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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | Meeting Abstract | Upregulation of AEG-1 Expression Attenuates Granule Cell Dispersion and Seizure Development by Suppressing mTORC1 Activation in KA-Induced TLE Mice | Kim, Sehwan; Nam, Youngpyo; Woo, Hanwoong; Kim, Sang Ryong | Kyungpook Natl Univ, Sch Life Sci, Daegu, South Korea; Kyungpook Natl Univ, Brain Sci & Engn Inst, Daegu, South Korea | MOLECULAR THERAPY | MOL THER | 1525-0016 | 1525-0024 | 31 | 4 | SCIE | BIOTECHNOLOGY & APPLIED MICROBIOLOGY;GENETICS & HEREDITY;MEDICINE, RESEARCH & EXPERIMENTAL | 2023 | 12.1 | 2.0 | 0 | English | 2023 | 2023-05-01 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | ○ | Article | Comparison of Laparoscopic Versus Robot-Assisted Surgery for Rectal Cancers The COLRAR Randomized Controlled Trial | Objective:To evaluate whether robotic for middle or low rectal cancer produces an improvement in surgical outcomes compared with laparoscopic surgery in a randomized controlled trial (RCT). Background:There is a lack of proven clinical benefit of robotic total mesorectal excision (TME) compared with a laparoscopic approach in the setting of multicenter RCTs. Methods:Between July 2011 and February 2016, patients diagnosed with an adenocarcinoma located Results:The RCT was terminated prematurely because of poor accrual of data. In all, 295 patients were assigned randomly to a robot-assisted TME group (151 in R-TME) or a laparoscopy-assisted TME group (144 in L-TME). The rates of complete TME were not different between groups (80.7% in R-TME, 77.1% in L-TME). Pathologic outcomes including the circumferential resection margin and the numbers of retrieved lymph nodes were not different between groups. In a subanalysis, the positive circumferential resection margin rate was lower in the R-TME group (0% vs 6.1% for L-TME; P=0.031). Among the recovery parameters, the length of opioid use was shorter in the R-TME group (P=0.028). There was no difference in the postoperative complication rate between the groups (12.0% for R-TME vs 8.3% for L-TME). Conclusions:In patients with middle or low rectal cancer, robotic-assisted surgery did not significantly improve the TME quality compared with conventional laparoscopic surgery (ClinicalTrial.gov ID: NCT01042743). | Park, Jun Seok; Lee, Sung Min; Choi, Gyu-Seog; Park, Soo Yeun; Kim, Hye Jin; Song, Seung Ho; Min, Byung Soh; Kim, Nam Kyu; Kim, Seon Hahn; Lee, Kang Young | Kyungpook Natl Univ, Chilgok Hosp, Colorectal Canc Ctr, Sch Med, Daegu, South Korea; Yonsei Univ, Coll Med, Severance Hosp, Div Colorectal Surg,Dept Surg, Seoul, South Korea; Korea Univ, Coll Med, Anam Hosp, Div Colorectal Surg,Dept Surg, Seoul, South Korea | ; KIM, SEON HAHN/JTU-1415-2023; Kim, Hye/W-1059-2019; Kim, Nam/P-1552-2017; Park, Joonhong/AAZ-9885-2020 | 35226761100; 55236751200; 8058759100; 40561578300; 57204567554; 57221771693; 35269560900; 35269088900; 57218683789; 57219637721 | parkjs0802@knu.ac.kr;rhsm3814@gmail.com;kyuschoi@mail.knu.ac.kr;psy-flower@hanmail.net;chocogom@hanmail.net;jojocrom@naver.com;BSMIN@yuhs.ac;namkyuk@yuhs.ac;drkimsh@korea.ac.kr; | ANNALS OF SURGERY | ANN SURG | 0003-4932 | 1528-1140 | 278 | 1 | SCIE | SURGERY | 2023 | 7.9 | 2.2 | 24.13 | 2025-06-25 | 77 | 77 | laparoscopy; rectal cancer; robotic surgery; short-term outcomes | SHORT-TERM OUTCOMES; PATHOLOGICAL OUTCOMES; ANTERIOR RESECTION; RISK | laparoscopy; rectal cancer; robotic surgery; short-term outcomes | Humans; Laparoscopy; Margins of Excision; Rectal Neoplasms; Retrospective Studies; Robotic Surgical Procedures; Treatment Outcome; fentanyl; nalbuphine; adult; Article; cancer staging; clinical outcome; controlled study; drug use; female; human; intermethod comparison; laparoscopic surgery; lymph node; major clinical study; male; multicenter study; postoperative complication; postoperative pain; randomized controlled trial; rectal adenocarcinoma; robot assisted surgery; surgical margin; total mesorectal excision; tumor volume; clinical trial; laparoscopy; pathology; rectum tumor; retrospective study; treatment outcome | English | 2023 | 2023-07 | 10.1097/sla.0000000000005788 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Article | Donor Safety and Risk Factors of Pure Laparoscopic Living Donor Right Hepatectomy: A Korean Multicenter Study | Objective: The aim of this study was to identify safety and risk factors of living donor after pure laparoscopic donor right hepatectomy in a Korean multicenter cohort study. Background: Pure laparoscopic donor right hepatectomy is not yet a standardized surgical procedure due to lack of data. Methods: This retrospective study included 543 patients undergoing PLRDH between 2010 and 2018 in 5 Korean transplantation centers. Complication rates were assessed and multivariate logistic regression analyses were performed to identify risk factors of open conversion, overall complications, major complications, and biliary complications. Results: Regarding open conversion, the incidence was 1.7% and the risk factor was body mass index >30 kg/m(2) [P=0.001, odds ratio (OR)=22.72, 95% CI=3.56-146.39]. Rates of overall, major (Clavien-Dindo classification III-IV), and biliary complications were 9.2%, 4.4%, and 3.5%, respectively. For overall complications, risk factors were graft weight >700 g (P=0.007, OR=2.66, 95% CI=1.31-5.41), estimated blood loss (P400 minutes (P=0.01, OR=2.46, 95% CI=1.25-4.88). For major complications, risk factors were graft weight >700 g (P=0.002, OR=4.01, 95% CI=1.67-9.62) and operation time >400 minutes (P=0.003, OR=3.84, 95% CI=1.60-9.21). For biliary complications, risk factors were graft weight >700 g (P=0.01, OR=4.34, 95% CI=1.40-13.45) and operation time >400 minutes (P=0.01, OR=4.16, 95% CI=1.34-12.88). Conclusion: Careful donor selection for PLRDH considering body mass index, graft weight, estimated blood loss, and operation time combined with skilled procedure can improve donor safety. | Kim, Sang-Hoon; Kim, Ki-Hun; Cho, Hwui-Dong; Suh, Kyung-Suk; Hong, Suk Kyun; Lee, Kwang-Woong; Choi, Gyu-Seong; Kim, Jong Man; David, Kwon Choon Hyuck; Cho, Jai Young; Han, Ho-Seong; Han, Jaryung; Han, Young Seok | Univ Ulsan, Coll Med, Dept Surg, Asan Med Ctr,Div Hepatobiliary Surg & Liver Trans, Seoul, South Korea; Seoul Natl Univ, Seoul Natl Univ Hosp, Coll Med, Dept Surg, Seoul, South Korea; Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Dept Surg, Seoul, South Korea; Cleveland Clin, Digest Dis & Surg Inst, Dept Surg, Cleveland, OH USA; Seoul Natl Univ, Coll Med, Bundang Hosp, Dept Surg, Seoul, South Korea; Kyungpook Natl Univ, Natl Univ Hosp, Sch Med, Dept Surg, Daegu, South Korea; Catholic Univ Daegu, Daegu Catholic Univ Hosp, Sch Med, Dept Surg, Daegu, South Korea | Kim, Sunghoon/Z-2981-2019; Kim, Jong/AAH-5295-2020 | 57225920341; 57214859181; 57189067548; 7202645048; 57191476351; 56239685700; 22939899200; 36065224600; 58693181000; 35335935300; 7401969217; 57214671308; 7404096216 | khkim620@amc.seoul.kr;hwuidongcho@gmail.com;kssuh2000@gmail.com;nobel1210@naver.com;kwleegs@gmail.com;med9370@gmail.com;yjongman21@gmail.com;chdkwon@gmail.com;jychogs@gmail.com;hanhs@snubh.org;jh40356@gmail.com;gshys@knu.ac.kr; | ANNALS OF SURGERY | ANN SURG | 0003-4932 | 1528-1140 | 278 | 6 | SCIE | SURGERY | 2023 | 7.9 | 2.2 | 1.63 | 2025-06-25 | 8 | 5 | living donor liver transplantation; morbidity; open conversion; pure laparoscopic donor right hepatectomy; risk factors | LIVER-TRANSPLANTATION; DONATION; OUTCOMES; SURGERY | living donor liver