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      Automated computed tomography based parasitoid detection in mason bee rearings

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          Abstract

          In recent years, insect husbandry has seen an increased interest in order to supply in the production of raw materials, food, or as biological/environmental control. Unfortunately, large insect rearings are susceptible to pathogens, pests and parasitoids which can spread rapidly due to the confined nature of a rearing system. Thus, it is of interest to monitor the spread of such manifestations and the overall population size quickly and efficiently. Medical imaging techniques could be used for this purpose, as large volumes can be scanned non-invasively. Due to its 3D acquisition nature, computed tomography seems to be the most suitable for this task. This study presents an automated, computed tomography-based, counting method for bee rearings that performs comparable to identifying all Osmia cornuta cocoons manually. The proposed methodology achieves this in an average of 10 seconds per sample, compared to 90 minutes per sample for the manual count over a total of 12 samples collected around lake Zurich in 2020. Such an automated bee population evaluation tool is efficient and valuable in combating environmental influences on bee, and potentially other insect, rearings.

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            SciPy 1.0: fundamental algorithms for scientific computing in Python

            SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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              Backpropagation Applied to Handwritten Zip Code Recognition

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                13 October 2022
                2022
                : 17
                : 10
                : e0275891
                Affiliations
                [1 ] Department of Neurosurgery, Clinical Neuroscience Center, Universitätsspital and University of Zurich, Zurich, Switzerland
                [2 ] Division of Internal Medicine, Universitätsspital and University of Zurich, Zurich, Switzerland
                [3 ] Institut für Agrarökologie, Agroecology, Science, Aarau, Switzerland
                [4 ] Center for X-ray Analytics, EMPA, Dübendorf, Switzerland
                [5 ] Vetcom, University of Zurich, Zurich, Switzerland
                [6 ] Wildbiene + Partner AG, Zurich, Switzerland
                [7 ] Diagnostic Imaging Research Unit, University of Zurich, Zurich, Switzerland
                US Environmental Protection Agency, UNITED STATES
                Author notes

                Competing Interests: The authors do not declare any competing interests.

                Author information
                https://orcid.org/0000-0002-3073-9821
                https://orcid.org/0000-0003-0811-7396
                https://orcid.org/0000-0003-3696-6993
                Article
                PONE-D-22-15307
                10.1371/journal.pone.0275891
                9560145
                36227883
                f602ca28-3461-4648-a12a-c197c37acc0a
                © 2022 Thomson et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 June 2022
                : 23 September 2022
                Page count
                Figures: 3, Tables: 1, Pages: 13
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Hymenoptera
                Bees
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Bees
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Bees
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Computed Axial Tomography
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Computed Axial Tomography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Bone Imaging
                X-Ray Radiography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Bone Imaging
                X-Ray Radiography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Bone Imaging
                X-Ray Radiography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                X-Ray Radiography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                X-Ray Radiography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                X-Ray Radiography
                Biology and Life Sciences
                Developmental Biology
                Life Cycles
                Larvae
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Developmental Biology
                Diapause
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Diapause
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Hymenoptera
                Wasps
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Wasps
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Wasps
                Biology and Life Sciences
                Agriculture
                Pests
                Insect Pests
                Custom metadata
                The data that has been used in this publication is publicly accessible under the following DOI: https://doi.org/10.5281/zenodo.6977306.

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