Optical Microscopy Applied to Forensics

Barcoding for diatoms in the Yangtze River from the morphological observation and 18S rDNA polymorphic analysis

The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines.

Forensic DNA analysis has revolutionized criminal investigations by providing valuable insights into identifying perpetrators, exonerating the innocent and unravelling mysteries of the past. Various molecular biology techniques, such as Short Tandem Repeat (STR) analysis, Single Nucleotide Polymorphism (SNP) profiling, mitochondrial DNA (mtDNA) sequencing, epigenetics and DNA methylation analysis, have played crucial roles in forensic investigations. While these techniques offer exceptional sensitivity and specificity, they also come with unique challenges. This review explores the current approaches of forensic DNA analysis, highlighting each technique. STR analysis, regarded as the gold standard in forensic DNA profiling, provides high discrimination power. However, it is limited in its ability to analyze degraded or mixed samples. On the other hand, SNP profiling offers advantages in analyzing highly degraded DNA samples but lacks the discriminatory power of STRs. Meanwhile, mtDNA analysis, particularly useful in cases involving compromised nuclear DNA, presents challenges due to its maternal inheritance pattern and lower discriminatory power. Furthermore, explore the exciting realm of epigenetics and the analysis of DNA methylation in forensic investigations. Epigenetic markers offer insights into gene expression patterns influenced by environmental factors, potentially aiding in the determination of tissue origin and chronological age estimation. DNA methylation analysis holds promise in forensic applications, providing additional layers of information for identity verification and tissue differentiation. Despite these advancements, several challenges persist in forensic DNA analysis, including the interpretation of complex DNA mixtures, standardization of methodologies, ethical considerations, and privacy concerns associated with the use of genetic information. Moreover, the integration of multi-omics data and machine learning approaches presents both opportunities and challenges in enhancing the accuracy and reliability of forensic DNA analysis. Looking ahead, future directions in molecular biology research for forensic DNA analysis involve the development of novel techniques with increased sensitivity, scalability, and robustness. By addressing these challenges and embracing emerging technologies, the field of forensic DNA analysis is poised for further advancements, offering enhanced capabilities in criminal justice and humanitarian efforts.

This chapter introduces a variety of optical microscopy techniques available to soft matter researchers. It deals with basic principles and parameters important to all optical microscopy modalities. The chapter describes traditional optical microscopy techniques such as bright field and polarizing microscopy (PM), and discusses state-of-the-art three-dimensional (3D) imaging techniques such as fluorescence microscopy and fluorescence confocal microscopy. It provides an extended account of Non Linear Optical (NLO) microscopy techniques, and explores an example of a particle-tracking technique that utilizes a pre-engineered point spread function (PSF) and its application in imaging. Since many experiments in soft matter systems require simultaneous noncontact optical manipulation and 3D imaging, integration of these techniques is immensely useful. Finally, the chapter reviews on optical microscopy and discusses future perspectives of its use in the study of soft matter systems.

This study presents a systematic review of studies related to murder and criminal investigation, policing, and implications for forensic science application in Nigeria. A literature search was done in the English language, using primary and secondary keywords; such as murder investigation, forensic science, policing, criminal investigation, standard operating procedures guiding murder investigation, and application of forensic science in Nigeria. Four academic databases selected for the study were: SAGE Online, Wiley Online Library, Science Direct, and Google Scholar. Following the inclusion and exclusion criteria earmarked for the study, 16 selected online published materials published between 2012 and 2022 met the predetermined criteria. These articles are either surveys, reviews, case studies, expert opinion materials, or theoretical articles. Central to the findings of these articles is that the application of forensic science in murder investigations in Nigeria is still suboptimal, accounting for lots of cold and unresolved murder cases across the country. In addition, there are other issues such as inadequate legislation governing forensic science application in Nigeria, lack of forensic skills and poor capacity development of Nigerian police, and non-adherence to forensic validated procedures during murder investigations. Gaps identified from these studies suggest a need for research focusing on the importance of suspect forensic profiling, a criminal investigative tool that can aid in narrowing down the suspect pool which has become a perennial problem in Nigeria's justice system as evidenced by the rising number of murder suspects across the country’s penitentiaries. This would make murder investigation more effective and efficient for criminal justice experts.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0788/a.php" alt="Hit counter" /></p>

Biochemical analysis of biological substances found at a crime scene is an important aspect of forensic investigations, where the objective is to determine the identification of crime victims and suspects/persons-of-interest. One particular biochemistry/molecular biology-based area of interest in forensic analysis is forensic serology; this area focuses on the complex task of gathering information regarding the type, age, ethnicity, and/or gender of the sample originator from the biological fluids (blood, saliva, etc.) found at a crime scene. In this review, we cover various studies involving biocomputing techniques used for the analysis of biological samples – namely blood – in different forensic applications. The studies covered include the differentiation of gender and ethnicity using a biocomputing system that produces a binary result, as well as the determination of blood spot age. Each of the applications showcased in this review produces sometimes ambiguous results that would normally pose major problems for a study, however, the addition of biocomputing logic provides an extra level of certainty and clarity. We hope to convey the advantages of utilizing biocomputing methods in various forms of forensic analysis.

