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Xie H, Wei C, Wang W, Chen R, Cui L, Wang L, Chen D, Yu YL, Li B, Li YF. Screening the phytotoxicity of micro/nanoplastics through non-targeted metallomics with synchrotron radiation X-ray fluorescence and deep learning: Taking micro/nano polyethylene terephthalate as an example. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132886. [PMID: 37913659 DOI: 10.1016/j.jhazmat.2023.132886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/11/2023] [Accepted: 10/27/2023] [Indexed: 11/03/2023]
Abstract
Microplastics (MPs) and nanoplastics (NPs) are global pollutants with emerging concerns. Methods to predict and screen their toxicity are crucial. Elemental dyshomeostasis can be used to assess toxicity of environmental pollutants. Non-targeted metallomics, combining synchrotron radiation X-ray fluorescence (SRXRF) and machine learning, has successfully differentiated cancer patients from healthy individuals. The whole idea of this work is to screen the phytotoxicity of nano polyethylene terephthalate (nPET) and micro polyethylene terephthalate (mPET) through non-targeted metallomics with SRXRF and deep learning algorithms. Firstly, Seed germination, seedling growth, photosynthetic changes, and antioxidant activity were used to evaluate the toxicity of mPET and nPET. It was showed that nPET, at 10 mg/L, was more toxic to rice seedlings, inhibiting growth and impairing chlorophyll content, MDA content, and SOD activity compared to mPET. Then, rice seedling leaves exposed to nPET or mPET was examined with SRXRF, and the SRXRF data was differentiated with deep learning algorithms. It was showed that the one-dimensional convolutional neural network (1D-CNN) model achieved 98.99% accuracy without data preprocessing in screening mPET and nPET exposure. In all, non-targeted metallomics with SRXRF and 1D-CNN can effectively screen the exposure and phytotoxicity of nPET/mPET and potentially other emerging pollutants. Further research is needed to assess the phytotoxicity of different types of MPs/NPs using non-targeted metallomics.
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Affiliation(s)
- Hongxin Xie
- Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, Liaoning, China; CAS-HKU Joint Laboratory of Metallomics on Health and Environment, & CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, & Beijing Metallomics Facility, & National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Chaojie Wei
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Wei Wang
- College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Rui Chen
- Beijing Key Laboratory of Occupational Safety and Health, Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing 100054, China
| | - Liwei Cui
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liming Wang
- CAS-HKU Joint Laboratory of Metallomics on Health and Environment, & CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, & Beijing Metallomics Facility, & National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongliang Chen
- University of Chinese Academy of Sciences, Beijing 100049, China; Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Yong-Liang Yu
- Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, Liaoning, China.
| | - Bai Li
- CAS-HKU Joint Laboratory of Metallomics on Health and Environment, & CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, & Beijing Metallomics Facility, & National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Yu-Feng Li
- CAS-HKU Joint Laboratory of Metallomics on Health and Environment, & CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, & Beijing Metallomics Facility, & National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Brito de Jesus S, Vieira D, Gheller P, Cunha BP, Gallucci F, Fonseca G. Machine learning algorithms accurately identify free-living marine nematode species. PeerJ 2023; 11:e16216. [PMID: 37842061 PMCID: PMC10569207 DOI: 10.7717/peerj.16216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/11/2023] [Indexed: 10/17/2023] Open
Abstract
Background Identifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903. Methods A total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms. Results For both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified. Conclusion These results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.
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Affiliation(s)
- Simone Brito de Jesus
- Marine Science Institute, Federal University of São Paulo, Santos, São Paulo, Brazil
| | - Danilo Vieira
- Marine Science Institute, Federal University of São Paulo, Santos, São Paulo, Brazil
| | - Paula Gheller
- Institute Oceanographic, University of São Paulo, São Paulo, Brazil
| | - Beatriz P. Cunha
- Department of Animal Biology, State University of Campinas, Campinas, São Paulo, Brazil
| | - Fabiane Gallucci
- Marine Science Institute, Federal University of São Paulo, Santos, São Paulo, Brazil
| | - Gustavo Fonseca
- Marine Science Institute, Federal University of São Paulo, Santos, São Paulo, Brazil
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Hishamuddin MS, Lee SY, Syazwan SA, Ramlee SI, Lamasudin DU, Mohamed R. Highly divergent regions in the complete plastome sequences of Aquilaria are suitable for DNA barcoding applications including identifying species origin of agarwood products. 3 Biotech 2023; 13:78. [PMID: 36761338 PMCID: PMC9902582 DOI: 10.1007/s13205-023-03479-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023] Open
Abstract
Members of Aquilaria Lam. (Thymelaeaceae) are evergreen trees that are widely distributed in the Indomalesia region. Aquilaria is highly prized for its unique scented resin, agarwood, which is often the subject of unlawful trade activities. Survival of the tree is heavily threatened by destructive harvesting and agarwood poaching, leading to its protection under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Unfortunately, an efficient species identification method, which is crucial to aid in the conservation efforts of Aquilaria is lacking. Here, we described our search for a suitable specific DNA barcode for Aquilaria species using eight complete plastome sequences. We identified five highly variable regions (HVR) (matK-rps16, ndhF-rpl32, psbJ-petA, trnD, and trnT-trnL) in the plastomes. These regions were further analyzed using the neighbor-joining (NJ) method to assess their ability at discriminating the eight species. Coupled with in silico primer design, two potential barcoding regions, psbJ-petA and trnT-trnL, were identified. Their strengths in species delimitation were evaluated individually and in combination, via DNA barcoding analysis. Our findings showed that the combined dataset, psbJ-petA + trnT-trnL, effectively resolved members of the genus Aquilaria by clustering all species into their respective clades. In addition, we demonstrated that the newly proposed DNA barcode was capable at identifying the species of origin of six commercial agarwood samples that were included as unknown samples. Such achievement offers a new technical advancement, useful in the combat against illicit agarwood trades and in assisting the conservation of these valuable species in natural populations. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-023-03479-1.
