An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra.

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. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from 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.

Introduction

Evaluation of existing malaria control interventions such as insecticide-treated nets (ITNs) and indoor residual spraying (IRS) relies upon, among other factors, the assessment of the changes occurring in the mosquito parity structure prior to and after implementation of an intervention [1-3]. The parity status of mosquitoes corresponds with their capability to transmit Plasmodium parasites, with an assumption that parous mosquitoes are more highly capable than nulliparous mosquitoes, as they may have accessed parasite-infected blood. A shift in the parity structure towards a population with more nulliparous mosquitoes signifies a reduction in the risk of disease transmission [2, 4, 5], as the chances that mosquitoes carry the malaria parasite declines [6].

The current standard technique for estimating the parity status of female mosquitoes involves dissection of their ovaries to separate mosquitoes into those that have previously laid eggs, known as the parous group (assumed to be old and potentially infectious), and those that do not have a gonotrophic history, known as the nulliparous group (assumed to be young and non-infectious) [7]. Another standard technique also based on the dissection of ovaries determines the number of times a female mosquito has laid eggs [8]. However, both techniques are laborious, time consuming, and require skilled technicians. These technical difficulties lead to analysis of small sample sizes that often fail to capture the heterogeneity of a mosquito population.

Near infrared spectroscopy (NIRS) complimented by techniques from machine learning, have been demonstrated to be alternative tools for predicting age, species, and infectious status of laboratory and semi-field raised mosquitoes [9-20]. NIRS is a rapid, non-invasive, reagent-free technique that requires minimal skills to operate, allowing hundreds of samples to be analyzed in a day. However, the accuracy of NIRS techniques for predicting the parity status of wild mosquitoes has not been tested. Moreover, recently, it has been reported that models trained on NIR spectra using an artificial neural network (ANN) estimate the age of laboratory-reared An. arabiensis, An.gambiae, Aedes aegypti, and Aedes albopictus with accuracies higher than models trained on NIR spectra using partial least squares (PLS) [20].

In this study, we train ANN models on NIR spectra preprocessed according to an existing protocol [9] to estimate the parity status of wild An. gambiae s.s. and An. arabiensis. We then apply autoencoders to reduce the spectra feature space from 1851 to 10 and re-train ANN models. The ANN model achieved an average accuracy of 72% and 93% before and after applying the autoencoder, respectively. These results suggest ANN models trained on autoencoded NIR spectra as an alternative tool to estimate the parity status of wild An. gambiae and An. arabiensis. High-throughput, non-invasive, reagent free, and simple to use NIRS analyses compliment the limitations of ovary dissections.

Materials and methods

Ethics approvals

Ethics approvals for collecting mosquitoes in Minepa-ARA, Burkina-GA and Muleba-GA datasets from residents’ homes were obtained from Ethics Review Boards of the Ifakara Health Institute (IHI-IRB/No. 17–2015), the Colorado State University (approval No. 09-1148H), and the Kilimanjaro Christian Medical College (Certificate No. 781), respectively.

Data

We use data from wild An. arabiensis (Minepa-ARA) collected from Minepa, a village in southeastern Tanzania (published in [21] and publicly available for reuse), from wild An. gambiae s.s (Muleba-GA) collected from Muleba, northwestern Tanzania (mosquitoes published in [22], permission to reuse was obtained from the senior author) and from wild An. gambiae s.s collected from Bougouriba and Diarkadou-gou villages in Burkina Faso (Burkina-GA) (published in [12] and publicly available for reuse).

