OBJECTIVE: Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD.
METHODS: This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions.
RESULTS: The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations.
CONCLUSIONS: Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.

Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.

The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint\'\' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research has offered new methodological perspectives through the automatisation of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identification done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques\' face detector (Faster-RCNN model), reaching an accuracy of 82.2% and (ii) the creation of an individual recogniser for the Kōjima Island macaque population (YOLOv8n model), reaching an accuracy of 83%. We also created a Kōjima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.

Stoat (Mustela erminea) and weasel (M. nivalis) are hard to monitor as they are elusive of nature and leave few identifying marks in their surroundings. Stoat and weasel are both fully protected in Denmark and are thought to be widely distributed throughout the country. Despite this stoat and weasel were listed on the Danish Red List as Near Threatened in 2019, as their densities and population trends are unknown. Using a modified novel camera trapping device, the Double-Mostela, a wooden box comprising a tracking tunnel and two camera traps, we attempted to obtain density estimates based on identification of individual stoats and weasels. We deployed camera traps both inside Double-Mostela traps and externally in three different study areas in northern Zealand, Denmark, and tested commercial, American scent-based lures to attract stoat and weasel. We obtained very low seasonal trapping rates of weasel in two study areas, but in one study area, we obtained a seasonal trapping rate of stoat larger compared to another study using the Mostela. In one study area, both species were absent. We observed no effect of scent-based lures in attracting small mustelids compared to non-bait traps. Potential reasons behind low capture rates of weasel and stoat are suboptimal habitat placement and timing of deployment of the Double-Mostelas, land-use changes over the last 200 years, predation from larger predators, as well as unintended secondary poisoning with rodenticides. Due to the scarcity of weasel and stoat captures, we were unable to make density estimates based on identification of individuals; however, we identified potential features that could be used for identification and density estimates with more captures.

The Bangkaew dog is an indigenous dog breed in the Phitsanulok province of Thailand. This breed is recognized by the Fédération Cynologique Internationale (FCI), a global canine organization. The unique traits of the Bangkaew breed lead to purebred selection for breeding, while only their traits and pedigree from parental history are recorded. Determination of the risk of inbreeding depression and the origin of unknown DNA profiles is essential due to the challenges in predicting puppy characteristics, which are crucial for breed management and conservation.
This study aimed to emphasize that current allelic frequency data for the Bangkaew dog breed must be considered for precise individual identification.
Approximately 82 Bangkaew dogs from various Thai localities were studied using 15 microsatellite markers for genotypic monitoring and individual identification. Maternal genetic inheritance was assessed via mtDNA D-loop analysis.
The results revealed high genetic diversity in the Bangkaew breed, indicating low potential for inbreeding. We also found that using a 15 loci microsatellite panel was effective for the identification of Bangkaew dogs. The optimized 10 loci microsatellite genotyping panel developed in this study presents improved identification testing efficiency, promoting both time- and cost-effectiveness.
Analysis of microsatellite DNA markers in Bangkaew dogs using an optimized panel of 10 loci selected from 15 loci effectively facilitated individual identification. This approach not only enhances time and cost efficiency, but also provides accurate allelic frequency estimates, which are crucial for the realistic evaluation of DNA evidence.

Domestic dogs are helpers in outdoor human work and companions for families; thus, individual canine identification and parentage testing are crucial in certain fields, including forensics and breeding programs. In this study, a six-dye fluorescent labeling multiplex amplification system containing 29 canine short tandem repeats (STRs) and the sex-determining marker DAmel was developed. The system was called the Tronfo Canine 30-plex STR Kit and was further validated according to the Scientific Working Group on DNA Analysis Methods and the Organization of Scientific Area Committees for Wildlife Forensics guidelines, including tests for PCR conditions, precision, species specificity, sensitivity, stability, repeatability and reproducibility, a population study, and a study of 16 paternity test cases. The results indicated that the novel canine STR assay was accurate, specific, reproducible, stable, and robust. Complete profiles were obtained with 31.25 pg of canine DNA. Additionally, 500 unrelated canine individuals were investigated using this novel system, and the combined power of discrimination and exclusion values were 0.999999999999999999 and 0.999996451039850, respectively. These results suggest that the Tronfo Canine 30-plex STR Kit is highly polymorphic, informative, and suitable for individual canine identification and parentage testing.

