A DNA-based method for distinction of fly artifacts from human bloodstains

Fly artifacts resulting from insect activity could act as confounding factors on a crime scene and interfere with bloodstain pattern analysis interpretation. Several techniques have been proposed to distinguish fly artifacts from human bloodstains based on morphological approach and immunological assay, but a DNA-based method has not been developed so far. Even if in forensic genetic investigations the detection of human DNA is generally the primary goal, fly artifacts can provide useful information on the dynamics of crime events. The present study provides a molecular method to detect fly DNA from artifacts deposited by Calliphora vomitoria after feeding on human blood through the analysis of the mitochondrial cytochrome oxidase gene subunit I (COI). Fly artifacts originated from digestive process and of different morphology spanning from red and brownish/light brown, circular and elliptical stains to artifacts with sperm-like tail or a tear-shaped body were collected. The COI amplification was successfully obtained in 94% of fly artifact samples. The method showed high sensitivity and reproducibility, and no human DNA contamination was observed, offering specificity for use in confirmatory test. This molecular approach permits the distinction of fly artifacts from genuine bloodstains and the identification of fly’s species through the COI region sequencing by protocols usually applied in forensic genetic laboratories.

Variations in the Impact Spatter Patterns due to Blunt Weapons Inflicted by Different Gender and Age Groups

Bloodstain pattern analysis makes use of the mechanics of bloodstains to estimate the time, nature and the sequence of events at the crime scene. In this study, a quantitative methodology was developed which includes manually calculating the average size of the bloodstains and the synthetic bloodstains to distinguish the impact stain patterns created by different gender and age groups using the two blunt weapons. Impact stains were created by hitting sponges soaked in blood and synthetic blood using a hammer and a cricket bat. A smaller bloodstain size was observed of the group of males aged between 18 to 30 years. Greater amount of spatter and the distribution of the blood spatter were observed with the bat as compared to hammer due to larger surface area of bat. Cast off patterns were studied and were observed to be produced only by the bat which can be attributed to its higher surface area. Results showed difference in the bloodstain diameter produced from human blood and synthetic blood evaluating if the synthetic blood is suitable for use, as well as variation in the average stain size between produced by the four groups using both the fluids.

Application of DNA Containing Forensic Blood Substitute in the Bloodstain Pattern Analysis

We were able to diversify blood substitutes that emphasized the existing physical characteristics by adding the synthetic DNA, which was designed and produced in this study, to developed blood substitute. By doing so, we could improve the availability of developed blood substitute to the point where it can be identified similarly as in DNA analysis from the human blood.

Recognition of overlapping elliptical objects in a binary image

Recognition of overlapping objects is required in many applications in the field of computer vision. Examples include cell segmentation, bubble detection and bloodstain pattern analysis. This paper presents a method to identify overlapping objects by approximating them with ellipses. The method is intended to be applied to complex-shaped regions which are believed to be composed of one or more overlapping objects. The method has two primary steps. First, a pool of candidate ellipses are generated by applying the Euclidean distance transform on a compressed image and the pool is filtered by an overlaying method. Second, the concave points on the contour of the region of interest are extracted by polygon approximation to divide the contour into segments. Then, the optimal ellipses are selected from among the candidates by choosing a minimal subset that best fits the identified segments. We propose the use of the adjusted Rand index, commonly applied in clustering, to compare the fitting result with ground truth. Through a set of computational and optimization efficiencies, we are able to apply our approach in complex images comprised of a number of overlapped regions. Experimental results on a synthetic data set, two types of cell images and bloodstain patterns show superior accuracy and flexibility of our method in ellipse recognition, relative to other methods.