Kyungpook National University

About: Kyungpook National University is a education organization based out in Daegu, South Korea. It is known for research contribution in the topics: Population & Catalysis. The organization has 20497 authors who have published 42107 publications receiving 834608 citations.

Phenotypic Polarization of Activated Astrocytes: The Critical Role of Lipocalin-2 in the Classical Inflammatory Activation of Astrocytes

Abstract: Astrocytes provide structural and functional support for neurons, as well as display neurotoxic or neuroprotective phenotypes depending upon the presence of an immune or inflammatory microenvironment. This study was undertaken to characterize multiple phenotypes of activated astrocytes and to investigate the regulatory mechanisms involved. We report that activated astrocytes in culture exhibit two functional phenotypes with respect to pro- or anti-inflammatory gene expression, glial fibrillary acidic protein expression, and neurotoxic or neuroprotective activities. The two distinct functional phenotypes of astrocytes were also demonstrated in a mouse neuroinflammation model, which showed pro- or anti-inflammatory gene expression in astrocytes following challenge with classical or alternative activation stimuli; similar results were obtained in the absence of microglia. Subsequent studies involving recombinant lipocalin-2 (LCN2) protein treatment or Lcn2-deficient mice indicated that the pro- or anti-inflammatory functionally polarized phenotypes of astrocytes and their intracellular signaling pathway were critically regulated by LCN2 under in vitro and in vivo conditions. Astrocyte-derived LCN2 promoted classical proinflammatory activation of astrocytes but inhibited IL-4-STAT6 signaling, a canonical pathway involved in alternative anti-inflammatory activation. Our results suggest that the secreted protein LCN2 is an autocrine modulator of the functional polarization of astrocytes in the presence of immune or inflammatory stimuli and that LCN2 could be targeted therapeutically to dampen proinflammatory astrocytic activation and related pathologies in the CNS.

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.

Abstract: Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates

A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks

Abstract: One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

Mechanism of anchoring of OmpA protein to the cell wall peptidoglycan of the gram-negative bacterial outer membrane

Abstract: The outer membrane protein A (OmpA) plays important roles in anchoring of the outer membrane to the bacterial cell wall. The C-terminal periplasmic domain of OmpA (OmpA-like domain) associates with the peptidoglycan (PGN) layer noncovalently. However, there is a paucity of information on the structural aspects of the mechanism of PGN recognition by OmpA-like domains. To elucidate this molecular recognition process, we solved the high-resolution crystal structure of an OmpA-like domain from Acinetobacter baumannii bound to diaminopimelate (DAP), a unique bacterial amino acid from the PGN. The structure clearly illustrates that two absolutely conserved Asp271 and Arg286 residues are the key to the binding to DAP of PGN. Identification of DAP as the central anchoring site of PGN to OmpA is further supported by isothermal titration calorimetry and a pulldown assay with PGN. An NMR-based computational model for complexation between the PGN and OmpA emerged, and this model is validated by determining the crysta...