Network forensics is the capture, recording, and analysis of network events in order to discover the source of security attacks or other problem incidents. Network forensics is a sub-branch of digital forensics. Network device forensics is also a sub-branch of network digital forensics. Forensic IT applications and examination of suspicious users` personal computers to be copied to all of the data alone is not sufficient for the determination of guilt or innocence. Users before the event and during the event is logged on the network traffic to be carried out is important. These records are necessary to preserve the security measures on the network devices, the devices required by the user on the network can not use, the internet user`s IP address and MAC address of the output to be significant, the user attempts to disrupt the functioning of the network is very important in minimizing. Users, network devices must not interfere with the records. In this study, as well as to facilitate network forensics investigations on network devices to improve network security configuration settings to be discussed.

In many applications of forensic image analysis, state-of-the-art results are nowadays achieved with machine learning methods. However, concerns about their reliability and opacity raise the question whether such methods can be used in criminal investigations. So far, this question of legal compliance has hardly been discussed, also because legal regulations for machine learning methods were not defined explicitly. To this end, the European Commission recently proposed the artificial intelligence (AI) act, a regulatory framework for the trustworthy use of AI. Under the draft AI act, high-risk AI systems for use in law enforcement are permitted but subject to compliance with mandatory requirements. In this paper, we review why the use of machine learning in forensic image analysis is classified as high-risk. We then summarize the mandatory requirements for high-risk AI systems and discuss these requirements in light of two forensic applications, license plate recognition and deep fake detection. The goal of this paper is to raise awareness of the upcoming legal requirements and to point out avenues for future research.

The clinical and rehabilitation value of gait analysis is remarkable and indisputable and poised to grow as technological advancements unfold. This article aims to shed light on the advances in how gait is assessed, enabling those who have suffered an injury impairing their motor skills to be diagnosed more accurately and efficiently as well as to compare the hallmarks of rehabilitative and forensic gait analysis. The authors have conducted an analysis of relevant papers (published between 1967 and 2020) from a medicolegal perspective, cited in PubMed, MEDLINE, Cochrane Library, EMBASE, and available recommendations for the legal application of such techniques. Moreover, considering the use of gait analysis as a forensic tool, this study broadens the scope of research by including search engines, legal databases, and court filings (DeJure, Lexis Nexis, Justia) between 2000 and 2022. The instrumental assessment of movement (Gait Analysis) has come to constitute an essential analytical tool for the biomedical sector to objectively and accurately assess human movement and posture. The article is also aimed at elaborating differences and similarities between clinical and forensic gait analysis. When it comes to the forensic applicability of gait analysis and its evidentiary value, however, there is a pressing need for a review of its scientific basis. Therefore, it is necessary to conduct a thorough evaluation of its use in legal practice, as stressed in scientific literature and surveys. It is of utmost importance to highlight the procedural and assessment standards currently applied to forensic gait analysis, to evaluate its strengths and weaknesses, and to achieve standardized guidelines based on broad scientific consensus.

The National Institute of Standards and Technology (NIST) has coordinated numerous evaluations of speech and language technology over the past two decades. We discuss here evaluations in three general areas of speech processing that have security and forensic applications: large vocabulary conversational speech recognition (LVCSR), speaker recognition, and language recognition.

I. Introducing the Problem Here is a problem that arises in analyzing rehabilitation and life care plans, but can occur as well in other forensic economic applications and in related fields of financial analysis. McClenahan (2003, pp. 225-28), for example, explores a variant of the problem confronted by actuaries. In forensic economics the problem occurs whenever cost or damage estimate ranges have been established for several distinct categories and those range estimates subsequently must be aggregated. If there is less than perfect positive correlation in the random variables describing the various categories of damage or cost estimates, then the occurrence of extreme values (i.e., those near the minimum or maximum) in the aggregate range will be less likely (and indeed may be far less likely) than the occurrence of extreme values in any of the component categories. This article offers a method for truncating the reported aggregate range of values in recognition of the unlikelihood of extreme values. Consider the task facing the forensic economist in analyzing a life care plan. Certain categories within the plan may feature cost ranges rather than specific dollar amounts. After performing his or her ordinary duty of projecting the cost estimates forward in time (taking due account of such factors as the frequency and duration of treatments as presented by the life care planner), then discounting to present value, a forensic economist may wish ultimately to aggregate across the categories and report a lump-sum total in the form of an overall damage range. If the aggregate range is reported as merely the sum of all category minimums at the low end and the sum of all category maximums at the high end, then the aggregation procedure and its results are suspect (probabilistically speaking). A number of criteria ought to be satisfied by an aggregation procedure if it is to be deemed appropriate for practicing forensic economists. The procedure should be methodologically sound and readily applicable, yielding results that are realistic and capable of correct interpretation by subsequent users in the litigation process (such as other economists, negotiating attorneys, judges, and jury members). In an effort to identify the most appropriate procedure for ag

