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Wagner MG, Kutlu AZ, Davis B, Raval AN, Laeseke PF, Speidel MA. Topology observing 3D device reconstruction from continuous-sweep limited angle fluoroscopy. Med Phys 2024; 51:2882-2892. [PMID: 38308822 DOI: 10.1002/mp.16954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/20/2023] [Accepted: 01/12/2024] [Indexed: 02/05/2024] Open
Abstract
BACKGROUND Minimally invasive procedures usually require navigating a microcatheter and guidewire through endoluminal structures such as blood vessels and airways to sites of the disease. For numerous clinical applications, two-dimensional (2D) fluoroscopy is the primary modality used for real-time image guidance during navigation. However, 2D imaging can pose challenges for navigation in complex structures. Real-time 3D visualization of devices within the anatomic context could provide considerable benefits for these procedures. Continuous-sweep limited angle (CLA) fluoroscopy has recently been proposed to provide a compromise between conventional rotational 3D acquisitions and real-time fluoroscopy. PURPOSE The purpose of this work was to develop and evaluate a noniterative 3D device reconstruction approach for CLA fluoroscopy acquisitions, which takes into account endoluminal topology to avoid impossible paths between disconnected branches. METHODS The algorithm relies on a static 3D roadmap (RM) of vessels or airways, which may be generated from conventional cone beam CT (CBCT) acquisitions prior to navigation. The RM is converted to a graph representation describing its topology. During catheter navigation, the device is segmented from the live 2D projection images using a deep learning approach from which the centerlines are extracted. Rays from the focal spot to detector pixels representing 2D device points are identified and intersections with the RM are computed. Based on the RM graph, a subset of line segments is selected as candidates to exclude device paths through disconnected branches of the RM. Depth localization for each point along the device is then performed by finding the point closest to the previous 3D reconstruction along the candidate segments. This process is repeated as the projection angle changes for each CLA image frame. The approach was evaluated in a phantom study in which a catheter and guidewire were navigated along five pathways within a complex vessel phantom. The result was compared to static cCBCT acquisitions of the device in the final position. RESULTS The average root mean squared 3D distance between CLA reconstruction and reference centerline was1.87 ± 0.30 $1.87 \pm 0.30$ mm. The Euclidean distance at the device tip was2.92 ± 2.35 $2.92 \pm 2.35$ mm. The correct pathway was identified during reconstruction in100 % $100\%$ of frames (n = 1475 $n=1475$ ). The percentage of 3D device points reconstructed inside the 3D roadmap was91.83 ± 2.52 % $91.83 \pm 2.52\%$ with an average distance of0.62 ± 0.30 $0.62 \pm 0.30$ mm between the device points outside the roadmap and the nearest point within the roadmap. CONCLUSIONS This study demonstrates the feasibility of reconstructing curvilinear devices such as catheters and guidewires during endoluminal procedures including intravascular and transbronchial interventions using a noniterative reconstruction approach for CLA fluoroscopy. This approach could improve device navigation in cases where the structure of vessels or airways is complex and includes overlapping branches.
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Affiliation(s)
- Martin G Wagner
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Ayca Z Kutlu
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Brian Davis
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Amish N Raval
- Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA
| | - Paul F Laeseke
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Michael A Speidel
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA
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2
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Nair PR, Danilova L, Gómez-de-Mariscal E, Kim D, Fan R, Muñoz-Barrutia A, Fertig EJ, Wirtz D. MLL1 regulates cytokine-driven cell migration and metastasis. Sci Adv 2024; 10:eadk0785. [PMID: 38478601 PMCID: PMC10936879 DOI: 10.1126/sciadv.adk0785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/07/2024] [Indexed: 03/17/2024]
Abstract
Cell migration is a critical contributor to metastasis. Cytokine production and its role in cancer cell migration have been traditionally associated with immune cells. We find that the histone methyltransferase Mixed-Lineage Leukemia 1 (MLL1) controls 3D cell migration via cytokines, IL-6, IL-8, and TGF-β1, secreted by the cancer cells themselves. MLL1, with its scaffold protein Menin, controls actin filament assembly via the IL-6/8/pSTAT3/Arp3 axis and myosin contractility via the TGF-β1/Gli2/ROCK1/2/pMLC2 axis, which together regulate dynamic protrusion generation and 3D cell migration. MLL1 also regulates cell proliferation via mitosis-based and cell cycle-related pathways. Mice bearing orthotopic MLL1-depleted tumors exhibit decreased lung metastatic burden and longer survival. MLL1 depletion leads to lower metastatic burden even when controlling for the difference in primary tumor growth rates. Combining MLL1-Menin inhibitor with paclitaxel abrogates tumor growth and metastasis, including preexistent metastasis. These results establish MLL1 as a potent regulator of cell migration and highlight the potential of targeting MLL1 in patients with metastatic disease.
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Affiliation(s)
- Praful R. Nair
- Institute for Nanobiotechnology, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ludmila Danilova
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Estibaliz Gómez-de-Mariscal
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, 28911 Leganés, and Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Optical Cell Biology Group, Instituto Gulbenkian de Ciência, R. Q.ta Grande 6 2780, 2780-156 Oeiras, Portugal
| | - Dongjoo Kim
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Arrate Muñoz-Barrutia
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, 28911 Leganés, and Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
| | - Elana J. Fertig
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA
| | - Denis Wirtz
- Institute for Nanobiotechnology, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
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3
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Shvartzman B, Ram Y. Self-replicating artificial neural networks give rise to universal evolutionary dynamics. PLoS Comput Biol 2024; 20:e1012004. [PMID: 38547320 PMCID: PMC11003675 DOI: 10.1371/journal.pcbi.1012004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 04/09/2024] [Accepted: 03/17/2024] [Indexed: 04/11/2024] Open
Abstract
In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can naturally emerge in a self-replicator model when both selection and mutation are implicit and endogenous. We therefore suggest that SeRANN can be applied to explore and test various evolutionary dynamics and hypotheses.
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Affiliation(s)
- Boaz Shvartzman
- School of Zoology, Faculty of Life Sciences, Tel Aviv University; Tel Aviv, Israel
- School of Computer Science, Reichman University; Herzliya, Israel
| | - Yoav Ram
- School of Zoology, Faculty of Life Sciences, Tel Aviv University; Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University; Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University; Tel Aviv, Israel
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4
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Cantrell DR, Cho L, Zhou C, Faruqui SHA, Potts MB, Jahromi BS, Abdalla R, Shaibani A, Ansari SA. Background Subtraction Angiography with Deep Learning Using Multi-frame Spatiotemporal Angiographic Input. J Imaging Inform Med 2024; 37:134-144. [PMID: 38343209 PMCID: PMC10980661 DOI: 10.1007/s10278-023-00921-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/29/2023] [Accepted: 10/23/2023] [Indexed: 03/02/2024]
Abstract
Catheter Digital Subtraction Angiography (DSA) is markedly degraded by all voluntary, respiratory, or cardiac motion artifact that occurs during the exam acquisition. Prior efforts directed toward improving DSA images with machine learning have focused on extracting vessels from individual, isolated 2D angiographic frames. In this work, we introduce improved 2D + t deep learning models that leverage the rich temporal information in angiographic timeseries. A total of 516 cerebral angiograms were collected with 8784 individual series. We utilized feature-based computer vision algorithms to separate the database into "motionless" and "motion-degraded" subsets. Motion measured from the "motion degraded" category was then used to create a realistic, but synthetic, motion-augmented dataset suitable for training 2D U-Net, 3D U-Net, SegResNet, and UNETR models. Quantitative results on a hold-out test set demonstrate that the 3D U-Net outperforms competing 2D U-Net architectures, with substantially reduced motion artifacts when compared to DSA. In comparison to single-frame 2D U-Net, the 3D U-Net utilizing 16 input frames achieves a reduced RMSE (35.77 ± 15.02 vs 23.14 ± 9.56, p < 0.0001; mean ± std dev) and an improved Multi-Scale SSIM (0.86 ± 0.08 vs 0.93 ± 0.05, p < 0.0001). The 3D U-Net also performs favorably in comparison to alternative convolutional and transformer-based architectures (U-Net RMSE 23.20 ± 7.55 vs SegResNet 23.99 ± 7.81, p < 0.0001, and UNETR 25.42 ± 7.79, p < 0.0001, mean ± std dev). These results demonstrate that multi-frame temporal information can boost performance of motion-resistant Background Subtraction Deep Learning algorithms, and we have presented a neuroangiography domain-specific synthetic affine motion augmentation pipeline that can be utilized to generate suitable datasets for supervised training of 3D (2d + t) architectures.
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Affiliation(s)
- Donald R Cantrell
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA.
