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Screening Priority Factors Determining and Predicting the Reproductive Toxicity of Various Nanoparticles Zhan Ban, Li Mu, Qixing Zhou, Anqi Sun, and Xiangang Hu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b02757 • Publication Date (Web): 30 Jul 2018 Downloaded from http://pubs.acs.org on July 30, 2018

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Screening Priority Factors Determining and Predicting the Reproductive

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Toxicity of Various Nanoparticles

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Zhan Ban†, Qixing Zhou†, Anqi Sun†, Li Mu‡, Xiangang Hu†,*

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†

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Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution

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Control, College of Environmental Science and Engineering, Nankai University,

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Tianjin 300350, China

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‡

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Laboratory for Environmental Factors Control of Agro-Product Quality and Safety

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(Ministry of Agriculture), Institute of Agro-Environmental Protection, Ministry of

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Agriculture, Tianjin 300191, China

Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of

Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Key

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Corresponding author: Xiangang Hu, [email protected]

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Fax: 0086-022-23507800

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Tel.: 0086-022-23507800

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ABSTRACT

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Due to the numerous factors (e.g., nanoparticle [NP] properties and experimental

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conditions) influencing nanotoxicity, it is difficult to identify the priority factors

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dominating nanotoxicity. Herein, by integrating data from the literature and a random

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forest model, the priority factors determining reproductive toxicity were successfully

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screened from highly heterogeneous data. Among ten factors from more than eighteen

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different NPs, the NP type and the exposure pathway were found to dominantly

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determine NP accumulation. The reproductive toxicity of various NPs primarily

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depended on the NP type and the toxicity indicators. Nanoparticles containing major

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elements (e.g., Zn and Fe) tended to accumulate in rats but induced lower toxicity

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than NPs containing noble elements. Compared with other exposure pathways, i.p.

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injection posed significantly higher risks for NP accumulation. By combining

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similarity network analysis and hierarchical clustering, the sources of highly

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heterogeneous data were identified, the factor-toxicity dependencies were extracted

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and visualized, and the prediction of nanotoxicity was then achieved based on the

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screened priority factors. The present work provides insights for the design of animal

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experiments and the illustration and prediction of nanotoxicity.

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Keywords: nanotoxicology, carbon nanotube, graphene, Ag, TiO2, rat

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INTRODUCTION

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With unique and advanced properties, engineered nanoparticles (ENPs) have been

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widely applied in human activities (e.g., approximately 760 nano-enabled consumer

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products in the health and fitness category, with an increasing number of

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nano-enabled products for industry, agriculture and environmental protection).1 With

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the rapid development of NPs, nanosafety has attracted increasing attention

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worldwide due to environmental contamination or direct human exposure.2, 3 However,

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the numerous physicochemical properties (e.g., size, shape and surface chemistry) of

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ENPs and the various experimental conditions (e.g., various exposure pathways)

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affect nanotoxicity.4 For example, suspended graphene showed toxic effects through

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direct physical interaction with the cell membrane, generating reactive oxygen species

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(ROS) and trapping cells via aggregation.5-9 The chemical state of graphene (e.g., GO

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and rGO) also affected its interaction with cells.10 Based on the hypothesis that the

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physicochemical properties of ENPs and the experimental conditions determine

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nanotoxicology, the priority factors determining nanotoxicity were identified and

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nanotoxicity was predicted using models in the present work. In addition, the cost of

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animal experiments is very high, and the mechanisms of nanotoxicity are very

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complicated, especially in experiments involving reproductive toxicity. As a result,

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arguments concerning nanotoxicity are frequently reported.11-13 It is becoming

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increasingly important to effectively screen priority factors determining and 3


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predicting the toxicity of various NPs using other methods to supplement animal

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experiments, which is critical for the design of animal experiments and for the

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illustration and prediction of nanotoxicity.

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Multidimensional datasets (e.g., the physicochemical properties of ENPs and the

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various experimental conditions) challenge traditional tools (e.g., quantitative

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structure-activity relationships and qualitative classification-based models) for the

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assessment and prediction of nanotoxicity.14-16 Machine learning is a discipline that

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can be used to build models to explain observed data through experience.17, 18 With its

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robust capacity to recognize meaningful patterns, machine learning has a wide range

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of applications, including controlling robots,18 predicting toxicity19 and predicting

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synthetic reactions.19, 20 In the above approach, numerous data points were used to

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overcome the heterogeneous data. Thus, machine learning may solve the

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aforementioned problems of assessing nanosafety.

