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Next Generation Sequencing (NGS)-Based Post-Transplant Monitoring in Acute Myeloid Leukemia Patients Receiving Allogeneic Hematopoietic Stem Cell Transplantation (HCT)

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BLOOD 2017年 130卷 271-271页

作者： Kim, Taehyung Moon, Joon Ho Ahn, Jae-Sook Tyndel, Marc S. Kim, Yeo-Kyeoung Lee, Seung-Shin Ahn, Seo-Yeon Jung, Sung-Hoon Yang, Deok-Hwan Lee, Je-Jung Choi, Seunghyun Shin, Myung-Geun Kook, Hoon Kim, Sun-Hee Park, Seong Kyu Kim, Hyeoung-Joon Zhang, Zhaolei Kim, Dennis (Dong-Hwong) Donnelly Centre for Cellular and Biomolecular Research University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Kyungpook National University Hospital Daegu Korea Republic of (South) Department of Hematology-Oncology Chonnam National University Hwasun Hospital Hwasun-Gun Korea Republic of (South) The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Canada Genome Research Center for Hematopoietic Disease Chonnam National University Hwasun Hospital Hwasun Korea Republic of (South) Department of Hematology-Oncology Chonnam National University Hwasun Hospital Hwasun Korea Republic of (South) Department of Hematology-Oncology Chonnam National University Hwasun Hospital Hwasun-gun Korea Republic of (South) Hematology-Oncology Chonnam National University Hwasun Hospital Hwasun Korea Republic of (South) Hematopoietic Genomics Research Centre Chonnam National University Hwasun Hospital Hwasun-gun Korea Republic of (South) Departments of Laboratory Medicine Chonnam National Univ. Hwasun Hosp. Hwasun-gun South Korea Chonnam National Univ. Hwasun Hospital Hwasun-Gun KOR Samsung Medical Center Seoul KOR Soonchunhyang University Bucheon Hospital Bucheon Korea Republic of (South) Department of Molecular Genetics University of Toronto Toronto Canada Blood and Marrow Transplant Program Department of Medical Oncology and Hematology Princess Margaret Cancer Centre University Health Network Toronto Canada

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Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016 : Cadiz, KY, USA. 8-10 April 2016

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BMC bioinformatics 2016年第SUPPL 10期17卷 297页

作者： Eric C. Rouchka Julia H. Chariker Benjamin J. Harrison Juw Won Park Xueyuan Cao Stanley Pounds Susana Raimondi James Downing Raul Ribeiro Jeffery Rubnitz Jatinder Lamba Bernie J. Daigle Deborah Burgess Stephanie Gehrlich John C. Carmen Nicholas Johnson Chandrakanth Emani Kalpani De Silva Michael P. Heaton Theodore S. Kalbfleisch Teeradache Viangteeravat Rahul Mudunuri Oluwaseun Ajayi Fatih Şen Eunice Y. Huang Mohammad Mohebbi Luaire Florian Douglas J. Jackson John F. Naber AKM Sabbir Sally R. Ellingson Yuping Lu Charles A Phillips Michael A. Langston Rahul K. Sevakula Raghuveer Thirukovalluru Nishchal K. Verma Yan Cui Mohammed Sayed Jing Wang Qi Liu Yu Shyr Xiaofei Zhang Naresh Prodduturi Gavin R. Oliver Diane Grill Jie Na Jeanette Eckel-Passow Eric W. Klee Michael M. Goodin Mark Farman Harrison Inocencio Chanyong Jang Jerzy W. Jaromczyk Neil Moore Kelly Sovacool Leon Dent Mike Izban Sammed Mandape Shruti Sakhare Siddharth Pratap Dana Marshall M Scotty DePriest James N. MacLeod Hanady Adam Ethan Blandford Joel Campbell Joshua Castlen Brittany Dixon Ginger Gilbert Aaron Hall Philip Kreisle Jessica Lasher Bethany Oakes Allison Speer Maximilian Valentine Naga Satya V. Rao Nagisetty Rony Jose Robert Rooney David Hains Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core Louisville USA Department of Psychological and Brain Sciences University of Louisville Louisville USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville USA Department of Biostatistics St. Jude Children’s Research Hospital Memphis USA Department of Pathology St. Jude Children’s Research Hospital Memphis USA Department of Oncology St. Jude Children’s Research Hospital Memphis USA Department of Pharmacotherapy and Translational Research College of Pharmacy University of Florida Gainesville USA Department of Biological Sciences University of Memphis Memphis USA Department of Computer Science University of Memphis Memphis USA Department of Computer Science Northern Kentucky University Highland Heights USA Department of Biological Sciences Northern Kentucky University Highland Heights USA Department of Biology Western Kentucky University Bowling Green USA Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville USA United States Department of Agriculture Agricultural Research Service United States Meat Animal Research Center Clay Center USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville USA Biomedical Informatics Core Children’s Foundation Research Institute Memphis USA Department of Pediatrics University of Tennessee Health Science Center Memphis USA Department of Electrical and Computer Engineering University of Louisville Louisville USA Department of Computer Science University of Kentucky Lexington USA Division of Biomedical Informatics College of Medicine University of Kentucky Lexington USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA Department of Electrical E

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nasce

关键词： True Negative Differential Expression UCSC Genome Browser Laboratory Information Management System Biomedical Informatics

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Biomimetics: Biomimetic Microfingerprints for Anti‐Counterfeiting Strategies (Adv. Mater. 12/2015)

