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??????????????? ????????? ???????????? ???????????? ?????? ?????????????????? ??????????????? ?????? ???????????? ??????. ??? ??????????????? ??????????????? ??? ?????? ????????? ???????????? ????????? ??????????????? ????????? ???????????????. ?????? ??????????????? ????????? ??? ?????? ????????? ???????????????, ??? ?????? ????????? diethylnitrosamine (DEN)??? C3H/HeN ?????? ?????? ?????? ???????????? ??? ?????? ????????? ????????? ???????????????. DEN?????? ????????? ???????????? ?????? alkaline phosphatase (ALP) ??????, TUNEL positive ???????????? ??????, ??? ???????????? ?????? ???????????? duct??? ??????, ?????????????????? ????????????, Masson???s trichrome ???????????? ????????? ???????????? ???, ?????? ?????? ?????? ??? ????????? ????????? ???????????? ?????? ???????????? ?????? ?????? ????????? ??? ?????????. ?????????, ??????????????? ?????? ???????????? ????????? ?????? ?????????????????? ??????, ?????? ?????? ??? ????????? ???????????? ??????????????? ???????????? ???????????? ???????????? ?????? ?????? ????????? ??? ?????????. ???????????? ???????????? ????????? ???????????? ??????, ???????????? ???????????? ???????????? ?????? ????????? ???, solvent partition ????????? ???????????? ????????? ???????????? hexane, ethyl acetate, water ???????????? ???????????????. ?????? ??????????????? ?????? ????????? ??????????????? ??????????????? ???, ethyl acetate ???????????? ??????????????? ????????? ?????????????????? ??????????????? ?????? ??????????????? ???????????? ????????? ????????? ????????? ??? ?????????. ????????? ethyl acetate???????????? ???????????? ????????? ????????? ??? ?????????, ??????????????? ????????? ??? ?????? ????????? ????????? ?????????. ???????????????, ??????????????? ????????? ????????????????????? ?????? ????????? ??? ?????? ????????? ???????????? ?????? ??????????????? ????????? ??? ?????? ????????? ????????? ????????? ?????? ???????????????. ?????????, ?????? ??????????????? ?????? ???????????? ?????? ??? ?????? ?????? ???????????? ??????????????? ?????? ????????? ????????? ??? ?????? ????????? ???????????? ??????.clos

Cochlear Implantation after Bilateral Transverse Temporal Bone Fractures

Patients deafened by a severe head injury are rarely encountered. We report a case of a 65-yr-old man with bilateral transverse temporal bone fractures due to head injury. He underwent cochlear implant and achieved a satisfactory auditory rehabilitation. Imaging studies of temporal bone before performing a cochlear implantation provide important information on a patient with bilateral temporal bone fractures. Cochlear implantations with careful planning in such a patient may be a very effective method for aural rehabilitation

A novel β-glucan produced by Paenibacillus polymyxa JB115 induces nitric oxide production in RAW264.7 macrophages

The effect of extracellular β-(1→3), (1→6)-glucan, produced by Paenibacillus polymyxa JB115, on nitric oxide (NO) production in RAW264.7 macrophages was investigated. β-glucan induced the production of NO by RAW264.7 macrophages in a concentration- and time-dependent manner. Moreover, β-glucan stimulation increased the mRNA expression of iNOS, COX-2 and IL-6 in RAW264.7 macrophages in a concentration-dependent manner

Comparison of sampling methods in assessing the microbiome from patients with ulcerative colitis

Background Dysbiosis of ulcerative colitis (UC) has been frequently investigated using readily accessible stool samples. However, stool samples might insufficiently represent the mucosa-associated microbiome status. We hypothesized that luminal contents including loosely adherent luminal bacteria after bowel preparation may be suitable for diagnosing the dysbiosis of UC. Methods This study included 16 patients with UC (9 men and 7 women, mean age: 52.13 ± 14.09 years) and 15 sex- and age-matched healthy individuals (8 men and 7 women, mean age: 50.93 ± 14.11 years). They donated stool samples before colonoscopy and underwent luminal content aspiration and endoscopic biopsy during the colonoscopy. Then, the composition of each microbiome sample was analyzed by 16S rRNA-based next-generation sequencing. Results The microbiome between stool, luminal contents, and biopsy was significantly different in alpha and beta diversities. However, a correlation existed between stool and luminal contents in the Procrustes test (p = 0.001) and Mantel test (p = 0.0001). The stool microbiome was different between patients with UC and the healthy controls. Conversely, no difference was found in the microbiome of luminal content and biopsy samples between the two subject groups. The microbiome of stool and lavage predicted UC, with AUC values of 0.85 and 0.81, respectively. Conclusion The microbiome of stool, luminal contents, and biopsy was significantly different. However, the microbiome of luminal contents during colonoscopy can predict UC, with AUC values of 0.81. Colonoscopic luminal content aspiration analysis could determine microbiome differences between patients with UC and the healthy control, thereby beneficial in screening dysbiosis via endoscopy. Trial registration: This trial was registered at http://cris.nih.go.kr . Registration No.: KCT0003352), Date: 2018–11-13.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1G1A100923411)

The characteristics of population aging and the employment of aged workers in Seoul, Korea

This study examines the characteristics of population aging and the employment of aged workers in Seoul. Population indexes such as those on aged/children ratio and longevity degree are analyzed to identify the time-spatial changes and differentiation of population aging by gu (district). Employers, job seekers and employees related to the aged workers are also analyzed using data from the Center of Job Placement for the Aged in Seoul. The results indicate that population aging has progressed faster in Seoul than other regions in recent years. In addition, regional differences in tendency of population aging are obvious even within Seoul. The percentage of unskilled laborers is much higher in Seoul compared with other regions. There is also geographical differentiation in the employment of aged workers among regions within Seoul. Employment opportunities for the aged are relatively abundant in core economic areas of Seoul, while they are much scarcer in outer residential areas. This shows a geographical mismatch between employers and job seekers, which means that it is difficult for aged workers to secure jobs near their places of residence. Accordingly, governmental support and intervention is needed to address the lower level of mobility of aged workers

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers