Method For Identification Of Species Research Articles

PRECISION AND RELIABILITY OF NANOPLATE DIGITAL PCR SYSTEM FOR PORK DNA IDENTIFICATION AND QUANTIFICATION

The adulteration and mislabeling of food ingredients, particularly meat products, pose a significant concern for consumers. In order to address this issue, it is crucial to use precise and sensitive methods for species identification, mainly when dealing with samples that contain a limited number of copies of DNA. The primary objective of this study is to design a digital nanoplate polymerase chain reaction (PCR) technique for the purpose of quantifying species identification. The primer used in this study was cytochrome b gene. The results showed that target DNA can be amplified using the digital PCR nanoplate until the lowest sample concentration of 0.0013 ng/µL. This study can be applied to enhance species identification processes in food samples by utilizing digital PCR nanoplates.

Nontuberculous mycobacterial disease in children: A systematic review and meta-analysis

BackgroundThe prevalence of nontuberculous mycobacterial (NTM) disease in children is increasing worldwide. The clinical manifestations of pediatric NTM patients are significantly different from those of adult patients, but the knowledge of the disease is generally poor. MethodsEnglish databases (PubMed, Web of Science, Embase, BIOSIS) and Chinese databases (CNKI, Wanfan, VIP) were searched on October 15th, 2022. All the articles of cross-sectional and cohort studies reporting the species composition and lesion site of the NTM disease in children using well-recognized NTM species identification methods were taken into account. Using a random effects model, we assessed the disease lesion sites and the prevalence of different NTM species in pediatric NTM disease. Sources of heterogeneity were analyzed using Cochran's Q and the I2 statistic. All analyses were performed using CMA V3.0. ResultsThe prevalence rates of NTM disease in children ranged between 0.6 and 5.36/100,000 in different countries, and Europe reported the highest prevalence rate. The most common clinical lesion site was lymph node, accounting for 71.1 % (55.0 %–83.2 %), followed by lung (19.3 %, 9.8%–34.4 %）and then skin and soft tissue (16.6 %,13.5%–20.3 %). Mycobacterium avium complex (MAC) was the most isolated NTM pathogen in children, accounting for 54.9 % (39.4%–69.6 %). Inconsistent with adult patients, Mycobacterium avium accounted for a dominant proportion in MAC than Mycobacterium intracellulare. ConclusionsThe lymph node was the most affected organ in pediatric NTM disease, while Mycobacterium avium was the most isolated pathogenic species in children.

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species

BackgroundTraditional method of wood species identification involves the use of hand lens by wood anatomists, which is a time-consuming method that usually identifies only at the genetic level. Computer vision method can achieve "species" level identification but cannot provide an explanation on what features are used for the identification. Thus, in this study, we used computer vision methods coupled with deep learning to reveal interspecific differences between closely related tree species.ResultA total of 850 images were collected from the cross and tangential sections of 15 wood species. These images were used to construct a deep-learning model to discriminate wood species, and a classification accuracy of 99.3% was obtained. The key features between species in machine identification were targeted by feature visualization methods, mainly the axial parenchyma arrangements and vessel in cross section and the wood ray in tangential section. Moreover, the degree of importance of the vessels of different tree species in the cross-section images was determined by the manual feature labeling method. The results showed that vessels play an important role in the identification of Dalbergia, Pterocarpus, Swartzia, Carapa, and Cedrela, but exhibited limited resolutions on discriminating Swietenia species.ConclusionThe research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity.

Discrimination of ivory from extant and extinct elephant species using Raman spectroscopy: A potential non-destructive technique for combating illegal wildlife trade.