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Radiology, Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA.
| | - Leon Cho
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
| | - Chaochao Zhou
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
| | - Syed H A Faruqui
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
| | - Matthew B Potts
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Babak S Jahromi
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ramez Abdalla
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
| | - Ali Shaibani
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Radiology, Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Sameer A Ansari
- Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL, 60611, USA
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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5
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Dutt S, Shao H, Karawdeniya B, Bandara YMNDY, Daskalaki E, Suominen H, Kluth P. High Accuracy Protein Identification: Fusion of Solid-State Nanopore Sensing and Machine Learning. Small Methods 2023; 7:e2300676. [PMID: 37718979 DOI: 10.1002/smtd.202300676] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/25/2023] [Indexed: 09/19/2023]
Abstract
Proteins are arguably one of the most important class of biomarkers for health diagnostic purposes. Label-free solid-state nanopore sensing is a versatile technique for sensing and analyzing biomolecules such as proteins at single-molecule level. While molecular-level information on size, shape, and charge of proteins can be assessed by nanopores, the identification of proteins with comparable sizes remains a challenge. Here, solid-state nanopore sensing is combined with machine learning to address this challenge. The translocations of four similarly sized proteins is assessed using amplifiers with bandwidths (BWs) of 100 kHz and 10 MHz, the highest bandwidth reported for protein sensing, using nanopores fabricated in <10 nm thick silicon nitride membranes. F-values of up to 65.9% and 83.2% (without clustering of the protein signals) are achieved with 100 kHz and 10 MHz BW measurements, respectively, for identification of the four proteins. The accuracy of protein identification is further enhanced by classifying the signals into different clusters based on signal attributes, with F-value and specificity of up to 88.7% and 96.4%, respectively, for combinations of four proteins. The combined use of high bandwidth instruments, advanced clustering and machine learning methods allows label-free identification of proteins with high accuracy.
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Affiliation(s)
- Shankar Dutt
- Department of Materials Physics, Research School of Physics, Australian National University, Canberra, ACT, 2601, Australia
| | - Hancheng Shao
- Department of Materials Physics, Research School of Physics, Australian National University, Canberra, ACT, 2601, Australia
| | - Buddini Karawdeniya
- Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra, ACT, 2601, Australia
| | - Y M Nuwan D Y Bandara
- Research School of Chemistry, Australian National University, Canberra, ACT, 2601, Australia
| | - Elena Daskalaki
- School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT, 2601, Australia
| | - Hanna Suominen
- School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT, 2601, Australia
- Eccles Institute of Neuroscience, College of Health and Medicine, Australian National University, Canberra, ACT, 2601, Australia
| | - Patrick Kluth
- Department of Materials Physics, Research School of Physics, Australian National University, Canberra, ACT, 2601, Australia
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6
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Vu HH, Behrmann H, Hanić M, Jeyasankar G, Krishnan S, Dannecker D, Hammer C, Gunkel M, Solov'yov IA, Wolf E, Behrmann E. A marine cryptochrome with an inverse photo-oligomerization mechanism. Nat Commun 2023; 14:6918. [PMID: 37903809 PMCID: PMC10616196 DOI: 10.1038/s41467-023-42708-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/19/2023] [Indexed: 11/01/2023] Open
Abstract
Cryptochromes (CRYs) are a structurally conserved but functionally diverse family of proteins that can confer unique sensory properties to organisms. In the marine bristle worm Platynereis dumerilii, its light receptive cryptochrome L-CRY (PdLCry) allows the animal to discriminate between sunlight and moonlight, an important requirement for synchronizing its lunar cycle-dependent mass spawning. Using cryo-electron microscopy, we show that in the dark, PdLCry adopts a dimer arrangement observed neither in plant nor insect CRYs. Intense illumination disassembles the dimer into monomers. Structural and functional data suggest a mechanistic coupling between the light-sensing flavin adenine dinucleotide chromophore, the dimer interface, and the C-terminal tail helix, with a likely involvement of the phosphate binding loop. Taken together, our work establishes PdLCry as a CRY protein with inverse photo-oligomerization with respect to plant CRYs, and provides molecular insights into how this protein might help discriminating the different light intensities associated with sunlight and moonlight.
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Affiliation(s)
- Hong Ha Vu
- Institute of Molecular Physiology (IMP), Johannes Gutenberg-University Mainz, Hanns-Dieter-Hüsch-Weg 17, 55128, Mainz, Germany
| | - Heide Behrmann
- University of Cologne, Faculty of Mathematics and Natural Sciences, Institute of Biochemistry, Zülpicher Straße 47, 50674, Cologne, Germany
| | - Maja Hanić
- Institute of Physics, Carl von Ossietzky University of Oldenburg, Carl-von-Ossietzky Straße 9-11, 26129, Oldenburg, Germany
| | - Gayathri Jeyasankar
- University of Cologne, Faculty of Mathematics and Natural Sciences, Institute of Biochemistry, Zülpicher Straße 47, 50674, Cologne, Germany
| | - Shruthi Krishnan
- Institute of Molecular Physiology (IMP), Johannes Gutenberg-University Mainz, Hanns-Dieter-Hüsch-Weg 17, 55128, Mainz, Germany
| | - Dennis Dannecker
- University of Cologne, Faculty of Mathematics and Natural Sciences, Institute of Biochemistry, Zülpicher Straße 47, 50674, Cologne, Germany
| | - Constantin Hammer
- Institute of Molecular Physiology (IMP), Johannes Gutenberg-University Mainz, Hanns-Dieter-Hüsch-Weg 17, 55128, Mainz, Germany
| | - Monika Gunkel
- University of Cologne, Faculty of Mathematics and Natural Sciences, Institute of Biochemistry, Zülpicher Straße 47, 50674, Cologne, Germany
| | - Ilia A Solov'yov
- Institute of Physics, Carl von Ossietzky University of Oldenburg, Carl-von-Ossietzky Straße 9-11, 26129, Oldenburg, Germany
- Research Center for Neurosensory Sciences, Carl von Ossietzky University of Oldenburg, Carl-von-Ossietzky Straße 9-11, 26111, Oldenburg, Germany
- Center for Nanoscale Dynamics (CENAD), Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstr. 114-118, 26129, Oldenburg, Germany
| | - Eva Wolf
- Institute of Molecular Physiology (IMP), Johannes Gutenberg-University Mainz, Hanns-Dieter-Hüsch-Weg 17, 55128, Mainz, Germany.
- Institute of Molecular Biology (IMB), 55128, Mainz, Germany.
| | - Elmar Behrmann
- University of Cologne, Faculty of Mathematics and Natural Sciences, Institute of Biochemistry, Zülpicher Straße 47, 50674, Cologne, Germany.
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7
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Case D, Aktulga HM, Belfon K, Cerutti DS, Cisneros GA, Cruzeiro VD, Forouzesh N, Giese TJ, Götz AW, Gohlke H, Izadi S, Kasavajhala K, Kaymak MC, King E, Kurtzman T, Lee TS, Li P, Liu J, Luchko T, Luo R, Manathunga M, Machado MR, Nguyen HM, O’Hearn KA, Onufriev AV, Pan F, Pantano S, Qi R, Rahnamoun A, Risheh A, Schott-Verdugo S, Shajan A, Swails J, Wang J, Wei H, Wu X, Wu Y, Zhang S, Zhao S, Zhu Q, Cheatham TE, Roe DR, Roitberg A, Simmerling C, York DM, Nagan MC, Merz KM. AmberTools. J Chem Inf Model 2023; 63:6183-6191. [PMID: 37805934 PMCID: PMC10598796 DOI: 10.1021/acs.jcim.3c01153] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 10/10/2023]
Abstract
AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.
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Affiliation(s)
- David
A. Case
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Hasan Metin Aktulga
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Kellon Belfon
- FOG
Pharmaceuticals Inc., Cambridge 02140, Massachusetts, United States
| | - David S. Cerutti
- Psivant, 451 D Street, Suite 205, Boston 02210, Massachusetts, United States
| | - G. Andrés Cisneros
- Department
of Physics, Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson 75801, Texas, United States
| | - Vinícius
Wilian D. Cruzeiro
- Department
of Chemistry and The PULSE Institute, Stanford
University, Stanford 94305, California, United States
| | - Negin Forouzesh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Timothy J. Giese
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Andreas W. Götz
- San
Diego Supercomputer Center, University of
California San Diego, La Jolla 92093-0505, California, United States
| | - Holger Gohlke
- Institute
for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Saeed Izadi
- Pharmaceutical
Development, Genentech, Inc., South San Francisco 94080, California, United
States
| | - Koushik Kasavajhala
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Mehmet C. Kaymak
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Edward King
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Tom Kurtzman
- Ph.D.
