Yonsei University

About: Yonsei University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Cancer. The organization has 50162 authors who have published 106172 publications receiving 2279044 citations. The organization is also known as: Yonsei.

Observation of two resonant structures in e+ e- → π+π- ψ(2S) via initial-state radiation at belle

Abstract: Reference EPFL-ARTICLE-154576doi:10.1103/PhysRevLett.99.142002View record in Web of Science Record created on 2010-11-05, modified on 2017-12-10

The generalizability of the Youth Self-Report syndrome structure in 23 societies.

Abstract: As a basis for theories of psychopathology, clinical psychology and related disciplines need sound taxonomies that are generalizable across diverse populations. To test the generalizability of a statistically derived 8-syndrome taxonomic model for youth psychopathology, confirmatory factor analyses (CFAs) were performed on the Youth Self-Report (T. M. Achenbach & L. A. Rescorla, 2001) completed by 30,243 youths 11-18 years old from 23 societies. The 8-syndrome taxonomic model met criteria for good fit to the data from each society. This was consistent with findings for the parent-completed Child Behavior Checklist (Achenbach & Rescorla, 2001) and the teacher-completed Teacher's Report Form (Achenbach & Rescorla, 2001) from many societies. Separate CFAs by gender and age group supported the 8-syndrome model for boys and girls and for younger and older youths within individual societies. The findings provide initial support for the taxonomic generalizability of the 8-syndrome model across very diverse societies, both genders, and 2 age groups.

Synthetic ion transporters can induce apoptosis by facilitating chloride anion transport into cells

Abstract: Anion transporters based on small molecules have received attention as therapeutic agents because of their potential to disrupt cellular ion homeostasis. However, a direct correlation between a change in cellular chloride anion concentration and cytotoxicity has not been established for synthetic ion carriers. Here we show that two pyridine diamide-strapped calix[4]pyrroles induce coupled chloride anion and sodium cation transport in both liposomal models and cells, and promote cell death by increasing intracellular chloride and sodium ion concentrations. Removing either ion from the extracellular media or blocking natural sodium channels with amiloride prevents this effect. Cell experiments show that the ion transporters induce the sodium chloride influx, which leads to an increased concentration of reactive oxygen species, release of cytochrome c from the mitochondria and apoptosis via caspase activation. However, they do not activate the caspase-independent apoptotic pathway associated with the apoptosis-inducing factor. Ion transporters, therefore, represent an attractive approach for regulating cellular processes that are normally controlled tightly by homeostasis.

Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks

Abstract: This paper addresses the problems of feature representation of skeleton joints and the modeling of temporal dynamics to recognize human actions. Traditional methods generally use relative coordinate systems dependent on some joints, and model only the long-term dependency, while excluding short-term and medium term dependencies. Instead of taking raw skeletons as the input, we transform the skeletons into another coordinate system to obtain the robustness to scale, rotation and translation, and then extract salient motion features from them. Considering that Long Shortterm Memory (LSTM) networks with various time-step sizes can model various attributes well, we propose novel ensemble Temporal Sliding LSTM (TS-LSTM) networks for skeleton-based action recognition. The proposed network is composed of multiple parts containing short-term, mediumterm and long-term TS-LSTM networks, respectively. In our network, we utilize an average ensemble among multiple parts as a final feature to capture various temporal dependencies. We evaluate the proposed networks and the additional other architectures to verify the effectiveness of the proposed networks, and also compare them with several other methods on five challenging datasets. The experimental results demonstrate that our network models achieve the state-of-the-art performance through various temporal features. Additionally, we analyze a relation between the recognized actions and the multi-term TS-LSTM features by visualizing the softmax features of multiple parts.