Functions Recover after Fumigants Removal in Different Soils

Chenglei Zhang,Hao Chen,Guitong Li,Qimei Lin 한국토양비료학회 2014 한국토양비료학회 학술발표회 초록집 Vol.2014 No.6

Monitoring Response to Neoadjuvant Chemotherapy of Primary Osteosarcoma Using Diffusion Kurtosis Magnetic Resonance Imaging: Initial Findings

Chenglei Liu,Yan Xi,Mei Li,Qiong Jiao,Huizhen Zhang,Qingcheng Yang,Weiwu Yao 대한영상의학회 2019 Korean Journal of Radiology Vol.20 No.5

Objective: To determine whether diffusion kurtosis imaging (DKI) is effective in monitoring tumor response to neoadjuvant chemotherapy in patients with osteosarcoma. Materials and Methods: Twenty-nine osteosarcoma patients (20 men and 9 women; mean age, 17.6 ± 7.8 years) who had undergone magnetic resonance imaging (MRI) and DKI before and after neoadjuvant chemotherapy were included. Tumor volume, apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), and change ratio (ΔX) between preand post-treatment were calculated. Based on histologic response, the patients were divided into those with good response (≥ 90% necrosis, n = 12) and those with poor response (< 90% necrosis, n = 17). Several MRI parameters between the groups were compared using Student’s t test. The correlation between image indexes and tumor necrosis was determined using Pearson’s correlation, and diagnostic performance was compared using receiver operating characteristic curves. Results: In good responders, MDpost, ADCpost, and MKpost values were significantly higher than in poor responders (p < 0.001, p < 0.001, and p = 0.042, respectively). The ΔMD and ΔADC were also significantly higher in good responders than in poor responders (p < 0.001 and p = 0.01, respectively). However, no significant difference was observed in ΔMK (p = 0.092). MDpost and ΔMD showed high correlations with tumor necrosis rate (r = 0.669 and r = 0.622, respectively), and MDpost had higher diagnostic performance than ADCpost (p = 0.037) and MKpost (p = 0.011). Similarly, ΔMD also showed higher diagnostic performance than ΔADC (p = 0.033) and ΔMK (p = 0.037). Conclusion: MD is a promising biomarker for monitoring tumor response to preoperative chemotherapy in patients with osteosarcoma.

Isolation, identification and characterization of a novel elastase from Chryseobacterium indologenes

Yunlong Lei,Peipei Zhao,Chenglei Li,Haixia Zhao,Zhi Shan,Qi Wu 한국응용생명화학회 2018 Applied Biological Chemistry (Appl Biol Chem) Vol.61 No.3

Elastase is a type of protease that specifically degrades elastin. It has broad application prospects in medicine, food industry, and daily-use chemical industry. In this study, we isolated a bacterial strain WZE87 with high elastin-hydrolysis activity, which was identified as Chryseobacterium indologenes based on morphology, physiological and biochemical characteristics, and 16S rDNA sequence analysis. The elastase produced by this strain was purified by three steps: ammonium sulfate precipitation, Q-Sepharose fast-flow anion-exchange chromatography, and Sephadex G-75 gel-filtration chromatography. The purified elastase was 2376.5 U/mg in activity (a 8.3-fold increase in specific activity), and the recovery was 5.8%. Its molecular mass was estimated to be 26 kDa by sodium dodecyl sulfate–polyacrylamide gel electrophoresis. This enzyme was stable in the pH range of 5.0–10.5 at 37 C. The optimal temperature and pH were 37 C and 7.5, respectively. The activity of this elastase was found to decrease when the temperature was higher than 50 C. The activity was also inhibited by Zn2?, Fe2?, Fe3?, and Mn2? ions. The specific hydrolytic ability of this enzyme was similar to that of papain on substrates like gelatin, casein, soybean-isolated protein and bovine hemoglobin. However, this elastase preferentially hydrolyzed elastin in a protein mixture because of its specific adsorption. Considering its promising properties, this protease may be considered a potential candidate for applications in related industries.

Isolation, identification and characterization of a novel elastase from Chryseobacterium indologenes

Lei, Yunlong,Zhao, Peipei,Li, Chenglei,Zhao, Haixia,Shan, Zhi,Wu, Qi The Korean Society for Applied Biological Chemistr 2018 Applied Biological Chemistry (Appl Biol Chem) Vol.61 No.3

Elastase is a type of protease that specifically degrades elastin. It has broad application prospects in medicine, food industry, and daily-use chemical industry. In this study, we isolated a bacterial strain WZE87 with high elastin-hydrolysis activity, which was identified as Chryseobacterium indologenes based on morphology, physiological and biochemical characteristics, and 16S rDNA sequence analysis. The elastase produced by this strain was purified by three steps: ammonium sulfate precipitation, Q-Sepharose fast-flow anion-exchange chromatography, and Sephadex G-75 gel-filtration chromatography. The purified elastase was 2376.5 U/mg in activity (a 8.3-fold increase in specific activity), and the recovery was 5.8%. Its molecular mass was estimated to be 26 kDa by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. This enzyme was stable in the pH range of 5.0-10.5 at $37^{\circ}C$. The optimal temperature and pH were $37^{\circ}C$ and 7.5, respectively. The activity of this elastase was found to decrease when the temperature was higher than $50^{\circ}C$. The activity was also inhibited by $Zn^{2+}$, $Fe^{2+}$, $Fe^{3+}$, and $Mn^{2+}$ ions. The specific hydrolytic ability of this enzyme was similar to that of papain on substrates like gelatin, casein, soybean-isolated protein and bovine hemoglobin. However, this elastase preferentially hydrolyzed elastin in a protein mixture because of its specific adsorption. Considering its promising properties, this protease may be considered a potential candidate for applications in related industries.