Programs in Chemistry, Biochemistry, and Biology, The Graduate Center of the City University of New York, 365 Fifth Avenue, New York 10016, New York, United States
- Department
of Chemistry, Lehman College, 250 Bedford Park Blvd West, Bronx 10468, New York, United States
| | - Tai-Sung Lee
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Pengfei Li
- Department
of Chemistry and Biochemistry, Loyola University
Chicago, Chicago 60660, Illinois, United States
| | - Jian Liu
- Beijing
National Laboratory for Molecular Sciences, Institute of Theoretical
and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Tyler Luchko
- Department
of Physics and Astronomy, California State
University, Northridge, Northridge 91330, California, United States
| | - Ray Luo
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Madushanka Manathunga
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | | | - Hai Minh Nguyen
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Kurt A. O’Hearn
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Alexey V. Onufriev
- Departments
of Computer Science and Physics, Virginia
Tech, Blacksburg 24061, Virginia, United
States
| | - Feng Pan
- Department
of Statistics, Florida State University, Tallahassee 32304, Florida, United States
| | - Sergio Pantano
- Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Ruxi Qi
- Cryo-EM
Center, Southern University of Science and
Technology, Shenzhen 518055, China
| | - Ali Rahnamoun
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Ali Risheh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Stephan Schott-Verdugo
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Akhil Shajan
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | - Jason Swails
- Entos, 4470 W Sunset
Blvd, Suite 107, Los Angeles 90027, California, United States
| | - Junmei Wang
- Department
of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh 15261, Pennsylvania, United States
| | - Haixin Wei
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Xiongwu Wu
- Laboratory
of Computational Biology, NHLBI, NIH, Bethesda 20892, Maryland, United States
| | - Yongxian Wu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Shi Zhang
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Shiji Zhao
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
- Nurix Therapeutics, Inc., San Francisco 94158, California, United States
| | - Qiang Zhu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Thomas E. Cheatham
- Department
of Medicinal Chemistry, The University of
Utah, 30 South 2000 East, Salt Lake City 84112, Utah, United
States
| | - Daniel R. Roe
- Laboratory
of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda 20892, Maryland, United States
| | - Adrian Roitberg
- Department
of Chemistry, The University of Florida, 440 Leigh Hall, Gainesville 32611-7200, Florida, United States
| | - Carlos Simmerling
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Darrin M. York
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Maria C. Nagan
- Department
of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Kenneth M. Merz
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
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8
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Ranieri CM, Moioli RC, Vargas PA, Romero RAF. A neurorobotics approach to behaviour selection based on human activity recognition. Cogn Neurodyn 2023; 17:1009-1028. [PMID: 37522044 PMCID: PMC10374508 DOI: 10.1007/s11571-022-09886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 08/04/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.
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Affiliation(s)
- Caetano M. Ranieri
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
| | - Renan C. Moioli
- Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Avenida Senador Salgado Filho, 3000, Natal, RN 59078-970 Brazil
| | - Patricia A. Vargas
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS Scotland, UK
| | - Roseli A. F. Romero
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
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9
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Abstract
We present PepQuery2, which leverages a new tandem mass spectrometry (MS/MS) data indexing approach to enable ultrafast, targeted identification of novel and known peptides in any local or publicly available MS proteomics datasets. The stand-alone version of PepQuery2 allows directly searching more than one billion indexed MS/MS spectra in the PepQueryDB or any public datasets from PRIDE, MassIVE, iProX, or jPOSTrepo, whereas the web version enables users to search datasets in PepQueryDB with a user-friendly interface. We demonstrate the utilities of PepQuery2 in a wide range of applications including detecting proteomic evidence for genomically predicted novel peptides, validating novel and known peptides identified using spectrum-centric database searching, prioritizing tumor-specific antigens, identifying missing proteins, and selecting proteotypic peptides for targeted proteomics experiments. By putting public MS proteomics data directly into the hands of scientists, PepQuery2 opens many new ways to transform these data into useful information for the broad research community.
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Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
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10
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Koger B, Deshpande A, Kerby JT, Graving JM, Costelloe BR, Couzin ID. Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision. J Anim Ecol 2023. [PMID: 36945122 DOI: 10.1111/1365-2656.13904] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 02/07/2023] [Indexed: 03/23/2023]
Abstract
Methods for collecting animal behaviour data in natural environments, such as direct observation and biologging, are typically limited in spatiotemporal resolution, the number of animals that can be observed and information about animals' social and physical environments. Video imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals' locations, into geographical coordinates. We demonstrate a new system for studying behaviour in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals' body postures (poses) and extract environmental features, including topography of the landscape and animal trails. By quantifying animal movement and posture while reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments.
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Affiliation(s)
- Benjamin Koger
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Adwait Deshpande
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Jeffrey T Kerby
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
- Neukom Institute for Computational Science, Dartmouth College, Hanover, New Hampshire, USA
- Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark
| | - Jacob M Graving
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
- Advanced Research Technology Unit, Max Planck Institute of Animal Behaviour, Konstanz, Germany
| | - Blair R Costelloe
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Iain D Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
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11
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Baskerville AL, Gray C, Cox H. Correlated energy from radial density-energy relations. R Soc Open Sci 2023; 10:221402. [PMID: 36938537 PMCID: PMC10014244 DOI: 10.1098/rsos.221402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Here, we demonstrate that the radial distribution function can be mapped into a radial density-energy space and the relationship between the radial density and radial energy is linear for the ground and excited states of helium-like systems; the gradient of the resulting straight line delivers the energy of the state considered. To utilize this finding, a simple analytical expression for the total energy in terms of the density at the most probable nucleus-electron distance of the systems considered is derived using a fitting procedure.
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Affiliation(s)
- Adam L. Baskerville
- Department of Chemistry, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, UK
| | - Conor Gray
- Department of Chemistry, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, UK
| | - Hazel Cox
- Department of Chemistry, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QJ, UK
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12
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Hanna J, Flöel A. An accessible and versatile deep learning-based sleep stage classifier. Front Neuroinform 2023; 17:1086634. [PMID: 36938361 PMCID: PMC10017438 DOI: 10.3389/fninf.2023.1086634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.
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Affiliation(s)
- Jevri Hanna
- Greifswald University Hospital, Greifswald, Germany
- *Correspondence: Jevri Hanna,
| | - Agnes Flöel
- Greifswald University Hospital, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany
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13
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Rodríguez-Moreno I, Martínez-Otzeta JM, Goienetxea I, Sierra B. Sign language recognition by means of common spatial patterns: An analysis. PLoS One 2022; 17:e0276941. [PMID: 36315481 PMCID: PMC9621452 DOI: 10.1371/journal.pone.0276941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/17/2022] [Indexed: 01/24/2023] Open
Abstract
Currently there are around 466 million hard of hearing people and this amount is expected to grow in the coming years. Despite the efforts that have been made, there is a communication barrier between deaf and hard of hearing signers and non-signers in environments without an interpreter. Different approaches have been developed lately to try to deal with this issue. In this work, we present an Argentinian Sign Language (LSA) recognition system which uses hand landmarks extracted from videos of the LSA64 dataset in order to distinguish between different signs. Different features are extracted from the signals created with the hand landmarks values, which are first transformed by the Common Spatial Patterns (CSP) algorithm. CSP is a dimensionality reduction algorithm and it has been widely used for EEG systems. The features extracted from the transformed signals have been then used to feed different classifiers, such as Random Forest (RF), K-Nearest Neighbors (KNN) or Multilayer Perceptron (MLP). Several experiments have been performed from which promising results have been obtained, achieving accuracy values between 0.90 and 0.95 on a set of 42 signs.
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Affiliation(s)
- Itsaso Rodríguez-Moreno
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
- * E-mail:
| | - José María Martínez-Otzeta
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
| | - Izaro Goienetxea
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
| | - Basilio Sierra
- Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
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14
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Basha SHS, Pulabaigari V, Mukherjee S. An information-rich sampling technique over spatio-temporal CNN for classification of human actions in videos. Multimed Tools Appl 2022; 81:40431-40449. [PMID: 35572387 PMCID: PMC9084266 DOI: 10.1007/s11042-022-12856-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 01/27/2022] [Accepted: 03/09/2022] [Indexed: 06/15/2023]
Abstract
We propose a novel video sampling scheme for human action recognition in videos, using Gaussian Weighing Function. Traditionally in deep learning-based human activity recognition approaches, either a few random frames or every k t h frame of the video is considered for training the 3D CNN, where k is a small positive integer, like 4, 5, or 6. This kind of sampling reduces the volume of the input data, which speeds-up the training network and also avoids overfitting to some extent, thus enhancing the performance of the 3D CNN model. In the proposed video sampling technique, consecutive k frames of a video are aggregated into a single frame by computing a Gaussian-weighted summation of the k frames. The resulting frame preserves the information in a better way than the conventional approaches and experimentally shown to perform better. In this paper, a 3-Dimensional deep CNN is proposed to extract the spatio-temporal features and follows Long Short-Term Memory (LSTM) to recognize human actions. The proposed 3D CNN architecture is capable of handling the videos where the camera is placed at a distance from the performer. Experiments are performed with KTH, WEIZMANN, and CASIA-B Human Activity and Gait datasets, whereby it is shown to outperform state-of-the-art deep learning based techniques. We achieve 95.78%, 95.27%, and 95.27% over the KTH, WEIZMANN, and CASIA-B human action and gait recognition datasets, respectively.
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Affiliation(s)
- S. H. Shabbeer Basha
- Computer Vision Group, Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh 517646 India
| | - Viswanath Pulabaigari
- Computer Vision Group, Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh 517646 India
| | - Snehasis Mukherjee
- Computer Science and Engineering Department, Shiv Nadar University, Greater Noida, India
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15
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Sinn L, Giese SH, Stuiver M, Rappsilber J. Leveraging Parameter Dependencies in High-Field Asymmetric Waveform Ion-Mobility Spectrometry and Size Exclusion Chromatography for Proteome-wide Cross-Linking Mass Spectrometry. Anal Chem 2022; 94:4627-4634. [PMID: 35276035 PMCID: PMC8943524 DOI: 10.1021/acs.analchem.1c04373] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/21/2021] [Indexed: 11/29/2022]
Abstract
Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography-mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m/z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.
