Estimation of post-mortem interval (PMI) is crucial for time of death determination. The advent of DNA-based identification techniques forensic entomology saw the beginning of a proliferation of molecular studies into forensically important Calliphoridae (Diptera). The use of DNA to characterise morphologically indistinguishable immature calliphorids was recognised as a valuable molecular tool with enormous practical utility. The local entomofauna in most cases is important for the examination of entomological evidences. The survey of the local entomofauna has become a fundamental first step in forensic entomological studies, because different geographical distributions, seasonal and environmental factors may influence the decomposition process and the occurrence of different insect species on corpses. In this study, calliphorids were collected from 13 human corpses recovered from indoors, outdoors and aquatic conditions during the post-mortem examination by pathologists from the government hospitals in Malaysia. Only two species, Chrysomya megacephala and Chrysomya rufifacies were recovered from human corpses. DNA sequencing was performed to study the mitochondrial encoded COI gene and to evaluate the suitability of the 1300 base pairs of COI fragments for identification of blow fly species collected from real crime scene. The COI gene from blow fly specimens were sequenced and deposited in GenBank to expand local databases. The sequenced COI gene was useful in identifying calliphorids retrieved from human corpses.
Forensic entomology applies knowledge about insects associated with decedent in crime scene investigation. It is possible to calculate a minimum postmortem interval (PMI) by determining the age and species of the oldest blow fly larvae feeding on decedent. This study was conducted in Malaysia to identify maggot specimens collected during crime scene investigations. The usefulness of the molecular and morphological approach in species identifications was evaluated in 10 morphologically identified blow fly larvae sampled from 10 different crime scenes in Malaysia. The molecular identification method involved the sequencing of a total length of 2.2 kilo base pairs encompassing the 'barcode' fragments of the mitochondrial cytochrome oxidase I (COI), cytochrome oxidase II (COII) and t-RNA leucine genes. Phylogenetic analyses confirmed the presence of Chrysomya megacephala, Chrysomya rufifacies and Chrysomya nigripes. In addition, one unidentified blow fly species was found based on phylogenetic tree analysis.
Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.