Search Results (66)

Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection. Full article

Madagascar harbors a rich marine biodiversity, yet detailed knowledge of its fish species remains limited. Of the 1689 species listed in 2018, only 22% had accessible cytochrome oxidase I (COI) sequences in public databases. In response to growing pressure on fishery resources, this study aims to strengthen biodiversity monitoring tools. Its objectives were to enrich the COI database for Malagasy marine fishes, create the first 12S reference library, and evaluate the taxonomic resolution of different 12S metabarcodes for eDNA analysis, namely MiFish, Teleo1, AcMDB, Ac12S, and 12SF1/R1. An integrated approach combining morphological, molecular, and phylogenetic analyses was applied for specimen identification of fish captured using various types of fishing gear in Toliara and Ranobe Bays from 2018 to 2023. The Malagasy COI database now includes 2146 sequences grouped into 502 Barcode Index Numbers (BINs) from 82 families, with 14 BINs newly added to BOLD (The Barcode of Life Data Systems), and 133 cryptic species. The 12S library comprises 524 sequences representing 446 species from 78 families. Together, the genetic datasets cover 514 species from 84 families, with the most diverse being Labridae, Apogonidae, Gobiidae, Pomacentridae, and Carangidae. However, the two markers show variable taxonomic resolution: 67 species belonging to 35 families were represented solely in the COI dataset, while 10 species from nine families were identified exclusively in the 12S dataset. For 319 species with complete 12S gene sequences associated with COI BINs (Barcode Index Numbers), 12S primer sets were used to evaluate the taxonomic resolution of five 12S metabarcodes. The MiFish marker proved to be the most effective, with an optimal similarity threshold of 98.5%. This study represents a major step forward in documenting and monitoring Madagascar’s marine biodiversity and provides a valuable genetic reference for future environmental DNA (eDNA) applications. Full article

The advent of alignment-free k-mer barcoding has revolutionized taxonomic analysis, enabling bacterial identification at phylogroup resolution within natural communities. We applied this approach to characterize Escherichia coli intraspecific diversity in human gut microbiomes using publicly available datasets representing diverse human physiological states. By estimating the relative abundance of eight E. coli phylogroups defined by their 18-mer markers in 558 fecal samples, we compared their distribution between gut microbiomes of healthy individuals, patients with chronic bowel diseases and volunteers subjected to various external interventions. Across all datasets, phylogroups exhibited bidirectional abundance shifts in response to host physiological changes, indicating an inherent bimodality in their adaptive strategies. Correlation analysis of phylogroup persistence revealed positive intraspecific connectivity networks and dependence of their patterns on both acute interventions like antibiotic or probiotic treatment and chronic bowel disorders. Along with predominantly negative correlations with Bacteroides, we observed a transition from positive to negative associations with Prevotella in Prevotella-rich microbiomes. Several interspecific correlations individually established by E. coli phylogroups with dominant taxa suggest their potential role in shaping intraspecific networks. Machine learning techniques statistically confirmed an ability of phylogroup patterns to discriminate the physiological state of the host and virtual diagnostic assays opened a way to optimize intraspecific phylotyping for medical applications. Full article

DNA metabarcoding is a powerful biodiversity monitoring tool, enabling simultaneous assessments of diverse biological communities. However, its accuracy depends on the reliability of reference databases that assign taxonomic identities to obtained sequences. Here we provide a DNA barcode dataset for aquatic fauna of the Panama Canal, a region that connects the Western Atlantic and Eastern Pacific oceans. This unique setting creates opportunities for trans-oceanic dispersal while acting as a modern physical dispersal barrier for some terrestrial organisms. We sequenced 852 specimens from a diverse array of taxa (e.g., fishes, zooplankton, mollusks, arthropods, reptiles, birds, and mammals) using COI, and in some cases, 12S and 16S barcodes. These data were collected for a variety of studies, many of which have sought to understand recent changes in aquatic communities in the Panama Canal. The DNA barcodes presented here are all from captured specimens, which confirms their presence in Panama and, in many cases, inside the Panama Canal. Both native and introduced taxa are included. This dataset represents a valuable resource for environmental DNA (eDNA) work in the Panama Canal region and across the Neotropics aimed at monitoring ecosystem health, tracking non-native and potentially invasive species, and understanding the ecology and distribution of these freshwater and euryhaline taxa. Full article

