Precise Species Identification Research Articles

A Review of Semantic Segmentation and Instance Segmentation Techniques in Forestry Using LiDAR and Imagery Data

The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, emphasizing their applications using LiDAR and imagery data. This review covers the challenges, progress, and application of these strategies in ecological monitoring, forest inventory, and tree species classification. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. This review highlights the effectiveness of semantic and instance segmentation techniques in forestry applications, such as precise tree species identification and individual tree monitoring. However, challenges such as occlusions, overlapping branches, and varying data quality remain. Future research should focus on overcoming these obstacles to enhance the precision and applicability of these segmentation methodologies.

Phylogenomic analyses of the Listeriaceae family support species reclassification and proposal of a new family and new genera.

The taxonomy of the Listeriaceae family has undergone substantial revisions, expanding the Listeria genus from 6 to 29 species since 2009. However, these classifications have relied on 16S rRNA gene sequences and conventional polyphasic taxonomy, with limited use of genomic approaches. This study aimed to employ genomic tools, including phylogenomics, Overall Genomic Relatedness Indices (OGRIs), and core-genome phylogenomic analyses, to reevaluate the taxonomy of the Listeriaceae family. The analyses involved the construction of phylogenetic and phylogenomic trees based on 16S rRNA gene sequences and core genomes from 34 type strain genomes belonging to Listeriaceae family. OGRIs, which encompass Average Amino acid Identity (AAI), core-proteome AAI (cAAI), and Percentage of Conserved Proteins (POCP), were calculated, and specific threshold values of 70%, 87%, and 72-73% were established, respectively, to delimitate genera in the Listeriaceae family. These newly proposed OGRI thresholds unveiled distinct evolutionary lineages. The outcomes of this taxonomic re-evaluation were: (i): the division of the Listeria genus into an emended Listeria genus regrouping only Listeria senso stricto species; (ii): the remaining Listeria senso lato species were transferred into three newly proposed genera: Murraya gen. nov., Mesolisteria gen. nov., and Paenilisteria gen. nov. within Listeriaceae; (iii): Brochothrix was transferred to the newly proposed family Brochothricaceae fam. nov. within the Caryophanales order; (iiii): Listeria ivanovii subsp. londonensis was elevated to the species level as Listeria londonensis sp. nov.; and (iiiii): Murraya murrayi comb. nov. was reclassified as a later heterotypic synonym of Murraya grayi comb. nov. This taxonomic framework enables more precise identification of pathogenic Listeriaceae species, with significant implications for important areas such as food safety, clinical diagnostics, epidemiology, and public health.

Assembly-free reads accurate identification (AFRAID) approach outperforms other methods of DNA barcoding in the walnut family (Juglandaceae)

DNA barcoding has been extensively used for species identification. However, species identification of mixed samples or degraded DNA is limited by current DNA barcoding methods. In this study, we use plant species in Juglandaceae to evaluate an assembly-free accurate reads identification (AFRAID) method of species identification, a novel approach for precise species identification in plants. Specifically, we determined (1) the accuracy of DNA barcoding approaches in delimiting species in Juglandaceae, (2) the minimum size of chloroplast dataset for species discrimination, and (3) minimum amount of next generation sequencing (NGS) data required for species identification. We found that species identification rates were highest when whole chloroplast genomes were used, followed by taxon-specific DNA barcodes, and then universal DNA barcodes. Species identification of 100% was achieved when chloroplast genome sequence coverage reached 20% and the original sequencing data reached 500,000 reads. AFRAID accurately identified species for all samples tested after 500,000 clean reads, with far less computing time than common approaches. These results provide a new approach to accurately identify species, overcoming limitations of traditional DNA barcodes. Our method, which uses next generation sequencing to generate partial chloroplast genomes, reveals that DNA barcode regions are not necessarily fixed, accelerating the process of species identification.

