Search Results (146)

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

Barnyard grass is the most problematic weed in paddy fields in Ningxia. Its substantial morphological variation complicates both identification and control, yet the genetic diversity of barnyard grass infesting paddy fields in Ningxia has not been thoroughly studied. In this research, we analyzed the genetic diversity of 46 barnyard grass populations from Ningxia’s paddy fields based on the assessment of morphological traits, DNA barcoding, and SCoT-targeted gene markers. Nine morphological traits were quantitatively analyzed, among which three phenological traits, i.e., leaf length, stem diameter, and plant height, exhibited notable variations. Correlational analysis revealed a positive relationship between morphological traits and multi-herbicide resistance profiles. To assess genetic diversity, four DNA barcodes (ITS, psbA, matK, and trnL-F) were used, among which ITS demonstrated the strongest potential in single-gene barcoding for barnyard grass species identification. Cluster analysis based on ITS barcode sequences was performed to group the populations into five main categories. Additionally, SCoT marker analysis using six primers was performed to classify the 46 barnyard grass samples into five groups. The results showed that the predominant barnyard grass species in Ningxia were E. colona, E. crus-galli var. Formosensis, E. crusgalli, E. oryzoides, and E. crusgalli var. Zelayensis, with E. colona being the most prevalent. The differences observed between the morphological and molecular marker-based classifications were method-dependent. However, both SCoT molecular marker technology and DNA barcoding contributed to identifying the genetic diversity of barnyard grass. Taken together, our study revealed significant morphological and genetic variations among barnyard grass populations, which correlated with herbicide sensitivity in Ningxia’s paddy fields, underscoring the necessity for an integrated weed management approach to combat this troublesome weed species. Full article

Molecular species identification plays an increasingly important role in forensic entomology and is centered on selecting appropriate DNA barcodes, which there are not yet enough of. Such identification is decisive in discovering a better DNA barcode for the identification of necrophilous fly species. Here, we analyzed 10 common necrophilous fly species found on Hainan Island; designed 12 pairs of fly-specific primers from different mitochondrial regions; screened two fly DNA barcodes with better results than those of published studies, which were used as controls; and employed a high-resolution melting (HRM) curve to construct PCR-HRM technology systems for rapid and efficient necrophilous fly species identification. The results showed that, among the 14 DNA barcoding PCR-HRM systems, the newly designed COXII-519/COXII-615 primer was the best, which identified 10 necrophilous fly species in one test. The second-best system was the C1-J-2495/C1-N-2800 primer published in the literature, which identified six fly species in one test. Moreover, since the COXII-519/COXII-615 primer system performed successfully in both stale (stored over two years) and larval samples due to its short amplificated fragment (shorter than 97 bp), it may serve as a new efficient DNA barcode for necrophilic fly species identification. The new DNA barcoding PCR-HRM system established in this study enables the rapid and accurate identification of necrophilic fly species. Full article

We highlight the lack of researchers studying freshwater zooplankton in Latin America and contextualize it within the global extinction of taxonomists, global loss of biodiversity, and regional reality to visualize the intensity of this threat and possible strategies for addressing it. The scarcity of researchers working on freshwater zooplankton currently threatens the future of these studies in the world. This global trend of the decreasing interest of scientists and local governments in learning about this important component of freshwater biodiversity is more accentuated in Latin America by regional drivers, such as brain drain, a lack of support by stakeholders, and the absence of planning for the rational use and conservation of this essential natural resource. All these drivers interact and have more dramatic consequences for regional research due to the recent reduction in government funds for science in some of these countries. In the context of Global Change; a loss of biodiversity due to this fact; and the misuse of drainage basins, overexploitation, and regional pressures, the lack of researchers studying freshwater zooplankton and, in general, all aquatic life has emerged as a critical threat to the delicate equilibrium of these ecosystems. Within this situation, scientific integration through intra-regional and extra-regional collaboration networks has emerged as an unavoidable strategy for the survival and future strengthening of studies on biodiversity and the conservation of freshwater zooplankton in Latin America. The development of new technologies such as DNA barcoding, metabarcoding, and metagenomics has emerged as a solution to this problem. Nevertheless, they should be considered as new tools towards integrative taxonomy and not as replacements for taxonomical studies. Full article

