167 results on '"Su O"'
Search Results
2. Understanding the Space-Charge Layer in SnO2 for Enhanced Electron Extraction in Hybrid Perovskite Solar Cells
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Sarah Su-O Youn, Jihyun Kim, Junhong Na, William Jo, and Gee Yeong Kim
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General Materials Science - Published
- 2022
3. Cost analysis of mine roadways driven by drilling and blasting method and a roadheader
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Su, O., primary and Akkaş, M., additional
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- 2019
- Full Text
- View/download PDF
4. A machine learning approach to predict foot care self-management in older adults with diabetes
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Su Özgür, Serpilay Mum, Hilal Benzer, Meryem Koçaslan Toran, and İsmail Toygar
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Foot care ,Older adults ,Self-management ,Machine learning ,Diabetes ,Nutritional diseases. Deficiency diseases ,RC620-627 - Abstract
Abstract Background Foot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach. Method This cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study: XGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score. Results XGBoost had the best performance (AUC: 0.7469). The common predictors of the models were age (0.0534), gender (0.0038), perceived health status (0.0218), and treatment regimen (0.0027). The XGBoost model, which had the highest AUC value, also identified income level (0.0055) and A1c (0.0020) as predictors of the FCS-OD score. Conclusion The study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group.
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- 2024
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- View/download PDF
5. Challenges of Large Vessel Vasculitis Stroke Patient with Complicated Endovascular Thrombectomy: A Case Report
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Hsi-Yen, Lee, Su-O, Wang, Ya-Wen, Ciou, Chun-Ching, Chiu, and Yang-Hao, Ou
- Abstract
The causes of acute stroke in patients with SLE are multifactorial. Antiphospholipid-associated hypercoagulability and inflammation-induced platelet activation are major causes of ischemic stroke in SLE patients. As such patients underwent intravenous thrombolysis and endovascular thrombectomy, they may have higher risk of complications and less favorable outcome.A 30-year-old woman with underline SLE and Takayasu arteritis who presented with right CCA and MCA occlusion status post rtPA and endovascular thrombectomy. Twelve hours after the procedure, head CT was ordered due to anisocoria with loss of pupillary light reflex. The head CT showed partial obliteration of suprasellar and quadrigeminal cistern due to extensive brain edema, leading to her decompressive craniectomy. Two days later, patient's both pupil became dilated with head CT showing occlusion of the left MCA. Her condition drastically went downhill when complications such as central DI and myocardial stunning occurred.Although autoimmune vasculitis is not listed as an absolute contraindication to endovascular thrombectomy, given the antecedent reports, it is prudent to disclose possible complications to both the patient and family while making the decision.
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- 2022
6. Understanding the Space-Charge Layer in SnO2 for Enhanced Electron Extraction in Hybrid Perovskite Solar Cells
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Youn, Sarah Su-O, primary, Kim, Jihyun, additional, Na, Junhong, additional, Jo, William, additional, and Kim, Gee Yeong, additional
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- 2022
- Full Text
- View/download PDF
7. A machine learning approach to determine the risk factors for fall in multiple sclerosis
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Su Özgür, Meryem Koçaslan Toran, İsmail Toygar, Gizem Yağmur Yalçın, and Mefkure Eraksoy
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Multiple sclerosis ,Machine learning ,Fall ,Risk factors,Risk prediction ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract Background Falls in multiple sclerosis can result in numerous problems, including injuries and functional loss. Therefore, determining the factors contributing to falls in people with Multiple Sclerosis (PwMS) is crucial. This study aims to investigate the contributing factors to falls in multiple sclerosis using a machine learning approach. Methods This cross-sectional study was conducted with 253 PwMS admitted to the outpatient clinic of a university hospital between February and August 2023. A sociodemographic data collection form, Fall Efficacy Scale (FES-I), Berg Balance Scale (BBS), Fatigue Severity Scale (FSS), Expanded Disability Status Scale (EDSS), Multiple Sclerosis Impact Scale (MSIS-29), and Timed 25 Foot Walk Test (T25-FW) were used for data collection. Gradient-boosting algorithms were employed to predict the important variables for falls in PwMS. The XGBoost algorithm emerged as the best performed model in this study. Results Most of the participants (70.0%) were female, with a mean age of 40.44 ± 10.88 years. Among the participants, 40.7% reported a fall history in the last year. The area under the curve value of the model was 0.713. Risk factors of falls in PwMS included MSIS-29 (0.424), EDSS (0.406), marital status (0.297), education level (0.240), disease duration (0.185), age (0.130), family type (0.119), smoking (0.031), income level (0.031), and regular exercise habit (0.026). Conclusions In this study, smoking and regular exercise were the modifiable factors contributing to falls in PwMS. We recommend that clinicians facilitate the modification of these factors in PwMS. Age and disease duration were non-modifiable factors. These should be considered as risk increasing factors and used to identify PwMS at risk. Interventions aimed at reducing MSIS-29 and EDSS scores will help to prevent falls in PwMS. Education of individuals to increase knowledge and awareness is recommended. Financial support policies for those with low income will help to reduce the risk of falls.
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- 2024
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8. Regulation of SHP2 by PTEN/AKT/GSK-3β signaling facilitates IFN-γ resistance in hyperproliferating gastric cancer
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Tseng, Po-Chun, Huang, Wei-Ching, Chen, Chia-Ling, Sheu, Bor-Shyang, Shan, Yan-Shen, Tsai, Cheng-Chieh, Wang, Chi-Yun, Chen, Su-O, Hsieh, Chia-Yuan, and Lin, Chiou-Feng
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- 2012
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9. Design and expected performances of the large acceptance calorimeter for the HERD space mission
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Pacini, L., Adriani, O., Bai, Y. -L., Bao, T. -W., Berti, E., Bottai, S., Cao, W. -W., Casaus, J., Cui, X. -Z., D’Alessandro, R., Formato, V., Gao, J. -R., Li, R., Liu, X., Lorusso, L., Lyu, L. -W., Marin, J., Martinez, G., Pizzolotto, C., Qin, J. -J., Quan, Z., Shi, D. -L., Starodubtsev, O., Tang, Z. -C., Tiberio, A., Vagelli, V., Velasco, M. A., Wang, B., Wang, Hongmei, R. -J., Z. -G., Xu, M., Yang, Y., Zhang, L., Zheng, J. -K., Alemanno, F., Aloisio, R., Altomare, G., Ambrosi, G., An, Q., Antonelli, M., Azzarello, P., Bai, L., Bai, Y. L., Bao, T. W., Barbanera, M., Barbato, F. C. T., Bernardini, P., Berti, B., Bertucci, B., X. J., Bi, Bigongiari, G., Bongi, M., Bonvicini, V., Bordas, P., Bosch-Ramon, V., Brogi, P., Cadoux, F., Campana, D., Cao, W. W., Cao, Z., Catanzani, E., Cattaneo, P. W., Chang, J., Chang, Y. H., Chen, G. M., Chen, F., Cianetti, F., Comerma, A., Cortis, D., Cui, X. H., Cui, X. Z., Dai, C., Dai, Z. G., Gaetanoe, De, Mitri, De, Palma, De, Felice, Di, Giovanni, Di, Santo, Di, Venere, Di, Dong, L., Dong, J. N., Donvito, Y. W., Duranti, G., D’Urso, M., Evoli, D., Fang, C., Fariña, K., Favre, L., Feng, Y., Feng, C. Q., Feng, H., Feng, H. B., Finetti, Z. K., Formato, N., Frieden, V., Fusco, J. M., Gao, P., Gargano, J. R., Gascon-Fora, F., Gasparrini, D., Giglietto, D., Giovacchini, N., Gomez, F., Gong, S., Gou, K., Guida, Q. B., Guo, R., Guo, D. Y., Guo, J. H., Y. Q., He, H. H., Hu, H. B., Hu, J. Y., Hu, Hu, P., Huang, Y. M., Huang, G. S., Huang, J., Huang, W. H., Huang, X. T., Huang, Y. B., Ionica, Y. F., Jouvin, M., Kotenko, L., Kyratzis, A., Marra, La, Li, D., M. J., Li, Q. Y., Li, S. L., Li, Li, T., Li, X., Li, Z., Liang, Z. H., Liang, E. W., Liao, M. J., Licciulli, C. L., Lin, F., Liu, S. J., Liu, D., Liu, H. B., Liu, H., Liu, J. B., Liu, S. B., Liu, X. W., Loparco, Y. Q., Loporchio, F., Lu, S., Lyu, X., Lyu, J. G., Maestro, L. W., Mancini, E., Manera, E., Marin, R., Marrocchesi, J., Marsella, P. S., Marzullo, M., Mauricio, D., Mocchiutti, J., Morettini, G., Mori, G., Mussolin, L., Nicola, Mazziotta, Oliva, M., Orlandi, A., Osteria, D., Pacini, G., Panico, L., Pantalei, B., Papa, F. R., Papini, S., Paredes, P., Parenti, J. M., Pauluzzi, A., Pearce, M., Peng, M., Perfetto, W. X., Perrina, F., Perrotta, C., Pillera, G., Pizzolotto, R., Qiao, C., Qin, R., Quadrani, J. J., Quan, L., Rappoldi, Z., Raselli, A., Ren, G., Renno, X. X., Ribo, F., Rico, M., Rossella, J., Ryde, M., Sanmukh, F., Scotti, A., Serini, V., Shi, D., Shi, D. L., Silveri, Q. Q., Starodubtsev, L., Su, O., D. T., Su, Sukhonos, M., Suma, D., Sun, A., Sun, X. L., Surdo, Z. T., Tang, A., Tiberio, Z. C., Tykhonov, A., Vagelli, A., Vannuccini, V., Velasco, E., Walter, M., Wang, R., Wang, A. Q., Wang, J. C., Wang, J. M., Wang, J. J., Wang, L., Wang, M., Wang, R. J., Wang, S., Wang, X. Y., Wang, X. L., Wei, Z. G., Wei, D. M., J. J., Wu, B. B., Wu, Wu, J., L. B., Wu, Wu, X., Xin, X. F., Y. L., Xu, Yan, Z. Z., Yang, H. R., Yin, Y., P. F., Yu, Yuan, Y. W., Zampa, Q., Zampa, G., Zha, N., Zhang, M., Zhang, C., Zhang, F. Z., Zhang, L. F., Zhang, S. N., Zhang, Y., Zhao, Y. L., Zheng, Z. G., Zhou, J. K., Zhu, Y. L., Zhu, F. R., and K. J.
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Space experiments ,Energy ,Scintillating fiber ,Performance ,Monte Carlo methods ,Measurements of ,Space stations ,Space missions ,Cosmology ,Cosmic rays ,Intelligent systems ,Scintillation counters ,Silicon detectors ,Charge detectors ,Fiber trackers ,Radiation detection ,Read out systems ,Calorimeters - Published
- 2022
10. Gamma-ray performance study of the HERD payload
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Adriani, O., Alemanno, F., Aloisio, R., Altomare, C., Ambrosi, G., An, Q., Antonelli, M., Azzarello, P., Bai, L., Bai, Y. L., Bao, T. W., Barbanera, M., Barbato, F. C. T., Bernardini, P., Berti, E., Bertucci, B., X. J., Bi, Bigongiari, G., Bongi, M., Bonvicini, V., Bordas, P., Bosch-Ramon, V., Bottai, S., Brogi, P., Cadoux, F., Campana, D., Cao, W. W., Cao, Z., Casaus, J., Catanzani, E., Cattaneo, P. W., Chang, J., Chang, Y. H., Chen, G. M., Chen, Y., Cianetti, F., Comerma, A., Cortis, D., Cui, X. H., Cui, X. Z., Dai, C., Dai, Z. G., D'Alessandro, R., Gaetano, De, Mitri, De, Palma, De, Felice, Di, Giovanni, Di, Santo, Di, Venere, Di, Dong, L., Dong, J. N., Donvito, Y. W., Duranti, G., D'Urso, M., Evoli, D., Fang, C., Fariña, K., Favre, L., Feng, Y., Feng, C. Q., Feng, H., Feng, H. B., Finetti, Z. K., Formato, N., Frieden, V., Fusco, J. M., Gao, P., Gargano, J. R., Gascon-Fora, F., Gasparrini, D., Giglietto, D., Giovacchini, N., Gomez, F., Gong, S., Gou, K., Guida, Q. B., Guo, R., Guo, D. Y., Guo, J. H., Y. Q., He, H. H., Hu, H. B., Hu, J. Y., Hu, Hu, P., Huang, Y. M., Huang, G. S., Huang, J., Huang, W. H., Huang, X. T., Huang, Y. B., Ionica, Y. F., Jouvin, M., Kotenko, L., Kyratzis, A., Marra, La, Li, D., M. J., Li, Q. Y., Li, Li, R., S. L., Li, Li, T., Li, X., Li, Z., Liang, Z. H., Liang, E. W., Liao, M. J., Licciulli, C. L., Lin, F., Liu, S. J., Liu, D., Liu, H. B., Liu, H., Liu, J. B., Liu, S. B., Liu, X., Liu, X. W., Loparco, Y. Q., Loporchio, F., Lu, S., Lyu, X., Lyu, J. G., Maestro, L. W., Mancini, E., Manera, E., Marin, R., Marrocchesi, J., Marsella, P. S., Martinez, G., Marzullo, M., Mauricio, D., Mocchiutti, J., Morettini, G., Mori, G., Mussolin, L., Nicola, Mazziotta, Oliva, M., Orlandi, A., Osteria, D., Pacini, G., Panico, L., Pantaleo, B., Papa, F. R., Papini, S., Paredes, P., Parenti, J. M., Pauluzzi, A., Pearce, M., Peng, M., Perfetto, W. X., Perrina, F., Perrotta, C., Pillera, G., Pizzolotto, R., Qiao, C., Qin, R., Quadrani, J. J., Quan, L., Rappoldi, Z., Raselli, A., Ren, G., Renno, X. X., Ribo, F., Rico, M., Rossella, J., Ryde, M., Sanmukh, F., Scotti, A., Serini, V., Shi, D., Shi, D. L., Silveri, Q. Q., Starodubtsev, L., Su, O., D. T., Su, Sukhonos, M., Suma, D., Sun, A., Sun, X. L., Surdo, Z. T., Tang, A., Tiberio, Z. C., Tykhonov, A., Vagelli, A., Vannuccini, V., Velasco, E., Walter, M., Wang, R., Wang, A. Q., Wang, B., Wang, J. C., Wang, J. M., Wang, J. J., Wang, L., Wang, M., Wang, R. J., Wang, S., Wang, X. Y., Wang, X. L., Wei, Z. G., Wei, D. M., J. J., Wu, B. B., Wu, Wu, J., L. B., Wu, Wu, X., Xin, X. F., Y. L., Xu, Xu, M., Yan, Z. Z., Yang, H. R., Yin, Y., P. F., Yu, Yuan, Y. W., Zampa, Q., Zampa, G., Zha, N., Zhang, M., Zhang, C., Zhang, F. Z., Zhang, L., Zhang, L. F., Zhang, S. N., Zhang, Y., Zhao, Y. L., Zheng, Z. G., Zhou, J. K., Zhu, Y. L., Zhu, F. R., and K. J.
