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125th ASEE Annual Conference and Exposition

作者： Riskin, Eve A. Milford, Jana Callahan, Janet Cosman, Pamela Schneider, John B. Pitts, Kevin Knaphus-Soran, Emily Llewellyn, Donna C. Delaney, Ann E. Myers, Beth A. Tetrick, Katherine Christine Cunningham, Sonya Ennis, Tanya D. O'Connor, Kevin Ferrez, Michelle Pan, Tiffany D. Baldis, Jessica EE Department University of Washington United States Engineering GoldShirt Program University of Colorado Boulder United States Micron School of Materials Science and Engineering Boise State University United States Department of Electrical and Computer Engineering University of California San Diego United States Voiland College of Engineering and Architecture United States Department of Physics University of Illinois Urbana-Champaign United States University of Washington United States Institute for STEM and Diversity Initiatives Boise State University United States Public Policy and Administration Boise State University United States Department of Analytics Assessment and Accreditation University of Colorado Boulder United States Program Washington State University United States University of Washington STARS Program Diversity and Access College of Engineering United States Engineering GoldShirt Program University of Colorado Boulder College of Engineering and Applied Science United States Department of Educational Psychology and Learning Sciences University of Colorado Boulder United States United States

The NSF-funded Redshirt in engineering Consortium was formed in 2016 with the goal of enhancing the ability of academically talented but underprepared students coming from low-income backgrounds to successfully graduate with engineering degrees. The Consortium takes its name from the practice of redshirting in college athletics, with the idea of providing an extra year and support to help promising engineering students complete a bachelor's degree. The Consortium builds on the success of three existing "academic redshirt" programs and expands the model to three new schools. The Existing Redshirt Institutions (ERIs) help mentor and train the new Student Success Partners (SSPs), and SSPs contribute their unique expertise to help ERIs improve existing Redshirt programs. The Redshirt model consists of seven main programmatic components aimed at improving the engagement, retention, and graduation of students underrepresented in engineering. These components include: "intrusive" academic advising and support services, an intensive first-year academic curriculum, community-building (including pre-matriculation summer programs), career awareness and vision, faculty mentorship, NSF S-STEM scholarships, and second-year support. Successful implementation of these activities is intended to produce two main long-term outcomes: a six-year graduation rate of 60%-75% for Redshirt students, and increased rates of enrollment and graduation of Pell-eligible, URM, and women students in engineering at participating universities. In the first year of the grant (AY 16-17), SSPs developed their own Redshirt programs, hired and trained staff, and got their programs off the ground. ERIs implemented faculty mentorship programs and expanded support to Redshirt students into their sophomore year. In the second year (AY 17-18), Redshirt programs were expanded at the ERIs while SSPs welcomed their first cohorts of Redshirt students. This Work in Progress paper describes the Redshirt programs at e

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Charting brain growth in tandem with brain templates at school age

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science Bulletin 2020年第22期65卷 1924-1934,M0004页

作者： Hao-Ming Dong F.Xavier Castellanos Ning Yang Zhe Zhang Quan Zhou Ye He Lei Zhang Ting Xu Avram J.Holmes B.T.Thomas Yeo Feiyan Chen Bin Wang Christian Beckmann Tonya White Olaf Sporns Jiang Qiu Tingyong Feng Antao Chen Xun Liu Xu Chen Xuchu Weng Michael P.Milham Xi-Nian Zuo Research Center for Lifespan Development of Brain and Mind Institute of PsychologyChinese Academy of Sciences(CAS)Beijing 100101China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Child and Adolescent Psychiatry New York University School of MedicineNew YorkNY 10016USA Nathan Kline Institute for Psychiatric Research OrangeburgNew YorkNY 10962USA Department of Psychology University of CASBeijing 100049China Department of Psychological and Brain Sciences Indiana UniversityBloomingtonIN 47405USA Child Mind Institute New YorkNY 10022USA Department of Psychology Yale UniversityNew HavenCT 06511USA Department of Electrical and Computer Engineering Clinical Imaging Research CentreN.1 Institute for Health and Memory Networks ProgramNational University of SingaporeSingapore 117574Singapore Department of Physics Zhejiang UniversityHangzhou 310058China Binzhou Medical University Yantai 264100China Donders Institute Radboud University Medical CentreNijmegen 6525 ENNetherlands Department of Child and Adolescent Psychiatry Erasmus UniversityRotterdam 3000 CBNetherlands Department of Radiology Erasmus University Medical CentreRotterdam 3000 CANetherlands School of Psychology Southwest UniversityChongqing 400715China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou 510631China Institute of Children Health Changzhou Children’s HospitalChangzhou 213003China Key Laboratory of Brain and Education School of Education ScienceNanning Normal UniversityNanning 530001China CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China

Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental *** present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging *** accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and *** magnetic resonance imaging(MRI)of typically developing school-age children(TDC)was obtained up to three times at nominal intervals of1.25 *** protocol generated and compared population-and age-specific brain templates and growth charts,respectively.A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American ***-and age-specific brain templates were used to quantify warp cost,the differences between individual brains and brain *** growth charts for labeled brain network areas were *** analyses of cost functions supported the necessity of age-specific and ethnicitymatched brain templates,which was confirmed by growth chart *** analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas,regions which are most variable across individuals in regard to their structure and ***-and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts,indicating the necessity of the brain charts and brain templates generated in *** templates and growth charts as well as related codes have been made freely available to the public for open neuroscience(https://***/zuoxinian/CCS/tree/master/H3/Growth Charts).

关键词： Growth charts Neuroimaging Ethnicity Brain templates Development

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Cytocompatible magnetostrictive microstructures for nano- A nd microparticle manipulation on linear strain response piezoelectrics

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Multifunctional Materials 2018年第1期1卷

作者： Xiao, Zhuyun Khojah, Reem Chooljian, Marc Conte, Roberto Lo Schneider, Joseph D. Fitzell, Kevin Chopdekar, Rajesh V. Wang, Yilian Scholl, Andreas Chang, Jane Carman, Gregory P. Bokor, Jeffrey Carlo, Dino Di Candler, Rob N. Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Bioengineering University of California Los Angeles United States University of California Berkeley UCSF Graduate Program in Bioengineering Berkeley United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Mechanical and Aerospace Engineering University of California Los Angeles United States Department of Chemical and Biomolecular Engineering University of California Los Angeles United States Advanced Light Source Lawrence Berkeley National Laboratory Berkeley United States California Nanosystems Institute Los Angeles United States

In this work, we investigate polycrystalline Ni and FeGamagnetostrictivemicrostructures on prepoled (011)-cut single crystal [Pb(Mg1/3Nb2/3)O3]1-x-[PbTiO3]x (PMN-PT, x ≈ 0.31) with linear strain profile versus applied electric field. Magnetostrictive microstructure arrays with various geometries are patterned on PMN-PT. Functionalizedmagnetic beads are trapped by localized stray fields originating fromthe microstructures. With an applied electric field, themagnetic domains are actuated, inducing the motion of the coupled particles with sub-micrometer precision. This work shows promise of using energy-efficient electric-field-controlled magnetostrictivemicro- A nd nanostructures formanipulatingmagnetic beads via a linear strain response. The work also demonstrates the viability of cells suspended in solution on these structures when subject to applied electric fields, proving the cytocompatibility of the platform for live cell sorting applications. © 2018 IOP Publishing Ltd.

关键词： Electric fields

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Multi-UAVs communication for payload data link enhancement under asynchronous access

Multi-UAVs communication for payload data link enhancement u...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Yun-Ruei Li Hsin-Piao Lin Li-Chun Wang Abebe Belay Adege Li-Yong Chen Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Chiao Tung University Hsinchu Taiwan Department of International Graduate Program in Electrical Engineering and Computer Science (EECS) National Taipei University of Technology Taipei Taiwan

Recently, unmanned Aerial Vehicles (UAVs) are widely applied in civilian and commercial areas. In order to rapidly arrange the UAVs in a variable and changeable environment, the technique of maintaining stable and reliable communication link between Ground Control Station (GCS) and UAVs is one of the main issues [1]. However, UAVs deployment can significantly increase the efficiency of missions execution and the flexibility of task allocation. As discussed by different scholars, multi-UAVs have a number of advantages depending on the application areas: disaster areas monitoring, wireless sensor data collection in precision agriculture, and relay-aided of UAVs network topology [2].

