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Article ; Online: Low dimensional approximation and generalization of multivariate functions on smooth manifolds using deep ReLU neural networks.

Labate, Demetrio / Shi, Ji

Neural networks : the official journal of the International Neural Network Society

2024 Volume 174, Page(s) 106223

Abstract: The expressive power of deep neural networks is manifested by their remarkable ability to approximate multivariate functions in a way that appears to overcome the curse of dimensionality. This ability is exemplified by their success in solving high- ... ...

Abstract	The expressive power of deep neural networks is manifested by their remarkable ability to approximate multivariate functions in a way that appears to overcome the curse of dimensionality. This ability is exemplified by their success in solving high-dimensional problems where traditional numerical solvers fail due to their limitations in accurately representing high-dimensional structures. To provide a theoretical framework for explaining this phenomenon, we analyze the approximation of Hölder functions defined on a d-dimensional smooth manifold M embedded in R
MeSH term(s)	Neural Networks, Computer
Language	English
Publishing date	2024-03-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106223
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Article: Advances in quantitative analysis of astrocytes using machine learning.

Labate, Demetrio / Kayasandik, Cihan

Neural regeneration research

2022 Volume 18, Issue 2, Page(s) 313–314

Language	English
Publishing date	2022-08-24
Publishing country	India
Document type	Journal Article
ZDB-ID	2388460-5
ISSN	1876-7958 ; 1673-5374
ISSN (online)	1876-7958
ISSN	1673-5374
DOI	10.4103/1673-5374.346474
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Article ; Online: High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery.

Stossi, Fabio / Singh, Pankaj K / Safari, Kazem / Marini, Michela / Labate, Demetrio / Mancini, Michael A

Biochemical pharmacology

2023 Volume 216, Page(s) 115770

Abstract: Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered ... ...

Abstract	Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
Language	English
Publishing date	2023-09-01
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	208787-x
ISSN	1873-2968 ; 0006-2952
ISSN (online)	1873-2968
ISSN	0006-2952
DOI	10.1016/j.bcp.2023.115770
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Article ; Online: A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue.

Kayasandik, Cihan Bilge / Ru, Wenjuan / Labate, Demetrio

Scientific reports

2020 Volume 10, Issue 1, Page(s) 5137

Abstract: While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise ... ...

Abstract	While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. To provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. Extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.
MeSH term(s)	Algorithms ; Astrocytes/physiology ; Brain/cytology ; Brain/physiology ; Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer
Language	English
Publishing date	2020-03-20
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-61953-9
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Article ; Online: Automated detection of GFAP-labeled astrocytes in micrographs using YOLOv5.

Huang, Yewen / Kruyer, Anna / Syed, Sarah / Kayasandik, Cihan Bilge / Papadakis, Manos / Labate, Demetrio

Scientific reports

2022 Volume 12, Issue 1, Page(s) 22263

Abstract: Astrocytes, a subtype of glial cells with a complex morphological structure, are active players in many aspects of the physiology of the central nervous system (CNS). However, due to their highly involved interaction with other cells in the CNS, made ... ...

Abstract	Astrocytes, a subtype of glial cells with a complex morphological structure, are active players in many aspects of the physiology of the central nervous system (CNS). However, due to their highly involved interaction with other cells in the CNS, made possible by their morphological complexity, the precise mechanisms regulating astrocyte function within the CNS are still poorly understood. This knowledge gap is also due to the current limitations of existing quantitative image analysis tools that are unable to detect and analyze images of astrocyte with sufficient accuracy and efficiency. To address this need, we introduce a new deep learning framework for the automated detection of GFAP-immunolabeled astrocytes in brightfield or fluorescent micrographs. A major novelty of our approach is the applications of YOLOv5, a sophisticated deep learning platform designed for object detection, that we customized to derive optimized classification models for the task of astrocyte detection. Extensive numerical experiments using multiple image datasets show that our method performs very competitively against both conventional and state-of-the-art methods, including the case of images where astrocytes are very dense. In the spirit of reproducible research, our numerical code and annotated data are released open source and freely available to the scientific community.
MeSH term(s)	Astrocytes ; Central Nervous System ; Microscopy, Confocal
Language	English
Publishing date	2022-12-23
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-26698-7
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Article ; Online: A Learning Based Framework for Disease Prediction from Images of Human-Derived Pluripotent Stem Cells of Schizophrenia Patients.

Fularczyk, Nickolas / Di Re, Jessica / Stertz, Laura / Walss-Bass, Consuelo / Laezza, Fernanda / Labate, Demetrio

Neuroinformatics

2022 Volume 20, Issue 2, Page(s) 513–523

Abstract: Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new ... ...

Abstract	Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ) patients. We show that, by using Support Vector Machines or selected Artificial Neural Networks trained on image-based features associated with somas of hiPSCs derived neurons, we can predict very reliably SCZ and healthy control cells. In addition, our method reveals that [Formula: see text]III tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.
MeSH term(s)	Fibroblast Growth Factors ; Glycogen Synthase Kinase 3 ; Humans ; Induced Pluripotent Stem Cells ; Pluripotent Stem Cells ; Schizophrenia/diagnostic imaging ; Tubulin
Chemical Substances	FGF12 protein, human ; Tubulin ; Fibroblast Growth Factors (62031-54-3) ; Glycogen Synthase Kinase 3 (EC 2.7.11.26)
Language	English
Publishing date	2022-01-22
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2111941-7
ISSN	1559-0089 ; 1539-2791
ISSN (online)	1559-0089
ISSN	1539-2791
DOI	10.1007/s12021-022-09561-y
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Article ; Online: Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis.