Investigating the antioxidant and anticancer potential of Daucus spp. extracts against human prostate cancer cell line C4-2, and lung cancer cell line A549

The study evaluated 297 carrot germplasm lines, focusing on 52 cultivars to explore their therapeutic potential and address challenges related to the accessibility and affordability of nutraceuticals and health promoting foods. The investigation explores the application of DNA barcoding using the ITS region for precise species identification, highlighting genetic diversity among the examined cultivars. Through ITS sequence-based analysis and phylogenetic examination, six diverse Daucus spp. genotypes were differentiated and classified into distinct groups, indicating the presence of vast genetic variation. Evaluation of antioxidant activities using the DPPH radical scavenging assay revealed varying degrees of scavenging ability among genotypes with SKAU-C-15, SKAU-C-17, and SKAU-C-16 exhibiting the highest activity, suggesting their potential for antioxidant-rich products. Thin Layer Chromatography (TLC) bioautography confirmed the presence of bioactive compounds in carrot extracts responsible for their antioxidant properties. In cell culture studies, specific carrot genotype extracts demonstrated potential anti-proliferative and anti-invasive effects on recurrent prostate cancer cell line - C4-2 (SKAU-C-30, SKAU-C-10, and SKAU-C-42) and non-small cell lung cancer cell line - A549 (SKAU-C-18 and SKAU-C-11) cancer cells, as indicated by MTT assay, wound healing assay, and Colony Forming Unit assay. These findings suggest the promising therapeutic potential of carrot genotypes for developing anti-cancer functional foods, nutraceuticals and health supplements.Therefore, the study contributes to the nutrition security, paving the way for advancements in functional foods and health applications, particularly in cancer treatment and prevention.

Rapid Diagnostic PCR Assay Method for Species Identification of Mantidis Ootheca (Sangpiaoxiao) Based on Cytochrom C Oxidase I (COI) Barcode Analysis.

Mantidis Ootheca (sangpiaoxiao), the egg case of the mantis, is a type of insect-derived traditional medicine widely used in East Asia. However, species identification based on egg morphology is challenging, leading to the distribution of counterfeit and adulterated products. The use of inauthentic ingredients can pose serious health risks to consumers. This study aimed to develop PCR markers that can rapidly and accurately differentiate between authentic and counterfeit Mantidis Ootheca. The mitochondrial cytochrome c oxidase I (COI) region was sequenced in thirteen samples from four mantis species: Tenodera angustipennis, Statilia maculata, Hierodula patellifera, and T. sinensis. Four sets of SCAR primers were designed based on species-specific nucleotide polymorphisms, and a multiplex SCAR assay was developed by combining all sets of the primers. The sequence-characterized amplified region (SCAR) primers successfully produced amplicons for each target species, even with low-DNA templates or templates containing DNA from multiple samples. No amplification was observed for nontarget species. This study presents a novel approach for identifying authentic Mantidis Ootheca species using DNA-based diagnostic marker assays, which enable rapid and precise species identification. The SCAR assays developed in this study will aid in maintaining quality control and promoting the standardization of commercial Mantidis Ootheca products.

DNA Barcoding of the Endemic Freshwater Catfish Encheloclarias tapeinopterus (Bleeker, 1853) (Siluriformes:Clariidae) from Bangka Island, Indonesia

Abstract Encheloclarias tapeinopterus, an endemic catfish exclusively found in Bangka Island, Indonesia. The population of this species has been observed to be decreasing at an alarming rate, which has resulted in the IUCN Red List classifying it as a species that is vulnerable (VU). During our expedition on the 25th and 26th of January 2023, a total of Two specimens of E. tapeinopterus were collected from Kelumpik River, Pinang Sebatang Village, Central Bangka Regency, Bangka Island, Indonesia. In this groundbreaking study, we proudly present the pioneering documentation of E. tapeinopterus DNA barcoding, focusing on the Cytochrome C oxidase Subunit I (COI) gene. The recorded DNA Barcode of E. tapeinopterus has been officially registered in the Genbank under the accession code OQ281707. By employing DNA barcoding, we offer an advanced and expedient method for precise species identification and discovery, revolutionizing the field of taxonomy. Phylogenetic analysis revealed that E. tapeinopterus forms a distinct clade alongside the Clarias and Heterobranchus genus, shedding light on its evolutionary relationships and genetic affinities.