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However, publications and data regarding reproductive toxicity are relatively

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limited due to the high cost and complexity of performing such studies. To evaluate

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the reproductive toxicity of ENPs, the reported data from animal experiments are

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valuable sources of toxicological information for meta-analysis, which could produce

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general answers to questions regarding diverse data.19-23 Given the highly

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heterogeneous data, it is difficult to reveal the relationships between the

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multidimensional factors of ENPs and nanotoxicity using traditional models. 4



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Therefore, accurately extracting the hidden multidimensional factor-toxicity

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dependencies through machine learning is critical for toxicity assessment and

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prediction19, 24 and remains a challenge for current workflows.25

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As a new interdisciplinary technique, machine learning can be used to build models

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to explain observed data through experience.17, 18 Random forest (RF)26 is a powerful

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algorithm based on the decision tree method and is suitable for dealing with big

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data.19, 27 In the present work, RF was used to examine heterogeneous data from a

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meta-analysis. To explore the heterogeneity of the datasets, similarity network

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analysis was performed to visualize the similarity between data points with the

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proximity matrix in RF. The proximity matrix, a by-product of RF, records the

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frequency of two data points appearing in the same nodes.19 The priority factors for

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determining reproductive toxicity were screened by RF and are critical to the

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scientific design of experiments, as well as the accurate illustration and prediction of

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nanotoxicity. Finally, multiple methods were integrated to predict nanotoxicity by

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revealing factor-toxicity dependencies.

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MATERIALS AND METHODS

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Data extraction

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Data regarding the female reproductive toxicity induced by NPs (e.g., those related to the uterus, ovaries and sex hormones) are highly heterogeneous.28, 29 In contrast, the indexes of male reproductive toxicity showed a high representation of fertility (e.g., 5


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the sperm count and the sperm abnormality rate).30 Therefore, the present work

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focused on reproductive toxicity in male rodents. The tested data were obtained from

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the ISI Web of Knowledge, PubMed and Scopus databases; the searched topics are

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presented in the Supporting Information (search time, July 23, 2017). To obtain a

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comprehensive understanding of the relationships between different factors (e.g., ENP

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types) and reproductive toxicity, the present work collected data from animal

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experiments related to nanotoxicity in the literature, as shown in Tables S1-S3. The

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chosen NPs, including carbon nanotubes, graphene, and Ag and TiO2 NPs, have been

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widely applied in various fields and have high potential for environmental or human

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exposure; for example, Ag and TiO2 NPs have been detected in various surface

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waters.31, 32 More importantly, the chosen NPs cause obvious reproductive toxicity by

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reducing sperm parameters and accumulating in the testicles.33, 34 The present work

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intended to study the relationships between different ENP types (e.g., carbon

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nanotubes and Ag and Au NPs) and reproductive toxicity in the testes. All NPs

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causing reproductive toxicity in male rats were analyzed. A summary of toxicity

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information from each publication collected in the present work is provided in Table

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S3 to ensure the reliability of the results of the model. Ten factors, including five

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physicochemical properties (ENP size, shape, type, surface ligand and element) and

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five experimental conditions (rodent age, dosage and exposure time, duration and

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method), and two adverse biological impacts (reproductive toxicity and accumulation 6



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of ENPs) were extracted from each publication, as shown in Tables S1 and S2. The

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organ tested for the accumulation of ENPs was the testis. The tested reproductive

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toxicity indicators were sperm parameters, blood testosterone concentration and testis

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index. Among the above three indicators, sperm parameters were the priority

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parameters for measuring the male reproductive toxicity of ENPs. The analyzed

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sperm parameters included the sperm count (106/ml), rate of motile sperm (%),

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progressive motility (%), sperm abnormalities (%) and total sperm production (106/g

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testis). Among the above sperm parameters, the sperm count (106/ml) was set as a

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priority sperm parameter.

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Treatment of special data

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Approximately one hundred data samples did not include the age of the tested

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rodents. The curve-fitting method was used to calculate the missing ages based on the

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age-weight relationships of different species (details are presented in the

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Supplementary Information and Table S4). In a few publications without information

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regarding rodent age or weight, the median age of overall rodents was used to replace

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the missing age values. For nanowires or nanorods, the widths were recorded in the

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present analysis. According to respiratory rates in previous research,35 μg/݉ଷ or

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mg/݉ଷ was converted to mg/݇݃ for rodents based on the formula described by

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Nouwen.36 The unit of exposure dosage was normalized to mg/(݇݃ ∙ ݀ܽ‫)ݕ‬. To

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normalize the diverse toxicity indicators, the ratios of data from treated groups to data

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from control groups were used in the present analysis.

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RF regression and validation

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In RF, each tree is built by a bootstrap sample from the overall data and the best

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partitions among a subset of attributes that are randomly selected for each node. The

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RF algorithm performs the predictions by aggregating the predictions of each tree,

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using the majority vote for classification and the average for regression. To measure

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the relative importance of factors, the percent increase in the RF mean square error

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(MSE) was calculated using the R package randomForest. To find the sources of

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heterogeneity and to identify the priority factors determining the reproductive toxicity

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of ENPs, the original datasets were classified into subsets for further analysis

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according to the above priority factors. Given that the type of ENP was crucial for

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ENP toxicity and accumulation, metallic ENPs vs metal-free ENPs and carbonaceous

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ENPs vs carbon-free ENPs were set as two important classifications. Regarding the

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exposure method in accumulation studies, the data were divided according to injection

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(e.g., intravenous injection, intraperitoneal injection, abdominal subcutaneous

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injection and intratracheal injection) and noninjection (e.g., gavage and inhalation)

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exposure methods.

