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| 1 | Validity of depression rating scales during pregnancy and the postpartum period: Impact of trimester and parity显示文摘 | Shuang Ji Qi Long D. Jeffrey Newport Hyeji Na Bettina Knight Elizabeth B. Zach Natalie J. Morris Michael Kutner Zachary N. Stowe | 2010 | Journal of Psychiatric Research2010，，2： | 1 |
| 2 | Finding type 2 Diabe- tes Causal Single Nueleotide Polymorphism Combinations and Functional Modules from Genome-wide Association Data显示文摘 | CHIYONG Kang HYEJI Yu GWAN-Su Yi | 2013 | BMC Medical Informatics and Decision Making2013，13，11： | 1 |
| 3 | Deep Learning-based Wireless Signal Classification in the IoT Environment显示文摘With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%. | Hyeji Roh Sheungmin Oh Hajun Song Jinseo Han Sangsoon Lim | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 4 | Potential Alzheimer’s disease therapeutic nano-platform: Discovery of amyloid-beta plaque disaggregating agent and brain-targeted delivery system using porous silicon nanoparticles显示文摘There has been a lot of basic and clinical research on Alzheimer’s disease(AD)over the last 100 years,but its mechanisms and treatments have not been fully clarified.Despite some controversies,the amyloid-beta hypothesis is one of the most widely accepted causes of AD.In this study,we disclose a new amyloid-beta plaque disaggregating agent and an AD brain-targeted delivery system using porous silicon nanoparticles(pSiNPs)as a therapeutic nano-platform to overcome AD.We hypothesized that the negatively charged sulfonic acid functional group could disaggregate plaques and construct a chemical library.As a result of the in vitro assay of amyloid plaques and library screening,we confirmed that 6-amino-2-naphthalenesulfonic acid(ANA)showed the highest efficacy for plaque disaggregation as a hit compound.To confirm the targeted delivery of ANA to the AD brain,a nano-platform was created using porous silicon nanoparticles(pSiNPs)with ANA loaded into the pore of pSiNPs and biotin-polyethylene glycol(PEG)surface functionalization.The resulting nano-formulation,named Biotin-CaCl2-ANA-pSiNPs(BCAP),delivered a large amount of ANA to the AD brain and ameliorated memory impairment of the AD mouse model through the disaggregation of amyloid plaques in the brain.This study presents a new bioactive small molecule for amyloid plaque disaggregation and its promising therapeutic nano-platform for AD brain-targeted delivery. | Jaehoon Kim Hyeji Um Na Hee Kim Dokyoung Kim | 2023 | Bioactive Materials2023，，6： | 0 |
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