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Energy ,Performance ,Cosmology ,Gamma rays ,Optical transfer function ,Space stations ,Dark matter searches ,Detector geometry ,Full simulations ,Gamma-rays ,Knee energy ,Performance study ,Radiation detection ,Space astronomy ,Cosmic rays - Published
- 2022
11. The High Energy cosmic-Radiation Detector (HERD) Trigger System
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Velasco, M. A., Bao, T., Berti, E., Bonvicini, V., Casaus, J., Giovacchini, F., Liu, X., Marco, R., Marín, J., Martínez, G., Mori, N., Oliva, A., Pacini, L., Quan, Z., Tang, Z., Xu, M., Zampa, G., Zampa, N., Adriani, O., Alemanno, F., Aloisio, R., Altomare, G., Ambrosi, G., An, Q., Antonelli, M., Azzarello, P., Bai, L., Bai, Y. L., Bao, T. W., Barbanera, M., Barbato, F. C. T., Bernardini, B., Bertucci, B., X. J., Bi, Bigongiari, G., Bongi, M., Bordas, P., Bosch-Ramon, V., Bottai, S., Brogi, P., Cadoux, F., Campana, D., Cao, W. W., Cao, Z., Catanzani, E., Cattaneo, P. W., Chang, J., Chang, Y. H., Chen, G. M., Chen, F., Cianetti, F., Comerma, A., Cortis, D., Cui, X. H., Cui, X. Z., Dai, C., Dai, Z. G., D'Alessandro, R., Gaetanoe, De, Mitri, De, Palma, De, Felice, Di, Giovanni, Di, Santo, Di, Venere, Di, Dong, L., Dong, J. N., Donvito, Y. W., Duranti, G., D'Urso, M., Evoli, D., Fang, C., Fariña, K., Favre, L., Feng, Y., Feng, C. Q., Feng, H., Feng, H. B., Finetti, Z. K., Formato, N., Frieden, V., Fusco, J. M., Gao, P., Gargano, J. R., Gascon-Fora, F., Gasparrini, D., Giglietto, D., Gomez, N., Gong, S., Gou, K., Guida, Q. B., Guo, R., Guo, D. Y., Guo, J. H., Y. Q., He, H. H., Hu, H. B., Hu, J. Y., Hu, Hu, P., Huang, Y. M., Huang, G. S., Huang, J., Huang, W. H., Huang, X. T., Huang, Y. B., Ionica, Y. F., Jouvin, M., Kotenko, L., Kyratzis, A., Marra, La, Li, D., M. J., Li, Q. Y., Li, Li, R., S. L., Li, Li, T., Li, X., Li, Z., Liang, Z. H., Liang, E. W., Liao, M. J., Licciulli, C. L., Lin, F., Liu, S. J., Liu, D., Liu, H. B., Liu, H., Liu, J. B., Liu, S. B., Liu, X. W., Loparco, Y. Q., Loporchio, F., Lu, S., Lyu, X., Lyu, J. G., Maestro, L. W., Mancini, E., Manera, E., Marrocchesi, R., Marsella, P. S., Martinez, G., Marzullo, M., Mauricio, D., Mocchiutti, J., Morettini, G., Mussolin, L., Nicola, Mazziotta, Orlandi, M., Osteria, D., Panico, G., Pantalei, B., Papa, F. R., Papini, S., Paredes, P., Parenti, J. M., Pauluzzi, A., Pearce, M., Peng, M., Perfetto, W. X., Perrina, F., Perrotta, C., Pillera, G., Pizzolotto, R., Qiao, C., Qin, R., Quadrani, J. J., Rappoldi, L., Raselli, A., Ren, G., Renno, X. X., Ribo, F., Rico, M., Rossella, J., Ryde, M., Sanmukh, F., Scotti, A., Serini, V., Shi, D., Shi, D. L., Silveri, Q. Q., Starodubtsev, L., Su, O., D. T., Su, Sukhonos, M., Suma, D., Sun, A., Sun, X. L., Surdo, Z. T., Tang, A., Tiberio, Z. C., Tykhonov, A., Vagelli, A., Vannuccini, V., Velasco, E., Walter, M., Wang, R., Wang, A. Q., Wang, B., Wang, J. C., Wang, J. M., Wang, J. J., Wang, L., Wang, M., Wang, R. J., Wang, S., Wang, X. Y., Wang, X. L., Wei, Z. G., Wei, D. M., J. J., Wu, B. B., Wu, Wu, J., L. B., Wu, Wu, X., Xin, X. F., Y. L., Xu, Yan, Z. Z., Yang, H. R., Yin, Y., P. F., Yu, Yuan, Y. W., Zha, Q., Zhang, M., Zhang, C., Zhang, F. Z., Zhang, L., Zhang, L. F., Zhang, S. N., Zhang, Y., Zhao, Y. L., Zheng, Z. G., Zhou, J. K., Zhu, Y. L., Zhu, F. R., and K. J.
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Cosmology ,Gamma rays ,Intelligent systems ,Monte Carlo methods ,Space stations ,Tellurium compounds ,Topology ,Dark matter ,Electron spectrum ,Energy ,Fundamental physics ,Measurements of ,Plastic scintillator detector ,Precise measurements ,Radiation detection ,Space-borne ,Trigger systems ,Cosmic rays - Published
- 2022
12. The Plastic Scintillator Detector of the HERD space mission
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Kyratzis, D., Alemanno, F., Altomare, C., Barbato, F. C. T., Bernardini, P., Cattaneo, P. W., Mitri, De, Palma, De, Venere, Di, Santo, Di, Fusco, M., Gargano, P., Loparco, F., Loporchio, F., Marsella, S., Mazziotta, G., Pantaleo, M. N., Parenti, F. R., Pillera, A., Rappoldi, R., Raselli, A., Rossella, G., Serini, M., Silveri, D., Surdo, L., Wu, A., Adriani, L., Aloisio, O., Ambrosi, G., An, G., Antonelli, Q., Azzarello, M., Bai, P., Bai, L., Bao, Y. L., Barbanera, T. W., Berti, M., Bertucci, B., Bi, B., Bigongiari, X. J., Bongi, G., Bonvicini, M., Bordas, V., Bosch-Ramon, P., Bottai, V., Brogi, S., Cadoux, P., Campana, F., Cao, D., Cao, W. W., Casaus, Z., Catanzani, J., Chang, E., Chang, J., Chen, Y. H., Chen, G. M., Cianetti, F., Comerma, F., Cortis, A., Cui, D., Cui, X. H., Dai, X. Z., Dai, C., D'Alessandro, Z. G., Gaetano, De, Felice, Di, Giovanni, Di, Dong, A., Dong, J. N., Donvito, Y. W., Duranti, G., D'Urso, M., Evoli, D., Fang, C., Fariña, K., Favre, L., Feng, Y., Feng, C. Q., Feng, H., Feng, H. B., Finetti, Z. K., Formato, N., Frieden, V., Gao, J. M., Gascon-Fora, J. R., Gasparrini, D., Giglietto, D., Giovacchini, N., Gomez, F., Gong, S., Gou, K., Guida, Q. B., Guo, R., Guo, D. Y., Guo, J. H., Y. Q., He, H. H., Hu, H. B., Hu, J. Y., Hu, Hu, P., Huang, Y. M., Huang, G. S., Huang, J., Huang, W. H., Huang, X. T., Huang, Y. B., Ionica, Y. F., Jouvin, M., Kotenko, L., Marra, La, Li, D., M. J., Li, Q. Y., Li, Li, R., S. L., Li, Li, T., Li, X., Li, Z., Liang, Z. H., Liang, E. W., Liao, M. J., Licciulli, C. L., Lin, F., Liu, S. J., Liu, D., Liu, H. B., Liu, H., Liu, J. B., Liu, S. B., Liu, X., Liu, X. W., Y. Q., Lu, Lyu, X., Lyu, J. G., Maestro, L. W., Mancini, E., Manera, E., Marin, R., Marrocchesi, J., Martinez, P. S., Martinez, G., Marzullo, M., Mauricio, D., Mocchiutti, J., Morettini, G., Mori, G., Mussolin, L., Oliva, L., Orlandi, A., Osteria, D., Pacini, G., Panico, L., Papa, B., Papini, S., Paredes, P., Pauluzzi, J. M., Pearce, M., Peng, M., Perfetto, W. X., Perrina, F., Perrotta, C., Pizzolotto, G., Qiao, C., Qin, R., Quadrani, J. J., Quan, L., Ren, Z., Renno, X. X., Ribo, F., Rico, M., Ryde, J., Sanmukh, F., Scotti, A., Shi, V., Shi, D. L., Starodubtsev, Q. Q., Su, O., D. T., Su, Sukhonos, M., Suma, D., Sun, A., Sun, X. L., Tang, Z. T., Tiberio, Z. C., Tykhonov, A., Vagelli, A., Vannuccini, V., Velasco, E., Walter, M., Wang, R., Wang, A. Q., Wang, B., Wang, J. C., Wang, J. M., Wang, J. J., Wang, L., Wang, M., Wang, R. J., Wang, S., Wang, X. Y., Wang, X. L., Wei, Z. G., Wei, D. M., J. J., Wu, B. B., Wu, Wu, J., L. B., Wu, Wu, X., Xin, X. F., Y. L., Xu, Xu, M., Yan, Z. Z., Yang, H. R., Yin, Y., P. F., Yu, Yuan, Y. W., Zampa, Q., Zampa, G., Zha, N., Zhang, M., Zhang, C., Zhang, F. Z., Zhang, L., Zhang, L. F., Zhang, S. N., Zhang, Y., Zhao, Y. L., Zheng, Z. G., Zhou, J. K., Zhu, Y. L., Zhu, F. R., and K. J.
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Energy ,Gamma-ray astronomy ,Charged particles ,Gamma rays ,High energy gamma rays ,Measurements of ,Space-borne instruments ,Space stations ,Mass composition ,Space missions ,Cosmology ,Cosmic ray energy spectrum ,Plastic scintillator detector ,Precise measurements ,Scintillation counters ,Cosmic rays - Published
- 2022
13. Autophagy facilitates an IFN-γ response and signal transduction
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Chang, Yu-Ping, Chen, Chia-Ling, Chen, Su-O, Lin, Yee-Shin, Tsai, Cheng-Chieh, Huang, Wei-Ching, Wang, Chi-Yun, Hsieh, Chia-Yuan, Choi, Pui-Ching, and Lin, Chiou-Feng
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- 2011
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- View/download PDF
14. Treatment of localized pagetoid reticulosis with imiquimod: a case report and literature review
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Su, O., Dizman, D., Onsun, N., Bahali, A. G., Biyik Ozkaya, D., Tosuner, Z., and Demirkesen, C.
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- 2016
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15. Exploring the Predictive Role of Inflammatory Markers in Neuropathic Bladder-Related Kidney Damage with Machine Learning
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Su Özgür, Sevgin Taner, Gülnur Gülnaz Bozcuk, and Günay Ekberli
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neuropathic bladder dysfunction ,kidney damage ,inflammatory markers ,machine learning ,k-nearest neighbor ,random forest ,Medicine ,Pediatrics ,RJ1-570 - Abstract
Aim: The main objective of this study was to predict upper urinary tract damage utilizing novel approaches, such as machine learning models, by incorporating simple predictors alongside established radiological and clinical factors. Materials and Methods: In this retrospective study, a total of 191 patients who underwent blood tests, urine analysis, imaging, and urodynamic studies (UDS) in order to assess their nephrological and urological status were included. Basic statistical analyses were conducted using IBM SPSS Version 25. A significance level of p
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- 2024
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16. <scp>eFLORA PROJECT IN THE</scp> D.P.R. <scp>KOREA</scp> : <scp>CURRENT SITUATION AND PROSPECT</scp>
- Author
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Il‐Yob Ju, Il‐Bong Ri, Sok‐Jun Ri, Myong‐Hwa Pak, Chol‐Su O, and Myong‐Ho Sin
- Subjects
Political science ,Plant Science ,Economic system ,Current (fluid) ,Ecology, Evolution, Behavior and Systematics - Published