The use of elephant ivory as a commodity is a factor in declining elephant populations. Despite recent worldwide elephant ivory trade bans, mammoth ivory trade remains unregulated. This complicates law enforcement efforts, as distinguishing between ivory from extant and extinct species requires costly, destructive and time consuming methods. Elephant and mammoth ivory mainly consists of dentine, a mineralized connective tissue that contains an organic collagenous component and an inorganic component of calcium phosphate minerals, similar in structure to hydroxyapatite crystals. Raman spectroscopy is a non-invasive laser-based technique that has previously been used for the study of bone and mineral chemistry. Ivory and bone have similar biochemical properties, making Raman spectroscopy a promising method for species identification based on ivory. This study aimed to test the hypothesis that it is possible to identify differences in the chemistry of mammoth and elephant ivory using Raman spectroscopy. Mammoth and elephant tusks were obtained from the Natural History Museum in London, UK. Included in this study were eight samples of ivory from Mammuthus primigenius, two samples of carved ivory bangles from Africa (Loxodonta species), and one cross section of a tusk from Elephas maximus. The ivory was scanned using an inVia Raman micro spectrometer equipped with a x50 objective lens and a 785nm laser. Spectra were acquired using line maps and individual spectral points were acquired randomly or at points of interest on all samples. The data was then analysed using principal component analysis (PCA) with use of an in-house MATLAB script. Univariate analysis of peak intensity ratios of phosphate to amide I and III peaks, and carbonate to phosphate peaks showed statistical differences (p<0.0001) in the average peak intensity ratios between Mammuthus primigenius, Loxodonta spp. and Elephas maximus. Full width at half maximum hight (FWHM)analysis of the phosphate peak demonstrated higher crystal maturity of Mammuthus primigenius compared to living elephant species. The results of the study have established that spectra acquired by Raman spectroscopy can be separated into distinct classes through PCA. In conclusion, this study has shown that well-preserved mammoth and elephant ivory has the potential to be characterized using Raman spectroscopy, providing a promising method for species identification. The results of this study will be valuable in developing quick and non-destructive methods for the identification of ivory, which will have direct applications in archaeology and the regulation of international trade.

Genetic identification of three CITES-listed sharks using a paper-based Lab-on-a-Chip (LOC).

Threatened shark species are caught in large numbers by artisanal and commercial fisheries and traded globally. Monitoring both which shark species are caught and sold in fisheries, and the export of CITES-restricted products, are essential in reducing illegal fishing. Current methods for species identification rely on visual examination by experts or DNA barcoding techniques requiring specialist laboratory facilities and trained personnel. The need for specialist equipment and/or input from experts means many markets are currently not monitored. We have developed a paper-based Lab-on-a-Chip (LOC) to facilitate identification of three threatened and CITES-listed sharks, bigeye thresher (Alopias superciliosus), pelagic thresher (A. pelagicus) and shortfin mako shark (Isurus oxyrinchus) at market source. DNA was successfully extracted from shark meat and fin samples and combined with DNA amplification and visualisation using Loop Mediated Isothermal Amplification (LAMP) on the LOC. This resulted in the successful identification of the target species of sharks in under an hour, with a working positive and negative control. The LOC provided a simple "yes" or "no" result via a colour change from pink to yellow when one of the target species was present. The LOC serves as proof-of-concept (PoC) for field-based species identification as it does not require specialist facilities. It can be used by non-scientifically trained personnel, especially in areas where there are suspected high frequencies of mislabelling or for the identification of dried shark fins in seizures.

Molecular Identification of the Diversity of Insects, Spiders, Lizards, Birds and Mammals of Tuensang District, Nagaland, India

ABSTRACT: DNA barcoding is a method of species identification that revolutionized the way we study and understand biodiversity. With advancements in molecular genetics and DNA sequence archives, it has become possible to use short DNA sequences to identify species, even when the specimens are difficult to distinguish by traditional morphological methods. In this article, we explore the utility of DNA barcoding in the Tuensang ecosystem of Nagaland, India and evaluate its effectiveness for species identification, informing ongoing conservation of populations and species. A total of 62 species, which included insects, spiders, lizards, birds and mammals, were collected from Tuensang areas of northeast India and identified using DNA barcodes. DNA was extracted from muscle tissue and PCR was done with two pairs of primers targeting the mitochondrial COI gene. Sanger sequencing was employed and the obtained sequences were analysed to identify the species and reconstruct the evolutionary relationships amongst them. Our results provided molecular characterization of species from Tuensang areas of Nagaland for the first time.

Identification of breadfruit (Artocarpus altilis) and South American crops introduced during early settlement of Rapa Nui (Easter Island), as revealed through starch analysis.

