DNA barcoding is commonly used for species identification. Despite this, there has not been a comprehensive assessment of the utility of DNA barcoding in crayfishes (Decapoda: Astacidea). Here we examined the extent to which local barcoding gaps (used for species identification) and global barcoding gaps (used for species discovery) exist among crayfishes, and whether global gaps met a previously suggested 10× threshold (mean interspecific difference being 10× larger than mean intra specific difference). We examined barcoding gaps using publicly available mitochondrial COI sequence data from the National Center for Biotechnology Information's nucleotide database. We created two versions of the COI datasets used for downstream analyses: one focused on the number of unique haplotypes ( ) per species, and another that focused on total number of sequences ( ; i.e., including redundant haplotypes) per species. A total of 81 species were included, with 58 species and five genera from the family Cambaridae and 23 species from three genera from the family Parastacidae. Local barcoding gaps were present in only 30 species (20 Cambaridae and 10 Parastacidae species). We detected global barcoding gaps in only four genera (, , , and ), which were all below (4.2× to 5.2×) the previously suggested 10× threshold. We propose that a ~5× threshold would be a more appropriate working hypothesis for species discovery. While the and datasets yielded largely similar results, there were some discrepant inferences. To understand why some species lacked a local barcoding gap, we performed species delimitation analyses for each genus using the dataset. These results suggest that current taxonomy in crayfishes may be inadequate for the majority of examined species, and that even species with local barcoding gaps present may be in need of taxonomic revisions. Currently, the utility of DNA barcoding for species identification and discovery in crayfish is quite limited, and caution should be exercised when mitochondrial-based approaches are used in place of taxonomic expertise. Assessment of the evidence for local and global barcoding gaps is important for understanding the reliability of molecular species identification and discovery, but outcomes are dependent on the current state of taxonomy. As this improves (e.g., via resolving species complexes, possibly elevating some subspecies to the species-level status, and redressing specimen misidentifications in natural history and other collections), so too will the utility of DNA barcoding.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11260883 | PMC |
http://dx.doi.org/10.1002/ece3.70050 | DOI Listing |
J Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
View Article and Find Full Text PDFComput Biol Med
January 2025
Computer Engineering Department, Technology Faculty, Marmara University, Maltepe, Istanbul, Turkey. Electronic address:
Background: To address critical security challenges in the Internet of Medical Things (IoMT), this study develops a feature selection framework to improve detection accuracy and computational efficiency in IoMT cybersecurity. By optimizing feature selection, the framework aims to enhance the security and operational integrity of real-time healthcare systems.
Method: This study integrates Random Subset Feature Selection (RSFS) with Correlation Feature Selection (CFS) to create a novel feature selection framework tailored to IoMT datasets.
BMC Prim Care
December 2024
School of Life Course & Population Sciences, Department of Population Health Sciences, King's College London, London, UK.
Background: We aimed to identify and characterise the longitudinal patterns of multimorbidity associated with stroke.
Methods: We used an unsupervised patient-oriented clustering approach to analyse primary care electronic health records (EHR) of 30 common long-term conditions (LTC) in patients with stroke aged over 18, registered in 41 general practices in south London between 2005 and 2021.
Results: Of 849,968 registered patients, 9,847 (1.
BMC Plant Biol
December 2024
Department of Biochemistry, Faculty of Science and Technology, University of Nairobi, P.O. Box 30197, Nairobi, Kenya.
Background: The genus Physalis belongs to the Solanaceae family and has different species with important nutritional and medicinal values. Species within this genus have limited morphological differences, a characteristic that hinders accurate identification, safe utilization and genetic conservation of promising genotypes. In addition, to prevent the perceived loss of Physalis diversity due to habitat destruction, species delimitation needs attention.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!