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Bayes’ theorem is regarded as the most basic theory in much of the literature that discusses forensic science comparison methods. This paper analyzes the problems faced by using this theorem in forensic science, especially the significance of different representations in the theorem and the logical reasoning problem, and argues that Bayes’ theorem cannot provide meaningful support for the conclusion of feature comparison, only as a calculation tool. If the forensic science comparison method is based on Bayes’ theorem, the theoretical basis is not solid. At a time when forensic science methods are facing change, it is even more necessary to get to the bottom of the problem in theory.
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Fingerprints represent one of the most common types of physical evidence found at crime scene, characterized by their uniqueness to each individual, lifelong stability, capacity to leave traces upon contact, and ability to identify individuals. Recently, aggregation-induced emission (AIE) materials, particularly tetraphenylethylene, have garnered increasing research interest in forensic science due to their potential applications. Tetraphenylethylene can effectively develop latent fingerprints at crime scenes by selectively adsorbing endogenous fingerprint secretions. This study aimed to determine the optimal ratio and excitation wavelength of tetraphenylethylene-incorporated magnetic powder for fingerprint development. We evaluated its efficacy in developing fingerprints with varying substance compositions, on diverse surfaces, in continuous impressions, and at different remaining time, while comparing it to AIE magnetic fingerprint powder and green emitting phosphor. The results indicated that a mass ratio of tetraphenylethylene to iron powder at 1:70, combined with at 365 nm ultraviolet excitation, yielded the most favorable fingerprint development outcomes. This formulation exhibited high fluorescence intensity while maintaining low background powder deposition. Tetraphenylethylene magnetic powder demonstrated robust performance in revealing both sweat and sebaceous fingerprints, demonstrating versatility across various surfaces. In case of continuous fingerprinting, it clearly visualized the tenth sweat fingerprints impression. Additionally, the powder showed potential in developing aged fingerprints, successfully revealing sweat fingerprints up to 15 days old and sebaceous fingerprints up to 30 days old. In summary, tetraphenylethylene magnetic powder has demonstrated exceptional performance and broad application potential in fingerprint development.
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In this study, the methods for the identification of propoxate and isopropoxate in seized e-cigarette oil by gas chromatography-mass spectrometry (GC-MS) and ultra performance liquid chromatography-quadrupole/orbitrap high resolution mass spectrometry (UPLC-Q/Orbitrap HRMS) were established. Based on established methods, two unknown components were identified as propoxate and isopropoxate through structural analysis by MS and the characteristic fragment ions information acquired by EI-MS and Full MS/dd MS2 were similar to the known etomidate. The detection limits of propoxate and isopropoxate were both 0.1μg/mL for GC-MS and 0.1ng/mL for UPLC-Q/Orbitrap HRMS. This method provides a quick and simple approach for the identification of new carboxylate drugs with imidazole ring in the absence of standard substances and it could be applied to the analysis of actual cases.
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This study investigates the detection capability of whole genome sequencing (WGS) technology for varying DNA input levels, aiming to establish a novel technical approach for SNP-based genealogical inference in forensic biological samples. On the SalusPro sequencing platform (China), WGS with a depth of 25× were performed on samples containing 0.5, 0.2, and 0.05 ng of DNA respectively. From these data, 645 199 autosomal SNP loci (Wegene GSA chip) were extracted and subjected to quality control. Genetic relationships were predicted by calculating the total length of identity by descent (IBD) fragments between individuals using IBD algorithms. The results demonstrated that when DNA input was ≥0.2 ng, the locus detection rate and genotype concordance rate exceeded 95% and 99%, respectively. The IBD fragment lengths showed no significant difference compared to the standard samples (P>0.05), with an average confidence interval accuracy of 91.40% for 1st- to 7th-degree relationships. In contrast, at 0.05 ng DNA input, the locus detection rate and genotype concordance rate dropped below 80% and 97%, respectively. The IBD fragment lengths and confidence interval accuracy were significantly lower than those of the standard samples (P=0.004), rendering this input level unsuitable for reliable genealogical inference.