Development of nucleic acid lateral flow immunoassay for duplex detection of Leishmania martiniquensis and Leishmania orientalis in asymptomatic patients with HIV.

Leishmaniasis, a neglected tropical disease caused by parasitic protozoa of the Leishmania genus, remains a global health concern with significant morbidity and mortality. In Thailand, the rising incidence of autochthonous leishmaniasis cases involving Leishmania (Mundinia) martiniquensis and novel Leishmania (Mundinia) orientalis underscores the critical need for accurate diagnosis and effective control strategies. This study presents a sensitive and specific nucleic acid lateral flow immunoassay (NALFIA) that integrates a duplex PCR assay with a lateral flow device (LFD) strip format. Targeting the internal transcribed spacer 1 (ITS1) region, known for its unique combination of conserved and variable sequences, this assay employs primers labeled with biotin, digoxigenin, and fluorescein isothiocyanate (FITC) markers, enabling precise species identification and differentiation of these two Leishmania species. Remarkably, the assay achieves a sensitivity that surpasses agarose gel electrophoresis, detecting as few as 10-2 parasite/μL for L. martiniquensis and 10-4 parasite/μL for L. orientalis. Notably, the assay exhibited reliable specificity, revealing no cross-amplification with other major viscerotropic Leishmania species or reference organisms. Evaluation using 62 clinical samples further confirms the effectiveness of the PCR-LFD assay, with a sensitivity of 100% for L. martiniquensis and 83.3% for L. orientalis, and an excellent agreement (κ value = 0.948) with nested PCR. This integrated assay represents a promising advancement in diagnostic tools, offering rapid and accurate results that can significantly contribute to effective disease management and control. Given the increasing relevance of these Leishmania species in current public health scenarios, this assay serves as a valuable tool for both diagnostic and research applications.

Molecular identification and genetic variation of forensically important fly species (Order: Diptera) in Thailand using DNA barcoding

Forensic entomology plays a crucial role in criminal investigations by providing vital insights into minimum postmortem interval (PMImin) and corpse relocation by identifying insect species that colonize in decomposing remains. This study aimed to identify and analyze the genetic variation of forensically significant fly species in Thailand, using DNA barcoding of the mitochondrial cytochrome c oxidase subunit I COI gene. A total of 3,220 fly specimens were collected from 18 provinces across six regions of Thailand from October 2017 to September 2022. These specimens were classified by morphological identification into 21 species among three Dipteran families: Calliphoridae, Muscidae, and Sarcophagidae, with Chrysomya megacephala Diptera: Calliphoridae being the most abundant species. DNA barcoding confirmed the morphological identifications with 100 % accuracy, showing low intraspecific K2P distances0.0 to 1.1 %) and significant interspecific K2P distances 2.5 % to 17.2 %. A Neighbour-Joining (NJ) analysis was conducted to assess the molecular identification capabilities of the barcoding region. This analysis successfully recovered nearly all species as distinct monophyletic groups. The species groupings obtained were generally consistent with both morphological and molecular identifications. These findings underscore the effectiveness of DNA barcoding for precise species identification and contribute to a comprehensive database of forensically important flies in Thailand, thus facilitating improved forensic investigations and biodiversity studies.

Integrated Histological and Molecular Analysis of Filarial Species and Associated Wolbachia Endosymbionts in Human Filariasis Cases Presenting Atypically in Thailand.

