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Author Correction: Disrupting cellular memory to overcome drug resistance

Nature communications 2024年第1期15卷 4264页

作者： Guillaume Harmange Raúl A Reyes Hueros Dylan L Schaff Benjamin Emert Michael Saint-Antoine Laura C Kim Zijian Niu Shivani Nellore Mitchell E Fane Gretchen M Alicea Ashani T Weeraratna M Celeste Simon Abhyudai Singh Sydney M Shaffer Cellular and Molecular Biology Graduate Group Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Biochemistry and Molecular Biophysics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania Philadelphia PA USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA 91125 USA. Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA. Abramson Family Cancer Research Institute Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Chemistry College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Physics College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Biology College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. The Wharton School University of Pennsylvania Philadelphia PA USA. Cancer Signaling and Microenvironment Research Program Fox Chase Cancer Center Philadelphia PA USA. Department of Biochemistry and Molecular Biology Johns Hopkins School of Public Health Baltimore MD USA. Sidney Kimmel Cancer Center Johns Hopkins School of Medicine Baltimore MD USA. Department of Cell and Developmental Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania Philadelphia PA USA. sydshaffer@***. Department of Pathology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. sydshaffer@***.

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A methodology to investigate the relationship between cancer cells cell-cycle phase and their migratory behaviors

A methodology to investigate the relationship between cancer...

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Clinical and Translational Biophotonics, TRANSLATIONAL 2018

作者： Pourfarhangi, Kamyar Esmaeili Bayarmagnai, Battuya de la Hoz, Edgar Cardenas Cohen, Andrew Gligorijevic, Bojana Bioengineering department College of Engineering Temple University PhiladelphiaPA19122 United States Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA19104 United States Cancer Biology Program Fox Chase Cancer Center PhiladelphiaPA19111 United States

We present a methodology integrating live-cell fluorescent microscopy, cell labeling, PDMS fabricated micro-channels (2D) and tumor spheroids (3D), and automated cell tracking in order to investigate cell-cycle phase ... 详细信息

ISBN: (纸本)9781943580415

关键词： Cancer cells

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Iterative Motion Compensation reconstruction ultra-short TE(iMoCo UTE) for high resolution free breathing pulmonary MRI

arXiv

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arXiv 2020年

作者： Zhu, Xucheng Chan, Marilynn Lustig, Michael Johnson, Kevin Larson, Peder E.Z. UCSF/UC Berkeley Graduate Program in Bioengineering University of California San Francisco San FranciscoCA United States Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCA United States Department of Pediatrics Division of Pediatric Pulmonology University of California San Francisco San FranciscoCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Department of Medical Physics University of Wisconsin Madison MadisonWI United States Department of Radiology University of Wisconsin Madison MadisonWI United States

Purpose: To develop a high scanning efficiency, motion corrected imaging strategy for free-breathing pulmonary MRI by combining a motion compensation reconstruction with a UTE acquisition, called iMoCo UTE. Methods: An optimized golden angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, then motion resolved data was reconstructed by XD Golden-angle RAdial Sparse Parallel reconstruction (XD-GRASP). One state from the motion resolved images was selected as a reference, and motion fields from the other states to the reference were derived via non-rigid registration. Finally, all motion resolved data and motion fields were reconstructed by using an iterative motion compensation reconstruction with a total generalized variation sparse constraint. Results: The iMoCo UTE strategy was evaluated in volunteers and non-sedated pediatric patient(4-6 y/o) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures, and higher apparent SNR and CNR, compared to using other motion correction strategies, such as soft-gating, motion resolved reconstruction, and non-rigid motion compensation(MoCo). iMoCo UTE also showed promising results in an infant study. Conclusions: The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency, SNR, and reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free breathing lung MRI scans, especially in very challenging application situations, such as pediatric MRI studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A micromirror array with annular partitioning for high-speed random-access axial focusing

arXiv

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arXiv 2020年

作者： Ersumo, Nathan Tessema Yalcin, Cem Antipa, Nick Pégard, Nicolas Waller, Laura Lopez, Daniel Muller, Rikky University of California Berkeley - University of California San Francisco Graduate Program in Bioengineering United States Departement of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel HillNC27514 United States Chan Zuckerberg Biohub San FranciscoCA94158 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States

Dynamic axial focusing functionality has recently seen widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics, and material processing. Yet the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to drive (ex: liquid crystal, elastomeric or optofluidic lenses) suffer from low (~100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as dwelling capacity (ex: acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (ex: deformable mirror arrays). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion, 48 μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1 100 nm with 10-90% settling in 64.8 μs (i.e. 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires less than 30 V/channel. Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct, resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

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Reproducible brain-wide association studies require thousands of individuals (Mar, 10.1038/s41586-022-04492-9, 2022)

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NATURE 2022年第7911期605卷 E11-E11页