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A total of 328 soil samples were collected from Guangling County and classified into habitat-based categories, including vegetable soil, wetland soil, park soil, roadside soil, and hilly soil. The study aimed to investigate the distribution characteristics of bacterial community diversity across different land-use types at the county scale, as well as the geographic traceability of soil of unknown origin. High-throughput sequencing was conducted using the Illumina MiSeq PE250 platform, and the microbiome 16S rRNA gene amplicon sequencing data were analyzed using the Qiime 2 pipeline. Additionally, physicochemical indicators of 14 soil samples were determined. The soil bacterial community structure profiling demonstrated that, the relative abundance of dominant bacterial groups varied across different habitats. Principal coordinate analysis (PCoA) results indicated that intra-group differences among soil sample sites within the same subgroups were small, while inter-group differences among soil type sample sites from different subgroups were significant. Under this categorization, pH, TN, WC, IC, TP, Al, Mn, and Pb were identified as important environmental factors driving changes in soil microbial community composition in the region. Finally, a random forest model was established, and its parameters were optimized. Using the collected samples as a training set, the model achieved the highest prediction accuracy when utilizing soil microbial data, with an accuracy of 83.58%.
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Detection of Bitcoin illegal transactions is a significant challenge in blockchain technology, particularly when faced with complex transaction patterns and issues of class imbalance. This paper proposes a feature-enhanced graph neural network approach aimed at improving the accuracy of Bitcoin illegal transaction detection. First, the BERT model is employed to enhance transaction features, leveraging its powerful contextual modeling capabilities to extract more expressive features. Second, an innovative model architecture is designed, combining LSTM with a dual-channel ONGNNConv, where the latter passes its output to an attention mechanism for weighted aggregation, thereby enabling more effective capture of latent patterns in the transaction network. Finally, to address the class imbalance problem, a weighted binary cross-entropy loss is incorporated into the loss function to enhance the detection of illegal transactions. Experimental results demonstrate that the proposed method outperforms existing baseline models across multiple evaluation metrics, validating its effectiveness and robustness in Bitcoin illegal transaction detection.
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To evaluate the application potential of the one-step mRNA-PCR fluorescence multiplex amplification detection technology for identifying body fluid stains in case samples, a total of 89 samples suspected to be blood, saliva, semen, and vaginal secretions were collected. These samples were analyzed collaboratively with two forensic DNA laboratories lacking prior RNA operation experience. The laboratories performed total RNA extraction, one-step RT-PCR amplification, and capillary electrophoresis detection using different experimental instruments. The results demonstrated that three housekeeping genes (GAPDH, ACTB, and PRL19) were successfully detected in all 89 case samples. Additionally, specific markers for each body fluid were identified: HBB and HBA for peripheral blood; MMP7 and MMP10 for menstrual blood; STATH and HTN3 for saliva; PRM2 and SEMG1 for semen; and CYP2B7P1 and HBD1 for vaginal fluid. Notably, each participating DNA laboratory was able to independently complete the detection of RNA markers in the case samples, and parallel testing of the same samples across different laboratories yielded consistent results. This study provides robust evidence supporting the application of the one-step mRNA-PCR fluorescence multiplex amplification detection system for the source identification of body fluid stains in forensic case samples.
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This study aimed to establish a gas chromatography-mass spectrometry (GC-MS) method for detecting the components in condom lubricants, and to identify different brands of condom lubricants through statistical analysis. Ethyl acetate was used as extraction solvent, and the components in 17 types of condom lubricants from 6 commercial brands were detected by GC-MS. A statistical classification model for condom lubricants was established. A total of 48 components were detected, with a relative peak area of inter-day precision of 0.14% to 13.94%, intra-day precision of 0.10% to 14.14%, and repeatability of 0.13% to 14.79%. The accuracy of discriminant analysis is 87.4%. The developed GC-MS detection method features simple operation, rapid analysis, comprehensive substances extraction, high precision, and good repeatability. The successful classification of 17 condom lubricants provided a reliable technical reference and theoretical basis for the detection and traceability of condoms in sexual assault cases investigations.
