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Shen Z, Zhong Y, Wang Y, Zhu H, Liu R, Yu S, Zhang H, Wang M, Yang T, Zhang M. A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues based on animal experiments. Int J Legal Med 2024; 138:1093-1107. [PMID: 37999765 DOI: 10.1007/s00414-023-03127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/27/2023] [Indexed: 11/25/2023]
Abstract
The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation.
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Affiliation(s)
- Zefang Shen
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Yue Zhong
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Yucong Wang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Haibiao Zhu
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Ran Liu
- Forensic Science Center of Beijing Huatong Junjian Science and Technology Company Limited, Beijing, 100016, China
| | - Shengnan Yu
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Haidong Zhang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Min Wang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Tiantong Yang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China.
| | - Mengzhou Zhang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China.
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Zhang F, Liu Y, Song C, Yang C, Hong S. Empirical study of college students' extracurricular reading preference by functional data analysis of the library book borrowing behavior. PLoS One 2024; 19:e0297357. [PMID: 38277367 PMCID: PMC10817177 DOI: 10.1371/journal.pone.0297357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/02/2024] [Indexed: 01/28/2024] Open
Abstract
Library data contains many students' reading records that reflect their general knowledge acquisition. The purpose of this study is to deeply mine the library book-borrowing data, with concerns on different book catalogues and properties to predict the students' extracurricular interests. An intelligent computing framework is proposed by the fusion of a neural network architecture and a partial differential equations (PDE) function module. In model designs, the architecture is constructed as an adaptive learning backpropagation neural network (BPNN), with automatic tuning of its hyperparameters. The PDE module is embedded into the network structure to enhance the loss functions of each neural perceptron. For model evaluation, a novel comprehensive index is designed using the calculus of information entropy. Empirical experiments are conducted on a diverse and multimodal time-series dataset of library book borrowing records to demonstrate the effectiveness of the proposed methodology. Results validate that the proposed framework is capable of revealing the students' extracurricular reading interests by processing related book borrowing records, and expected to be applied to "big data" analysis for a wide range of various libraries.
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Affiliation(s)
- Fan Zhang
- The Library of Guangzhou Huashang College, Guangzhou, China
| | - Yuling Liu
- School of Data Science, Guangzhou Huashang College, Guangzhou, China
| | - Chao Song
- School of Accounting, Guangzhou Huashang College, Guangzhou, China
| | - Chun Yang
- School of Accounting, Guangzhou Huashang College, Guangzhou, China
| | - Shaoyong Hong
- School of Data Science, Guangzhou Huashang College, Guangzhou, China
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Woess C, Huck CW, Badzoka J, Kappacher C, Arora R, Lindtner RA, Zelger P, Schirmer M, Rabl W, Pallua J. Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review. JOURNAL OF BIOPHOTONICS 2023; 16:e202300189. [PMID: 37494000 DOI: 10.1002/jbio.202300189] [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: 05/24/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 07/27/2023]
Abstract
Estimating postmortem intervals (PMI) is crucial in forensic investigations, providing insights into criminal cases and determining the time of death. PMI estimation relies on expert experience and a combination of thanatological data and environmental factors but is prone to errors. The lack of reliable methods for assessing PMI in bones and soft tissues necessitates a better understanding of bone decomposition. Several research groups have shown promise in PMI estimation in skeletal remains but lack valid data for forensic cases. Current methods are costly, time-consuming, and unreliable for PMIs over 5 years. Raman spectroscopy (RS) can potentially estimate PMI by studying chemical modifications in bones and teeth correlated with burial time. This review summarizes RS applications, highlighting its potential as an innovative, nondestructive, and fast technique for PMI estimation in forensic medicine.
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Affiliation(s)
- C Woess
- Institute of Forensic Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innsbruck, Austria
| | - J Badzoka
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innsbruck, Austria
| | - C Kappacher
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innsbruck, Austria
| | - R Arora
- Department of Orthopaedics and Traumatology, Medical University of Innsbruck, Innsbruck, Austria
| | - R A Lindtner
- Department of Orthopaedics and Traumatology, Medical University of Innsbruck, Innsbruck, Austria
| | - Philipp Zelger
- University Clinic for Hearing, Voice and Speech Disorders, Medical University of Innsbruck, Innsbruck, Austria
| | - M Schirmer
- Department of Internal Medicine, Clinic II, Medical University of Innsbruck, Innsbruck, Austria
| | - W Rabl
- Institute of Forensic Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Pallua
- Department of Orthopaedics and Traumatology, Medical University of Innsbruck, Innsbruck, Austria
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