Up-regulation of miR-20a ameliorates sevoflurane anesthesia-induced cognitive impairment in rats by targeting EphA4

Purpose: To evaluate the role of miR-20a in sevoflurane (SEV)-induced cognitive impairment in rats and to elucidate the mechanism of action. Methods: A SEV-induced cognitive impairment rat model was developed. The Morris water maze test and fear assay were carried out to assess impaired learning and memory. A cellular SEV-impaired model was developed and the miR-20a level was measured in the animal and cellular models. TUNEL staining and immunoblot assay were performed to determine the SEV effect on apoptosis. Bioinformatic analysis and luciferase assay were conducted to identify the target of miR-20a action. A rescue assay involving miR-20a overexpression in cellular and animal models was developed and used to evaluate function of miR-20a in cognitive defects. Results: The rats showed significant cognitive impairment upon SEV treatment, which inhibited the expression of miR-20a and promoted neuronal apoptosis. Further findings identified EphA4 as a target of miR-20a, which regulates its expression. Overexpression of miR-20a in rats effectively reduced cognitive dysfunction and apoptosis of hippocampus somatic cells caused by SEV treatment. Conclusion: Evidently, miR-20a ameliorates SEV anesthesia-induced cognitive impairment in rats and thus has the potential to serve as a therapeutic target for the treatment of post-operative cognitive dysfunction

Revealing New Technologies in Ocean Engineering Research using Machine Learning

On par with aerospace engineering, ocean engineering has caught a lot of attention re-cently. In this paper we employ machine learning and natural language processing methods to reveal new technologies and research hotspots in the ocean engineering field. Our data collection includes 14 high-impact journals, and the abstracts of almost 30,000 papers pub- lished from 2010 to 2019. We employed two topic models, Latent Dirichlet Allocation (LDA) and PhraseLDA. Used independently, the LDA model may lack interpretability and the PhraseLDA result may lose information in the final topics. We hence combined these two models and discovered the research hotspots for each year using affinity propagation cluster- ing and word-cloud-based visualization. The results reveal that several topics such as "wind power" and "ship structure", areas such as the European and Arctic seas, and some common research methods are increasing in popularity. This work consists of data collection, topic modelling, clustering, and visualization, which can help researchers understand the trends and important topics in ocean engineering as well as other fields

Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training

This paper investigates a novel method for designing linear precoders with finite alphabet inputs based on autoencoders (AE) without the knowledge of the channel model. By model-free training of the autoencoder in a multiple-input multiple-output (MIMO) system, the proposed method can effectively solve the optimization problem to design the precoders that maximize the mutual information between the channel inputs and outputs, when only the input-output information of the channel can be observed. Specifically, the proposed method regards the receiver and the precoder as two independent parameterized functions in the AE and alternately trains them using the exact and approximated gradient, respectively. Compared with previous precoders design methods, it alleviates the limitation of requiring the explicit channel model to be known. Simulation results show that the proposed method works as well as those methods under known channel models in terms of maximizing the mutual information and reducing the bit error rate.Comment: Accepted by GLOBECOM 202

Joint Beamforming and Antenna Movement Design for Moveable Antenna Systems Based on Statistical CSI

This paper studies a novel movable antenna (MA)-enhanced multiple-input multiple-output (MIMO) system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of wireless communications. We aim to maximize the achievable rate by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To solve the resulting design problem, we develop a constrained stochastic successive convex approximation (CSSCA) algorithm applicable for the general movement mode. Furthermore, we propose two simplified antenna movement modes, namely the linear movement mode and the planar movement mode, to facilitate efficient antenna movement and reduce the computational complexity of the CSSCA algorithm. Numerical results show that the considered MA-enhanced system can significantly improve the achievable rate compared to conventional MIMO systems employing uniform planar arrays (UPAs) and that the proposed planar movement mode performs closely to the performance upper bound achieved by the general movement mode