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Affiliation(s)
- Muhammad Syahmi Hishamuddin
- Forest Biotechnology Laboratory, Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Shiou Yih Lee
- Forest Biotechnology Laboratory, Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Samsuddin Ahmad Syazwan
- Forest Biotechnology Laboratory, Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
- Mycology and Pathology Branch, Forest Biodiversity Division, Forest Research Institute Malaysia (FRIM), Jalan FRIM, 52109 Kuala Lumpur, Selangor Malaysia
| | - Shairul Izan Ramlee
- Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Dhilia Udie Lamasudin
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Rozi Mohamed
- Forest Biotechnology Laboratory, Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
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The Anthropometric Measurement of Nasal Landmark Locations by Digital 2D Photogrammetry Using the Convolutional Neural Network. Diagnostics (Basel) 2023; 13:diagnostics13050891. [PMID: 36900035 PMCID: PMC10000550 DOI: 10.3390/diagnostics13050891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/15/2023] [Accepted: 02/19/2023] [Indexed: 03/02/2023] Open
Abstract
Measuring and labeling human face landmarks are time-consuming jobs that are conducted by experts. Currently, the applications of the Convolutional Neural Network (CNN) for image segmentation and classification have made great progress. The nose is arguably one of the most attractive parts of the human face. Rhinoplasty surgery is increasingly performed in females and also in males since surgery can help to enhance patient satisfaction with the resulting perceived beautiful ratio following the neoclassical proportions. In this study, the CNN model is introduced to extract facial landmarks based on medical theories: it learns the landmarks and recognizes them based on feature extraction during training. The comparison between experiments has proved that the CNN model can detect landmarks depending on desired requirements. Anthropometric measurements are carried out by automatic measurement divided into three images with frontal, lateral, and mental views. Measurements are performed including 12 linear distances and 10 angles. The results of the study were evaluated as satisfactory with a normalized mean error (NME) of 1.05, an average error for linear measurements of 0.508 mm, and 0.498° for angle measurements. Through its results, this study proposed a low-cost automatic anthropometric measurement system with high accuracy and stability.
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Distributed adaptive fixed-time neural networks control for nonaffine nonlinear multiagent systems. Sci Rep 2022; 12:8459. [PMID: 35590095 PMCID: PMC9120193 DOI: 10.1038/s41598-022-12634-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 05/12/2022] [Indexed: 11/11/2022] Open
Abstract
This paper, with the adaptive backstepping technique, presents a novel fixed-time neural networks leader–follower consensus tracking control scheme for a class of nonaffine nonlinear multiagent systems. The expression of the error system is derived, based on homeomorphism mapping theory, to formulate a set of distributed adaptive backstepping neural networks controllers. The weights of the neural networks controllers are trained, by an adaptive law based on fixed-time theory, to determine the adaptive control input. The control algorithm can guarantee that the output of the follower agents of the system effectively follow the output of the leader of the system in a fixed time, while the upper bound of the settling time can be calculated without initial parameters. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed consensus tracking control approach. A step-by-step procedure for engineers and researchers interested in applications is proposed.
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Caveat emptor: On the Need for Baseline Quality Standards in Computer Vision Wood Identification. FORESTS 2022. [DOI: 10.3390/f13040632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Computer vision wood identification (CVWID) has focused on laboratory studies reporting consistently high model accuracies with greatly varying input data quality, data hygiene, and wood identification expertise. Employing examples from published literature, we demonstrate that the highly optimistic model performance in prior works may be attributed to evaluating the wrong functionality—wood specimen identification rather than the desired wood species or genus identification—using limited datasets with data hygiene practices that violate the requirement of clear separation between training and evaluation data. Given the lack of a rigorous framework for a valid methodology and its objective evaluation, we present a set of minimal baseline quality standards for performing and reporting CVWID research and development that can enable valid, objective, and fair evaluation of current and future developments in this rapidly developing field. To elucidate the quality standards, we present a critical revisitation of a prior CVWID study of North American ring-porous woods and an exemplar study incorporating best practices on a new dataset covering the same set of woods. The proposed baseline quality standards can help translate models with high in silico performance to field-operational CVWID systems and allow stakeholders in research, industry, and government to make informed, evidence-based modality-agnostic decisions.
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