Violence seems deeply rooted in human nature and an endemic potential for such is today frequently associated with differing ethnic, religious or socio-economic backgrounds. Ethnic nepotism is believed to be one of the main causes of inter-group violence in multi-ethnic societies. At the site of Els Trocs in the Spanish Pyrenees, rivalling groups of either migrating early farmers or farmers and indigenous hunter-gatherers collided violently around 5300 BCE. This clash apparently resulted in a massacre of the Els Trocs farmers. The overkill reaction was possibly triggered by xenophobia or massive disputes over resources or privileges. In the present, violence and xenophobia are controlled and sanctioned through social codes of conduct and institutions. So that, rather than representing an insurmountable evolutionary inheritance, violence and ethnic nepotism can be overcome and a sustainable future achieved through mutual respect, tolerance and openness to multi-ethnic societies.

Cannabis sativa (C. sativa) is commonly chemically classified based on its Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) content ratios. However, the plant contains nearly 150 additional cannabinoids, referred to as minor cannabinoids. Minor cannabinoids are gaining interest for improved plant and product characterization, e.g., for medical use, and bioanalytical questions in the medico-legal field. This study describes the development and validation of an analytical method for the elucidation of minor cannabinoid fingerprints, employing liquid chromatography coupled to high-resolution mass spectrometry. The method was used to characterize inflorescences from 18 different varieties of C. sativa, which were cultivated under the same standardized conditions. Complementing the targeted detection of 15 cannabinoids, untargeted metabolomics employing in silico assisted data analysis was used to detect additional plant ingredients with focus on cannabinoids. Principal component analysis (PCA) was used to evaluate differences between varieties. The overall purpose of this study was to examine the ability of targeted and non-targeted metabolomics using the mentioned techniques to distinguish cannabis varieties from each other by their minor cannabinoid fingerprint. Quantitative determination of targeted cannabinoids already gave valuable information on cannabinoid fingerprints as well as inter- and intra-variety variability of cannabinoid contents. The untargeted workflow led to the detection of 19 additional compounds. PCA of the targeted and untargeted datasets revealed further subgroups extending commonly applied phenotype classification systems of cannabis. This study presents an analytical method for the comprehensive characterization of C. sativa varieties. Graphical abstract

Cannabis is the most frequently consumed illegal substance worldwide. More recently, an increasing number of legal cannabis products low in psychoactive Δ⁹‐tetrahydrocannabinol (THC) but high in non‐intoxicating cannabidiol (CBD) are being more widely consumed. While the detection and quantification of THC and its metabolites in biological matrices is an important forensic‐toxicological task, additional detection of CBD is also important, for example, when examining the plausibility of consumer's statements. This report describes the method validation for the quantitative determination of THC and its two major metabolites, 11‐hydroxy‐THC (OH‐THC) and 11‐nor‐9‐carboxy‐THC (THC‐COOH), as well as CBD and cannabinol (CBN) in whole blood and urine. The method employs automated on‐line solid phase extraction coupled to gas chromatography tandem mass spectrometry (GC–MS/MS). The method was fully validated according to guidelines of the Swiss Society of Legal Medicine (SGRM) and the Society of Toxicological and Forensic Chemistry (GTFCh). The method fulfilled the validation criteria regarding analytical limits, accuracy and precision, extraction efficacy, and sample stability. The limits of detection (LODs) in whole blood and urine were 0.15 ng/mL for THC, OH‐THC and CBD, 0.1 ng/mL for CBN, and 1.0 ng/mL for THC‐COOH. The limits of quantification (LOQ) in whole blood and urine were 0.3 ng/mL for THC, OH‐THC and CBD, 0.2 ng/mL for CBN, and 3.0 ng/mL for THC‐COOH. The fully validated and automated method allows sensitive and robust measurement of cannabinoids in whole blood and urine. Detection of CBD provides additional information regarding consumed products.

Three-dimensional (3D) crime scene documentation using 3D scanners and medical imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) are increasingly applied in forensic casework. Together with digital photography, these modalities enable comprehensive and non-invasive recording of forensically relevant information regarding injuries/pathologies inside the body and on its surface. Furthermore, it is possible to capture traces and items at crime scenes. Such digitally secured evidence has the potential to similarly increase case understanding by forensic experts and non-experts in court. Unlike photographs and 3D surface models, images from CT and MRI are not self-explanatory. Their interpretation and understanding requires radiological knowledge. Findings in tomography data must not only be revealed, but should also be jointly studied with all the 2D and 3D data available in order to clarify spatial interrelations and to optimally exploit the data at hand. This is technically challenging due to the heterogeneous data representations including volumetric data, polygonal 3D models, and images. This paper presents a novel computer-aided forensic toolbox providing tools to support the analysis, documentation, annotation, and illustration of forensic cases using heterogeneous digital data. Conjoint visualization of data from different modalities in their native form and efficient tools to visually extract and emphasize findings help experts to reveal unrecognized correlations and thereby enhance their case understanding. Moreover, the 3D case illustrations created for case analysis represent an efficient means to convey the insights gained from case analysis to forensic non-experts involved in court proceedings like jurists and laymen. The capability of the presented approach in the context of case analysis, its potential to speed up legal procedures and to ultimately enhance legal certainty is demonstrated by introducing a number of representative forensic cases.

During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progressively used as an important imaging tool for supporting autopsies. Additionally, previous works showed that deep learning applications yielded robust results for in vivo medical imaging interpretation. In this work, we propose a pipeline to identify fatal cerebral haemorrhage on three-dimensional PMCT data. We retrospectively selected 81 PMCT cases from the database of our institute, whereby 36 cases suffered from a fatal cerebral haemorrhage as confirmed by autopsy. The remaining 45 cases were considered as neurologically healthy. Based on these datasets, six machine learning classifiers (k-nearest neighbour, Gaussian naive Bayes, logistic regression, decision tree, linear discriminant analysis, and support vector machine) were executed and two deep learning models, namely a convolutional neural network (CNN) and a densely connected convolutional network (DenseNet), were trained. For all algorithms, 80% of the data was randomly selected for training and 20% for validation purposes and a five-fold cross-validation was executed. The best-performing classification algorithm for fatal cerebral haemorrhage was the artificial neural network CNN, which resulted in an accuracy of 0.94 for all folds. In the future, artificial neural network algorithms may be applied by forensic pathologists as a helpful computer-assisted diagnostics tool supporting PMCT-based evaluation of cause of death.