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Affiliation(s)
- Ludwig
R. Sinn
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
| | - Sven H. Giese
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
- Data
Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, 14482 Potsdam, Germany
- Digital
Engineering Faculty, University of Potsdam, 14469 Potsdam, Germany
| | - Marchel Stuiver
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
- Wellcome
Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, U.K.
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16
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Fischhoff IR, Castellanos AA, Rodrigues JPGLM, Varsani A, Han BA. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2. Proc Biol Sci 2021; 288:20211651. [PMID: 34784766 PMCID: PMC8596006 DOI: 10.1098/rspb.2021.1651] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/25/2021] [Indexed: 12/13/2022] Open
Abstract
Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.
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Affiliation(s)
- Ilya R. Fischhoff
- Cary Institute of Ecosystem Studies, Box AB Millbrook, NY 12545, USA
| | | | | | - Arvind Varsani
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
- Structural Biology Research Unit, Department of Integrative Biomedical Sciences, University of Cape Town, 7700 Cape Town, Rondebosch, South Africa
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies, Box AB Millbrook, NY 12545, USA
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17
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Chklovski T, Jung R, Anderson R, Young K. Comparing 2 Years of Empowering Families to Solve Real-World Problems with AI. Kunstliche Intell (Oldenbourg) 2021; 35:207-219. [PMID: 34316096 PMCID: PMC8297433 DOI: 10.1007/s13218-021-00738-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 06/14/2021] [Indexed: 11/30/2022]
Abstract
Over the course of 2 years a global technology education nonprofit engaged ~ 20,000 under-resourced 3rd-8th grade students, parents and educators from 13 countries in a multi-week AI competition. Families worked together with the help of educators to identify meaningful problems in their communities and developed AI-prototypes to address them. Key findings included: (1) Identifying a high level of interest in underserved communities to develop and apply AI-literacy skills; (2) Determining curricular and program implementation elements that enable families to apply AI knowledge and skills to real problems; (3) Identifying effective methods of engaging industry mentors to support participants; (4) Measuring and identifying changes in self-efficacy and ability to apply AI-based tools to real-world problems; (5) Determining effective curricula around value-sensitive design and ethical innovation.
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18
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Reiman D, Manakkat Vijay GK, Xu H, Sonin A, Chen D, Salomonis N, Singh H, Khan AA. Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination. PLoS Comput Biol 2021; 17:e1008094. [PMID: 33939691 PMCID: PMC8118552 DOI: 10.1371/journal.pcbi.1008094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 05/13/2021] [Accepted: 03/30/2021] [Indexed: 11/19/2022] Open
Abstract
Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNAseq data into a low-dimensional manifold that maps the transcriptional states a cell can occupy. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results revealed an ordering of key transcription factors regulating CSR to the IgG1 isotype, including the concomitant expression of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors suggest a poised IL-4 signaling state that preceeds CSR to the IgG1 isotype.
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Affiliation(s)
- Derek Reiman
- University of Illinois at Chicago, Department of Bioengineering, Chicago, Illinois, United States of America
| | - Godhev Kumar Manakkat Vijay
- University of Pittsburgh, Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, Pittsburgh, Pennsylvania, United States of America
| | - Heping Xu
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China
| | - Andrew Sonin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia
| | - Dianyu Chen
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China
| | - Nathan Salomonis
- Division of Biomedical Informatics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Harinder Singh
- University of Pittsburgh, Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (HS); (AAK)
| | - Aly A. Khan
- University of Chicago, Department of Pathology, Chicago, Illinois, United States of America
- * E-mail: (HS); (AAK)
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19
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Pereira GRC, Vieira BDAA, De Mesquita JF. Comprehensive in silico analysis and molecular dynamics of the superoxide dismutase 1 (SOD1) variants related to amyotrophic lateral sclerosis. PLoS One 2021; 16:e0247841. [PMID: 33630959 PMCID: PMC7906464 DOI: 10.1371/journal.pone.0247841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/15/2021] [Indexed: 12/29/2022] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most frequent motor neuron disorder, with a significant social and economic burden. ALS remains incurable, and the only drugs approved for its treatments confers a survival benefit of a few months for the patients. Missense mutations in superoxide dismutase 1 (SOD1), a major cytoplasmic antioxidant enzyme, has been associated with ALS development, accounting for 23% of its familial cases and 7% of all sporadic cases. This work aims to characterize in silico the structural and functional effects of SOD1 protein variants. Missense mutations in SOD1 were compiled from the literature and databases. Twelve algorithms were used to predict the functional and stability effects of these mutations. ConSurf was used to estimate the evolutionary conservation of SOD1 amino-acids. GROMACS was used to perform molecular dynamics (MD) simulations of SOD1 wild-type and variants A4V, D90A, H46R, and I113T, which account for approximately half of all ALS-SOD1 cases in the United States, Europe, Japan, and United Kingdom, respectively. 233 missense mutations in SOD1 protein were compiled from the databases and literature consulted. The predictive analyses pointed to an elevated rate of deleterious and destabilizing predictions for the analyzed variants, indicating their harmful effects. The ConSurf analysis suggested that mutations in SOD1 mainly affect conserved and possibly functionally essential amino acids. The MD analyses pointed to flexibility and essential dynamics alterations at the electrostatic and metal-binding loops of variants A4V, D90A, H46R, and I113T that could lead to aberrant interactions triggering toxic protein aggregation. These alterations may have harmful implications for SOD1 and explain their association with ALS. Understanding the effects of SOD1 mutations on protein structure and function facilitates the design of further experiments and provides relevant information on the molecular mechanism of pathology, which may contribute to improvements in existing treatments for ALS.
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Affiliation(s)
- Gabriel Rodrigues Coutinho Pereira
- Department of Genetics and Molecular Biology, Bioinformatics and Computational Biology Laboratory, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Joelma Freire De Mesquita
- Department of Genetics and Molecular Biology, Bioinformatics and Computational Biology Laboratory, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
- * E-mail:
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20
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Karim MR, Beyan O, Zappa A, Costa IG, Rebholz-Schuhmann D, Cochez M, Decker S. Deep learning-based clustering approaches for bioinformatics. Brief Bioinform 2021; 22:393-415. [PMID: 32008043 PMCID: PMC7820885 DOI: 10.1093/bib/bbz170] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 11/28/2019] [Accepted: 12/11/2019] [Indexed: 12/14/2022] Open
Abstract
Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.
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Affiliation(s)
- Md Rezaul Karim
- Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Oya Beyan
- Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany
- Information Systems and Databases, RWTH Aachen University, Aachen, Germany
| | - Achille Zappa
- Insight Centre for Data Analytics, National University of Ireland Galway, Ireland
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Medical School, Aachen, Germany
| | | | - Michael Cochez
- Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany
- Department of Computer Science, Vrije Univeriteit Amsterdam, The Netherlands
| | - Stefan Decker
- Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany
- Information Systems and Databases, RWTH Aachen University, Aachen, Germany
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21
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Yang KD, Belyaeva A, Venkatachalapathy S, Damodaran K, Katcoff A, Radhakrishnan A, Shivashankar GV, Uhler C. Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nat Commun 2021; 12:31. [PMID: 33397893 PMCID: PMC7782789 DOI: 10.1038/s41467-020-20249-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 11/19/2020] [Indexed: 12/19/2022] Open
Abstract
The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.
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Affiliation(s)
| | | | - Saradha Venkatachalapathy
- Mechanobiology Institute, National University of Singapore, Singapore, Singapore
- ETH Zurich and Paul Scherrer Institute, Villigen, Switzerland
| | - Karthik Damodaran
- Mechanobiology Institute, National University of Singapore, Singapore, Singapore
| | | | | | - G V Shivashankar
- Mechanobiology Institute, National University of Singapore, Singapore, Singapore
- ETH Zurich and Paul Scherrer Institute, Villigen, Switzerland
- FIRC Institute of Molecular Oncology (IFOM), Milano, Italy
| | - Caroline Uhler
- Massachusetts Institute of Technology, Cambridge, MA, USA.
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22
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Zhou Y, Eimen RL, Seibel EJ, Bowden AK. Cost-Efficient Video Synthesis and Evaluation for Development of Virtual 3D Endoscopy. IEEE J Transl Eng Health Med 2021; 9:1800711. [PMID: 34950539 PMCID: PMC8673697 DOI: 10.1109/jtehm.2021.3132193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/11/2021] [Accepted: 11/13/2021] [Indexed: 11/06/2022]
Affiliation(s)
- Yaxuan Zhou
- Department of Electrical and Computer EngineeringUniversity of Washington Seattle WA 98195 USA
- Human Photonics LaboratoryDepartment of Mechanical EngineeringUniversity of Washington Seattle WA 98195 USA
| | - Rachel L Eimen
- Department of Biomedical EngineeringVanderbilt University Nashville TN 37232 USA
| | - Eric J Seibel
- Human Photonics LaboratoryDepartment of Mechanical EngineeringUniversity of Washington Seattle WA 98195 USA
| | - Audrey K Bowden
- Department of Biomedical EngineeringVanderbilt University Nashville TN 37232 USA
- Department of Electrical Engineering and Computer ScienceVanderbilt University Nashville TN 37232 USA
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23
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Bova A, Gaidica M, Hurst A, Iwai Y, Hunter J, Leventhal DK. Precisely timed dopamine signals establish distinct kinematic representations of skilled movements. eLife 2020; 9:e61591. [PMID: 33245045 PMCID: PMC7861618 DOI: 10.7554/elife.61591] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 11/24/2020] [Indexed: 12/23/2022] Open
Abstract
Brain dopamine is critical for normal motor control, as evidenced by its importance in Parkinson Disease and related disorders. Current hypotheses are that dopamine influences motor control by 'invigorating' movements and regulating motor learning. Most evidence for these aspects of dopamine function comes from simple tasks (e.g. lever pressing). Therefore, the influence of dopamine on motor skills requiring multi-joint coordination is unknown. To determine the effects of precisely timed dopamine manipulations on the performance of a complex, finely coordinated dexterous skill, we optogenetically stimulated or inhibited midbrain dopamine neurons as rats performed a skilled reaching task. We found that reach kinematics and coordination between gross and fine movements progressively changed with repeated manipulations. However, once established, rats transitioned abruptly between aberrant and baseline reach kinematics in a dopamine-dependent manner. These results suggest that precisely timed dopamine signals have immediate and long-term influences on motor skill performance, distinct from simply 'invigorating' movement.
