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García-Vázquez FA. Artificial intelligence and porcine breeding. Anim Reprod Sci 2024; 269:107538. [PMID: 38926001 DOI: 10.1016/j.anireprosci.2024.107538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.
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Affiliation(s)
- Francisco A García-Vázquez
- Departamento de Fisiología, Facultad de Veterinaria, Campus de Excelencia Mare Nostrum, Universidad de Murcia, Murcia 30100, Spain; Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain.
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2
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Rosen J, Alford S, Allan B, Anand V, Arnon S, Arockiaraj FG, Art J, Bai B, Balasubramaniam GM, Birnbaum T, Bisht NS, Blinder D, Cao L, Chen Q, Chen Z, Dubey V, Egiazarian K, Ercan M, Forbes A, Gopakumar G, Gao Y, Gigan S, Gocłowski P, Gopinath S, Greenbaum A, Horisaki R, Ierodiaconou D, Juodkazis S, Karmakar T, Katkovnik V, Khonina SN, Kner P, Kravets V, Kumar R, Lai Y, Li C, Li J, Li S, Li Y, Liang J, Manavalan G, Mandal AC, Manisha M, Mann C, Marzejon MJ, Moodley C, Morikawa J, Muniraj I, Narbutis D, Ng SH, Nothlawala F, Oh J, Ozcan A, Park Y, Porfirev AP, Potcoava M, Prabhakar S, Pu J, Rai MR, Rogalski M, Ryu M, Choudhary S, Salla GR, Schelkens P, Şener SF, Shevkunov I, Shimobaba T, Singh RK, Singh RP, Stern A, Sun J, Zhou S, Zuo C, Zurawski Z, Tahara T, Tiwari V, Trusiak M, Vinu RV, Volotovskiy SG, Yılmaz H, De Aguiar HB, Ahluwalia BS, Ahmad A. Roadmap on computational methods in optical imaging and holography [invited]. APPLIED PHYSICS. B, LASERS AND OPTICS 2024; 130:166. [PMID: 39220178 PMCID: PMC11362238 DOI: 10.1007/s00340-024-08280-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/10/2024] [Indexed: 09/04/2024]
Abstract
Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography. Supplementary Information The online version contains supplementary material available at 10.1007/s00340-024-08280-3.
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Affiliation(s)
- Joseph Rosen
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
| | - Simon Alford
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Blake Allan
- Faculty of Science Engineering and Built Environment, Deakin University, Princes Highway, Warrnambool, VIC 3280 Australia
| | - Vijayakumar Anand
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
- Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Computing and Engineering Technologies, Optical Sciences Center, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122 Australia
| | - Shlomi Arnon
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Francis Gracy Arockiaraj
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
| | - Jonathan Art
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Bijie Bai
- Electrical and Computer Engineering Department, Bioengineering Department, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA USA
| | - Ganesh M. Balasubramaniam
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Tobias Birnbaum
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel VUB), Pleinlaan 2, 1050 Brussel, Belgium
- Swave BV, Gaston Geenslaan 2, 3001 Leuven, Belgium
| | - Nandan S. Bisht
- Applied Optics and Spectroscopy Laboratory, Department of Physics, Soban Singh Jeena University Campus Almora, Almora, Uttarakhand 263601 India
| | - David Blinder
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel VUB), Pleinlaan 2, 1050 Brussel, Belgium
- IMEC, Kapeldreef 75, 3001 Leuven, Belgium
- Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba Japan
| | - Liangcai Cao
- Department of Precision Instruments, Tsinghua University, Beijing, 100084 China
| | - Qian Chen
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
| | - Ziyang Chen
- Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science and Engineering, Huaqiao University, Xiamen, 361021 Fujian China
| | - Vishesh Dubey
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Karen Egiazarian
- Computational Imaging Group, Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland
| | - Mert Ercan
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM), Bilkent University, 06800 Ankara, Turkey
- Department of Physics, Bilkent University, 06800 Ankara, Turkey
| | - Andrew Forbes
- School of Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - G. Gopakumar
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Vallikavu, Kerala India
| | - Yunhui Gao
- Department of Precision Instruments, Tsinghua University, Beijing, 100084 China
| | - Sylvain Gigan
- Laboratoire Kastler Brossel, Centre National de la Recherche Scientifique (CNRS) UMR 8552, Sorbonne Universite ´, Ecole Normale Supe ´rieure-Paris Sciences et Lettres (PSL) Research University, Collège de France, 24 rue Lhomond, 75005 Paris, France
| | - Paweł Gocłowski
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | | | - Alon Greenbaum
- Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695 USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695 USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695 USA
| | - Ryoichi Horisaki
- Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 Japan
| | - Daniel Ierodiaconou
- Faculty of Science Engineering and Built Environment, Deakin University, Princes Highway, Warrnambool, VIC 3280 Australia
| | - Saulius Juodkazis
- Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Computing and Engineering Technologies, Optical Sciences Center, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122 Australia
- World Research Hub Initiative (WRHI), Tokyo Institute of Technology, 2-12-1, Ookayama, Tokyo, 152-8550 Japan
| | - Tanushree Karmakar
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Vladimir Katkovnik
- Computational Imaging Group, Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland
| | - Svetlana N. Khonina
- IPSI RAS-Branch of the FSRC “Crystallography and Photonics” RAS, 443001 Samara, Russia
- Samara National Research University, 443086 Samara, Russia
| | - Peter Kner
- School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602 USA
| | - Vladislav Kravets
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Ravi Kumar
- Department of Physics, SRM University – AP, Amaravati, Andhra Pradesh 522502 India
| | - Yingming Lai
- Laboratory of Applied Computational Imaging, Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Université du Québec, Varennes, QC J3X1Pd7 Canada
| | - Chen Li
- Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695 USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695 USA
| | - Jiaji Li
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Shaoheng Li
- School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602 USA
| | - Yuzhu Li
- Electrical and Computer Engineering Department, Bioengineering Department, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA USA
| | - Jinyang Liang
- Laboratory of Applied Computational Imaging, Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Université du Québec, Varennes, QC J3X1Pd7 Canada
| | - Gokul Manavalan
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Aditya Chandra Mandal
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Manisha Manisha
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Christopher Mann
- Department of Applied Physics and Materials Science, Northern Arizona University, Flagstaff, AZ 86011 USA
- Center for Materials Interfaces in Research and Development, Northern Arizona University, Flagstaff, AZ 86011 USA
| | - Marcin J. Marzejon
- Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland
| | - Chané Moodley