- 2020
17. Understanding the Space-Charge Layer in SnO2 for Enhanced Electron Extraction in Hybrid Perovskite Solar Cells.
- Author
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Youn, Sarah Su-O, Kim, Jihyun, Na, Junhong, Jo, William, and Kim, Gee Yeong
- Published
- 2022
- Full Text
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18. Pan-cancer analysis of whole genomes
- Author
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Campbell, P. J. a., Abpemail, Author, Getz, G. b., C, D, Eemail, Author, Korbel, J. O. f., Gemail, Author, Stuart, hEmail Author, J. M., Jennings, J. L. i., Stein, J, L. D. k., Lemail, Author, Perry, M. D. m., Nahal-Bose, N, Ouellette, H. K. n., B. F. F. o., P, C. H. k., Li, Rheinbay, Q, E. b., E, Nielsen, R, Sgroi, G. P. r., D. C. r., Wu, C. -L. r., Faquin, W. C. r., Deshpande, V. r., Boutros, P. C. k., Q, S, Lazar, T, Hoadley, A. J. u., K. A. v., W, Louis, D. N. r., Dursi, L. J. k., Yung, X, Bailey, C. K. n., M. H. y., Z, Saksena, G. b., Raine, Abp, K. M., Buchhalter, Aa, I., Ab, Ac, Kleinheinz, Aa, K., Schlesner, Ac, Aa, M., Zhang, Ad, Wang, J. n., Ae, W., Wheeler, D. A., Af, Ding, Ag, L. y., Z, Simpson, Ah, J. T. k., Ai, O’Connor, B. D. n., Yakneen, Aj, Ellrott, S. g., Ak, K., Miyoshi, Al, N., Butler, Abp, A. P., Royo, Am, R., Shorser, S. I. k., Vazquez, Am, M., Rausch, An, Tiao, T. g., Waszak, G. b., Rodriguez-Martin, S. M. g., Ao, B., Ap, Aq, Shringarpure, Ar, S., D. -Y., As, Demidov, G. M., At, Au, Av, Delaneau, Aw, O., Ax, Ay, Hayashi, Al, S., Imoto, Habermann, N. g., Segre, A. V. b., Garrison, Az, Abp, E., Cafferkey, A. f., Alvarez, E. G., Ao, Heredia-Genestar, J. M., Ba, Muyas, At, F., Drechsel, At, O., Bruzos, Av, A. L., Ao, Temes, Ao, J., Zamora, Ap, Abp, Baez-Ortega, Bb, A., Kim, H. -L., Bc, Mashl, R. J. z., Bd, Ye, Be, K., Dibiase, Bf, Bg, A., Huang, K. -L. z., Letunic, Bh, Bi, I., Mclellan, M. D. y., Z, Ah, Newhouse, S. J. f., Shmaya, As, T., Kumar, Bj, S., Wedge, Bk, D. C., Bl, Abp, Bm, Wright, M. H., Ar, Yellapantula, V. D., Bn, Gerstein, Bo, Bj, M., Bk, Bp, Khurana, Bq, E., Br, Bs, Marques-Bonet, Bt, Bu, T., Bv, Bw, Navarro, Bx, Bu, A., Bustamante, C. D., Ar, Siebert, By, Bz, R., Nakagawa, Ca, Cb, H., Easton, D. F., Cc, Ossowski, Cd, At, S., Tubio, J. M. C., Ao, De La Vega, F. M., Ar, As, By, Estivill, At, X., Yuen, Ce, Mihaiescu, D. k., Omberg, G. L. n., Cf, L., Ferretti, V. n., Sabarinathan, Cg, Ch, R., Ci, Cj, Pich, Ch, O., Gonzalez-Perez, Cj, Ch, A., Taylor-Weiner, Cj, Ck, A., Fittall, M. W., Cl, Demeulemeester, Cl, J., Tarabichi, Cm, Cl, M., Abp, Roberts, Abp, N. D., Van, Loo, Cl, P., Cortés-Ciriano, Cm, Cn, I., Co, Cp, Urban, L. f., Park, G, Co, P., Zhu, Cp, Cq, B., Pitkänen, E. g., Abp, Y., Saini, Cr, N., Klimczak, L. J., Cs, Weischenfeldt, J. g., Ct, Cu, Sidiropoulos, Cu, N., Alexandrov, L. B., Cv, Abp, Rabionet, At, R., Av, Cw, Escaramis, At, G., Cx, Cy, Bosio, At, Av, Holik, A. Z., At, Susak, At, H., Prasad, Av, Av, A., Erkek, S. g., Calabrese, C. f., Raeder, G, Harrington, B. g., Cz, E., Mayes, Da, S., Turner, Da, D., Juul, Cz, S., Roberts, S. A., Db, Song, Cq, L., Koster, Dc, R., Mirabello, Hua, Cq, X., Tanskanen, T. J., Dd, Tojo, Aq, M., Chen, Bk, J., Aaltonen, De, L. A., Df, Rätsch, Dg, G., Dh, Di, Dj, Dk, Schwarz, Dl, R. F. f., Dm, Dn, Do, Butte, A. J., Dp, Brazma, A. f., Chanock, S. J., Cq, Chatterjee, Dq, N., Stegle, Dr, O. f., G, Harismendy, Ds, Dt, O., Bova, G. S., Du, Gordenin, D. A., Cr, Haan, D. h., Sieverling, Dv, L., Feuerbach, Dw, Chalmers, Dx, D., Joly, Dy, Y., Knoppers, Dy, B., Molnár-Gábor, Dz, F., Phillips, Dy, M., Thorogood, Dy, A., Townend, Dy, D., Goldman, Ea, M., Fonseca, N. A. f., Xiang, Eb, Craft, Q. n., Ea, B., Piñeiro-Yáñez, Ec, E., Muñoz, A. f., Petryszak, R. f., Füllgrabe, A. f., Al-Shahrour, Ec, F., Keays, M. f., Haussler, Ea, D., Weinstein, Ed, Ee, J., Huber, Ef, Valencia, W. g., Am, A., Papatheodorou, Bw, Zhu, I. f., Ea, J., Fan, Ae, Y., Torrents, Am, D., Bieg, Bw, Eg, M., Chen, Eh, Ei, K., Chong, Ej, Z., Cibulskis, K. b., Eils, Aa, R., Ac, Ek, Fulton, El, R. S. y., Z, Gelpi, Ah, J. L., Am, Gonzalez, Em, S. f., G, Gut, I. G., Av, Hach, Bu, En, F., Heinold, Eo, Ac, Hu, Ep, T., Huang, V. k., Hutter, Eh, B., Eq, Er, Jäger, Aa, N., Jung, Es, J., Ep, Y., Lalansingh, C. k., Leshchiner, I. b., Livitz, D. b., E. Z., Ep, Maruvka, Y. E. b., R, Et, Milovanovic, Nielsen, M. M., Eu, Paramasivam, Pedersen, Eh, J. S., Eu, Puiggròs, Ev, Sahinalp, S. C., Eo, Ew, Ex, Sarrafi, Eo, I., Stewart, Ex, Stobbe, C. b., M. D., Av, Wala, Bu, J. A. b., E, Wang, Ey, J. z., Be, Wendl, Ez, M. z., Fa, Werner, Fb, Aa, J., Fc, Wu, Ep, Z., Xue, Ep, H., Yamaguchi, T. N. k., Bn, V., Davis-Dusenbery, Bo, B. N., Fd, Grossman, R. L., Fe, Ff, Y., Heinold, Fg, M. C., Aa, Hinton, Ac, Abp, J., Jones, Abp, D. R., Menzies, Abp, A., Stebbings, Abp, L., Hess, J. M. b., Rosenberg, Et, M. b., R, Dunford, A. J. b., Gupta, M. b., Imielinski, Fh, M., Meyerson, Fi, M. b., E, Beroukhim, Ey, R. b., E, Reimand, Fj, J. k., Q, Dhingra, Br, P., Favero, Bt, Fk, F., Dentro, Bl, S., Abp, Cl, Wintersinger, Fl, J., Fm, Fn, Rudneva, V. g., Park, J. W., Fo, Hong, E. P., Fo, Heo, S. G., Fo, Kahles, Dg, A., Lehmann, K. -V., Dg, Di, Dj, Fp, Fq, Soulette, C. M., Aj, Shiraishi, Al, Y., Liu, Fr, F., Fs, He, Fr, Y., Demircioğlu, Ft, D., Davidson, Fu, N. R., Dg, Dl, Fp, Greger, L. f., Fv, S., Liu, Fw, Fv, D., Stark, Fw, S. G., Dj, Fp, Fx, Zhang, Fy, Amin, S. B., Fz, Ga, Gb, Bailey, Gc, P., Chateigner, A. n., Frenkel-Morgenstern, Gd, M., Hou, Fv, Y., Huska, Fw, M. R., Dm, Kilpinen, Ge, H., Lamaze, F. C. k., Fv, C., Fw, Li, Fv, X., Marin, Fw, M. G., Aj, Markowski, Dm, J., Nandi, Gf, T., Ojesina, A. I., Gg, Gh, Gi, Pan-Hammarström, Fv, Q., Park, Gj, P. J., Co, Pedamallu, Cp, C. S. b., E, Fj, Su, Fv, H., Tan, Fw, Gf, P., Gk, Gl, Teh, Gm, B. T., Gk, Gl, Gm, Gn, Go, Wang, Fv, J., Xiong, Fw, Ye, Yung, Fw, Zhang, C. n., Zheng, Fr, L., Zhu, Awadalla, Fw, P. k., L, Creighton, C. J., Gp, Fv, K., Yang, Fw, Göke, Ft, J., Zhang, Gq, Fr, Z., Brooks, Gr, A. 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G. k., Gao, Ez, Y., Lalansingh, Iv, Teague, C. M. k., Abp, J. W., Wendl, M. C. z., Fa, Fb, Abascal, Abp, F., Bader, G. D. l., Bandopadhayay, P. b., Iw, Ix, Barenboim, J. k., Brunak, Iy, S., Carlevaro-Fita, Iz, Ja, J., Jb, Jc, Chakravarty, Jd, D., Chan, Je, C. W. Y., Aa, Choi, Dw, J. K., Jf, Diamanti, Jg, K., Fink, Frigola, Jh, Gu, J., Gambacorti-Passerini, Ji, C., Garsed, D. W., Jj, Haradhvala, N. J. b., Harmanci, R, A. O., Bk, Helmy, Jk, Fm, M., Herrmann, Aa, C., Ac, Jl, Hobolth, Ev, A., Hodzic, Gv, Ex, E., Dv, C., Isaev, Dw, K. k., Q, Izarzugaza, J. M. G., Iy, Jb, R., Juul, Jm, R. I., Eu, Kim, J. b., J. K., Jn, Jan, Komorowskijg, Lanzós, Jo, Jb, A., Jc, Jm, Larsson, Dg, E., Lee, Bk, D., Bk, S., Bk, X., Lin, Z. b., Liu, Jp, E. M., Br, Bt, Jq, Lochovsky, Bj, L., Bk, Gb, Lou, Madsen, Bk, Eu, T., Marchal, Jr, K., Martinez-Fundichely, Js, Br, A., Bs, Bt, Mcgillivray, P. 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W., Mp, Vicentini, Pa, C., Mp, J., Zeps, Po, N., Behren, Pp, Pq, A., Burke, Pr, H., Cebon, Pq, J., Dagg, R. A., Ps, Paoli-Iseppi, De, Pt, R., Dutton-Regester, Field, M. A., Pu, Fitzgerald, Pv, A., Hersey, Pr, P., Jakrot, Pr, V., Johansson, P. A., Ma, Kakavand, Pt, H., Kefford, R. F., Pw, Lau, L. M. S., Px, Long, G. V., Py, Pickett, H. A., Px, Pritchard, A. L., Ma, Pupo, G. M., Pz, Saw, R. P. M., Py, Schramm, S. -J., Qa, Shang, C. A., Pv, Py, P., Spillane, A. J., Py, Stretch, J. R., Py, Tembe, Ot, V., Thompson, Qa, J. F., Py, Vilain, R. E., Qb, Wilmott, J. S., Py, J. Y., Qc, Hayward, N. K., Ma, Mann, Pr, G. J., Ot, Scolyer, Qd, R. A., Ou, Py, Qb, Bartlett, Qe, Qf, J., Bavi, Qg, Qh, P., Chadwick, D. E., Qi, Chan-Seng-Yue, Qh, M., Cleary, Qh, S., Connor, Qj, A. A., Qj, Czajka, Qk, If, K., Denroche, R. E., Qh, Dhani, N. C., Ql, Eagles, If, J., Gallinger, Qj, Qk, Grant, R. C., Qh, Hedley, Qk, Ql, D., Hollingsworth, M. A., Qm, Jang, G. H., Qh, Kalimuthu, S. -B., Qn, Lungu, Qh, I., Luo, Qo, Mbabaali, X. k., If, F., T. A., Qk, J. K., If, Moore, M. J., Ql, Notta, Qh, F., Pasternack, Qp, If, D., Petersen, G. M., Qq, Roehrl, M. H. A. q., Qh, Qr, Qs, Qt, Sam, If, M., Selander, Qk, I., Serra, Os, S., Shahabi, Qn, S., Thayer, S. P., Qm, Timms, L. E., If, Wilson, G. W. k., Wilson, Qh, J. M., Qh, Wouters, B. G., Qu, J. D., If, Qh, Qv, Beck, T. A. n., Bhandari, Qw, Collins, V. k., C. C., Eo, Fleshner, N. E., Qx, Fox, N. S. k., Fraser, M. k., Heisler, L. E., Qy, Lalonde, E. k., Livingstone, J. k., Meng, Qz, A., Sabelnykova, V. Y. k., Shiah, Y. -J. k., Van der Kwast, Ra, T., Bristow, R. G. q., Rb, Rc, Rd, Re, Ding, Rf, S., Rg, D., Fv, L., Nie, Rg, Y., Xiao, Rh, Xing, Hm, R., Yang, Ri, Rj, Y., Banks, R. E., Rk, Bourque, Rl, G., Brennan, Rm, Rn, P., Letourneau, Ro, L., Riazalhosseini, Rm, Y., Scelo, Rn, G., Vasudev, Rk, N., Viksna, Rp, Rq, J., Lathrop, Rm, M., Tost, Rr, J., Ahn, S. -M., Rs, Aparicio, Rt, S., Arnould, Ru, L., Aure, M. R., Rv, Bhosle, Abp, S. G., Birney, E. f., Borg, Rw, A., Boyault, Rx, S., Brinkman, A. B., Ry, Brock, J. E., Rz, Broeks, Sa, A., Børresen-Dale, A. -L., Rv, Caldas, Sb, C., Chin, Sc, S. -F., Sb, Davies, Sc, Mu, H., Abp, Mv, Desmedt, Sd, C., Dirix, Se, Sf, L., Dronov, Ehinger, Sg, A., Eyfjord, J. E., Sh, Fatima, Gt, A., Foekens, J. A., Si, Futreal, P. A., Sj, Garred, Sk, Ø., Giri, Sl, D. D., Sm, Glodzik, Abp, D., Grabau, Sn, D., Hilmarsdottir, Sh, H., Hooijer, G. K., So, Jacquemier, Sp, J., S. J., Sq, Jonasson, J. G., Sh, Jonkers, Sr, J., H. -Y., Sp, King, T. A., Ss, Knappskog, St, Su, S., Abp, Kong, Sp, G., Krishnamurthy, Sv, S., Lakhani, S. R., Sw, Langerød, Rv, A., Larsimont, Sx, D., H. J., Sq, J. -Y., Sy, M. T. M., Sj, Lingjærde, O. C., Sz, Macgrogan, Ta, G., Martens, J. W. M., Si, O’Meara, Pauporté, He, I., Pinder, Tb, S., Pivot, Tc, X., Provenzano, Td, E., Purdie, C. A., Te, Ramakrishna, Abp, M., Ramakrishnan, Abp, K., Reis-Filho, Sm, J., Richardson, A. L., Gt, Ringnér, Rw, M., Rodriguez, J. B., Am, Rodríguez-González, F. G., Iz, Romieu, Tf, G., Salgado, Os, R., Sauer, Sz, T., Shepherd, Abp, R., Sieuwerts, A. M., Si, Simpson, P. T., Sw, Smid, Si, M., Sotiriou, Span, P. N., Tg, Stefánsson, Ó. A., Th, Stenhouse, Ti, A., Stunnenberg, H. G., Fw, Sweep, Tj, Tk, F., Tan, B. K. T., Tl, Thomas, Tm, G., Thompson, A. M., Ti, Tommasi, Tn, S., Treilleux, To, I., Tutt, Tp, Ueno, N. T., Nv, Van, Laere, Sf, S., Van den Eynden, G. G., Sf, Vermeulen, Sf, P., Viari, Vincent-Salomon, Tj, A., Wong, B. H., Tq, Yates, Abp, X., Van, Deurzen, C. H. M., Tr, van de Vijver, M. J., Os, Van’T, Veer, Ts, L., Ammerpohl, Tt, O., Tu, Tv, Aukema, Tu, S., Tv, Tw, Bergmann, A. K., Tx, Bernhart, S. 