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Moreno-Rodríguez JL, Larrañaga P, Bielza C. NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools. Front Neuroinform 2023; 17:1092967. [PMID: 36938360 PMCID: PMC10016263 DOI: 10.3389/fninf.2023.1092967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/31/2023] [Indexed: 02/19/2023] Open
Abstract
Nowadays, an enormous amount of high dimensional data is available in the field of neuroscience. Handling these data is complex and requires the use of efficient tools to transform them into useful knowledge. In this work we present NeuroSuites, an easy-access web platform with its own architecture. We compare our platform with other software currently available, highlighting its main strengths. Thanks to its defined architecture, it is able to handle large-scale problems common in some neuroscience fields. NeuroSuites has different neuroscience-oriented applications and tools to integrate statistical data analysis and machine learning algorithms commonly used in this field. As future work, we want to further expand the list of available software tools as well as improve the platform interface according to user demands.
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Affiliation(s)
- José Luis Moreno-Rodríguez
- Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain
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Lara H, Li Z, Abels E, Aeffner F, Bui MM, ElGabry EA, Kozlowski C, Montalto MC, Parwani AV, Zarella MD, Bowman D, Rimm D, Pantanowitz L. Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association. Appl Immunohistochem Mol Morphol 2021; 29:479-493. [PMID: 33734106 PMCID: PMC8354563 DOI: 10.1097/pai.0000000000000930] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/12/2021] [Indexed: 01/19/2023]
Abstract
Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.
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Affiliation(s)
- Haydee Lara
- GlaxoSmithKline-R&D, Cellular Biomarkers, Collegeville, PA
| | - Zaibo Li
- The Ohio State University, Columbus, OH
| | | | - Famke Aeffner
- Translational Safety and Bioanalytical Sciences, Amgen Research, Amgen Inc
| | | | | | | | | | | | | | | | - David Rimm
- Yale University School of Medicine, New Haven, CT
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Lin Y, Lin H, Zhu X, Chen G. Three-dimensional characterizations of two-photon excitation fluorescence images of elastic fibers affected by cutaneous scar duration. Quant Imaging Med Surg 2021; 11:3584-3594. [PMID: 34341733 DOI: 10.21037/qims-20-1051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/18/2021] [Indexed: 11/06/2022]
Abstract
Background The type or duration of a scar determines the choice of therapy available. Traditional detection methods can easily cause secondary trauma, so there is an urgent need for a non-invasive, rapid diagnostic approach. Methods A strategy for quantitative analysis of three-dimensional (3D) elastic fibers in human cutaneous scars was designed, which included 3D reconstruction, skeleton extraction, quantitative analysis, and random forest regression. Results Four reconstruction methods were used to reconstruct 3D two-photon excitation fluorescence images of elastic fibers for comparison. In the skeleton extraction stage, the 3D thinning algorithm was improved to prepare for accurate quantitative analysis, in which eight parameters comprising branches number (B-NUM), nodes number (N-NUM), averaged branch broken-line length (AB-BL), averaged linear branch length (AB-LL), averaged branch tortuosity (AB-T), branch direction consistency (B-DC), averaged branch volume (AB-V), and averaged branch sectional area (AB-SA) were presented. Six of them, except averaged branch tortuosity (AB-T) and branch direction consistency (B-DC), showed an explicit tendency to change with scar duration. In the random forests regression analysis, the six extracted parameters could be used to predict scar duration with R2=0.981 and RMSE=0.513. Conclusions The parameters we extracted had a distinct relationship with scar duration, and random forests regression showed better performance in forecasting scar duration than unitary models.