Accurate species identification of small aquatic insects remains challenging due to their morphological similarities. This study addresses this issue by developing a DNA barcode reference library for the globally distributed Brillia (Diptera: Chironomidae). We analyzed cytochrome c oxidase subunit I (COI) sequences of 241 specimens belonging to 13 Brillia species from 18 countries, including 56 newly generated and 185 publicly available COI barcodes. Our integrated approach included genetic distance analysis, haplotype network construction, and ecological niche modeling. The results revealed remarkable cryptic diversity, with sequences clustering into 30 Barcode Index Numbers and 158 unique haplotypes, most being region-specific. Notably, East Asian and North American populations showed complete genetic distinctness, suggesting long-term isolation. Environmental factors, particularly temperature and precipitation gradients, were identified as key drivers of this diversification. The study also corrected several misidentifications in existing databases. These findings significantly advance our understanding of Brillia diversity and provide a reliable molecular tool for freshwater ecosystem monitoring, with important implications for biodiversity conservation and environmental assessment. Full article

Complete chloroplast genome sequences are widely used in the analyses of phylogenetic relationships among angiosperms. As a species-rich genus, species diversity centers of Saxifraga L. include mountainous regions of Eurasia, such as the Alps and the Qinghai–Tibetan Plateau (QTP) sensu lato. However, to date, datasets of chloroplast genomes of Saxifraga have been concentrated on the QTP species; those from European Alps are largely unavailable, which hinders comprehensively comparative and evolutionary analyses of chloroplast genomes in this genus. Here, complete chloroplast genomes of 19 Saxifraga species were de novo sequenced, assembled and annotated, and of these 15 species from Alps were reported for the first time. Subsequent comparative analysis and phylogenetic reconstruction were also conducted. Chloroplast genome length of the 19 Saxifraga species range from 149,217 bp to 152,282 bp with a typical quadripartite structure. All individual chloroplast genome included in this study contains 113 unique genes, including 79 protein-coding genes, four rRNAs and 30 tRNAs. The IR boundaries keep relatively conserved with minor expansion in S. consanguinea. mVISTA analysis and identification of polymorphic loci for molecular markers shows that six intergenic regions (ndhC-trnV, psbE-petL, rpl32-trnL, rps16-trnQ, trnF-ndhJ, trnS-trnG) can be selected as the potential DNA barcodes. A total of 1204 SSRs, 433 tandem repeats and 534 Large sequence repeats were identified in the 19 Saxifraga chloroplast genomes. The codon usage analysis revealed that Saxifraga chloroplast genome codon prefers to end in A/T. Phylogenetic reconstruction of 33 species (31 Saxifraga species included) based on 75 common protein coding genes received high bootstrap support values for nearly all identified nodes, and revealed a tree topology similar to previous studies. Full article

Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications. Full article

Background/Objectives: The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data—including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)—into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. Methods: The dataset consists of 958 anonymous patient records. Each record includes four first trimester screening markers, hCG, PAPP-A, and NT, expressed as multiples of the median. The DS risk outcome was categorized into three classes: high, medium, and low. Three transformer architectures—DeiT3, MaxViT, and Swin—are employed to extract high-level features from the generated barcodes. The extracted features are combined into a unified set, and dimensionality reduction is performed using two feature selection techniques: minimum Redundancy Maximum Relevance (mRMR) and RelieF. Intersecting features from both selectors are retained to form a compact and informative feature subset. The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. Results: The proposed approach achieved up to 100% classification accuracy using the Naive Bayes classifier with 1250 features selected by RelieF and 527 intersecting features from mRMR. By selecting a smaller but more informative subset of features, the system significantly reduced hardware and processing demands while maintaining strong predictive performance. Conclusions: The results suggest that the proposed hybrid AI method offers a promising and resource-efficient solution for DS risk assessment in first trimester screening. However, further comparative studies are recommended to validate its performance in broader clinical contexts. Full article

