Development of a two-layer machine learning model for the forensic application of legal and illegal poppy classification based on sequence data.

Poppies are beneficial plants with a variety of applications, including medicinal, edible, ornamental, and industrial purposes. Some Papaver species are forensically significant plants because they contain opium, a narcotic substance. Internationally trafficked species of illegal poppies are being identified by DNA barcoding employing multiple markers in response to their forensic value. However, effective markers for precise species identification of legal and illegal poppies are still under discussion, with research on illegal poppies focusing on Papaver somniferum L., and species identification studies of Papaver bracteatum and Papaver setigerum DC. still lacking. As a result, in order to evaluate the performance of genetic markers and classify their DNA sequences in the genus Papaver, this study developed the first machine learning-based two-layer model, in which the first layer classifies legal and illegal poppies from the given sequence and the second layer identifies species of illegal poppies using their sequences. We constructed the dataset and investigated biological features from four markers, internal transcribed spacer 1 (ITS1), internal transcribed spacer 2 (ITS2), transfer RNA Leucine (trnL), transfer RNA Leucine - transfer RNA Phenylalanine intergenic spacer (trnL-trnF intergenic spacer) and their combination, using four machine learning algorithms, K-nearest neighbor (KNN), Naïve Bayes (NB), extreme gradient boost (XGBoost) and Random Forest (RF). According to our findings, for Layer 1 to classify legal and illegal poppies, KNN-based models using combined ITS region achieved the greatest performance of accuracy 0.846 and 0.889 using training and test sets, respectively. Additionally, for Layer 2 to identify illegal poppy species, KNN-based models using combined ITS region achieved the best performance of 0.833 and 1.000 for using training and test sets, respectively. To validate the model, the combined ITS region, which includes ITS 1 and 2 sequences, from blind poppy samples were used as a case study, with the Layer 1 correctly classifying legal and illegal poppies with over 0.830 accuracy. Layer 2 correctly identified P. setigerum DC., however, only one of the three P. somniferum L. species was accurately identified. Nevertheless, our research shows that machine learning can be used to classify and identify legal and illegal poppy species using DNA barcodes which can then be used as an efficient and effective forensic tool for improved law enforcement and a safer society.

Identification of Gluconacetobacter xylinus LYP25 and application to bacterial cellulose production in biomass hydrolysate with acetic acid.

Bacterial cellulose (BC) is a remarkable biomacromolecule with potential applications in food, biomedical, and other industries. However, the low economic feasibility of BC production processes hinders its industrialization. In our previous work, we obtained candidate strains with improved BC production through random mutations in Gluconacetobacter. In this study, the molecular identification of LYP25 strain with significantly improved productivity, the development of chestnut pericarp (CP) hydrolysate medium, and its application in BC fermentation were performed for cost-effective BC production process. As a result, the mutant strain was identified as Gluconacetobacter xylinus. The CP hydrolysate (CPH) medium contained 30 g/L glucose with 0.4 g/L acetic acid, whereas other candidates known to inhibit fermentation were not detected. Although acetic acid is generally known as a fermentation inhibitor, it improves the BC production by G. xylinus when present within about 5 g/L in the medium. Fermentation of G. xylinus LYP25 in CPH medium resulted in 17.3 g/L BC, a 33 % improvement in production compared to the control medium, and BC from the experimental and control groups had similar physicochemical properties. Finally, the overall process of BC production from biomass was evaluated and our proposed platform showed the highest yield (17.9 g BC/100 g biomass).

A case study on DNA barcoding for pet food mislabeling in South Korea.

Due to the close relationship between pets and humans, pet owners are highly invested in proper diets for their pets. Even though pet food mislabeling is concerning, there are few studies on this topic. This study investigated pet food mislabeling in South Korea's market based on DNA barcoding. In total, 10 pet food products were purchased, and 200 sequences of the partial Cytochrome c oxidase subunit 1 (COI) gene were generated from clones of the samples. The obtained sequences were compared to available public databases to identify species present in the ingredients. The data analyses showed that the labeled species were consistent with species detected by COI sequences in 6 of the products. However, the expected species were not detected in 4 products, revealing possible mislabeling in these samples. Our findings indicated that DNA barcoding might represent a promising tool to detect pet food mislabeling.