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RF, as a data-driven model, includes two important parameters: ntree, the number

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of trees in the forest; and mtry, the number of attributes randomly selected for a subset 8



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at each node.26 The values of ntree and mtry for each study were adjusted to obtain

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the best predictive accuracy. During tree construction, approximately 63% of the raw

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data were used to build the trees in each RF bootstrap sample, and the remaining

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out-of-bag (OOB) data (not in the bootstrap samples) were used to validate the

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performance of the model.37 As the RF algorithm is inherently tolerant to

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overfitting,37 the OOB predictive accuracy was selected to represent the performance

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and predictive accuracy of the model. To measure the performance, the correlation

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coefficient (R2) and root mean square error (RMSE) between the predictions and

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observations were calculated as predictive accuracy metrics.24 Then, to evaluate the

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predictive accuracy of the RF model, linear regression of quantitative factors was

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performed to predict nanotoxicity (accumulation and toxicity of ENPs). To evaluate

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the performance of the model with different data, training datasets (from 20% to

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100%) were randomly selected from original datasets, with ten repetitions, and the R2

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and RMSE were then calculated.

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Similarity network analysis

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The similarity network was drawn using the Kamada-Kawai layout algorithm.38

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Similarity network nodes with higher than average values were linked in the

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proximity matrix using the R package igraph. Subsequently, to find tight-knit

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subgroups with intrinsic commonality and to visualize the heterogeneity of the

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datasets, the walktrap community algorithm,39 a hierarchical clustering method, was 9


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applied to the similarity network. Then, two important measurements, the density and

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clustering coefficient (transitivity), reflecting the kernel density estimation and the

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probability of a vertex connecting to adjacent vertices, were applied to measure the

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tendency of the data to cluster.40, 41 Given the heterogeneity in the datasets, the first to

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the third quartiles and the most frequent categories were applied to represent the

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distribution of quantitative and qualitative data in each cluster, respectively.

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Factor-toxicity dependence analysis and nanotoxicity prediction

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Two methods were used to analyze the complex factor-toxicity dependencies. The

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first method was to visualize the average partial relationships between the adverse

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impacts (accumulation and toxicity) of ENPs and factors by partial dependence plots42,

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43

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was then used to estimate statistically significant differences (p < 0.05) between

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observations (p1) and predictions (p2). The above method was suitable for visualizing

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low-dimensional (one or two) parameters. Similarity network analysis with

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hierarchical clustering could identify tight-knit clusters with intrinsic similarity and

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common properties. Similarity network analysis was also employed to visualize

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high-dimensional parameters. The classification analysis of RF was used to extract

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reliable factor-toxicity dependencies shared in clusters. The prediction analysis used

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data with a uniform distribution over a certain range for each factor. The median

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values or most frequent categories were used to describe nonsignificant attributes

and individual conditional expectation (ICE) plots.44 Analysis of variance (ANOVA)

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(rodent age, ENP shape, exposure duration, surface ligands for accumulation studies

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and indicators for toxicity studies). The sperm parameters were selected for measuring

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reproductive toxicity due to their low heterogeneity.

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To validate the analysis of multidimensional factor-toxicity dependence, the

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over-80% vote rate and the classification rate were calculated to measure the data

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matching the cluster and the RF classification performance. To estimate the predictive

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performance, the predictions (TP and CP) were compared with the observations (TO

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and CO) of data meeting the conditions of the six important attributes (ENP type, size,

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exposure method, exposure time, dosage, element for accumulation studies and

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surface ligand for toxicity studies) screened by variable importance, shown in Figure

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2, and the observations in the original cluster (TC and CC). ANOVA with Tukey’s

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multiple comparison test was performed using IBM SPSS Statistics 20. In the RF

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analysis, partial dependence plots and ICE plots were generated using the

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randomForest package and the pdp package in R software version 3.3.2, respectively.

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RESULTS AND DISCUSSION

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Analysis of highly heterogeneous data

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Mining data regarding the reproductive toxicity of ENPs followed the process

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described in the Methods section (Tables S1 and S2). The data from eighty-two

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publications directly related to the male reproductive toxicity of ENPs were analyzed. 11


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Regarding the accumulation and the reproductive toxicity of ENPs, 242 and 250 data

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points were obtained, respectively, as summarized in Table 1. The quantitative

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attributes (rodent age, ENP size, dosage, exposure time and duration) and qualitative

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attributes (ENP type, shape, surface ligand, exposure method, and element) were

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tested in the RF analysis (Table 1). The aforementioned ten qualitative and

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quantitative attributes covered the main issues related to the reproductive impacts of

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ENPs.45 The distribution of the extracted data points is presented in Figure S1. No

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particular studies dominating the databases were observed, and further analysis was

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performed.