Mosquitoes in the Minepa-ARA and Muleba-GA datasets were captured using CDC light traps placed inside residential homes. Mosquitoes that were morphologically identified as members of the Anopheles gambiae complex were processed further. Prior to scanning, wild mosquitoes collected in Minepa were killed by freezing them for 20 minutes in a freezer that is calibrated to -20º C. After freezing the mosquitoes were re-equilibrated to room temperature for 30 minutes. Wild mosquitoes collected in Muleba were killed using 75% ethanol, dissected according to the technique described by Detinova [23] to determine their parity status, and preserved in silica gel. Mosquitoes in Minepa-ARA were dissected after scanning. Following a previous published protocol to collect spectra [9], mosquitoes in both Minepa-ARA and Muleba-GA were scanned using a LabSpec 5000 near-infrared spectrometer with an integrated light source (ASD Inc., Malvern, UK). After spectra collection, mosquitoes in Minepa-ARA were dissected to score their parity status. Then polymerase chain reaction (PCR) was conducted on DNA extracted from mosquito legs (in both Minepa-ARA and Muleba-GA) to identify species type as previously described [24]. Each mosquito was labeled with a unique identifier code linking each NIR spectrum to parity dissection and PCR information.

Data from wild An. gambiae s.s from Burkina Faso were published in [12] and publicly available for reuse. These mosquitoes are referred to as independent test sets 2 and 3 (ITS 2 and ITS 3) in [12]. ITS 2 has 40 nulliparous and 40 parous mosquitoes, and ITS 3 has 40 nulliparous and 38 parous mosquitoes. In this study, we combine these two datasets into one dataset and refer it as Burkina-GA. Mosquitoes in Burkina-GA (N = 158) were collected in 2013 in Burkina Faso from Bougouriba and Diarkadou-gou villages using either indoor aspiration or a human baited tent trap, and their ovaries were dissected according to the Detinova method [23]. Mosquitoes were preserved in silica gel before their spectra were collected using a LabSpec4i spectrometer (ASD Inc., Boulder, CO, USA).

Model training and testing

We trained models on four datasets, namely Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA (Muleba-GA and Burkina-GA combined). Before training models, spectra in all datasets were pre-processed according to the previously published protocol [9] and divided into two groups (nulliparous and parous). Spectra in the nulliparous and parous groups were labeled zero and one, respectively. The two groups were then merged, randomized, and divided into a training set (75%; N = 927 for Minepa-ARA, N = 140 for the Muleba-GA, N = 158 for Burkina-GA and N = 298 for Muleba-Burkina-GA) and a test set (the remaining 25% in each dataset). On each dataset, using ten Monte-Carlo cross validations [20, 25] and Levenberg-Marquardt optimization, a one hidden layer, ten-neuron feed-forward ANN model with logistic regression as a transfer function was trained and tested in MATLAB (Fig 1).

Fig 1

Training and testing ANN model on spectra preprocessed according to Mayagaya et al. [9].

“M” is either Minepa-ARA, Muleba-GA, Burkina-GA, or Muleba-Burkina-GA.

Based on the accuracy of the model presented in Table 1 in the Results and Discussion section, we explored how to improve the model accuracy. Normally a parous class, unlike a nulliparous class, often is represented by a limited number of samples, posing a problem of data imbalance during model training. In this case, a large amount of data is required to obtain enough samples in a parous class for a model to learn and characterize it accurately. Obtaining enough data for model training is always challenging. The most common ways of dealing with the data imbalance are either to discard samples from a nulliparous class to equal the number of samples in a parous class or to bootstrap samples in a parous class [26]. However, discarding data to equalize the data distribution in two classes in the training set leaves an imbalanced test set. Also, it is this imbalanced scenario to which the model will be applied in real cases. In addition, throwing away samples, especially from data sets with a high dimension feature space, can lead to over-fitting the model. Alternatively, for datasets with a high dimension feature space, instead of discarding data from a class with a large number of samples, feature reduction techniques are employed [26]. Feature reduction reduces the size of the hypothesis space initially presented in the original data, thereby reducing the size of data required to adequately train the model. Principal component analysis (PCA) and partial least squares (PLS) are the commonly used unsupervised and supervised feature reduction methods, respectively, especially for cases whose features are linearly related [27, 28]. Autoencoders recently are used as an alternative to PCA in cases involving both linear and non-linear relationships [29-32].

Table 1

Accuracies of reconstructing original feature spaces from encoded feature spaces.

MSE = mean square error.

