Data integration aids understanding of butterfly–host plant networks

Muto-Fujita, Ai; Takemoto, Kazuhiro; Kanaya, Shigehiko; Nakazato, Takeru; Tokimatsu, Toshiaki; Matsumoto, Natsushi; Kono, M.; Chubachi, Yuko; Ozaki, Katsuhisa; Kotera, Masaaki

doi:10.1038/srep43368

Cited by 28 publications

(23 citation statements)

References 48 publications

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“…Machine learning deals with large and diverse datasets to extract relevant information, being an increasingly critical computing tool in ecology 30 , healthcare and life sciences 31 – 33 . Artificial intelligence is also considered important for the control of infectious diseases 34 , 35 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the meta-analyses of microbial pathogens’ volatile signatures

Palma

Traguedo

Porteira

et al. 2018

Sci Rep

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Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62–100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86–90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

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Section: Introductionmentioning

confidence: 99%

Machine learning for the meta-analyses of microbial pathogens’ volatile signatures

Palma

Traguedo

Porteira

et al. 2018

Sci Rep

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“…Data integration and the establishment of databases have significant value for ecology and biodiversity sciences (Muto‐Fujita et al, ). With the development of ICT, the number of web‐based databases that provide multiple data types has been continuously increasing.…”

Section: A Data Reuse Support Mechanismmentioning

confidence: 99%

Perspectives on biodiversity informatics for ecology

Osawa

2019

Ecological Research

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Biodiversity informatics is the application of informatics techniques to ecology and biodiversity sciences. The premise is utilizing natural history collections/data, such as specimens and biodiversity observations, with information and communication technology. During the past two decades, biodiversity informatics has improved dramatically and has been applied increasingly in ecology science. In this paper, I review biodiversity informatics for ecology with a particular focus on the treatment of data. First, I discuss the traditional perspective of data collection and its usage in the ecological community. Then, I provide an overview of the trajectory of changing perspectives of data treatment relative to developing biodiversity informatics (i.e., the infiltration of the data reuse and sharing concepts). Specifically, I discuss the significance of data reuse, which offers numerous benefits for research, and data sharing with their supporting mechanisms and case studies. Finally, I discuss potential future developments in biodiversity informatics relative to the field of ecology.

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“…Plants of the family Fabaceae, as well as Brassicaceae and Rutaceae, are common hosts for various butterfly species (Ferrer‐Paris et al ., ; Muto‐Fujita et al ., ). Fabaceae is also the most likely host family used by the ancestral species in Pieridae and Papilionidae (Braby & Trueman, ; Fordyce, ).…”

Section: Introductionmentioning

confidence: 97%

A cyanogenic glucoside of Trifolium repens deters oviposition by the common grass yellow Eurema mandarina

2019

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The common grass yellow Eurema mandarina (Pieridae, Coliadinae) widely inhabits Japan, feeds on various fabaceous plants such as silktree (Albizia julibrissin) and uses d-pinitol, a cyclitol omnipresent in Fabaceae, as a primary oviposition stimulant. However, E. mandarina has a clear host preference within the Fabaceae; for example, white clover (Trifolium repens) is a nonhost despite containing d-pinitol. The present study aims to identify plant chemicals in white clover that inhibit oviposition of E. mandarina. Females lay very few eggs on T. repens foliage and plastic plant models treated with a methanolic extract of the foliage. The foliage extract is fractionated by successive extraction with chloroform, isobutanol and water. None of these fractions induce egg-laying responses. The aqueous fraction is further separated into four subfractions (Tr-3-1 to Tr-3-4) by column chromatography. Among these subfractions, females show high egg-laying responses to Tr-3-1, which is known to contain d-pinitol. Interestingly, Tr-3-2, when mixed with Tr-3-1, significantly decreases egg-laying responses, indicating that it contains oviposition deterrents. Chemical analyses reveal that two cyanogenic glucosides, linamarin and lotaustralin, are the major constituents of Tr-3-2. Authentic linamarin does not elicit egg-laying responses and significantly inhibits female oviposition when mixed with Tr-3-1 at the natural concentration. Although these cyanogenic glucosides are reported to synergistically induce oviposition of a coliadine species Colias erate on white clover, we conclude that linamarin acts as an oviposition deterrent for E. mandarina, restricts its host range and regulates their differential host acceptance.

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Data integration aids understanding of butterfly–host plant networks

Cited by 28 publications

References 48 publications

Machine learning for the meta-analyses of microbial pathogens’ volatile signatures

Machine learning for the meta-analyses of microbial pathogens’ volatile signatures

Perspectives on biodiversity informatics for ecology

A cyanogenic glucoside of Trifolium repens deters oviposition by the common grass yellow Eurema mandarina

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