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E., Ze, Ghossein, Zf, Sm, R., Giama, N. H., Zg, Gibbs, R. A., Ag, Gomez, Zh, C., Govindan, R. y., Hayes, D. N. w., Zi, Zj, Hegde, A. M., Ee, Heiman, Ef, Heins, D. I. b., Jq, Z., Hepperla, A. J. w., Holbrook, Ym, A., Holt, R. A., Yc, Hoyle, A. P. w., Hruban, R. H., Yv, Ag, J., Xg, M., Huntsman, Zk, D., Huse, Jq, J., Iacobuzio-Donahue, C. A., Sm, Ittmann, Zl, M., Jayaseelan, Zm, J. C., Ag, Jefferys, S. R. w., C. D., Zn, S. J. M., Zo, Juhl, Zp, H., Kang, K. J., Zq, Karlan, Zr, B., Kasaian, Zo, K., Kebebew, Zs, E., Kim, Zt, H. K., Zu, Korchina, Ag, V., Kundra, Wt, R., Lai, Yd, P. H., Ym, Lander, E. b., Zv, X., Levine, D. A., Jq, Lewis, Zw, Ag, L., Ley, Zx, T., H. I., Yc, Lin, P. b., Linehan, W. M., Zy, F. F., No, Ef, Y., Lype, Zz, L., Yc, Y., Maglinte, D. T., Ym, Aaa, Mardis, E. R. z., Yp, Aab, Marks, Ow, J., Aac, Marra, M. A., Yc, Matthew, T. J., Aj, Mayo, Mccune, Aad, K., Meier, S. R. b., Meng, S. w., Mieczkowski, P. A. v., Mikkelsen, Aae, T., Miller, C. A. z., Mills, Aaf, G. B., Morrison, Aag, Mose, Moser, L. E. w., C. D., Zg, Mungall, A. J., Yc, Yc, K., Mutch, Aah, D., Muzny, Aai, D. M., Myers, Aaj, J., Newton, Aj, Y., Noble, M. S. b., O’Donnell, Aak, P., Aal, B. P., Ochoa, Aam, J. -W., Parker, Aan, J. S., Pass, Aao, H., Pastore, Pennell, Aap, N. A., Perou, Aaq, C. M., Petrelli, Aar, N., Potapova, Aas, O., Rader, Aat, J. S., Ramalingam, Aau, S., Rathmell, Aav, W. K., Reuter, Sm, V., Reynolds, S. M., Zz, Ringel, Aaw, M., Roach, Aax, J., L. R., Zg, A. G., Yc, Sadeghi, Yc, S., Saller, Aay, C., Sanchez-Vega, Wt, F., Schadendorf, Yd, Eq, D., Aaz, Schein, J. E., Yc, Schmidt, H. K. z., Schultz, Yd, N., Seethala, Aba, R., Senbabaoglu, Dg, Y., Shelton, Yw, T., Shi, Y. w., Shih, J. b., Shmulevich, Fj, Zz, I., Shriver, Abb, C., Signoretti, Fj, S., Abc, Jb, Simons, J. V. w., Singer, Abd, Sipahimalani, Yc, P., Skelly, T. J. v., Smith-McCune, Socci, N. D., Dg, Soloway, Aan, M. G., Sood, Abe, A. K., Tam, Tan, D. v., Tarnuzzer, Hv, R., Thiessen, Abf, R. H., L. B., Xg, Tsao, Xe, M., Umbricht, Ye, Lk, C., Abg, Wv, Van Den Berg, D. J., Ym, Van, Meir, Abh, E. G., Veluvolu, U. v., Voet, D. b., Weinberger, Abi, P., Weisenberger, Wigle, Abj, D., Wilkerson, M. D. v., Wilson, R. K. z., Abk, Winterhoff, Abl, B., Wiznerowicz, Abm, M., Abn, Wong, T. z., Yc, Abo, W., Yau, Zhang, H. b., Yd, H., Hv, J., The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium View Correspondence (jump link), Medical Oncology, Pathology, IBM, Pharmacyclics, Novartis, Celgene, AstraZeneca, Bayer, Janssen Biotech, University of Chicago, Ipsen, Pfizer, Ono Pharmaceutical, Ariad Pharmaceuticals, Gilead Sciences, Bristol-Myers Squibb, University of St Andrews. School of Medicine, University of St Andrews. Statistics, University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews. Cellular Medicine Division, RS: CAPHRI - R4 - Health Inequities and Societal Participation, Metamedica, Háskóli Íslands, University of Iceland, Faculty of Medicine, University of Helsinki, Department of Medical and Clinical Genetics, Research Programs Unit, Lauri Antti Aaltonen / Principal Investigator, ATG - Applied Tumor Genomics, Helsinki Institute of Life Science HiLIFE, Organismal and Evolutionary Biology Research Programme, Helsinki Institute for Information Technology, Institute of Biotechnology, Bioinformatics, Department of Computer Science, STEMM - Stem Cells and Metabolism Research Program, Centre of Excellence in Stem Cell Metabolism, Genome-Scale Biology (GSB) Research Program, Department of Physics, HUS Helsinki and Uusimaa Hospital District, University of Zurich, ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Apollo - University of Cambridge Repository, Graduate School, Laboratory Genetic Metabolic Diseases, AGEM - Endocrinology, metabolism and nutrition, 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Hong, J, Hudson, T, Hubschmann, D, Ivkovic, S, Jeon, S, Jiao, W, Kabbe, R, Kerssemakers, J, Kim, J, Koscher, M, Koures, A, Kovacevic, M, Lawerenz, C, Liu, J, Mijalkovic, S, Mijalkovic-Lazic, A, Miyano, S, Nastic, M, Nicholson, J, Ocana, D, Ohi, K, Ohno-Machado, L, Pihl, T, Prinz, M, Radovic, P, Short, C, Sofia, H, Spring, J, Struck, A, Tijanic, N, Vicente, D, Wang, Z, Williams, A, Woo, Y, Wright, A, Yang, L, Hamilton, M, Johnson, T, Kahraman, A, Kellis, M, Polak, P, Sallari, R, Sinnott-Armstrong, N, von Mering, C, Beltran, S, Gerhard, D, Gut, M, Trotta, J, Whalley, J, Niu, B, Espiritu, S, Gao, S, Huang, Y, Teague, J, Abascal, F, Bader, G, Bandopadhayay, P, Barenboim, J, Brunak, S, Carlevaro-Fita, J, Chakravarty, D, Chan, C, Choi, J, Diamanti, K, Fink, J, Frigola, J, Gambacorti Passerini, C, Garsed, D, Haradhvala, N, Harmanci, A, Helmy, M, Herrmann, C, Hobolth, A, Hodzic, E, Hong, C, Isaev, K, Izarzugaza, J, Johnson, R, Juul, R, Jan, K, Lanzos, A, Larsson, E, Lee, D, Lin, Z, Liu, E, 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K, Bruxner, T, Christ, A, Cordner, S, Cowin, P, Drapkin, R, Fereday, S, George, J, Hamilton, A, Holmes, O, Hung, J, Kassahn, K, Kazakoff, S, Kennedy, C, Leonard, C, Mileshkin, L, Miller, D, Arnau, G, Mitchell, C, Newell, F, Nones, K, Patch, A, Quinn, M, Taylor, D, Thorne, H, Traficante, N, Vedururu, R, Waring, P, Wood, S, Xu, Q, Defazio, A, Anderson, M, Antonello, D, Barbour, A, Bassi, C, Bersani, S, Cataldo, I, Chantrill, L, Chiew, Y, Chou, A, Cingarlini, S, Cloonan, N, Corbo, V, Davi, M, Duthie, F, Gill, A, Graham, J, Harliwong, I, Jamieson, N, Johns, A, Kench, J, Landoni, L, Lawlor, R, Mafficini, A, Merrett, N, Miotto, M, Musgrove, E, Nagrial, A, Oien, K, Pajic, M, Pinese, M, Robertson, A, Rooman, I, Rusev, B, Samra, J, Scardoni, M, Scarlett, C, Scarpa, A, Sereni, E, Sikora, K, Simbolo, M, Taschuk, M, Toon, C, Vicentini, C, Wu, J, Zeps, N, Behren, A, Burke, H, Cebon, J, Dagg, R, De Paoli-Iseppi, R, Dutton-Regester, K, Field, M, Fitzgerald, A, Hersey, P, Jakrot, V, Johansson, P, Kakavand, H, Kefford, R, Lau, L, Long, G, Pickett, H, Pritchard, A, Pupo, G, Saw, R, Schramm, S, Shang, C, Shang, P, Spillane, A, Stretch, J, Tembe, V, Thompson, J, Vilain, R, Wilmott, J, Yang, J, Hayward, N, Mann, G, Scolyer, R, Bartlett, J, Bavi, P, Chadwick, D, Chan-Seng-Yue, M, Cleary, S, Connor, A, Czajka, K, Denroche, R, Dhani, N, Eagles, J, Gallinger, S, Grant, R, Hedley, D, Hollingsworth, M, Jang, G, Johns, J, Kalimuthu, S, Liang, S, Lungu, I, Luo, X, Mbabaali, F, Mcpherson, T, Miller, J, Moore, M, Notta, F, Pasternack, D, Petersen, G, Roehrl, M, Sam, M, Selander, I, Serra, S, Shahabi, S, Thayer, S, Timms, L, Wilson, G, Wilson, J, Wouters, B, Beck, T, Bhandari, V, Collins, C, Fleshner, N, Fox, N, Fraser, M, Heisler, L, Lalonde, E, Livingstone, J, Meng, A, Sabelnykova, V, Shiah, Y, Van der Kwast, T, Bristow, R, Ding, S, Fan, D, Li, L, Nie, Y, Xiao, X, Xing, R, Yang, S, Yu, Y, Zhou, Y, Banks, R, Bourque, G, Brennan, P, Letourneau, L, Riazalhosseini, Y, Scelo, G, Vasudev, N, Viksna, J, Lathrop, M, Tost, J, Ahn, S, Aparicio, S, Arnould, L, Aure, M, Bhosle, S, Birney, E, Borg, A, Boyault, S, Brinkman, A, Brock, J, Broeks, A, Borresen-Dale, A, Caldas, C, Chin, S, Davies, H, Desmedt, C, Dirix, L, Dronov, S, Ehinger, A, Eyfjord, J, Fatima, A, Foekens, J, Futreal, P, Garred, O, Giri, D, Glodzik, D, Grabau, D, Hilmarsdottir, H, Hooijer, G, Jacquemier, J, Jang, S, Jonasson, J, Jonkers, J, King, T, Knappskog, S, Kong, G, Krishnamurthy, S, Lakhani, S, Langerod, A, Larsimont, D, Lee, H, Lee, M, Lingjaerde, O, Macgrogan, G, Martens, J, O'Meara, S, Pauporte, I, Pinder, S, Pivot, X, Provenzano, E, Purdie, C, Ramakrishna, M, Ramakrishnan, K, Reis-Filho, J, Richardson, A, Ringner, M, Rodriguez, J, Rodriguez-Gonzalez, F, Romieu, G, Salgado, R, Sauer, T, Shepherd, R, Sieuwerts, A, Simpson, P, Smid, M, Sotiriou, C, Span, P, Stefansson, O, Stenhouse, A, Stunnenberg, H, Sweep, F, Tan, B, Thomas, G, Thompson, A, Tommasi, S, Treilleux, I, Tutt, A, Ueno, N, Van Laere, S, Van den Eynden, G, Vermeulen, P, Viari, A, Vincent-Salomon, A, Wong, B, Yates, L, Zou, X, van Deurzen, C, van de Vijver, M, van't Veer, L, Ammerpohl, O, Aukema, S, Bergmann, A, Bernhart, S, Borkhardt, A, Borst, C, Burkhardt, B, Claviez, A, Goebler, M, Haake, A, Haas, S, Hansmann, M, Hoell, J, Hummel, M, Karsch, D, Klapper, W, Kneba, M, Kreuz, M, Kube, D, Kuppers, R, Lenze, D, Loeffler, M, Lopez, C, Mantovani-Loffler, L, Moller, P, Ott, G, Radlwimmer, B, Richter, J, Rohde, M, Rosenstiel, P, Rosenwald, A, Schilhabel, M, Schreiber, S, Stadler, P, Staib, P, Stilgenbauer, S, Sungalee, S, Szczepanowski, M, Toprak, U, Trumper, L, Wagener, R, Zenz, T, Hovestadt, V, von Kalle, C, Kool, M, Korshunov, A, Landgraf, P, Lehrach, H, Northcott, P, Pfister, S, Reifenberger, G, Warnatz, H, Wolf, S, Yaspo, M, Assenov, Y, Gerhauser, C, Minner, S, Schlomm, T, Simon, R, Sauter, G, Sultmann, H, Biswas, N, Maitra, A, Majumder, P, Sarin, R, Barbi, S, Bonizzato, G, Cantu, C, Dei Tos, A, Fassan, M, Grimaldi, S, Luchini, C, Malleo, G, Marchegiani, G, Milella, M, Paiella, S, Pea, A, Pederzoli, P, Ruzzenente, A, Salvia, R, Sperandio, N, Arai, Y, Hama, N, Hiraoka, N, Hosoda, F, Nakamura, H, Ojima, H, Okusaka, T, Totoki, Y, Urushidate, T, Fukayama, M, Ishikawa, S, Katai, H, Katoh, H, Komura, D, Rokutan, H, Saito-Adachi, M, Suzuki, A, Taniguchi, H, Tatsuno, K, Ushiku, T, Yachida, S, Yamamoto, S, Aikata, H, Arihiro, K, Ariizumi, S, Chayama, K, Furuta, M, Gotoh, K, Hayami, S, Hirano, S, Kawakami, Y, Maejima, K, Nakamura, T, Nakano, K, Ohdan, H, Sasaki-Oku, A, Tanaka, H, Ueno, M, Yamamoto, M, Yamaue, H, Choo, S, Cutcutache, I, Khuntikeo, N, Ong, C, Pairojkul, C, Popescu, I, Ahn, K, Aymerich, M, Lopez-Guillermo, A, Lopez-Otin, C, Puente, X, Campo, E, Amary, F, Baumhoer, D, Behjati, S, Bjerkehagen, B, Myklebost, O, Pillay, N, Tarpey, P, Tirabosco, R, Zaikova, O, Flanagan, A, Boultwood, J, Bowen, D, Cazzola, M, Green, A, Hellstrom-Lindberg, E, Malcovati, L, Nangalia, J, Papaemmanuil, E, Vyas, P, Ang, Y, Barr, H, Beardsmore, D, Eldridge, M, Gossage, J, Grehan, N, Hanna, G, Hayes, S, Hupp, T, Khoo, D, Lagergren, J, Lovat, L, Macrae, S, O'Donovan, M, O'Neill, J, Parsons, S, Preston, S, Puig, S, Roques, T, Sanders, G, Sothi, S, Tavare, S, Tucker, O, Turkington, R, Underwood, T, Welch, I, Fitzgerald, R, Berney, D, De Bono, J, Cahill, D, Camacho, N, Dennis, N, Dudderidge, T, Edwards, S, Fisher, C, Foster, C, Ghori, M, Gill, P, Gnanapragasam, V, Gundem, G, Hamdy, F, Hawkins, S, Hazell, S, Howat, W, Isaacs, W, Karaszi, K, Kay, J, Khoo, V, Kote-Jarai, Z, Kremeyer, B, Kumar, P, Lambert, A, Leongamornlert, D, Livni, N, Luxton, H, Marsden, L, Massie, C, Matthews, L, Mayer, E, Mcdermott, U, Merson, S, Neal, D, Nicol, D, Ogden, C, Rowe, E, Shah, N, Thomas, S, Verrill, C, Visakorpi, T, Warren, A, Whitaker, H, Zhang, H, van