Metric	Steps	Encoded-Minepa-ARA	Encoded-Muleba-GA	Encoded-Burkina-GA
MSE	Step 1	0.0046	0.0029	0.0031
	Step 2	0.00005	0.0027	0.0022
	Step 3	0.00008	0.0029	0.0011

An autoencoder is an unsupervised ANN that learns both linear and non-linear relationships present in data and represents them in a new reduced dimension data space (which also can be used to regenerate the original data space) without losing important information [33-35]. The autoencoder has two parts, the encoder part, where an original dataset is encoded to a desired reduced feature space (encoded dataset) and the decoder part where the encoded dataset is decoded to an original dataset to determine how accurately the encoded dataset represents the original dataset. Fig 2 illustrates an example of an autoencoder in which an 1850-feature dataset is stepwise encoded to a 10-feature dataset. There is no formula for the number and size of steps to take to get to a desired feature size. However, taking several steps results on losing very little information, compared with taking a single step.

Fig 2

Autoencoder reducing feature space dimension.

Once an encoded feature space can reconstruct the original feature space with an acceptable accuracy, the decoder is detached, and a desired model (in our case an ANN binary classifier) is trained on the encoded feature space as shown in Fig 3.

Fig 3

ANN model trained on a dataset with an encoded feature space.

Egg laying appears to be affected by both linear and non-linear relationships. Hence, we separately train autoencoders on the Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA datasets to reduce spectra feature dimensions from 1851 to 10 (Fig 4).

Fig 4

Reducing spectra feature space using an autoencoder and re-constructing original feature spaces from their respective encoded feature spaces (reconstruction accuracies presented in Table 1).

Figures generated from MATLAB.

Table 1 presents accuracies of reconstructing original feature spaces from their respective encoded feature spaces. We refer to the autoencoded Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA datasets as Encoded-Minepa-ARA, Encoded-Muleba-GA, Encoded-Burkina-GA, and Encoded-Muleba-Burkina-GA, respectively. We then train ANN models on Encoded-Minepa-ARA, Encoded-Muleba-GA, Encoded-Burkina-GA, and Encoded-Muleba-Burkina-GA (Fig 5).

Fig 5

Training and testing of ANN model on autoencoded spectra.

M is either Minepa-ARA, Muleba-GA, Burkina-GA, or Muleba-Burkina-GA dataset.

Finally, we used the Encoded-Burkina-GA and the Encoded-Muleba-GA datasets as independent test sets to test accuracies of ANN models trained on the Encoded-Muleba-GA dataset and on the Encoded-Burkina-GA dataset, respectively (Fig 6A and 6B).

Fig 6

Independent testing of ANN model trained on encoded datasets.

A) Applying an ANN model trained on the Encoded-Muleba-GA dataset to estimate the parity status of mosquitoes in the autoencoded Burkina-GA dataset. B) Applying the ANN model trained on the Encoded-Burkina-GA dataset to estimate the parity status of mosquitoes in the Encoded-Muleba-GA dataset.

Results and discussion

In this study, we demonstrated that near-infrared spectroscopy (NIRS) can estimate accurately the parity status of wild collected An. arabiensis and An. gambiae s.s. Referring to the published results in [11] (ANN models achieve higher accuracies than PLS models), we trained and tested an ANN model on NIRS spectra in four different datasets, pre-processed according to a previously published protocol [9]. The model achieved accuracies between 68.7 and 81.9% (Table 2, Figs 7 and 8). Table 2 further presents various metrics to score the performance of our classifiers, namely sensitivity, specificity, precision, and area under the receiver operating characteristic (ROC) curve (AUC). We calculated sensitivity, specificity, precision and accuracy of the model using Equs 1, 2, 3, and 4, respectively [36-39]. Sensitivity (also known as recall) is the percentage of correctly predicted parous mosquitoes, specificity is the percentage of correctly predicted nulliparous mosquitoes [20], and precision is the proportion of true parous mosquitoes out of all mosquitoes estimated by the model as parous [39]. We presented both sensitivity and precision because different scholars prefer one metric to another especially for cases with imbalanced data [39].

Table 2

Performance of an ANN model trained on 75% of mosquito spectra with 1851 features (before autoencoder) and tested on the remaining 25% spectra (out of the sample testing).

