Aims: In criminal investigations, it is necessary to determine the date and time of the death of a person. Different techniques are used. In this study, we try to analyze the necrobioma that characterizes all the bacteria that populate a corpse. It would be necessary to determine which bacteria first inhabit a dead organism? Which bodies are the first organs to be affected? Which microorganisms will tend to multiply post-mortem? How to establish a dynamics of bacterial diffusion and an occupation gradient according to the moment of death? Several factors are involved in this dynamic. Mathematical modeling becomes very complex. In this study, we propose an intelligent system to predict the exact date of death of the number and species found at time (t). Materials and Methods: The purpose is to determine and enumerate the bacterial colonies in the study organ. Establish the bacterial dynamics as a function of time. In this study, an artificial neural network is established. The input variables are bacterial species, their growth rates, growth conditions (temperature, humidity, soil type, and bacterial species). The rate of bacterial species in specified organ is considered as output variable. The time taken for a bacterial species to reach this rate under defined conditions determines the date of death of the person. Results: Since input variables are considered complex, uncertain, an artificial neural network demonstrates its ability to solve such complexity. After the learning phase of the network from the real data, this creates a function of correspondence between the space of inputs and output. The established system makes it possible to instantly read the time elapsed after death from the introduction of the random values at the input with the maximum precision. The proposed system remains extensible to enter variables that may have an effect on the output.
Published in | Computational Biology and Bioinformatics (Volume 5, Issue 6) |
DOI | 10.11648/j.cbb.20170506.13 |
Page(s) | 90-96 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Necrobiome, Microbial Dynamics, Post Mortem, Intelligent Systems, ANN
[1] | Kaliszan, M., Hauser, R. & Kernbach-Wighton, G. Estimation of the time of death based on the assessment of post mortem processes with emphasis on body cooling. Legal Medicine. 2009. 11(3): 111-117. |
[2] | Metcalf et al. Jessica L Metcalf et al. A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. 2013. eLife; 2:e01104. DOI: 10.7554/eLife.01104. |
[3] | Pechal JL, Crippen TL, Benbow ML, et al. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int J Legal Med. 2013. doi: 10.1007/s00414-013-0872-1. |
[4] | Vass A. A. Beyond the grave-understanding human decomposition. Microbiol Today 2001. 28:190–192. |
[5] | Mondor E. B, Tremblay MN, Tomberlin JK, et al. The ecology of carrion decomposition. Nat Educ Knowledge 2012. 3:21. |
[6] | Hyde ER, Haarmann DP, Lynne AM, et al. The living dead: bacterial community structure of a cadaver at the onset and end of the bloat stage of decomposition. 2013. PLoS One 8, e77733. doi:10.1371/journal.pone.0077733. |
[7] | Hyde ER, Haarmann DP, Petrosino JF, et al. Initial insights into bacterial succession during human decomposition. Int J Legal Med 2015. 129:661–671. |
[8] | Metcalf JL, Wegener Parfrey L, Gonzalez A, et al. A microbial clock provides an accurate estimate of the postmortem interal in a mouse model system. 2013. ELife 2, e01104. doi:10.7554/eLife.01104 |
[9] | Weiss, S, Carter DO, Metcalf JL, Knight R. Carcass mass has little influence on the structure of gravesoil microbial communities. Int J Legal Med. 2015. doi:10.1007/s00414-015-1206-2. |
[10] | Payne J. A summer carrion study of the baby pig Sus Scrofa Linnaeus. Ecology 1965. 46:592–602. doi:10.2307/1934999 |
[11] | Pechal JL, Crippen TL, Benbow ME, et al. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int J Legal Med 2014. 128:193–205. doi:10.1007/s00414-013-0872-1. |
[12] | Carter DO, Yellowlees D, Tibbett M Temperature affects microbial decomposition of cadavers (Rattus rattus) in contrasting soils. Faculty Publications. Department of Entomology. 2008. |
[13] | Pechal J, Crippen T, Benbow ME, et al. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. International Journal of Legal Medicine: 2013. 1–13. |
[14] | Hopkins DW, Wiltshire PEJ, Turner BD. Microbial characteristics of soils from graves: an investigation at the interface of soil microbiology and forensic science. Applied Soil Ecology 2000. 14: 283–288. |