transplantation; morbidity; open conversion; pure laparoscopic donor right hepatectomy; risk factors | Cohort Studies; Hepatectomy; Humans; Laparoscopy; Liver Transplantation; Living Donors; Postoperative Complications; Republic of Korea; Retrospective Studies; Risk Factors; Tissue and Organ Harvesting; antibiotic agent; abdominal bleeding; adult; Article; bile duct injury; bile leakage; biliary tract surgery; body mass; cholestasis; cohort analysis; conversion to open surgery; female; graft size; hepatic portal vein; hospital readmission; human; incidence; Korean (people); liver transplantation; living donor; lung embolism; major clinical study; male; operation duration; operative blood loss; partial hepatectomy; pleura effusion; portal vein thrombosis; postoperative hemorrhage; retrospective study; risk factor; safety procedure; shock; steatosis; surgical infection; surgical mortality; vein injury; clinical trial; graft harvesting; hepatectomy; laparoscopy; living donor; multicenter study; postoperative complication; procedures; risk factor; South Korea | English | 2023 | 2023-12 | 10.1097/sla.0000000000005976 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Article | An unsupervised image registration method employing chest computed tomography images and deep neural networks | Background: Deformable image registration is crucial for multiple radiation therapy applications. Fast registration of computed tomography (CT) lung images is challenging because of the large and nonlinear deformation between inspiration and expiration. With advancements in deep learning techniques, learning-based registration methods are considered efficient alternatives to traditional methods in terms of accuracy and computational cost. Method: In this study, an unsupervised lung registration network (LRN) with cycle-consistent training is proposed to align two acquired CT-derived lung datasets during breath-holds at inspiratory and expiratory levels without utilizing any ground-truth registration results. Generally, the LRN model uses three loss functions: image similarity, regularization, and Jacobian determinant. Here, LRN was trained on the CT datasets of 705 subjects and tested using 10 pairs of public CT DIR-Lab datasets. Furthermore, to evaluate the effectiveness of the registration technique, target registration errors (TREs) of the LRN model were compared with those of the conventional algorithm (sum of squared tissue volume difference; SSTVD) and a state-of-the-art unsupervised registration method (VoxelMorph).Results: The results showed that the LRN with an average TRE of 1.78 +/- 1.56 mm outperformed VoxelMorph with an average TRE of 2.43 +/- 2.43 mm, which is comparable to that of SSTVD with an average TRE of 1.66 +/- 1.49 mm. In addition, estimating the displacement vector field without any folding voxel consumed less than 2 s, demonstrating the superiority of the learning-based method with respect to fiducial marker tracking and the overall soft tissue alignment with a nearly real-time speed.Conclusions: Therefore, this proposed method shows significant potential for use in time-sensitive pulmonary studies, such as lung motion tracking and image-guided surgery. | Ho, Thao Thi; Kim, Woo Jin; Lee, Chang Hyun; Jin, Gong Yong; Chae, Kum Ju; Choi, Sanghun | Kyungpook Natl Univ, Sch Mech Engn, Daegu, South Korea; Kangwon Natl Univ, Kangwon Natl Univ Hosp, Sch Med, Dept Internal Med, Chunchon, South Korea; Kangwon Natl Univ, Kangwon Natl Univ Hosp, Environm Hlth Ctr, Sch Med, Chunchon, South Korea; Seoul Natl Univ, Seoul Natl Univ Hosp, Coll Med, Dept Radiol, Seoul, South Korea; Univ Iowa, Coll Med, Dept Radiol, Iowa City, IA USA; Jeonbuk Natl Univ, Jeonbuk Natl Univ Hosp, Biomed Res Inst, Dept Radiol,Res Inst Clin Med, Jeonju, South Korea | Choi, Sanghun/AGS-7430-2022; Kim, Woo/A-8216-2019 | 57221374670; 56560422500; 57196253438; 55663719500; 57195310676; 55847101000 | s-choi@knu.ac.kr; | COMPUTERS IN BIOLOGY AND MEDICINE | COMPUT BIOL MED | 0010-4825 | 1879-0534 | 154 | SCIE | BIOLOGY;COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY | 2023 | 7 | 2.3 | 3.1 | 2025-06-25 | 18 | 26 | Deep learning; Unsupervised learning; Image registration; CT lung | PRESERVING NONRIGID REGISTRATION; LEARNING FRAMEWORK; CT; MOTION; DEFORMATION | CT lung; Deep learning; Image registration; Unsupervised learning | Algorithms; Humans; Image Processing, Computer-Assisted; Lung; Neural Networks, Computer; Tomography; Tomography, X-Ray Computed; Biological organs; Computerized tomography; Deep neural networks; Learning systems; Tissue; Unsupervised learning; Computed tomography images; Computed tomography lung; Deep learning; Deformable image registration; Images registration; Larger deformations; Network models; Nonlinear deformations; Registration methods; Target registration errors; adult; algorithm; Article; breath holding; comparative effectiveness; computer assisted tomography; controlled study; deep learning; deep neural network; female; human; human experiment; image registration; learning; loss of function mutation; lung; major clinical study; male; motion; soft tissue; thorax; velocity; diagnostic imaging; image processing; lung; procedures; tomography; x-ray computed tomography; Image registration | English | 2023 | 2023-03 | 10.1016/j.compbiomed.2023.106612 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Collective behaviors of stochastic agent-based models and applications to finance and optimization | In this paper, we present a survey of recent progress on the emergent behaviors of stochastic particle models which arise from the modeling of collective dynamics. Collective dynamics of interacting autonomous agents is ubiquitous in nature, and it can be understood as a formation of concentration in a state space. The jargons such as aggregation, herding, flocking and synchronization describe such concentration phenomena. Recently it became one of the emerging topics in the applied mathematics community due to possible engineering applications and close relation with nonlocal partial differential equations. When an autonomous agent system interacts with unknown environment as an open system, the effects of hidden and unidentified interactions between the environment and the autonomous system are often realized by stochastic noises in agent dynamics, and the temporal evolution of the autonomous system results in stochastic collective models. From the viewpoint of dynamical systems theory, it is very interesting how collective dynamics emerges from initial state. As concrete examples, we consider four specific stochastic collective models (stochastic Winfree and Kuramoto models for synchronization, the stochastic Cucker-Smale model for flocking, and a first-order stochastic nonlinear consensus model), and we also briefly review the state-of-the-art results for these models on the emergence of collective dynamics and discuss their applications in finance and optimization. | Ko, Dongnam; Ha, Seung-Yeal; Lee, Euntaek; Shim, Woojoo | Catholic Univ Korea, Dept Math, Bucheon 14662, Gyeonggido, South Korea; Seoul Natl Univ, Dept Math Sci, Seoul 08826, South Korea; Seoul Natl Univ, Res Inst Math, Seoul 08826, South Korea; Seoul Natl Univ, Dept Math Sci, Seoul 08826, South Korea; Kyungpook Natl Univ, Dept Math Educ, Daegu 41566, South Korea | Ko, Dongnam/MCK-1551-2025; Shim, Woojoo/GYJ-0778-2022 | 56823032100; 7202500884; 58242095600; 57204943631 | dongnamko@catholic.ac.kr;syha@snu.ac.kr;tngkrqks21@snu.ac.kr;cosmo.shim@gmail.com; | MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES | MATH MOD METH APPL S | 0218-2025 | 1793-6314 | 33 | 07 | SCIE | MATHEMATICS, APPLIED | 2023 | 3.6 | 2.3 | 0.91 | 2025-06-25 | 5 | 5 | Consensus; collective dynamics; Cucker-Smale model; flocking; Kuramoto model; Winfree model; synchronization | PHASE-LOCKED STATES; CUCKER-SMALE MODEL; KURAMOTO MODEL; GLOBAL OPTIMIZATION; EULERIAN DYNAMICS; HERD BEHAVIOR; WINFREE MODEL; SYNCHRONIZATION; FLOCKING; STABILITY | collective dynamics; Consensus; Cucker-Smale model; flocking; Kuramoto model; synchronization; Winfree model | Autonomous agents; Computational methods; Dynamical systems; Dynamics; Stochastic models; Stochastic systems; Collective behaviour; Collective dynamics; Consensus; Cucke-smale model; Flocking; Kuramoto models; Optimisations; Smale model; Stochastics; Winfree model; Synchronization | English | 2023 | 2023-06-30 | 10.1142/s021820252350032x | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Review | Emerging Imaging Technologies for Parathyroid Gland Identification and Vascular Assessment in Thyroid Surgery A Review From the American Head and Neck Society Endocrine Surgery Section | Importance Identification and preservation of parathyroid glands (PGs) remain challenging despite advances in surgical techniques. Considerable morbidity and even mortality result from hypoparathyroidism caused by devascularization or inadvertent removal of PGs. Emerging imaging technologies hold promise to improve identification and preservation of PGs during thyroid surgery. Observation This narrative review (1) comprehensively reviews PG identification and vascular assessment using near-infrared autofluorescence (NIRAF)-both label free and in combination with indocyanine green-based on a comprehensive literature review and (2) offers a manual for possible implementation these emerging technologies in thyroid surgery. Conclusions and Relevance Emerging technologies hold promise to improve PG identification and preservation during thyroidectomy. Future research should address variables affecting the degree of fluorescence in NIRAF, standardization of signal quantification, definitions and standardization of parameters of indocyanine green injection that correlate with postoperative PG function, the financial effect of these emerging technologies on near-term and longer-term costs, the adoption learning curve and effect on surgical training, and long-term outcomes of key quality metrics in adequately powered randomized clinical trials evaluating PG preservation. | Karcioglu, Amanda Silver L.; Triponez, Frederic; Solorzano, Carmen C.; Iwata, Ayaka J.; Ahmed, Amr Abdelhamid H.; Almquist, Martin; Angelos, Peter; Benmiloud, Fares; Berber, Eren; Bergenfelz, Anders; Cha, Jaepyeong; Colaianni, C. Alessandra; Davies, Louise; Duh, Quan-Yang; Hartl, Dana; Kandil, Emad; Kim, Wan Wook; Kopp, Peter A.; Liddy, Whitney; Mahadevan-Jansen, Anita; Lee, Kang-Dae; Mannstadt, Michael; McMullen, Caitlin P.; Shonka Jr, David C.; Shin, Jennifer J.; Singer, Michael C.; Slough, Cristian M.; Stack Jr, Brendan C.; Tearney, Guillermo; Thomas, Giju; Tolley, Neil; Vidal-Fortuny, Jordi; Randolph, Gregory W. | Harvard Med Sch, Massachusetts Eye & Ear Infirm, Dept Otolaryngol Head & Neck Surg, Div Thyroid & Parathyroid Endocrine Surg, Boston, MA USA; NorthShore Univ HealthSyst, Dept Surg, Div Otolaryngol Head & Neck Surg, Evanston, IL USA; Univ Chicago, Pritzker Sch Med, Chicago, IL USA; Univ Hosp, Dept Surg, Thorac & Endocrine Surg, Geneva, Switzerland; Vanderbilt Univ, Med Ctr, Dept Surg, Div Surg Oncol & Endocrine Surg, Nashville, TN USA; Kaiser Permanente, Dept Otolaryngol Head & Neck Surg, Santa Clara, CA USA; Lund Univ, Skane Univ Hosp, Inst Clin Sci, Dept Surg, Lund, Sweden; Univ Chicago, MacLean Ctr Clin Med Ethics, Dept Surg, Chicago, IL USA; Hop Europeen Marseille, Endocrine Surg Unit, Marseille, France; Cleveland Clin, Dept Endocrine Surg, Div Otolaryngol Head & Neck Surg, 9669 Kenton Ave,Ste 206, Skokie, OH USA; Lund Univ, Dept Clin Sci Lund, Lund, Sweden; Childrens Natl Hosp, Zayed Inst Pediat Surg Innovat, Washington, DC USA; George Washington Univ, Dept Pediat, Sch Med & Hlth Sci, Washington, DC USA; Oregon Hlth & Sci Univ, Dept Otolaryngol Head & Neck Surg, Div Head & Neck Surg, Portland, OR USA; VA Outcomes Grp, White River Jct, VT USA; Geisel Sch Med Dartmouth, Sect Otolaryngol Head & Neck Surg, Hanover, NH USA; Univ Calif San Francisco, Dept Surg, Sect Endocrine Surg, San Francisco, CA USA; VA Med Ctr, San Francisco, CA USA; Univ Paris Saclay, Dept Surg, Thyroid Surg Unit, Paris, France; Tulane Univ, Sch Med, Endocrine & Oncol Surg, New Orleans, LA USA; Kyungpook Natl Univ, Dept Surg, Breast & Thyroid Div, Daegu, South Korea; Univ Lausanne, Div Endocrinol Diabetol & Metab, Lausanne, Switzerland; Northwestern Univ, Feinberg Sch Med, Div Endocrinol Metab & Mol Med, Chicago, IL USA; Northwestern Med, Dept Otolaryngol Head & Neck Surg, Thyroid & Parathyroid Surg, Chicago, IL USA; Vanderbilt Univ, Dept Biomed Engn, Nashville, TN USA; Vanderbilt Univ, Dept Surg Otolaryngol & Neurol Surg, Med Ctr, Nashville, TN USA; Kosin Univ Gospel Hosp, Dept Otolaryngol Head & Neck Surg, Busan, South Korea; Harvard Med Sch, Massachusetts Gen Hosp, Endocrine Unit, Boston, MA USA; H Lee Moffitt Canc Ctr & Res Inst, Dept Head & Neck Endocrine Oncol, Tampa, FL USA; Univ Virginia, Dept Otolaryngol Head & Neck Surg, Div Head & Neck Surg, Charlottesville, VA USA; Harvard Med Sch, Dept Otolaryngol Head & Neck Surg, Boston, MA USA; Brigham & Womens Hosp, Ctr Surg & Publ Hlth, Boston, MA USA; Henry Ford Hlth Syst, Dept Otolaryngol Head & Neck Surg, Div Thyroid & Parathyroid Surg, Detroit, MI USA; Hawkes Bay Dist Hlth Board, Hawkes Bay Fallen Soldiers Memorial Hosp, Dept Otolaryngol Head & Neck Surg, Hastings, New Zealand; Southern Illinois Univ, Otolaryngol Head & Neck Surg, Sch Med, Springfield, IL USA; Massachusetts Gen Hosp, Wellman Ctr Photomed, Boston, MA USA; Harvard Med Sch, Boston, MA USA; Vanderbilt Univ, Vanderbilt Biophoton Ctr, Nashville, TN USA; Vanderbilt Univ, Dept Biomed Engn, Nashville, TN USA; Imperial Coll NHS Healthcare Trust, London, England; Univ Hosp Geneva, Dept Thorac & Endocrine Surg, Geneva, Switzerland; Dept Thorac & Endocrine Surg, Geneva, Switzerland; Harvard Med Sch, Massachusetts Gen Hosp, Dept Surg, Boston, MA USA | Almquist, Martin/H-7209-2019; Shin, Jennifer/A-3169-2016; Karcioglu, Amanda Silver/AAJ-6058-2021; Mannstadt, Michael/AAO-1124-2020; Solorzano, Carmen/AFM-9897-2022; Thomas, Giju/AAE-1425-2019; Stack, Brendan/AAQ-5580-2020; Triponez, Frederic/A-6237-2013 | 57225070721; 6701652175; 7003938563; 57194141787; 57193251840; 6506075044; 7004398304; 8438454500; 7003767467; 7006187279; 55542319800; 57199227916; 13406943900; 7005311392; 7103313154; 35299453200; 26023273400; 7007034567; 56520377500; 7004319217; 55587784800; 6602573731; 37026462000; 8711306100; 35184205000; 35489234800; 14018736800; 7005654324; 7006907866; 57198751619; 7003967365; 56229507000; 7005319533 | amandalsilver@gmail.com; | JAMA OTOLARYNGOLOGY-HEAD & NECK SURGERY | JAMA OTOLARYNGOL | 2168-6181 | 2168-619X | 149 | 3 | SCIE | OTORHINOLARYNGOLOGY;SURGERY | 2023 | 6.1 | 2.3 | 6.75 | 2025-06-25 | 32 | 37 | INDOCYANINE GREEN FLUORESCENCE; HYPOPARATHYROIDISM; LOCALIZATION; ANGIOGRAPHY; MANAGEMENT; FEASIBILITY; ASSOCIATION; STATEMENT; PERFUSION | Humans; Hypoparathyroidism; Indocyanine Green; Optical Imaging; Parathyroid Glands; Thyroid Gland; Thyroidectomy; indocyanine green; adoption; autofluorescence; endocrine surgery; human; imaging; learning curve; medical