- School of Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - Junko Morikawa
- World Research Hub Initiative (WRHI), Tokyo Institute of Technology, 2-12-1, Ookayama, Tokyo, 152-8550 Japan
| | - Inbarasan Muniraj
- LiFE Lab, Department of Electronics and Communication Engineering, Alliance School of Applied Engineering, Alliance University, Bangalore, Karnataka 562106 India
| | - Donatas Narbutis
- Institute of Theoretical Physics and Astronomy, Faculty of Physics, Vilnius University, Sauletekio 9, 10222 Vilnius, Lithuania
| | - Soon Hock Ng
- Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Computing and Engineering Technologies, Optical Sciences Center, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122 Australia
| | - Fazilah Nothlawala
- School of Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - Jeonghun Oh
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141 South Korea
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, Bioengineering Department, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA USA
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141 South Korea
- Tomocube Inc., Daejeon, 34051 South Korea
| | - Alexey P. Porfirev
- IPSI RAS-Branch of the FSRC “Crystallography and Photonics” RAS, 443001 Samara, Russia
| | - Mariana Potcoava
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Shashi Prabhakar
- Quantum Science and Technology Laboratory, Physical Research Laboratory, Navrangpura, Ahmedabad, 380009 India
| | - Jixiong Pu
- Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science and Engineering, Huaqiao University, Xiamen, 361021 Fujian China
| | - Mani Ratnam Rai
- Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695 USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695 USA
| | - Mikołaj Rogalski
- Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland
| | - Meguya Ryu
- Research Institute for Material and Chemical Measurement, National Metrology Institute of Japan (AIST), 1-1-1 Umezono, Tsukuba, 305-8563 Japan
| | - Sakshi Choudhary
- Department Chemical Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Shiva, Israel
| | - Gangi Reddy Salla
- Department of Physics, SRM University – AP, Amaravati, Andhra Pradesh 522502 India
| | - Peter Schelkens
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel VUB), Pleinlaan 2, 1050 Brussel, Belgium
- IMEC, Kapeldreef 75, 3001 Leuven, Belgium
| | - Sarp Feykun Şener
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM), Bilkent University, 06800 Ankara, Turkey
- Department of Physics, Bilkent University, 06800 Ankara, Turkey
| | - Igor Shevkunov
- Computational Imaging Group, Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland
| | - Tomoyoshi Shimobaba
- Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba Japan
| | - Rakesh K. Singh
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Ravindra P. Singh
- Quantum Science and Technology Laboratory, Physical Research Laboratory, Navrangpura, Ahmedabad, 380009 India
| | - Adrian Stern
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Jiasong Sun
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Shun Zhou
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Chao Zuo
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Zack Zurawski
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Tatsuki Tahara
- Applied Electromagnetic Research Center, Radio Research Institute, National Institute of Information and Communications Technology (NICT), 4-2-1 Nukuikitamachi, Koganei, Tokyo 184-8795 Japan
| | - Vipin Tiwari
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
| | - Maciej Trusiak
- Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland
| | - R. V. Vinu
- Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science and Engineering, Huaqiao University, Xiamen, 361021 Fujian China
| | - Sergey G. Volotovskiy
- IPSI RAS-Branch of the FSRC “Crystallography and Photonics” RAS, 443001 Samara, Russia
| | - Hasan Yılmaz
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM), Bilkent University, 06800 Ankara, Turkey
| | - Hilton Barbosa De Aguiar
- Laboratoire Kastler Brossel, Centre National de la Recherche Scientifique (CNRS) UMR 8552, Sorbonne Universite ´, Ecole Normale Supe ´rieure-Paris Sciences et Lettres (PSL) Research University, Collège de France, 24 rue Lhomond, 75005 Paris, France
| | - Balpreet S. Ahluwalia
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Azeem Ahmad
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
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Bhatt S, Butola A, Acuña S, Hansen DH, Tinguely JC, Nystad M, Mehta DS, Agarwal K. Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing. F&S SCIENCE 2024; 5:215-224. [PMID: 38977198 DOI: 10.1016/j.xfss.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/23/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
OBJECTIVE To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis. DESIGN Anonymized sperm samples were videographed under a quantitative phase microscope, followed by generating and analyzing superresolution motion traces of individual spermatozoa. SETTING Not applicable. PATIENT(S) Centrifuged human sperm samples. INTERVENTION(S) Not applicable. MAIN OUTCOME MEASURE(S) Precision of motion trace of individual sperms, presence of a helical pattern in the motion trace, mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression. RESULT(S) Spatially sensitive quantitative phase imaging with a superresolution computational technique MUltiple SIgnal Classification ALgorithm allowed achieving motion precision of 340 nm using ×10, 0.25 numerical aperture lens whereas the diffraction-limited resolution at this setting was 1,320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with a speed >25 μm/s were randomly selected from the same healthy donor semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. In addition, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (e.g., >70 μm/s) may demonstrate irregular motion and need further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUltiple SIgnal Classification ALgorithm is an image analysis technique that may vaguely fall under the machine learning category, but the conventional metrics for reporting found in Enhancing the QUAlity and Transparency Of health Research network do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility. CONCLUSION(S) Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion patterns, and whether irregularity of motion indicates poor quality regarding artificial insemination needs further investigation. The presented technique can be generalized for sperm analysis for a variety of fertility conditions.
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Affiliation(s)
- Sunil Bhatt
- Bio-photonics and Green-photonics Laboratory, Department of Physics, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi, India
| | - Ankit Butola
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Sebastian Acuña
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Daniel Henry Hansen
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Jean-Claude Tinguely
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Mona Nystad
- Department of Clinical Medicine, Women's Health and Perinatology Research Group, UiT The Arctic University of Norway, Tromsø, Norway; Department of Obstetrics and Gynecology, University Hospital of North Norway, Tromsø, Norway
| | - Dalip Singh Mehta
- Bio-photonics and Green-photonics Laboratory, Department of Physics, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi, India
| | - Krishna Agarwal
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway.
