Contactless Visualization of Latent Fingerprints on Nonporous Curved Surfaces of Circular Cross Section—A Statistical Evaluation on the Materials as Plane Mirror

A new contactless technique for latent fingerprint visualization on nonporous curved surfaces of circular cross section was introduced by Low et al. (1). The technique utilizes a plane mirror to convey the light rays toward the inspected surfaces for latent fingerprint visualization. This research activity came up as an extension of the previous study which utilized an aluminum plate as the plane mirror to illuminate the inspected surfaces. Dulling spray was used to increase the diffuse component of the reflective aluminum plate. However, the amount of dulling spray will affect the uniformity of the illumination on the inspected curved surfaces. In this study, a study on the new materials for the plane mirror was carried out. Coated aluminum, opal, and ground glass diffusers were selected as the new materials. The performance of the new materials was compared to the aluminum based on the quality of the captured images on various nonporous cylindrical surfaces. A statistical approach known as randomized complete block design was used to design the experiment. The quality of the captured images was obtained using Spectral Image Validation and Verification. Two‐way analysis of variance and Fisher's least significant difference test were used to analyze the quality of the images. From the results of the statistical analysis, coated aluminum has the best performance compared to aluminum, opal, and ground glass diffusers.     

Body Morph: Feasibility Testing of an Interactive CD-ROM to Teach Young Adolescents about Puberty

The aim of this pilot study was to examine the feasibility of an interactive computer program among 34 sixth and seventh grade children and to assess the potential for knowledge acquisition about puberty. Based on a developmental self-esteem approach to teach children about their bodies, the Body Morph program was designed to maximize the educational experience by: (1) tailoring puberty information to boys or girls, and (2) creating a confidential experience. Preliminary results indicated that students significantly improved their knowledge about puberty after using the program. Satisfaction among boys and girls for this type of educational approach suggests that it is a compelling format from which to learn about puberty.This research project was funded with the support of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) 1R43DK061269-01A2 to develop an educational computer program to improve body image in middle schoolers.     

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 10¹⁰ magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.     

Capturing high-resolution digital images for use in forensic document examination

In the past, pattern disciplines within forensic science have periodically faced criticism due to their subjective and qualitative nature and the perceived absence of research evaluating and supporting the foundations of their practices. Recently, however, forensic scientists and researchers in the field of pattern evidence analysis have developed and published approaches that are more quantitative, objective, and data driven. This effort includes automation, algorithms, and measurement sciences, with the end goal of enabling conclusions to be informed by quantitative models. Before employing these tools, forensic evidence must be digitized in a way that adequately balances high-quality detail and content capture with minimal background noise imparted by the selected technique. While the current work describes the process of optimizing a method to digitize physical documentary evidence for use in semi-automated trash mark examinations, it could be applied to assist other disciplines where the digitization of physical items of evidence is prevalent. For trash mark examinations specifically, it was found that high-resolution photography provided optimal digital versions of evidentiary items when compared to high-resolution scanning.     

Additional predictions for forensic DNA phenotyping of externally visible characteristics using the ForenSeq and Imagen kits

Multiplex DNA typing methods using massively parallel sequencing can be used to predict externally visible characteristics (EVCs) in forensic DNA phenotyping through the analysis of single-nucleotide polymorphisms. The focus of EVC determination has focused on hair color, eye color, and skin tone as well as visible biogeographical ancestry features. In this study, we researched off-label applications beyond what is currently marketed by the manufacturer of the Verogen ForenSeq kit primer set B and Imagen primer set E SNP loci. We investigated additional EVC predictions by examining published genome wide sequencing studies and reported allele-specific gene expression and predictive values. We have identified 15 SNPs included in the ForenSeq kit panel and Imagen kits that have additional EVC prediction capabilities beyond what is published in the Verogen manuals. The additional EVCs that can be predicted include hair graying, ephelides hyperpigmented spots, dermatoheliosis, facial pigmented spots, standing height, pattern balding, helix-rolling ear morphology, hair shape, hair thickness, facial morphology, eyebrow thickness, sarcoidosis, obesity, vitiligo, and tanning propensity. The loci can be used to augment and refine phenotype predictions with software such as MetaHuman for missing persons, cold case, and historic case investigations.     