Phlegmariurus, a diverse genus within the Lycopodiaceae family, has wide diversity in tropical regions, including Thailand. Accurate species delimitation in the tropical clubmoss genus Phlegmariurus is challenged by high morphological plasticity and genetic complexity. This study applied an integrative multilocus approach combining morphometric analysis of 27 complete specimens, 35 Phlegmariurus and one Lycopodiella accessions for AFLP genotyping (926 loci; PIC 0.32), SSR profiling (44 loci; PIC 0.57; expected heterozygosity 0.35), and chloroplast barcoding using rbcL (1308 bp; bootstrap 89–99%) and the psbA-trnH intergenic spacer (308 bp; bootstrap ≥ 94%). A total of 13 were identified as belonging to seven known species, including P. nummulariifolius (NST01, NST15, NST36), P. goebelii (JP04), P. phlegmaria (NST13), P. verticillatus (PHI16), P. squarrosus (NST21, NST22, MY31), P. tetrastichus (NST30), and P. carinatus (MY32, MY33, NST34). Morphological clustering and molecular markers consistently distinguished Phlegmariurus accessions from the Lycopodiella outgroup. Additionally, 19 previously unclassified Phlegmariurus accessions were successfully identified as belonging to the species P. nummulariifolius (NST23), P. goebelii (NST03, JP05, STN12, PNA14, SKA25, CPN26, KRB27, PNA28), P. phlegmaria (NWT07, STN08, NST09, NST10, PHI29), P. squarrosus (NST17), and P. carinatus (PNA06, STN18, CPN19, JP24). Moreover, this study identified three novel lineages (NST02, STN11, NST20) with strong support across datasets. The combination of broad genomic coverage (AFLP), fine-scale allelic resolution (SSR), deep-branch backbone (rbcL), and terminal-branch discrimination (psbA-trnH) yields a robust framework for species identification. These results define clear operational units for conservation prioritization and establish a foundation for marker-assisted development of ornamental Phlegmariurus cultivars. Full article

Barcode technology plays a crucial role in automatic identification and data acquisition systems, with extensive applications in retail, warehousing, healthcare, and industrial automation. However, barcode images often suffer from blurriness due to lighting conditions, camera quality, motion blur, and noise, adversely affecting their readability and system performance. This paper proposes a multi-scale real-time deblurring method based on edge feature guidance. Our designed multi-scale deblurring network integrates an edge feature fusion module (EFFM) to restore image edges better. Additionally, we introduce a feature filtering mechanism (FFM), which effectively suppresses noise interference by precisely filtering and enhancing critical signal features. Moreover, by incorporating wavelet reconstruction loss, the method significantly improves the restoration of details and textures. Extensive experiments on various barcode datasets demonstrate that our method significantly enhances barcode clarity and scanning accuracy, especially in noisy environments. Furthermore, our algorithm ensures robustness and real-time performance. The research results indicate that our method holds significant promise for enhancing barcode image processing, with potential applications in retail, logistics, inventory management, and industrial automation. Full article

Background/Objectives: Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples. Methods: Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer’s protocol and sequenced on an R9.4.1 flow cell for 24–48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression. Results: The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative–immune, proliferative–splicing, and proliferative–immune–splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing. Conclusions: This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care. Full article

Prior to this study, Myanmar was known to host 15 species of snakehead fishes (genus Channa) distributed across Southeast Asia. The region, characterized by its confluence of diverse river systems and two biodiversity hotspots, is presumed to have notable gaps in its biodiversity assessments. Recently, a new snakehead species, Channa shingon, was discovered in China, with its potential distribution in Myanmar warranting further investigation. This study focused on exploring Channa species in Kachin State and examined collected specimens using an integrated approach. The specimens were identified as C. shingon based on their distinct morphological characters, with a maximum standard length of 99.2 mm. Additionally, mitochondrial COI sequence data were generated, and species confirmation was achieved through nucleotide BLAST searches, genetic distance estimations, and phylogenetic analyses. The DNA sequences of C. shingon showed a mean inter-species genetic distance of 7.97% to 27.41% compared with other Channa species in the dataset, while the intra-species genetic distance between the Burmese and Chinese populations was 0.27%. Both Bayesian and maximum-likelihood phylogenetic analyses distinctly separated C. shingon from other congeners through a monophyletic clustering pattern, revealing its sister relationship with C. rubora. Overall, this study provides the first report of C. shingon from Myanmar and suggests its speciation from a common ancestor with C. rubora, likely driven by geographical barriers such as the Irrawaddy River. Furthermore, the study contributes a robust DNA barcode dataset encompassing 85.7% of the global Channa species diversity, which can serve as a valuable resource for further species identification, discovery, and diversity assessments of snakeheads from South and Southeast Asia. Full article