Starch residue analysis was carried out on stone tools recovered from the bottom layer of the Anakena site on Rapa Nui (Easter Island). These deposits have been dated to AD 1000-1300 AD and so far, represent the earliest evidence of human settlement on this island. Twenty obsidian tools were analyzed. Analysis of 46 starch grains recovered from 20 obsidian tools from the earliest dated level of the Anakena site on Rapa Nui provides direct evidence for translocation of traditional crop plants at initial stages of the colonization of this island. The analysis of starch grains was based mainly on statistical methods for species identification but was complemented by visual inspection in some cases. Our results identify taxons previously unknown to have been cultivated on the island, such as breadfruit (Artocarpus altilis), Zingiber officinale (ginger), and starch grains of the Spondias dulcis and Inocarpus fagifer tropical trees. Additionally, starch grains of Colocasia esculenta (taro) and Dioscorea sp. (yam), both common species in Pacific agriculture, were identified. Furthermore, the presence of four American taxa Ipomoea batatas (sweet potato), Canna sp. (achira), Manihot esculenta (manioc), and Xanthosoma sp., was detected. The occurrence of Canna sp., M. esculenta, and Xanthosoma sp. starch grains suggests the translocation of previously not described South American cultivars into the Pacific. The detection of I. batatas from this site in Rapa Nui constitutes the earliest record of this cultigen in the Pacific. Our study provides direct evidence for translocation of a set of traditional Polynesian and South American crop plants at the initial stages of colonization in Rapa Nui.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

Species Identification of Inuit Skin and Fur Clothing: Analyses of DNA, Hair Microscopy, and Macroscopical Identification

From approximately 1830 to 1940, through various expeditions to Siberia, Arctic North America, and Greenland, and through donations, the National Museum of Denmark acquired its collection of historical Inuit skin clothing. Unfortunately, original provenance information has been lost for 14% of the garments. In order to document the extensive collection, this study investigates three methods for species identification of animal skin: microscopy of hair, macroscopic identification, and DNA validation. Thus, the present study has two aims: first, to optimise and test hair microscopy for species identification by validating identifications by DNA analyses, and second, to use species identification to estimate the geographic and cultural provenance of Inuit skin clothing. Based on a dataset of well-documented clothing (for positive controls), this study describes an optimised species identification protocol via hair microscopy using transmitted light microscopy (TLM). We demonstrate that the TLM hair protocol is a reliable and inexpensive alternative to molecular approaches when macroscopic identification is doubtful and DNA validation or protein analyses are impossible. In this study we used photomicrographs to document the identifications of caribou (Rangifer tarandus), musk ox (Ovibos moschatus), species of the true seal family (Phocidae), domestic dog (Canis lupus familiaris), wolf (Canis lupus), Arctic fox (Vulpes lagopus), polar bear (Ursus maritimus), wolverine (Gulo gulo), ground squirrel (Urocitellus parryii richardsonii), ermine (Mustela erminea), and cattle (Bos taurus); these identifications can be used for future reference. We conclude that species identification analyses secure the documentation of garments and allow most objects to be contextualized by culture and geography. The studied garments are accessible at the museum website.

Bioinformatic Analysis Strategy in Restriction Enzyme Selection for Indonesian Panulirus homarus Identification by PCR-RFLP

The spiny lobster (Panulirus homarus) is a valuable fishery commodity in Indonesia, particularly in its juvenile life form. However, identifying the early life forms of the spiny lobster can be challenging, as it exhibits similar morphological features compared to the juveniles of other Panilurid lobsters. Molecular-based identification, specifically DNA sequencing, is the best method for species identification, but it requires advanced instruments and is costly. An alternative method is proposed here, using the PCR-RFLP technique, which is low-cost, rapid, and has standard instrumentation requirements. The challenge with this method is selecting the appropriate restriction enzyme to determine the targeted species’ identity. This study proposes using the REfind (https://github.com/indriatmoko07/REfind), R package to select the best restriction enzyme for identifying P. homarus, applicable to other species. The bioinformatics workflow used in this study successfully identified BseSI or BmgI as the most suitable restriction enzymes among 739 restriction enzymes to differentiate P. homarus from other Panilurid species. This result was validated by employing a wet lab test using the BseSI enzyme and successfully validated the bioinformatics result. These findings may be useful for biologists in conducting various studies that require rapid, low-cost, and identification of specific species in the future.