As, N, Eeles, R, Abeshouse, A, Agrawal, N, Akbani, R, Al-Ahmadie, H, Albert, M, Aldape, K, Ally, A, Appelbaum, E, Armenia, J, Asa, S, Auman, J, Balasundaram, M, Balu, S, Barnholtz-Sloan, J, Bathe, O, Baylin, S, Benz, C, Berchuck, A, Berrios, M, Bigner, D, Birrer, M, Bodenheimer, T, Boice, L, Bootwalla, M, Bosenberg, M, Bowlby, R, Boyd, J, Broaddus, R, Brock, M, Brooks, D, Bullman, S, Caesar-Johnson, S, Carey, T, Carlsen, R, Cerfolio, R, Chandan, V, Chen, H, Cherniack, A, Chien, J, Cho, J, Chuah, E, Cibulskis, C, Cope, L, Cordes, M, Curley, E, Czerniak, B, Danilova, L, Davis, I, Defreitas, T, Demchok, J, Dhalla, N, Dhir, R, Doddapaneni, H, El-Naggar, A, Felau, I, Ferguson, M, Finocchiaro, G, Fong, K, Frazer, S, Friedman, W, Fronick, C, Fulton, L, Gabriel, S, Gao, J, Gehlenborg, N, Gershenwald, J, Ghossein, R, Giama, N, Gibbs, R, Gomez, C, Govindan, R, Hayes, D, Hegde, A, Heiman, D, Heins, Z, Hepperla, A, Holbrook, A, Holt, R, Hoyle, A, Hruban, R, Hu, J, Huntsman, D, Huse, J, Iacobuzio-Donahue, C, Ittmann, M, Jayaseelan, J, Jefferys, S, Jones, C, Jones, S, Juhl, H, Kang, K, Karlan, B, Kasaian, K, Kebebew, E, Korchina, V, Kundra, R, Lai, P, Lander, E, Le, X, Levine, D, Lewis, L, Ley, T, Li, H, Lin, P, Linehan, W, Lype, L, Ma, Y, Maglinte, D, Mardis, E, Marks, J, Marra, M, Matthew, T, Mayo, M, Mccune, K, Meier, S, Meng, S, Mieczkowski, P, Mikkelsen, T, Miller, C, Mills, G, Moore, R, Morrison, C, Mose, L, Moser, C, Mungall, A, Mungall, K, Mutch, D, Muzny, D, Myers, J, Newton, Y, Noble, M, O'Donnell, P, O'Neill, B, Ochoa, A, Parker, J, Pass, H, Pastore, A, Pennell, N, Perou, C, Petrelli, N, Potapova, O, Rader, J, Ramalingam, S, Rathmell, W, Reuter, V, Reynolds, S, Ringel, M, Roach, J, Roberts, L, Sadeghi, S, Saller, C, Sanchez-Vega, F, Schadendorf, D, Schein, J, Schmidt, H, Schultz, N, Seethala, R, Senbabaoglu, Y, Shelton, T, Shi, Y, Shih, J, Shmulevich, I, Shriver, C, Signoretti, S, Simons, J, Singer, S, Sipahimalani, P, Skelly, T, Smith-McCune, K, Socci, N, Soloway, M, Sood, A, Tam, A, Tan, D, Tarnuzzer, R, Thiessen, N, Thompson, R, Thorne, L, Tsao, M, Umbricht, C, Van Den Berg, D, Van Meir, E, Veluvolu, U, Voet, D, Wang, L, Weinberger, P, Weisenberger, D, Wigle, D, Wilkerson, M, Wilson, R, Winterhoff, B, Wiznerowicz, M, Wong, T, Wong, W, Xi, L, Yau, C, Consortium, ICGC/TCGA Pan-Cancer Analysis of Whole Genomes, Demeulemeester, Jonas, Desmedt, Christine, Van Loo, Peter, Barcelona Supercomputing Center, Imperial College Healthcare NHS Trust- BRC Funding, Cancer Research UK, Basic (bio-) Medical Sciences, and Laboratory for Medical and Molecular Oncology
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Male ,tert promoter mutations ,Cancer development and immune defence Radboud Institute for Molecular Life Sciences [Radboudumc 2] ,DNA Mutational Analysis ,Normal tissue ,systematic analysis ,Germline ,Transcriptome ,0302 clinical medicine ,Aetiology ,Càncer ,Cellular Senescence ,Cancer ,0303 health sciences ,dna-damage ,Massive parallel sequencing ,Pan cancer ,REARRANGEMENTS ,High-Throughput Nucleotide Sequencing ,Genomics ,Sciences bio-médicales et agricoles ,Telomere ,COMPREHENSIVE ,3. Good health ,TERT PROMOTER MUTATIONS ,signatures ,030220 oncology & carcinogenesis ,Science & Technology - Other Topics ,Erfðarannsóknir ,Human ,Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC] ,Evolution ,RNA Splicing ,Article ,Evolution, Molecular ,Structural variation ,RC0254 ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,Genetic ,genomics ,SYSTEMATIC ANALYSIS ,Genetics ,Genomics--Databases ,Humans ,Genetic Testing ,Molecular Biology ,SIGNATURES ,Whole genome sequencing ,1000 Multidisciplinary ,Chromothripsis ,Science & Technology ,RC0254 Neoplasms. Tumors. Oncology (including Cancer) ,Information Dissemination ,ResearchInstitutes_Networks_Beacons/mcrc ,Prevention ,Biology and Life Sciences ,Molecular ,Oncogenes ,Cloud Computing ,medicine.disease ,Genòmica ,Compute clouds ,Mutation ,570 Life sciences ,biology ,COMPREHENSIVE CHARACTERIZATION ,Genètica ,Whole Genome Sequencing--methods ,Background information ,Genetic / genetics ,Genome ,Germ-Line Mutation / genetics ,Human / genetics ,ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium ,Medizin ,Whole-genome ,Genome mapping ,Neoplasms ,2.1 Biological and endogenous factors ,Promoter Regions, Genetic ,Càncer -- Aspectes genètics ,Telomerase ,Women's cancers Radboud Institute for Molecular Life Sciences [Radboudumc 17] ,Multidisciplinary ,Manchester Cancer Research Centre ,genomics, cancer, profiling ,3rd-DAS ,10124 Institute of Molecular Life Sciences ,Women's cancers Radboud Institute for Health Sciences [Radboudumc 17] ,Multidisciplinary Sciences ,Parallel sequencing ,Female ,profiling ,Medical Genetics ,Engineering sciences. Technology ,Biotechnology ,General Science & Technology ,The Cancer Genome Atlas ,610 Medicine & health ,Computational biology ,QH426 Genetics ,Biology ,Consortium of the International Cancer Genome Consortium ,Promoter Regions ,Germline mutation ,Pan-cancer analysis ,Krabbameinsrannsóknir ,medicine ,cancer ,ddc:610 ,QH426 ,Germ-Line Mutation ,Medicinsk genetik ,Krabbamein ,030304 developmental biology ,Cell Proliferation ,LANDSCAPE ,Genome, Human ,comprehensive characterization ,Pan-cancer analysis of whole genomes ,Point mutation ,Human Genome ,Reproducibility of Results ,SOMATIC MUTATIONS ,EVOLUTION ,Cancer, sequencing, Chromothripsis, telomere ,DNA-DAMAGE ,Mutagenesis ,PATTERNS ,3111 Biomedicine ,CHARACTERIZATION - Abstract
Publisher's version (útgefin grein), Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18., Competing interests Gad Getz receives research funds from IBM and Pharmacyclics and is an inventor on patent applications related to MuTect, ABSOLUTE, MutSig, MSMuTect, MSMutSig and POLYSOLVER. Hikmat Al-Ahmadie is consultant for AstraZeneca and Bristol-Myers Squibb. Samuel Aparicio is a founder and shareholder of Contextual Genomics. Pratiti Bandopadhayay receives grant funding from Novartis for an unrelated project. Rameen Beroukhim owns equity in Ampressa Therapeutics. Andrew Biankin receives grant funding from Celgene, AstraZeneca and is a consultant for or on advisory boards of AstraZeneca, Celgene, Elstar Therapeutics, Clovis Oncology and Roche. Ewan Birney is a consultant for Oxford Nanopore, Dovetail and GSK. Marcus Bosenberg is a consultant for Eli Lilly. Atul Butte is a cofounder of and consultant for Personalis, NuMedii, a consultant for Samsung, Geisinger Health, Mango Tree Corporation, Regenstrief Institute and in the recent past a consultant for 10x Genomics and Helix, a shareholder in Personalis, a minor shareholder in Apple, Twitter, Facebook, Google, Microsoft, Sarepta, 10x Genomics, Amazon, Biogen, CVS, Illumina, Snap and Sutro and has received honoraria and travel reimbursement for invited talks from Genentech, Roche, Pfizer, Optum, AbbVie and many academic institutions and health systems. Carlos Caldas has served on the Scientific Advisory Board of Illumina. Lorraine Chantrill acted on an advisory board for AMGEN Australia in the past 2 years. Andrew D. Cherniack receives research funding from Bayer. Helen Davies is an inventor on a number of patent applications that encompass the use of mutational signatures. Francisco De La Vega was employed at Annai Systems during part of the project. Ronny Drapkin serves on the scientific advisory board of Repare Therapeutics and Siamab Therapeutics. Rosalind Eeles has received an honorarium for the GU-ASCO meeting in San Francisco in January 2016 as a speaker, a honorarium and support from Janssen for the RMH FR meeting in November 2017 as a speaker (title: genetics and prostate cancer), a honorarium for an University of Chicago invited talk in May 2018 as speaker and an educational honorarium paid by Bayer & Ipsen to attend GU Connect ‘Treatment sequencing for mCRPC patients within the changing landscape of mHSPC’ at a venue at ESMO, Barcelona, on 28 September 2019. Paul Flicek is a member of the scientific advisory boards of Fabric Genomics and Eagle Genomics. Ronald Ghossein is a consultant for Veracyte. Dominik Glodzik is an inventor on a number of patent applications that encompass the use of mutational signatures. Eoghan Harrington is a full-time employee of Oxford Nanopore Technologies and is a stock holder. Yann Joly is responsible for the Data Access Compliance Office (DACO) of ICGC 2009-2018. Sissel Juul is a full-time employee of Oxford Nanopore Technologies and is a stock holder. Vincent Khoo has received personal fees and non-financial support from Accuray, Astellas, Bayer, Boston Scientific and Janssen. Stian Knappskog is a coprincipal investigator on a clinical trial that receives research funding from AstraZeneca and Pfizer. Ignaty Leshchiner is a consultant for PACT Pharma. Carlos López-Otín has ownership interest (including stock and patents) in DREAMgenics. Matthew Meyerson is a scientific advisory board chair of, and consultant for, OrigiMed, has obtained research funding from Bayer and Ono Pharma and receives patent royalties from LabCorp. Serena Nik-Zainal is an inventor on a number of patent applications that encompass the use of mutational signatures. Nathan Pennell has done consulting work with Merck, Astrazeneca, Eli Lilly and Bristol-Myers Squibb. Xose S. Puente has ownership interest (including stock and patents in DREAMgenics. Benjamin J. Raphael is a consultant for and has ownership interest (including stock and patents) in Medley Genomics. Jorge Reis-Filho is a consultant for Goldman Sachs and REPARE Therapeutics, member of the scientific advisory board of Volition RX and Paige.AI and an ad hoc member of the scientific advisory board of Ventana Medical Systems, Roche Tissue Diagnostics, InVicro, Roche, Genentech and Novartis. Lewis R. Roberts has received grant support from ARIAD Pharmaceuticals, Bayer, BTG International, Exact Sciences, Gilead Sciences, Glycotest, RedHill Biopharma, Target PharmaSolutions and Wako Diagnostics and has provided advisory services to Bayer, Exact Sciences, Gilead Sciences, GRAIL, QED Therapeutics and TAVEC Pharmaceuticals. Richard A. Scolyer has received fees for professional services from Merck Sharp & Dohme, GlaxoSmithKline Australia, Bristol-Myers Squibb, Dermpedia, Novartis Pharmaceuticals Australia, Myriad, NeraCare GmbH and Amgen. Tal Shmaya is employed at Annai Systems. Reiner Siebert has received speaker honoraria from Roche and AstraZeneca. Sabina Signoretti is a consultant for Bristol-Myers Squibb, AstraZeneca, Merck, AACR and NCI and has received funding from Bristol-Myers Squibb, AstraZeneca, Exelixis and royalties from Biogenex. Jared Simpson has received research funding and travel support from Oxford Nanopore Technologies. Anil K. Sood is a consultant for Merck and Kiyatec, has received research funding from M-Trap and is a shareholder in BioPath. Simon Tavaré is on the scientific advisory board of Ipsen and a consultant for Kallyope. John F. Thompson has received honoraria and travel support for attending advisory board meetings of GlaxoSmithKline and Provectus and has received honoraria for participation in advisory boards for MSD Australia and BMS Australia. Daniel Turner is a full-time employee of Oxford Nanopore Technologies and is a stock holder. Naveen Vasudev has received speaker honoraria and/or consultancy fees from Bristol-Myers Squibb, Pfizer, EUSA pharma, MSD and Novartis. Jeremiah A. Wala is a consultant for Nference. Daniel J. Weisenberger is a consultant for Zymo Research. Dai-Ying Wu is employed at Annai Systems. Cheng-Zhong Zhang is a cofounder and equity holder of Pillar Biosciences, a for-profit company that specializes in the development of targeted sequencing assays. The other authors declare no competing interests.