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Wheeler MB, Rabel RAC, Rubessa M, Popescu G. Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos†. Biol Reprod 2024; 110:1125-1134. [PMID: 38733568 PMCID: PMC11180620 DOI: 10.1093/biolre/ioae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 05/13/2024] Open
Abstract
Assisted reproduction is one of the significant tools to treat human infertility. Morphological assessment is the primary method to determine sperm and embryo viability during in vitro fertilization cycles. It has the advantage of being a quick, convenient, and inexpensive means of assessment. However, visual observation is of limited predictive value for early embryo morphology. It has led many to search for other imaging tools to assess the reproductive potential of a given embryo. The limitations of visual assessment apply to both humans and animals. One recent innovation in assisted reproduction technology imaging is interferometric phase microscopy, also known as holographic microscopy. Interferometric phase microscopy/quantitative phase imaging is the next likely progression of analytical microscopes for the assisted reproduction laboratory. The interferometric phase microscopy system analyzes waves produced by the light as it passes through the specimen observed. The microscope collects the light waves produced and uses the algorithm to create a hologram of the specimen. Recently, interferometric phase microscopy has been combined with quantitative phase imaging, which joins phase contrast microscopy with holographic microscopy. These microscopes collect light waves produced and use the algorithm to create a hologram of the specimen. Unlike other systems, interferometric phase microscopy can provide a quantitative digital image, and it can make 2D and 3D images of the samples. This review summarizes some newer and more promising quantitative phase imaging microscopy systems for evaluating gametes and embryos. Studies clearly show that quantitative phase imaging is superior to bright field microscopy-based evaluation methods when evaluating sperm and oocytes prior to IVF and embryos prior to transfer. However, further assessment of these systems for efficacy, reproducibility, cost-effectiveness, and embryo/gamete safety must take place before they are widely adopted.
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Affiliation(s)
- Matthew B Wheeler
- Department of Animal Sciences University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - R A Chanaka Rabel
- Department of Animal Sciences University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Marcello Rubessa
- Department of Animal Sciences University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Gabriel Popescu
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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Panner Selvam MK, Moharana AK, Baskaran S, Finelli R, Hudnall MC, Sikka SC. Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:279. [PMID: 38399566 PMCID: PMC10890589 DOI: 10.3390/medicina60020279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024]
Abstract
Background and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.
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Affiliation(s)
- Manesh Kumar Panner Selvam
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
| | - Ajaya Kumar Moharana
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
- Redox Biology & Proteomics Laboratory, Department of Zoology, School of Life Sciences, Ravenshaw University, Cuttack 753003, Odisha, India
| | - Saradha Baskaran
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
| | | | | | - Suresh C. Sikka
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
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Lustgarten Guahmich N, Borini E, Zaninovic N. Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection. Fertil Steril 2023; 120:729-734. [PMID: 37307892 DOI: 10.1016/j.fertnstert.2023.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/14/2023]
Abstract
Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplasmic sperm injection (ICSI) have been explored, mainly to improve fertilization outcomes and decrease variability within ICSI procedures. Although significant advances have been achieved in developing algorithms that track and rank single sperm in real-time during ICSI, the clinical benefits these might have in improving pregnancy rates from a single assisted reproductive technology cycle remain to be established.
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Affiliation(s)
- Nicole Lustgarten Guahmich
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Elena Borini
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Nikica Zaninovic
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York.
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7
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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8
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Ebrahimi S, Moreno-Pescador G, Persson S, Jauffred L, Bendix PM. Label-free optical interferometric microscopy to characterize morphodynamics in living plants. FRONTIERS IN PLANT SCIENCE 2023; 14:1156478. [PMID: 37284726 PMCID: PMC10239806 DOI: 10.3389/fpls.2023.1156478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/04/2023] [Indexed: 06/08/2023]
Abstract
During the last century, fluorescence microscopy has played a pivotal role in a range of scientific discoveries. The success of fluorescence microscopy has prevailed despite several shortcomings like measurement time, photobleaching, temporal resolution, and specific sample preparation. To bypass these obstacles, label-free interferometric methods have been developed. Interferometry exploits the full wavefront information of laser light after interaction with biological material to yield interference patterns that contain information about structure and activity. Here, we review recent studies in interferometric imaging of plant cells and tissues, using techniques such as biospeckle imaging, optical coherence tomography, and digital holography. These methods enable quantification of cell morphology and dynamic intracellular measurements over extended periods of time. Recent investigations have showcased the potential of interferometric techniques for precise identification of seed viability and germination, plant diseases, plant growth and cell texture, intracellular activity and cytoplasmic transport. We envision that further developments of these label-free approaches, will allow for high-resolution, dynamic imaging of plants and their organelles, ranging in scales from sub-cellular to tissue and from milliseconds to hours.
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Affiliation(s)
- Samira Ebrahimi
- Copenhagen Plant Science Center, Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg, Denmark
- Biocomplexity, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Guillermo Moreno-Pescador
- Copenhagen Plant Science Center, Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg, Denmark
- Biocomplexity, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Staffan Persson
- Copenhagen Plant Science Center, Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg, Denmark
- Joint International Research Laboratory of Metabolic and Developmental Sciences, State Key Laboratory of Hybrid Rice, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Liselotte Jauffred
- Biocomplexity, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Poul Martin Bendix
- Biocomplexity, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
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9
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Ahmad A, Hettiarachchi R, Khezri A, Singh Ahluwalia B, Wadduwage DN, Ahmad R. Highly sensitive quantitative phase microscopy and deep learning aided with whole genome sequencing for rapid detection of infection and antimicrobial resistance. Front Microbiol 2023; 14:1154620. [PMID: 37125187 PMCID: PMC10130531 DOI: 10.3389/fmicb.2023.1154620] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/16/2023] [Indexed: 05/02/2023] Open
Abstract
Current state-of-the-art infection and antimicrobial resistance (AMR) diagnostics are based on culture-based methods with a detection time of 48-96 h. Therefore, it is essential to develop novel methods that can do real-time diagnoses. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable rapid diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks-based classification. The workflow was benchmarked on 21 clinical isolates from four WHO priority pathogens that were antibiotic susceptibility tested, and their AMR profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Accurate classification based on the gram staining (100% recall for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% sensitivity in predicting 19 out of the 21 strains, with an overall accuracy of 95.45%). The results from this initial proof-of-concept study demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, strain-level classification, and the presence or absence of AMR, which WGS can follow up for confirmation. Overall, a combined workflow with QPM and WGS complemented with deep learning data analyses could, in the future, be transformative for detecting and identifying pathogens and characterization of the AMR profile and antibiotic susceptibility.