This study aimed to investigate the genetic polymorphisms and population characteristics of Chinese Mongolian group from northwest China (NCM) through a self-developed panel including 43 autosomal insertion/deletion (A-InDel) polymorphism genetic markers. Herein, 288 unrelated healthy individuals from the NCM group were employed to obtain the genetic data of 43 A-InDels through multiplex PCR amplification and InDel genotyping using capillary electrophoresis platform. In addition, multiplex population genetic analyses were performed between the NCM group and 27 reference populations. There were no deviations at 43 loci from Hardy-Weinberg equilibrium in the NCM group. The observed heterozygosity (Ho) values ranged from 0.312 8 to 0.559 2, and the combined power of discrimination (CPD) and cumulative probability of exclusion (CPE) values in the NCM group were 0.999 999 999 999 999 998 77 and 0.999 814, respectively. The forensic parameter values indicated that this panel was polymorphic and informative in the NCM group and could be used as an effective tool for forensic personal identification. Furthermore, the results of pairwise genetic distances, principal component analysis, multidimensional scaling analysis, phylogenetic tree construction, and admixture analysis among the NCM group and 27 reference populations revealed that there were closer genetic relationships between the NCM group and East Asian populations, especially Chinese Hui group (CHH) from the northwest China, which is consistent with the geographical location. These present findings contributed to the ongoing genetic explorations and insights into the genetic architecture of the NCM group.

In recent years, with the continuous progress of DNA extraction and detection technology, cell-free DNA(cfDNA)has been widely used in the life science field, and its potential application value in forensic identification is becoming more and more obvious. This paper reviews the concept, formation mechanism, and classification of cfDNA, etc., and describes the latest research progress of cfDNA in personal identification of crime scene touch DNA samples and non-invasive prenatal paternity testing (NIPPT). Meanwhile, this paper summarizes the potential application of cfDNA in injury inference, and discusses the advantages and disadvantages of common cfDNA analysis methods and techniques, and its application prospects, to provide a new idea for the wide application of cfDNA in the field of forensic science.
近年来，随着DNA提取和检测技术的不断进步，游离DNA（cell-free DNA，cfDNA）已经在生命科学领域得到了广泛应用，在法医学鉴定领域中的潜在应用价值也越来越明显。本文回顾了cfDNA概念、形成机制与分类等，并阐述了cfDNA在法医学现场接触检材的个体识别和无创产前亲缘关系鉴定应用中的最新研究进展，同时总结了cfDNA在损伤推断中的应用潜力，并探讨了常用cfDNA分析方法和技术的优缺点及应用展望，为cfDNA在法医学领域的广泛应用提供新思路。.

Biological evidence is relatively common evidence in criminal cases, and it has strong probative power because it carries DNA information for individual identification. At the scene of fire-related cases, the complex thermal environment, the escape of trapped people, the firefighting and rescue operations, and the deliberate destruction of criminal suspects will all affect the biological evidence in the fire scene. Scholars at home and abroad have explored and studied the effectiveness of biological evidence identification in fire scenes, and found that the blood stains, semen stains, bones, etc. are the main biological evidence which can be easily recovered with DNA in fire scenes. In order to analyze the research status and development trend of biological evidence in fire scenes, this paper systematically sorts out the relevant research, mainly including the soot removal technology, appearance method of typical biological evidence, and possibility of identifying other biological evidence. This paper also prospects the next step of research direction, in order to provide reference for the identification of biological evidence and improve the value of biological evidence in fire scenes.
生物物证是刑事案件中的常见证据，因其携带可供个体身份识别的DNA信息而有较强的证明力。在涉火案件现场，复杂的热环境、被困人员逃生、灭火救援行动，以及犯罪嫌疑人的故意破坏等，都会对火场生物物证造成影响。国内外学者对火场生物物证识别的有效性进行了探索和研究，发现在火场中能够有效回收DNA的生物物证主要有血迹、精斑、骨骼等。为了分析火场生物物证的研究现状和发展趋势，本文对相关研究进行了系统梳理，主要包括火场典型生物物证的烟尘清除技术、显现方法和识别其他生物物证的可能性，并对下一步研究方向进行了展望，以期为火场生物物证的识别、提升火场生物物证的证据价值提供参考。.