• Animal cells and systems
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• v.15 no.2
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• pp.115-121
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• 2011

Previous studies have demonstrated that N-methyl-D-aspartate (NMDA) receptors and acetylcholine receptors are related to learning and memory in rat and mice. In this study, we examined the effects of MK-801, a non-competitive NMDA receptor antagonist, on learning and memory in zebrafish using a passive avoidance test. We further tested whether or not nicotine, a nicotinic acetylcholine receptor agonist, and physostigmine, an acetylcholinesterase inhibitor, reverse the effects of MK-801. Crossing time was increased significantly in the training and test sessions for the controls. When 20 ${\mu}M$ MK-801 was administered prior to the training session, the crossing time did not increase in either session. The MK-801-induced learning deficit was rescued by pretreatment with 20 ${\mu}M$ physostigmine, and crossing time was increased in the training and test sessions compared to the MK-801-treated zebrafish. Further, the MK-801-induced learning deficit was prevented by pretreatment with 20 ${\mu}M$ nicotine, and crossing time was increased in the training session but not in the test session. These results show that MK-801 induced a learning deficit in zebrafish that was prevented by pretreatment with nicotine and physostigmine.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Journal of Science Education
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• v.36 no.2
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• pp.167-185
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• 2012

This study aimed at finding points of improvement in teaching expertise by analyzing the question patterns that appeared during teaching demonstrations which applied science teaching models prepared by a pre-service biology teacher. The question analysis frame for analyzing question types were categorized largely into the question types of Category 1 (questions in cognitive domain, questions with research function, questions in affective domain), Category 2 (repeated questions, questions for narrowing the range, practice questions), and Category 3 (questions on student activity progress, memory questions, and thinking questions). The results of analyzing question patterns from five different science teaching models revealed a high frequency of questions in the fields of cognition and memory. For the circular learning model, questions from the cognitive field appeared the most often, while, student activity progressive questions in particular were used mostly in the 'preliminary concept introduction stage' of the circular learning model and the 'secondary exploratory stage', in which experiments were conducted, and displayed the characteristics of these stages. The discovery learning model combined the courses of observation, measurement, classification and generalization, but, during teaching demonstrations, memory questions turned up the most, while the portion of inquisitive function questions was low. There were many questions from the inquisitive learning model, and, compared to other learning models, many exploratory function questions turned up during the 'experiment planning stage' and 'experiment stage'. Definitional questions and thought questions for the STS learning model turned up more than other learning models. During the change of concept learning model, the five concepts of students were stimulated and the modification of scientific concepts was very much aided by using many memory questions.

• The Korean Journal of Physiology and Pharmacology
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• v.18 no.3
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• pp.201-209
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• 2014

The present study was designed to investigate the efficacy of selective $ET_A$ receptor antagonist, ambrisentan on hyperhomocysteinemia-induced experimental vascular dementia. L-methionine was administered for 8 weeks to induce hyperhomocysteinemia and associated vascular dementia in male rats. Ambrisentan was administered to L-methionine-treated effect rats for 4 weeks (starting from $5^{th}$ to $8^{th}$ week of L-methionine treatment). On $52^{nd}$ day onward, the animals were exposed to the Morris water maze (MWM) for testing their learning and memory abilities. Vascular endothelial function, serum nitrite/nitrate levels, brain thiobarbituric acid reactive species (TBARS), brain reduced glutathione (GSH) levels, and brain acetylcholinesterase (AChE) activity were also measured. L-methionine-treated animals showed significant learning and memory impairment, endothelial dysfunction, decrease in/serum nitrite/nitrate and brain GSH levels along with an increase in brain TBARS levels and AChE activity. Ambrisentan significantly improved hyperhomocysteinemia-induced impairment of learning, memory, endothelial dysfunction, and changes in various biochemical parameters. These effects were comparable to that of donepezil serving as positive control. It is concluded that ambrisentan, a selective $ET_A$ receptor antagonist may be considered as a potential pharmacological agent for the management of hyperhomocysteinemia-induced vascular dementia.

