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Representative H&E images of Ahnak-/- and WT lungs in embryonic day 18.5 and postnatal day 1.

AHNAK is known to be a tumor suppressor in breast cancer due to its ability to activate the TGFβ signaling pathway. However, the role of AHNAK in lung tumor development and progression remains unknown. Here, the Ahnak gene was disrupted to determine its effect on lung tumorigenesis and the mechanism by which it triggers lung tumor development was investigated. First, AHNAK protein expression was determined to be decreased in human lung adenocarcinomas compared with matched nonneoplastic lung tissues. Then, Ahnak−/− mice were used to investigate the role of AHNAK in pulmonary tumorigenesis. Ahnak−/− mice showed increased lung volume and thicker alveolar walls with type II pneumocyte hyperplasia. Most importantly, approximately 20% of aged Ahnak−/− mice developed lung tumors, and Ahnak−/− mice were more susceptible to urethane-induced pulmonary carcinogenesis than wild-type mice. Mechanistically, Ahnak deficiency promotes the cell growth of lung epithelial cells by suppressing the TGFβ signaling pathway. In addition, increased numbers of M2-like alveolar macrophages (AM) were observed in Ahnak−/− lungs, and the depletion of AMs in Ahnak−/− lungs alleviated lung hyperplastic lesions, suggesting that M2-like AMs promoted the progression of lung hyperplastic lesions in Ahnak-null mice. Collectively, AHNAK suppresses type II pneumocyte proliferation and inhibits tumor-promoting M2 alternative activation of macrophages in mouse lung tissue. These results suggest that AHNAK functions as a novel tumor suppressor in lung cancer.Implications: The tumor suppressor function of AHNAK, in murine lungs, occurs by suppressing alveolar epithelial cell proliferation and modulating lung microenvironment. Mol Cancer Res; 16(8); 1287–98. ©2018 AACR.

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In Korean movies, general makeup and special makeup maximize reality and immersion as an expression of external images of a character in the movies, with the development of visual media and increasing opportunities to experience various video contents. This study aimed to identify the impact of the character image implementation elements in Korean movies with special makeup technique on viewing immersion and behavioral intention and provide a theoretical model for analyzing the movies with general makeup and special makeup. To achieve this, this study conducted a survey of 617 male and female viewers aged teens or higher who viewed Korean movies with special makeup technique and made an empirical analysis with SPSS WIN 21.0. The findings can be summarized as follows: First, as a result of analyzing the impact of the character image implementation elements in Korean movies with special makeup technique on viewing immersion and behavioral intention, it appeared that viewing immersion had a significant positive (+) impact on general makeup, costume, and special makeup and that behavioral intention had a significant positive (+) impact on general makeup, special makeup, hair, and costume. Second, as a result of analyzing the impact of viewing immersion on behavioral intention, it appeared that viewing immersion had a significant positive (+) on behavioral intention. It appeared that character image implementation elements in Korean movies with special makeup technique had a positive impact on viewing immersion and behavioral intention. It is expected that replicating an atmosphere or accident in the movie more realistically and using character image implementation elements that are based on new materials and techniques and various technologies with the development of genres in the future can create more viewers continuously.

Background: Allergy health problems worldwide are mainly caused by allergens from domestic cats ( Felis catus). Fel d 1 is a major allergen that causes severe allergic reactions in humans, including rhinitis, conjunctivitis, and life-threatening asthma. The treatment for allergic reactions to Fel d 1 in humans is still in its infancy. Patients with cat allergies anticipate hypoallergenic cats. Methods: We used the CRISPR-Cas9 system, microinjection, and embryo transfer to knock out the chain 2 (CH2) genome of Fel d 1 in cats. T7 endonuclease 1 (T7E1) assay and Sanger sequencing analysis were used to confirm CH2 knockout cats. Fel d 1 level in CH2 knockout cats was assessed by enzyme-linked immunosorbent assay (ELISA). Cytoplasm injection clone technology (CICT) was used to clone the CH2 knockout cat. Results: We report the first successful generation and cloning of CH2 knockout cats using the CRISPR-Cas9 system and CICT. CH2 knockout cats were confirmed using T7E1 and Sanger sequencing. Cloned CH2 knockout cat was verified by microsatellite analysis. Remarkably, ELISA proved that Fel d 1 level of CH2 knockout cats was extremely low compared with that of wild type domestic cats. Conclusion: CH2 knockout cats we generated showed an extremely low level of Fel d 1 and could be hypoallergenic cats. Our study indicates that creating hypoallergenic cats using the CRISPR-Cas9 system is a significant step forward in the consequence that these cats can safely approach allergic patients.

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features., 4 pages, 2 figures

Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transformers implemented only with attention with encoder-decoder structure following seq2seq without using RNN, but had better performance than RNN. Herein, we investigate the decoding technique for electroencephalography (EEG) composed of self-attention module from transformer architecture during imagined speech and overt speech. We performed classification of nine subjects using convolutional neural network based on EEGNet that captures temporal-spectral-spatial features from EEG of imagined speech and overt speech. Furthermore, we applied the self-attention module to decoding EEG to improve the performance and lower the number of parameters. Our results demonstrate the possibility of decoding brain activities of imagined speech and overt speech using attention modules. Also, only single channel EEG or ear-EEG can be used to decode the imagined speech for practical BCIs., submitted to IEEE BCI Winter Conference