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- 2020
19. Simulation of rock cutting mechanism and characterization of failure mode in PFC3D
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Su, O, primary
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- 2013
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20. Understanding the Space-Charge Layer in SnO2for Enhanced Electron Extraction in Hybrid Perovskite Solar Cells
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Youn, Sarah Su-O, Kim, Jihyun, Na, Junhong, Jo, William, and Kim, Gee Yeong
- Abstract
Tin oxide (SnO2) has been widely used as an n-type metal oxide electron transport layer in perovskite solar cells (PSCs) owing to its superior electrical and optical properties and low-temperature synthesis process. In particular, the interfacial effect between indium tin oxide (ITO) and SnO2is an important parameter that controls the charge transport properties and device performance of the PSCs. Therefore, understanding the interfacial effect of ITO/SnO2and its role in PSCs is crucial, but it is not studied intensively. Herein, we investigated the space-charge effect at the interface of ITO/SnO2using transfer length measurement and conductive atomic force microscopy as a function of SnO2thickness. Moreover, optical, morphologic, and device measurements were performed to determine the optimal SnO2thickness for PSCs. The space-charge effect was identified in ITO/SnO2when the SnO2layer was very thin due to electron depletion near the interface. Interestingly, a critical kink point was observed at approximately 10 nm SnO2thickness, indicating the electron depletion and weak charge transfer behavior of the device. Thus, a thickness around 20 nm was favorable for the best PSC performance because charge transport behavior in the thin SnO2layer was depressed by electron depletion. However, when the thickness of SnO2exceeded 50 nm, the device performance deteriorated due to increased series resistance. This study provides a strategy to tune the electron transport layer and boost the charge transport behavior in PSCs, making important contributions to optimizing SnO2-based PSCs.
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- 2022
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21. Inside Japan's North Korean community
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Lee, su-o
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Regional focus/area studies - Abstract
The views of Lee su-o about abduction of Japanese civilians by North Korean operatives are discussed.
- Published
- 2003
22. Innovative Technique for Evacuating Side Branch in Bifurcation Lesion
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Ramazan Gündüz, Bekir Serhat Yıldız, and Su Özgür
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coronary bifurcation thrombus aspiration catheter acute coronary syndrome ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Treatment of bifurcation lesions changes according to lesion characteristics and the patient’s clinical diagnosis, including acute or chronic coronary syndrome. Treatment of bifurcation lesions in patients with acute coronary syndrome (ACS) is more difficult. We presented an innovative treatment for a bifurcation lesion in a patient with ACS.
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- 2023
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23. Application of deep learning technique in next generation sequence experiments
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Su Özgür and Mehmet Orman
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Next generation sequencing ,Deep learning ,Machine learning ,Variant calling format ,Cloud computing ,Computer engineering. Computer hardware ,TK7885-7895 ,Information technology ,T58.5-58.64 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract In recent years, the widespread utilization of biological data processing technology has been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has become an integral component of biological research. NGS technologies enable the sequencing of billions of nucleotides in the entire genome, transcriptome, or specific target regions. This sequencing generates vast data matrices. Consequently, there is a growing demand for deep learning (DL) approaches, which employ multilayer artificial neural networks and systems capable of extracting meaningful information from these extensive data structures. In this study, the aim was to obtain optimized parameters and assess the prediction performance of deep learning and machine learning (ML) algorithms for binary classification in real and simulated whole genome data using a cloud-based system. The ART-simulated data and paired-end NGS (whole genome) data of Ch22, which includes ethnicity information, were evaluated using XGBoost, LightGBM, and DL algorithms. When the learning rate was set to 0.01 and 0.001, and the epoch values were updated to 500, 1000, and 2000 in the deep learning model for the ART simulated dataset, the median accuracy values of the ART models were as follows: 0.6320, 0.6800, and 0.7340 for epoch 0.01; and 0.6920, 0.7220, and 0.8020 for epoch 0.001, respectively. In comparison, the median accuracy values of the XGBoost and LightGBM models were 0.6990 and 0.6250 respectively. When the same process is repeated for Chr 22, the results are as follows: the median accuracy values of the DL models were 0.5290, 0.5420 and 0.5820 for epoch 0.01; and 0.5510, 0.5830 and 0.6040 for epoch 0.001, respectively. Additionally, the median accuracy values of the XGBoost and LightGBM models were 0.5760 and 0.5250, respectively. While the best classification estimates were obtained at 2000 epochs and a learning rate (LR) value of 0.001 for both real and simulated data, the XGBoost algorithm showed higher performance when the epoch value was 500 and the LR was 0.01. When dealing with class imbalance, the DL algorithm yielded similar and high Recall and Precision values. Conclusively, this study serves as a timely resource for genomic scientists, providing guidance on why, when, and how to effectively utilize deep learning/machine learning methods for the analysis of human genomic data.
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- 2023
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24. In vitro Antimicrobial Susceptibility of Urinary Tract Infection Pathogens in Children
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Sevgin Taner, Sabire Şöhret Aydemir, Su Özgür, Ezgi Aksoy, Ahmet Keskinoğlu, Alper Tünger, Caner Kabasakal, and İpek Kaplan Bulut
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antibiotic resistance ,antimicrobial resistance ,antimicrobial susceptibility ,e. coli ,paediatric ,urinary tract infections ,urine culture ,Medicine ,Pediatrics ,RJ1-570 - Abstract
Aim:Urinary tract infection (UTI) is one of the most common bacterial infections in children. Empirical treatment is commenced according to the patient’s characteristics and the antimicrobial susceptibility patterns in the region. Therefore, a determination of antimicrobial resistance patterns has a great importance in effective treatment. The aim of this study was to determine the pathogens which cause UTIs in patients admitted to a university hospital in Izmir and to determine their antimicrobial susceptibility pattern.Materials and Methods:The files of patients aged between 0-18 years, followed up with a diagnosis of UTI, vesicoureteral reflux and neurogenic bladder in Ege University Faculty of Medicine Paediatric Nephrology Unit between February, 2013 and November, 2018 were retrospectively reviewed.Results:A total of 1,126 positive urine cultures from 729 patients (65% female) were included in this study. Gram-negative pathogens constituted 88.2% of the cultures. Escherichia coli (E. coli) was the most commonly isolated bacteria with a prevalence of 59.1%, followed by Klebsiella pneumonia with 17.9%, and Enterococcus faecalis with 8.3% (n=93). Ampicillin, cefuroxime and trimethoprim-sulfamethoxazole with susceptibility rates of 18.6%, 39.6%, 49.0% respectively, constituted the highest resistant antimicrobials to Enterobacteriaceae. Enterococcus spp. showed the highest resistance to gentamycin with 50% resistance in tested cases. Pseudomonas spp. with 64.3% susceptibility showed the highest resistance to piperacillin-tazobactam.Conclusion:This study revealed that bacterial resistance to commonly used antimicrobials in UTI is an important and challenging problem which requires planning.
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- 2023
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25. Psychometric properties and factor structure of the diabetes eatıng problem survey- revised (DEPS-R) among adults with type 1 diabetes mellitus
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Yasemin Atik-Altınok, Beyza Eliuz-Tipici, Cemile İdiz, Su Özgür, Ayşe Merve Ok, and Kubilay Karşıdağ
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DEPS-R ,Distributed eating behaviors ,Type 1 diabetes mellitus ,Adults ,Nutritional diseases. Deficiency diseases ,RC620-627 - Abstract
Abstract Background and objective Although many studies on the Diabetes Eating Problem Survey–Revised (DEPS-R) in adolescents with type 1 diabetes mellitus (T1D), the number of studies validating this questionnaire in adults with T1D is limited. Therefore, this study aimed to examine the factor structure of the Turkish version of the DEPS-R in adults with T1D and internal consistency and construct validity. Methods A total of 100 patients with T1D, ages 18–50 years, completed the DEPS-R and EDE-Q. In addition to tests of validity, confirmatory factor analysis was conducted to investigate the factor structure of the 6-item Turkish version of DEPS-R. Results The Cronbach’s alpha coefficient of the DEPS-R Turkish version was 0.77, suggesting good internal consistency. The median (IQ) DEPS-R score was 15.0 (13.0) among all participants. DEPS-R score was significantly correlated with BMI (r = 0.210; p
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- 2023
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26. Circumventing the Menace of Seed Dormancy in Dormant Seeds of Parkia biglobosa and Prosopis africana
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Surukite O, O., primary, Clement Su, O., additional, Opeyemi Oy, S., additional, Ogun, L. Mautin, additional, and Oluwamayow, N., additional
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- 2019
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27. Proposal of some cuttability indexes for evaluating the performance of mechanical excavators using conical picks
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Su O., Wang X., and Zonguldak Bülent Ecevit Üniversitesi
- Abstract
2019 Rapid Excavation and Tunneling Conference, RETC 2019 -- 16 June 2019 through 19 June 2019 -- -- 149573, In this study, rock cuttability indexes are evaluated based on peak cutting force, specific energy and rock strength. For this purpose, a number of samples were collected from different parts of China and the mechanical properties of the samples were initially determined. Then, a series of cutting tests in unrelieved cutting mode were conducted at the linear rock cutting rig. The tests were performed at the cutting depths ranging from 3 mm to 18 mm. As a result of the tests, the effect of a significant cuttability index, which is the ratio of cutting force to cutting depth (FC/d), on the uniaxial compressive strength was investigated. Based on the acquired data from linear cutting test rig, the correlations between the ratio of FC/?c and cutting depth and also between the ratio of (SE/?C) and cutting depth were examined and their validity to be as new cuttability indexes were checked. High correlation coefficients among the cutting variables verified that the new cuttability index values can be successfully used for designing the cutterheads or the drums of mechanical excavators as well as evaluating their cutting efficiency. © 2019 Society for Mining, Metallurgy and Exploration. All rights reserved.