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Affiliation(s)
- Alexandra Bova
- Neuroscience Graduate Program, University of MichiganAnn ArborUnited States
| | - Matt Gaidica
- Neuroscience Graduate Program, University of MichiganAnn ArborUnited States
| | - Amy Hurst
- Department of Neurology, University of MichiganAnn ArborUnited States
| | - Yoshiko Iwai
- Department of Neurology, University of MichiganAnn ArborUnited States
| | - Julia Hunter
- Department of Neurology, University of MichiganAnn ArborUnited States
| | - Daniel K Leventhal
- Department of Neurology, University of MichiganAnn ArborUnited States
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Parkinson Disease Foundation Research Center of Excellence, University of MichiganAnn ArborUnited States
- Department of Neurology, VA Ann Arbor Health SystemAnn ArborUnited States
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24
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Abstract
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modeling framework for neural computations in the primate brain. Just like individual brains, each DNN has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using tools typically employed in systems neuroscience, we show that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations despite similar network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than misaligned category centroids. These results call into question the common practice of using single networks to derive insights into neural information processing and rather suggest that computational neuroscientists working with DNNs may need to base their inferences on groups of multiple network instances.
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Affiliation(s)
- Johannes Mehrer
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF, UK.
| | - Courtney J Spoerer
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF, UK
| | | | - Tim C Kietzmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Montessorilaan 3, 6525, HR, Nijmegen, Netherlands.
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25
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Abstract
Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring. Heterogeneity in environmental conditions can have profound effects on long-term evolutionary outcomes in structured populations. We consider a population evolving on a colored graph, wherein the color of a node represents the resources at that location. Using a combination of analytical and numerical methods, we quantify the effects of background heterogeneity on a population’s dynamics. In addition to considering the notion of an “optimal” coloring with respect to mutant invasion, we also study the effects of dynamic spatial redistribution of resources as the population evolves. Although the effects of static background heterogeneity can be quite striking, these effects are often attenuated by the movement (or “flow”) of the underlying resources.
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Affiliation(s)
- Kamran Kaveh
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, United States
- * E-mail: (KK); (AM)
| | - Alex McAvoy
- Department of Mathematics, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- * E-mail: (KK); (AM)
| | | | - Martin A. Nowak
- Department of Mathematics, Harvard University, Cambridge, Massachusetts, United States
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States
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26
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Abstract
How do we construct a sense of place in a real-world environment? Real-world environments are actively explored via saccades, head turns, and body movements. Yet, little is known about how humans process real-world scene information during active viewing conditions. Here, we exploited recent developments in virtual reality (VR) and in-headset eye-tracking to test the impact of active vs. passive viewing conditions on gaze behavior while participants explored novel, real-world, 360° scenes. In one condition, participants actively explored 360° photospheres from a first-person perspective via self-directed motion (saccades and head turns). In another condition, photospheres were passively displayed to participants while they were head-restricted. We found that, relative to passive viewers, active viewers displayed increased attention to semantically meaningful scene regions, suggesting more exploratory, information-seeking gaze behavior. We also observed signatures of exploratory behavior in eye movements, such as quicker, more entropic fixations during active as compared with passive viewing conditions. These results show that active viewing influences every aspect of gaze behavior, from the way we move our eyes to what we choose to attend to. Moreover, these results offer key benchmark measurements of gaze behavior in 360°, naturalistic environments.
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Affiliation(s)
- Amanda J Haskins
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA.
| | - Jeff Mentch
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Thomas L Botch
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - Caroline E Robertson
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA
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27
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Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp 2020; 4:50. [PMID: 32814998 PMCID: PMC7438418 DOI: 10.1186/s41747-020-00173-2] [Citation(s) in RCA: 186] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/30/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited. METHODS We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets. RESULTS Using different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024). CONCLUSIONS The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.
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Affiliation(s)
- Johannes Hofmanninger
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel, 18-20, Vienna, Austria.
| | - Forian Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel, 18-20, Vienna, Austria
| | - Jeanny Pan
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel, 18-20, Vienna, Austria
| | - Sebastian Röhrich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel, 18-20, Vienna, Austria
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel, 18-20, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel, 18-20, Vienna, Austria.
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28
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Dovgan E, Gradišek A, Luštrek M, Uddin M, Nursetyo AA, Annavarajula SK, Li YC, Syed-Abdul S. Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients. PLoS One 2020; 15:e0233976. [PMID: 32502209 PMCID: PMC7274378 DOI: 10.1371/journal.pone.0233976] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/16/2020] [Indexed: 12/29/2022] Open
Abstract
Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient's first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such models could allow policy-makers better planning and allocation of resources for treatment. Using data from 8,492 patients, we obtained the area under the receiver operating characteristic curve (AUC) of 0.773 for predicting RRT within 12 months from the time of CKD diagnosis. The results also show that there is no additional advantage in focusing only on patients with diabetes in terms of prediction performance. Although these results are not as such suitable for adoption into clinical practice, the study provides a strong basis and a variety of approaches for future studies of forecasting models in healthcare.
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Affiliation(s)
- Erik Dovgan
- Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Anton Gradišek
- Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Mitja Luštrek
- Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Mohy Uddin
- Executive Office, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard – Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Aldilas Achmad Nursetyo
- Taipei Medical University, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei, Taiwan
| | | | - Yu-Chuan Li
- Taipei Medical University, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei, Taiwan
| | - Shabbir Syed-Abdul
- Taipei Medical University, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei, Taiwan
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29
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Butt BG, Owen DJ, Jeffries CM, Ivanova L, Hill CH, Houghton JW, Ahmed MF, Antrobus R, Svergun DI, Welch JJ, Crump CM, Graham SC. Insights into herpesvirus assembly from the structure of the pUL7:pUL51 complex. eLife 2020; 9:e53789. [PMID: 32391791 PMCID: PMC7289601 DOI: 10.7554/elife.53789] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 05/07/2020] [Indexed: 12/19/2022] Open
Abstract
Herpesviruses acquire their membrane envelopes in the cytoplasm of infected cells via a molecular mechanism that remains unclear. Herpes simplex virus (HSV)-1 proteins pUL7 and pUL51 form a complex required for efficient virus envelopment. We show that interaction between homologues of pUL7 and pUL51 is conserved across human herpesviruses, as is their association with trans-Golgi membranes. We characterized the HSV-1 pUL7:pUL51 complex by solution scattering and chemical crosslinking, revealing a 1:2 complex that can form higher-order oligomers in solution, and we solved the crystal structure of the core pUL7:pUL51 heterodimer. While pUL7 adopts a previously-unseen compact fold, the helix-turn-helix conformation of pUL51 resembles the cellular endosomal complex required for transport (ESCRT)-III component CHMP4B and pUL51 forms ESCRT-III-like filaments, suggesting a direct role for pUL51 in promoting membrane scission during virus assembly. Our results provide a structural framework for understanding the role of the conserved pUL7:pUL51 complex in herpesvirus assembly.
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Affiliation(s)
- Benjamin G Butt
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
| | - Danielle J Owen
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
| | - Cy M Jeffries
- European Molecular Biology Laboratory (EMBL) Hamburg SiteHamburgGermany
| | - Lyudmila Ivanova
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
| | - Chris H Hill
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
| | - Jack W Houghton
- Cambridge Institute for Medical Research, University of CambridgeCambridgeUnited Kingdom
| | - Md Firoz Ahmed
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
| | - Robin Antrobus
- Cambridge Institute for Medical Research, University of CambridgeCambridgeUnited Kingdom
| | - Dmitri I Svergun
- European Molecular Biology Laboratory (EMBL) Hamburg SiteHamburgGermany
| | - John J Welch
- Department of Genetics, University of CambridgeCambridgeUnited Kingdom
| | - Colin M Crump
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
| | - Stephen C Graham
- Department of Pathology, University of CambridgeCambridgeUnited Kingdom
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30
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Kimmel JC, Hwang AB, Scaramozza A, Marshall WF, Brack AS. Aging induces aberrant state transition kinetics in murine muscle stem cells. Development 2020; 147:dev183855. [PMID: 32198156 PMCID: PMC7225128 DOI: 10.1242/dev.183855] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 02/17/2020] [Indexed: 12/12/2022]
Abstract
Murine muscle stem cells (MuSCs) experience a transition from quiescence to activation that is required for regeneration, but it remains unknown if the trajectory and dynamics of activation change with age. Here, we use time-lapse imaging and single cell RNA-seq to measure activation trajectories and rates in young and aged MuSCs. We find that the activation trajectory is conserved in aged cells, and we develop effective machine-learning classifiers for cell age. Using cell-behavior analysis and RNA velocity, we find that activation kinetics are delayed in aged MuSCs, suggesting that changes in stem cell dynamics may contribute to impaired stem cell function with age. Intriguingly, we also find that stem cell activation appears to be a random walk-like process, with frequent reversals, rather than a continuous linear progression. These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them.