[15] | Howard GT, Duos B, Watson-Horzelski EJ. Characterization of the soil microbial community associated with the decomposition of a swine carcass. International Biodeterioration & Biodegradation 2010. 64: 300–304. |
[16] | Melvin JR, Jr., Cronholm LS, Simson LR, Jr., Isaacs AM. Bacterial transmigration as an indicator of time of death. J Forensic Sci 1984. 29: 412–417. |
[17] | Dickson GC, Poulter RTM, Maas EW, Probert PK, Kieser JA. Marine bacterial succession as a potential indicator of postmortem submersion interval. Forensic Science International 2011. 209: 1–10. |
[18] | Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010. 7: 335–336. |
[19] | Carter DO, Yellowlees D, Tibbett M. Temperature affects microbial decomposition of cadavers (Rattus rattus) in contrasting soils. Faculty Publications. Department of Entomology. 2008. |
[20] | Evans WED. The Chemistry of Death. Springfield IL: Charles C. Thomas. 1963. |
[21] | Janaway R, Percival S, Wilson A. Decomposition of Human Remains. In: Percival S, Microbiology and Aging: Humana Press. 2009. pp. 313–334. |
[22] | Pechal J, Crippen T, Benbow ME, et al. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. International Journal of Legal Medicine: 2013. 1–13. |
[23] | Vass A. Beyond the Grave - Understanding Human Decomposition. Microbiology Today 2001. 28: 190–192. |
[24] | Hopkins DW, Wiltshire PEJ, Turner BD. Microbial characteristics of soils from graves: an investigation at the interface of soil microbiology and forensic science. Applied Soil Ecology 2000. 14: 283–288. |
[25] | Howard GT, Duos B, Watson-Horzelski EJ. Characterization of the soil microbial community associated with the decomposition of a swine carcass. International Biodeterioration & Biodegradation 2010. 64: 300–304. |
[26] | Stokes KL, Forbes SL, Tibbett M. Freezing skeletal muscle tissue does not affect its decomposition in soil: Evidence from temporal changes in tissue mass, microbial activity and soil chemistry based on excised samples. Forensic science international 2009. 183: 6–13. |
[27] | Evans WED. The Chemistry of Death. Springfield IL: Charles C. Thomas. 1963. |
[28] | Janaway R, Percival S, Wilson A. Decomposition of Human Remains. In: Percival S, Microbiology and Aging: Humana Press. 2009. pp. 313–334. |
[29] | [Vass A. Beyond the Grave - Understanding Human Decomposition. Microbiology Today 2001. 28: 190–192. |
[30] | Melvin JR, Jr., Cronholm LS, Simson LR, Jr., Isaacs AM. Bacterial transmigration as an indicator of time of death. J Forensic Sci 1984. 29: 412–417. |
[31] | Heimesaat MM, Boelke S, Fischer A, et al. Comprehensive postmortem analyses of intestinal microbiota changes and bacterial translocation in human flora associated mice. 2012. PLoS One.; 7: e40758. doi: 10.1371/journal.pone.0040758 PMID: 22808253. |
[32] | Swann LM, Forbes SL, Lewis SW. Analytical separations of mammalian decomposition products for forensic science: A review. Analytica Chimica Acta. 2010. 682: 9–22. doi: 10.1016/j.aca.2010.09.052 PMID: 21056711. |
[33] | Cobaugh KL, Schaeffer SM, DeBruyn JM. Functional and Structural Succession of Soil Microbial Communities below Decomposing Human Cadavers. 2015. PLoS ONE 10(6): e0130201. doi:10.1371/ journal.pone.0130201 |
[34] | Carter DO, Metcalf JL, Bibat A, Knight R. Seasonal variation of postmortem microbial communities. Forensic Sci Med Pathol 2015. 11:202–227. doi:10.1007/s12024-015-9667-7 |
[35] | Benbow ME, Pechal JL, Lang JM, et al. The potential of high-throughput metagenomic sequencing of aquatic bacterial communities to estimate the postmortem submersion interval. J Forensic Sci 2015. doi:10.1111/1556-4029.12859 |
[36] | Benbow ME, Lewis AJ, Tomberlin JK, Pechal JL. Seasonal necrophagous insect community assembly during vertebrate carrion decomposition. J Med Entomol 2013. 50:440–450. doi:10.1603/ME12194. |
[37] | Tomberlin JK, Mohr R, Benbow ME, et al. A roadmap for bridging basic and applied research in forensic entomology. Annu Rev Entomol 2011. 56:401–421. doi:10.1146/ annurev-ento-051710-103143. |
[38] | Powers RH. The decomposition of human remains. In: Rich J, Dean DE, Powers RH, editors. Forensic medicine of the lower extremity. Totowa: The Humana Press; 2005. p. 3-15. |
[39] | David O. Carter, David Y. and Mark T. Temperature affects microbial decomposition of cadavers (Rattus rattus) in contrasting soils Applied Soil Ecology 2008. 40:1 pp. 129–137; doi: 10.1016/j.apsoil.2008.03.010. |
[40] | Smart, J. L. & Kaliszan, M. The post mortem temperature plateau and its role in the estimation of time of death: A review. Legal Medicine 2012. 14(2): 55-62. |