Atypical presentations of filariasis have posed diagnostic challenges due to the complexity of identifying the causative species and the difficulties in both diagnosis and treatment. In this study, we present the integrative histological and molecular analysis of seven atypical filariasis cases observed in regions of nonendemicity of Thailand. All filariasis cases were initially diagnosed based on histological findings. To confirm the causative species, molecular characterization based on both filarial mitochondrial (mt 12S rRNA and COI genes) and nuclear ITS1 markers was performed, together with the identification of associated Wolbachia bacterial endosymbionts. Among the cases studied, Brugia pahangi (N = 3), Brugia malayi (N = 1), Dirofilaria sp. "hongkongensis" (N = 2), and a suspected novel filarial species genetically related to Pelecitus copsychi (N = 1) were identified. By targeting the 16S rRNA gene, Wolbachia was also molecularly amplified in two cases of infection with Dirofilaria sp. "hongkongensis." Phylogenetic analysis further revealed that the detected Wolbachia could be classified into supergroups C and F, indicating the high genetic diversity of this endosymbiont in Dirofilaria sp. "hongkongensis." Furthermore, this study demonstrates the consistency between histological findings and species identification based on mitochondrial loci rather than on the nuclear ITS1. This suggests the utility of mitochondrial markers, particularly COI, as a highly sensitive and reliable diagnostic tool for the detection and differentiation of filarial species in clinical specimens. Precise identification of the causative species will facilitate accurate diagnosis and treatment and is also essential for the development of epidemiological and preventive strategies for filariasis.

AI-powered revolution in plant sciences: advancements, applications, and challenges for sustainable agriculture and food security

Artificial intelligence (AI) is revolutionizing plant sciences by enabling precise plant species identification, early disease diagnosis, crop yield prediction, and precision agriculture optimization. AI uses machine learning and image recognition to aid ecological research and biodiversity conservation. It plays a crucial role in plant breeding by accelerating the development of resilient, high-yielding crops with desirable traits. AI models using climate and soil data contribute to sustainable agriculture and food security. In plant phenotyping, AI automates the measurement and analysis of plant characteristics, enhancing our understanding of plant growth. Ongoing research aims to improve AI models’ robustness and interpretability while addressing data privacy and algorithmic biases. Interdisciplinary collaboration is essential to fully harness AI’s potential in plant sciences for a sustainable, food-secure future.

Growth characteristics and molecular identification of indigenous Limnospira strains from Ethiopian soda lakes as a protein source

Malnutrition poses a significant global challenge in regions such as Ethiopia, particularly in children with limited dietary diversity. Arthrospira, a protein-rich cyanobacterium known for its rapid growth even under extreme conditions, is a promising solution for biomass production. This study addresses knowledge gaps in the morphological characterization and taxonomic classification of Arthrospira by focusing on precise species identification and strategic strain selection for optimal biomass production. Cyanobacterial strains were cultivated in a modified Spirulina Ogawa Terui medium at 35 °C under the photosynthetically active radiation of 160 μmol photons m−2 s−1 with a 12/12 h light/dark period in laboratory conditions. One hundred Arthrospira-like strains were isolated and characterized based on their morphological attributes according to accepted criteria, from Ethiopian soda lakes. Systematic evaluation of the growth rate comparison test identified four exceptionally fast-growing strains, with LC-30 exhibiting the highest dry weight (3.27 ± 0.51 g L˗1) and specific growth rate (1.63 ± 0.05 d˗1) among them. DNA analysis confirmed that all four examined strains belong to the Limnospira genus, specifically identified as Limnospira fusiformis. LC-30 and LA-08 demonstrated protein and phycocyanin levels (582.80 ± 0.07 and 557.90 ± 0.09 mg g˗1, respectively) comparable to those reported for Arthrospira platensis, and surpassing conventional protein sources, thus suggesting their potential for nutritional supplementation. The study highlights the importance of bioprospecting indigenous strains from soda lakes, revealing promising candidates for large-scale biomass production.

Analysis of Whole-Genome for Identification of Seven Penicillium Species with Significant Economic Value.