Nardino, Vanni / Guzzi, Donatella / Lastri, Cinzia / Palombi, Lorenzo / Coluccia, Giulio / Magli, Enrico / Labate, Demetrio / Raimondi, Valentina

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 4

Abstract: Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the ... ...

Abstract	Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV-Vis) stellar spectroscopy. The instrument-which is pixel-limited in the entire 300 nm-650 nm spectral range-features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system.
Language	English
Publishing date	2023-02-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23042269
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Article: SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data.

Stossi, Fabio / Singh, Pankaj K / Marini, Michela / Safari, Kazem / Szafran, Adam T / Tostado, Alejandra Rivera / Candler, Christopher D / Mancini, Maureen G / Mosa, Elina A / Bolt, Michael J / Labate, Demetrio / Mancini, Michael A

bioRxiv : the preprint server for biology

2024

Abstract: Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has ... ...

Abstract	Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software (
Language	English
Publishing date	2024-03-26
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.21.586132
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Book ; Online: Shearlets

Kutyniok, Gitta / Labate, Demetrio

multiscale analysis for multivariate data

(Applied and numerical harmonic analysis)

2012

Abstract: Demetrio Labate ... Over the last 20 years, multiscale methods and wavelets have revolutionized the field of applied mathematics by providing an efficient means of encoding isotropic phenomena. Directional multiscale systems, particularly shearlets, are ... ...

Author's details	Gitta Kutyniok; Demetrio Labate [eds.]
Series title	Applied and numerical harmonic analysis
Abstract	Demetrio Labate Over the last 20 years, multiscale methods and wavelets have revolutionized the field of applied mathematics by providing an efficient means of encoding isotropic phenomena. Directional multiscale systems, particularly shearlets, are now having the same dramatic impact on the encoding of multidimensional signals. Since its introduction about five years ago, the theory of shearlets has rapidly developed and gained wide recognition as the superior way of achieving a truly unified treatment in both a continuous and a digital setting. By now, it has reached maturity as a research field, with rich mathematics, efficient numerical methods, and various important applications. With a broad range of experience as a scholar and a professor over the past 15 years, Gitta Kutyniok has received numerous awards for her teaching and research, including the Weierstrass Prize for outstanding teaching at the Universität Paderborn in 1998, the Research Prize of the University Gießen in 2006, and the prestigious von Kaven Prize in 2007. More recently, she has served as an Associate Editor for the Journal of Wavelet Theory and Applications; as a Corresponding Editor for Acta Applicandae Mathematicae; and as an Advisory Board member for Birkhäuser's Applied and Numerical Harmonic Analysis series. She was a panelist for the NSF in 2008 and serves as a reviewer for the European Commission, the DFG-German Research Foundation, the Israel Science Foundation, the NSF, and the French National Research Agency, among others. She has published one book and over 75 peer-reviewed journal and conference publications. Demetrio Labate received his Ph.D. in Electrical Engineering from the Politecnico di Torino, Italy, and his M.S. in Applied Mathematics and his Ph.D. in Mathematics from the Georgia Institute of Technology, Atlanta, GA, where he received the Sigma Xi Best Ph.D. Thesis Award in 2000. In 2008, he was awarded the NSF Career Award for young investigators for his research on shearlets. His research is currently funded by the National Science Foundation, the Army Research Office, and the Norman Hackerman Advance Research Program. He is an Associate Editor for the Journal of Wavelet Theory and Applications, and he is author or coauthor of over 60 publications in both mathematical and engineering journals.
Keywords	Computer science ; Fourier analysis ; Mathematics ; Numerical analysis ; Computer Sonstiges ; Technik / Wissen Elektronik ; Technik / Wissen Mathematik
Language	English
Size	Online-Ressource
Publisher	Birkhäuser / Springer
Publishing place	Boston, Mass. u.a.
Document type	Book ; Online
Note	Description based upon print version of record
ISBN	9780817683153 ; 9780817683160 ; 0817683151 ; 081768316X
DOI	10.1007/978-0-8176-8316-0
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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Article ; Online: Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

Kayasandik, Cihan Bilge / Labate, Demetrio

Journal of neuroscience methods

2016 Volume 274, Page(s) 61–70

Abstract: Background: Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple ... ...

Abstract	Background: Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. New method: In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. Results: Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. Comparison with existing methods: We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. Conclusions: Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.
MeSH term(s)	Algorithms ; Animals ; Cell Body/physiology ; Cells, Cultured ; Embryo, Mammalian ; Hippocampus/cytology ; Image Processing, Computer-Assisted ; Microscopy, Confocal ; Microscopy, Fluorescence/methods ; Neurons/cytology ; Rats ; Software
Language	English
Publishing date	2016-09-26
Publishing country	Netherlands
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	282721-9
ISSN	1872-678X ; 0165-0270
ISSN (online)	1872-678X
ISSN	0165-0270
DOI	10.1016/j.jneumeth.2016.09.007
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