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An Assessment to Toxicological Risk of Pesticide Exposure

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 636)

Abstract

On the one hand, pesticides may be absorbed into the body orally, dermally, ocularly and by inhalation and the human exposure may be dietary, recreational and/or occupational where toxicity could be acute or chronic. On the other hand, the environmental fate and toxicity of the pesticide is contingent on the physico-chemical characteristics of pesticide, the soil composition and adsorption. Human toxicity is also dependent on the exposure time and individual’s susceptibility. Therefore, this work will focus on the development of an Artificial Intelligence based diagnosis support system to assess the pesticide toxicological risk to humanoid, built under a formal framework based on Logic Programming to knowledge representation and reasoning, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting.

Keywords

Pesticide exposure Toxicity Environmental fate Artificial intelligence Logic programming Knowledge representation and reasoning Artificial neuronal networks Incomplete information 

Notes

Acknowledgments

This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

References

  1. 1.
    Hodgson, E.: Introduction to pesticide biotransformation and disposition. In: Hodgson, E. (ed.) Pesticide Biotransformation and Disposition, pp. 1–3. Elsevier, Amsterdam (2012)CrossRefGoogle Scholar
  2. 2.
    Needham, L.L., Patterson, D.G., Barr, D.B., Grainger, J., Calafat, A.M.: Uses of speciation techniques in biomonitoring for assessing human exposure to organic environmental chemicals. Anal. Bioanal. Chem. 381, 397–404 (2005)CrossRefGoogle Scholar
  3. 3.
    Environmental Protection Agency: General Principles for Performing Aggregate Exposure and Risk Assessments. Item 6043. https://www.epa.gov/sites/production/files/2015-07/documents/aggregate.pdf
  4. 4.
    Renwick, A.G.: Pesticide residue analysis and its relationship to hazard characterisation (ADI/ARfD) and intake estimations (NEDI/NESTI). Pest Manag. Sci. 58, 1073–1082 (2002)CrossRefGoogle Scholar
  5. 5.
    Esteban, M., Castaño, A.: Non-invasive matrices in human biomonitoring: a review. Environ. Int. 35, 438–449 (2009)CrossRefGoogle Scholar
  6. 6.
    Angerer, J., Ewers, U., Wilhelm, M.: Human biomonitoring: state of the art. Int. J. Hyg. Environ. Health 210, 201–228 (2007)CrossRefGoogle Scholar
  7. 7.
    Antón, A., Castells, F., Montero, J.I., Huijbregts, M.: Comparison of toxicological impacts of integrated and chemical pest management in mediterranean greenhouses. Chemosphere 54, 1225–1235 (2004)CrossRefGoogle Scholar
  8. 8.
    Alister, C., Kogan, M.: ERI: Environmental risk index. A simple proposal to select agrochemicals for agricultural use. Crop Prot. 25, 202–211 (2006)CrossRefGoogle Scholar
  9. 9.
    Juraske, R., Antón, A., Castells, F., Huijbregts, M.A.: PestScreen: a screening approach for scoring and ranking pesticides by their environmental and toxicological concern. Environ. Int. 33, 886–893 (2007)CrossRefGoogle Scholar
  10. 10.
    Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)Google Scholar
  11. 11.
    Cortez, P., Rocha, M., Neves, J.: Evolving time series forecasting ARMA models. J. Heuristics 10, 415–429 (2004)CrossRefGoogle Scholar
  12. 12.
    Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)Google Scholar
  13. 13.
    Pereira, L.M., Anh, H.T.: Evolution prospection. In: Nakamatsu, K., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds.) New Advances in Intelligent Decision Technologies. SCI, vol. 199, pp. 51–64. Springer, Berlin (2009)CrossRefGoogle Scholar
  14. 14.
    Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 160–169. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Lucas, P.: Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., Mackintosh, A. (eds.) Research and Development in Intelligent Systems XX, pp. 309–321. Springer, London (2003)Google Scholar
  16. 16.
    Machado, J., Abelha, A., Novais, P., Neves, J., Neves, J.: Quality of service in healthcare units. In: Bertelle, C., Ayesh, A. (eds.) Proceedings of the ESM 2008, pp. 291–298. Eurosis – ETI Publication, Ghent (2008)Google Scholar
  17. 17.
    Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., Neves, J.: Artificial neural networks in diabetes control. In: Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370. IEEE Edition (2015)Google Scholar
  18. 18.
    National Pesticide Information Center. http://npic.orst.edu/index.html
  19. 19.
    Haykin, S.: Neural Networks and Learning Machines. Pearson Education, New Jersey (2009)Google Scholar
  20. 20.
    Mitra, S., Pal, S., Mitra, P.: Data mining in soft computing framework: a survey. IEEE Trans. Neural Netw. 13, 3–14 (2002)CrossRefGoogle Scholar
  21. 21.
    Riedmiller, M.: Advanced supervised learning in multilayer perceptrons—from backpropagation to adaptive learning algorithms. Comput. Stand. Interfaces 16, 265–278 (1994)CrossRefGoogle Scholar
  22. 22.
    Thimm, G., Fiesler, E.: Evaluating pruning methods. In: Proceedings of the International Symposium on Artificial Neural Networks, pp. 20–25. National Chiao-Tung University Edition (1995)Google Scholar
  23. 23.
    Kwok, T., Yeung, D.: Constructive algorithms for structure learning in feedforward neural networks for regression problems: a survey. IEEE Trans. Neural Netw. 8, 630–645 (1997)CrossRefGoogle Scholar
  24. 24.
    Vicente, H., Couto, C., Machado, J., Abelha, A., Neves, J.: Prediction of water quality parameters in a reservoir using artificial neural networks. Int. J. Des. Nat. Ecodyn. 7, 309–318 (2012)CrossRefGoogle Scholar
  25. 25.
    Vicente, H., Dias, S., Fernandes, A., Abelha, A., Machado, J., Neves, J.: Prediction of the quality of public water supply using artificial neural networks. J. Water Supply: Res. Technol. – AQUA 61, 446–459 (2012)CrossRefGoogle Scholar
  26. 26.
    Figueiredo, M., Neves, J., Vicente, H.: A soft computing approach to quality evaluation of general chemistry learning in higher education. In: Caporuscio, M., De la Prieta, F., Di Mascio, T., Gennari, R., Rodríguez, J.G., Vittorini, P. (eds.) Methodologies and Intelligent Systems for Technology Enhanced Learning. Advances in Intelligent and Soft Computing, vol. 478, pp. 81–89. Springer International Publishing, Cham (2016)CrossRefGoogle Scholar
  27. 27.
    Neves, J., Figueiredo, M., Vicente, L., Vicente, H.: A case based reasoning view of school dropout screening. In: Kim, K.J., Joukov, N. (eds.) Information Science and Applications. LNEE, vol. 376, pp. 953–964. Springer, Singapore (2016)Google Scholar
  28. 28.
    Neves, J., Martins, M.R., Candeias, F., Arantes, S., Piteira, A., Vicente, H.: An assessment of pharmacological properties of schinus essential oils – a soft computing approach. In: Proceedings 30th European Conference on Modelling and Simulation (ECMS 2016), pp. 107–113. European Council for Modelling and Simulation Edition (2016)Google Scholar
  29. 29.
    Neves, J., Martins, M.R., Candeias, F., Ferreira, D., Arantes, S., Cruz-Morais, J., Gomes, G., Macedo, J., Abelha, A., Vicente, H.: Logic programming and artificial neural networks in pharmacological screening of schinus essential oils. Int. J. Biol. Biomol. Agric. Food Biotechnol. Eng. 9, 706–711 (2015). World Academy of Science, Engineering and Technology, International Science Index 103Google Scholar
  30. 30.
    Vilhena, J., Vicente, H., Martins, M.R., Grañeda, J., Caldeira, F., Gusmão, R., Neves, J., Neves, J.: Antiphospholipid syndrome risk evaluation. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Teixeira, M.M. (eds.) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol. 444, pp. 157–167. Springer International Publishing, Cham (2016)CrossRefGoogle Scholar
  31. 31.
    Neves, J., Martins, M.R., Vilhena, J., Neves, J., Gomes, S., Abelha, A., Machado, J., Vicente, H.: A soft computing approach to kidney diseases evaluation. J. Med. Syst. 39, 131 (2015). doi: 10.1007/s10916-015-0313-4 CrossRefGoogle Scholar
  32. 32.
    Mendes, R., Kennedy, J., Neves, J.: Watch thy neighbor or how the swarm can learn from its environment. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS 2003), pp. 88–94. IEEE Edition (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Departamento de Química, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal
  2. 2.Departamento de Química, Laboratório HERCULES, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal
  3. 3.Centro de Engenharia Biológica, Micoteca da Universidade do MinhoUniversidade do MinhoBragaPortugal
  4. 4.Centro AlgoritmiUniversidade do MinhoBragaPortugal

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