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Affiliation(s)
- Jacob C Kimmel
- Eli and Edythe Broad Center for Regenerative Medicine, University of California, San Francisco, 35 Medical Center Way, San Francisco, CA 94143, USA
- Center for Cellular Construction, University of California, San Francisco, San Francisco, CA 94143, USA
- Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ara B Hwang
- Eli and Edythe Broad Center for Regenerative Medicine, University of California, San Francisco, 35 Medical Center Way, San Francisco, CA 94143, USA
| | - Annarita Scaramozza
- Eli and Edythe Broad Center for Regenerative Medicine, University of California, San Francisco, 35 Medical Center Way, San Francisco, CA 94143, USA
| | - Wallace F Marshall
- Center for Cellular Construction, University of California, San Francisco, San Francisco, CA 94143, USA
- Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Andrew S Brack
- Eli and Edythe Broad Center for Regenerative Medicine, University of California, San Francisco, 35 Medical Center Way, San Francisco, CA 94143, USA
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31
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Pereira GRC, Tavares GDB, de Freitas MC, De Mesquita JF. In silico analysis of the tryptophan hydroxylase 2 (TPH2) protein variants related to psychiatric disorders. PLoS One 2020; 15:e0229730. [PMID: 32119710 PMCID: PMC7051086 DOI: 10.1371/journal.pone.0229730] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 02/12/2020] [Indexed: 11/19/2022] Open
Abstract
The tryptophan hydroxylase 2 (TPH2) enzyme catalyzes the first step of serotonin biosynthesis. Serotonin is known for its role in several homeostatic systems related to sleep, mood, and food intake. As the reaction catalyzed by TPH2 is the rate-limiting step of serotonin biosynthesis, mutations in TPH2 have been associated with several psychiatric disorders (PD). This work undertakes an in silico analysis of the effects of genetic mutations in the human TPH2 protein. Ten algorithms were used to predict the functional and stability effects of the TPH2 mutations. ConSurf was used to estimate the evolutionary conservation of TPH2 amino acids. GROMACS was used to perform molecular dynamics (MD) simulations of TPH2 WT and P260S, R303W, and R441H, which had already been associated with the development of PD. Forty-six TPH2 variants were compiled from the literature. Among the analyzed variants, those occurring at the catalytic domain were shown to be more damaging to protein structure and function. The ConSurf analysis indicated that the mutations affecting the catalytic domain were also more conserved throughout evolution. The variants S364K and S383F were predicted to be deleterious by all the functional algorithms used and occurred at conserved positions, suggesting that they might be deleterious. The MD analyses indicate that the mutations P206S, R303W, and R441H affect TPH2 flexibility and essential mobility at the catalytic and oligomerization domains. The variants P206S, R303W, and R441H also exhibited alterations in dimer binding affinity and stability throughout the simulations. Thus, these mutations may impair TPH2 functional interactions and, consequently, its function, leading to the development of PD. Furthermore, we developed a database, SNPMOL (http://www.snpmol.org/), containing the results presented in this paper. Understanding the effects of TPH2 mutations on protein structure and function may lead to improvements in existing treatments for PD and facilitate the design of further experiments.
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Affiliation(s)
- Gabriel Rodrigues Coutinho Pereira
- Bioinformatics and Computational Biology Laboratory, Department of Genetics and Molecular Biology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gustavo Duarte Bocayuva Tavares
- Bioinformatics and Computational Biology Laboratory, Department of Genetics and Molecular Biology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marta Costa de Freitas
- Bioinformatics and Computational Biology Laboratory, Department of Genetics and Molecular Biology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | - Joelma Freire De Mesquita
- Bioinformatics and Computational Biology Laboratory, Department of Genetics and Molecular Biology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
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32
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Williams BM, Borroni D, Liu R, Zhao Y, Zhang J, Lim J, Ma B, Romano V, Qi H, Ferdousi M, Petropoulos IN, Ponirakis G, Kaye S, Malik RA, Alam U, Zheng Y. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia 2020; 63:419-430. [PMID: 31720728 PMCID: PMC6946763 DOI: 10.1007/s00125-019-05023-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/30/2019] [Indexed: 12/31/2022]
Abstract
AIMS/HYPOTHESIS Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics. METHODS Our deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy. RESULTS The intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria). CONCLUSIONS/INTERPRETATION These results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy. DATA AVAILABILITY The publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm.
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Affiliation(s)
- Bryan M Williams
- Department of Eye and Vision Science, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
- Data Science Institute, Lancaster University, Lancaster, UK
| | - Davide Borroni
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
- Department of Ophthalmology, Riga Stradins University, Riga, Latvia
| | - Rongjun Liu
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China
| | - Yitian Zhao
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
| | - Jiong Zhang
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jonathan Lim
- Department of Endocrinology and Diabetes, University Hospital Aintree, Longmoor Lane, Liverpool, UK
| | - Baikai Ma
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China
| | - Vito Romano
- Department of Eye and Vision Science, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Hong Qi
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China
| | - Maryam Ferdousi
- Department of Endocrinology and Diabetes, University Hospital Aintree, Longmoor Lane, Liverpool, UK
| | | | | | - Stephen Kaye
- Department of Eye and Vision Science, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | | | - Uazman Alam
- Diabetes and Neuropathy Research, Department of Eye and Vision Sciences and Pain Research Institute, Institute of Ageing and Chronic Disease, University of Liverpool and Aintree University Hospital NHS Foundation Trust, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK.
- Department of Diabetes and Endocrinology, Royal Liverpool and Broadgreen University NHS Hospital Trust, Liverpool, UK.
- Division of Endocrinology, Diabetes and Gastroenterology, University of Manchester, Manchester, UK.
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK.
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK.
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Kolhatkar V, Wu H, Cavasso L, Francis E, Shukla K, Taboada M. The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments. Corpus Pragmat 2019; 4:155-190. [PMID: 32685909 PMCID: PMC7357677 DOI: 10.1007/s41701-019-00065-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 10/15/2019] [Indexed: 06/02/2023]
Abstract
We present the SFU Opinion and Comments Corpus (SOCC ), a collection of opinion articles and the comments posted in response to the articles. The articles include all the opinion pieces published in the Canadian newspaper The Globe and Mail in the 5-year period between 2012 and 2016, a total of 10,339 articles and 663,173 comments. SOCC is part of a project that investigates the linguistic characteristics of online comments. The corpus can be used to study a host of pragmatic phenomena. Among other aspects, researchers can explore: the connections between articles and comments; the connections of comments to each other; the types of topics discussed in comments; the nice (constructive) or mean (toxic) ways in which commenters respond to each other; how language is used to convey very specific types of evaluation; and how negation affects the interpretation of evaluative meaning in discourse. Our current focus is the study of constructiveness and evaluation in the comments. To that end, we have annotated a subset of the large corpus (1043 comments) with four layers of annotations: constructiveness, toxicity, negation and Appraisal (Martin and White, The language of evaluation, Palgrave, New York, 2005). This paper details our corpus, the data collection process, the characteristics of the corpus and describes the annotations. While our focus is comments posted in response to opinion news articles, the phenomena in this corpus are likely to be present in many commenting platforms: other news comments, comments and replies in fora such as Reddit, feedback on blogs, or YouTube comments.