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However, the data heterogeneity was still a challenge for further analysis. Table 1

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provides an overview of the heterogeneity of databases regarding the accumulation

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and toxicity of ENPs. The large standard deviations for the accumulation

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concentration (e.g., 1437 ± 6201 ng/g) and the toxicity (e.g., 0.12 ± 0.25) of ENPs

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demonstrated the heterogeneity of the data (Table 1), leading to difficulty in drawing

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an unbiased conclusion. In addition to the influence of the physicochemical properties

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of ENPs and the experimental conditions, the method of exposure to NPs can lead to

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disruption of the cellular redox status and activation of compensatory mechanisms

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(e.g., changes in starch granules, lysosomes and autophagy), resulting heterogeneous

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toxicological data.46 First, linear regression was used to fit the heterogeneous data and

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explore the connections of quantitative factors with the accumulation concentration 12



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and toxicity of ENPs. As shown in Figure S2, the small R2 (less than 0.05 for

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accumulation studies and less than 0.12 for toxicity studies) and the large RMSE

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(greater than 6200 ng/g for accumulation studies and greater than 0.25 for

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reproductive toxicity) values indicated that the traditional linear regression model had

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no capacity to handle the database heterogeneity. To compare with the above linear

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regression model, the R2 and RMSE were also used to evaluate the performance of the

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RF model (Table 2 and Figure 1). Higher R2 (greater than 0.62) and smaller RMSE

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(less than 6000 ng/g) values indicated that the RF model was better for analyzing the

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heterogeneous data than the linear regression model. Moreover, the RMSE and R2

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gradually decreased and increased with increasing training data, respectively (Figure

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1), indicating that the RF model would exhibit better performance with more data.

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These measurements ensured the reliability of the meta-analysis machine learning

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workflow and the results of the model. Regarding the above heterogeneous data, the

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RF model combined with similarity network and hierarchical clustering analyses was

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used to handle and visualize data heterogeneity, to screen priority factors determining

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ENP toxicity and to reveal factor-toxicity dependencies, as presented below.

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Screening priority factors determining reproductive toxicity and contributing to

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heterogeneity

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The present work screened priority factors determining toxicity by measuring the

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increase in factor importance with increasing MSE. According to the MSE increase 13


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illustrated in Figure 2, two top priority factors with the highest MSE increases (greater

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than 32%) for NP accumulation were screened: the exposure method (e.g., i.p., i.v.

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and ga.) and ENP type (e.g., MWCNT, ZnO and Ag). The above two factors should

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be well considered in the design of animal experiments and the assessment of

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nanomaterial accumulation. In contrast, toxicity indicators (e.g., sperm parameters,

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testosterone level and testis index) and the ENP type (e.g., Ag, MWCNT and TiO2)

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with the highest MSE increase (greater than 15%) were identified as the two top

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priority factors for reproductive toxicity. Therefore, the determination of nanomaterial

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reproductive toxicity depended on the toxicity indicators and ENP type used in the

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experiments.

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To find the sources of heterogeneity, the present work classified the priority factors

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into subsets and evaluated the heterogeneity in these subsets using an RF regression

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model. As shown in Table 2, the RF model had good predictive performance in all of

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the subsets, and the R2 values were greater than 0.6. The differences between the

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observations and predictions modeled by the RMSE were obvious in the accumulation

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studies due to the presence of few data sources with large predictive errors (Figure

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S3a). In other words, the RMSE could be used to find the sources of large predictive

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errors and the contributors to heterogeneity. In the toxicity studies (Table 2), the small

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RMSE (e.g., 0.091 and 0.111) reflected the high predictive accuracy in those subsets.

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The limited data points (n = 37 and n = 56) of the subsets with carbonaceous ENPs 14



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and the testis index contributed to the high heterogeneity, indicated by small R2 values

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(0.489 and 0.383, respectively), in the prediction of reproductive toxicity (Table 2).

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However, compared with the accumulation studies (Figure S3a), the toxicity studies

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did not contain data that dominantly affected the regression performance or reduced

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the predictive accuracy (Figure S3b).

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To further identify sources of heterogeneity, the present work built similarity

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networks (Figures 3 and 4) to visualize the construction and heterogeneity of the

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datasets based on the RF proximity matrix. To analyze the major constructions of the

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similarity network, hierarchical clustering was performed to find tight-knit clusters,

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which are labeled with different colors in Figure 3. Table S5 clearly lists the densities

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and clustering coefficients, which were provided as measures of the aggregation of

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each cluster in the similarity network for NP accumulation and reproductive toxicity.

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With few connections, sparse clusters demonstrated the heterogeneity of accumulation

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or toxicity study-related data in terms of factor-toxicity dependencies. For example,

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the sparse cluster 8 (C8), with low density (0.692) and clustering coefficient (0.782)

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values, indicates high heterogeneity in terms of the properties of ENPs and the

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experimental conditions, consistent with the wide range (0-14191 ng/g) of ENP

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accumulation concentrations. Additionally, tight clusters tended to present lower

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accumulation concentrations (e.g., C1, C2 and C3 in accumulation studies) and higher

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toxicity levels (e.g., C7, C8 and C9 in toxicity studies) than sparse clusters. The 15


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accumulation studies contained more separate clusters (e.g., C1 and C3) with specific

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commonalities than the toxicity studies, indicating the diverse factor-accumulation

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dependencies in the accumulation studies. To assess the heterogeneity of the priority

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factors (i.e., ENP type and exposure method for accumulation studies; ENP type and

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indicator for toxicity studies), Figure 4 presents similarity networks colored by the