- Published
- 2019
28. Prognostic and predictive role of liquid biopsy in lung cancer patients
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Tuncay Goksel, Su Özgür, Aslı Tetik Vardarlı, Altuğ Koç, Haydar Soydaner Karakuş, Taha Reşid Özdemir, Kadri Murat Erdoğan, Ceyda Aldağ, Ali Veral, Berna Komurcuoglu, Pınar Gursoy, Mehmet Emin Arayici, Asim Leblebici, Türkan Yiğitbaşı, Hülya Ellidokuz, and Yasemin Basbinar
- Subjects
lung cancer ,liquid biopsy ,mutation ,EGFR ,real time PCR ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
IntroductionLung cancer (LC) is a leading cause of cancer-related mortality worldwide. Approximately 80% of LC cases are of the non-small cell lung cancer (NSCLC) type, and approximately two-thirds of these cases are diagnosed in advanced stages. Only systemic treatment methods can be applied to patients in the advanced stages when there is no chance of surgical treatment. Identification of mutations that cause LC is of vital importance in determining appropriate treatment methods. New noninvasive methods are needed to repeat and monitor these molecular analyses. In this regard, liquid biopsy (LB) is the most promising method. This study aimed to determine the effectiveness of LB in detecting EGFR executive gene mutations that cause LC.MethodsOne hundred forty-six patients in stages IIIB and IV diagnosed with non-squamous cell non-small cell LC were included. Liquid biopsy was performed as a routine procedure in cases where no mutation was detected in solid tissue or in cases with progression after targeted therapy. Liquid biopsy samples were also obtained for the second time from 10 patients who showed progression under the applied treatment. Mutation analyses were performed using the Cobas® EGFR Test, a real-time PCR test designed to detect mutations in exons 18, 20, and 21 and changes in exon 19 of the EGFR gene.ResultsMutation positivity in paraffin blocks was 21.9%, whereas it was 32.2% in LB. Solids and LB were compatible in 16 patients. Additionally, while no mutation was found in solid tissue in the evaluation of 27 cases, it was detected in LB. It has been observed that new mutations can be detected not only at the time of diagnosis, but also in LB samples taken during the follow-up period, leading to the determination of targeted therapy.DiscussionThe results showed that “liquid biopsy” is a successful and alternative non-invasive method for detecting cancer-causing executive mutations, given the limitations of conventional biopsies.
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- 2024
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29. Tool forces and specific energy prediction models in the process of sandstones cutting by using conical picks
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Wang X., Wang Q.-F., Su O., and Zonguldak Bülent Ecevit Üniversitesi
- Subjects
Specific energy ,Rock cutting ,Tool forces ,Conical pick ,Regression analysis - Abstract
In this study, unrelieved rock cutting experiments were conducted at the linear rock cutting machine and the characteristics of tool forces were discussed. The correlations among tool forces, specific energy, cutting depth, and rock strength were analyzed using single factor regression analysis method. Based on multiple non-linear regression method, the models of tool forces and specific energy were developed considering the rock strength and cutting depth. The results indicate that models of tool forces have the superior performance. When the model of specific energy is analyzed using the compressive strength of the rock, it was seen that the correlations are weak compared to the model related to tensile strength of rock. In conclusion, it is emphasized that the proposed models presented in this study are particularly recommended for performance prediction of soft and medium-hard strength sandstones in case conical picks are employed. © 2018 Union of Chambers of Engineers and Architects of Turkey. All Rights Reserved., UQaPAbT?bSA??UccAA]? ? WTPSaX?Sdb?caT]VcW?bP]SbcA?]Tb%? [X]TPa?aTVaTbbXA]??TcWAS%?IWT??PX]? R&A-0]1R7[d.(bXA(]/b(?0The 54 study is supported by the Open Fund of RP]?QSTa? Pf]?Pb?UAfive [[A?diffb1ferent sandstones under different levels of Chongqing Key Laboratory of Manufacturing cutting depths in unrelieved cutting IWT?modes. bcdShThe ? Equipment Xb? bdBBAaMechanism cTS?Design Qh? cand WT?Control DBT]?(Grant ;d]S? AU? effects of cutting depth on cutting and normal No. KFJJ2016032), the Chongqing science and (%:?gBA]T]cXP?U[d]RcXforces AfP]?b ? Uand Ad]?SUspecific Aa?Tgenergy BaTbbwere X?]V? 8cdiscussed WWAT ]VCX]V?in technology @Th? APQAainnovation PcAaleading h? AUtalent ?support BP]dUplan PRcdaX]V? aT[PcXA]bWXBb? QdTectafTil.T ]S? omRed cecmX]pVir ica?l m]A? oPad]?eSl?sP [w?e:rCUeA ddXaeBRveT?lbopTe]cd? BTRWP]Xb?? 9TbXV]? P]S? ?
- Published
- 2018
30. Effects of Dietary Carbohydrate Concentration and Glycemic Index on Blood Glucose Variability and Free Fatty Acids in Individuals with Type 1 Diabetes
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Selda Seckiner, Murat Bas, Ilgin Yildirim Simsir, Su Ozgur, Yasemin Akcay, Cigdem Gozde Aslan, Ozge Kucukerdonmez, and Sevki Cetinkalp
- Subjects
carbohydrate ,free fatty acid ,glycemic index ,glycemic load ,glycemic variability ,type 1 diabetes ,Nutrition. Foods and food supply ,TX341-641 - Abstract
Monitoring glycemic control status is the cornerstone of diabetes management. This study aimed to reveal whether moderate-carbohydrate (CHO) diets increase the risk of free fatty acid (FFA) levels, and it presents the short-term effects of four different diet models on blood sugar, glycemic variability (GV), and FFA levels. This crossover study included 17 patients with type 1 diabetes mellitus to identify the effects of four diets with different CHO contents and glycemic index (GI) on GV and plasma FFA levels. Diet 1 (D1) contained 40% CHO with a low GI, diet 2 (D2) contained 40% CHO with a high GI, diet 3 (D3) contained 60% CHO with a low GI, and diet 4 (D4) contained 60% CHO with a high GI. Interventions were performed with sensor monitoring in four-day periods and completed in four weeks. No statistical difference was observed among the groups in terms of blood glucose area under the curve (p = 0.78), mean blood glucose levels (p = 0.28), GV (p = 0.59), and time in range (p = 0.567). FFA and total triglyceride levels were higher in the D1 group (p < 0.014 and p = 0.002, respectively). Different diets may increase the risk of cardiovascular diseases by affecting GI, FFA, and blood glucose levels.
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- 2024
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31. Verrucous Bowen-s disease on groin with dermoscopic and siascopic images
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CENGIZ, F. P., EMIROGLU, NAZAN, Su, O., ONSUN, NAHİDE, and EMİROĞLU, Nazan
- Subjects
CENGIZ F. P. , EMIROGLU N., Su O., ONSUN N., -Verrucous Bowen-s disease on groin with dermoscopic and siascopic images-, JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY, cilt.31, ss.78, 2017 - Published
- 2017
32. Correlation of Clinical, Dermoscopical and histopathological features of all subtypes of basal cell carcinoma
- Author
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DIZMAN, DİDEM, OZKAYA, D. B., BAHALI, A. G., DIZMAN, B. T., YILDIZ, PELİN, TOSUNER, ZEYNEP, Demirkesen, C., ONSUN, NAHİDE, SU, O., ONSUN, Nahide, and YILDIZ, PELİN
- Subjects
DIZMAN D., OZKAYA D. B. , BAHALI A. G. , DIZMAN B. T. , YILDIZ P., TOSUNER Z., Demirkesen C., ONSUN N., SU O., -Correlation of clinical, dermoscopical and histopathological features of all subtypes of basal cell carcinoma-, JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY, cilt.31, ss.79-80, 2017 ,DİZMAN D., BIYIK ÖZKAYA D., BAHALI A. G. , TOPUKÇU B., YILDIZ P., TOSUNER Z., DEMİRKESEN C., ONSUN N., SU KÜÇÜK Ö. S. , -Correlation of Clinical, Dermoscopical and histopathological features of all subtypes of basal cell carcinoma-, 13th Congress of European Association of Dermato Oncology, 3 - 06 Mayıs 2017 - Published
- 2017
33. Gabapentin-induced aquagenic wrinkling of the palms
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Emiroglu N, Cengiz FP, Su O, and Onsun N.
- Published
- 2017
34. Left ventricular lead delivery system used to implant right ventricular lead via persistent left superior vena cava
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Bekir Serhat Yildiz, Ramazan Gündüz, and Su Ozgur
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coronary sinus sheath ,pacemaker implantation ,persistent left superior vena cava ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Published
- 2023
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35. Conversion of specific lncRNAs to biomarkers in exhaled breath condensate samples of patients with advanced stage non-small-cell lung cancer
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Aslı Tetik Vardarlı, Su Ozgur, Tuncay Goksel, Korcan Korba, Hardar Soydaner Karakus, Aycan Asık, Levent Pelit, and Cumhur Gunduz
- Subjects
lung cancer ,cfRNA ,lncRNA ,ebc ,biomarker ,Genetics ,QH426-470 - Abstract
Objectives: Lung cancer (LC) is one of the most prevalent cancers with the highest fatality rate worldwide. Long noncoding RNAs (lncRNAs) are being considered potential new molecular targets for early diagnosis, follow-up, and individual treatment decisions in LC. Therefore, this study evaluated whether lncRNA expression levels obtained from exhaled breath condensate (EBC) samples play a role in the occurrence of metastasis in the diagnosis and follow-up of patients with advanced lung adenocarcinoma (LA).Methods: A total of 40 patients with advanced primary LA and 20 healthy controls participated in the study. EBC samples were collected from patients (during diagnosis and follow-up) and healthy individuals for molecular analysis. Liquid biopsy samples were also randomly obtained from 10 patients with LA and 10 healthy people. The expression of lncRNA genes, such as MALAT1, HOTAIR, PVT1, NEAT1, ANRIL, and SPRY4-IT1 was analyzed using cfRNA extracted from all clinical samples.Results: In the diagnosis and follow-up of patients with LA, lncRNA HOTAIR (5-fold), PVT1 (7.9-fold), and NEAT1 (12.8-fold), PVT1 (6.8-fold), MALAT1 (8.4-fold) expression levels were significantly higher than those in healthy controls, respectively. Additionally, the distinct lncRNA expression profiles identified in EBC samples imply that decreased ANRIL–NEAT1 and increased ANRIL gene expression levels can be used as biomarkers to predict the development of bone and lung metastases, respectively.Conclusion: EBC is an innovative, easily reproducible approach for predicting the development of metastases, molecular diagnosis, and follow-up of LC. EBC has shown potential in elucidating the molecular structure of LC, monitoring changes, and discovering novel biomarkers.
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- 2023
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36. Frequency and risk factors for secondary malignancies in patients with mycosis fungoides
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EMIROGLU, NAZAN, OZKAYA, D. Biyik, BAHALI, A. Gulsel, CENGIZ, F. P., Su, O., ONSUN, NAHİDE, and CENGİZ, Fatma Pelin
- Subjects
hemic and lymphatic diseases ,CENGIZ F. P. , EMIROGLU N., OZKAYA D. B. , BAHALI A. G. , Su O., ONSUN N., -Frequency and risk factors for secondary malignancies in patients with mycosis fungoides-, MELANOMA RESEARCH, cilt.26, 2016 - Abstract
Mycosis fungoides (MF), the most common form of cutaneous T-cell lymphoma (CTCL), has an incidence of 6.4 per million people [1]. Patients with CTCL have an increased risk of the development of secondary malignancies, particularly lymphomas [2,3]. We conducted a 20-year population-based cohort study to assess the risk factors of secondary cancers in MF patients from our center.
- Published
- 2016
37. Prognostic factors of patients with mycosis fungoides
- Author
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BAHALI, A. G., Su, O., CENGIZ, F. P., EMIROGLU, NAZAN, OZKAYA, D. Biyik, ONSUN, NAHİDE, SU KÜÇÜK, ÖZLEM, EMİROĞLU, NAZAN, and ONSUN, NAHIDE
- Published
- 2016
38. Autophagy facilitates an IFN-γ response and signal transduction
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Pui Ching Choi, Cheng Chieh Tsai, Chi Yun Wang, Chiou Feng Lin, Su O. Chen, Chia Yuan Hsieh, Wei Ching Huang, Chia Ling Chen, Yu Ping Chang, and Yee Shin Lin
- Subjects
Inflammation ,Immunology ,Autophagy ,Immune escape ,Biology ,Communicable Diseases ,Microbiology ,Cell biology ,Interferon-gamma ,Infectious Diseases ,medicine ,medicine.symptom ,Signal transduction ,Intracellular ,Immune Evasion ,Signal Transduction - Abstract
Autophagy, that is directly triggered by invaded pathogens and indirectly triggered by IFN-γ, acts as a defense by mediating intracellular microbial recognition and clearance. In addition, autophagy contributes to inflammation by facilitating an IFN-γ response and signal transduction. For immune escape, downregulated autophagy may be a strategy used by microbes.
- Published
- 2011
39. Penisola iberica e colonie americane: una relazione 'eccezionale'. Il caso brasiliano
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Alfredo Sorrini è dottorando di ricerca in Iberistica con un progetto dedicato alla Morfologia di una forma breve e al genere della 'estória' nelle letterature brasiliana e mozambicana and è, inoltre, professore della scuola secondaria di primo e secondo grado. Ha coordinato un interscambio educativo fra scuole italiane e mozambicane. Ha svolto il ruolo di docente esterno presso la Faculdade Metropolitana de Belo Horizonte, tenendo un ciclo di lezioni su ―O Modernismo‖. Attualmente sta curando la traduzione – dal portoghese all‘italiano – di due volumi dell‘autore mozambicano João Paulo Borges Coelho, Indicios I e II (Meridião e Setentrião).