A Stray Capacitances Model of Inductors with Partial Layer of Windings

Bingxin Xu,Zhan Shen,Chenglei Liu,Cungang Hu,Bi Liu,Long Jin,Jiangfeng Wang,Xin Li,Zhike Xu,Wu Chen,Xiaohui Qu,Zhixiang Zou 전력전자학회 2023 ICPE(ISPE)논문집 Vol.2023 No.-

With the high precision requirements of electronic devices, more accurate analysis and modelling of stray capacitance is required in order to reduce electromagnetic interference (EMI) from the stray capacitance in inductors. This paper proposes an improved analytical model of the stray capacitances of the inductor, which takes into account the capacitances between the windings and the central limb, the side limb and the yokes of the core. A general model of the stray capacitance with each winding as a complete layer is calculated, and the potential of the floating core is derived analytically. For the case of a partial winding layer near the side limb, calculations are derived and the stray capacitance changes are compared for different winding layers. Finally, the stray capacitance model of the partial layer is verified by finite element simulations and experimental results on the prototype.

Sentienl-1 SAR 영상을 활용한 유류 분포특성과 CNN 구조에 따른 유류오염 탐지모델 성능 평가

박소연 ( Soyeon Park ),안명환 ( Myoung-hwan Ahn ),이성뢰 ( Chenglei Li ),김준우 ( Junwoo Kim ),전현균 ( Hyungyun Jeon ),김덕진 ( Duk-jin Kim ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.5

SAR 이미지의 통계적 특징을 이용하여 유류오염영역을 특정하는 방법은 분류규칙이 복잡하고 이상값에 의한 영향을 많이 받는다는 한계가 있어, 최근 인공신경망을 기반으로 유류오염영역을 특정하는 연구가 활발히 이루어지고 있다. 하지만, 다양한 유류오염 사례에 대해 모델의 탐지 성능 및 특성을 평가한 연구는 부족하였다. 따라서, 본 연구에서는 기본적인 구조의 CNN인 Simple CNN과 픽셀 단위의 영상 분할이 가능한 U-net을 이용하여, CNN의 구조와, 유류오염의 분포특성에 따른 모델의 탐지성능차이가 존재하는지 분석하였다. 연구결과, 축소경로만 존재하는Simple CNN과 축소경로와 확장경로가 모두 존재하는U-net의 F1 score는 86.24%와 91.44%로 나타나, 두 모델 모두 비교적 높은 탐지 정확도를 보여주었지만, U-net의 탐지성능이 더 높은 것으로 나타났다. 또한 다양한 유류오염 사례에 따른 모델의 성능 비교를 위해, 유류오염의 공간적 분포특성(유류오염 주변의 육지의 분포)과 선명도(유출된 기름과 해수의 경계면이 뚜렷한 정도)를 기준으로, 유류오염 발생 사례를 4가지 유형으로 구분하여 탐지 정확도를 평가하였다. Simple CNN은 각각의 유형에 대해 F1 score가 85.71%, 87.43%, 86.50%, 85.86% 로 유형별 최대 편차가 1.71%인 것으로 나타났으며, U-net은 동일한 지표에 대해 89.77%, 92.27%, 92.59%, 92.66%의 F1 score를 보여 최대 편차가 2.90% 로 두 CNN모델 모두 유류오염 분포 특성에 따른 수치상 탐지성능의 차이는 크지 않은 것으로 나타났다. 하지만 모든 유류오염 유형에서 Simple CNN은 오염영역을 과대탐지 하는 경향을, U-net은 과소탐지 하는 경향을 보여, 모델의 구조와 유류오염의 유형에 따라 서로 다른 탐지 특성을 가진다는 것을 확인하였고, 이러한 특성은 유류오염과 해수의 경계면이 뚜렷하지 않은 경우 더 두드러지게 나타났다. Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures (Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

이중 편파 Sentinel-1 SAR 영상의 편파 지표를 활용한 인공지능 기반 선박 탐지

송주영,김덕진,김준우,이성뢰,Song, Juyoung,Kim, Duk-jin,Kim, Junwoo,Li, Chenglei 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.5

전천후 자료 취득이 가능한 SAR 영상을 기반으로 한 선박 탐지와 인공지능 기반 탐지 알고리즘과 함께 사용하는 것은 안정적인 선박 모니터링에 효과적이다. 기존의 SAR 영상에서는 인공지능 기반 선박 탐지 알고리즘에 진폭 영상만을 주로 사용하였으며, 물체의 산란 특성을 구분할 수 있는 다중 편파 SAR 영상의 편파 지표는 사용되지 않았다. 이에, 본 연구에서는 이중 편파 Sentinel-1 SAR 영상으로부터 고유값 분해를 통해 취득한 4개의 편파 지표인 H, p₁, DoP, DPRVI와 방사 보정을 통해 취득한 2개 편파의 산란계수인 γ₀, _VV, γ₀, _VH를 이용하여 총 6개의 밴드를 가진 SAR 영상 52장의 데이터베이스를 구축하고, 이와 상응하는 시간에 취득한 선박의 실시간 위치 및 속도 정보인 AIS 자료를 사용하여 학습자료를 추출하였다. 구축된 밴드 조합에 대해 선박탐지 정확도를 평가한 결과, 이중 편파 지표를 진폭과 함께 사용한 경우 진폭 값만을 사용했을 때에 비해 개선된 탐지 정확도를 보였다. Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.