• 생명과학회지
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• 제29권5호
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• pp.564-569
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• 2019

최근 알코올 소비량이 증가함에 따라 과량의 에탄올을 섭취하는 경우 또한 늘어나고 있다. 이런 과도한 에탄올 섭취는 ${\gamma}$-aminobutyric acid (GABA) 수용체의 활성화와 glutamate 수용체의 활성 억제를 통해 신경계를 교란시켜 단기 기억 형성을 방해 한다. 알코올에 의한 인지기능의 저하는 알코올성 black out을 유도할 수 있으며, 반복될 경우 알코올성 치매로 이어질 수 있기 때문에 black out을 예방하는 치료제의 개발이 필요하다. 따라서 본 연구자는 해당 연구를 통하여 Cassia obtusifolia seeds 에탄올 추출물(COE)이 가진 black out 예방제로써의 가능성을 평가하였다. 본 연구에서는 에탄올에 의해 유도된 기억 장애에 대한 COE의 효과를 확인하였다. 실험 동물의 기억력을 측정하기 위하여 수동 회피 실험과 Y자 미로 실험을 수행하였고, 마우스 해마 절편을 사용하여 에탄올이 기억의 형성과 관련하여 장기 강화(long term potentiation; LTP)에 어떠한 영향을 끼치는지 전기생리학을 통해 확인하였다. 또한 ${\alpha}$-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid 수용체 길항제인 NBQX ($50{\mu}M$)를 사용하여 에탄올에 의한 인지기능 장애와 관련이 있다고 알려진 N-Methyl-D-aspartate (NMDA) 매개 field 흥분성 시냅스 후 전위를 측정하였다. 결과적으로, COE는 에탄올에 의한 기억력의 손상을 방지하였고, 해마 절편에서 에탄올에 의해 감소된 LTP와 NMDA 매개 흥분성 시냅스 후 전위를 대조군과 비슷한 수준까지 회복시켰다.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제55권8호
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• pp.399-403
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• 2006

An accurate constant output voltage provided by the analog memory cell may be used by the low power oscillator to generate an accurate low frequency output signal. This accurate low frequency output signal may be used to maintain long-term timing accuracy in host devices during sleep modes of operation when an external crystal is not available to provide a clock signal. Further, incorporation of the analog memory cell in the low power oscillator is fully implementable in a 0.35um Samsung standard CMOS process. Therefore, the analog memory cell incorporated into the low power oscillator avoids the previous problems in a oscillator by providing a temperature-stable, low power consumption, size-efficient method for generating an accurate reference clock signal that can be used to support long sleep mode operation.

• International journal of advanced smart convergence
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• 제7권3호
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• pp.1-7
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• 2018

This study relates to a composition for improvement of memory or prevention and treatment of cognitive impairment using waste resources rich in beneficial substances. This study makes good effects to inhibit the activity of acetylcholinesterase in brain tissue and to improve the cognitive functions in a simulation model of cognitive impairment induced by scopolamine, so it can be available in the promotion of memory and the prevention and treatment of cognitive impairment. The composition uses the extract of fruit peels, which have long been used without causing toxicity in a wide range of food applications; therefore, it can be used safely without a risk of side effects, even in the case of a long-term administration for the preventive purpose. Furthermore, this research is a very beneficial invention in the environment-friendly aspect in association with the recycling of resources, as it is based on the novel efficacies of fruit peels, which have been conventionally disposed as a refuse of fruits due to their poor sensory qualities despite the content of beneficial substances.

• Molecules and Cells
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• 제39권7호
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• pp.515-523
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• 2016

Long-term immunity to many viral and bacterial pathogens requires$ CD8^+$ memory T cell development, and the induction of long-lasting$ CD8^+$ memory T cells from a $na{\ddot{i}}ve$, undifferentiated state is a major goal of vaccine design. Formation of the memory$ CD8^+$ T cell compartment is highly dependent on the early activation cues received by $na{\ddot{i}ve}$ $CD8^+$ T cells during primary infection. This review aims to highlight the cellularity of various niches within the lymph node and emphasize recent evidence suggesting that distinct types of T cell activation and differentiation occur within different immune contexts in lymphoid organs.