society; parathyroid gland; Review; standardization; systematic review; thyroid surgery; adverse event; diagnostic imaging; fluorescence imaging; hypoparathyroidism; procedures; surgery; thyroid gland; thyroidectomy | English | 2023 | 2023-03 | 10.1001/jamaoto.2022.4421 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Review | Precision studies of QCD in the low energy domain of the EIC | This White Paper aims at highlighting the important benefits in the science reach of the EIC. High luminosity operation is generally desirable, as it enables producing and harvesting scientific results in a shorter time period. It becomes crucial for programs that would require many months or even years of operation at lower luminosity. & COPY; 2023 Elsevier B.V. All rights reserved. | Burkert, V. D.; Elouadrhiri, L.; Afanasev, A.; Arrington, J.; Contalbrigo, M.; Cosyn, W.; Deshpande, A.; Glazier, D. I.; Ji, X.; Liuti, S.; Oh, Y.; Richards, D.; Satogata, T.; Vossen, A.; Abdolmaleki, H.; Albataineh, A.; Aidala, C. A.; Alexandrou, C.; Avagyan, H.; Bacchetta, A.; Baker, M.; Benmokhtar, F.; Bernauer, J. C.; Bissolotti, C.; Briscoe, W.; Byers, D.; Cao, Xu; Carlson, C. E.; Cichy, K.; Cloet, I. C.; Cocuzza, C.; Cole, P. L.; Constantinou, M.; Courtoy, A.; Dahiyah, H.; Dehmelt, K.; Diehl, S.; Dilks, C.; Djalali, C.; Dupre, R.; Dusa, S. C.; El-Bennich, B.; El Fassi, L.; Frederico, T.; Freese, A.; Gamage, B. R.; Gamberg, L.; Ghoshal, R. R.; Girod, F. X.; Goncalves, V. P.; Gotra, Y.; Guo, F. K.; Guo, X.; Hattawy, M.; Hatta, Y.; Hayward, T.; Hen, O.; Huber, G. M.; Hyde, C.; Isupov, E. L.; Jacak, B.; Jacobs, W.; Jentsch, A.; Ji, C. R.; Joosten, S.; Kalantarians, N.; Kang, Z.; Kim, A.; Klein, S.; Kriesten, B.; Kumano, S.; Kumar, A.; Kumericki, K.; Kuchera, M.; Lai, W. K.; Li, Jin; Li, Shujie; Li, W.; Li, X.; Lin, H. -W.; Liu, K. F.; Liu, Xiaohui; Markowitz, P.; Mathieu, V; McEneaney, M.; Mekki, A.; de Melo, J. P. B. C.; Meziani, Z. E.; Milner, R.; Mkrtchyan, H.; Mochalov, V.; Mokeev, V.; Morozov, V.; Moutarde, H.; Murray, M.; Mtingwa, S.; Nadel-Turonski, P.; Okorokov, V. A.; Onyie, E.; Pappalardo, L. L.; Papandreou, Z.; Pecar, C.; Pilloni, A.; Pire, B.; Polys, N.; Prokudin, A.; Przybycien, M.; Qiu, J. -W.; Radici, M.; Reed, R.; Ringer, F.; Roy, B. J.; Sato, N.; Schaefer, A.; Schmookler, B.; Schnell, G.; Schweitzer, P.; Seidl, R.; Semenov-Tian-Shansky, K. M.; Serna, F.; Shaban, F.; Shabestari, M. H.; Shiells, K.; Signori, A.; Spiesberger, H.; Strakovsky, I.; Sufian, R. S.; Szczepaniak, A.; Teodorescu, L.; Terry, J.; Teryaev, O.; Tessarotto, F.; Timmer, C.; Tawfik, Abdel Nasser; Cazares, L. Valenzuela; Vladimirov, A.; Voutier, E.; Watts, D.; Wilson, D.; Winney, D.; Xiao, B.; Ye, Z.; Ye, Zh.; Yuan, F.; Zachariou, N.; Zahed, I.; Zhang, J. L.; Zhang, Y.; Zhou, J. | Thomas Jefferson Natl Accelerator Facil, Newport News, VA 23606 USA; George Washington Univ, Dept Phys, Washington, DC 20052 USA; Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; INFN Ferrara, I-44122 Ferrara, Italy; Florida Int Univ, Dept Phys, Miami, FL 33199 USA; Univ Ghent, Dept Phys & Astron, B-9000 Ghent, Belgium; SUNY Stony Brook, Stony Brook, NY 11794 USA; Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow G12 8QQ, Lanark, Scotland; Univ Maryland, College Pk, MD 20742 USA; Ctr Nucl Femtog, 1201 New York Ave, Washington, DC 20005 USA; Univ Virginia, Dept Phys, 382 McCormick Rd, Charlottesville, VA 22904 USA; Kyungpook Natl Univ, Dept Phys, Daegu 41566, South Korea; Asia Pacific Ctr Theoret Phys, Pohang 37673, Gyeongbuk, South Korea; Duke Univ, Durham, NC 27708 USA; Inst Res Fundamental Sci IPM, Sch Particles & Accelerators, POB 19395-5531, Tehran, Iran; Yarmouk Univ, Irbid 21163, Jordan; Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA; Univ Cyprus, Dept Phys, CY-1678 Nicosia, Cyprus; Cyprus Inst, CY-1645 Nicosia, Cyprus; Univ Pavia, Dipartimento Fis, Pavia, Italy; Duquesne Univ, Pittsburgh, PA 15282 USA; Brookhaven Natl Lab, RIKEN BNL Res Ctr, Upton, NY 11973 USA; Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China; Coll William & Mary, Dept Phys & Astron, Williamsburg, VA USA; Adam Mickiewicz Univ, Ul Uniwersytetu Poznanskiego 2, PL-61614 Poznan, Poland; Argonne Natl Lab, Lemont, IL 60439 USA; Temple Univ, Philadelphia, PA 19122 USA; Lamar Univ, Dept Phys, Beaumont, TX USA; Univ Nacl Autonoma Mexico, Inst Fis, Apartado Postal 20-364, Mexico City 01000, DF, Mexico; Dr BR Ambedkar Natl Inst Technol, Dept Phys, Jalandhar 144027, Punjab, India; Univ Connecticut, Dept Phys, 196A Auditorium Rd, Storrs, CT 06269 USA; Justus Liebig Univ, Phys Inst, Giessen, Germany; Ohio Univ, Dept Phys & Astron, Athens, OH 45701 USA; Univ Paris Saclay, CNRS, IN2P3, Lab Phys Joliot Curie, Gif Sur Yvette, France; Univ Cidade Sao Paulo, Rua Galvao Bueno 868, BR-01506000 Sao Paulo, SP, Brazil; Mississippi State Univ, Mississippi State, MS 39762 USA; Inst Tecnol Aeronaut, BR-12228900 Sao Jose Dos Campos, Brazil; Univ Washington, Dept Phys B464, Seattle, WA USA; Penn State Univ Berks, Div Sci, Reading, PA 19610 USA; Westfalische Wilhelms Univ Munster, Inst Theoret Phys, Wilhelm Klemm Str 9, D-48149 Munster, Germany; Univ Fed Pelotas, Inst Phys & Math, Postal Code 354, BR-96010900 Pelotas, RS, Brazil; Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China; Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China; Old Dominion Univ, Dept Phys, 4600 Elkhorn Ave, Norfolk, VA 23529 USA; Brookhaven Natl Lab, Dept Phys, Upton, NY 11973 USA; MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA; Univ Regina, Regina, SK S4S 0A2, Canada; Lomonosov Moscow State Univ, Skobeltsyn Nucl Phys Inst, Moscow, Russia; Lomonosov Moscow State Univ, Dept Phys, Moscow, Russia; Indiana Univ, CEEM, Bloomington, IN 47408 USA; North Carolina State Univ, Raleigh, NC USA; Virginia Union Univ, Dept Nat Sci, Richmond, VA USA; Univ Calif Los Angeles, Dept Phys & Astron, Los Angeles, CA USA; Univ Calif Los Angeles, Mani L Bhaumik Inst Theoret Phys, Los Angeles, CA USA; SUNY Stony Brook, Ctr Frontiers Nucl Sci, Stony Brook, NY USA; KEK, High Energy Accelerator Res Org, Tsukuba, Ibaraki, Japan; Dr Rammanohar Lohia Avadh Univ, Ayodhya 224001, UP, India; Univ Zagreb, Dept Phys, Fac Sci, Zagreb 10000, Croatia; Davidson Coll, Dept Math & Comp Sci, Dept Phys, Box 7133, Davidson, NC 28035 USA; South China Normal Univ, Inst Quantum Matter, Guangdong Prov Key Lab Nucl Sci, Guangzhou 510006, Peoples R China; South China Normal Univ, Southern Nucl Sci Comp Ctr, Guangdong Hong Kong Joint Lab Quantum Matter, Guangzhou, Peoples R China; Nanjing Normal Univ, Dept Phys, Nanjing 210023, Peoples R China; Coll William & Mary, Williamsburg, VA 23185 USA; Los Alamos Natl Lab, Los Alamos, NM USA; Michigan State Univ, E Lansing, MI 48824 USA; Univ Kentucky, Coll Arts & Sci Phys & Astron, Lexington, KY 40506 USA; Beijing Normal Univ, Dept Phys, Ctr Adv Quantum Studies, Beijing 100875, Peoples R China; Peking Univ, Ctr High Energy Phys, Beijing 100871, Peoples R China; Univ Barcelona, Dept Fis Quant & Astrofis, E-08028 Barcelona, Spain; Univ Barcelona, Inst Ciencies Cosmos, E-08028 Barcelona, Spain; Univ Complutense Madrid, Dept Fis Teor, E-28040 Madrid, Spain; IPARCOS, E-28040 Madrid, Spain; Univ Khartoum, Fac Sci, Dept Phys, POB 321, Khartoum 11115, Sudan; Univ Cidade Sao Paulo, Cruzeiro Sul Univ, Lab Theoret & Computat Phys LFTC, BR-01506000 Sao Paulo, SP, Brazil; AI Alikhanyan Natl Sci Lab, Yerevan 0036, Armenia; NRC Kurchatov Inst IHEP, Protvino 142281, Russia; Natl Res Nucl Univ MEPhI, Moscow 115409, Russia; Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA; IRFU