作者： Marek, Scott Tervo-Clemmens, Brenden Calabro, Finnegan J. Montez, David F. Kay, Benjamin P. Hatoum, Alexander S. Donohue, Meghan Rose Foran, William Miller, Ryland L. Hendrickson, Timothy J. Malone, Stephen M. Kandala, Sridhar Feczko, Eric Miranda-Dominguez, Oscar Graham, Alice M. Earl, Eric A. Perrone, Anders J. Cordova, Michaela Doyle, Olivia Moore, Lucille A. Conan, Gregory M. Uriarte, Johnny Snider, Kathy Lynch, Benjamin J. Wilgenbusch, James C. Pengo, Thomas Tam, Angela Chen, Jianzhong Newbold, Dillan J. Zheng, Annie Seider, Nicole A. Van, Andrew N. Metoki, Athanasia Chauvin, Roselyne J. Laumann, Timothy O. Greene, Deanna J. Petersen, Steven E. Garavan, Hugh Thompson, Wesley K. Nichols, Thomas E. Yeo, B. T. Thomas Barch, Deanna M. Luna, Beatriz Fair, Damien A. Dosenbach, Nico U. F. Department of Psychiatry Washington University School of Medicine St Louis MO USA Department of Neurology Washington University School of Medicine St Louis MO USA Department of Psychological and Brain Sciences Washington University in St Louis St Louis MO USA Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Psychology University of Pittsburgh Pittsburgh PA USA Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Department of Bioengineering University of Pittsburgh Pittsburgh PA USA Department of Biomedical Engineering Washington University in St Louis St Louis MO USA Department of Radiology Washington University School of Medicine St Louis MO USA Department of Neurological Surgery Washington University School of Medicine St Louis MO USA Program in Occupational Therapy Washington University School of Medicine St Louis MO USA Department of Pediatrics Washington University School of Medicine St Louis MO USA University of Minnesota Informatics Institute University of Minnesota Minneapolis MN USA Department of Psychology University of Minnesota Minneapolis MN USA Masonic Institute for the Developing Brain University of Minnesota Medical School Minneapolis MN USA Department of Pediatrics University of Minnesota Medical School Minneapolis MN USA Department of Psychiatry Oregon Health and Science University Portland OR USA Minnesota Supercomputing Institute University of Minnesota Minneapolis MN USA Institute of Child Development University of Minnesota Medical School Minneapolis MN USA Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Sleep and Cognition National University of Singapore Singapore Singapore Centre for Translational MR Research National University of Singapore Singapore Singapore N.1 Institute for Health Institute for Digital Medicine National University of Singapore Singapore Sing

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Brain tumor segmentation in magnetic resonance images using genetic algorithm clustering and adaboost classifier 5

Brain tumor segmentation in magnetic resonance images using ...

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5th International Conference on Bioimaging, BIOIMAGING 2018 - Part of 11th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Oliveira, Gustavo C. Varoto, Renato Cliquet, Alberto University of São Paulo Interunits Graduate Program in Bioengineering University of São Paulo São Carlos - SP Brazil Campinas - SP Brazil Department of Electrical and Computer Engineering University of São Paulo São Carlos - SP Brazil

ISBN: (纸本)9789897582783

We present a technique for automatic brain tumor segmentation in magnetic resonance images, combining a modified version of a Genetic Algorithm Clustering method with an AdaBoost Classifier. In a group of 42 FLAIR images, segmentations produced by the algorithm were compared to the ground truth information produced by radiologists. The mean Dice similarity coefficient reached by the algorithm was 70.3%. In most cases, the AdaBoost classifier increased the quality of the segmentation, improving, on average, the DSC in about 10%. Our implementation of the Genetic Algorithm Clustering method presents improvements compared to the original method. The use of a fixed, small number of groups and smaller population allowed for less computational effort. In addition, adaptive restriction in the initial segmentation was achieved by using the information of the groups with highest and 2nd-highest mean intensities. By exploring intensity and spatial information of the pixels, the AdaBoost classifier improved segmentation results. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Image segmentation

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Characterization of TOF-PET detectors for a second generation radiofrequency-penetrable PET insert for simultaneous PET/MRI

Characterization of TOF-PET detectors for a second generatio...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Qian Dong Brian J. Lee Chen-Ming Chang Ilaria Sacco Ronald D. Watkins Peter Fischer Emily Anaya Craig S. Levin Departments of Electrical Engineering and Radiology Stanford University Stanford CA USA Departments of Mechanical Engineering and Radiology Stanford University Stanford CA USA Departments of Applied Physics and Radiology Stanford University Stanford CA USA Department of Radiology Stanford University Stanford CA USA Department of Computer Science Heidelberg University Mannheim Germany Departments of Radiology Electrical Engineering Bioengineering and Physics Stanford University and Molecular Imaging Program at Stanford (MIPS) Stanford CA USA

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Simultaneous PET/MRI combines two powerful and complementary modalities, providing multiparametric information to assess the anatomic as well as biochemical basis of disease. However, the high cost of the current commercial integrated PET/MRI systems limit the long-term potential, accessibility, and availability of PET/MRI. A portable PET insert provides a cost- effective alternative to achieve simultaneous PET/MRI for the sites that are already equipped with MR systems. Therefore, we are developing a radiofrequency (RF)-penetrable MR-compatible time-of-flight (TOF)-PET insert that can acquire simultaneous PET/MR data using the MR systems built-in body coil for RF transmission. In this study, we characterized performance of two detector modules in the MR bore under four different MRI conditions: B 0 only, echo planar imaging (EPI), gradient echo (GRE), and fast spin echo (FSE), each running for 15 minutes. The one-hour study results for coincidence timing resolution, energy resolution, coincidence count rate and ASIC chip temperature of two detector modules demonstrate the PET performance stability as well as the MR compatibility of the PET detectors under different MRI sequences. The global coincidence timing and energy resolution achieved was 264.21 ± 4.82 ps FWHM and 13.66 ± 0.08 % FWHM, respectively.