A validation study was performed on the new STR (Short tandem repeat) multiplex PCR Kit for human identification: AmpFℓSTR® Identifiler® Plus PCR Amplification Kit (IDPlus) released from Applied Biosystems. IDPlus contains the same fifteen loci as AmpFℓSTR® Identifiler® PCR Amplification Kit (ID) which is currently widely utilized in forensic DNA analysis. Consequently, IDPlus has the same discrimination ability as ID. According to the manufacturer, this kit has higher sensitivity, more resistant to PCR inhibitors and less background noise in electrophoresis. Thus, IDPlus was expected to be applied to forensic samples difficult to DNA profiling using ID. An applicability of IDPlus to forensic STR analysis was evaluated. Our study confirmed that IDPlus was more sensitive than ID. IDPlus has about 1.4 times to 1.5 times higher peak than ID when using the same PCR cycle number (28) for both kits. While ID has only one PCR protocol, IDPlus has two PCR protocols differing in cycle number: 28 and 29. To clarify the basic ability we compared heterozygous peak height ratio, stutter, intra-color balance, inter-locus balance among IDPlus to both protocols and ID from 94 individual DNA samples. Lower peak height ratio and the larger standard deviation in heterozygous samples were observed when using IDPlus 29-cycle compared to IDPlus 28-cycle. There was no significant difference between ID and IDPlus 28-cycle and between ID and IDPlus 29-cycle about the peak height ratio. Stutter ratios significantly were different in some loci between ID and IDPlus. Although Applied Biosystems supplies one stutter ratio to filter peaks for each locus to IDPlus in spite of having two PCR protocols, it was confirmed that the same stutter ratio could proper filter out stutter peaks for both cycle numbers. With regard to intra-color balance and inter-locus balance, IDPlus 28-cycle tended to be the highest in balance and ID tended to be the lowest in balance across all the samples and colors. Our validation study indicates IDPlus kit is useful in forensic applications, especially for analysis of trace DNA samples when using 29 cycles. However, forensic biologists must be cautious in interpreting DNA profiles obtained using IDPlus 29 cycles, because peak balance in heterozygous samples tended to be imbalanced when using 29 cycles compared to 28 and ID.

Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

Nanotechnology has a great influence on modern technology, just like the development of DNA profiling on biotechnology in the past 50 years. Because of applying knowledge and techniques from natural science, forensic science aims to identify, individualize and evaluate evidence. Evidence will then be used to reconstruct crime scenes, guide investigations and bring criminals to justice. Nanotechnology has been applied towards these purposes. Among the various nanotechnologies, nano‐analysis is most commonly seen in forensic science with instrumentations including transmission electron microscope (TEM), scanning electron microscope (SEM), atomic force microscope (AFM) and Raman microspectroscopy (Micro‐Raman). This paper will introduce the principles of nanotechnology, instrumentations, and known forensic applications. In addition, the toxicity of nanomaterials and future prospects will be discussed.

Background As a new kind of diallelic genetic marker, insertion/deletion (InDel) polymorphisms have recently been used in forensic science. However, there are relatively few studies on the forensic evaluation of InDel genetic polymorphisms from different populations. Aim The aim of the present work is to assess the genetic polymorphism and forensic applicability of 57 InDels from the Yi ethnic group and explore the genetic background of this group. Subjects and methods A total sample of 122 unrelated individuals of Yi group from the Yunnan province were genotyped by the AGCU indel 60 Kit. Multiplex population genetic analyses on the same 57 InDels were carried out among the Yunnan Yi group and 29 reference populations. Results The average allele frequency of these loci in the Yi ethnic group was 0.485. Heterozygosity, polymorphism information content, and the power of discrimination were 0.477, 0.362, and 0.612, respectively. The combined power of discrimination and the combined power of exclusion reached to 0.99999999999999999669 and 0.999962965, respectively. The results showed that 57 InDels polymorphisms have high genetic polymorphisms in the Yi ethnic group. Conclusions The 57 InDels could be used for forensic individual identification, paternity testing, and intercontinental population discrimination, with the potential for use in biogeographic ancestry inference.