DNA BARCODING OF Caranx senegallus FROM THREE WATER BODIES ADJOINING THE GULF OF GUINEA, NIGERIA

Fish species show astonishing phenotypic and genetic diversity in populations. The delimitation and recognition of fish species are not only of interest to taxonomists but also a requirement in studies of ecology. In this study, the genetic structure of Caranx senegallus collected from three water bodies adjoining the Gulf of Guinea, Nigeria was characterized using the cytochrome c oxidase I (COI) barcode gene region. Fish samples collected from Qua Iboe estuary, Escravos estuary, and Lagos lagoon were identified morphologically, and muscle tissues were used for DNA barcoding using Fish1 and Fish2 primers. Evolutionary analysis showed the formation of diverging clades in the neighbor-joining tree and indicated the presence of multiple species. The result also revealed that morphological identification was not entirely successful, as the Basic Local Alignment Search Tool (BLAST) prediction confirmed the presence of three specimens initially identified as C. senegallus but genetically matching C. fischeri and Trachinotus goreensis. This observation further emphasized the need for an integrative method for species identification.

Nontuberculous mycobacteria testing and culture positivity in the United States

BackgroundNontuberculous mycobacteria (NTM) are environmental bacteria which may cause chronic lung disease. The prevalence of NTM pulmonary infection and disease has been increasing in the United States and globally. The predominant clinically relevant species of NTM in the United States are Mycobacterium avium complex (MAC) species and Mycobacterium abscessus. With the development of rapid species identification methods for NTM (e.g. PCR probes), more testing for NTM is being conducted through commercial labs, such as Laboratory Corporation of America (Labcorp), which provides deidentified real-time testing data to the Centers for Disease Control (CDC) pursuant to a data sharing agreement. Because NTM lung infections are not reportable in most states, other data sources are key to understanding NTM testing patterns, positivity rates, and species distributions to track infection trends and identify clinical care needs.MethodsWe obtained national Labcorp data for the period January 2019 through mid-April 2022. We subset the data to only respiratory samples sent for Acid Fast Bacilli (AFB) cultures. NTM positive results were defined as those which identified an NTM species and are not Mycobacterium tuberculosis, Mycobacterium bovis, or Mycobacterium gordonae.ResultsOverall, 112,528 respiratory samples were sent for AFB testing during the study period; 26.3% were from the Southeast U.S., identified as HSS Region IV in the Labcorp dataset, and 23.0% were from the Pacific and South Pacific region (Region IX). The culture positive prevalence ranged from 20.2% in the Southeast to 9.2% in the East North Central region (Region V). In the Southeast US, M. abscessus prevalence was 4.0%. For MAC, the highest prevalence was observed in the Mountain region (Region VII) (13.5%) and the lowest proportion was in the East South Central region (7.3%, Region III). Among positive tests, the proportion which was MAC varied from 61.8% to 88.9% and was highest in the Northeast U.S. The proportion of positive samples which were M. abscessus ranged from 3.8% to 19.7% and was highest in the Southeast.ConclusionsThe Southeastern region of the U.S. has the highest rate of culture positivity in Labcorp tests for total NTM and, of all positive tests, the highest proportion of M.abscessus. These estimates may underrepresent the true number of M.abscessus infections because M. absesscus-specific probes are not commercially available and not all NTM testing in the United States is done by Labcorp. Analysis of real-time testing data from commercial laboratories may provide insights into risk factors for NTM culture positivity in ‘hotspot’ areas.

Analysis of Whole-Genome as a Novel Strategy for Animal Species Identification.

Survival crises stalk many animals, especially endangered and rare animals. Accurate species identification plays a pivotal role in animal resource conservation. In this study, we developed an animal species identification method called Analysis of whole-GEnome (AGE), which identifies species by finding species-specific sequences through bioinformatics analysis of the whole genome and subsequently recognizing these sequences using experimental technologies. To clearly demonstrate the AGE method, Cervus nippon, a well-known endangered species, and a closely related species, Cervus elaphus, were set as model species, without and with published genomes, respectively. By analyzing the whole genomes of C. nippon and C. elaphus, which were obtained through next-generation sequencing and online databases, we built specific sequence databases containing 7,670,140 and 570,981 sequences, respectively. Then, the species specificities of the sequences were confirmed experimentally using Sanger sequencing and the CRISPR-Cas12a system. Moreover, for 11 fresh animal samples and 35 commercially available products, our results were in complete agreement with those of other authoritative identification methods, demonstrating AGE's precision and potential application. Notably, AGE found a mixture in the 35 commercially available products and successfully identified it. This study broadens the horizons of species identification using the whole genome and sheds light on the potential of AGE for conserving animal resources.