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postcolonial identity ,Portuguese colonialism ,Brasile ,lcsh:D204-475 ,letteratura brasiliana ,Machado de Assis ,colonialismo portoghese ,identità postcoloniale ,lcsh:History (General) ,Brazilian literature ,lcsh:D1-2009 ,Brazil ,lcsh:Modern history, 1453 - Abstract
Il Portogallo è una colonia informale dell’Inghilterra che si comporta da colonizzatore, un paese Prospero nelle sue colonie e Calibano in Europa. Il problema di rappresentazione, che ne consegue, si ripercuote “doppiamente” sul colonizzato, perché se da una parte il Portogallo ha un problema di autorappresentazione rispetto ad una storia coloniale scritta in inglese, per l’ ex colonia portoghese il problema di autorappresentazione diventa duplice, prima nei confronti del colonizzatore diretto e poi di chi ha scritto la storia del suo assoggettamento. La lettura di questa relazione “eccezionale” ci è fornita essenzialmente dall’opera di scrittori come Machado de Assis, le cui “narrazioni” consentono di scarnificare il desiderio coagulato intorno al dispositivo giuridico imperiale e l’ambiguità di una norma applicativa concepita, essenzialmente, per giustificare l’atto coloniale.
- Published
- 2011
40. Effort Rupture of Esophagus: One Case.
- Author
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Un Song, Hyon-Jong Kim, Yong-Jin Ryu, Ki-Yong Ri, Song-Rim Pak, and Kyong-Su O.
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- 2023
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41. Enterovirus 71 Proteins 2A and 3D Antagonize the Antiviral Activity of Gamma Interferon via Signaling Attenuation
- Author
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Po Chun Tseng, Li Chiu Wang, Su O. Chen, Yi Ping Lee, Shih Ping Chang, Chiou Feng Lin, Shun Hua Chen, Chia Yuan Hsieh, Chia Ling Chen, and Chun Keung Yu
- Subjects
Viral protein ,Receptor expression ,Immunology ,Biology ,medicine.disease_cause ,Microbiology ,Cell Line ,Transactivation ,Interferon-gamma ,Viral Proteins ,Virology ,medicine ,Enterovirus Infections ,Animals ,Humans ,STAT1 ,Mice, Knockout ,Enterovirus A, Human ,Immunosurveillance ,Mice, Inbred C57BL ,Disease Models, Animal ,IRF1 ,Immunoediting ,Insect Science ,Host-Pathogen Interactions ,biology.protein ,STAT protein ,Cancer research ,Pathogenesis and Immunity ,Signal Transduction - Abstract
Enterovirus 71 (EV71) infection causes severe mortality involving multiple possible mechanisms, including cytokine storm, brain stem encephalitis, and fulminant pulmonary edema. Gamma interferon (IFN-γ) may confer anti-EV71 activity; however, the claim that disease severity is highly correlated to an increase in IFN-γ is controversial and would indicate an immune escape initiated by EV71. This study, investigating the role of IFN-γ in EV71 infection using a murine model, showed that IFN-γ was elevated. Moreover, IFN-γ receptor-deficient mice showed higher mortality rates and more severe disease progression with slower viral clearance than wild-type mice. In vitro results showed that IFN-γ pretreatment reduced EV71 yield, whereas EV71 infection caused IFN-γ resistance with attenuated IFN-γ signaling in IFN regulatory factor 1 (IRF1) gene transactivation. To study the immunoediting ability of EV71 proteins in IFN-γ signaling, 11 viral proteins were stably expressed in cells without cytotoxicity; however, viral proteins 2A and 3D blocked IFN-γ-induced IRF1 transactivation following a loss of signal transducer and activator of transcription 1 (STAT1) nuclear translocation. Viral 3D attenuated IFN-γ signaling accompanied by a STAT1 decrease without interfering with IFN-γ receptor expression. Restoration of STAT1 or blocking 3D activity was able to rescue IFN-γ signaling. Interestingly, viral 2A attenuated IFN-γ signaling using another mechanism by reducing the serine phosphorylation of STAT1 following the inactivation of extracellular signal-regulated kinase without affecting STAT1 expression. These results demonstrate the anti-EV71 ability of IFN-γ and the immunoediting ability by EV71 2A and 3D, which attenuate IFN-γ signaling through different mechanisms. IMPORTANCE Immunosurveillance by gamma interferon (IFN-γ) may confer anti-enterovirus 71 (anti-EV71) activity; however, the claim that disease severity is highly correlated to an increase in IFN-γ is controversial and would indicate an immune escape initiated by EV71. IFN-γ receptor-deficient mice showed higher mortality and more severe disease progression, indicating the anti-EV71 property of IFN-γ. However, EV71 infection caused cellular insusceptibility in response to IFN-γ stimulation. We used an in vitro system with viral protein expression to explore the novel IFN-γ inhibitory properties of the EV71 2A and 3D proteins through the different mechanisms. According to this study, targeting either 2A or 3D pharmacologically and/or genetically may sustain a cellular susceptibility in response to IFN-γ, particularly for IFN-γ-mediated anti-EV71 activity.
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- 2015
42. Development of STSAT-2 Ground Station Baseband Control System
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Seung-Han O and Dae-Su O
- Subjects
Ground station ,Engineering ,Frequency-shift keying ,business.industry ,Gate array ,Control system ,Telecommunications link ,Electronic engineering ,Baseband ,Satellite ,business ,Field-programmable gate array ,Computer hardware - Abstract
STSAT-2 is the first satellite which will be launched by the first Korean Space Launch Vehicle(KSLV). Ground station Baseband Control system(GBC) is now developed for STSAT-2. GBC has two functions. One is control data path between satellite control computers and ground station antennas(1.5M, 3.7M, 13M) automatically. The other is sending and receiving data between ground station and satellite. GBC is implemented by FPGA(Field-Programmable Gate Array) which includes almost all logic(for MODEM, PROTOCOL and GBC system control). MODEM in GBC has two uplink FSK modulators(1.2[kbps], 9.6[kbps]) and six downlink FSK demodulators(9.6[kbps], 38.4[kbps]). In hardware, STSAT-2 GBC is smaller than STSAT-1 GBC. In function, STSAT-2 GBC has more features than STSAT-1 GBC. This paper is about GBC structure, functions and test results.
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- 2006
43. XI Int. Conference on Computational Plasticity
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Rojek, J, Oxf1ate E, Labra C. and Su O., Rojek, J., Oxf1ate E., Labra C., and Su O.
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- 2011
44. Performance Evaluation of Machine Learning Algorithms for Sarcopenia Diagnosis in Older Adults
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Su Ozgur, Yasemin Atik Altinok, Devrim Bozkurt, Zeliha Fulden Saraç, and Selahattin Fehmi Akçiçek
- Subjects
predictive preventive personalized medicine (3P/PPPM) ,sarcopenia ,machine learning ,early diagnosis ,older adults ,Medicine - Abstract
Background: Sarcopenia is a progressive and generalized skeletal muscle disorder. Early diagnosis is necessary to reduce the adverse effects and consequences of sarcopenia, which can help prevent and manage it in a timely manner. The aim of this study was to identify the important risk factors for sarcopenia diagnosis and compare the performance of machine learning (ML) algorithms in the early detection of potential sarcopenia. Methods: A cross-sectional design was employed for this study, involving 160 participants aged 65 years and over who resided in a community. ML algorithms were applied by selecting 11 features—sex, age, BMI, presence of hypertension, presence of diabetes mellitus, SARC-F score, MNA score, calf circumference (CC), gait speed, handgrip strength (HS), and mid-upper arm circumference (MUAC)—from a pool of 107 clinical variables. The results of the three best-performing algorithms were presented. Results: The highest accuracy values were achieved by the ALL (male + female) model using LightGBM (0.931), random forest (RF; 0.927), and XGBoost (0.922) algorithms. In the female model, the support vector machine (SVM; 0.939), RF (0.923), and k-nearest neighbors (KNN; 0.917) algorithms performed the best. Regarding variable importance in the ALL model, the last HS, sex, BMI, and MUAC variables had the highest values. In the female model, these variables were HS, age, MUAC, and BMI, respectively. Conclusions: Machine learning algorithms have the ability to extract valuable insights from data structures, enabling accurate predictions for the early detection of sarcopenia. These predictions can assist clinicians in the context of predictive, preventive, and personalized medicine (PPPM).
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- 2023
- Full Text
- View/download PDF
45. Effect of elastic and strength properties of rocks during blasthole drilling
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Su, O, primary, Sakız, U, additional, and Akçın, N, additional
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- 2016
- Full Text
- View/download PDF
46. Disseminated scar sarcoidosis may predict pulmonary involvement in sarcoidosis
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Su, O, ONSUN, NAHİDE, TOPUKÇU, B, OZÇELIK, HK, CAKıTER, AU, BÜYÜKPıNARBAŞıLı, NUR, and BÜYÜKPINARBAŞILI, NUR
- Subjects
Su O., ONSUN N., TOPUKÇU B., OZÇELIK H., CAKıTER A., BÜYÜKPıNARBAŞıLı N., -Disseminated scar sarcoidosis may predict pulmonary involvement in sarcoidosis.-, Acta dermatovenerologica Alpina, Pannonica, et Adriatica, cilt.22, ss.71-4, 2013 - Abstract
Sarcoidosis is a chronic, inflammatory, multi-organ disease of unknown origin that is characterized by non-caseating granuloma formation in affected organs. Cutaneous involvement is reported in 25% of patients with sarcoidosis. Scar sarcoidosis is rare but is clinically specific for skin sarcoidosis. Systemic involvement is seen in most patients with scar sarcoidosis. We present a case of scar sarcoidosis in a 30-year-old male that developed infiltrated nodules on old scars, including on his penile shaft, which is rare, and that also had pulmonary involvement. Scar sarcoidosis should be considered in the differential diagnosis of changes in all scar areas and should be investigated for systemic involvement. PURPOSE. Translucency of all-ceramic restorations is an important factor which affects the final appearance and esthetic outcome of the restoration. The aim of this study was to evaluate the effect of the shade of coloring liquid on the translucency of zirconia framework. MATERIALS AND METHODS. Thirty zirconium oxide core plate (15 × 12 × 0.5 mm) were divided into 6 groups of 5 plates each. Each group was classified according to the shade of coloring liquid based on Vita Classic Scale (A2, A3, B1, C2, and D2), and each sample was immersed in coloring liquid for 3 seconds as recommended by the manufacturer, except for the control group. Contrast ratio, as a translucency parameter, was calculated using a spectrophotometer and the data were analyzed with oneway analysis of variance (ANOVA) and Tukey’s honestly significant differences (HSD) tests (α=.05). RESULTS. Significant differences in translucency among the control and test groups, and the B1 shaded group and other shades was observed. There were no significant differences among A2, A3, C2, and D2 shaded groups. CONCLUSION. The translucency of the zirconium oxide cores was affected by the coloring procedure and significant differences in the translucency measurements were identified between specific shades
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- 2013
47. Non-healing ulcer on the foot: early onset unilateral Mali-type acroangiodermatitis
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OZKAYA, DB, Su, O, ONSUN, NAHİDE, ULUSAL, H, DEMIRKESEN, C, and ONSUN, Nahide
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early onset unilateral Mali-type acroangiodermatitis.-, Acta dermatovenerologica Alpina, Pannonica, et Adriatica, cilt.22, ss.49-51, 2013 [OZKAYA D., Su O., ONSUN N., ULUSAL H., DEMIRKESEN C., -Non-healing ulcer on the foot] - Abstract
Acroangiodermatitis (pseudo-Kaposi's sarcoma, AAD) is a benign vascular dermatosis that resembles Kaposi's sarcoma clinically and histopathologically (1). Four types have been defined: the Stewart-Bluefarb type accompanying chronic arteriovenous malformations, the Mali type accompanying stasis dermatitis, a type accompanying the first gestation, and a type accompanying arteriovenous shunts in patients with chronic kidney failure (3). Although AAD development is associated with chronic venous failure, less frequently AAD can develop as a complication of extremity paralysis, hemodialysis, post-traumatic arteriovenous fistula, amputated extremities, and vascular malformations (e.g., Klippel-Trénaunay syndrome). Pseudo-Kaposi's sarcoma can be histopathologically and clinically confused with malignant diseases such as Kaposi's sarcoma (1, 4). A 22-year-old male was referred to our outpatient clinic with a complaint of a non-healing wound on the distal phalanx of the left first toe. The patient was referred to various centers for 2 years and stated that he had received infection treatments but that his complaints did not disappear. An AAD diagnosis was established for the patient based on clinical and histopathologic evidence. Because he had early-onset disease and it was unilateral, the diagnosis was delayed. In addition, due to the rare occurrence of the disease, we histopathologically diagnosed this patient as having acroangiodermatitis.
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- 2013
48. Disseminated scar sarcoidosis may predict pulmonary involvement in sarcoidosis
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Su O, Onsun N, Topukçu B, Ozçelik HK, Cakıter AU, and Büyükpınarbaşılı N.
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- 2013
49. Non-healing ulcer on the foot: early onset unilateral Mali-type acroangiodermatitis
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Ozkaya DB, Su O, Onsun N, Ulusal H, and Demirkesen C.
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- 2013
50. Simulation of rock cutting mechanism and characterization of failure mode in PFC3D
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Su O. and Zonguldak Bülent Ecevit Üniversitesi
- Abstract
3rd ISRM Symposium on Rock Characterisation, Modelling and Engineering Design Methods, SINOROCK 2013 -- 18 June 2013 through 20 June 2013 -- Shanghai -- 97711, Rock cutting mechanisms were used for the prediction of the cutting performance of mechanical excavators in the mid of 1900s. However, rock cutting tests performed in the laboratory both in small and full scales have been successfully applied for the same purpose as a more realistic method for the last few decades. Daily advance rate, the specificenergy, and the performance of the excavation machine can accurately be predicted by means of those tests. In accordance with the advanced technology, the cutting tests can also be modeled by utilizing the discrete element method as in Particle Flow Code in 3D (PFC3D) and hence the performance of a machine can be assessedfrom this code as well. This paper initially summarizes the methods which have been applied to predict the forces. Then, rock cutting simulations by using a conical bit in PFC3D are discussed and the pick forces recordedin the course ofmodeling are given. The results are compared with the ones obtained from rock cutting mechanisms. In addition, an understanding of how discrete assemblies are failed during cutting and how the tool forces on the bit are influencedby the failure type are examined. © 2013 Taylor & Francis Group.
- Published
- 2013
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