CEA Saclay, F-91191 Gif Sur Yvette, France; Dept Phys & Astron, Malott Hall,1251 Wescoe Hall Dr, Lawrence, KS 66045 USA; US Nucl Regulatory Commiss, Triangle Sci, 138 W Hatterleigh Ave, Hillsborough, NC 27278 USA; Univ Ferrara, I-44122 Ferrara, Italy; Univ Regina, Dept Phys, 3737 Wascana Pkwy, Regina, SK, Canada; Ist Nazl Fis Nucl, Sez Catania, I-95123 Catania, Italy; Univ Messina, Dipartimento Sci Matemat & Informat Sci Fis & Sci, I-98122 Messina, Italy; Ecole Polytech, CNRS, CPHT, IP Paris, F-91128 Palaiseau, France; Virginia Tech Univ, Blacksburg, VA 24061 USA; AGH Univ Sci & Technol, FPACS, PL-30059 Krakow, Poland; Ist Nazl Fis Nucl, Sez Pavia, Pavia, Italy; Lehigh Univ, Dept Phys, 16 Mem Dr East Off 406, Bethlehem, PA 18015 USA; Bhabha Atom Res Ctr, Div Nucl Phys, Mumbai 400085, Maharashtra, India; Univ Regensburg, Inst Theoret Phys, D-93040 Regensburg, Germany; Univ Calif Riverside, Dept Phys & Astron, Riverside, CA USA; DESY, Hamburg, Germany; RIKEN, Nishina Ctr Accelerator Based Sci, Wako, Saitama 3510198, Japan; Petersburg Nucl Phys Inst, Kurchatov Inst, Natl Res Ctr, Gatchina 188300, Russia; Natl Res Univ, Higher Sch Econ, St Petersburg 194100, Russia; Univ Sucre, Dept Fis, Carrera 28 5-267, Barrio Puerta Roja, Sincelejo, Colombia; Univ Cidade Sao Paulo, Lab Fis Teor & Computac, Rua Galvao Bueno 868, BR-01506000 Sao Paulo, SP, Brazil; Cairo Univ, Fac Sci, Cairo, Egypt; Univ West Florida, Pensacola, FL 23514 USA; Univ Turin, Dept Phys, Via P Giuria 1, I-10125 Turin, Italy; Ist Nazl Fis Nucl, Turin Sect, Via P Giuria 1, I-10125 Turin, Italy; Johannes Gutenberg Univ Mainz, Inst Kernphys, Inst Phys, D-55099 Mainz, Germany; Indiana Univ, Bloomington, IN USA; Brunel Univ London, Uxbridge UB8 3PH, Middx, England; Joint Inst Nucl Res, Bogoliubov Lab Theoret Phys, Dubna, Russia; Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; St 90,Fifth Settlement, New Cairo 11835, Egypt; Iowa State Univ, Iowa City, IA USA; Univ Complutense Madrid, Dept Fis Teor, E-28040 Madrid, Spain; Univ Complutense Madrid, IPARCOS, E-28040 Madrid, Spain; Univ York, Sch Phys Engn & Technol, Heslington YO10 5DD, England; Univ Cambridge, DAMTP, Cambridge, England; South China Normal Univ, Southern Nucl Sci Comp Ctr, Guangdong Hong Kong Joint Lab Quantum Matter, Guangzhou 510006, Peoples R China; Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China; Univ Illinois, Chicago, IL 60607 USA; Tsinghua Univ, Dept Phys, 30 Shuangqing Rd, Beijing 100084, Peoples R China; Inst Frontier & Interdisciplinary Sci, Key Lab Particle Phys & Particle Irradiat MOE, Qingdao 266237Q, Shandong, Peoples R China | ; Ye, Zhihong/E-6651-2017; de Melo, Joao/O-4139-2018; Mekki, Abdelkrim/B-6422-2015; Goncalves, Victor/AAQ-1485-2021; Vladimirov, Alexey/AFS-1287-2022; Mochalov, Vasilii/G-9679-2017; Al-bataineh, Ayman/AAK-1197-2021; Bacchetta, Alessandro/F-3199-2012; Huber, Garth/JNS-0022-2023; Li, Xuan/K-6034-2017; Jacazio, Nicolò/HLW-2357-2023; Peng, Chao/MTG-0314-2025; Sufian, Raza/C-7844-2018; Semenov, Kirill/AAL-9290-2021; Cao, Xu/B-2070-2014; Pappagallo, Marco/R-3305-2016; Mathieu, Vincent/N-9218-2018; Burkert, Volker/AAF-7395-2020; Markowitz, Pete/AAC-3382-2020; Nunes, Ana/N-4747-2017; Joosten, Sylvester/HZL-4182-2023; Cosyn, Wim/ABC-1099-2020; Liu, Xiaohui/ABI-6636-2020; de Melo, Joao Pacheco/O-4139-2018; Tawfik, Abdel Nasser/M-6220-2013; Holtrop, Maurik/A-9017-2010; cao, xu/B-2070-2014; Albataineh, Ayman Ahmad/AAK-1197-2021; Courtoy, Aurore/GRY-7619-2022; Bo-Wen, Xiao/AHD-3216-2022; Terry, John/ABA-2609-2020; Kuchera, Michelle/LIC-8128-2024; Guo, Feng-Kun/F-5325-2010; Frederico, Tobias/F-4348-2011; El-Bennich, Bruno/AER-3286-2022; Pire, Bernard/AAV-3689-2020; Abdolmaleki, Hamed/Y-3622-2019; Pilloni, Alessandro/M-3626-2014 | 7004440244; 35227171600; 7003494602; 57206493481; 7003468594; 14017732400; 35227223500; 8258896400; 59817416300; 6603447397; 58254043900; 7401579410; 8729955200; 36934412800; 57193749765; 57727242800; 9241383300; 7003977292; 57222760261; 7004059162; 7403074941; 57218527298; 22133369700; 57218177062; 7005532059; 58003171000; 35365845700; 7202968485; 23987687800; 54790508300; 57204916710; 35227101500; 15047628800; 23026784600; 58003015900; 57451472100; 57217562965; 56014525700; 35374416600; 35069234100; 56543636400; 6507578062; 14041647600; 7005910634; 55887011800; 55970259800; 6602753782; 58003326700; 35227280900; 56187738200; 35225777000; 26434168900; 58003016000; 56115055200; 7006292368; 57206656408; 36993853800; 35516536200; 57204617911; 35227460400; 57531222100; 7201781287; 56210730700; 57203068254; 23034837300; 35285763100; 18037495900; 36604596000; 35227530600; 57216439510; 7007147401; 59121475900; 6507719807; 59157891300; 57224851647; 58003248200; 57326065200; 58664376600; 57192494892; 7405571889; 57197929289; 57209203159; 57330473400; 14825520500; 57425379200; 6603781128; 7006284991; 24500876300; 57203254691; 6603829178; 7004356384; 7004546205; 7402759461; 6506281916; 57199298922; 6602088698; 6603294089; 35227782600; 57219758788; 7004207376; 7004120059; 57205680783; 55364479600; 6701363261; 6506870986; 6603897565; 57202557646; 7403310021; 7003341934; 25641606400; 55586634600; 7201660483; 57129645300; 7202243676; 55803697000; 8721812700; 7102806030; 57207901251; 12244533300; 57188968931; 57962617800; 22986573900; 57191538718; 56070212700; 6701908288; 22969481600; 57189986493; 57203070364; 35228088300; 57217511563; 57211728507; 8292956700; 57197669895; 36635081200; 58144042600; 7102276477; 6603350317; 7201539565; 57000663400; 57211078940; 9840056500; 26327166500; 57222151615; 55148879800; 36836386600; 35569502200; 57189001467; 59821255500; 37462420900 | burkert@jlab.org; | PROGRESS IN PARTICLE AND NUCLEAR PHYSICS | PROG PART NUCL PHYS | 0146-6410 | 1873-2224 | 131 | SCIE | PHYSICS, NUCLEAR;PHYSICS, PARTICLES & FIELDS | 2023 | 14.5 | 2.3 | 3.85 | 2025-06-25 | 49 | 53 | EIC; QCD; Spin; Mass; Pressure; GPDs | DEEP-INELASTIC-SCATTERING; NUCLEAR PARTON DISTRIBUTIONS; VIRTUAL COMPTON-SCATTERING; QUANTUM-FIELD THEORIES; TRANSVERSE-MOMENTUM; FORM-FACTORS; EXCLUSIVE PHOTOPRODUCTION; RADIATIVE-CORRECTIONS; MECHANICAL-PROPERTIES; EVOLUTION-EQUATIONS | EIC; GPDs; Mass; Pressure; QCD; Spin | Luminance; EIC; Energy domain; GPD; Lower energies; Mass; Precision studies; QCD; Scientific results; Spin; Time-periods; High energy physics | English | 2023 | 2023-07 | 10.1016/j.ppnp.2023.104032 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Phage-targeting bimetallic nanoplasmonic biochip functionalized with bacterial outer membranes as a biorecognition element | The use of phages-a natural predator of bacteria-has emerged as a therapeutic strategy for treating multidrugresistant bacterial infections; thus, the isolation and detection of phages from the environment is crucial for advancing phage therapy. Herein, for the first time, we propose a nanoplasmonic-based biodetection platform for phages that utilizes bacterial outer membranes (OMs) as a biorecognition element. Conventional biosensors based on phage-bacteria interactions encounter multiple challenges due to the bacteriolytic phages and potentially toxic bacteria, resulting in instability and risk in the measurement. Therefore, instead of whole living bacteria, we employ a safe biochemical OMs fraction presenting phage-specific receptors, allowing the robust and reliable phage detection. In addition, the biochip is constructed on bimetallic nanoplasmonic islands through solid-state dewetting for synergy between Au and Ag, whereby sensitive detection of phage-OMs interactions is