AUC values are the area of the ROC curves in Fig 8. Minepa-ARA (Nulliparous = 656, Parous = 271), Muleba-GA (Nulliparous = 119, Parous = 21) Burkina-GA (Nulliparous = 80, Parous = 78).

	Minepa-ARA (N = 927)	Muleba-GA (N = 140)	Burkina-GA (N = 158)	Muleba-Burkina-GA (N = 298)
Accuracy (%)	81.9 ± 2.8	68.7 ± 4.8	80.3 ± 2.0	75.7 ± 2.5
Sensitivity (%)	79.7 ± 3.2	37.8 ± 6.6	76.5 ± 2.1	70.2 ± 3.1
Specificity (%)	86.0 ± 1.6	80.1 ± 2.7	88.3 ± 2.3	77.6 ± 2.9
Precision (%)	74.3 ± 3.4	31.3 ± 5.2	77.8 ± 1.8	68.8 ± 3.2
AUC (%)	77.2	55.9	83.6	76.4

Fig 7

Box plots of parity estimation score when ANN models trained on 75% of spectra before the autoencoder was applied and tested on the remaining spectra (25%) (out of the sample testing).

A, B, C, and D represent results for the Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA (mosquitoes in Muleba-GA and Burkina-GA datasets combined) datasets, respectively.

Fig 8

ROC curves (AUCs presented in the last row of Table 2) showing results when ANN models trained on 75% of spectra before the autoencoder was applied and tested on the remaining spectra (25%) (out of the sample testing).

A, B, C, and D represent results for the Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA (mosquitoes in Muleba-GA and Burkina-GA datasets combined) datasets, respectively. In each ROC curve, a threshold of 0.5 was used to compute true positive rate and false positive rate.

Let

True Positive (TP) = Number of mosquitoes correctly classified by the model as parous,

False Positive (FP) = Number of mosquitoes wrongly classified by the model as parous,

True Negative (TN) = Number of mosquitoes correctly classified by the model as nulliparous,

Positive (P) = Total number of mosquitoes in test set that are parous, and

Negative (N) = Total number of mosquitoes in test set that are nulliparous.

Then

AUC was computed from the receiver operating characteristic (ROC) curve shown in Fig 8 generated by plotting the true parous rate against the false parous rate at different threshold settings. A higher AUC is interpreted as higher predictivity performance of the model [40, 41]. The ROC curve normally presents the performance of the model at different thresholds (cut off points), providing more information on the accuracy of the classifier [40, 41]. Table 3 provides confusion matrices from the last (tenth) Monte-Carlo cross validation showing model accuracy in absolute values.

Table 3

Confusion matrices showing accuracies of the models in absolute values when the models were trained on spectra before feature reduction by autoencoder.

A) Minepa-ARA, B) Muleba-GA, C) Burkina-GA and D) Muleba-Burkina-GA. Results from the last Monte-Carlo cross validation.

		Actual Parity
	Estimates	Nulliparous	Parous	Total
	Nulliparous	165	17	182
A	Parous	31	61	92
	Total	196	78	274
	Nulliparous	28	4	32
B	Parous	8	3	11
	Total	36	7	43
	Nulliparous	20	6	26
C	Parous	4	18	22
	Total	24	24	48
	Nulliparous	46	9	55
D	Parous	14	22	36
	Total	60	31	91

We hypothesized that results presented in Tables 2 and 3, and in Figs 7 and 8 were influenced by the size of a dataset used to train the model. The model that was trained on a dataset with a relatively larger number of mosquitoes, especially in the parous class, performed better than the model trained on the dataset with fewer mosquitoes.

The current standard preprocessing technique [9] leaves a mosquito spectrum with an 1851- dimensional feature space. Mathematically, binary inputs with a 1851-dimensional feature space present hypothesis space dimensions for the model to learn [42-44]. Successful learning of such hypothesis space dimensions requires many data points (mosquitoes in our case). Finding enough wild mosquitoes, especially parous mosquitoes, for a model to learn such a hypothesis space is expensive and time consuming. Feature reduction is an alternative to overcome this, as it reduces the hypothesis space dimension initially presented by the original data, hence lowering the number of data required to train the model efficiently. Techniques such as principal component analysis (PCA) [27, 28], partial least squares (PLS) [27, 45, 46], singular value decomposition (SVD) [30, 46, 47], and autoencoders can reduce a feature space to a size that can be learned by the available data without losing important information. PCA, PLS, and SVD are commonly used when features are linearly dependent [27, 28], otherwise, an autoencoder, which can be thought as a nonlinear version of PCA, is used [29-32].

