Postmortem interval (PMI) estimation is a necessary but often difficult task that must completed during a death investigation. The level of difficulty rises as time since death increases, especially with the case of skeletonized remains (long PMI). While challenging, a reliable PMI estimate may be of great importance for investigative direction and cost-savings (e.g. suspect identification, tailoring missing persons searches, non-forensic remains exclusion). Long PMI can be estimated by assessing changes in the organic content of bone (i.e. collagen), which degrades and is lost as the PMI lengthens. Visible-near infrared (VNIR) spectroscopy is one method that can be used for analyzing organic constituents, including proteins, in solid specimens. A 2013 preliminary investigation using a limited number of human cortical bone samples suggested that VNIR spectroscopy could provide a fast, reliable technique for assessing PMI in human skeletal remains. Clear separation was noted between "forensic" and "archaeological" specimen spectra within the near-infrared (NIR) bands. The goal of this research was to develop reliable multivariate classification models that could assign skeletal remains to appropriate PMI classes (e.g. "forensic" and "non-forensic"), based on NIR spectra collected from human cortical bone. Working with a large set of cortical samples (n=341), absorbance spectra were collected with an ASD/PANalytical LabSpec® 4 full range spectrometer. Sample spectra were then randomly assigned to training and test sets, where training set spectra were used to build internally cross-validated models in Camo Unscrambler® X 10.4; external validations of the models were then performed on test set spectra. Selected model algorithms included soft independent modeling of class analogy (SIMCA), linear discriminant analysis on principal components (LDA-PCA), and partial least squares discriminant analysis (PLSDA); an application of support vector machines on principal components (SVM-PCA) was attempted as well. Multivariate classification models were built using both raw and transformed spectra (standard normal variate, Savitzky-Golay) that were collected from the longitudinally cut cortical surfaces (Set A models) and the superficial cortical surface following light grinding (Set B models). SIMCA models were consistently the poorest performers, as were many of the SVM-PCA models; LDA-PCA models were generally the best performers for these data. Transformed-spectra model classification accuracies were generally the same or lower than corresponding raw spectral models. Set A models out-performed Set B counterparts in most cases; Set B models often yielded lower classification accuracy for older forensic and non-forensic spectra. A limited number of Set B transformed-spectra models out-performed the raw model counterparts, suggesting that these transformations may be removing scattering-related noise, leading to improvements in model accuracy. This study suggests that NIR spectroscopy may represent a reliable technique for assessing the PMI of unknown human skeletal remains. Future work will require identifying new sources of remains with established extended PMI values. Broadening the number of spectra collected from older forensic samples would allow for the determination of how many narrower potential PMI classes can be discriminated within the forensic time-frame.

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.

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.

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.