achieved by monitoring the absorption peak shift. For high detection performance, the nanoplasmonic chip is optimized by systematically investigating the morphological features, e.g., size and packing density of the nanoislands. Using our optimized device, phages are detected with high sensitivity (& GE;-104 plaques), specificity (little cross-reactivity), and affinity (stronger binding to the host OMs than anti-bacterial antibodies), further exhibiting the cell-killing activities. | Kim, Moon-Ju; Bae, Hyung Eun; Kwon, Soonil; Park, Mi-Kyung; Yong, Dongeun; Kang, Min-Jung; Pyun, Jae-Chul | Yonsei Univ, Dept Mat & Sci & Engn, Seoul 03722, South Korea; Kyungpook Natl Univ, Sch Food Sci & Biotechnol, Daegu 41566, South Korea; Yonsei Univ, Coll Med, Dept Lab Med, Seoul 03722, South Korea; Yonsei Univ, Res Inst Bacterial Resistance, Coll Med, Seoul 03722, South Korea; Korea Inst Sci & Technol, Mol Recognit Res Ctr, Seoul 02792, South Korea | ; pyun, jae-chul/J-2662-2012; Park, Mi-Kyung/J-9643-2017 | 57192182433; 58537649000; 57954561900; 7404491155; 7007146314; 15042583100; 35234792000 | jcpyun@yonsei.ac.kr; | BIOSENSORS & BIOELECTRONICS | BIOSENS BIOELECTRON | 0956-5663 | 1873-4235 | 238 | SCIE | BIOPHYSICS;BIOTECHNOLOGY & APPLIED MICROBIOLOGY;CHEMISTRY, ANALYTICAL;ELECTROCHEMISTRY;NANOSCIENCE & NANOTECHNOLOGY | 2023 | 10.7 | 2.4 | 0.27 | 2025-06-25 | 2 | 2 | Bacterial outer membranes; Localized surface plasmon resonance; Bimetallic nanoplasmonic islands; Phage-targeting biosensor; Anti-Bacterial antibody | SURFACE-PLASMON RESONANCE; NANOPARTICLES; RESISTANCE; AU; ABLATION; THERAPY; INNATE; OPTICS | Anti-Bacterial antibody; Bacterial outer membranes; Bimetallic nanoplasmonic islands; Localized surface plasmon resonance; Phage-targeting biosensor | Antibodies, Bacterial; Apoptosis; Bacterial Outer Membrane; Bacteriophages; Biosensing Techniques; Bacteria; Bacteriophages; Biochips; Risk assessment; Surface plasmon resonance; bacteriophage receptor; bacterium antibody; gold; silver; bacterium antibody; Anti-bacterial; Anti-bacterial antibody; Bacterial outer membrane; Bimetallic nanoplasmonic island; Bimetallics; Biorecognition elements; Localized surface plasmon resonance; Nanoplasmonics; Outer membrane; Phage-targeting biosensor; absorption; Article; bacterial outer membrane; bacteriolysis; bacteriophage; binding affinity; cell killing; controlled study; cross reaction; density; host bacterium interaction; molecular recognition; morphology; nonhuman; risk; sensitivity and specificity; size; solid state; surface plasmon resonance; apoptosis; bacterial outer membrane; genetic procedures; Antibodies | English | 2023 | 2023-10-15 | 10.1016/j.bios.2023.115598 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | Article | Tumor-derived OBP2A promotes prostate cancer castration resistance | Androgen deprivation therapy (ADT) is a systemic therapy for advanced prostate cancer (PCa); although most patients initially respond to ADT, almost all cancers eventually develop castration-resistant PCa (CRPC). Currently, most research focuses on castration-resistant tumors, and the role of tumors in remission is almost completely ignored. Here, we report that odorant-binding protein (OBP2A) released from tumors in remission during ADT catches survival factors, such as CXCL15/IL8, to promote PCa cell androgen-independent growth and enhance the infiltration of myeloid-derived suppressor cells (MDSCs) into tumor microenvironment, leading to the emergence of castration resistance. OBP2A knockdown significantly inhibits CRPC and metastatic CRPC development and improves therapeutic efficacy of CTLA-4/PD-1 antibodies. Treatment with OBP2A-binding ligand α-pinene interrupts the function of OBP2A and suppresses CRPC development. Furthermore, α-pinene–conjugated doxorubicin/docetaxel can be specifically delivered to tumors, resulting in improved anticancer efficacy. Thus, our studies establish a novel concept for the emergence of PCa castration resistance and provide new therapeutic strategies for advanced PCa. © 2022. | Jeong, Ji-Hak; Zhong, Shangwei; Li, Fuzhuo; Huang, Changhao; Chen, Xueyan; Liu, Qingqing; Peng, Shoujiao; Park, Hajeung; Lee, You Mie; Dhillon, Jasreman; Luo, Jun-Li | Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, United States, Vessel-Organ Interaction Research Center (VOICE, MRC), College of Pharmacy, Kyungpook National University, Daegu, South Korea; Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, United States, The Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, China; Department of Chemistry, The Scripps Research Institute, Jupiter, FL, United States; Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, United States; Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, United States; The Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, China; Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, United States; X-ray Core Facility, The Scripps Research Institute, Jupiter, FL, United States; Vessel-Organ Interaction Research Center (VOICE, MRC), College of Pharmacy, Kyungpook National University, Daegu, South Korea; Department of Pathology, Moffitt Cancer Center, Tampa, FL, United States; Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, United States, The Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, China | 55913671500; 55639445400; 57226692479; 57190050818; 59784802100; 58156307500; 56025735200; 7601570080; 8230508600; 26024982200; 26327010000 | jlluo@usc.edu.cn; | Journal of Experimental Medicine | J EXP MED | 0022-1007 | 1540-9538 | 220 | 3 | SCIE | IMMUNOLOGY;MEDICINE, RESEARCH & EXPERIMENTAL | 2023 | 12.8 | 2.4 | 1.59 | 2025-06-25 | 12 | cytotoxic T lymphocyte antigen 4; docetaxel; doxorubicin; interleukin 8; pinene; programmed death 1 ligand 1; tumor protein; animal cell; animal experiment; animal model; animal tissue; Article; cancer regression; castration resistant prostate cancer; cell growth; cell infiltration; cell migration; cell proliferation; controlled study; drug efficacy; gene knockdown; human; human cell; male; mouse; myeloid-derived suppressor cell; nonhuman; tumor microenvironment | English | Final | 2023 | 10.1084/jem.20211546 | 바로가기 | 바로가기 | 바로가기 | ||||||||
| ○ | ○ | Article | A Provably Secure Mobile User Authentication Scheme for Big Data Collection in IoT-Enabled Maritime Intelligent Transportation System | The emergence of contemporary technologies like cloud computing and the Internet of Things (IoT) has revolutionized the trends in the cyber world to serve humanity. There are plenty of applications in which they are being used, especially in smart cities and their constituents, Maritime Transportation System (MTS) is one of them. The IoT-enabled MTS has the potential to entertain the growing challenges of modern-day ship transportation. Secure real-time data access from numerous smart IoT devices is the most critical and crucial exercise for Big Data acquisition in IoT-enabled MTS. Therefore, we have developed a Physically Unclonable Function (PUF) based authenticated key agreement solution to deal with this challenge. This solution enables the mobile user and IoT node to mutually authenticate each other via Cloud-Gateway before real-time data exchange and transmission in IoT-enabled MTS. The use of PUF in our solution brings invincibility against physical security threats. An inclusive security analysis under the assumption of the specified threat model is carried out to substantiate the security resilience of our solution. The conduct of our solution is realized through