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Affiliation(s)
- Azeem Ahmad
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Ramith Hettiarachchi
- Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Sri Lanka
- Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, United States
| | - Abdolrahman Khezri
- Department of Biotechnology, Inland Norway University of Applied Sciences, Hamar, Norway
| | - Balpreet Singh Ahluwalia
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
- Department of Clinical Science, Intervention and Technology, Karolinska Insitute, Stockholm, Sweden
| | - Dushan N. Wadduwage
- Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, United States
| | - Rafi Ahmad
- Department of Biotechnology, Inland Norway University of Applied Sciences, Hamar, Norway
- Institute of Clinical Medicine, Faculty of Health Sciences, UiT—The Arctic University of Norway, Tromsø, Norway
- *Correspondence: Rafi Ahmad,
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10
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Arcab P, Mirecki B, Stefaniuk M, Pawłowska M, Trusiak M. Experimental optimization of lensless digital holographic microscopy with rotating diffuser-based coherent noise reduction. OPTICS EXPRESS 2022; 30:42810-42828. [PMID: 36522993 DOI: 10.1364/oe.470860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/23/2022] [Indexed: 06/17/2023]
Abstract
Laser-based lensless digital holographic microscopy (LDHM) is often spoiled by considerable coherent noise factor. We propose a novel LDHM method with significantly limited coherent artifacts, e.g., speckle noise and parasitic interference fringes. It is achieved by incorporating a rotating diffuser, which introduces partial spatial coherence and preserves high temporal coherence of laser light, crucial for credible in-line hologram reconstruction. We present the first implementation of the classical rotating diffuser concept in LDHM, significantly increasing the signal-to-noise ratio while preserving the straightforwardness and compactness of the LDHM imaging device. Prior to the introduction of the rotating diffusor, we performed LDHM experimental hardware optimization employing 4 light sources, 4 cameras, and 3 different optical magnifications (camera-sample distances). It was guided by the quantitative assessment of numerical amplitude/phase reconstruction of test targets, conducted upon standard deviation calculation (noise factor quantification), and resolution evaluation (information throughput quantification). Optimized rotating diffuser LDHM (RD-LDHM) method was successfully corroborated in technical test target imaging and examination of challenging biomedical sample (60 µm thick mouse brain tissue slice). Physical minimization of coherent noise (up to 50%) was positively verified, while preserving optimal spatial resolution of phase and amplitude imaging. Coherent noise removal, ensured by proposed RD-LDHM method, is especially important in biomedical inference, as speckles can falsely imitate valid biological features. Combining this favorable outcome with large field-of-view imaging can promote the use of reported RD-LDHM technique in high-throughput stain-free biomedical screening.
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11
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Cywińska M, Rogalski M, Brzeski F, Patorski K, Trusiak M. DeepOrientation: convolutional neural network for fringe pattern orientation map estimation. OPTICS EXPRESS 2022; 30:42283-42299. [PMID: 36366685 DOI: 10.1364/oe.465094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Fringe pattern based measurement techniques are the state-of-the-art in full-field optical metrology. They are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe orientation map can significantly facilitate the measurement process in various ways, e.g., fringe filtering (denoising), fringe pattern boundary padding, fringe skeletoning (contouring/following/tracking), local fringe spatial frequency (fringe period) estimation, and fringe pattern phase demodulation. Considering all of that, the accurate, robust, and preferably automatic estimation of local fringe orientation map is of high importance. In this paper we propose a novel numerical solution for local fringe orientation map estimation based on convolutional neural network and deep learning called DeepOrientation. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepOrientation comparing it with a representative of the classical approach to orientation estimation called combined plane fitting/gradient method. The example proving the effectiveness of DeepOrientation in fringe pattern analysis, which we present in this paper, is the application of DeepOrientation for guiding the phase demodulation process in Hilbert spiral transform. In particular, living HeLa cells quantitative phase imaging outcomes verify the method as an important asset in label-free microscopy.
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12
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Lipke W, Winnik J, Trusiak M. Numerical analysis of the effect of reduced temporal coherence in quantitative phase microscopy and tomography. OPTICS EXPRESS 2022; 30:21241-21257. [PMID: 36224847 DOI: 10.1364/oe.458167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/20/2022] [Indexed: 06/16/2023]
Abstract
We present the numerical analysis of the effect of the temporarily partially coherent illumination on the phase measurement accuracy in digital holography microscopy (DHM) and optical diffraction tomography (ODT), as reconstruction algorithms tend to assume purely monochromatic conditions. In the regime of reduced temporal coherence, we simulate the hologram formation in two different optical setups, representing classical off-axis two-beam and grating common-path configurations. We consider two ODT variants: with sample rotation and angle-scanning of illumination. Besides the coherence degree of illumination, our simulation considers the influence of the sample normal dispersion, shape of the light spectrum, and optical parameters of the imaging setup. As reconstruction algorithms we employ Fourier hologram method and first-order Rytov approximation with direct inversion and nonnegativity constraints. Quantitative evaluation of the measurement results deviations introduced by the mentioned error sources is comprehensively analyzed, for the first time to the best of our knowledge. Obtained outcomes indicate low final DHM/ODT reconstruction errors for the grating-assisted common-path configuration. Nevertheless, dispersion and asymmetric spectrum introduce non-negligible overestimated refractive index values and noise, and should be thus carefully considered within experimental frameworks.