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categories of the priority factors. The data for some categories (e.g., Au in Figure 4a,

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i.v. in Figure 4b and sperm parameters in Figure 4c) were separated from neighbor

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clusters and gathered in tight clusters with great homogeneity. For example, the Au

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NPs gathered in two tight clusters for NP accumulation. Compared with the clusters in

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Figure 4b, the two clusters of Au NPs were separated by different exposure methods

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(i.v. and i.p.). The data also suggested that the exposure method was the priority

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factor that more dominantly determined ENP accumulation than the ENP type, which

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was verified by the variable importance in Figure 2. Furthermore, in Figure 4d,

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compared with the cluster of sperm parameters, the testosterone and testis index

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clusters contained sparse connections, indicating the high heterogeneity of the data

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regarding testis index-toxicity and testosterone-toxicity. Compared with Figure 4a,

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Figure 4c, with nodes of diverse types of ENPs clustering closely together, shows the

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high homogeneity of the relationships between ENP type and ENP toxicity. The

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figure also confirms that the exposure methods with tight clusters play more important

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roles in ENP toxicity than the ENP types in Figure 2. The employed similarity 16



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networks identified the heterogeneity and homogeneity of factor-toxicity

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dependencies from sparse and tight clusters, respectively. The above analysis of

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factor-toxicity dependence provided a reliable foundation from which to predict the

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reproductive toxicity of ENPs, as described below.

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Predicting the reproductive toxicity of ENPs

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Visualizing multidimensional factor-toxicity dependencies is effective for

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interpreting the relationship between factors and toxicity hidden in the RF model and

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contributes to the prediction of nanotoxicity. The partial dependence plots shown in

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Figure 5 provided an easy way to deeply understand the relationships between factors

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(ENP properties and exposure conditions) and biological impacts (ENP accumulation

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and reproductive toxicity) (more details are presented in Figure S4). As shown in

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Figure 5a, sperm parameters exhibited higher sensitivity for indicating reproductive

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toxicity risk than did other reproductive toxicity indicators (e.g., testosterone and the

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testis index) (p1, p2 < 0.0001). The above result was consistent with the better

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predictive performance of RF in the sperm parameter indicator subset than other

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subsets of reproductive indicators (Table 2). Compared with other types of ENPs,

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CdTe quantum dots and Si NPs were likely to induce significantly high reproductive

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toxicity (p1 = 0.023, p2 = 0.007). Previous studies have demonstrated that CdTe

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quantum dots can result in acute reproductive toxicity by destroying the structure of

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testicular tissue.47 As shown in Figure 5b, Fe3O4, Fe2O3 and ZnO NPs exhibited 17


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significantly higher accumulation concentrations than the other types of ENPs. Figure

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S4 also illustrates that the ENPs with major elements (e.g., Zn and Fe) tended to reach

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significantly high accumulation concentrations in the testes (p1, p2 < 0.0001). As

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demonstrated by animal experiments,48, 49 essential elements (e.g., Fe and Zn) with

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high absorptivity in spermatogenesis could cross the blood-testis barrier and easily

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accumulate in the testes. Interestingly, these NPs (e.g., Fe and Zn ENPs) reached

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higher accumulation concentrations, but with less reproductive toxicity, as shown in

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Figures 5a and 5b. Such inconsistency between the accumulation concentration and

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reproductive toxicity of ENPs deserves attention in the assessment and control of

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nanotoxicity.50, 51 Figure 5b shows that i.p. injection posed a significantly higher risk

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of ENP accumulation in the testes than other exposure pathways (p1, p2 < 0.0001),

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probably because i.p. injection could effectively transport ENPs to the testes.48 The

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above result suggests that assessments of nanotoxicity must consider the exposure

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pathway: i.p. injection may overestimate the nanotoxicity or pose a higher risk.

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To reveal any multidimensional factor-toxicity dependencies, the present work

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applied data mining technology to the similarity network (Figure 3), a platform for

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predicting the accumulation and reproductive toxicity of ENPs under specific

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conditions. The tight clusters in the similarity networks shared commonalities in terms

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of ENP properties and experimental conditions. Extracting the reliable commonalities

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shared in the clusters could visualize the multidimensional factor-toxicity 18



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dependencies. The present work applied RF classification analysis to achieve

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excellent classification accuracy based on hierarchical clustering with high vote rates

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(greater than 95%) and small error rates (less than 4%), as listed in Table S6. The high

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classification accuracy demonstrated the good affiliation of the extracted

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factor-dependencies (Table 3) with their original clusters.