关键词： OFDM Resource management Interference Wireless communication Payloads Bandwidth Optical filters

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P-2: Hydrogenated SnO for p-channel oxide Thin Film Transistor

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SID Symposium Digest of Technical Papers 2020年第1期51卷

作者： Alex Wootae Lee Kosuke Matsuzaki Kenji Nomura Materials Science and Engineering program University of California San Diego La Jolla California 92092 USA Materials and Structures Laboratory Tokyo Institute of Technology Nagatsuta Midori-ku Yokohama 226-8503 Japan Department of Electrical and Computer Engineering University of California San Diego La Jolla California 92092 USA

Subgap defect termination for p-type Tin monoxide (SnO) is developed with thermal annealing in hydrogen ambient for p-channel oxide thin-film transistor (TFT). High-performance p-channel oxide TFT with the TFT mobility of ~1.4-1.8 cm 2 /Vs and large on-to-off current as large as 10 5 was demonstrated. Hydrogen-containing annealing also maintains SnO phase stable up to 390 °C and effectively reduces the subgap defect, which offer the improvement in TFT characteristics.

关键词： p-channel oxide thin film transistor tin monoxide hydrogen defect termination hydrogenated SnO

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Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Enhanced plasmonic photocatalysis through cooperative plasmonic-photonic hybridization

arXiv

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arXiv 2019年

作者： Huang, Qinglan Canady, Taylor D. Gupta, Rohit Li, Nantao Singamaneni, Srikanth Cunningham, Brian T. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Engineering & Materials Science Washington University in St. Louis St. LouisMO63130 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Plasmonic nanoparticles (NPs) hold tremendous promise for catalyzing light-driven chemical reactions. The conventionally assumed detrimental absorption loss from plasmon damping can now be harvested to drive chemical transformations of the NP adsorbent, through the excitation and transfer of energetic "hot" carriers. The rate and selectivity of plasmonic photocatalysis are dependent on the characteristics of the incident light. By engineering the strength and wavelength of the light harvesting of a NP, it is possible to achieve more efficient and predictive photocatalysts. We report a plasmonic-photonic resonance hybridization strategy to substantially enhance hot electron generation at tunable, narrow-band wavelengths. By coupling the plasmon resonance of silver NPs to the guided mode resonance in a photonic crystal (PC) slab, the reaction rate of a hot-electron-driven reduction conversion is greatly accelerated. The mechanism is broadly compatible with NPs with manifold materials and shapes optimized for the targeted chemistry. The novel enhancement platform sheds light on rational design of high-performance plasmonic photocatalysts. Copyright © 2019, The Authors. All rights reserved.

关键词： Plasmonics

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[email protected] core-shell nanoparticles as a p-type gas sensor: Novel synthesis, characterization, and their gas sensing properties with sensing mechanism

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Sensors and Actuators B: Chemical 2018年 268卷 223-231页

作者： Sanjit Manohar Majhi Gautam Kumar Naik Hu-Jun Lee Ho-Geun Song Cheul-Ro Lee In-Hwan Lee Yeon-Tae Yu Division of Advanced Materials Engineering and Research Center for Advanced Materials Development College of Engineering Chonbuk National University Jeonju 561-756 South Korea Electrical Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Ogam Technology Jeonju 54882 South Korea Department of Materials Science and Engineering Korea University Seoul 02841 South Korea

In this work, [email protected] core-shell nanoparticles (C-S NPs) as a p-type gas sensing material was synthesized by a facile wet-chemical method, and evaluated their gas sensing properties as compared to the pristine NiO NPs gas sensors. Transmission electron microscope (TEM) results exhibited the well-dispersed formation of [email protected] C-S NPs having the total size of 70–120 nm and NiO shells having 30–50 nm thickness. The C-S morphology as well as the overall particle sizes are unchanged even at 500 °C. The gas sensing result reveals that the response of [email protected] C-S NPs gas sensor is higher than pristine NiO NPs gas sensor for 100 ppm of ethanol at 200 °C operating temperature. The baseline resistance in the air for [email protected] C-S NPs sensor is lowered as compared to pristine NiO NPs, which is due to the increased number of holes as charge carriers in [email protected] C-S NPs. The high response of [email protected] core-shell NPs as compared to pristine NiO NPs is attributed to electronic and chemical sensitization effects of Au. In [email protected] C-S structure, the contact between metal (Au) and semiconductor (NiO) formed a Schottky junction since Au metal acted as electron acceptor, a withdrawal of electrons from NiO by Au metal core leaved behind number of holes as charge carriers in [email protected] C-S NPs. Therefore, the baseline resistance of [email protected] C-S NPs greatly decreased than pristine NiO NPs, as a result the [email protected] C-S NPs showed higher response. On the other hand, in chemical sensitization effect, Au NPs catalyzed to dissociate O 2 molecules into ionic species. This work will give some clue to the researchers for the further development of p-type based C-S NPs sensors.

关键词： [email protected] Core-shell NPs Baseline resistance Sensitivity [email protected]

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