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13
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Wiesehöfer C, Wiesehöfer M, Dankert JT, Chung JJ, von Ostau NE, Singer BB, Wennemuth G. CatSper and its CaM-like Ca 2+ sensor EFCAB9 are necessary for the path chirality of sperm. FASEB J 2022; 36:e22288. [PMID: 35438819 PMCID: PMC9835897 DOI: 10.1096/fj.202101656rr] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/11/2022] [Accepted: 03/17/2022] [Indexed: 01/14/2023]
Abstract
Successful fertilization depends on sperm motility adaptation. Ejaculated and activated sperm beat symmetrically in high frequency, move linearly, and swim with clockwise chirality. After capacitation, sperm beat asymmetrically with lower amplitude and a high lateral head excursion. This motility change called hyperactivation requires CatSper activation and an increase in intracellular Ca2+ . However, whether CatSper-mediated Ca2+ influx participates in controlling the swim path chirality is unknown. In this study, we show that the clockwise path chirality is preserved in mouse sperm regardless of capacitation state but is lost in the sperm either lacking the entire CatSper channel or its Ca2+ sensor EFCAB9. Pharmacological inhibition of CatSper with either mibefradil or NNC 55-0396 leads to the same loss in swim path chirality. Exposure of sperm to the recombinant N-terminal part of the zona pellucida protein 2 randomizes chirality in capacitated cells, but not in non-capacitated ones. We conclude that Ca2+ sensitive regulation of CatSper activity orchestrates clockwise swim path chirality of sperm and any substantial change, such as the physiological stimulus of zona pellucida glycoproteins, results in a loss of chirality.
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Affiliation(s)
| | - Marc Wiesehöfer
- Department of Anatomy, University Duisburg-Essen, D-45147 Essen, Germany
| | | | - Jean-Ju Chung
- Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Nicola Edith von Ostau
- Department of Anatomy, University Duisburg-Essen, D-45147 Essen, Germany,Department of Urology, University Hospital Essen, D-45147 Essen, Germany
| | | | - Gunther Wennemuth
- Department of Anatomy, University Duisburg-Essen, D-45147 Essen, Germany,Correspondence to
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14
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Juskewitz E, Mishchenko E, Dubey VK, Jenssen M, Jakubec M, Rainsford P, Isaksson J, Andersen JH, Ericson JU. Lulworthinone: In Vitro Mode of Action Investigation of an Antibacterial Dimeric Naphthopyrone Isolated from a Marine Fungus. Mar Drugs 2022; 20:md20050277. [PMID: 35621928 PMCID: PMC9147123 DOI: 10.3390/md20050277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/13/2022] [Accepted: 04/16/2022] [Indexed: 01/27/2023] Open
Abstract
Treatment options for infections caused by antimicrobial-resistant bacteria are rendered ineffective, and drug alternatives are needed—either from new chemical classes or drugs with new modes of action. Historically, natural products have been important contributors to drug discovery. In a recent study, the dimeric naphthopyrone lulworthinone produced by an obligate marine fungus in the family Lulworthiaceae was discovered. The observed potent antibacterial activity against Gram-positive bacteria, including several clinical methicillin-resistant Staphylococcus aureus (MRSA) isolates, prompted this follow-up mode of action investigation. This paper aimed to characterize the antibacterial mode of action (MOA) of lulworthinone by combining in vitro assays, NMR experiments and microscopy. The results point to a MOA targeting the bacterial membrane, leading to improper cell division. Treatment with lulworthinone induced an upregulation of genes responding to cell envelope stress in Bacillus subtilis. Analysis of the membrane integrity and membrane potential indicated that lulworthinone targets the bacterial membrane without destroying it. This was supported by NMR experiments using artificial lipid bilayers. Fluorescence microscopy revealed that lulworthinone affects cell morphology and impedes the localization of the cell division protein FtsZ. Surface plasmon resonance and dynamic light scattering assays showed that this activity is linked with the compound‘s ability to form colloidal aggregates. Antibacterial agents acting at cell membranes are of special interest, as the development of bacterial resistance to such compounds is deemed more difficult to occur.
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Affiliation(s)
- Eric Juskewitz
- Research Group for Host Microbe Interactions, Department of Medical Biology, Faculty of Health Sciences, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (E.M.); (V.K.D.)
- Correspondence: (E.J.); (J.U.E.)
| | - Ekaterina Mishchenko
- Research Group for Host Microbe Interactions, Department of Medical Biology, Faculty of Health Sciences, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (E.M.); (V.K.D.)
| | - Vishesh K. Dubey
- Research Group for Host Microbe Interactions, Department of Medical Biology, Faculty of Health Sciences, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (E.M.); (V.K.D.)
| | - Marte Jenssen
- Marbio, The Norwegian College of Fishery Science, Faculty of Biosciences, Fisheries and Economics, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (M.J.); (J.H.A.)
| | - Martin Jakubec
- Department of Chemistry, Faculty of Science and Technology, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (M.J.); (P.R.); (J.I.)
| | - Philip Rainsford
- Department of Chemistry, Faculty of Science and Technology, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (M.J.); (P.R.); (J.I.)
| | - Johan Isaksson
- Department of Chemistry, Faculty of Science and Technology, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (M.J.); (P.R.); (J.I.)
| | - Jeanette H. Andersen
- Marbio, The Norwegian College of Fishery Science, Faculty of Biosciences, Fisheries and Economics, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (M.J.); (J.H.A.)
| | - Johanna U. Ericson
- Research Group for Host Microbe Interactions, Department of Medical Biology, Faculty of Health Sciences, UiT the Arctic University of Norway, 9019 Tromsø, Norway; (E.M.); (V.K.D.)
- Correspondence: (E.J.); (J.U.E.)
