Targeting cell-derived markers to improve the detection of invisible biological traces for the purpose of genetic-based criminal identification.

At a crime scene, investigators are faced with a multitude of traces. Among them, biological traces are of primary interest for the rapid genetic-based identification of individuals. "Touch DNA" consists of invisible biological traces left by the simple contact of a person's skin with objects. To date, these traces remain undetectable with the current methods available in the field. This study proposes a proof-of-concept for the original detection of touch DNA by targeting cell-derived fragments in addition to DNA. More specifically, adhesive-structure proteins (laminin, keratin) as well as carbohydrate patterns (mannose, galactose) have been detected with keratinocyte cells derived from a skin and fingermark touch-DNA model over two months in outdoor conditions. Better still, this combinatory detection strategy is compatible with DNA profiling. This proof-of-concept work paves the way for the optimization of tools that can detect touch DNA, which remains a real challenge in helping investigators and the delivery of justice.

A mobile DNA laboratory for forensic science adapted to coronavirus SARS-CoV-2 diagnosis.

The Forensic Science Institute of the French "Gendarmerie Nationale" (IRCGN™) developed in 2015 an ISO 17025 certified mobile DNA laboratory for genetic analyses. This Mobil'DNA laboratory is a fully autonomous and adaptable mobile laboratory to perform genetic analyses in the context of crime scenes, terrorism attacks or disasters. To support the hospital task force in Paris during the peak of the COVID-19 epidemic, we adapted this mobile genetic laboratory to perform high-throughput molecular screening for coronavirus SARS-CoV-2 by real-time PCR. We describe the adaptation of this Mobil'DNA lab to assist in Coronavirus SARS-CoV-2 diagnosis.

SCOPE11 Method for Estimating Aircraft Black Carbon Mass and Particle Number Emissions.

Black carbon (BC) emissions from aircraft engines lead to an increase in the atmospheric burden of fine particulate matter (PM2.5). Exposure to PM2.5 from sources, including aviation, is associated with an increased risk of premature mortality, and BC suspended in the atmosphere has a warming impact on the climate. BC particles emitted from aircraft also serve as nuclei for contrail ice particles, which are a major component of aviation's climate impact. To facilitate the evaluation of these impacts, we have developed a method to estimate BC mass and number emissions at the engine exit plane, referred to as the Smoke Correlation for Particle Emissions-CAEP11 (SCOPE11). We use a data set consisting of SN-BC mass concentration pairs, collected using certification-compliant measurement systems, to develop a new relationship between smoke number (SN) and BC mass concentration. In addition, we use a complementary data set to estimate measurement system loss correction factors and particle geometric mean diameters to estimate BC number emissions at the engine exit plane. Using this method, we estimate global BC emissions from aircraft landing and takeoff (LTO) operations for 2015 to be 0.74 Gg/year (95% CI = 0.64-0.84) and 2.85 × 1025 particles/year (95% CI = 1.86-4.49 × 1025).

The systematic profiling of false identity documents: method validation and performance evaluation using seizures known to originate from common and different sources.

False identity documents constitute a potential powerful source of forensic intelligence because they are essential elements of transnational crime and provide cover for organized crime. In previous work, a systematic profiling method using false documents' visual features has been built within a forensic intelligence model. In the current study, the comparison process and metrics lying at the heart of this profiling method are described and evaluated. This evaluation takes advantage of 347 false identity documents of four different types seized in two countries whose sources were known to be common or different (following police investigations and dismantling of counterfeit factories). Intra-source and inter-sources variations were evaluated through the computation of more than 7500 similarity scores. The profiling method could thus be validated and its performance assessed using two complementary approaches to measuring type I and type II error rates: a binary classification and the computation of likelihood ratios. Very low error rates were measured across the four document types, demonstrating the validity and robustness of the method to link documents to a common source or to differentiate them. These results pave the way for an operational implementation of a systematic profiling process integrated in a developed forensic intelligence model.