• Biomolecules & Therapeutics
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• 제28권6호
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• pp.512-518
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• 2020

Stroke is a leading cause of long-term disability in ischemic survivors who are suffering from motor, cognitive, and memory impairment. Previously, we have reported suppressing LPA₅ activity with its specific antagonist can attenuate acute brain injuries after ischemic stroke. However, it is unclear whether suppressing LPA₅ activity can also attenuate chronic brain injuries after ischemic stroke. Here, we explored whether effects of LPA₅ antagonist, TCLPA5, could persist a longer time after brain ischemic stroke using a mouse model challenged with tMCAO. TCLPA5 was administered to mice every day for 3 days, starting from the time immediately after reperfusion. TCLPA5 administration improved neurological function up to 21 days after tMCAO challenge. It also reduced brain tissue loss and cell apoptosis in mice at 21 days after tMCAO challenge. Such long-term neuroprotection of TCLPA5 was associated with enhanced neurogenesis and angiogenesis in post-ischemic brain, along with upregulated expression levels of vascular endothelial growth factor. Collectively, results of the current study indicates that suppressing LPA₅ activity can provide long-term neuroprotection to mice with brain ischemic stroke.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Animal cells and systems
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• 제2권1호
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• pp.65-69
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• 1998

The hippocampus and prefrontal cortex are regarded as the highest-order association cortices. The hippocampus has been proposed to store "cognitive maps" of external environments, and the prefrontal cortex is known to be involved in the planning of behavior, among other functions. Considering the prominent functional roles played by these structures, it is not surprising to find direct monosynaptic projections from the hippocampus to the prefrontal cortex. Rhythmic stimulation of this projection patterned after the hippocampal EEG theta rhythm induced stable long-term potentiation of field potentials in the prefrontal cortex. Comparison of behavioral correlates of hippocampal and prefrontal cortical neurons during an a-arm radial maze, working memory task shows a striking contrast. Hippocampal neurons exhibit clear place-specific firing patterns, whereas prefrontal cortical neurons do not show spatial selectivity, but are correlated to different stages of the behavioral task. These data lead to the hypothesis that the role of hippocampal projection to the prefrontal cortex is not to impose spatial representations upon prefrontal activity, but to provide a mechanism for learning the spatial context in which particular behaviors are appropriate.propriate.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.559-560
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• 2018

순환 신경망(RNN) 기반의 Long Short-Term Memory(LSTM)는 자연어처리 분야에서 우수한 성능을 보이는 모델이다. 음성을 문자로 변환해주는 Speech to Text (STT)를 이용해 자막을 생성하고, 생성된 자막을 다른 언어로 동시에 번역을 해주는 서비스가 활발히 진행되고 있다. STT를 사용하여 자막을 추출하는 경우에는 마침표가 없이 전부 연결된 문장이 생성되기 때문에 정확한 번역이 불가능하다. 본 논문에서는 영어자막의 자동 번역 시, 정확도를 높이기 위해 텍스트를 문장으로 분할하여 마침표를 생성해주는 방법을 제안한다. 이 때, LSTM을 이용하여 데이터를 학습시킨 후 테스트한 결과 62.3%의 정확도로 마침표의 위치를 예측했다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(B)
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• pp.321-323
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• 2012

스마트폰 사용의 보편화와 센서기술의 발달로 이를 응용하는 다양한 연구가 진행되고 있다. 특히 가속도, GPS, 조도, 방향센서 등의 센서들이 스마트폰에 부착되어 출시되고 있어서, 이를 이용한 상황인지, 행동인식 등의 관련 연구들이 활발하다. 하지만 다양한 클래스를 분류하면서 높은 인식률을 유지하는 것은 어려운 문제이다. 본 논문에서는 인식률 향상을 위해 계층적 구조의 순환 신경망을 이용하여 제스처를 인식한다. 스마트폰의 가속도 센서를 이용하여 사용자의 제스처 데이터를 수집하고 BLSTM(Bidirectional Long Short-Term Memory) 구조의 순환신경망을 계층적으로 사용하여, 20가지 사용자의 제스처와 비제스처를 분류한다. 약 24,850개의 시퀀스 데이터를 사용하여 실험한 결과, 기존 BLSTM은 평균 89.17%의 인식률을 기록한 반면 계층적 BLSTM은 평균 91.11%의 인식률을 나타내었다.

• 통합자연과학논문집
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• 제11권4호
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.