security features, communication, and computation cost and It has been observed that our solution achieves efficiency of 37.3% and 9.7% in communication and computation overhead, respectively. Moreover, the network performance effectiveness of our solution is demonstrated in NS3 implementation. | Mahmood, Khalid; Ferzund, Javed; Saleem, Muhammad Asad; Shamshad, Salman; Das, Ashok Kumar; Park, Youngho | Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan; COMSATS Univ Islamabad, Dept Comp Sci, Sahiwal Campus, Sahiwal 57000, Pakistan; Univ Lahore, Dept Software Engn, Lahore 54590, Pakistan; Int Inst Informat Technol, Ctr Secur Theory & Algorithm Res, Hyderabad 500032, India; Old Dominion Univ, Virginia Modeling Anal & Simulat Ctr, Suffolk, VA 23435 USA; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea | Saleem, Muhammad Asad/KZG-8796-2024; Shamshad, Salman/AEN-4853-2022; Das, Ashok Kumar/U-2790-2019; Mahmood, Dr. Khalid/AAE-9552-2020; Mahmood, Khalid/AAE-9552-2020 | 57342911900; 25927016300; 57215832850; 57214118050; 55450732800; 56962990300 | khalidm.research@gmail.com;jferzund@cuisahiwal.edu.pk;masad@cuisahiwal.edu.pk;salmanshamshad01@gmail.com;iitkgp.akdas@gmail.com;parkyh@knu.ac.kr; | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | IEEE T INTELL TRANSP | 1524-9050 | 1558-0016 | 24 | 2 | SCIE | ENGINEERING, CIVIL;ENGINEERING, ELECTRICAL & ELECTRONIC;TRANSPORTATION SCIENCE & TECHNOLOGY | 2023 | 7.9 | 2.5 | 2.89 | 2025-06-25 | 22 | 25 | Protocols; Security; Internet of Things; Authentication; Big Data; Logic gates; Transportation; Authentication protocol; mutual authentication; key agreement; security protocol | KEY; PROTOCOLS; INTERNET | Authentication protocol; key agreement; mutual authentication; security protocol | Big data; Computation theory; Cryptography; Data acquisition; Electronic data interchange; Gateways (computer networks); Intelligent systems; Internet of things; Internet protocols; Network security; Security systems; Authentication protocols; Key agreement; Maritime transportation system; Mobile users; Mutual authentication; Physically unclonable functions; Provably secure; Security; Security protocol.; Security protocols; Authentication | English | 2023 | 2023-02 | 10.1109/tits.2022.3177692 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | Meeting Abstract | Airflow and Particle Transport Simulations for Subjects With Cement Dust Exposure | Hwang, J.; Hong, Y.; Nguyen, Q.; Lee, C.; Kim, W.; Chae, K.; Jin, G.; Choi, S. | Kyungpook Natl Univ, Sch Mech Engn, Daegu, South Korea; Kyungpook Natl Univ, Dept Bioind Machinery Engn, Daegu, South Korea; Seoul Nat Univ Hosp, Radiol, Seoul, South Korea; Kangwon Natl Univ Hosp, Dept Internal Med, Chunchon, South Korea; Kangwon Natl Univ Hosp, Ctr Environm Hlth, Chunchon, South Korea; Jeonbuk Natl Univ, Res Inst Clin Med, Dept Radiol, Biomed Res Inst, Jeonju, South Korea | AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE | AM J RESP CRIT CARE | 1073-449X | 1535-4970 | 207 | SCIE | CRITICAL CARE MEDICINE;RESPIRATORY SYSTEM | 2023 | 19.3 | 2.5 | 0 | English | 2023 | 2023-05-01 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | ○ | Article | Edge-based 3D vibration measurement of rotating cylinder-shaped structure through epipolar line-based corresponding point extraction between two camera images | Three-dimensional (3D) vibration measurements are important for monitoring rotating structures in order to reproduce the directional displacements of vibration signals such that they are similar to the actual movement. Photogrammetric techniques, which can be used to determine the relationship between 3D objects and their two-dimensional images, allow 3D vibrations in a structure to be measured without direct contact. Therefore, we herein present a multipoint 3D vibration measurement approach for rotating cylinder-shaped structures, which performs the imaging of a structure from two directions using two cameras. First, an epipolar line-based cor-responding point extraction technique is applied to extract the corresponding regions of interest (ROIs) in videos acquired by the two cameras placed at 90 degrees to the structure. Subsequently, an edge-based vibration measurement approach is used to detect vibrations in the corresponding ROIs. Noise reduction is then applied to reduce the noise, induced by the cameras or edge-based vibration measurement technique, by extracting and employing the frequencies at which the vibrations were occurring to the phase-based motion magnification technique. Subsequently, vibrations in the corresponding ROIs of the magnified videos are detected. For 3D plotting, the two vibration signals are combined. Simulation and real datasets are prepared for the qualitative and quantitative assessments of the proposed method. The simulation data results are compared with the results obtained before noise reduction, those obtained after noise reduction via band-pass filtering, and the reference data. A 3D vibration analysis is performed on the real data in multiple ROIs. The results yielded by the proposed method can be used to identify the weak points in a rotating cylinder-shaped structure that cause the structure to function abnormally. | Javed, Aisha; Park, Jueon; Lee, Changno; Lee, Hyeongill; Kim, Byeongil; Han, Youkyung | Seoul Natl Univ Sci & Technol, Dept Civil Engn, Seoul 01811, South Korea; Kyungpook Natl Univ, Sch Automot Engn, Sangju 37224, South Korea; Yeungnam Univ, Sch Mech Engn, Gyongsan 38541, South Korea | ; Javed, Aisha/LQK-3075-2024 | 57215897698; 57218222782; 57203055998; 8261483800; 56843890300; 55457676600 | javedaisha123@seoultech.ac.kr;jpark14@seoultech.ac.kr;changno@seoultech.ac.kr;hilee@knu.ac.kr;bikim@yu.ac.kr;han602@seoultech.ac.kr; | MECHANICAL SYSTEMS AND SIGNAL PROCESSING | MECH SYST SIGNAL PR | 0888-3270 | 1096-1216 | 187 | SCIE | ENGINEERING, MECHANICAL | 2023 | 7.9 | 2.5 | 0.88 | 2025-06-25 | 7 | 7 | Three-dimensional vibration measurement; Targetless photogrammetry; Epipolar geometry; Subpixel-based edge detection | COMPUTER VISION; TRACKING | Epipolar geometry; Subpixel-based edge detection; Targetless photogrammetry; Three-dimensional vibration measurement | Cameras; Cylinders (shapes); Extraction; Image processing; Noise abatement; Vibration analysis; Vibration measurement; 3D vibration measurements; Edge-based; Epipolar geometry; Region-of-interest; Regions of interest; Sub-pixels; Subpixel-based edge detection; Targetless photogrammetry; Three-dimensional vibration measurement; Three-dimensional vibrations; Photogrammetry | English | 2023 | 2023-03-15 | 10.1016/j.ymssp.2022.109981 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Merged LSTM-based pattern recognition of structural behavior of cable-supported bridges | Structural responses of bridges occur based on their structural characteristics and conditions. After the structural pattern is identified from the long-term measured response datasets, the structural responses can be evaluated and predicted using a pattern model. In the absence of significant variations in the structural condition, the difference between the predicted and measured responses is negligible. Otherwise, the differences can be identified, and this would be evidence of the variation in the structural condition. Therefore, the structural pattern model can be used effectively to investigate variations in the structural state and conditions. This study proposes an effective structural pattern recognition method using deep learning. A merged model is proposed by combining