Seoul National University

About: Seoul National University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Catalysis. The organization has 65879 authors who have published 138759 publications receiving 3715170 citations. The organization is also known as: SNU & Seoul-dae.

Degradation of misfolded proteins in neurodegenerative diseases: therapeutic targets and strategies

Abstract: Mammalian cells remove misfolded proteins using various proteolytic systems, including the ubiquitin (Ub)-proteasome system (UPS), chaperone mediated autophagy (CMA) and macroautophagy. The majority of misfolded proteins are degraded by the UPS, in which Ub-conjugated substrates are deubiquitinated, unfolded and cleaved into small peptides when passing through the narrow chamber of the proteasome. The substrates that expose a specific degradation signal, the KFERQ sequence motif, can be delivered to and degraded in lysosomes via the CMA. Aggregation-prone substrates resistant to both the UPS and the CMA can be degraded by macroautophagy, in which cargoes are segregated into autophagosomes before degradation by lysosomal hydrolases. Although most misfolded and aggregated proteins in the human proteome can be degraded by cellular protein quality control, some native and mutant proteins prone to aggregation into β-sheet-enriched oligomers are resistant to all known proteolytic pathways and can thus grow into inclusion bodies or extracellular plaques. The accumulation of protease-resistant misfolded and aggregated proteins is a common mechanism underlying protein misfolding disorders, including neurodegenerative diseases such as Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD), prion diseases and Amyotrophic Lateral Sclerosis (ALS). In this review, we provide an overview of the proteolytic pathways in neurons, with an emphasis on the UPS, CMA and macroautophagy, and discuss the role of protein quality control in the degradation of pathogenic proteins in neurodegenerative diseases. Additionally, we examine existing putative therapeutic strategies to efficiently remove cytotoxic proteins from degenerating neurons.

The sixth visual object tracking VOT2018 challenge results

Abstract: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

Novel LiCoO2 Cathode Material with Al2O3 Coating for a Li Ion Cell

Abstract: A high-performance LiCoO2 cathode was successively fabricated by a sol−gel coating of Al2O3 to the LiCoO2 particle surfaces and subsequent heat treatment at 600 °C for 3 h. Unlike bare LiCoO2, the ...

Input feature selection by mutual information based on Parzen window

Abstract: Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms. However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.