Therefore, we applied an autoencoder as illustrated in Figs 2 and 4 to reduce the spectra feature space from 1851 features to 10 features (Table 1 presents the accuracies of reconstructing original feature spaces from the encoded (reduced) feature spaces), cutting down the hypothesis space dimensions from to , and re-trained ANN models (Figs 3 and 5). As presented in Tables 4 and 5, and in Figs 9 and 10, the accuracy of the model improved from an average of 72% to 93%, suggesting an ANN model trained on autoencoded NIR spectra as an appropriate tool to estimate the parity status of wild mosquitoes.

Table 4

Performance of an ANN model trained on 75% of the encoded mosquito spectra (10 features) and tested on the remaining 25% of the encoded mosquito spectra.

AUC values are the area of the ROC curves in Fig 10. Minepa-ARA (Nulliparous = 656, Parous = 271), Muleba-GA (Nulliparous = 119, Parous = 21), Burkina-GA (Nulliparous = 80, Parous = 78).

	Minepa-ARA (N = 927)	Muleba-GA (N = 140)	Burkina-GA (N = 158)	Muleba-Burkina-GA (N = 298)
Accuracy (%)	97.1 ± 2.2	89.8 ± 1.7	93.3 ± 1.2	92.7 ± 1.8
Sensitivity (%)	94.9 ± 1.6	70.1 ± 2.3	91.7 ± 1.9	88.2 ± 2.9
Specificity (%)	98.6 ± 1.3	96.9 ± 1.2	96.4 ± 1.6	94.7 ± 2.1
Precision (%)	93.7 ± 2.4	62.5 ± 3.2	91.3 ± 1.4	93.1 ± 2.5
AUC (%)	96.7	91.5	93.1	94.9

Table 5

Confusion matrices showing accuracies of the models in absolute values when the models were trained on spectra after feature reduction by autoencoder.

A) Minepa-ARA, B) Muleba-GA, C) Burkina-GA, and D) Muleba-Burkina-GA. Results from the last Monte-Carlo cross validation.

		Actual Parity
	Estimates	Nulliparous	Parous	Total
	Nulliparous	192	7	199
A	Parous	4	71	95
	Total	196	78	274
	Nulliparous	33	2	35
B	Parous	3	5	8
	Total	36	7	43
	Nulliparous	22	3	25
C	Parous	2	21	23
	Total	24	24	48
	Nulliparous	58	4	62
D	Parous	2	27	29
	Total	60	31	91

Fig 9

Box plots showing results when ANN models trained on 75% of encoded spectra in datasets were tested on the remaining encoded spectra (25%).

A, B, C, and D represent results for the Encoded-Minepa-ARA, Encoded-Muleba-GA, Encoded-Burkina-GA, and Encoded-Muleba & Burkina-GA (mosquitoes in Encoded-Muleba-GA and Encoded-Burkina-GA datasets combined) datasets, respectively.

Fig 10

ROC curves (AUCs presented in the last row of Table 4) showing results when ANN models trained on 75% of encoded spectra were tested on the remaining encoded spectra (25%).

A, B, C, and D represent results for the Encoded-Minepa-ARA, Encoded-Muleba-GA, Encoded-Burkina-GA, and Encoded-Muleba-Burkina-GA (mosquitoes in Encoded-Muleba-GA and Encoded-Burkina-GA datasets combined) datasets, respectively. In all ROC curves, a 0.5 threshold was used to calculate false positive rate and true positive rate.

