• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3293-3299
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• 2015

Background: Arm protein lost in epithelial cancers, on chromosome X (ALEX) is a novel subgroup within the armadillo (ARM) family, which has one or two ARM repeat domains as opposed to more than six-thirteen repeats in the classical Armadillo family members. Materials and Methods: In the study, we explore the biological functions of ALEX1 in breast cancer cells. Overexpression of ALEX1 and silencing of ALEX1 were performed with SK-BR3 and MCF-7 cell lines. Cell proliferation and colony formation assays, along with flow cytometry, were carried out to evaluate the roles of ALEX1. Results: ALEX1 overexpression in SK-BR3 breast cancer cells inhibited proliferation and induced apoptosis. Furthermore, depletion of ALEX1 in MCF-7 breast cancer cells increased proliferation and inhibited apoptosis. Additional analyses demonstrated that the overexpression of ALEX1 activated the intrinsic apoptosis cascades through up-regulating the expression of Bax, cytosol cytochrome c, active caspase-9 and active caspase-3 and down-regulating the levels of Bcl-2 and mitochondria cytochrome c. Simultaneouly, silencing of ALEX1 inhibited intrinsic apoptosis cascades through down-regulating the expression of Bax, cytosol cytochrome c, active caspase-9, and active caspase-3 and up-regulating the level of Bcl-2 and mitochondria cytochrome c. Conclusions: Our data suggest that ALEX1 as a crucial tumor suppressor gene has been involved in cell proliferation and apoptosis in breast cancer, which may serve as a novel candidate therapeutic target.

• Medical Lasers
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• v.8 no.1
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• pp.24-27
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• 2019

Seborrheic keratoses (SKs) have been treated with non-ablative longpulsed (LP) lasers, including LP 532-nm neodymium (Nd): yttrium aluminum garnet (YAG), LP 695-nm ruby, LP 755-nm alexandrite (Alex), and LP 1,064-nm Nd:YAG lasers, with a pulse durations of 1-300 msec. Dual-wavelength LP 755-nm Alex/1,064-nm Nd:YAG laser systems have been used to remove hair follicles and treat various vascular and pigmented disorders by sequentially delivering two pulses of different wavelengths with interpulse intervals in the millisecond range. This paper reports the case of a female patient with multiple, discrete, light brown SKs on the dorsum of both hands that were treated effectively with one session of dual-wavelength LP 1,064-nm Nd:YAG/755-nm Alex laser treatment. The treatment settings for the LP Nd:YAG laser were comprised of a wavelength of 1,064 nm, fluence of 50 J/cm², pulse duration of 5 msec, and beam size of 3 mm. The settings for the LP Alex laser were comprised of a wavelength of 755 nm, fluence of 50 J/cm², pulse duration of 5 msec, and beam size of 3 mm. A hybrid mode was used to automatically deliver LP Nd:YAG and LP Alex laser pulses in succession at interpulse intervals of 20 msec. Six weeks after treatment, the patient exhibited remarkable improvement of the light brown seborrheic keratoses and was satisfied with the results.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.205-217
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• 2018

Deep learning using convolutional neural networks (CNNs) is being studied in various fields of image recognition and these studies show excellent performance. In this paper, we compare the performance of CNN architectures, KCR-AlexNet and KCR-GoogLeNet. The experimental data used in this paper is obtained from PHD08, a large-scale Korean character database. It has 2,187 samples of each Korean character with 2,350 Korean character classes for a total of 5,139,450 data samples. In the training results, KCR-AlexNet showed an accuracy of over 98% for the top-1 test and KCR-GoogLeNet showed an accuracy of over 99% for the top-1 test after the final training iteration. We made an additional Korean character dataset with fonts that were not in PHD08 to compare the classification success rate with commercial optical character recognition (OCR) programs and ensure the objectivity of the experiment. While the commercial OCR programs showed 66.95% to 83.16% classification success rates, KCR-AlexNet and KCR-GoogLeNet showed average classification success rates of 90.12% and 89.14%, respectively, which are higher than the commercial OCR programs' rates. Considering the time factor, KCR-AlexNet was faster than KCR-GoogLeNet when they were trained using PHD08; otherwise, KCR-GoogLeNet had a faster classification speed.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.60.1-60.1
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• 2017

In this study we apply Convolution Neural Network(CNN) to solar flare occurrence prediction with various parameter options using the 00:00 UT MDI images from 1996 to 2010 (total 4962 images). We assume that only X, M and C class flares correspond to "flare occurrence" and the others to "non-flare". We have attempted to look for the best options for the models with two CNN pre-trained models (AlexNet and GoogLeNet), by modifying training images and changing hyper parameters. Our major results from this study are as follows. First, the flare occurrence predictions are relatively good with about 80 % accuracies. Second, both flare prediction models based on AlexNet and GoogLeNet have similar results but AlexNet is faster than GoogLeNet. Third, modifying the training images to reduce the projection effect is not effective. Fourth, skill scores of our flare occurrence model are mostly better than those of the previous models.