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Affiliation(s)
- Varada Kolhatkar
- Department of Computer Science, University of British Columbia, Vancouver, Canada
| | - Hanhan Wu
- Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, Canada
| | - Luca Cavasso
- Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, Canada
| | - Emilie Francis
- Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, Canada
| | - Kavan Shukla
- Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, Canada
| | - Maite Taboada
- Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, Canada
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Graving JM, Chae D, Naik H, Li L, Koger B, Costelloe BR, Couzin ID. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8:e47994. [PMID: 31570119 PMCID: PMC6897514 DOI: 10.7554/elife.47994;select dbms_pipe.receive_message(chr(79)||chr(103)||chr(106)||chr(65),5) from dual--] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
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Affiliation(s)
- Jacob M Graving
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Daniel Chae
- Department of Computer SciencePrinceton UniversityPrincetonUnited States
| | - Hemal Naik
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
- Chair for Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
| | - Liang Li
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Benjamin Koger
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Blair R Costelloe
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Iain D Couzin
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
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Graving JM, Chae D, Naik H, Li L, Koger B, Costelloe BR, Couzin ID. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8:e47994. [PMID: 31570119 PMCID: PMC6897514 DOI: 10.7554/elife.47994;select dbms_pipe.receive_message(chr(79)||chr(103)||chr(106)||chr(65),0) from dual--] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
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Affiliation(s)
- Jacob M Graving
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Daniel Chae
- Department of Computer SciencePrinceton UniversityPrincetonUnited States
| | - Hemal Naik
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
- Chair for Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
| | - Liang Li
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Benjamin Koger
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Blair R Costelloe
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Iain D Couzin
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
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Graving JM, Chae D, Naik H, Li L, Koger B, Costelloe BR, Couzin ID. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 2019; 8:e47994. [PMID: 31570119 PMCID: PMC6897514 DOI: 10.7554/elife.47994] [Citation(s) in RCA: 199] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/18/2019] [Indexed: 12/24/2022] Open
Abstract
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
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Affiliation(s)
- Jacob M Graving
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Daniel Chae
- Department of Computer SciencePrinceton UniversityPrincetonUnited States
| | - Hemal Naik
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
- Chair for Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
| | - Liang Li
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Benjamin Koger
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Blair R Costelloe
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
| | - Iain D Couzin
- Department of Collective BehaviourMax Planck Institute of Animal BehaviorKonstanzGermany
- Department of BiologyUniversity of KonstanzKonstanzGermany
- Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
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Unberath M, Zaech JN, Gao C, Bier B, Goldmann F, Lee SC, Fotouhi J, Taylor R, Armand M, Navab N. Enabling machine learning in X-ray-based procedures via realistic simulation of image formation. Int J Comput Assist Radiol Surg 2019; 14:1517-1528. [PMID: 31187399 PMCID: PMC7297499 DOI: 10.1007/s11548-019-02011-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 06/03/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE Machine learning-based approaches now outperform competing methods in most disciplines relevant to diagnostic radiology. Image-guided procedures, however, have not yet benefited substantially from the advent of deep learning, in particular because images for procedural guidance are not archived and thus unavailable for learning, and even if they were available, annotations would be a severe challenge due to the vast amounts of data. In silico simulation of X-ray images from 3D CT is an interesting alternative to using true clinical radiographs since labeling is comparably easy and potentially readily available. METHODS We extend our framework for fast and realistic simulation of fluoroscopy from high-resolution CT, called DeepDRR, with tool modeling capabilities. The framework is publicly available, open source, and tightly integrated with the software platforms native to deep learning, i.e., Python, PyTorch, and PyCuda. DeepDRR relies on machine learning for material decomposition and scatter estimation in 3D and 2D, respectively, but uses analytic forward projection and noise injection to ensure acceptable computation times. On two X-ray image analysis tasks, namely (1) anatomical landmark detection and (2) segmentation and localization of robot end-effectors, we demonstrate that convolutional neural networks (ConvNets) trained on DeepDRRs generalize well to real data without re-training or domain adaptation. To this end, we use the exact same training protocol to train ConvNets on naïve and DeepDRRs and compare their performance on data of cadaveric specimens acquired using a clinical C-arm X-ray system. RESULTS Our findings are consistent across both considered tasks. All ConvNets performed similarly well when evaluated on the respective synthetic testing set. However, when applied to real radiographs of cadaveric anatomy, ConvNets trained on DeepDRRs significantly outperformed ConvNets trained on naïve DRRs ([Formula: see text]). CONCLUSION Our findings for both tasks are positive and promising. Combined with complementary approaches, such as image style transfer, the proposed framework for fast and realistic simulation of fluoroscopy from CT contributes to promoting the implementation of machine learning in X-ray-guided procedures. This paradigm shift has the potential to revolutionize intra-operative image analysis to simplify surgical workflows.
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Affiliation(s)
- Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA.
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA.
| | - Jan-Nico Zaech
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
| | - Cong Gao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
| | - Bastian Bier
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
| | - Florian Goldmann
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
| | - Sing Chun Lee
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
| | - Javad Fotouhi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
| | - Russell Taylor
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
| | - Mehran Armand
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Nassir Navab
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Laboratory for Computational Sensing + Robotics, Johns Hopkins University, Baltimore, MD, USA
- Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, MD, USA
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Padmanaban N, Konrad R, Wetzstein G. Autofocals: Evaluating gaze-contingent eyeglasses for presbyopes. Sci Adv 2019; 5:eaav6187. [PMID: 31259239 PMCID: PMC6598771 DOI: 10.1126/sciadv.aav6187] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 05/22/2019] [Indexed: 05/13/2023]
Abstract
As humans age, they gradually lose the ability to accommodate, or refocus, to near distances because of the stiffening of the crystalline lens. This condition, known as presbyopia, affects nearly 20% of people worldwide. We design and build a new presbyopia correction, autofocals, to externally mimic the natural accommodation response, combining eye tracker and depth sensor data to automatically drive focus-tunable lenses. We evaluated 19 users on visual acuity, contrast sensitivity, and a refocusing task. Autofocals exhibit better visual acuity when compared to monovision and progressive lenses while maintaining similar contrast sensitivity. On the refocusing task, autofocals are faster and, compared to progressives, also significantly more accurate. In a separate study, a majority of 23 of 37 users ranked autofocals as the best correction in terms of ease of refocusing. Our work demonstrates the superiority of autofocals over current forms of presbyopia correction and could affect the lives of millions.
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Abstract
Although distinct categories are reliably decoded from fMRI brain responses, it has proved more difficult to distinguish visually similar inputs, such as different faces. Here, we apply a recently developed deep learning system to reconstruct face images from human fMRI. We trained a variational auto-encoder (VAE) neural network using a GAN (Generative Adversarial Network) unsupervised procedure over a large data set of celebrity faces. The auto-encoder latent space provides a meaningful, topologically organized 1024-dimensional description of each image. We then presented several thousand faces to human subjects, and learned a simple linear mapping between the multi-voxel fMRI activation patterns and the 1024 latent dimensions. Finally, we applied this mapping to novel test images, translating fMRI patterns into VAE latent codes, and codes into face reconstructions. The system not only performed robust pairwise decoding (>95% correct), but also accurate gender classification, and even decoded which face was imagined, rather than seen.
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Affiliation(s)
- Rufin VanRullen
- CerCo, CNRS, UMR 5549, Université de Toulouse, Toulouse, 31052 France
| | - Leila Reddy
- CerCo, CNRS, UMR 5549, Université de Toulouse, Toulouse, 31052 France
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De Oliveira CCS, Pereira GRC, De Alcantara JYS, Antunes D, Caffarena ER, De Mesquita JF. In silico analysis of the V66M variant of human BDNF in psychiatric disorders: An approach to precision medicine. PLoS One 2019; 14:e0215508. [PMID: 30998730 PMCID: PMC6472887 DOI: 10.1371/journal.pone.0215508] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 04/04/2019] [Indexed: 11/19/2022] Open
Abstract
Brain-derived neurotrophic factor (BDNF) plays an important role in neurogenesis and synapse formation. The V66M is the most prevalent BDNF mutation in humans and impairs the function and distribution of BDNF. This mutation is related to several psychiatric disorders. The pro-region of BDNF, particularly position 66 and its adjacent residues, are determinant for the intracellular sorting and activity-dependent secretion of BDNF. However, it has not yet been fully elucidated. The present study aims to analyze the effects of the V66M mutation on BDNF structure and function. Here, we applied nine algorithms, including SIFT and PolyPhen-2, for functional and stability prediction of the V66M mutation. The complete theoretical model of BNDF was generated by Rosetta and validated by PROCHECK, RAMPAGE, ProSa, QMEAN and Verify-3D algorithms. Structural alignment was performed using TM-align. Phylogenetic analysis was performed using the ConSurf server. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS 2018.2 package. The V66M mutation was predicted as deleterious by PolyPhen-2 and SIFT in addition to being predicted as destabilizing by I-Mutant. According to SNPeffect, the V66M mutation does not affect protein aggregation, amyloid propensity, and chaperone binding. The complete theoretical structure of BDNF proved to be a reliable model. Phylogenetic analysis indicated that the V66M mutation of BDNF occurs at a non-conserved position of the protein. MD analyses indicated that the V66M mutation does not affect the BDNF flexibility and surface-to-volume ratio, but affects the BDNF essential motions, hydrogen-bonding and secondary structure particularly at its pre and pro-domain, which are crucial for its activity and distribution. Thus, considering that these parameters are determinant for protein interactions and, consequently, protein function; the alterations observed throughout the MD analyses may be related to the functional impairment of BDNF upon V66M mutation, as well as its involvement in psychiatric disorders.
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Affiliation(s)
- Clara Carolina Silva De Oliveira
- Department of Genetics and Molecular Biology, Bioinformatics and Computational Biology Laboratory, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gabriel Rodrigues Coutinho Pereira
- Department of Genetics and Molecular Biology, Bioinformatics and Computational Biology Laboratory, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jamile Yvis Santos De Alcantara
- Department of Genetics and Molecular Biology, Bioinformatics and Computational Biology Laboratory, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
| | - Deborah Antunes
- Computational Biophysics and Molecular Modeling Group, Scientific Computing Program (PROCC), Fundação Oswaldo Cruz, Manguinhos, Rio de Janeiro, Brazil
| | - Ernesto Raul Caffarena
- Computational Biophysics and Molecular Modeling Group, Scientific Computing Program (PROCC), Fundação Oswaldo Cruz, Manguinhos, Rio de Janeiro, Brazil
| | - Joelma Freire De Mesquita
- Department of Genetics and Molecular Biology, Bioinformatics and Computational Biology Laboratory, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Rio de Janeiro, Brazil
- * E-mail:
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Baldominos A, Cervantes A, Saez Y, Isasi P. A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices. Sensors (Basel) 2019; 19:E521. [PMID: 30691177 PMCID: PMC6386875 DOI: 10.3390/s19030521] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/16/2019] [Accepted: 01/22/2019] [Indexed: 01/16/2023]
Abstract
We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.