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Metonitazene has appeared on the illicit drug market, which is a new type of synthetic opioid with an analgesic effect stronger than morphine and a toxicity similar to traditional opioids. This study aimed to develop a rapid method for the detection of metonitazene in human hair by UPLC-MS/MS, to provide basic data on the addictive and harmful effects of metonitazene, and accumulate the evidence of metonitazene abuse. The hair samples were ground and extracted by methanol and then filtered by microporous membrane. The filtrate was analyzed on UPLC with an Agilent ZORBAX Eclipse Plus C18 (100 mm×2.1 mm×1.8 μm) column, and the gradient elution was performed with aqueous containing 0.1% formic acid and acetonitrile solution containing 0.1% formic acid, at a flow rate of 0.4 mL/min. The positive ion scan mode with multiple reaction monitoring (MRM) was used for the MS detection. The results demonstrated that the extraction efficiency was the highest when methanol as extraction solvent, and the chromatographic peaks of metonitazene in the hair samples were good shape and symmetry. The precursor ion of metonitazene was m/z 383.2, and the fragment ions were m/z 100.0、120.9、72.0、44.0. The linear relationship was good in the range of 0.01、1.0 ng/mg, R2> 0.998. The limits of detection (LOD) were 0.005 ng/mg and the limits of quantification (LOQ) were 0.01 ng/mg. Besides, the recoveries were 97.3%、111.9%, the matrix effects were 101.5%、113.3%, and the relative standard deviations (RSD) of the intra-day and inter-day precision were both less than 10%. The method has the advantages of simple operation, rapid detection, high sensitivity, high recovery, and good reproducibility. The method is proved reliability on real samples, which can meet the requirements of metonitazene detection in forensic identification.
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In forensic document examination, investigations of altered documents typically focus on variations in printing characteristics and font types, with limited attention to discrepancies arising from different versions of the same font. In this study, 16 distinct versions of Songti typeface files were collected and conducted a comparative analysis of their typographic differences within the framework of 3 500 commonly used Chinese characters. The methodology comprised two phases: (1) Digital characterization through Python-based image rendering and differential comparison of font files, establishing a computational approach for version discrimination; (2) Validation using the VSC8000 document inspection system to perform superimposition analysis on printed specimens containing selected characters. Comparative analysis revealed significant typographic variations between Version 5 and earlier Songti releases. When cross-referenced with version release timelines, these discrepancies enable the detection of temporal attributes of the documents, thereby offering innovative methodologies for criminal investigations.
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This study focuses on two typical types of writing robots, namely linear joint-type and articulated joint-type writing robots. The Orbotech 8000 and RTI Hyper-Vision Intelligent Imaging System were used to analyze the similarities and differences in pen stroke characteristics between those two types of robots and human writers in the context of signature writing. The experiment found that the imitated signatures by linear writing robots significantly differ from human writers in terms of pen pressure uniformity, variations in pen trace angles, and flexibility in ink width. Articulated joint writing robots show a higher degree of similarity to human writers in these aspects. However, neither of the two writing robots produced any sister lines in their imitated signatures when writing vertically; linear joint writing robot produce sister lines when writing at an angle, but the depth ratio of these sister lines is fixed, indicating a constant pen-paper angle during writing. In contrast, human-written signatures exhibit multiple instances of sister lines with varying depth ratios across different strokes, indicating a variable pen-paper angle during writing. The results demonstrate that sister line characteristics and pen-paper angle features are effective for identifying the handwriting of writing robots.