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Affiliation(s)
- Yongping Lin
- School of Optoelectronic and Communication Engineering, Fujian Provincial Key Laboratory of Optoelectronic Technology and Devices, Xiamen University of Technology, Xiamen, China
| | - Hongxin Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.,Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou, China
| | - Xiaoqin Zhu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.,Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou, China
| | - Guannan Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.,Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou, China
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Navarro D, Alvarado M, Figueroa A, Gonzalez-Liencres C, Salas-Lucia F, Pacheco P, Sanchez-Vives MV, Berbel P. Distribution of GABAergic Neurons and VGluT1 and VGAT Immunoreactive Boutons in the Ferret ( Mustela putorius) Piriform Cortex and Endopiriform Nucleus. Comparison With Visual Areas 17, 18 and 19. Front Neuroanat 2019; 13:54. [PMID: 31213994 PMCID: PMC6554450 DOI: 10.3389/fnana.2019.00054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 05/14/2019] [Indexed: 12/12/2022] Open
Abstract
We studied the cellular organization of the piriform network [comprising the piriform cortex (PC) and endopiriform nucleus (EP)] of the ferret (Mustela putorius)-a highly excitable region prone to seizures-and, more specifically, the distribution and morphology of different types of gamma-aminobutyric acid (GABA)ergic neurons, and the distribution and ratio of glutamatergic and GABAergic boutons, and we compared our findings to those in primary visual area 17, and secondary areas 18 and 19. We accomplished this by using cytochrome oxidase and immunohistochemistry for mature neuronal nuclei (NeuN), GABAergic neurons [glutamic acid decarboxylase-67 (GAD67), calretinin (CR) and parvalbumin (PV)], and for excitatory (vesicular glutamate transporter 1; VGluT1) and inhibitory (vesicular GABA transporter; VGAT) boutons. In the ferret, the cellular organization of the piriform network is similar to that described in other species such as cats, rats and opossums although some differences also exist. GABAergic immunolabeling showed similarities between cortical layers I-III of the PC and visual areas, such as the relative distribution of GABAergic neurons and the density and area of VGluT1- and VGAT-immunoreactive boutons. However, multiple differences between the piriform network and visual areas (layers I-VI) were found, such as the percentage of GABAergic neurons with respect to the total number of neurons and the ratio of VGluT1- and VGAT-immunoreactive boutons. These findings are relevant to better understand the high excitability of the piriform network.
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Affiliation(s)
- Daniela Navarro
- Departamento de Histología y Anatomía, Facultad de Medicina, Universidad Miguel Hernández (UMH), Alicante, Spain.,Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico
| | - Mayvi Alvarado
- Departamento de Histología y Anatomía, Facultad de Medicina, Universidad Miguel Hernández (UMH), Alicante, Spain.,Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico.,Instituto de Neurociencias, UMH-Consejo Superior de Investigaciones Científicas (CSIC), Alicante, Spain
| | | | - Cristina Gonzalez-Liencres
- Àrea Neurociència de Sistemes, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Federico Salas-Lucia
- Departamento de Histología y Anatomía, Facultad de Medicina, Universidad Miguel Hernández (UMH), Alicante, Spain
| | - Pablo Pacheco
- Instituto de Neurociencias, UMH-Consejo Superior de Investigaciones Científicas (CSIC), Alicante, Spain
| | - Maria V Sanchez-Vives
- Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico.,Àrea Neurociència de Sistemes, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Generalitat de Catalunya, Barcelona, Spain
| | - Pere Berbel
- Departamento de Histología y Anatomía, Facultad de Medicina, Universidad Miguel Hernández (UMH), Alicante, Spain.,Instituto de Neuroetología, Universidad Veracruzana, Xalapa, Mexico
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Li S, Quan T, Zhou H, Yin F, Li A, Fu L, Luo Q, Gong H, Zeng S. Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites. Neuroinformatics 2019; 17:497-514. [PMID: 30635864 PMCID: PMC6841657 DOI: 10.1007/s12021-018-9414-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.
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Affiliation(s)
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,School of Mathematics and Economics, Hubei University of Education, Wuhan, 430205, Hubei, China.
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - FangFang Yin
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
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