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15
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Zhu H, Zhu Y, Sun C, Jiang F. A Preliminary Study on the Evaluation of Human Sperm Head Morphology with a Domestic Digital Holographic Image System. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:130-135. [PMID: 36939764 PMCID: PMC9590537 DOI: 10.1007/s43657-022-00046-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/15/2022] [Accepted: 01/19/2022] [Indexed: 11/29/2022]
Abstract
The head of sperm was imaged with domestic digital holographic microscopy (DHM), and then the quantitative three-dimensional size information of normal sperm and teratozoospermic sperm was compared and analyzed. DHM sperm imaging and repeated quantitative evaluation were used to determine the morphology of the sperm head in two patients with teratozoospermia and four volunteers with normal semen parameters. Sixty and 139 sperm of teratozoospermia patients and normal people were photographed by digital hologram, respectively. The differences in head height and width were compared and statistically analyzed. The sperm head height of the teratozoospermia group was 3.06 ± 1.66 μm, which was significantly lower than that of the normal sperm group (4.54 ± 1.60 μm, p < 0.01), but there was no significant difference in the head width between the two groups. Compared with the traditional two-dimensional optical microscope observation method, the DHM system can provide three-dimensional quantitative information for the sperm head and thus may help in the comprehensive clinical evaluation of the sperm head structure.
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Affiliation(s)
- Hong Zhu
- Department of Urology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011 China
| | - Yong Zhu
- Human Sperm Bank of Fudan University, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011 China
| | - Can Sun
- Human Sperm Bank of Fudan University, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011 China
| | - Feng Jiang
- Shanghai JiAi Genetics and IVF Institute China-USA Center, Shanghai, 200011 China
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16
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Computer software (SiD) assisted real-time single sperm selection correlates with fertilization and blastocyst formation. Reprod Biomed Online 2022; 45:703-711. [DOI: 10.1016/j.rbmo.2022.03.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 03/22/2022] [Accepted: 03/30/2022] [Indexed: 11/22/2022]
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17
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Belashov AV, Zhikhoreva AA, Belyaeva TN, Salova AV, Kornilova ES, Semenova IV, Vasyutinskii OS. Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images. Cells 2021; 10:2587. [PMID: 34685568 PMCID: PMC8533984 DOI: 10.3390/cells10102587] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/20/2021] [Accepted: 09/24/2021] [Indexed: 12/20/2022] Open
Abstract
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.
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Affiliation(s)
- Andrey V. Belashov
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
| | - Anna A. Zhikhoreva
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
| | - Tatiana N. Belyaeva
- Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia; (T.N.B.); (A.V.S.); (E.S.K.)
| | - Anna V. Salova
- Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia; (T.N.B.); (A.V.S.); (E.S.K.)
| | - Elena S. Kornilova
- Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia; (T.N.B.); (A.V.S.); (E.S.K.)
- Institute for Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 29, Polytekhnicheskaya, 195251 St. Petersburg, Russia
| | - Irina V. Semenova
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
| | - Oleg S. Vasyutinskii
- Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia; (A.A.Z.); (I.V.S.); (O.S.V.)
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18
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Gocłowski P, Cywińska M, Ahmad A, Ahluwalia B, Trusiak M. Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform. OPTICS EXPRESS 2021; 29:31632-31649. [PMID: 34615253 DOI: 10.1364/oe.435001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/15/2021] [Indexed: 06/13/2023]
Abstract
Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD - we have considerably increased algorithm's application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data.
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You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. Machine learning for sperm selection. Nat Rev Urol 2021; 18:387-403. [PMID: 34002070 DOI: 10.1038/s41585-021-00465-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2021] [Indexed: 02/04/2023]
Abstract
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 108 gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.
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Affiliation(s)
- Jae Bem You
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.,Department of Chemical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Christopher McCallum
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Yihe Wang
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Jason Riordon
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Reza Nosrati
- Department of Mechanical & Aerospace Engineering, Monash University, Clayton, VIC, Australia
| | - David Sinton
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.
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20
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Shemshaki G, Murthy ASN, Malini SS. Assessment and Establishment of Correlation between Reactive Oxidation Species, Citric Acid, and Fructose Level in Infertile Male Individuals: A Machine-Learning Approach. J Hum Reprod Sci 2021; 14:129-136. [PMID: 34316227 PMCID: PMC8279050 DOI: 10.4103/jhrs.jhrs_26_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 02/14/2021] [Accepted: 03/23/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Biochemical complexity of seminal plasma and obesity has an important role in male infertility (MI); so far, it has not been possible to provide evidence of clinical significance for all of them. Aims: Our goal here is to evaluate the correlation between biochemical markers with semen parameters, which might play a role in MI. Study Setting and Design: We enlisted 100 infertile men as patients and 50 fertile men as controls to evaluate the sperm parameters and biochemical markers in ascertaining MI. Materials and Methods: Semen analyses, seminal fructose, citric acid, and reactive oxidation species (ROS) were measured in 100 patients and 50 controls. Statistical Analysis: Descriptive statistics, an independent t-test, Pearson correlation, and machine-learning approaches were used to integrate the various biochemical and seminal parameters measured to quantify the inter-relatedness between these measurements. Results: Pearson correlation results showed a significant positive correlation between body mass index (BMI) and fructose levels. Citric acid had a positive correlation with sperm count, morphology, motility, and volume but displayed a negative correlation with BMI and basal metabolic rate (BMR). However, BMI and BMR had a positive correlation with ROS. Sperm count, morphology, and motility were negative correlations with ROS. The machine-learning approach detected that pH was the most critical parameter with an inverse effect on citric acid, and BMI and motility were the most critical parameter for ROS. Conclusion: We recommend that evaluation of biochemical markers of seminal fluid may benefit in understanding the etiology of MI based on the functionality of accessory glands and ROS levels.
