Species discrimination of novel chloroplast DNA barcodes and their application for identification of Panax (Aralioideae, Araliaceae)

Certain species within the genus Panax L. (Araliaceae) contain pharmacological precious ginsenosides, also known as ginseng saponins. Species containing these compounds are of high commercial value and are thus of particular urgency for conservation. However, within this genus, identifying the particular species that contain these compounds by morphological means is challenging. DNA barcoding is one method that is considered promising for species level identification. However, in an evolutionarily complex genus such as Panax, commonly used DNA barcodes such as nrITS, matK, psbA-trnH, rbcL do not provide species-level resolution. A recent in silico study proposed a set of novel chloroplast markers, trnQ-rps16, trnS-trnG, petB, and trnE-trnT for species level identification within Panax. In the current study, the discriminatory efficiency of these molecular markers is assessed and validated using 91 reference barcoding sequences and 38 complete chloroplast genomes for seven species, one unidentified species and one sub-species of Panax, and two outgroup species of Aralia L. along with empirical data of Panax taxa present in Vietnam via both distance-based and tree-based methods. The obtained results show that trnQ-rps16 can classify with species level resolution every clade tested here, including the highly valuable Panax vietnamensis Ha et Grushv. We thus propose that this molecular marker to be used for identification of the species within Panax to support both its conservation and commercial trade.

Assessment of Deep Convolutional Neural Network Models for Species Identification of Forensically-Important Fly Maggots Based on Images of Posterior Spiracles

Forensic entomology is the branch of forensic science that is related to using arthropod specimens found in legal issues. Fly maggots are one of crucial pieces of evidence that can be used for estimating post-mortem intervals worldwide. However, the species-level identification of fly maggots is difficult, time consuming, and requires specialized taxonomic training. In this work, a novel method for the identification of different forensically-important fly species is proposed using convolutional neural networks (CNNs). The data used for the experiment were obtained from a digital camera connected to a compound microscope. We compared the performance of four widely used models that vary in complexity of architecture to evaluate tradeoffs in accuracy and speed for species classification including ResNet-101, Densenet161, Vgg19_bn, and AlexNet. In the validation step, all of the studied models provided 100% accuracy for identifying maggots of 4 species including Chrysomya megacephala (Diptera: Calliphoridae), Chrysomya (Achoetandrus) rufifacies (Diptera: Calliphoridae), Lucilia cuprina (Diptera: Calliphoridae), and Musca domestica (Diptera: Muscidae) based on images of posterior spiracles. However, AlexNet showed the fastest speed to process the identification model and presented a good balance between performance and speed. Therefore, the AlexNet model was selected for the testing step. The results of the confusion matrix of AlexNet showed that misclassification was found between C. megacephala and C. (Achoetandrus) rufifacies as well as between C. megacephala and L. cuprina. No misclassification was found for M. domestica. In addition, we created a web-application platform called thefly.ai to help users identify species of fly maggots in their own images using our classification model. The results from this study can be applied to identify further species by using other types of images. This model can also be used in the development of identification features in mobile applications. This study is a crucial step for integrating information from biology and AI-technology to develop a novel platform for use in forensic investigation.

DNA sequence-based identification of Fusarium: A work in progress

Accurate species-level identification of an etiological agent is crucial for disease diagnosis and management because knowing the agent’s identity connects it with what is known about its host range, geographic distribution, and toxin production potential. This is particularly true in publishing peer-reviewed disease reports, where imprecise and/or incorrect identifications weaken the public knowledge base. This can be a daunting task for phytopathologists and other applied biologists that need to identify Fusarium in particular, because published and ongoing multilocus molecular systematic studies have highlighted several confounding issues. Paramount among these are: (i) this agriculturally and clinically important genus is currently estimated to comprise over 400 phylogenetically distinct species (i.e., phylospecies), with over 80% of these discovered within the past 25 years; (ii) approximately one-third of the phylospecies have not been formally described; (iii) morphology alone is inadequate to distinguish most of these species from one another; and (iv) the current rapid discovery of novel fusaria from pathogen surveys and accompanying impact on the taxonomic landscape is expected to continue well into the foreseeable future. To address the critical need for accurate pathogen identification, our research groups are focused on populating two web-accessible databases (FUSARIUM-ID v.3.0 and the non-redundant NCBI nucleotide collection that includes GenBank) with portions of three phylogenetically informative genes (i.e., TEF1, RPB1 and RPB2) that resolve at or near the species level in every Fusarium species. The objectives of this Special Report, and its companion in this issue (Torres-Cruz et al. 2022), are to provide a progress report on our efforts to populate these databases and to outline a set of best practices for DNA sequence-based identification of fusaria.

Multicenter Evaluation of Rapid BACpro® II for the Accurate Identification of Microorganisms Directly from Blood Cultures Using MALDI-TOF MS

The identification of microorganisms directly from blood cultures using MALDI-TOF MS has been shown to be the most impacting application of this methodology. In this study, a novel commercial method was evaluated in four clinical microbiology laboratories. Positive blood culture samples (n = 801) were processed using a rapid BACpro® II kit and then compared with the routine gold standard. A subset of monomicrobial BCs (n = 560) were analyzed in parallel with a Sepsityper® Kit (Bruker Daltonics, Bremen, Germany) and compared with the rapid BACpro® II kit. In addition, this kit was also compared with two different in-house methods. Overall, 80.0% of the monomicrobial isolates (609/761; 95% CI 71.5–88.5) were correctly identified by the rapid BACpro® II kit at the species level (92.3% of the Gram negative and 72.4% of the Gram positive bacteria). The comparison with the Sepsityper® Kit showed that the rapid BACpro® II kit generated higher rates of correct species-level identification for all categories (p > 0.0001), except for yeasts identified with score values > 1.7. It also proved superior to the ammonium chloride method (p > 0.0001), but the differential centrifugation method allowed for higher rates of correct identification for Gram negative bacteria (p > 0.1). The percentage of accurate species-level identification of Gram positive bacteria was particularly noteworthy in comparison with other commercial and in-house methods.

An integrated approach for synonymization of Rotylenchus rhomboides with R. goodeyi (Nematoda: Hoplolaimidae) reveals high intraspecific mitogenomic variation

Rotylenchus is a widely-distributed economically important plant-parasitic nematode group whose species-level identification relies largely on limited morphological characters including character-based tabular keys and molecular data of ribosomal and mitochondrial genes. In this study, a combined morphological and molecular analysis of three populations of R. goodeyi from Belgium, Poland and the Netherlands revealed important character variations of this species leading to synonymisation of R. rhomboides with R. goodeyi, and a high nucleotide variation within cox1 gene sequences in these populations. Additional Illumina sequencing of DNA from individuals of the Dutch population revealed two variants of mitogenomes each of approximately 23 Kb in size, differing by about 9% and containing eleven protein coding genes, two ribosomal RNA genes and up to 29 transfer RNA genes. In addition to the first representative whole genome shotgun sequence datasets of the genus Rotylenchus, this study also provides the full length mitogenome and the ribosomal DNA sequences of R. goodeyi.