Comparative Transcriptome Analysis of Eriocheir sinensis from Wild Habitats in Han River, Korea.

Eriocheir sinensis is an euryhaline crab found from East Asia to Europe and North America. This species can live in freshwater and seawater due to the unique physiological characteristics of their life cycle, which allows them to adapt and inhabit different habitats in a wide range of environments. Despite the wealth of studies focusing on adaptation mechanism of E. sinensis to specific environmental factors, the adaptation mechanisms to wild habitats with coexisting environmental factors are not well understood. In this study, we conducted a transcriptome analysis to investigate gene expression differences related to habitat adaptation of E. sinensis from two wild habitats with different environmental factors in the Han River, Korea. A total of 138,261 unigenes were analyzed, of which 228 were analyzed as differentially expressed genes (DEGs) between the two wild habitats. Among 228 DEGs, 110 DEGs were annotated against databases; most DEGs were involved in energy metabolism, immunity, and osmoregulation. Moreover, DEG enrichment analysis showed that upregulated genes were related to biosynthesis, metabolism, and immunity in an habitat representing relatively high salinity whereas downregulated genes were related to ion transport and hypoxia response in habitats with relatively low salinity and dissolved oxygen. The present findings can serve as foundation for future E. sinensis culture or conservation approaches in natural conditions.

Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles.

A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms' response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from Daphnia magna, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems.

Seasonal Diversity of Microeukaryotes in the Han River, Korea Through 18S rRNA Gene Metabarcoding.

Freshwater ecosystems contain a large diversity of microeukaryotes that play important roles in maintaining their structure. Microeukaryote communities vary in composition and abundance on the basis of temporal and environmental variables and may serve as useful bioindicators of environmental changes. In the present study, 18S rRNA metabarcoding was employed to investigate the seasonal diversity of microeukaryote communities during four seasons in the Han River, Korea. In total, 882 unique operational taxonomic units (OTUs) were detected, including various diatoms, metazoans (e.g., arthropods and rotifers), chlorophytes, and fungi. Although alpha diversity revealed insignificant differences based on seasons, beta diversity exhibited a prominent variation in the community composition as per seasons. The analysis revealed that the diversity of microeukaryotes was primarily driven by seasonal changes in the prevailing conditions of environmental water temperature and dissolved oxygen. Moreover, potential indicator OTUs belonging to diatoms and chlorophytes were associated with seasonal and environmental factors. This analysis was a preliminary study that established a continuous monitoring system using metabarcoding. This approach could be an effective tool to manage the Han River along with other freshwater ecosystems in Korea.

Development of the new microsatellite markers of Lucilia sericata (Diptera: Calliphoridae) from Korea.

BACKGROUND: Lucilia sericata is a medical and veterinary important insect species because its larvae feed on tissues of vertebrates including humans. Very few microsatellite makers have been reported from the species to illuminate its genetic variability and population genetic structure. METHODS AND RESULTS: In this study, L. sericata samples were collected from four different localities in Korea to develop the microsatellite markers to provide basic information on the genetic variability and population genetic structure in Korea of this species. In total, ten new microsatellite markers were sequenced and analyzed. Genetic diversity was performed using these microsatellite markers. The observed heterozygosity varied from 0.205 to 0.824, with an average of 0.546. The expected heterozygosity ranged from 0.579 to 0.886, with an average of 0.804. PIC value varied from 0.553 to 0.876. CONCLUSIONS: The markers developed in the present study are expected as informative for estimating genetic diversity of L. sericata.

Development of polymorphic microsatellite markers for the Trichoglossus haematodus and cross-species amplification in Trichoglossus moluccanus.