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This paper mainly introduces the research progress of deep learning technology in fingerprint information since 2018, including fingerprint image processing, fingerprint recognition, fake fingerprint detection technology, fingerprint dataset generation, and multimodal biometric recognition involving fingerprints. Fingerprint image processing includes fingerprint image segmentation, fingerprint image enhancement, and fingerprint image correction. Fingerprint recognition includes deep learning network-based fingerprint recognition, contactless fingerprint recognition, and fingerprint recognition based on three-level features of fingerprints. With the continuous deepening of research on deep learning technology, the study of fingerprint recognition algorithms using deep learning technology on large-scale fingerprint datasets will still be a research direction in the future. Moreover, using deep learning techniques to detect and identify fake fingerprints will be another direction for future research. The dataset of fake fingerprints needs to be dynamically updated and expanded in order to improve the ability of deep learning networks to detect and recognize fake fingerprints. In addition, one of the future research directions is how fingerprints, as an important biometric feature of the human body, can be integrated into multimodal biometric recognition based on large language models to achieve better identity recognition results.
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Sudden cardiac death cases are common in forensic pathology practice. The diagnosis of death caused by early ischemic heart disease is difficult due to the lack of specific pathologic changes. Postmortem biochemical examination can provide objective evidence for the postmortem diagnosis of cause of death. It has been proved that postmortem biochemical analysis of markers of sudden cardiac death, such as the classical markers NT-proBNP, CK-MB, cTnT, cTnI, as well as the newer markers sLOX-1, H-FABP, and sST2 reported in recent studies, have auxiliary diagnostic significance and practical application potential in forensic identification of ischemic heart disease. Based on forensic medicine and clinical research, this paper summarizes the research reports on both classic and new markers, and discusses the establishment of a multi-marker diagnostic system to facilitate its application in forensic pathological practice.
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Over the last few decades, many new discoveries at the neurobiological, cellular, and molecular levels have helped researchers to further understand chronobiology. The issue of time inference in forensic medicine is always an important problem that needs to be solved, as it is closely related to the investigation of criminal cases. Current research indicates that it is possible to infer the time of cases within a day based on the rhythmic expression of biomarkers. Therefore, utilizing the circadian clocks could improve the accuracy of estimating the postmortem interval, wound age, and the deposition time of body fluid stains within a day. In this paper, the mechanisms of circadian clocks and their application in forensic medicine were reviewed, in order to provide new ideas for the research of time prediction in forensic medicine.
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In criminal cases, individuals lacking or with limited civil capacity are not often promptly aware of being raped and pregnant, causing key evidence lost over time. DNA identification of embryonic tissue in these cases is vital for investigations and court proceedings. This paper examines the current state and recent advancements in forensic DNA analysis, considering the developmental stage of the embryo, methods for terminating pregnancies, and the preservation and extraction of embryonic tissue DNA. It also explores the selection and application of molecular markers in identifying DNA from challenging miscarriage samples. The paper discusses strategies and methodologies for DNA analysis of embryonic tissue in pregnancies resulting from rape and the potential applications of various molecular genetic markers in such analyses.
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In the process of fingerprint identification, sometimes the number of regional features is insufficient to obtain a definite positive or negative result. Referring to other fingerprint traces on site or case information for auxiliary comparison and judgment can increase the reliability of the identification result. Through enumeration and case analysis methods, this paper discusses the method of comprehensively utilizing linked fingers, different regions of the same finger, finger joint prints or palm prints, as well as case information such as geography, age, and gender as references to determine whether the examined fingerprint matches the sample fingerprint. This approach can effectively solve some difficult fingerprint identification problems and improve the accuracy and reliability.
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This paper investigates the application effect of FastSTR, a domestic forensic DNA data analysis software, in forensic DNA testing. The FastSTR software was used to analyze STR detection data from different genetic analyzers, and the results were compared with those obtained from GeneMapper® ID-X Software, to assess its application performance. The results indicate that FastSTR is fully compatible with the data files of ABI series genetic analyzers. The STR detection data analysis results from different genetic instruments using FastSTR shows no significant difference from those obtained with the GeneMapper® ID-X. As a domestically developed STR data analysis software, FastSTR’s core functionalities encompass those of similar imported analysis software. Its data analysis results are also accurate and reliable, indicating that it can replace imported software for the analysis and deep processing of DNA data collected and preprocessed by the platform. FastSTR has achieved automation in the forensic DNA testing process and can fully meet the current forensic DNA data analysis requirements.
