Copycatting the smell of death: Deciphering the role of cadaveric scent components used by detection dogs to locate human remains

Human remains detection dogs (HRDD) are commonly used by law enforcement agencies to search for cadavers. Biological material is typically used as a training stimulus, also called aids, to train dogs to recognize the smell of cadavers. While HRDD training approaches have received extensive attention, information remains limited on the olfactory cues used to train them. Here, we aimed to decipher the chemical basis of detection dog olfaction. Five specific objectives were explored to precise whether the composition or the concentration of the training aids drives the HRDDs responses. We recorded the behavioral responses of four HRDDs exposed to different cadaveric-like smells. We found that HRDDs recognized a simplified synthetic aid composed of cadaveric compounds. The lowest concentration at which HRDDs continued to perceive the cadaveric smell was determined. HRDDs were not impacted by slight modifications to the chemical composition of a blend of odors that they have been trained with. HRDDs associated sulfur and nitrogen compounds as human cadaver. Our findings highlight a lack of specificity of HRDDs to cadaveric compounds, which could lead to error of detection. Moreover, all dogs did not positively respond to the same blends, despite being trained with the same aids and procedure. However, we confirmed that dogs could be trained with a simplified blend of molecules. The chemical composition of a training aid has, therefore, high consequences on the performance of the trained animal, and this conclusion opens additional questions regarding olfaction-based detection animals.     

Identification of document paper using hybrid feature extraction

Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray-level co-occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.     

Rapid and non-destructive approach for characterization and differentiation of sealing wax using ATR-FTIR spectroscopy

Sealing wax is used for maintaining the integrity and authenticity of a document or physical evidence. Any tampering with the seal calls into question the overall integrity and authenticity of the tangible evidence or document. In these circumstances, determining the authenticity of the sealing material (physical and chemical) becomes imperative. In this study, ATR-FTIR spectroscopy supported by chemometrics has been used to differentiate sealing wax samples belonging to 12 different brands available across India. All the samples were first melted, cooled, and then analyzed using ATR-FTIR spectroscopy in the mid-infrared region (4000–600 cm⁻¹). The obtained spectra were first examined visually for the presence of different functional groups. Principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) were employed to analyze the sample clustering patterns and to categorize them into their respective groups, respectively. For classification, a PCA-LDA training model was applied, and it demonstrated 95.83% accuracy. The validation test resulted in an accuracy of 83.33%. PCA-LDA model offered 100% accurate prediction for samples on various substrates, including cloth, cardboard, and paper. A blind study was also performed using five unknown samples, which were accurately classified into their respective groups. PCA-LDA model will be helpful in providing investigative leads by linking a questioned sealing wax sample with its respective group.     

Subnuclear renal tubular vacuoles in alcohol use disorder

Subnuclear vacuoles in the proximal renal tubules have been reported as a histologic sign of ketoacidosis. Originally described in diabetic ketoacidosis, renal vacuoles can be found in other ketogenic states such as alcoholic ketoacidosis (AKA), starvation, and hypothermia, underpinned by deranged fatty acid metabolism. A retrospective analysis of 133 deaths associated with alcohol use disorder (AUD) examined at autopsy between 2017 and 2020 was undertaken. This study aimed to determine the prevalence of subnuclear vacuoles in deaths of those with AUD and their specificity for deaths from AKA, and to elucidate what demographic, biochemical, and pathologic findings are associated with subnuclear vacuoles. In each case, vitreous humor biochemistry including electrolytes, glucose, and beta-hydroxybutyrate (BHB) was analyzed alongside postmortem hemoglobin A1c and renal and liver histology. Renal histology was graded for the presence of vacuoles as absent (0), scanty (1), or easily identifiable (2). Liver histology was graded for steatosis and for fibrosis if Masson trichrome staining was available. Vacuoles were commonly seen in the deaths of those with AUD. They were seen in deaths due to AKA but were not specific to that cause of death. With vacuoles present, lower vitreous sodium (139 vs. 142 mmol/L; p = 0.005), higher vitreous BHB (1.50 vs. 1.39 mmol/L; p = 0.04), severe hepatic steatosis, and severe hepatic fibrosis were seen, compared with those without renal vacuoles.     

IoT forensics: Exploiting log records from the DAHUA technology CCTV systems

CCTV surveillance systems are ubiquitous IoT appliances. Their forensic examination has proven critical for investigating crimes. DAHUA Technology is a well-known manufacturer of such products. Despite its global market share, research regarding digital forensics of DAHUA Technology CCTV systems is scarce and currently limited to extracting their video footage, overlooking the potential presence of valuable artifacts within their log records. These pieces of evidence remain unexploited by major commercial forensic software, yet they can hide vital information for an investigation. For instance, these log records document user actions, such as formatting the CCTV system's hard drive or disabling camera recording. This information can assist in attributing nefarious actions to specific users and hence can be invaluable for understanding the sequence of events related to incidents. Therefore, in this paper, several DAHUA Technology CCTV systems are thoroughly analyzed for these unexplored pieces of evidence, and their forensic value is presented.     

Radiographic age estimation based on degenerative changes of vertebrae

Age estimation is an important component of decedent identification. When assessing adult remains, anthropologists frequently use gross examination of skeletal elements, such as clavicles, ribs, and pubic symphyses. For fleshed bodies, this requires the removal of these elements and maceration prior to analysis. A new method was developed using radiographic imaging to estimate age from degenerative changes of the lower thoracic and upper lumbar vertebrae. This technique will complement anthropological age estimation methods in young and middle-aged adults and may serve as a stand-alone method for older individuals. Digital radiographs from 240 medical examiner cases were evaluated. The sample included 120 females and 120 males between the ages of 18 and 101 years. A 3-phased scoring system was used for the target vertebrae. Transition analysis was conducted on binned average scores and a Bayesian approach was used to assign age intervals. At the 90% credible interval, individuals in Bin 1 were under 36 years of age while those in Bin 3 were over 47 years of age. Individuals in Bin 2 showed too much age variation to be informative. No significant differences were found between males and females. These findings will be especially useful in the age estimation of older adults and may eliminate the need for skeletal sampling in medicolegal cases where advanced degenerative changes are radiographically observed in the lower thoracic and/or upper lumbar vertebrae. This method was developed for use on fleshed individuals but may also be applicable to skeletonized remains.