Rice (Oryza sativa L.) is a stable food for over half of the world population, contributing 50–80% of the daily calorie intake. The completion of rice genome sequencing marks a significant milestone in understanding functional genomics, yet the systematic identification of gene functions remains a bottleneck for rice improvement. Large-scale mutant libraries in which the functions of genes are lost or gained (e.g., through chemical/physical treatments, T-DNA, transposons, RNAi, CRISPR/Cas9) have proven to be powerful tools for the systematic linking of genotypes to phenotypes. So far, using different mutagenesis approaches, a million mutant lines have been established and about 5–10% of the predicted rice gene functions have been identified due to the high demands of labor and low-throughput utilization. DNA-barcoding-based large-scale mutagenesis offers unprecedented precision and scalability in functional genomics. This review summarizes large-scale loss-of-function and gain-of-function mutant library development approaches and emphasizes the integration of DNA barcoding for pooled analysis. Unique DNA barcodes can be tagged to transposons/retrotransposons, DNA constructs, miRNA/siRNA, gRNA, and cDNA, allowing for pooling analysis and the assignment of functions to genes that cause phenotype alterations. In addition, the integration of high-throughput phenotyping and OMICS technologies can accelerate the identification of gene functions. Full article

Morphological classification is the gold standard for identifying necrophilous flies, but its complexity and the scarcity of experts make accurate classification challenging. The development of artificial intelligence for autonomous recognition holds promise as a new approach to improve the efficiency and accuracy of fly morphology identification. In our previous study, we developed a GLB-ViT (Global–Local Balanced Vision Transformer)-based deep learning model for fly species identification, which demonstrated improved identification capabilities. To expand the model’s application scope to meet the practical needs of forensic science, we extended the model based on the forensic science practice scenarios, increased the database of identifiable sarcosaphagous fly species, and successfully developed a WeChat Mini Program based on the model. The results show that the model can achieve fast and effective identification of ten common sarcosaphagous flies in Hainan, and the overall correct rate reaches 94.00%. For the few cases of identification difficulties and suspicious results, we have also constructed a rapid molecular species identification system based on DNA Barcoding technology to achieve accurate species identification of the flies under study. As the local fly database continues to be improved, the model is expected to be applicable to local forensic practice. Full article

The comparative analysis of the feeding ecology among sympatric small carnivores reveals both differentiation and overlap in resource utilization patterns, which serves as a critical pathway for understanding interspecific interactions and maintaining ecosystem stability. In this study, we collected fecal samples from sympatric Pallas’s cats (Otocolobus manul, n = 26) and red foxes (Vulpes vulpes, n = 13) within the Sanjiangyuan National Park (SNP) in China. Subsequently, DNA barcoding technology was employed to analyze the dietary composition and interspecific differences of these two small carnivores. The results demonstrated that both species primarily prey on plateau pikas (Ochotona curzoniae) and small rodents. Despite a high trophic niche overlap between Pallas’s cats and red foxes (O_jk = 0.81), interspecific competition is mitigated through differentiate feeding proportions of shared prey species. Furthermore, the trophic niche breadth of red foxes (B = 267.89) exceeds that of Pallas’s cats (B = 162.94), reflecting a greater diversity of prey resources utilized by red foxes. Consequently, the two small carnivores achieve sympatric coexistence via differentiated resource utilization. These findings enhance our understanding of the coexistence mechanisms within carnivore communities and provide a scientific basis for the conservation of wildlife in the SNP. Full article

Discogobio is a genus of small, economically important freshwater fishes that are widely distributed in Southwestern China. The species of the genus are morphologically very similar, which makes their taxonomic identification quite challenging. DNA barcoding technology can identify species at the molecular level, thus overcoming the limitations of morphological classification. In this study, we collected 16 morphological species of Discogobio from China, analyzed the mitochondrial cytochrome oxidase I subunit (COI) gene sequences of 206 samples, and applied DNA barcoding to identify the species. The COI amplicon was 651 bp in length, and the mean base contents were: (T) 28.83%, (C) 27.63%, (A) 25.97%, (G) 17.57%. The AT content (54.8%) was higher, and the base composition was biased. The intraspecific differences in the genus Discogobio were not significant, and the genetic distances were all less than 2%. The average interspecific genetic distances (3.94%) were about 18.8 times the average intraspecific genetic distances (0.21%), suggesting that there are barcode gaps among the species of the genus Discogobio. Five different species definition methods, Automatic Barcode Gap Discovery (ABGD), Assemble Species by Automatic Partitioning (ASAP), Bayesian Poisson Tree Process (bPTP), Generalized Mixed Yule Combination (GMYC), and Refined Single Linkage (RESL), were used to infer molecular operational taxonomic units (MOTU). The number of MOTUs ranged from 9 to 18. Phylogenetic analysis based on COI gene haplotypes showed that most species formed well-evolved branches on the phylogenetic tree, and the clustering among species was obvious without mixing. The results of this study provide reliable DNA barcoding information for species identification within the genus Discogobio, which is of great significance for taxonomic identification. 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