关键词： Detectors Clocks Energy resolution Magnetic resonance imaging Timing Positron emission tomography Radio frequency

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Concentric Micromirror Array for High-Speed Optical Dynamic Focusing

Concentric Micromirror Array for High-Speed Optical Dynamic ...

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IEEE/LEOS International Conference on Optical MEMS

作者： Nathan Tessema Ersumo Cem Yalcin Nicolas Pégard Laura Waller Daniel Lopez Rikky Muller San Francisco Graduate Program in Bioengineering University of California Berkeley - University of California USA Departement of Electrical Engineering & Computer Sciences University of California Berkeley USA Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel Hill USA Center for Nanoscale Materials Argonne National Laboratory Argonne USA

Optical dynamic focusing has emerged as a speed, complexity and efficiency bottleneck across various applications, which include scanning multi-photon microscopy in biology, maskless lithography and micromachining in material processing, and wavefront correction. Here, we present a 32-ring 23,852-pixel concentric micromirror array capable of performing dynamic focusing for wavelengths of up to 1040 nm with a response rate of up to 8.75 kHz, 30 V drive and a focusing full-width-half-maximum to range ratio of 4.8%.

关键词： Optimized production technology High-speed optical techniques Micromechanical devices Nanophotonics Micromirrors Focusing

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Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers for Early Assessment of Renal Transplants 25

Role of Integrating Diffusion Mr Image-Markers with Clinical...

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25th IEEE International Conference on Image Processing, ICIP 2018

作者： Shehata, Mohammad Ghazal, Mohammed Beache, Garth Ei-Ghar, Mohamed Abou Dwyer, Amy Hajjdiab, Hassan Khalil, Ashraf Ei-Baz, Ayman Bioengineering Department University of Louisville LouisvilleKY United States Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Radiology Department University of Louisville LouisvilleKY United States Radiolozy Department Urology and Nephrology Center Mansoura University Mansoura Egypt Kidney Disease Program University of Louisville LouisvilleKY United States Computer Science and Information Technology Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781479970612

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status. © 2018 IEEE.

关键词： Magnetic resonance imaging

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Early Assessment of Acute Renal Rejection Post-transplantation: A Combined Imaging and Clinical Biomarkers Protocol

Early Assessment of Acute Renal Rejection Post-transplantati...

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2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018

作者： Shehata, Mohamed Taher, Fatma Ghazal, Mohammed Mahmoud, Ali Beache, Garth El-Ghar, Mohamed Abou Dwyer, Amy C. Elmaghraby, Adel El-Baz, Ayman BioImaging Lab Bioengineering Department University of Louisville LouisvilleKY United States College of Technological Innovation Zayed University Dubai United Arab Emirates Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Radiology Department University of Louisville LouisvilleKY United States Radiology Department Urology and Nephrology Center Mansoura University Mansoura Egypt Kidney Disease Program University of Louisville LouisvilleKY United States Computer Engineering and Computer Science Department University of Louisville LouisvilleKY United States

ISBN: (纸本)9781538675687

Non-invasive evaluation of renal transplant function is crucial. Hence, a computer-assisted diagnostic (CAD) system is introduced in this paper to evaluate kidney function post-transplantation. The developed CAD system integrates clinical-based with diffusion weighted (DW) MR image-based biomarkers. The latter are derived from 3D DW-MRIs at multiple strengths and duration of the magnetic field (i.e. b-values). These DW-MRI scans were acquired at multiple geographical areas (Egypt and USA) using different scanner types (GE and Philips). The developed CAD system first segments kidneys using level-sets method and then estimates the DW-MRI image-markers, known as apparent diffusion coefficients (ADCs), from the segmented kidney. Then, the clinical biomarkers (serum creatinine and creatinine clearance) are integrated with the DW-MR image-markers (ADCs) resulted in new integrated markers known as integrated ADCs (IADCs). These IADCs are then used to construct cumulative distribution functions (CDFs) at multiple b-values. Finally, these markers (i.e. CDFs of the IADCs) are used to assess renal transplant status using different classifiers. Our CAD system demonstrates an almost consistent accuracy of 93%, sensitivity of 93%, and specificity of 92% in distinguishing acute rejection (AR) from non-rejection (NR) renal transplants, making the proposed diagnostic platform independent from the geographical area, scanner type, and classifier. These promising preliminary results are of high diagnostic accuracy and suggest that the developed CAD system might be noninvasively able to diagnose renal allograft status. © 2018 IEEE.

关键词： Creatinine

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