Using capillary electrophoresis to identify Anopheline species in routine sampling sites.

In the Anopheles genus, various mosquito species are able to transmit the Plasmodium parasites responsible for malaria, while others are non-vectors. In an effort to better understand the biology of Anopheles species and to quantify transmission risk in an area, the identification of mosquito species collected in the field is an essential but problematic task. Morphological identification requires expertise and cannot be checked after processing samples in a destructive treatment, while sequencing of numerous samples is costly. Here, we introduce a method of Species identification via Simple Observation Coupled with Capillary Electrophoresis Technology (SOCCET). This molecular technique of species identification is based on precise determination of ITS2 length combined with a simple visual observation, the colour of mosquito hindleg tip. DNA extracted from field-collected Anopheles mosquitoes was amplified with universal Anopheles ITS2 primers and analysed with a capillary electrophoresis device, which precisely determines the size of the fragments. We defined windows of amplicon sizes combined with fifth hind tarsus colour, which allows discrimination of the major Anopheles species found in our collections. We validated our parameters via Sanger sequencing of ITS2 amplicons. Using the SOCCET method, we characterised the composition of Anopheles populations in five locations of French Guiana, where we detected a total of nine species. Anopheles braziliensis and Anopheles darlingi were detected in four locations each and represented 13 and 67% of our samples, respectively. The SOCCET method can be particularly useful when working with routine sampling sites with a moderate species diversity, that is, when the number of local species is too high to define species-specific primers but low enough to avoid individual ITS2 sequencing. This tool will be of interest to evaluate local malaria transmission risk and this approach may be further implemented for other mosquito genera.

A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples.

Fish species identification plays a vital role in marine fisheries resource exploration, yet datasets related to marine fish resources are scarce. In open-water environments, various fish species often exhibit similar appearances and sizes. To solve these issues, we propose a few-shot learning approach to identifying fish species. Our approach involves two key components. Firstly, the embedding module was designed to address the challenges posed by a large number of fish species with similar phenotypes by utilizing the distribution relationships of species in the embedding space. Secondly, a metric function was introduced, effectively enhancing the performance of fish species classification and successfully addressing the issue of limited sample quantity. The proposed model is trained end to end on fish species public datasets including the Croatian fish dataset, Fish4Knowledge and WildFish. Compared with the prototypical networks, our method performs more effectively and improves accuracy by 2% to 10%; it is able to identify fish effectively in small samples sizes and complex scene scenarios. This method provides a valuable technological tool for the development of fisheries resources and the preservation of fish biodiversity.

Geometric morphometric methods for identification of oyster species based on morphology.

Both genetic and environmental factors affect the morphology of oysters. Molecular identification is currently the primary means of species identification, but it is inconvenient and costly. In this research, we evaluated the effectiveness of geometric morphometric (GM) techniques in distinguishing between two oyster species, Crassostreagigas and C.ariakensis. We used traditional morphometric and GM methods, including principal component analysis (PCA), thin-plate spline analysis (TPS) and canonical variable analysis (CVA), to identify specific features that distinguish the two species. We found that differences in shape can be visualised using GM methods. The Procrustes analysis revealed significant differences in shell morphology between C.gigas and C.ariakensis. The shells of C.ariakensis are more prominent at the widest point and are more scattered and have a greater variety of shapes. The shells of C.gigas are more oval in shape. PCA results indicated that PC1 explained 45.22%, PC2 explained 22.09% and PC3 explained 10.98% of the variation between the two species, which suggests that the main morphological differences are concentrated in these three principal components. Combining the TPS analysis function plots showed that the shell shape of C.ariakensis is mainly elongated and spindle-shaped, whereas the shell shape of C.gigas is more oval. The CVA results showed that the classification rate for the two species reached 100% which means that C.ariakensis and C.gigas have distinct differences in shell morphology and can be completely separated, based on morphological characteristics. Through these methods, a more comprehensive understanding of the morphological characteristics of different oyster populations can be obtained, providing a reference for oyster classification and identification.