Alternaria is a genus that contains several important plant pathogens affecting nearly 400 plant species worldwide, including economically important crops such as grapes, citrus, and ornamental plants. Rapid, scalable, and efficient methods of pathogen detection are crucial for managing plant diseases and ensuring agricultural productivity. Current amplicon sequencing protocols for Alternaria detection often require the enzymatic barcoding of amplicons, increasing hands-on time, cost, and contamination risk. We present a proof-of-concept study using custom barcoded primers, combining universal primers targeting ITS1 and ITS2 regions (600 bp) coupled with Oxford Nanopore Technologies (ONT) barcode sequences. Sequencing was performed on infected grapevine, mandarin orange, thuja, and maple tree samples. In total, we analyzed 38 samples using qPCR; 8 tested positive for Alternaria, which were sequenced using a newly developed protocol. As a result, we could identify Alternaria in every positive sample, and besides the pathogen of interest, we could identify the associated mycobiome. This protocol reduces hands-on time and cost, making a significant advancement over current sequencing methods. Future work will focus on optimizing our approach for high-throughput sequencing of up to 96 samples and determining the method’s applicability for large-scale mycobiome analysis. Full article

Addressing the pervasive issue of food adulteration and fraud driven by economic interests has long presented a complex challenge. Such adulteration not only compromises the safety of the food supply chain and destabilizes the market economy but also poses significant risks to public health. Food adulteration encompasses practices such as substitution, process manipulation, mislabeling, the introduction of undeclared ingredients, and the adulteration of genetically modified foods. Given the diverse range of deceptive methods employed, genomics-based identification techniques have increasingly been utilized for detecting food adulteration. Compared to traditional detection methods, technologies such as polymerase chain reaction (PCR), next-generation sequencing (NGS), high-resolution melt (HRM) analysis, DNA barcoding, and the CRISPR–Cas system have demonstrated efficacy in accurately and sensitively detecting even trace amounts of adulterants. This paper provides an overview of genomics-based approaches for identifying food adulteration, summarizes the latest applications in certification procedures, discusses current limitations, and explores potential future trends, thereby offering new insights to enhance the control of food quality and contributing to the development of more robust regulatory frameworks and food safety policies. 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

Background/Objectives: Illicium verum Hook. f. and Illicium difengpi K. I. B.et K. I. M. are two important medicinal plants which grow in the mountainous areas of Guangxi, China. Their similar morphological characteristics frequently lead to their misidentification. Chloroplast genome (cp)-based barcode technology has been used to effectively identify two closely related species; however, at present, there is no systematic comparative study of the cp genome sequences between I. verum and I. difengpi. Methods: Here, the cp genomes of the two plants were sequenced and analyzed. Results: The cp genome sizes were 142,689 and 142,689 bp for I. verum and I. difengpi, respectively. Each of the cp genomes annotated 122 genes, with 79 protein coding genes, 8 ribosomal RNA genes, and 35 transfer RNA genes. Amino acid frequencies of 1.17–10.19% (I. verum) and 1.18–10.17% (I. difengpi) were found in the coding genes. There were also 104 and 96 SSRs as well as 26 and 25 long repeats identified in I. verum and I. difengpi, respectively, among which the most common were A/T base repeats. Both cp genomes had SSC/IRa junctions located in gene ycf1-trnN. The ycf1 and trnL-trnV-rps7 genes were positioned at the IRb/SSC and LSC/IR boundaries, respectively. A phylogenetic relationship was constructed and the two species were fully nested within the genus Illicium. Conclusions: The comparative cp genomes of I. verum and I. difengpi are presented in this study, and this provides valuable phylogenetic information for subsequent molecular marker development and research of I. verum and I. difengpi. Full article

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia. 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