We further applied a model trained on encoded Muleba-GA dataset to estimate the parity status of mosquitoes in the encoded Burkina-GA dataset and a model trained on encoded Burkina-GA dataset to estimate the parity status of mosquitoes in encoded Muleba-GA. Here we wanted to test how the model performs on mosquitoes from different cohorts. As presented in Table 6, the model performed with accuracies of 68.6% and 88.3%, respectively, showing a model trained on encoded Burkina-GA dataset extrapolates well to mosquitoes from a different cohort than a model trained on the encoded Muleba-GA dataset.

Table 6

Independent testing of ANN models trained on Muleba-GA and Burkina-GA encoded datasets.

	ANN model trained on Encoded-Muleba-GA, tested on Encoded-Burkina-GA	ANN model trained on Encoded-Burkina-GA, tested on Encoded-Muleba-GA
Accuracy (%)	68.6	88.3
Sensitivity (%)	26.5	86.1
Specificity (%)	94.4	92.2

A possible explanation of the results shown in Table 6 could be that, unlike for the Burkina-GA dataset, the number of parous mosquitoes (N = 21) in the Muleba-GA dataset was not representative enough for a model to learn important characteristics that extrapolate to mosquitoes in a cohort other than the one used to train the model. Although the Muleba-GA model had the poor sensitivity as presented in Table 6, the Burkina-GA model results still suggest that ANN model trained on acceptable number of both encoded parous and nulliparous can be applied to estimate parity status of mosquitoes from different cohorts other than the one used to train the model.

Conclusion

These results suggest that applying autoencoders and artificial neural networks to NIRS spectra as an appropriate complementary method to ovary dissections to estimate parity status of wild mosquitoes. The high-throughput nature of near-infrared spectroscopy provides a statistically acceptable sample size to draw conclusions on parity status of a particular wild mosquito population. Before this method can be used as a stand-alone method to estimate the parity status of wild mosquitoes, we suggest repeating of the analysis on different datasets with much larger mosquito sample sizes to test the reproducibility of the results. Hence, with the results presented in this manuscript, we recommend complementing ovary dissection with ANN models trained on NIRS spectra with their feature reduced by an autoencoder to estimate parity status of wild mosquito population.

Zip folder with Minepa-ARA, Muleba-GA and Burkina-GA datasets.

Column header, wavelengths in ‘nm’.

(ZIP)

Click here for additional data file.

1 Apr 2020

PONE-D-20-02482

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

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Reviewer #1: In this paper, authors have trained ANN models on NIR spectra to estimate the parity status of wild mosquitoes based on four different datasets. Applying autoencoders in ANN is a way to develop smart chemometrics for rapid detections based on NIR technology. It is a somewhat valuable research. Nevertheless, some issues are also developing. I cordially raise some opinions for consideration and hope they can be of help for improving the paper.

Line 103: please clarify the way you control the killing temperature at -20oC?

Lines 127-129: how can you determine the division for training and test samples at the ratio of 75%/25%? I suppose 75% for training is over large because you don’t have a third sample set for model evaluation. Did you try other ratios?

Line 128: we can see that Minepa-ARA sample is much more than other samples, which will severely affect the prediction results for classification. Please demonstrate the methods for treating the problem of sample imbalance.

Line 280: please interpret the meaning of 2^2^(1851).

In Fig.2, how can you determine to use 10 feature nodes in the encoded feature space?

In Fig.4, can you explain why using logistic function for encoder’s activation but a linear function for decoder’s?

In Fig.8, you should identify the thresholds for each ROC curve.

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Reviewer #1: No

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15 May 2020

May 7, 2020

Re: Resubmission of a Manuscript PONE-D-20-02482, An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

Jie Zhang

Academic Editor

PLOS ONE

Dear Editor:

Thank you for the opportunity to revise our manuscript PONE-D-20-02482, An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra. We appreciate the careful review and constructive suggestions from you and the reviewer. It is our belief that the manuscript is substantially improved after making the suggested edits.

Following this letter are the editor and reviewer comments with our responses, including how and where the text was modified. As suggested, we also have uploaded an unmarked version of the revised manuscript, along with a marked-up copy highlighting changes made to the original version. The revision has been developed in consultation with all co-authors, and each author has given approval to the final form of this revision.

Thank you for your consideration.

Sincerely,

Masabho P. Milali.