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Affiliation(s)
- Alejandro Baldominos
- Department of Computer Science, University Carlos III of Madrid, 28911 Leganés, Madrid, Spain.
| | - Alejandro Cervantes
- Department of Computer Science, University Carlos III of Madrid, 28911 Leganés, Madrid, Spain.
| | - Yago Saez
- Department of Computer Science, University Carlos III of Madrid, 28911 Leganés, Madrid, Spain.
| | - Pedro Isasi
- Department of Computer Science, University Carlos III of Madrid, 28911 Leganés, Madrid, Spain.
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Aganj I, Harisinghani MG, Weissleder R, Fischl B. Unsupervised Medical Image Segmentation Based on the Local Center of Mass. Sci Rep 2018; 8:13012. [PMID: 30158534 PMCID: PMC6115387 DOI: 10.1038/s41598-018-31333-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 08/17/2018] [Indexed: 01/30/2023] Open
Abstract
Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large training datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary, can be used in the absence of training data to segment new images. We introduce a new approach to unsupervised image segmentation that is based on the computation of the local center of mass. We propose an efficient method to group the pixels of a one-dimensional signal, which we then use in an iterative algorithm for two- and three-dimensional image segmentation. We validate our method on a 2D X-ray image, a 3D abdominal magnetic resonance (MR) image and a dataset of 3D cardiovascular MR images.
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Affiliation(s)
- Iman Aganj
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Mukesh G Harisinghani
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ralph Weissleder
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce Fischl
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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Duster AW, Wang CH, Lin H. Adaptive QM/MM for Molecular Dynamics Simulations: 5. On the Energy-Conserved Permuted Adaptive-Partitioning Schemes. Molecules 2018; 23:E2170. [PMID: 30154373 PMCID: PMC6225285 DOI: 10.3390/molecules23092170] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/23/2018] [Accepted: 08/24/2018] [Indexed: 11/16/2022] Open
Abstract
In combined quantum-mechanical/molecular-mechanical (QM/MM) dynamics simulations, the adaptive-partitioning (AP) schemes reclassify atoms on-the-fly as QM or MM in a smooth manner. This yields a mobile QM subsystem with contents that are continuously updated as needed. Here, we tailor the Hamiltonian adaptive many-body correction (HAMBC) proposed by Boreboom et al. [J. Chem. Theory Comput.2016, 12, 3441] to the permuted AP (PAP) scheme. The treatments lead to the HAMBC-PAP method (HPAP), which both conserves energy and produces accurate solvation structures in the test of "water-in-water" model system.
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Affiliation(s)
- Adam W Duster
- Department of Chemistry, University of Colorado Denver, Denver, CO 80217, USA.
| | - Chun-Hung Wang
- Department of Chemistry, University of Colorado Denver, Denver, CO 80217, USA.
| | - Hai Lin
- Department of Chemistry, University of Colorado Denver, Denver, CO 80217, USA.
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Hou B, Khanal B, Alansary A, McDonagh S, Davidson A, Rutherford M, Hajnal JV, Rueckert D, Glocker B, Kainz B. 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images. IEEE Trans Med Imaging 2018; 37:1737-1750. [PMID: 29994453 PMCID: PMC6077949 DOI: 10.1109/tmi.2018.2798801] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 01/19/2018] [Accepted: 01/23/2018] [Indexed: 05/23/2023]
Abstract
Limited capture range, and the requirement to provide high quality initialization for optimization-based 2-D/3-D image registration methods, can significantly degrade the performance of 3-D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion, such as fetal in-utero imaging, complicate the 3-D image and volume reconstruction process. In this paper, we present a learning-based image registration method capable of predicting 3-D rigid transformations of arbitrarily oriented 2-D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations, we utilize a convolutional neural network architecture to learn the regression function capable of mapping 2-D image slices to a 3-D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated magnetic resonance imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2-D/3-D registration initialization problem and is suitable for real-time scenarios.
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Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T. Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning. IEEE Trans Med Imaging 2018; 37:1562-1573. [PMID: 29969407 PMCID: PMC6051485 DOI: 10.1109/tmi.2018.2791721] [Citation(s) in RCA: 259] [Impact Index Per Article: 43.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 05/22/2023]
Abstract
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine tuning. We applied this framework to two applications: 2-D segmentation of multiple organs from fetal magnetic resonance (MR) slices, where only two types of these organs were annotated for training and 3-D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only the tumor core in one MR sequence was annotated for training. Experimental results show that: 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.
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Markiewicz PJ, Ehrhardt MJ, Erlandsson K, Noonan PJ, Barnes A, Schott JM, Atkinson D, Arridge SR, Hutton BF, Ourselin S. NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis. Neuroinformatics 2018; 16:95-115. [PMID: 29280050 PMCID: PMC5797201 DOI: 10.1007/s12021-017-9352-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.
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Affiliation(s)
- Pawel J Markiewicz
- Translational Imaging Group, CMIC, Department of Medical Physics, Biomedical Engineering, University College London, London, UK.
| | - Matthias J Ehrhardt
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Kjell Erlandsson
- Institute of Nuclear Medicine, University College London, London, UK
| | - Philip J Noonan
- Translational Imaging Group, CMIC, Department of Medical Physics, Biomedical Engineering, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London, London, UK
| | | | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Simon R Arridge
- Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, UK
| | - Sebastien Ourselin
- Translational Imaging Group, CMIC, Department of Medical Physics, Biomedical Engineering, University College London, London, UK
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47
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Xiao S, Bucher F, Wu Y, Rokem A, Lee CS, Marra KV, Fallon R, Diaz-Aguilar S, Aguilar E, Friedlander M, Lee AY. Fully automated, deep learning segmentation of oxygen-induced retinopathy images. JCI Insight 2017; 2:97585. [PMID: 29263301 PMCID: PMC5752269 DOI: 10.1172/jci.insight.97585] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 11/15/2017] [Indexed: 12/29/2022] Open
Abstract
Oxygen-induced retinopathy (OIR) is a widely used model to study ischemia-driven neovascularization (NV) in the retina and to serve in proof-of-concept studies in evaluating antiangiogenic drugs for ocular, as well as nonocular, diseases. The primary parameters that are analyzed in this mouse model include the percentage of retina with vaso-obliteration (VO) and NV areas. However, quantification of these two key variables comes with a great challenge due to the requirement of human experts to read the images. Human readers are costly, time-consuming, and subject to bias. Using recent advances in machine learning and computer vision, we trained deep learning neural networks using over a thousand segmentations to fully automate segmentation in OIR images. While determining the percentage area of VO, our algorithm achieved a similar range of correlation coefficients to that of expert inter-human correlation coefficients. In addition, our algorithm achieved a higher range of correlation coefficients compared with inter-expert correlation coefficients for quantification of the percentage area of neovascular tufts. In summary, we have created an open-source, fully automated pipeline for the quantification of key values of OIR images using deep learning neural networks.
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Affiliation(s)
- Sa Xiao
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Felicitas Bucher
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
- Eye Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, Washington, USA
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Kyle V. Marra
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
- Department of Bioengineering, University of California, San Diego, San Diego, California, USA
| | - Regis Fallon
- Lowy Medical Research Institute, La Jolla, California, USA
| | - Sophia Diaz-Aguilar
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
| | - Edith Aguilar
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
| | - Martin Friedlander
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
- Lowy Medical Research Institute, La Jolla, California, USA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
- eScience Institute, University of Washington, Seattle, Washington, USA
- Department of Ophthalmology, Puget Sound Veteran Affairs, Seattle, Washington, USA
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48
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Tacchetti A, Isik L, Poggio T. Invariant recognition drives neural representations of action sequences. PLoS Comput Biol 2017; 13:e1005859. [PMID: 29253864 PMCID: PMC5749869 DOI: 10.1371/journal.pcbi.1005859] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 01/02/2018] [Accepted: 10/31/2017] [Indexed: 11/19/2022] Open
Abstract
Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.
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Affiliation(s)
- Andrea Tacchetti
- Center for Brains Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Leyla Isik
- Center for Brains Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Tomaso Poggio
- Center for Brains Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA, United States
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Lopes-Ramos CM, Paulson JN, Chen CY, Kuijjer ML, Fagny M, Platig J, Sonawane AR, DeMeo DL, Quackenbush J, Glass K. Regulatory network changes between cell lines and their tissues of origin. BMC Genomics 2017; 18:723. [PMID: 28899340 PMCID: PMC5596945 DOI: 10.1186/s12864-017-4111-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 09/01/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. RESULTS We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. CONCLUSIONS Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.
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Affiliation(s)
- Camila M. Lopes-Ramos
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Joseph N. Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Cho-Yi Chen
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Marieke L. Kuijjer
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Maud Fagny
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA USA
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215 USA
| | - Kimberly Glass
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA USA
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