Hydrogen Inhalation Reduces Lung Inflammation and Blood Pressure in the Experimental Model of Pulmonary Hypertension in Rats

Hydrogen has been shown to exhibit selective antioxidant properties against hydroxyl radicals, and exerts antioxidant and anti-inflammatory effects. The monocrotaline-induced model of pulmonary hypertension is suitable for studying substances with antioxidant activity because oxidative stress is induced by monocrotaline. On day 1, male Wistar rats were subcutaneously injected with a water-alcohol solution of monocrotaline or a control with an only water-alcohol solution. One group of monocrotaline-injected animals was placed in a plastic box that was constantly ventilated with atmospheric air containing 4% of molecular hydrogen, and the two groups of rats, injected with monocrotaline or vehicle, were placed in boxes ventilated with atmospheric air. After 21 days, hemodynamic parameters were measured under urethane narcosis. The results showed that, although hydrogen inhalation had no effect on the main markers of pulmonary hypertension induced by monocrotaline injection, there was a reduction in systemic blood pressure due to its systolic component, and a decrease in TGF-β expression, as well as a reduction in tryptase-containing mast cells.

Self-supervised recurrent depth estimation with attention mechanisms

Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improvements have been introduced to make self-supervised depth prediction more precise. However, most existing approaches still focus on single-frame depth estimation, even in the self-supervised setting. Since most methods can operate with frame sequences, we believe that the quality of current models can be significantly improved with the help of information about previous frames. In this work, we study different ways of integrating recurrent blocks and attention mechanisms into a common self-supervised depth estimation pipeline. We propose a set of modifications that utilize temporal information from previous frames and provide new neural network architectures for monocular depth estimation in a self-supervised manner. Our experiments on the KITTI dataset show that proposed modifications can be an effective tool for exploiting temporal information in a depth prediction pipeline.

Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.