The Penicillium genus exhibits a broad global distribution and holds substantial economic value in sectors including agriculture, industry, and medicine. Particularly in agriculture, Penicillium species significantly impact plants, causing diseases and contamination that adversely affect crop yields and quality. Timely detection of Penicillium species is crucial for controlling disease and preventing mycotoxins from entering the food chain. To tackle this issue, we implement a novel species identification approach called Analysis of whole GEnome (AGE). Here, we initially applied bioinformatics analysis to construct specific target sequence libraries from the whole genomes of seven Penicillium species with significant economic impact: P. canescens, P. citrinum, P. oxalicum, P. polonicum, P. paneum, P. rubens, and P. roqueforti. We successfully identified seven Penicillium species using the target we screened combined with Sanger sequencing and CRISPR-Cas12a technologies. Notably, based on CRISPR-Cas12a technology, AGE can achieve rapid and accurate identification of genomic DNA samples at a concentration as low as 0.01 ng/µL within 30 min. This method features high sensitivity and portability, making it suitable for on-site detection. This robust molecular approach provides precise fungal species identification with broad implications for agricultural control, industrial production, clinical diagnostics, and food safety.

Digital Twin System of Pest Management Driven by Data and Model Fusion

Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers’ personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM).

Comparative genomics of Tn6411 transposons carrying the blaIMP-1 gene in Pseudomonas aeruginosa.

We aimed to determine the molecular characteristics of carbapenem-resistant Pseudomonas aeruginosa strains 18081308 and 18083286, which were isolated from the urine and the sputum of two Chinese patients, respectively. Additionally, we conducted a comparative analysis between Tn6411 carrying blaIMP-1 in strain 18083286 and transposons from the same family available in GenBank. Bacterial genome sequencing was carried out on strains 18081308 and 18083286 to obtain their whole genome sequence. Average nucleotide identity (ANI) was used for their precise species identification. Serotyping and multilocus sequence typing were performed. Furthermore, the acquired drug resistance genes of these strains were identified. The carbapenem-resistant P. aeruginosa strains isolated in the present study were of sequence type ST865 and serotype O6. They all carried the same resistance genes (aacC2, tmrB, and blaIMP-1). Tn6411, a Tn7-like transposon carrying blaIMP-1, was found in strain 18083286 by single molecule real time (SMRT) sequencing. We also identified the presence of this transposon sequence in other chromosomes of P. aeruginosa and plasmids carried by Acinetobacter spp. in GenBank, indicating the necessity for heightening attention to the potential transferability of this transposon.

Genetic characterization and phylogenetic analysis of the Nigella sativa (black seed) plastome

In this study, the complete plastome sequence of Nigella sativa (black seed), was analyzed for the first time. The plastome spans approximately 154,120 bp, comprising four sections: the Large Single-Copy (LSC) (85,538 bp), the Small Single-Copy (SSC) (17,984 bp), and two Inverted Repeat (IR) regions (25,299 bp). A comparative study of N. sativa’s plastome with ten other species from various genera in the Ranunculaceae family reveals substantial structural variations. The contraction of the inverted repeat region in N. sativa influences the boundaries of single-copy regions, resulting in a shorter plastome size than other species. When comparing the plastome of N. sativa with those of its related species, significant divergence is observed, particularly except for N. damascena. Among these, the plastome of A. glaucifolium displays the highest average pairwise sequence divergence (0.2851) with N. sativa, followed by A. raddeana (0.2290) and A. coerulea (0.1222). Furthermore, the study identified 12 distinct hotspot regions characterized by elevated Pi values (> 0.1). These regions include trnH-GUG-psbA, matK-trnQ-UUG, psbK-trnR-UCU, atpF-atpI, rpoB-psbD, ycf3-ndhJ, ndhC-cemA, petA-psaJ, trnN-GUU-ndhF, trnV-GAC-rps12, ycf2-trnI-CAU, and ndhA-ycf1. Approximately, 24 tandem and 48 palindromic and forward repeats were detected in N. sativa plastome. The analysis revealed 32 microsatellites with the majority being mononucleotide repeats. In the N. sativa plastome, phenylalanine had the highest number of codons (1982 codons), while alanine was the least common amino acid with 260 codons. A phylogenetic tree, constructed using protein-coding genes, revealed a distinct monophyletic clade comprising N. sativa and N. damascene, closely aligned with the Cimicifugeae tribe and exhibiting robust support. This plastome provides valuable genetic information for precise species identification, phylogenetic resolution, and evolutionary studies of N. sativa.

