Synaptic Plasticity and Nicotine Addiction

AbstractNicotine, the main addictive component of tobacco, activates and desensitizes nicotinic acetylcholine receptors (nAChRs). In that way, nicotine alters normal nicotinic cholinergic functions. Among the myriad of psychopharmacological effects that underlie the addiction process, nicotine influences nAChR participation in synaptic plasticity. This influence has particular importance in the mesocorticolimbic dopamine system, which serves during the reinforcement of rewarding behaviors

Domain Adaptation For Vehicle Detection In Traffic Surveillance Images From Daytime To Nighttime

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle data and rich traffic flow parameters. Recently, deep learning based methods have been widely used in vehicle detection with high accuracy and efficiency. However, deep learning based methods require a large number of manually labeled ground truths (bounding box of each vehicle in each image) to train the Convolutional Neural Networks (CNN). In the modern urban surveillance cameras, there are already many manually labeled ground truths in daytime images for training CNN, while there are little or much less manually labeled ground truths in nighttime images. In this paper, we focus on the research to make maximum usage of labeled daytime images (Source Domain) to help the vehicle detection in unlabeled nighttime images (Target Domain). For this purpose, we propose a new method based on Faster R-CNN with Domain Adaptation (DA) to improve the vehicle detection at nighttime. With the assistance of DA, the domain distribution discrepancy of Source and Target Domains is reduced. We collected a new dataset of 2,200 traffic images (1,200 for daytime and 1,000 for nighttime) of 57,059 vehicles for training and testing CNN. In the experiment, only using the manually labeled ground truths of daytime data, Faster R- CNN obtained 82.84% as F-measure on the nighttime vehicle detection, while the proposed method (Faster R-CNN+DA) achieved 86.39% as F-measure on the nighttime vehicle detection

Analytical Studies on a Modified Nagel-Schreckenberg Model with the Fukui-Ishibashi Acceleration Rule

We propose and study a one-dimensional traffic flow cellular automaton model of high-speed vehicles with the Fukui-Ishibashi-type (FI) acceleration rule for all cars, and the Nagel-Schreckenberg-type (NS) stochastic delay mechanism. By using the car-oriented mean field theory, we obtain analytically the fundamental diagrams of the average speed and vehicle flux depending on the vehicle density and stochastic delay probability. Our theoretical results, which may contribute to the exact analytical theory of the NS model, are in excellent agreement with numerical simulations.Comment: 3 pages previous; now 4 pages 2 eps figure

Elevated serum Activin A in chronic obstructive pulmonary disease with skeletal muscle wasting

OBJECTIVE: Muscle wasting contributes to the reduced quality of life and increased mortality in chronic obstructive pulmonary disease (COPD). Muscle atrophy in mice with cachexia was caused by Activin A binding to ActRIIB. The role of circulating Activin A leading to muscle atrophy in COPD remains elusive. METHODS: In the present study, we evaluated the relationship between serum levels of Activin A and skeletal muscle wasting in COPD patients. The expression levels of serum Activin A were measured in 78 stable COPD patients and in 60 healthy controls via ELISA, which was also used to determine the expression of circulating TNF-a levels. Total skeletal muscle mass (SMM) was calculated according to a validated formula by age and anthropometric measurements. The fat-free mass index (FFMI) was determined as the fat-free mass (FFM) corrected for body surface area. RESULTS: Compared to the healthy controls, COPD patients had upregulated Activin A expression. The elevated levels of Activin A were correlated with TNF-a expression, while total SMM and FFMI were significantly decreased in COPD patients. Furthermore, serum Activin A expression in COPD patients was negatively associated with both FFMI and BMI. CONCLUSION: The above results showed an association between increased circulating Activin A in COPD patients and the presence of muscle atrophy. Given our previous knowledge, we speculate that Activin A contributes to skeletal muscle wasting in COPD

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

© 1982-2012 IEEE. Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance

Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs

Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) community. The capability to uncover the underlined table data of chart figures is a critical key to automatic chart understanding. We introduce ChartT5, a V+L model that learns how to interpret table information from chart images via cross-modal pre-training on plot table pairs. Specifically, we propose two novel pre-training objectives: Masked Header Prediction (MHP) and Masked Value Prediction (MVP) to facilitate the model with different skills to interpret the table information. We have conducted extensive experiments on chart question answering and chart summarization to verify the effectiveness of the proposed pre-training strategies. In particular, on the ChartQA benchmark, our ChartT5 outperforms the state-of-the-art non-pretraining methods by over 8% performance gains.Comment: Accepted by Findings of ACL 202

Light Field Salient Object Detection: A Review and Benchmark

Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides the first comprehensive review and benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce preliminary knowledge on light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, one comparative study, and one brief review. Existing datasets for light field SOD are also summarized with detailed information and statistical analyses. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved. Besides, due to the inconsistency of datasets in their current forms, we further generate complete data and supplement focal stacks, depth maps and multi-view images for the inconsistent datasets, making them consistent and unified. Our supplemental data makes a universal benchmark possible. Lastly, because light field SOD is quite a special problem attributed to its diverse data representations and high dependency on acquisition hardware, making it differ greatly from other saliency detection tasks, we provide nine hints into the challenges and future directions, and outline several open issues. We hope our review and benchmarking could help advance research in this field. All the materials including collected models, datasets, benchmarking results, and supplemented light field datasets will be publicly available on our project site https://github.com/kerenfu/LFSOD-Survey

Ventricular fibrillation induced by fever in structurally normal hearts

Ventricular fibrillation (VF) is a life-threatening arrhythmia that usually happens in patients with structural heart diseases. However, fever-induced ventricular fibrillation in structurally normal hearts was reported, and the four main diseases associated with these cases were Brugada syndrome, long QT syndrome, idiopathic ventricular fibrillation, and non-cardiovascular diseases. In this review, we analyzed this phenomenon and its clinical characteristics