Global ETD Search

21	Short-term effects of hydrated lime and quicklime on the decay of human remains using pig cadavers as human body analogues: Laboratory experiments Schotsmans, Eline M.J., Denton, J., Fletcher, Jonathan N., Janaway, Robert C., Wilson, Andrew S. January 2014 (has links) No / Contradictions and misconceptions regarding the effect of lime on the decay of human remains have demonstrated the need for more research into the effect of different types of lime on cadaver decomposition. This study follows previous research by the authors who have investigated the effect of lime on the decomposition of human remains in burial environments. A further three pig carcasses (Sus scrofa), used as human body analogues, were observed and monitored for 78 days without lime, with hydrated lime (Ca(OH)2) and with quicklime (CaO) in the taphonomy laboratory at the University of Bradford. The results showed that in the early stages of decay, the unlimed and hydrated lime cadavers follow a similar pattern of changes. In contrast, the application of quicklime instigated an initial acceleration of decay. Microbial investigation demonstrated that the presence of lime does not eliminate all aerobic bacteria. The experiment also suggested that lime functions as a sink, buffering the carbon dioxide evolution. This study complements the field observations. It has implications for the investigation of time since death of limed remains. Knowledge of the effects of lime on decomposition processes is of interest to forensic pathologists, archaeologists, humanitarian organisations and those concerned with disposal of animal carcasses or human remains in mass disasters. Animals Body temperature Calcium compounds Forensic pathology Humans Laboratories Models Oxides Postmortem changes Skin Swine Desiccation Histology Lime Microbiology Taphonomy Time since death
22	Population genetic analysis of the black blow fly Phormia regina (Meigen) (Diptera: Calliphoridae) Whale, John W. January 2015 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / The black blow fly, Phormia regina (Diptera: Calliphoridae), is a widely abundant fly autochthonous to North America. Like many other Calliphorids, P. regina plays a key role in several disciplines particularly in estimating post-mortem intervals (PMI). The aim of this work was to better understand the population genetic structure of this important ecological species using microsatellites from populations collected in the U.S. during 2008 and 2013. Additionally, it sought to determine the effect of limited genetic diversity on a quantitative trait throughout immature development; larval length, a measurement used to estimate specimen age. Observed heterozygosity was lower than expected at five of the six loci and ranged from 0.529-0.880 compared to expected heterozygosity that ranged from 0.512-0.980, this is indicative of either inbreeding or the presence of null alleles. Kinship coefficients indicate that individuals within each sample are not strongly related to one another; values for the wild-caught populations ranged from 0.033-0.171 and a high proportion of the genetic variation (30%) can be found among samples within regions. The population structure of this species does not correlate well to geography; populations are different to one another resulting from a lack of gene flow irrespective of geographic distance, thus inferring temporal distance plays a greater role on the genetic variation of P. regina. Among colonized samples, flies lost much of their genetic diversity, ≥67% of alleles per locus were lost, and population samples became increasingly more related; kinship coefficient values increased from 0.036 for the wild-caught individuals to 0.261 among the F10 specimens. Colonized larvae also became shorter in length following repeated inbreeding events, with the longest recorded specimen in F1 18.75 mm in length while the longest larva measured in F11 was 1.5 mm shorter at 17.25 mm. This could have major implications in forensic entomology, as the largest specimen is often assumed to be the oldest on the corpse and is subsequently used to estimate a postmortem interval. The reduction in length ultimately resulted in a greater proportion of individuals of a similar length; the range of data became reduced. Consequently, the major reduction in genetic diversity indicates that the loss in the spread of length distributions of the larvae may have a genetic influence or control. Therefore, this data highlights the importance when undertaking either genetic or development studies, particularly of blow flies such as Phormia regina, that collections of specimens and populations take place not only from more than one geographic location, but more importantly from more than one temporal event. Life cycles (Biology) -- Genetic aspects Molecular biology -- Mathematical models Gene expression -- Statistical methods Postmortem changes
23	De novo genome assembly of the blow fly Phormia regina (Diptera: Calliphoridae) Andere, Anne A. January 2014 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Phormia regina (Meigen), commonly known as the black blow fly is a dipteran that belongs to the family Calliphoridae. Calliphorids play an important role in various research fields including ecology, medical studies, veterinary and forensic sciences. P. regina, a non-model organism, is one of the most common forensically relevant insects in North America and is typically used to assist in estimating postmortem intervals (PMI). To better understand the roles P. regina plays in the numerous research fields, we re-constructed its genome using next generation sequencing technologies. The focus was on generating a reference genome through de novo assembly of high-throughput short read sequences. Following assembly, genetic markers were identified in the form of microsatellites and single nucleotide polymorphisms (SNPs) to aid in future population genetic surveys of P. regina. A total 530 million 100 bp paired-end reads were obtained from five pooled male and female P. regina flies using the Illumina HiSeq2000 sequencing platform. A 524 Mbp draft genome was assembled using both sexes with 11,037 predicted genes. The draft reference genome assembled from this study provides an important resource for investigating the genetic diversity that exists between and among blow fly species; and empowers the understanding of their genetic basis in terms of adaptations, population structure and evolution. The genomic tools will facilitate the analysis of genome-wide studies using modern genomic techniques to boost a refined understanding of the evolutionary processes underlying genomic evolution between blow flies and other insect species. Life cycles (Biology) -- Genetic aspects DNA microarrays -- Statistical methods Gene expression -- Statistical methods Forensic entomology -- Research Genomics -- Technological innovations Molecular biology -- Mathematical models Combinatorial analysis Graph theory Bioinformatics -- Research Postmortem changes
24	Applied Machine Learning Predicts the Postmortem Interval from the Metabolomic Fingerprint Arpe, Jenny January 2024 (has links) In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project. In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance. This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R2 of 0.68 and an MAE of 1.51 days compared to RF's R2 of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are necessary for comparable results with maintained predictive accuracy. Furthermore, Principal Component Analysis (PCA) reveals that these informative metabolites are primarily located within a specific cluster on the first and second principal components (PC), suggesting a need for further research into the biological context of these metabolites. In conclusion, the dataset has proven valuable for predicting PMI. This indicates significant potential for employing machine learning models in PMI estimation, thereby assisting forensic pathologists in determining the time of death. Notably, the model shows promise in surpassing current methods and filling crucial gaps in the field, representing an important step towards achieving accurate PMI estimations in forensic practice. This project suggests that machine learning will play a central role in assisting with determining time since death in the future. Forensic autopsies postmortem interval (PMI) postmortem metabolomics biological systems metabolomic fingerprints postmortem changes National Board of Forensic Medicine RMV Rättsmedicinalverket forensic science PMI estimation machine learning algorithms metabolomic data metabolites Random Forest (RF) Artificial Neural Network (ANN) feature selection Principal Component Analysis (PCA) forensic pathologists forensic practice. Rättsmedicinska obduktioner postmortalt intervall (PMI) postmortal metabolomik biologiska system metabolomiska fingeravtryck postmortala förändringar Rättsmedicinalverket RMV PMI-estimering maskininlärningsalgoritmer metabolomiska data metaboliter Random Forest (RF) Artificiella neurala nätverk (ANN) feature selection Principal Component Analysis (PCA) rättsläkare rättsmedicinsk praxis. Other Medical Engineering Annan medicinteknik

Page generated in 0.0507 seconds