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The specific dependencies of significant factors (e.g., ENP type and size) on the

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accumulation concentration and reproductive toxicity of ENPs are listed in Table 3

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(details are presented in the Methods section). The RF classification obtained high

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vote rates (greater than 80%) for each factor dependency, as shown in Table 3,

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indicating the good affiliation of each extracted factor dependency with their original

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clusters. The predictions (CP and TP) and observations (CP and TP) were compared to

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measure the predictive performance. In the accumulation studies, the similar

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prediction and observation ranges (e.g., C1, 4-15 ng/g vs 0.9-18.9 ng/g; C3, 177-252

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ng/g vs 183-287 ng/g) verified the acceptable predictive performance. However, there

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was remarkable predictive error (1562-12110 ng/g vs 200 ng/g) in C7 accumulation

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studies, indicating the difficulty in predicting the accumulation concentrations of

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ENPs with essential elements (e.g., Zn and Fe). In the toxicity studies, there were

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slight differences between the predicted and observed ranges, and comparable toxicity

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levels were achieved in the predictions and observations (e.g., C8: 0.26-0.33 vs

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0.14-0.42). As denoted for the C9 toxicity studies, the model performed well in 19


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Page 21 of 45


380

predicting the reproductive toxicity of three ENP types in terms of a high vote rate

381

(0.89). However, there was a slight difference between the predicted and observed

382

ranges (0.14-0.19 vs 0.01-0.15), which indicated the complicated nature of

383

reproductive toxicity, especially for carbon nanomaterials. There are some works

384

related to the complicated biocompatibility of graphene, for example, the roles of

385

surface modification in the growth, proliferation and differentiation of cells.52, 53 In

386

addition to cytotoxic materials, graphene materials also showed genotoxic effects,

387

which were dependent on size, shape and concentration.5-10 As a result, the toxicity

388

study predictions presented low predictive accuracy in terms of greater error between

389

predictions and observations due to the high heterogeneity of the toxicity studies, and

390

the accumulation study predictions exhibited higher accuracy in comparison.

391

According to the prediction results, Ag NPs could attain high accumulation

392

concentrations (177-252 ng/g) at small sizes (15-40 nm), medium-term exposure

393

times (14-28 days) and wide dosage ranges (1-100 mg/kg) by ga. or i.v. exposure. The

394

above results indicated that dosage over a certain range was not a sensitive factor for

395

the accumulation of Ag NPs in the testes. Furthermore, compared with Ag NPs in two

396

dependencies (C4 vs C7 of toxicity studies in Table 3), a citrate coating could reduce

397

the reproductive toxicity of Ag NPs (0.04-0.06 vs 0.06-0.08). This result supported

398

the idea that the surface ligands of Ag NPs should be taken into consideration for

399

controlling and reducing Ag nanotoxicity. 20



400

Perspectives of machine learning in nanotoxicity assessment and prediction

401

The rapid development and application of ENPs in various fields have attracted

402

much attention to nanosafety worldwide.2, 54 There are two challenges to the scientific

403

assessment of nanosafety: the numerous attributes (various ENP properties and

404

experimental conditions) related to nanosafety and the high heterogeneity of the data.

405

There are some special biological end points and mechanisms for the toxicological

406

study of nanomaterials compared with common pollutants or xenobiotics, such as

407

cellular uptake, cell membrane and cell wall damage by sharp edges and the release of

408

monomers and ions with stronger toxicity than the parent nanomaterials.55, 56 The

409

above issues contributed the high heterogeneity of the data. With the robust capacity

410

to recognize meaningful patterns in large and heterogeneous datasets, machine

411

learning can be used in a wide range of applications, such as cellular toxicity

412

prediction,19 synthetic reaction prediction24 and personality judgment.57 Thus,

413

machine learning may solve the above two problems in nanosafety assessment and

414

prediction. The present work applied a workflow combining machine learning (RF)

415

with similarity network analysis to provide a roadmap to robustly handle highly

416

heterogeneous data, reveal multidimensional factor-toxicity dependencies and predict

417

the nanotoxicity of various ENPs (more than eighteen types). However, the data

418

quality influences the performance of data-driven RF models.19 As stated above, the

419

lack of ENP characterization and the scarcity of data for some ENPs in the analyzed 21


Page 22 of 45

Page 23 of 45


420

references have influenced the performance of the model. Integrating RF with

421

similarity network analysis is an option for analyzing heterogeneous data from the

422

literature (Table 3 and Figure 3) for other nanotoxicological studies (including those

423

of female reproductive toxicity). Machine learning also reduces the use of animals in

424

experiments and will be a powerful tool in the assessment and prediction of complex

425

nanosafety issues. In addition, it is well known that reproductive toxicity evaluations

426

are very complicated and require detailed mechanistic studies. The method applied in

427

this research revealed the complex relationships between each piece of data in the RF

428

model and allowed the visualization of hidden complex factor-toxicity dependencies

429

via combination with a similarity network model. Animal experiments provide data

430

and information to support such analyses; thus, animal experiments, especially those

431

investigating toxicological mechanisms, are irreplaceable in assessing the

432

reproductive toxicity of ENPs. Notably, the data or databases are the most important

433

issue in machine learning. The intentional establishment of big data or databases

434

determines the quality of machine learning applications. In the present work, the

435

priority factors determining reproductive toxicity were successfully screened from

436

highly heterogeneous data by integrating data mined from the literature and an RF

437

model. By combining similarity network analysis and hierarchical clustering, the

438

sources of highly heterogeneous data were identified. Then, the factor-toxicity

439

dependencies were extracted and visualized. Subsequently, the prediction of 22



Page 24 of 45

440

nanotoxicity was achieved based on the screened priority factors. The above

441

workflow and used models are not specific to male toxicity; they are also suitable for

442

examine female reproductive toxicity, among others.

