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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Female Blow Fly (Diptera: Calliphoridae) Arrival Patterns and Consequences for Larval Development on Ephemeral ResourcesMohr, Rachel 2012 May 1900 (has links)
This investigation explored the environmental and physiological factors affecting adult blow fly (Diptera: Calliphoridae) arrival and attendance at pig (Sus scrofa domesticus L.) carcasses in Brazos Co, TX in the summer and winter, and validated a new technique for estimating the pre-colonization interval. It also examined how the offspring of said blow flies compensate for adverse developmental conditions such as starvation or the presence of older competitors by determining the function of minimum viable weight, critical weight, and the terminal growth period in Cochliomyia macellaria (F.) (Diptera: Calliphoridae). Adult blow fly carcass attendance is poorly explained by temperature, wind speed, ambient light intensity, or body size for either winter or summer-active species. Time of day explained approximately 10% in carcass size variation for all four of the most common species. For summer flies, the degree of ovarian development changed significantly from 96%/98% fully developed on day 1 postmortem to 7%/2% fully developed on day 2 postmortem for C. macellaria and Chrysomya rufifacies (Macquart)
iv
respectively. Using the binomial distribution, the minimum postmortem interval was correctly estimated for 4/6 validation tests. Minimum viable weight for C. macellaria was found to be ~ 0.02 g, and was stable under conditions of starvation and simulated competition. Under starvation conditions, time to pupariation was not altered, whereas under simulated competition, growth rate was increased and terminal growth period shortened. Starved flies under simulated competition entered the pupal state ~12 h faster than starved flies without competition, but required ~12 longer to complete development. These effects should be considered when estimating post-colonization intervals.
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Applied Machine Learning Predicts the Postmortem Interval from the Metabolomic FingerprintArpe, 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.
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