Reviewers’ comments:

General Comment

In this paper, authors have trained ANN models on NIR spectra to estimate the parity status of wild mosquitoes based on four different datasets. Applying autoencoders in ANN is a way to develop smart chemometrics for rapid detections based on NIR technology. It is a somewhat valuable research. Nevertheless, some issues are also developing. I cordially raise some opinions for consideration and hope they can be of help for improving the paper.

Author’s response:

Thank you.

Specific Comments

Comment # 1:

Line 103: please clarify the way you control the killing temperature at -20o C?

Author’s response:

Prior to scanning, mosquitoes were killed by freezing them for 20 minutes in a freezer that is calibrated to -20o C. After freezing the mosquitoes were re-equilibrated to room temperature for 30 minutes. We have reflected this on lines 102 – 104.

Comment # 2

Lines 127-129: how can you determine the division for training and test samples at the ratio of 75%/25%? I suppose 75% for training is over large because you don’t have a third sample set for model evaluation. Did you try other ratios?

Author’s response:

The ratio was picked based on the size of the data. We tried 70% / 30% data division and the results were consistent with the 75% / 25% split.

Training and testing of the models presented in the manuscript was performed in MATLAB using 10-fold Monte Carlo cross validation. The technique generates estimates of variance and was described by Korjus et al (1). The library in MATLAB divides the 75% data into training, validation and testing making our 25% sample as the third sample set for model evaluation.

We have modified the text on lines 130 – 133.

Comment # 3

Line 128: we can see that Minepa-ARA sample is much more than other samples, which will severely affect the prediction results for classification. Please demonstrate the methods for treating the problem of sample imbalance.

Author’s response:

To make sure that results from Minepa-ARA are not influenced by class imbalance, we repeated analysis while the number of mosquitoes in classes were matched (by randomly selecting mosquitoes from the class with large samples to match the number of mosquitoes from the class with few samples) and the results were similar. Furthermore, we computed and presented precision and recall (sensitivity) that are known to capture data imbalance (2). (Line 224 – 229).

Comment # 4

Line 280: please interpret the meaning of 2^2^(1851).

Author’s response:

Let n be the number of binary features, then the number of hypotheses is 2^(2^n). To better understand why this is so, we need to split the problem into two parts. First, we need to determine how many unique instances there are. Second, we need to determine the number of possible sets of instance we can form. The answer to the second question is the size of the power set of X (2^|X|). The first is easy if we have binary features, just take 2 to the number of features we have. So, for one binary feature, we have two (2^1) unique instances; x = 0 or x = 1. The size of the power set of two instances is 2^2 or four. So we have four possible hypotheses for a single binary feature. The hypotheses are "never in the class", "when x == 0 in the class", "when x == 1 in the class", and "always in the class".

Comment # 5

In Fig.2, how can you determine to use 10 feature nodes in the encoded feature space?

Author’s response:

The number 10 was chosen to allow the salient features of the data to be identified. This is based on our knowledge of the data.

Comment # 6

In Fig.4, can you explain why using logistic function for encoder’s activation but a linear function for decoder’s?

Author’s response:

We used logistic function for encoder part to capture the non-linear relationship between features. We used linear function for decoder to regenerate features in the domain similar to the original feature space.

Comment # 7

In Fig.8, you should identify the thresholds for each ROC curve.

Author’s response:

In all ROC curves, we calculated false positive rate and true positive rate using a 0.5 threshold. Reflected on lines 270 – 271, 325 – 326.

References

1. Korjus K, Hebart MN, Vicente R. An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable. PloS One. 2016;11(8):e0161788.

2. Saito T, Rehmsmeier M. The Precision-recall Plot is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PloS One. 2015;10(3):e0118432.

29 May 2020

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

PONE-D-20-02482R1

Dear Dr. Milali,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Jie Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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Reviewer #1: All comments have been addressed

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Reviewer #1: Partly

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Reviewer #1: Yes

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Reviewer #1: No

5 Jun 2020

PONE-D-20-02482R1

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

Dear Dr. Milali:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

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on behalf of

Dr. Jie Zhang

Academic Editor

PLOS ONE