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Advanced Materials 2015年第12期27卷

作者： Hyung Jong Bae Sangwook Bae Cheolheon Park Sangkwon Han Junhoi Kim Lily Nari Kim Kibeom Kim Suk‐Heung Song Wook Park Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Center for Nanoparticle Research Institute for Basic Science (IBS) Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Institute of Entrepreneurial Bio Convergence Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Interdisciplinary Program for Bioengineering Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Department of Electronics and Radio Engineering Institute for Laser Engineering Kyung Hee University Deongyeong‐daero Giheung‐gu Yongin‐si Gyeonggi‐do 446‐701 South Korea

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Zeeman polaritons as a platform for probing Dicke physics in condensed matter

arXiv

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arXiv 2024年

作者： Kritzell, T. Elijah Doumani, Jacques Asano, Tobias Yamada, Sota Tay, Fuyang Xu, Hongjing Yan, Han Katayama, Ikufumi Takeda, Jun Nevidomskyy, Andriy Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Physics Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Miyagi Sendai980-8577 Japan Rice Advanced Materials Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd3Ga5O12. The electron paramagnetic resonance of Gd3+ ions is tuned by a magnetic field into resonance with a Fabry–Pérot cavity mode, resulting in the formation of spin–photon hybrid states, or Zeeman polaritons. We observe that the light–matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin–boson system is more compatible with the Dicke model and has advantages over boson–boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light–matter hybrids. Copyright © 2024, The Authors. All rights reserved.

关键词： Phonons

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Carbon Nanospheres: Tunable Luminescent Carbon Nanospheres with Well-Defined Nanoscale Chemistry for Synchronized Imaging and Therapy (Small 36/2015)

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Small 2015年第36期11卷

作者： Prabuddha Mukherjee Santosh K. Misra Mark C. Gryka Huei-Huei Chang Saumya Tiwari William L. Wilson John W. Scott Rohit Bhargava Dipanjan Pan Department of Bioengineering University of Illinois at Urbana-Champaign 1304 W. Springfield Ave. Urbana IL 61801 USA Department of Chemistry University of Illinois at Urbana-Champaign 600 S. Mathews Ave. Urbana IL 61801 USA Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana IL 61801 USA Illinois Sustainability Technology Center University of Illinois at Urbana-Champaign Urbana IL 61801 USA Electrical and Computer Engineering Chemical and Biomolecular Engineering Chemistry and Mechanical Science and Engineering Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign 1304 W. Springfield Ave. Urbana IL 61801 USA Carle Foundation Hospital 502 N. Busey St. Urbana IL 61801 USA Departments of Bioengineering and Materials Science and Engineering 502 N. Busey St. Urbana IL 61801 USA Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL 61801 USA

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Author Correction: Macroscopic transition metal dichalcogenides monolayers with uniformly high optical quality

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Nature communications 2023年第1期14卷 6241页

作者： Qiuyang Li Adam Alfrey Jiaqi Hu Nathanial Lydick Eunice Paik Bin Liu Haiping Sun Yang Lu Ruoyu Wang Stephen Forrest Hui Deng Department of Physics University of Michigan Ann Arbor MI 48109 USA. Applied Physics Program University of Michigan Ann Arbor MI 48109 USA. Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA. Michigan Center for Materials Characterization College of Engineering University of Michigan Ann Arbor MI 48109 USA. Department of Physics University of Michigan Ann Arbor MI 48109 USA. dengh@umich.edu. Applied Physics Program University of Michigan Ann Arbor MI 48109 USA. dengh@umich.edu.

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Erratum to: electrical detection of dsDNA and polymerase chain reaction amplification

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Biomedical Microdevices 2011年第6期13卷 1095-1095页

作者： Salm, Eric Liu, Yi-Shao Marchwiany, Daniel Morisette, Dallas He, Yiping Razouk, Laila Bhunia, Arun K. Bashir, Rashid Department of Bioengineering University of Illinois at Urbana-Champaign Urbana USA Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign Urbana USA Taiwan Semiconductor Manufacturing Corporation Hsinchu Republic of China Department of Molecular and Cellular Biology University of Illinois at Urbana-Champaign Urbana USA BioVitesse Inc. West Lafayette USA Birck Nanotechnology Center Purdue University West Lafayette USA USDA-ARS-ERRC Wyndmoor USA Department of Food Science Purdue University West Lafayette USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Illinois USA

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Publisher’s Note: “Guided-mode-resonance-coupled plasmonic-active SiO2 nanotubes for surface enhanced Raman spectroscopy” [Appl. Phys. Lett. 100, 191114 (2012)]

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Applied Physics Letters 2012年第5期101卷

作者： Xiaobin Xu Dihan Hasan Lei Wang Swapnajit Chakravarty Ray T. Chen D. L. Fan Alan X. Wang 1Materials Science and Engineering Program Texas Materials Institute University of Texas at Austin Austin Texas 78712 USA 2School of Electrical Engineering and Computer Science Oregon State University Corvallis Oregon 97331 USA 3Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology 430074 Wuhan China 4Microelectronic Research Center University of Texas at Austin Austin Texas 78758 USA 5Omega Optics Inc. Austin Texas 78759 USA 6Department of Mechanical Engineering University of Texas at Austin Austin Texas 78712 USA

This article was originally published online on 10 May 2012 with an incorrect affiliation for co-author D. L. Fan and an incorrect footnote designation for co-a

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