443 444

ASSOCIATED CONTENT

445

Supporting Information Available

446

Methods of data extraction, data treatment and ENP characterization and related

447

results are provided in Tables S1-S6 and Figures S1-S5.

448 449

AUTHOR INFORMATION

450

Corresponding author

451

*Email:

452

86-22-23507800.

453

The authors declare no competing financial interests.

[email protected]

(X.H.).

Tel.:

86-22-23507800;

fax:

454 455

ACKNOWLEDGMENTS

456

This work was financially supported by the National Natural Science Foundation of

457

China (grant nos. 21577070 and 21307061).

458 459 23


Page 25 of 45


460

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461

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1036-1040.

630 631 632 633 634 635 636 637

32



Page 34 of 45

638

Table 1. Data analysis of ENP accumulation (242 data points) and reproductive

639

toxicity (250 data points). Five qualitative factors (rodent age, ENP size, and exposure

640

time, duration and dosage) and five quantitative factors (ENP type, surface ligand,

641

shape, exposure method, and element) were analyzed for each study.

642

“#”, the elements of the ENPs are major elements in organisms. “★”, the types of

643

ENPs include CeO2, Fe2O3, Fe3O4, VO, nano-alloy particles (Au and Cu), CoCr,

644

Eu2O3, MWCNTs and PbS NPs. “⚐”, special surface ligands, including

645

3-aminopropyltriethoxysilane (ATPS), carboxyl, amino, hydrophilic polymer, citrate,

646

polyvinyl pyrrolidone (PVP) and peptide. “⸙ ”, special ENP shapes, including colloid,

647

cluster, crystalline, rod, pentacle and line. “☼”, ENP types, including Se, CdTe,

648

CdSe/ZnS, Fe2O3, Si, PbS, Mn2O3, Ni and CoCr NPs. “⸙ ”, the surface ligands

649

polyethylene glycol-amino (PEG-NH2), polyethylene glycol and PVP. “♫”, ENP

650

shapes (line, rod and cube). MWCNTs, multiwalled carbon nanotubes; i.v.,

651

intravenous injection; i.p., intraperitoneal injection; i.t., intratracheal injection; ga.,

652

gavage; i.h., inhalation; sc., abdominal subcutaneous injection. Factor

Age (weeks) Mean (SD) Range Type of ENP (%) Au Ag TiO2 ZnO CdTe

Accumulation of ENPs Factor (n = 242) Age (weeks) 7.4 (2.3) Mean (SD) 1.1 to 13.0 Range Type of ENP (%) 32.2 Ag 21.9 TiO2 10.7 Au 7.0 MWCNT 6.6 ZnO 33


Toxicity of ENPs (n = 250)

8.0 (3.9) 1.1 to 19.3 30.8 18.8 8.4 7.6 7.2

Page 35 of 45


CdS …★ Surface ligand (%) None Glutathione Polyethylene glycol …⚐ Shape (%) Sphere …⸙ Exposure method (%) i.v. ga. i.p. i.h. i.t. Elements (%)#

3.3 18.1 62.4 8.3 8.3

21.1 77.7 22.3

50 26.9 14.5 7.0 1.7

Yes

28.9

No

71.1

Size (nm) Mean (SD) Range

GO …☼ Surface ligand (%) None Citrate Hydrophilic Polymer …₂ Shape (%) Sphere …♫ Exposure method (%) ga. i.v. i.p. i.h. sc. Indicator (%) Sperm Parameters Testosterone Testis index Size (nm) Mean (SD) Range Exposure time (days) Mean (SD) Range Duration time (days) Mean (SD) Range Dosage (mg/kg) Mean (SD) Range

37.7 (51.2) 1.9 to 323

Exposure time (days)

Mean (SD) Range

15.1 (34.0) 0 to 224

Duration time (days) Mean (SD) Range Dosage (mg/kg) Mean (SD) Range Accumulation Concentration Mean (SD) Range

17.4 (33.2) 0 to 180 233 (862) 0 to 5000

6.8 19.6 81.6 6.8 4.8

6.4 91.1 8.9

43.6 29.2 20.8 3.6 2.4 66.8 10.4 22.4 41.9 (48.5) 1.4 to 245

32.3 (38.9) 1 to 224 12.0 (37.7) 0 to 365 112 (519) 0 to 5000

Toxicity 1437 (6201) -3280 to 69856

Mean (SD) Range

653 654

34


0.12 (0.25) -1.3 to 0.95


Page 36 of 45

655

Table 2. Assessments of the performance of the random forest (RF) model using the

656

R2 correlation coefficient and root mean square error (RMSE). The subset of

657

metal-free engineering nanoparticles (ENPs) in NP accumulation studies was not

658

analyzed due to the limited number of samples (n = 3). For other subsets, the number

659

of tested samples was not less than 37. a, Number of data points in the subset. b,

660

Injection exposure methods, including intramuscular, intravenous, intraperitoneal,