[41] | Duday, H. & Guillon, M. Understanding the circumstances of decomposition when the body is skeletonized. In Forensic Anthropology and Medicine: Complimentary Sciences from Recovery to Cause of Death, edited by Schmitt, A., Cunha, E. & Pinheiro, J. Totowa: Humana Press Inc. 2006. pp. 117-157. |
[42] | Kelly JA, Van Der Linde TC, Anderson GS. The influence of clothing and wrapping on carcass decomposition and arthropod succession during the warmer seasons in Central South Africa. J Forensic Sci. 2009. 54(5): 1105-12. |
[43] | Prangnell J, McGowan G. Soil temperature calculation for burial site analysis. Forensic Sci Int. 2009. 191(1-3): 104-9. |
[44] | Chee H. T., Sri P., Amir H., et al. Post mortem changes in relation to different types of clothing Malaysian J Pathol; 2013. 35(1) : 77 – 85. |
[45] | Dent, B., Forbes, S. & Stuart, B. Review of human decomposition processes in soil. Environmental Geology 2004. 45(4): 576-585. |
[46] | Statheropoulos, M., Agapiou, A., Zorba, E., et al. Combined chemical and optical methods for monitoring the early decay stages of surrogate human models. Forensic Science International 2011. 210(1): 154-163. |
[47] | Fiedler S, Graw M. Decomposition of buried corpses, with special reference to the formation of adipocere. Naturwissenschaften. 2003. 90(7): 291-300. |
[48] | Ross, A. H. & Cunningham, S. L. Time-since-death and bone weathering in a tropical environment. Forensic Science International 2011. 204(1): 126-133. |
[49] | Goff ML. Early post-mortem changes and stages of decomposition in exposed cadavers. Exp Appl Acarol 2009. 49:21–36. doi:10. 1007/s10493-009-9284-9 |
[50] | Metcalf JL, Xu ZZ, Weiss S, et al. Microbial community assembly and met.abolic function during mammalian corpse decomposition. Science 2015. aad2646. |
[51] | Adams, V. I. Medicolegal autopsy and postmortem toxicology. In Handbook of Autopsy Practice, edited by Waters, B. L. Totowa: Humana Press Inc. 2009b. pp. 125-136. |
[52] | [52]. Smart, J. L. & Kaliszan, M. The post mortem temperature plateau and its role in the estimation of time of death: A review. Legal Medicine 2012. 14(2): 55-62. |
[53] | Madea, B. Is there recent progress in the estimation of the postmortem interval by means of thanatochemistry? Forensic Science International. 2005.151(2-3): 139-150. |
[54] | Forbes S. Time since death: a novel approach to dating skeletal remains. Aust J Forensic Sci. 2004. 36(2):67–72. |
[55] | Stephanie J. M, Paul F, Shari L. et al. Estimating post-mortem interval using accumulated degree-days and a degree of decomposition index in Australia: a validation study, Australian Journal of Forensic Sciences, 2015. DOI: 10.1080/00450618.2015.1021378. |
[56] | Mann R, Bass W, Meadows L. Time since death and decomposition of the human body: variables and observations in case and experimental field studies. J Forensic Sci. 1990; 35(1):103–111. |
[57] | Eilizabeth K. Costello et al. Bacterial Community Variation in Human Body Habitats Across Space and Time Science 2009. 326, 1694; DOI: 10.1126/science.1177486. |
[58] | Rhine S, Dawson J. Estimation of time since death in the south western United States. In: Reichs K, editor. Forensic Osteology: advances in the identification of human remains. Springfield, IL: Charles C. Thomas; 1998. p. 145–159. |
[59] | Finley S. J. et al. Microbial Signatures of Cadaver Gravesoil During Decomposition Microb Ecol. 2016. DOI 10.100/s00248-015-0725-1 |
[60] | Brion G. M, Neelakantan T. R, Lingireddy S. A neural-network-based classification scheme for sorting sources and ages of fecal contamination in water. Water research. 2002. 36 (15), 3765-3774. |
[61] | Bouharati S., Benamrani H., Alleg F., et al. Artificial Neural Networks In Prevention Of Nosocomials Infections. International journal of scientific & technology research 2013. volume 2, issue 10. |
APA Style
Khenchouche Abdelhalim, Bouharati Khaoula, Bouharati Saddek, Mahnane Abbas, Hamdi-Cherif Mokhtar. (2017). Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks. Computational Biology and Bioinformatics, 5(6), 90-96. https://doi.org/10.11648/j.cbb.20170506.13
ACS Style
Khenchouche Abdelhalim; Bouharati Khaoula; Bouharati Saddek; Mahnane Abbas; Hamdi-Cherif Mokhtar. Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks. Comput. Biol. Bioinform. 2017, 5(6), 90-96. doi: 10.11648/j.cbb.20170506.13
AMA Style
Khenchouche Abdelhalim, Bouharati Khaoula, Bouharati Saddek, Mahnane Abbas, Hamdi-Cherif Mokhtar. Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks. Comput Biol Bioinform. 2017;5(6):90-96. doi: 10.11648/j.cbb.20170506.13
