cell, such as transcription termination, mRNA stability and transportation, and mRNA translation in eukaryotic cells. A large number of human and mouse genes have multiple polyadenylation sites (referred to as poly(A) sites) that lead to variable transcripts, some of which are translated into various protein products with different functions. However, the details about when and where the polyadenylation occurs, and how pre-mRNA switches from one poly(A) site to another are still unknown. This kind of 3 \u27-end processing can be regulated by the cell environment, cell cycle stage, and tissue type. It is generally accepted that the cleavage of pre-mRNA is based on the sequence of nucleotides around the poly(A) sites. So it is possible to predict the poly(A) sites accurately based on the pre-mRNA sequence. To accomplish the supervised prediction of a poly(A) site, a set of statistical models has been used, such as linear discriminant analysis, quadratic discriminant analysis, and support vector machine (SVM). Among these, SVM was chosen as the classification algorithm for the prediction of poly(A) sites in this work. A program called polya svm has been developed using PERL. The true positive and accuracy results obtained using this method are better than the results obtained using other commonly used algorithms. Compared with the microarray technique, serial analysis of gene expression (SAGE) is another powerful technology for measuring the mRNA expression levels. Our study is the first investigation of the regulation of the transcripts from the same gene by analyzing the SAGE data. By filtering the noise data from the database and calculating the correlation between transcripts from the same unigene cluster, some significant genes are found to have multiple transcripts with opposite expression levels. These genes might be very interesting to biologists and they are worth being verified by biological experiments. Alternative polyadenylation has been found to be very common in human and mouse genes recently. It has been believed that the selection of different poly(A) sites is related to biological factors such as the developmental stages, cell conditions, and the availability and abundance of some protein factors. However, it is not clear how these factors affect alternative polyadenylation. Mathematical modeling is applied to understand the dynamical selection of poly(A) sites. Cleavage stimulation Factor (CstF) is a very important protein complex required for efficient cleavage, containing subunits of 77, 64, and 50 kD (CstF-77, CstF-64, CstF-50). It has been found that human cstf-77 gene has several different transcripts due to the alternative polyadenylation and the expression levels of these transcripts display some auto-regulation. A mathematical model with a time delay is constructed to simulate the dynamical gene expression levels of gene cstf-77. Experimental data are compared with the model. This kind of mathematical model can also be extended to some other polyadenylation factors that have similar alternative polyadenylation patterns

MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild

Face tracking serves as the crucial initial step in mobile applications trying to analyse target faces over time in mobile settings. However, this problem has received little attention, mainly due to the scarcity of dedicated face tracking benchmarks. In this work, we introduce MobiFace, the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over $95K$ bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking. 36 state-of-the-art trackers, including facial landmark trackers, generic object trackers and trackers that we have fine-tuned or improved, are evaluated. The results suggest that mobile face tracking cannot be solved through existing approaches. In addition, we show that fine-tuning on the MobiFace training data significantly boosts the performance of deep learning-based trackers, suggesting that MobiFace captures the unique characteristics of mobile face tracking. Our goal is to offer the community a diverse dataset to enable the design and evaluation of mobile face trackers. The dataset, annotations and the evaluation server will be on \url{https://mobiface.github.io/}.Comment: To appear on The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019

CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation

The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this paper, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results