Machine learning algorithms accurately identify free-living marine nematode species.

Background: Identifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903. Methods: A total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms. Results: For both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified. Conclusion: These results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

Friction ridge analysis in disaster victim identification (DVI): Brazilian case studies.

Depending on the magnitude and nature of a disaster, identifying the victims can be a complex task that requires coordinated work by disaster victim identification (DVI) teams based on pre-established protocols. Thus, the analysis of fingerprints has been presented as a method to establish, when possible, the identity of the victims during the DVI process. This study discusses the importance of this primary method of identification and the results obtained in four different disasters in which Brazilian DVI teams were involved: the Air France Flight AF447 plane crash in the Atlantic Ocean, floods and mudslides in the State of Rio de Janeiro, Brazil, the LaMia Flight 2933 plane crash in Colombia, and the tailings dam collapse in Brumadinho, Brazil. Here, we also report the use of the automatic fingerprint capture and identification system, called Alethia, developed by the Federal Police of Brazil and used in the victim identification process in the two latter events mentioned above.Key pointsThis article presents four different disasters that occurred in Brazil and overseas and involved Brazilian DVI teams in the identification process, focusing on fingerprint identification (Air France Flight AF447, floods and mudslides in the State of Rio de Janeiro, Brazil, LaMia Flight 2933, and the Brumadinho tailings dam collapse).This article also describes the evolution of the DVI process in Brazil, including a description of the technology currently used by Brazilian fingerprint experts (Alethia).This article reports how the Alethia System was used in the disasters and how it optimized the human identification process when compared to traditional methods.