An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra
An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra
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DOI：
10.1371/journal.pone.0234557
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发表时间：
2020-06-18
期刊：
影响因子：
3.7
通讯作者：
Povinelli, Richard J.
中科院分区：
文献类型：
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作者：
Milali, Masabho P.;Kiware, Samson S.;Povinelli, Richard J.
交配后，雌性蚊子需要动物的血液来发育卵。在获取血液的过程中，他们可能会获得病原体，这些病原体可能会导致人类不同的疾病，如疟疾，寨卡病毒，登革热和基孔肯雅病。因此，了解蚊子的产次状况有助于控制和评估由蚊子传播的传染病，其中经产蚊子被认为具有潜在的传染性。卵巢解剖目前用于确定蚊子的产次状态，非常繁琐，并且仅限于少数专家。卵巢解剖的一种替代方法是近红外光谱法（NIRS），它可以估计实验室和半野外饲养的蚊子的年龄（以天为单位）和感染状态，准确率在80%至99%之间。没有研究测试NIRS用于估计野生蚊子的产次状态的准确性。在这项研究中，我们训练了一个人工神经网络（ANN）模型的近红外光谱估计的奇偶性状态的野生蚊子。我们使用四个不同的数据集：从坦桑尼亚Minepa（Minepa-ARA）选择的阿拉伯按蚊;从坦桑尼亚Muleba（Muleba-GA）选择的冈比亚按蚊;从布基纳法索（Burkina-GA）选择的冈比亚按蚊;以及从Muleba和布基纳法索组合（Muleba-Burkina-GA）选择的冈比亚按蚊。我们在根据以前的协议预处理光谱的数据集上训练ANN模型。然后，我们使用自动编码器将光谱特征维度从1851减少到10，并重新训练ANN模型。在应用自动编码器之前，ANN模型估计Minepa-ARA、Muleba-GA、Burkina-GA和Muleba-Burkina-GA中蚊子的产次状态，样本外准确度分别为81.9 +/- 2.8（N = 274）、68.7 +/- 4.8（N = 43）、80.3 +/- 2.0（N = 48），和75.7 ± 2.5（N = 91）。使用自动编码器，在样本外数据上测试的ANN模型分别实现了Minepa-ARA、Muleba-GA、Burkina-GA和Muleba-Burkina-GA的97.1 +/- 2.2%（N = 274）、89.8 +/- 1.7%（N = 43）、93.3 +/- 1.2%（N = 48）和92.7 +/- 1.8%（N = 91）准确度。这些结果表明，一个自动编码器和一个人工神经网络的组合训练的近红外光谱估计野生蚊子的奇偶校验状态产生的模型，可作为一种替代工具来估计野生蚊子的奇偶校验状态，特别是因为近红外光谱是一个高通量，无试剂，和简单的使用技术相比，卵巢解剖。
After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets:An.arabiensiscollected from Minepa, Tanzania (Minepa-ARA);An.gambiae s.scollected from Muleba, Tanzania (Muleba-GA);An.gambiae s.scollected from Burkina Faso (Burkina-GA); andAn.gambiae s.sfrom Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9 +/- 2.8 (N = 274), 68.7 +/- 4.8 (N = 43), 80.3 +/- 2.0 (N = 48), and 75.7 +/- 2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 +/- 2.2% (N = 274), 89.8 +/- 1.7% (N = 43), 93.3 +/- 1.2% (N = 48), and 92.7 +/- 1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.