• Journal of Dental Rehabilitation and Applied Science
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• v.23 no.2
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• pp.179-186
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• 2007

The decrease of masticatory function caused by tooth loss leads to a decrease of cerebral blood flow volume resulting in impairment of cognitive function and learning memory disorder. However, the reduced mastication-mediated morphological alteration in the central nervous system (CNS) responsible for senile deficit of cognition, learning and memory has not been well documented. In this study, the effect of the loss of the molar teeth (molarless condition) on the hippocampal expression of glial fibrillary acidic protein (GFAP) protein was studied by immunohistochemical techniques. The results were as follows : 1. The molarless mice showed a lower density of pyramidal cells in the cornu ammonis 1 (CA1) and dentate gyrus (DG) region of the hippocampus than control mice. 2. Immunohistochemical analysis showed that the molarless condition enhanced the time-dependent increase in the cell density and hypertrophy of GFAP immunoreactivity in the CA1 region of the hippocampus. The molarless condition enhanced an time-dependent decrease in the number of neurons in the hippocampal formation and the time-dependent increase in the number and hypertrophy of GFAP-labeled cells in the same region. The data suggest a possible link between reduced mastication and histological changes in hippocampal formation that may be one risk factor for senile impairment of cognitive function and spatial learning memory.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• Journal of Haehwa Medicine
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• v.12 no.1
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• pp.11-26
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• 2003

Alzheimer's disease (AD) is characterized by amyloid deposition and associated loss of neunons in brain regions involved in learning and memory processes. Several causes of evidence support that the congnitive disturbance is closed associated with the deficit of cerebral acetylcholine neurotransmission, and the effect of carboxyl terminal 105 amino acid fragment (CT105) of the amyloid precursor protein (APP) on the gene expression of proinflammatory cytokines. We tested it on the scopolamine-induced amnesia model of the ICR mouse using the Morris water maze with repeated orally administration of 1st Green-Tea extract (200 mg/kg) and 2nd Green-Tea extract (200 mg/kg). The Green-Tea prevents impairment of learning and memory and neuronal loss in mouse models of cognitive disturbance and it demonstrated selectivity for inhibition of acetylcholinesterase (AChE). Furthermore, the repeated administration of Green-Tea, CT105-induced alzheimer's mouse model showed central cholinergic activity by ameliorates learning and memory impairment, and isolation of CD14 microglia showed significantly decreases intracellular release of the proinflammatory cytokines tumor necrosis factor-${\alpha}$, interleukin-$1{\beta}$ and reactive oxygen species (ROS). Because of its composite profile, oral therapeutic index and a prophylactic, Green-Tea is considered the better therapeutic candidate for the treatment of Alzheimer's disease.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.499-509
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• 2018

Recently, hybrid transactional memory (HyTM) has gained much interest from researchers because it combines the advantages of hardware transactional memory (HTM) and software transactional memory (STM). To provide the concurrency control of transactions, the existing HyTM-based studies use a bloom filter. However, they fail to overcome the typical false positive errors of a bloom filter. Though the existing studies use a global lock, the efficiency of global lock-based memory allocation is significantly low in multi-core environment. In this paper, we propose an efficient hybrid transactional memory scheme using near-optimal retry computation and sophisticated memory management in order to efficiently process transactions in multi-core environment. First, we propose a near-optimal retry computation algorithm that provides an efficient HTM configuration using machine learning algorithms, according to the characteristic of a given workload. Second, we provide an efficient concurrency control for transactions in different environments by using a sophisticated bloom filter. Third, we propose a memory management scheme being optimized for the CPU cache line, in order to provide a fast transaction processing. Finally, it is shown from our performance evaluation that our HyTM scheme achieves up to 2.5 times better performance by using the Stanford transactional applications for multi-processing (STAMP) benchmarks than the state-of-the-art algorithms.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.29-38
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• 1997

Dementia is defined as a syndrome which is characterized by various impairments in cognitive functions, especially memory function. Most of the diagnostic criteria for dementia include memory impairment as on essential feature. Memory decline can be present as a consequence of the aging process. But it does not cause significant distress or impairment in social and occupational functionings while dementiadoes. Depression may also be associated with memory impairment. However, unlike dementia, depression dose not cause decrease in delayed verbal learning and recognition memory. In dementia, different features of memory impairment may be present depending on the involved area. Memory impairment in cortical dementia is affected by the disturbance of encoding of information and memory consolidation, while memory imparnene in subcortical denentiy is affected by the disturbance of retrieval in subcortical dementia.