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Affiliation(s)
- Golnaz Shemshaki
- Department of Studies in Zoology, University of Mysore, Mysore, Karnataka, India
| | | | - Suttur S Malini
- Department of Studies in Zoology, University of Mysore, Mysore, Karnataka, India
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21
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Trusiak M. Fringe analysis: single-shot or two-frames? Quantitative phase imaging answers. OPTICS EXPRESS 2021; 29:18192-18211. [PMID: 34154081 DOI: 10.1364/oe.423336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/28/2021] [Indexed: 06/13/2023]
Abstract
Conditions of the digital recording of the fringe pattern determine the phase reconstruction procedure, which in turn directly shapes the final accuracy and throughput of the full-field (non-scanning) optical measurement technique and defines the system capabilities. In this way, the fringe pattern analysis plays a crucial role in the ubiquitous optical measurements and thus is under constant development focused on high temporal/spatial resolution. It is especially valuable in the quantitative phase imaging technology, which emerged in the high-contrast label-free biomedical microscopy. In this paper, I apply recently blossomed two-frame phase-shifting techniques to the QPI and merge them with advanced adaptive interferogram pre-filtering algorithms. Next, I comprehensively test such frameworks against classical and adaptive single-shot methods applied for phase reconstruction in dynamic QPI enabling highest phase time-space-bandwidth product. The presented study systematically tackles important question: what is the gain, if any, in QPI realized by recording two phase-shifted interferograms? Counterintuitively, the results show that single-shot demodulation exhibited higher phase reconstruction accuracy than two-frame phase-shifting methods in low and medium interferogram signal-to-noise ratio regimes. Thus, the single-shot approach is promoted due to not only high temporal resolution but also larger phase-information throughput. Additionally, in the majority of scenarios, the best option is to shift the paradigm and employ two-frame pre-filtering rather than two-frame phase retrieval. Experimental fringe analysis in QPI of LSEC/RWPE cell lines successfully corroborated all novel numerical findings. Hence, the presented numerical-experimental research advances the important field of fringe analysis solutions for optical full-field measurement methods with widespread bio-engineering applications.
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22
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Kandel ME, Kim E, Lee YJ, Tracy G, Chung HJ, Popescu G. Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity. ACS Sens 2021; 6:1864-1874. [PMID: 33882232 PMCID: PMC8815662 DOI: 10.1021/acssensors.1c00100] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Primary neuronal cultures have been widely used to study neuronal morphology, neurophysiology, neurodegenerative processes, and molecular mechanism of synaptic plasticity underlying learning and memory. However, the unique behavioral properties of neurons make them challenging to study, with phenotypic differences expressed as subtle changes in neuronal arborization rather than easy-to-assay features such as cell count. The need to analyze morphology, growth, and intracellular transport has motivated the development of increasingly sophisticated microscopes and image analysis techniques. Due to its high-contrast, high-specificity output, many assays rely on confocal fluorescence microscopy, genetic methods, or antibody staining techniques. These approaches often limit the ability to measure quantitatively dynamic activity such as intracellular transport and growth. In this work, we describe a method for label-free live-cell cell imaging with antibody staining specificity by estimating the associated fluorescence signals via quantitative phase imaging and deep convolutional neural networks. This computationally inferred fluorescence image is then used to generate a semantic segmentation map, annotating subcellular compartments of live unlabeled neural cultures. These synthetic fluorescence maps were further applied to study the time-lapse development of hippocampal neurons, highlighting the relationships between the cellular dry mass production and the dynamic transport activity within the nucleus and neurites. Our implementation provides a high-throughput strategy to analyze neural network arborization dynamically, with high specificity and without the typical phototoxicity and photobleaching limitations associated with fluorescent markers.
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Affiliation(s)
- Mikhail E Kandel
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Eunjae Kim
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Young Jae Lee
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Gregory Tracy
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana Champaign, Urbana, Illinois 61801, United States
| | - Hee Jung Chung
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana Champaign, Urbana, Illinois 61801, United States
| | - Gabriel Popescu
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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23
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Cauzzo J, Jayakumar N, Ahluwalia BS, Ahmad A, Škalko-Basnet N. Characterization of Liposomes Using Quantitative Phase Microscopy (QPM). Pharmaceutics 2021; 13:pharmaceutics13050590. [PMID: 33919040 PMCID: PMC8142990 DOI: 10.3390/pharmaceutics13050590] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 11/16/2022] Open
Abstract
The rapid development of nanomedicine and drug delivery systems calls for new and effective characterization techniques that can accurately characterize both the properties and the behavior of nanosystems. Standard methods such as dynamic light scattering (DLS) and fluorescent-based assays present challenges in terms of system's instability, machine sensitivity, and loss of tracking ability, among others. In this study, we explore some of the downsides of batch-mode analyses and fluorescent labeling, while introducing quantitative phase microscopy (QPM) as a label-free complimentary characterization technique. Liposomes were used as a model nanocarrier for their therapeutic relevance and structural versatility. A successful immobilization of liposomes in a non-dried setup allowed for static imaging conditions in an off-axis phase microscope. Image reconstruction was then performed with a phase-shifting algorithm providing high spatial resolution. Our results show the potential of QPM to localize subdiffraction-limited liposomes, estimate their size, and track their integrity over time. Moreover, QPM full-field-of-view images enable the estimation of a single-particle-based size distribution, providing an alternative to the batch mode approach. QPM thus overcomes some of the drawbacks of the conventional methods, serving as a relevant complimentary technique in the characterization of nanosystems.
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Affiliation(s)
- Jennifer Cauzzo
- Drug Transport and Delivery Research Group, Department of Pharmacy, Faculty of Health Sciences, University of Tromsø The Arctic University of Norway, N-9037 Tromsø, Norway;
| | - Nikhil Jayakumar
- Optical Nanoscopy Research Group, Department of Physics and Technology, Faculty of Science and Technology, University of Tromsø The Arctic University of Norway, N-9037 Tromsø, Norway; (N.J.); (B.S.A.); (A.A.)
| | - Balpreet Singh Ahluwalia
- Optical Nanoscopy Research Group, Department of Physics and Technology, Faculty of Science and Technology, University of Tromsø The Arctic University of Norway, N-9037 Tromsø, Norway; (N.J.); (B.S.A.); (A.A.)
| | - Azeem Ahmad
- Optical Nanoscopy Research Group, Department of Physics and Technology, Faculty of Science and Technology, University of Tromsø The Arctic University of Norway, N-9037 Tromsø, Norway; (N.J.); (B.S.A.); (A.A.)