Lipophilicity of fentalogs: Comparison of experimental and computationally derived data

Although fentanyl and a small number of derivatives used for medical or veterinary procedures are well characterized, physiochemical properties have not been determined for many of the newer fentanyl analogs. Partition coefficients (Log P) were determined for 19 fentalogs using the shake-flask method and liquid chromatography–tandem mass spectrometry (LC–MS/MS). Experimentally determined partition coefficients were compared with computationally derived data using six independent software sources (ACD/LogP, LogKOWWIN v 1.69, miLogP 2.2, OsirisP, XLOGP 3.0, ALogPS 2.1). Fentalogs with a wide variety of structural modifications were intentionally selected, yielding Log P values ranging from 1.21 to 4.90. Comparison of experimental and computationally derived Log P values were highly correlated (R² 0.854–0.967). Overall, substructure-based modeling using fragmental methods or property-based topological approaches aligned more closely with experimentally determined Log P values. LC–MS/MS was also used to estimate pK_a values for fentalogs with no previously reported data. Lipophilicity and pK_a are important considerations for analytical detection and toxicological interpretation. In silico methods allow the determination of physicochemical information prior to certified reference materials being readily available for in vitro or in vivo studies. Computationally derived data can provide insight regarding physiochemical characteristics of future fentalogs and other classes of synthetic analogs that have yet to emerge.     

A comprehensive ATR-FTIR spectroscopic analysis for the identification and differentiation of lip balms

Lip balm may be encountered as physical evidence in cases involving sexual assaults, homicides, and kidnappings. Lip balm can be used as corroborative evidence by providing a potential link between the victim, accused, and the crime scene. For lip balms to be used as evidence, it is important to understand the diversity and their aging process under different conditions. Therefore, in this study, ATR-FTIR spectroscopy in conjunction with chemometric tools such as principal component analysis (PCA) and linear discriminant analysis (LDA) has been used for the objective identification and differentiation of 20 brands of lip balms. Moreover, lip balms on different substrates and wearing effects over time were also investigated. The results show that the PCA-LDA training accuracy was 92.5%, whereas the validation accuracy comes out to be 83.33%. A blind study using pristine samples was also performed which resulted in 80% PCA-LDA accuracy. PCA-LDA prediction of samples on various substrates showed a higher chemometric prediction accuracy for nonporous substrates (glass, plastic, and steel), than for porous substrates (cotton cloth, cotton swab stick, dry tissue paper, and white paper) for samples kept in room temperature and under sunlight for 15 days. The substrate study showed that the samples from various substrates could effectively generate respective spectra which can help in brand-level identification even after several days. The present method demonstrates a potential for lip balm samples to be used in forensic casework applications.     

The role of DNA in criminal indictments in Israel

In their investigations of criminal cases, law enforcement agencies rely heavily on forensic evidence. Numerous studies have examined the scientific and technological advancements of DNA testing, but little evidence exists on how the availability of DNA evidence influences prosecutors' decisions to move cases forward in the criminal justice system. We created a new database by juxtaposing data from the Forensics Division of the Israel Police, which recorded the presence (or not) of DNA profiles in criminal cases (n = 9862), and data on the indictment decision for each case (2008–2019). Rates of indictments are computed for each case, and trend lines are used to present variations in the rates of indictment decisions with and without DNA profiles. Approximately 15% of all criminal cases without DNA presented to the prosecutor's office are subsequently prosecuted, compared with nearly 55% of cases with DNA profiles. The presence of DNA evidence influences the prosecutor's decision to move a case forward in the criminal justice system. Utilizing a scientific approach to prosecute offenders is a welcome development; however, DNA evidence is not infallible, and caution must be exercised in regard to DNA's overuse in the legal system.     

The power of presentation: How attire,cosmetics, and posture impact the source credibility of women expert witnesses

Gender stereotypes may negatively affect perceptions of women professionals' credibility, including forensic experts. This study investigated the impact of behavior-based and appearance-based factors on women expert witness's credibility. Jury-eligible adults were shown one of 16 conditions depicting a woman expert which varied based on combinations of three primary independent variables: (1) attire, (2) cosmetic use, and (3) posture. Expert attractiveness and participants' sexist attitudes served as covariates. Results revealed that women experts were seen as marginally more credible when wearing a skirt suit with a closed posture stance than when wearing a pant suit with a closed posture. Secondary analyses indicated expert attractiveness and participant sexist attitudes accounted for the most variability in credibility scores. Credibility of women expert witnesses may be impacted by irrelevant peripheral cues. Findings can inform discussions aimed at mitigating extraneous factors that inadvertently undermine the reception of women expert witness testimony.