DNA Barcoding of Economically Important Fruit Flies (Diptera: Tephritidae) from the Lower Gangetic Plains of Eastern India

The Lower Gangetic Plains are India's central horticultural crop production regions. Fruit flies (Diptera: Tephritidae) are a significant insect pest that significantly hinders the effective crop production of various commercially valuable fruit and vegetable crops. Therefore, rapid and precise species identification techniques are essential to controlling, managing, and quarantining these pests. Furthermore, reliably identifying fruit flies is exceedingly challenging due to their striking resemblances in morphological characters. In this work, we investigated the effectiveness of mitochondrial cytochrome c oxidase I (COI) gene sequences for identifying seven fruit fly species, involving only the genus Bactrocera from the lower Gangetic Plains in eastern India. The sequences of the studied species showed 99%–100% similarity with sequences from within and outside the country. COI barcodes were able to distinguish between species, with K2P intraspecific genetic divergences ranging from 0-1% and K2P interspecific genetic divergences ranging from 3.65-28.46%, thus maintaining a proper barcode gap. Phylogenetic analyses were performed, and the results showed moderate to high supported monophyly among species. Therefore, COI barcodes have proven a highly successful alternative for quickly identifying fruit flies. This is also the first account of molecular identification of the previously reported tephritid fruit flies infesting fruits and vegetables in eastern India's agriculturally important lower Gangetic Plains.

DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.

Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets. This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection-based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences. The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization. Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model. DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.

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.

Hybrid Machine Learning Approach for Mosquito Species Classification Using Wingbeat Analysis

Effective and precise techniques for mosquito species identification are required as mosquito-borne illnesses continue to pose serious threats to public health across the world. We provide a new hybrid machine-learning technique in this research work for the classification of mosquito species through the Wingbeat analysis. It analyzes the wingbeat of the mosquito species based on which it can identify the mosquito species. This method makes use of deep learning techniques. The hybrid technique attempts to provide robust and dependable classification performance by utilizing a wide range of machine learning methods, such as k-Nearest Neighbors (KNN), Random Forest, Multi-layer Perceptron (MLP), Support Vector Machines (SVM), and Gradient Boosting. To improve feature extraction and normalization, we apply a rigorous set of preprocessing techniques to a large dataset that includes wingbeat recordings from many mosquito species. By means of comprehensive testing and analysis, we prove that our method is effective in correctly detecting mosquito species, exhibiting better results than using separate machine learning algorithms. Our findings demonstrate how deep learning methods may support more conventional machine learning strategies in problems involving the categorization of mosquito species. We also address the implications of our results for ecological research and disease management initiatives, highlighting the significance of precise species identification in vector monitoring and epidemiological investigations.

Size-Dependent Catalysis in Fenton-like Chemistry: From Nanoparticles to Single Atoms.

State-of-the-art Fenton-like reactions are crucial in advanced oxidation processes (AOPs) for water purification. This review explores the latest advancements in heterogeneous metal-based catalysts within AOPs, covering nanoparticles (NPs), single-atom catalysts (SACs), and ultra-small atom clusters. A distinct connection between the physical properties of these catalysts, such as size, degree of unsaturation, electronic structure, and oxidation state, and their impacts on catalytic behavior and efficacy in Fenton-like reactions. In-depth comparative analysis of metal NPs and SACs is conducted focusing on how particle size variations and metal-support interactions affect oxidation species and pathways. The review highlights the cutting-edge characterization techniques and theoretical calculations, indispensable for deciphering the complex electronic and structural characteristics of active sites in downsized metal particles. Additionally, the review underscores innovative strategies for immobilizing these catalysts onto membrane surfaces, offering a solution to the inherent challenges of powdered catalysts. Recent advances in pilot-scale or engineering applications of Fenton-like-based devices are also summarized for the first time. The paper concludes by charting new research directions, emphasizing advanced catalyst design, precise identification of reactive oxygen species, and in-depth mechanistic studies. These efforts aim to enhance the application potential of nanotechnology-based AOPs in real-world wastewater treatment.