@article{10.11648/j.cbb.20170506.13, author = {Khenchouche Abdelhalim and Bouharati Khaoula and Bouharati Saddek and Mahnane Abbas and Hamdi-Cherif Mokhtar}, title = {Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks}, journal = {Computational Biology and Bioinformatics}, volume = {5}, number = {6}, pages = {90-96}, doi = {10.11648/j.cbb.20170506.13}, url = {https://doi.org/10.11648/j.cbb.20170506.13}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20170506.13}, abstract = {Aims: In criminal investigations, it is necessary to determine the date and time of the death of a person. Different techniques are used. In this study, we try to analyze the necrobioma that characterizes all the bacteria that populate a corpse. It would be necessary to determine which bacteria first inhabit a dead organism? Which bodies are the first organs to be affected? Which microorganisms will tend to multiply post-mortem? How to establish a dynamics of bacterial diffusion and an occupation gradient according to the moment of death? Several factors are involved in this dynamic. Mathematical modeling becomes very complex. In this study, we propose an intelligent system to predict the exact date of death of the number and species found at time (t). Materials and Methods: The purpose is to determine and enumerate the bacterial colonies in the study organ. Establish the bacterial dynamics as a function of time. In this study, an artificial neural network is established. The input variables are bacterial species, their growth rates, growth conditions (temperature, humidity, soil type, and bacterial species). The rate of bacterial species in specified organ is considered as output variable. The time taken for a bacterial species to reach this rate under defined conditions determines the date of death of the person. Results: Since input variables are considered complex, uncertain, an artificial neural network demonstrates its ability to solve such complexity. After the learning phase of the network from the real data, this creates a function of correspondence between the space of inputs and output. The established system makes it possible to instantly read the time elapsed after death from the introduction of the random values at the input with the maximum precision. The proposed system remains extensible to enter variables that may have an effect on the output.}, year = {2017} }
TY - JOUR T1 - Post Mortem Interval: Necrobiome Analysis Using Artificial Neural Networks AU - Khenchouche Abdelhalim AU - Bouharati Khaoula AU - Bouharati Saddek AU - Mahnane Abbas AU - Hamdi-Cherif Mokhtar Y1 - 2017/12/08 PY - 2017 N1 - https://doi.org/10.11648/j.cbb.20170506.13 DO - 10.11648/j.cbb.20170506.13 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 90 EP - 96 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20170506.13 AB - Aims: In criminal investigations, it is necessary to determine the date and time of the death of a person. Different techniques are used. In this study, we try to analyze the necrobioma that characterizes all the bacteria that populate a corpse. It would be necessary to determine which bacteria first inhabit a dead organism? Which bodies are the first organs to be affected? Which microorganisms will tend to multiply post-mortem? How to establish a dynamics of bacterial diffusion and an occupation gradient according to the moment of death? Several factors are involved in this dynamic. Mathematical modeling becomes very complex. In this study, we propose an intelligent system to predict the exact date of death of the number and species found at time (t). Materials and Methods: The purpose is to determine and enumerate the bacterial colonies in the study organ. Establish the bacterial dynamics as a function of time. In this study, an artificial neural network is established. The input variables are bacterial species, their growth rates, growth conditions (temperature, humidity, soil type, and bacterial species). The rate of bacterial species in specified organ is considered as output variable. The time taken for a bacterial species to reach this rate under defined conditions determines the date of death of the person. Results: Since input variables are considered complex, uncertain, an artificial neural network demonstrates its ability to solve such complexity. After the learning phase of the network from the real data, this creates a function of correspondence between the space of inputs and output. The established system makes it possible to instantly read the time elapsed after death from the introduction of the random values at the input with the maximum precision. The proposed system remains extensible to enter variables that may have an effect on the output. VL - 5 IS - 6 ER -