Traditionally, classifying species has required taxonomic experts to carefully examine unique physical characteristics, a time-intensive and complex process. Machine learning offers a promising alternative by utilizing computational power to detect subtle distinctions more quickly and accurately. This technology can classify both known (described) and unknown (undescribed) species, assigning known samples to specific species and grouping unknown ones at the genus level—an improvement over the common practice of labeling unknown species as outliers. In this paper, we propose a novel ensemble approach that integrates neural networks with support vector machines (SVM). Each animal is represented by an image and its DNA barcode. Our research investigates the transformation of one-dimensional vector data into two-dimensional three-channel matrices using discrete wavelet transform (DWT), enabling the application of convolutional neural networks (CNNs) that have been pre-trained on large image datasets. Our method significantly outperforms existing approaches, as demonstrated on several datasets containing animal images and DNA barcodes. By enabling the classification of both described and undescribed species, this research represents a major step forward in global biodiversity monitoring. Full article

(1) Background: Single-cell multi-omics is a powerful method for the dissection and detection of complicated immunologic functions and synapses. However, most currently available technologies merge datasets of different omics from separate portions of the same sample to generate combined multi-omics. This process is a source of bias, mainly in the field of immunology on cells originating from pluripotent hematopoietic stem cells with high flexibility during maturation. (2) Methods: Although new multi-omics approaches have been developed to use the advantages of cellular and molecular barcoding and next-generation sequencing to solve this issue, one of the main current challenges is intracellular proteomics, which should be combined with other omics data with high importance for immune system studies. We designed this study to evaluate previously recommended minimal permeabilization and fixation methods on the quality and quantity of transcriptomics and proteomics data generated by the BD Rhapsody™ Single-Cell Analysis System. (3) Results: Our findings showed that high-throughput sequencing with advanced quality and read-out is required for the combination of multi-omics outcomes from a permeabilized single cell. Therefore, the HiseqX platform was selected for further analysis. The effect of immune stimulation was observed clearly as the separated clusters of helper and cytotoxic T cells using unsupervised clustering. Importantly, fixation and permeabilization did not affect the general expression profile of unstimulated cells. However, fixation and permeabilization were proved to negatively impact the detection of the whole transcriptome for single-cell assay. Nevertheless, about 60% of the transcriptomic signature of the stimulation was detected. If the measurement of combined surface and intracellular markers is required to be achieved, the modified fixation and permeabilization method is recommended because of a lower transcriptomic loss and more precise proteomic fingerprint detected. (4) Conclusions: The findings of this study support the potential possibility for integrating intracellular proteomics, which needs to be optimized and tested with newly designed oligonucleotide-tagged antibodies targeting intracellular proteins. Full article

The diversity of non-biting midges (Chironomidae, Diptera) remains an unresolved topic, with estimates of species numbers ranging from 6000 to 15,000 according to various authors. To assess Chironomidae diversity in Lithuania, we evaluate the effectiveness of COI gene-based species delimitation methods for providing rapid diversity estimates. Nevertheless, differences between tree-based and distance-based approaches can result in varying group classifications, which may cause species numbers to be overestimated or underestimated. For our study, we analyzed a dataset of 109 specimens sampled from six Lithuanian streams. By applying multiple methods, such as Assemble Species by Automatic Partitioning (ASAP), Automatic Barcode Gap Discovery (ABGD), the generalized mixed Yule-coalescent (GMYC) model, and the Bayesian implementation of the Poisson Tree Processes (bPTP) model, we found that species estimates ranged from 28 to 58. Among these methods, ASAP proved to be the most effective for our dataset, identifying 58 putative species. These results reinforce our assumption that the current understanding of Chironomidae species diversity is incomplete. Full article