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以及中间形式:几何形态测量学和人工神经网络助力形态识别。

, and intermediate forms: geometric morphometrics and an artificial neural network to help morphological identification.

作者信息

Sumruayphol Suchada, Siribat Praphaiphat, Dujardin Jean-Pierre, Dujardin Sébastien, Komalamisra Chalit, Thaenkham Urusa

机构信息

Department of Medical Entomology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.

Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand.

出版信息

PeerJ. 2020 Feb 18;8:e8597. doi: 10.7717/peerj.8597. eCollection 2020.

DOI:10.7717/peerj.8597
PMID:32117632
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7034386/
Abstract

BACKGROUND

and cause fascioliasis in both humans and livestock. Some adult specimens of sp. referred to as "intermediate forms" based on their genetic traits, are also frequently reported. Simple morphological criteria are unreliable for their specific identification. In previous studies, promising phenotypic identification scores were obtained using morphometrics based on linear measurements (distances, angles, curves) between anatomical features. Such an approach is commonly termed "traditional" morphometrics, as opposed to "modern" morphometrics, which is based on the coordinates of anatomical points.

METHODS

Here, we explored the possible improvements that modern methods of morphometrics, including landmark-based and outline-based approaches, could bring to solving the problem of the non-molecular identification of these parasites. and intermediate forms suitable for morphometric characterization were selected from Thai strains following their molecular identification. Specimens of were obtained from the Liverpool School of Tropical Medicine (UK). Using these three taxa, we tested the taxonomic signal embedded in traditional linear measurements versus the coordinates of anatomical points (landmark- and outline-based approaches). Various statistical techniques of validated reclassification were used, based on either the shortest Mahalanobis distance, the maximum likelihood, or the artificial neural network method.

RESULTS

Our results revealed that both traditional and modern morphometric approaches can help in the morphological identification of sp. We showed that the accuracy of the traditional approach could be improved by selecting a subset of characters among the most contributive ones. The influence of size on discrimination by shape was much more important in traditional than in modern analyses. In our study, the modern approach provided different results according to the type of data: satisfactory when using pseudolandmarks (outlines), less satisfactory when using landmarks. The different reclassification methods provided approximately similar scores, with a special mention to the neural network, which allowed improvements in accuracy by combining data from both morphometric approaches.

CONCLUSION

We conclude that morphometrics, whether traditional or modern, represent a valuable tool to assist in species recognition. The general level of accuracy is comparable among the various methods, but their demands on skills and time differ. Based on the outline method, our study could provide the first description of the shape differences between species, highlighting the more globular contours of the intermediate forms.

摘要

背景

[寄生虫名称]可导致人类和牲畜患片形吸虫病。一些基于遗传特征被称为“中间形态”的[寄生虫名称]成虫标本也经常被报道。简单的形态学标准对其进行物种鉴定并不可靠。在先前的研究中,通过基于解剖特征之间的线性测量(距离、角度、曲线)的形态测量学获得了有前景的表型鉴定分数。这种方法通常被称为“传统”形态测量学,与基于解剖学点坐标的“现代”形态测量学相对。

方法

在此,我们探讨了现代形态测量学方法,包括基于地标和基于轮廓的方法,在解决这些寄生虫非分子鉴定问题方面可能带来的改进。在进行分子鉴定后,从泰国菌株中选择适合形态测量表征的[寄生虫名称]和中间形态。[寄生虫名称]的标本来自英国利物浦热带医学院。使用这三个分类单元,我们测试了传统线性测量中嵌入的分类信号与解剖学点坐标(基于地标和基于轮廓的方法)。基于最短马氏距离、最大似然或人工神经网络方法,使用了各种经过验证的重新分类统计技术。

结果

我们的结果表明,传统和现代形态测量方法都有助于[寄生虫名称]的形态鉴定。我们表明,通过在最具贡献性的特征中选择一个子集,可以提高传统方法的准确性。在传统分析中,大小对形状辨别力的影响比在现代分析中重要得多。在我们的研究中,现代方法根据数据类型提供了不同的结果:使用伪地标(轮廓)时令人满意,使用地标时不太令人满意。不同的重新分类方法提供了大致相似的分数,特别提到神经网络,它通过结合两种形态测量方法的数据提高了准确性。

结论

我们得出结论,形态测量学,无论是传统的还是现代的,都是协助[寄生虫名称]物种识别的有价值工具。各种方法的总体准确率相当,但它们对技能和时间的要求不同。基于轮廓方法,我们的研究可以首次描述物种之间的形状差异,突出中间形态更呈球形的轮廓。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/de30f9710c36/peerj-08-8597-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/1e35df639a77/peerj-08-8597-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/60be7b562f2c/peerj-08-8597-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/34fd4d6e83cf/peerj-08-8597-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/de30f9710c36/peerj-08-8597-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/1e35df639a77/peerj-08-8597-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/60be7b562f2c/peerj-08-8597-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/34fd4d6e83cf/peerj-08-8597-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da14/7034386/de30f9710c36/peerj-08-8597-g004.jpg

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