Geometry of posterior larval spiracles to identify medically and forensically important calliphorids in Thailand

Fly identification is the primary step of analysis in forensic entomology. Although morphology and molecular techniques are considered satisfactory methods, some constraints may arise from a financial or even human point of view. Over the past decade, the geometric morphometric approach has been increasingly advocated for the classification and identification of arthropods. This study explored the method for species identification of 800 third-instar larvae of eight blow fly species of medical and forensic importance: Chrysomya chani Kurahashi, Chrysomya megacephala (Fabricius), Chrysomya (Ceylonomyia) nigripes Aubertin, Chrysomya pinguis (Walker), Chrysomya (Achoetandrus) rufifacies (Macquart), Hemipyrellia ligurriens (Wiedemann), Lucilia cuprina (Wiedemann), and Lucilia porphyrina (Walker). Based on the posterior spiracles geometry, the cross-validation revealed a relatively high percentage of correct classification in most species, ranking from 86% to 100%. The results of this study confirmed that the geometric morphometric (GM) analysis of posterior spiracles might be utilized as a larva identification tool. Therefore, this GM method represents one way of overcoming difficulties with the identification of blow fly larvae and can support further studies of these flies.

Artificial neuronal networks are revolutionizing entomological research

AbstractThe application of artificial intelligence (AI) in entomological research has gained significant attention in recent years. This review summarizes the current state of research on the potential of AI methods in various subfields of entomology, such as behavioural biology, biodiversity research, climate change research, pest management, and disease vector control. In some cases, AI‐based species identification methods based on deep learning neuronal network models have been shown to outperform traditional morphological identification methods in terms of accuracy and speed. Behavioural biology research has been enhanced through the use of AI‐based tracking systems that can classify insect behaviour and movement patterns. Habitat modelling has also been improved with the use of AI, allowing for the creation of more accurate models that can predict insect distribution and abundance. Climate change and biodiversity research have benefited from AI‐driven tools that can analyse large datasets and predict the impact of environmental changes on insect populations. In agriculture, pest management has been revolutionized by AI methods, with the development of smart traps and monitoring systems that can detect and identify pest species in real‐time, enabling targeted control measures. Disease vector control has also been improved through the use of AI‐based predictive models, which can identify areas at risk of disease transmission and aid in the development of effective control strategies. In conclusion, AI methods have the potential to revolutionize many aspects of entomological research. Future research should focus on developing more sophisticated AI tools and integrating them into entomological research to address even more complex ecological questions.

Molecular based identification and phylogenetic relationship by using cytochrome b gene of Pangasius pangasius.

Molecular appraoch for identification of unknown species by using Cytochrome b gene is an effective and reliable as compared with morphological based identification. For DNA barcoding universal molecular genes were used to identify the species. Cytochrome b is a specific gene used for identification purpose. DNA barcoding is a reliable and effective method compared to the different traditional morphological methods of specie identification. So,in the present study which was conducted to identify the species, a total of 50 fish samples were collected from five different sites. DNA was extracted by using the Phenol Chloroform method from muscle tissue. Five sequences were sequenced (one from each site), analyzed, and identified specific species as Pangasius pangasius. Identified sequences were variable in length from 369 bp (Site 1), 364 bp (Site 2), 364 bp (Site 3), 352 bp (Site 4), and 334 bp (Site 5). Identity matches on the NCBI database confirmed the specific specie as P. pangasius. A distancing tree was drawn to show maximum likelihood among the same and different species. Yet, in many cases fishes on diverse development stages are difficult to identify by morphological characters. DNA-based identification methods offer an analytically powerful addition or even an alternative tool for species identification and phylogenetic study. This work intends to provide an updated and extensive overview on the DNA based methods for fish species identification by using Cytochrome b gene as targeted markers for identification purpose.

Challenges and knowledge gaps in the management of non-tuberculous mycobacterial pulmonary disease in sub-Saharan African countries with a high tuberculosis burden: a scoping review

IntroductionIn sub-Saharan African (SSA) countries endemic for tuberculosis (TB), previous TB is a significant risk factor for non-tuberculous mycobacterial pulmonary disease (NTM-PD). The deployment of GeneXpert MTB/RIF in pulmonary TB...