deep neural network (DNN) and long short-term memory (LSTM) algorithms to handle long-term responses from various sensors in the time domain and reflect statistical properties. Long-term (five-year) measured response datasets of an existing cable-supported bridge were used to validate the proposed method. According to the study, the proposed method can effectively identify the structural behavioral pattern of a cable-supported bridge. | Min, Seongi; Lee, Yunwoo; Byun, Yong-Hoon; Kang, Young Jong; Kim, Seungjun | Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 02841, South Korea; Chungbuk Natl Univ, Sch Civil Engn, Cheongju 28644, South Korea; Kyungpook Natl Univ, Sch Agr Civil & Bioind Engn, Daegu 41566, South Korea | Lee, Eun-Hye/KDN-5679-2024; Kim, Seungjun/R-3294-2019; Byun, Yong-Hoon/JKI-8441-2023; Kang, Young-Jong/D-3053-2013; Kang, Young/D-3053-2013 | 57226591242; 57218166232; 42761048000; 7402784706; 55498261300 | rocksmell@korea.ac.kr; | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | ENG APPL ARTIF INTEL | 0952-1976 | 1873-6769 | 125 | SCIE | AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ENGINEERING, ELECTRICAL & ELECTRONIC;ENGINEERING, MULTIDISCIPLINARY | 2023 | 7.5 | 2.5 | 1.11 | 2025-06-25 | 8 | 11 | Cable -supported bridge; Structural pattern recognition; Structural health monitoring; Deep learning; Long short-term memory; Long-term measured data | DAMAGE DETECTION; ALGORITHM | Cable-supported bridge; Deep learning; Long short-term memory; Long-term measured data; Structural health monitoring; Structural pattern recognition | Behavioral research; Brain; Cables; Deep neural networks; Pattern recognition; Structural health monitoring; Time domain analysis; Cable-supported bridges; Deep learning; Long-term measured data; Structural behaviors; Structural characteristics; Structural condition; Structural pattern; Structural pattern recognition; Structural response; Structural state; Long short-term memory | English | 2023 | 2023-10 | 10.1016/j.engappai.2023.106774 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Multi-slice Nested Recurrence Plot (MsNRP): A robust approach for person identification using daily ECG or PPG signals | This study presents a novel approach called Multi-slice Nested Recurrence Plot (MsNRP) for person iden-tification using noisy bio-signals. Prior studies in biometrics have predominantly relied on ideal datasets of 5-10 min, which introduces uncertainty in accuracy when dealing with noisy bio-signals. The proposed MsNRP method captures features from one and multiple cycles without the need for preprocessing, making it well-suited for photoplethysmograms(PPG) and electrocardiograms(ECG). By overcoming the limitations of traditional recurrence plots(RP), MsNRP demonstrates robustness to noisy bio-signal datasets, thus offering a reliable solution for identification in practical scenarios. We demonstrate the experiments of MsNRP on both 5-10 min datasets, similar to previous related work and day-long datasets, measured in daily life emphasizing its robustness in handling noisy data. | Jeon, YeongJun; Kang, Soon Ju | Kyungpook Natl Univ, Coll IT Engn, Sch Elect & Elect Engn, 80,Daehak Ro, Daegu 41566, South Korea | 57208863636; 55666313900 | sjkang@ee.knu.ac.kr; | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | ENG APPL ARTIF INTEL | 0952-1976 | 1873-6769 | 126 | SCIE | AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ENGINEERING, ELECTRICAL & ELECTRONIC;ENGINEERING, MULTIDISCIPLINARY | 2023 | 7.5 | 2.5 | 0.55 | 2025-06-25 | 5 | 5 | Person identification; Recurrence Plot (RP); Convolutional neural network (CNN); Biometrics; Electrocardiogram (ECG); Photoplethysmography (PPG); Internet of Medical Things (IoMT) | HEALTH-CARE; SLEEP-APNEA; CLASSIFICATION; SYSTEM | Biometrics; Convolutional neural network (CNN); Electrocardiogram (ECG); Internet of Medical Things (IoMT); Person identification; Photoplethysmography (PPG); Recurrence Plot (RP) | Biomedical signal processing; Convolutional neural networks; Data handling; Electrocardiography; Photoplethysmography; Biosignals; Convolutional neural network; Electrocardiogram; Internet of medical thing; Multi slices; Person identification; Photoplethysmography; Recurrence plot; Biometrics | English | 2023 | 2023-11 | 10.1016/j.engappai.2023.106799 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature | This paper proposes a spatiotemporal graph neural network capable of effective representation learning of the spatiotemporal interrelationships and interdependencies of in-situ observation data from multiple locations for multivariate multi-step ahead time-series forecasting. The propose model is largely composed of graph learning, spatial encoder, and temporal decoder, and ablation studies on variants of the three modules and comparative experiments with state-of-the-art deep neural networks for sequence modeling were also performed extensively. The proposed model showed improved predictability than conventional numerical model-based approaches or state-of-the-art models by applying consecutive multi-step ahead time-series prediction of sea surface temperature at multiple locations along the coast. For more rigorous performance evaluation, not only the overall performance of the test data, but also the performance of extreme cases included in the test data based on historical records were separately assessed. The prediction rationales were also presented through quantified relative contributions between neighbor locations using the trained adjacency matrix obtained through graph learning. The results showed that it is well consistent with the ocean physics and geographical domain knowledge, demonstrating the feasibility and reliability of the proposed method. Therefore, the proposed method shows sufficient potential to be used as a scientific tool for decision-making in extreme events such as marine heat waves or for operational ocean forecasting. | Kim, Jinah; Kim, Taekyung; Ryu, Joon-Gyu; Kim, Jaeil | Korea Inst Ocean Sci & Technol, Coastal Disaster Res Ctr, Busan, South Korea; Elect & Telecommun Res Inst, Satellite Wide Area Infra Res Sect, Daejeon, South Korea; Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea | 55720345100; 59471665700; 7401868766; 57211615348 | jakim@kiost.ac.kr;jgryurt@etri.re.kr; | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | ENG APPL ARTIF INTEL | 0952-1976 | 1873-6769 | 126 | SCIE | AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ENGINEERING, ELECTRICAL & ELECTRONIC;ENGINEERING, MULTIDISCIPLINARY | 2023 | 7.5 | 2.5 | 1.66 | 2025-06-25 | 16 | 16 | Graph neural network; Deep learning; Multivariate multiple time-series; Multi-step-ahead time-series prediction; Extreme events; Sea surface temperature | SURFACE-TEMPERATURE; PREDICTION; MODEL; LSTM | Deep learning; Extreme events; Graph neural network; Multi-step-ahead time-series prediction; Multivariate multiple time-series; Sea surface temperature | Atmospheric temperature; Decision making; Deep neural networks; Domain Knowledge; Forecasting; Location; Oceanography; Submarine geophysics; Surface waters; Time series; Ahead-time; Deep learning; Extreme events; Graph neural networks; Multi-step-ahead time-series prediction; Multiple time series; Multisteps; Multivariate multiple time-series; Sea surface temperature; Sea surfaces; Surface temperatures; Time series prediction; Surface temperature | English | 2023 | 2023-11 | 10.1016/j.engappai.2023.106854 | 바로가기 | 바로가기 | 바로가기 | 바로가기 |
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