| | - Nataša Škalko-Basnet
- Drug Transport and Delivery Research Group, Department of Pharmacy, Faculty of Health Sciences, University of Tromsø The Arctic University of Norway, N-9037 Tromsø, Norway;
- Correspondence: ; Tel.: +47-776-46-640
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24
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Li Y, Di J, Wang K, Wang S, Zhao J. Classification of cell morphology with quantitative phase microscopy and machine learning. OPTICS EXPRESS 2020; 28:23916-23927. [PMID: 32752380 DOI: 10.1364/oe.397029] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/21/2020] [Indexed: 06/11/2023]
Abstract
We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifier including various individual models such as artificial neural network, extreme learning machine and generalized logistic regression. In another approach, we apply a pretrained convolutional neural network using transfer learning for the classification. As a validation, we show the performances of both approaches on classification between macrophages cultured in normal gravity and microgravity with quantitative phase imaging. The multilevel integrated classifier achieves average accuracy 93.1%, which is comparable to the average accuracy 93.5% obtained by convolutional neural network. The presented quantitative phase imaging system with two classification approaches could be helpful to biomedical scientists for easy and accurate cell analysis.
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25
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Singh R, Dubey V, Wolfson D, Ahmad A, Butola A, Acharya G, Mehta DS, Basnet P, Ahluwalia BS. Quantitative assessment of morphology and sub-cellular changes in macrophages and trophoblasts during inflammation. BIOMEDICAL OPTICS EXPRESS 2020; 11:3733-3752. [PMID: 33014563 PMCID: PMC7510918 DOI: 10.1364/boe.389350] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/14/2020] [Accepted: 05/21/2020] [Indexed: 05/06/2023]
Abstract
In pregnancy during an inflammatory condition, macrophages present at the feto-maternal junction release an increased amount of nitric oxide (NO) and pro-inflammatory cytokines such as TNF-α and INF-γ, which can disturb the trophoblast functions and pregnancy outcome. Measurement of the cellular and sub-cellular morphological modifications associated with inflammatory responses are important in order to quantify the extent of trophoblast dysfunction for clinical implication. With this motivation, we investigated morphological, cellular and sub-cellular changes in externally inflamed RAW264.7 (macrophage) and HTR-8/SVneo (trophoblast) using structured illumination microscopy (SIM) and quantitative phase microscopy (QPM). We monitored the production of NO, changes in cell membrane and mitochondrial structure of macrophages and trophoblasts when exposed to different concentrations of pro-inflammatory agents (LPS and TNF-α). In vitro NO production by LPS-induced macrophages increased 22-fold as compared to controls, whereas no significant NO production was seen after the TNF-α challenge. Under similar conditions as with macrophages, trophoblasts did not produce NO following either LPS or the TNF-α challenge. Super-resolution SIM imaging showed changes in the morphology of mitochondria and the plasma membrane in macrophages following the LPS challenge and in trophoblasts following the TNF-α challenge. Label-free QPM showed a decrease in the optical thickness of the LPS-challenged macrophages while TNF-α having no effect. The vice-versa is observed for the trophoblasts. We further exploited machine learning approaches on a QPM dataset to detect and to classify the inflammation with an accuracy of 99.9% for LPS-challenged macrophages and 98.3% for TNF-α-challenged trophoblasts. We believe that the multi-modal advanced microscopy methodologies coupled with machine learning approach could be a potential way for early detection of inflammation.
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Affiliation(s)
- Rajwinder Singh
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Author with equal contribution
| | - Vishesh Dubey
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
- Author with equal contribution
| | - Deanna Wolfson
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
| | - Azeem Ahmad
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
| | - Ankit Butola
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Ganesh Acharya
- Department of Clinical Science, Intervention and Technology Karolinska Univ. Hospital, Sweden
| | - Dalip Singh Mehta
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Purusotam Basnet
- Womeńs Health and Perinatology Research Group, Department of Clinical Medicine, UiT The Arctic University of Norway and Department of Obstetrics and Gynecology, University Hospital of North Norway, Tromsø, Norway
| | - Balpreet Singh Ahluwalia
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
- Department of Clinical Science, Intervention and Technology Karolinska Univ. Hospital, Sweden
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26
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Tayal S, Singh V, Kaur T, Singh N, Mehta DS. Simultaneous fluorescence and quantitative phase imaging of MG63 osteosarcoma cells to monitor morphological changes with time using partially spatially coherent light source. Methods Appl Fluoresc 2020; 8:035004. [PMID: 32325433 DOI: 10.1088/2050-6120/ab8c5d] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Quantitative phase imaging (QPI) technique is used to determine various biophysical parameters, such as refractive index, cell thickness, morphology, etc. On the other hand, fluorescence microscopy is used to acquire information regarding molecular specificity of the biological cells and tissues. Conventionally, a fully coherent light source such as laser is used in QPI technique to obtain the interference fringes with ease; however, its high coherence is also responsible for the generation of speckle and spurious fringes, which results in degraded image quality and affects the phase measurement results too. In this paper, we report a multi-modal system that can be effectively utilized to acquire time varied diverse information about the biological specimen with high spatial phase sensitivity. Herein, a single unit comprising of a fluorescence microscope and the Linnik based interferometer specially equipped with a partially spatially coherent light source illumination was developed. The integrated system is capable to procure molecular specificity and phase information of biological specimen, in a single shot, utilizing a single-chip color CCD camera. Here, we performed experiments on MG63 osteosarcoma cells, and the composite interferometric-fluorescence images were obtained and then digitally decomposed into red and green colors; and, the phase maps were reconstructed using the Fourier fringe analysis method. Furthermore, the cultured cells were monitored over a time-span to observe and investigate the time dependent morphological changes along with the quantification of cellular adhesion and spreading. Hence, the proposed system can be utilized to quantify time dependent changes in the cell's morphology and in cell adhesion which can be an indicator for the detection of various range of diseases such as arthritis, cancer, osteoporosis and atherosclerosis.
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Affiliation(s)
- Shilpa Tayal
- Bio-Photonics and Green Photonics Laboratory, Department of Physics, Indian Institute of Technology Delhi, New Delhi 110016, India
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