661

intra-articular and abdominal subcutaneous injection. c, Noninjection exposure

662

methods, including inhalation and gavage. Accumulation of ENPs Priority Factor Overall Type of ENP Exposure method Priority Factor Overall Type of ENP Toxicity indicator

Nd a

R2

242 135 104 156 86

0.744 0.634 0.757 0.769 0.758

RMSE (ng/g) 4218 392 6177 5033 851

Subset

Nd a

R2

RMSE

Metallic Metal-free Carbonaceous Carbon-free Sperm parameters Testosterone Testis index

250 194 56 37 213 168 26 56

0.624 0.605 0.553 0.485 0.619 0.543 0.471 0.383

0.198 0.215 0.131 0.091 0.212 0.181 0.424 0.111

Subset Single-metallic element Multiple-metallic element Injectionb Noninjectionc Toxicity of ENPs

663

35


Page 37 of 45


664

Table 3. Predictions of nanomaterial accumulation and reproductive toxicity. The factors are normalized for each study as follows: rodent age, 8

665

weeks; ENP shape, particles; duration, 14 days; ENP surface ligands, none; toxicity indicator, sperm parameters. ⚐, cluster number; *, duration,

666

0 to 4 weeks; $, lowest vote rates of data points. Cp and Tp represent the predictions of the extracted data points in the RF model for the

667

accumulation and toxicity studies, respectively. Co and To represent the observations of actual data points meeting the conditions of the six

668

important attributes (ENP type, size, exposure method, exposure time, dosage, and element for accumulation studies and surface ligand for

669

toxicity studies) for the accumulation and reproductive toxicity of ENPs, respectively. Nd and Np represent the number of actual data points and

670

publications meeting the conditions of the six attributes, respectively. Cc and Tc represent the accumulation concentration and toxicity

671

distribution of each cluster, respectively. The first to third quartiles were used as the main range.

C⚐

ENP

Element

Size Exposure (nm) Method

1 3 3

Au TiO2 Ag ZnO, Fe2O3

N Y N

10-40 i.v., i.h. 5-100 ga., i.v. 15-40 ga., i.v.

Y

20-40

7*

ga.

Predictions for Accumulation Studies Exposure Dosage Time Cp (ng/g) Nd (mg/kg) (days) 1 0.01-1 4-15 31 1-90 10-200 405-1123 5 14-28 1-100 177-252 6 10-40

30-200

1562-12110

36


2

Np

Co (ng/g)

4 3 3

0.9-18.9 230-3860 183-287

Vote Rate$ (%) 96.3 80.0 87.0

2

200

82.5

Cc (ng/g)

2-18 3-174 3-174 130-3080


⚐

C

4 7 8

ENP

Ag Ag TiO2 MWCNT, 9 GO, Ag

Predictions for Toxicity Studies Exposure Dosage Surface Size Exposure Time Tp (mg/kg) Ligand (nm) Method (days) citrate 5-40 ga. 90-365 10-150 0.04-0.06 n 10-15 i.v. 10-15 0.1-5 0.06-0.08 n 20-70 ga. 30-60 20-200 0.26-0.33 n

3-20

i.p.

1-15

0.5-70

0.14-0.19

672

37


Page 38 of 45

Nd

Np

To

2 3 6

1 1 4

0.00-0.01 0.00-0.03 0.14-0.42

Vote Rate$ (%) 80.0 86.0 87.0

24

3

0.01-0.15

89.0

Tc

-0.03-0.04 0.00-0.18 0.04-0.35 0.08-0.36

Page 39 of 45


673

Figure Captions

674

Figure 1. Performance of the RF model. The training sets were randomly selected

675

from original datasets, and each training set was repeated ten times. a and b,

676

accumulation of ENPs; b and d, reproductive toxicity of ENPs.

677 678

Figure 2. Importance of attributes to the reproductive toxicity and accumulation

679

concentration of ENPs analyzed by the RF model. The increase in the MSE was used

680

to measure the importance of the attributes.

681 682

Figure 3. Similarity network analysis based on the proximity matrix of the RF model.

683

The present work connected adjacent nodes with higher than average values in the

684

proximity matrix, which measures the probability of two data points occurring in the

685

same node of a tree in the RF model, and vice versa. The tight connections in the

686

similarity network indicate the high homogeneity of the clusters. The clusters with

687

different colors were classified by hierarchical clustering.

688 689

Figure 4. Similarity network analysis of priority factors based on the proximity

690

matrix of the RF model. The nodes were colored according to the categories of the

691

priority factors.

38



692

Figure 5. Bivariate partial dependence plots of ENP types, exposure methods and

693

toxicity indicators. a, Reproductive toxicity of ENPs; b, accumulation concentration

694

of ENPs.

695 696 697 698 699 700 701 702 703 704 705 706

39


Page 40 of 45

Page 41 of 45


707 708

Figure 1.

709

40



710 711

Figure 2.

712 713 714 715 716 717 718 719 41


Page 42 of 45

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720 721

Figure 3. 42



722 723

724 725

Figure 4.

43


Page 44 of 45

Page 45 of 45


726 727

Figure 5.

728

44


Screening Priority Factors Determining and ... - ACS Publications

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