• Smart Media Journal
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• v.12 no.7
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.61-67
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• 2017

In this paper we introduce two experimental results from classification of Instagram images and some valuable lessons from them. We have tried some experiments for evaluating the competitive power of Convolutional Neural Network(CNN) in classification of real social network images such as Instagram images. We used AlexNet and ResNet, which showed the most outstanding capabilities in ImageNet Large Scale Visual Recognition Challenge(ILSVRC) 2012 and 2015, respectively. And we used 240 Instagram images and 12 pre-defined categories for classifying social network images. Also, we performed fine-tuning using Inception V3 model, and compared those results. In the results of four cases of AlexNet, ResNet, Inception V3 and fine-tuned Inception V3, the Top-1 error rates were 49.58%, 40.42%, 30.42%, and 5.00%. And the Top-5 error rates were 35.42%, 25.00%, 20.83%, and 0.00% respectively.

• Journal of IKEEE
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• v.25 no.4
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• pp.664-671
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• 2021

This paper proposes a GPGPU structure that can reduce the number of operations and memory access by effectively applying a data reuse method to a convolutional neural network(CNN). Convolution is a two-dimensional operation using kernel and input data, and the operation is performed by sliding the kernel. In this case, a reuse method using an internal register is proposed instead of loading kernel from a cache memory until the convolution operation is completed. The serial operation method was applied to the convolution to increase the effect of data reuse by using the principle of GPGPU in which instructions are executed by the SIMT method. In this paper, for register-based data reuse, the kernel was fixed at 4×4 and GPGPU was designed considering the warp size and register bank to effectively support it. To verify the performance of the designed GPGPU on the CNN, we implemented it as an FPGA and then ran LeNet and measured the performance on AlexNet by comparison using TensorFlow. As a result of the measurement, 1-iteration learning speed based on AlexNet is 0.468sec and the inference speed is 0.135sec.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.293-307
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• 2020

Every year, the floating macroalgae, green and golden tide, are massively detected at the Yellow Sea and East China Sea. After influx of them to the aquaculture facility or beach, it occurs enormous economic losses to remove them. Currently, remote sensing is used effectively to detect the floating macroalgae flowed into the coast. But it has difficulties to detect the floating macroalgae exactly because of the wavelength overlapped with other targets in the ocean. Also, it is difficult to distinguish between green and golden tide because they have similar spectral characteristics. Therefore, we tried to distinguish between green and golden tide applying the Deep learning method to the satellite images. To determine the network, the optimal training conditions were searched to train the AlexNet. Also, Gaofen-1 WFV images were used as a dataset to train and validate the network. Under these conditions, the network was determined after training, and used to confirm the test data. As a result, the accuracy of test data is 88.89%, and it can be possible to distinguish between green and golden tide with precision of 66.67% and 100%, respectively. It is interpreted that the AlexNet can be pick up on the subtle differences between green and golden tide. Through this study, it is expected that the green and golden tide can be effectively classified from various objects in the ocean and distinguished each other.

• Korea journal of population studies
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• v.17 no.1
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• pp.115-126
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• 1994

• Microbiology and Biotechnology Letters
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• v.47 no.4
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• pp.621-629
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• 2019

In this study we attempted to screen bacteria and fungi that generate electricity while treating wastewater using optimized double-chamber microbial fuel cell (MFC) system parameters. Optimization was carried out for five best exoelectrogenic isolates (two bacteria and three fungi) at pH values of 6.0, 7.5, 8.5, and 9.5, and temperatures of 30, 35, 40, and 45℃; the generated power densities were measured using a digital multimeter (DT9205A). The isolates were identified using molecular characterization, followed by the phylogenetic analysis of isolates with known exoelectrogenic microorganisms. The bacterium, Proteus species, N6 (KX548358.1) and fungus, Candida parapsilosis, S10 (KX548360) produced the highest power densities of 1.59 and 1.55 W/m² (at a pH of 8.5 and temperatures of 35 and 40℃) within 24 h, respectively. Other fungi-Clavispora lusitaniae, S9 (KX548359.1) at 40℃, Clavispora lusitaniae, S14 (KX548361.1) at 35℃-and bacterium-Providencia species, N4 (KX548357.1) at 40℃-produced power densities of 1.51, 1.46, and 1.44 W/m², respectively within 24 h. The MFCs achieved higher power densities at a pH of 8.5, temperature of 40℃ within 24 h. The bacterial isolates have a close evolutionary relationship with other known exoelectrogenic microorganisms. These findings helped us determine the optimal pH, temperature, evolutionary relationship, and exoelectrogenic fungal species other than bacteria that enhance MFC performance.