BACKGROUD: Trichoglossus haematodus is the most popular parrots globally and one of the most bred species in Korea's zoos. However, despite its popularity, there are limited studies on the population genetics of this species. METHODS AND RESULTS: In this study, 10 polymorphic microsatellite markers were developed for T. haematodus. The number of alleles ranged from 6 to 9 (mean 7.9). Null alleles were present in two loci (TH-07 and TH-08). The observed heterozygosity ranged from 0.4444 to 1.0000 (mean 0.7000). One locus (TH-08) indicated a significant deviation from the Hardy-Weinberg equilibrium after Bonferroni correction (p < 0.005). The mean inbreeding coefficient (FIS) of the 10 loci was positive, suggesting that there is inbreeding in the population. Since the polymorphism information content (PIC) values were more than 0.7 in all loci, all markers developed in this study were classified as informative. The parentage exclusion probabilities considering all loci were higher than 0.99 in all three cases (P1, P2, and P3). The cross-species amplification of the 10 markers was tested in T. moluccanus, a close relative species of T. haematodus. These markers were also informative for T. moluccanus with PIC values higher than 0.7 in all loci. Additionally, the parentage exclusion probabilities (P1, P2 and P3) for T. moluccanus were above 0.99. However, due to the small number of T. haematodus and T. moluccanus investigated in this study, the 10 microsatellite markers should be analyzed with more individuals of these two species in future studies. CONCLUSIONS: The markers developed in this study might be helpful for investigations of genetic diversity and parentage analysis of T. haematodus and T. moluccanus.

The complete mitochondrial genome of Scyra compressipes Stimpson, 1857 (Decapoda: Epialtidae).

The family Epialtidae is the most diversified family within superfamily Majoidea but there is no report for complete mitogenome of any species in this family. This study was performed to sequence a complete mitogenome of Scyra compressipes Stimpson, 1857, as the first mitochondrial genome report from the Epialtidae. The complete mitogenome sequence of S. compressipes was 16,415 bp and it consisted of 36 genes, including 13 protein-coding genes (PCGs), two rRNA genes, and 21 tRNA genes excluding tRNA-Leu (UAG). tRNA genes ranged from 63 bp to 72 bp in length. The base composition of a complete mitogenome of S. compressipes is 34.7% A, 15.3% C, 10.2% G, and 39.8% T. The phylogenetic position of S. compressipes in the superfamily Majoidea was examined based on 13 PCGs. The phylogenetic analysis showed that S. compressipes was most closely related to Maguimithrax spinosissimus, a representative of the family Mithracidae.

Characterization of the complete mitochondrial genome of Okenia hiroi (Baba, 1938) (Nudibranchia, Goniodorididae).

Okenia is a speciose genus of the family Goniodorididae with more than 50 valid species. The phylogenetic relationships within the genus are little known. The mitogenome is a good marker to understand the phylogenetic relationships of relative species. This study was performed to sequence the mitogenome of O. hiroi. The mitogenome of O. hiroi was 14,583 bp in size and was composed of 37 genes, including 13 protein-coding genes, two ribosomal RNA genes, and 22 tRNA genes. The nucleotide composition was 30.5% A, 13.6% C, 16.5% G, and 39.4% T. The phylogenetic analysis showed that O. hiroi is sister to Notodoris gardineri (Aegiridae). This study recorded the first mitochondrial genome sequence of the family Goniodorididae.

Comparative analyses of the three complete mitochondrial genomes from forensic important beetle genus Dermestes with phylogenetic relationships.

Necrophagous Dermestes species have high forensic importance in relation to the estimation of elapsed time since death or death season. To further supplement the genome-level features for related species, the complete mitochondrial genome (mitogenome) of Dermestes species D. essellatocollis, D. frischii and D. coarctatus are amplified, sequenced, annotated, analyzed, and compared with other twelve species of the infraorder Bostrichoidea. The mitochondrial genomes were typical circular molecules with 16,218, 15,873 and 15,873 bp in length, respectively. They included 13 protein coding genes, two rRNAs, and 22 tRNAs, as well as the putative control region. The gene orders and orientations are identical to those of other recorded bostrichiformian species and had the ancestral insect gene composition. Furthermore, phylogenetic analyses based on all the mitochondrial protein coding genes for 13 Bostrichoidea and 16 outgroup taxa were performed using Bayesian and Maximum Likelihood analyses. The inferred trees indicate that the genus Dermestes is monophyletic. The monophyly of infraorder Bostrichiformia is not supported. This study provides genomic data for mitochondrial genome library of the genus Dermestes to investigate evolutionary and systematic studies.