Identification Of Virus Research Articles

Effectiveness of Structured Teaching Programme on Knowledge Regarding HIV/AIDS among B.sc Nursing 1st Semester Students: A Review Analysis

HIV/AIDS remains one of the most significant global public health challenges. Since the identification of Human Immunodeficiency Virus (HIV) in the early 1980s, significant advancements have been achieved in comprehending, preventing, and managing the infection. The human immunodeficiency virus (HIV) targets the cells of the human immune system, they either destroy them or damage their normal functioning. In the initial stages of infection, there are no apparent signs or symptoms. However, when the illness becomes more severe, the patient's immune system weakens, making them more susceptible to opportunistic infections such as tuberculosis (TB) and Kaposi's sarcoma. AIDS is a disease that leads to a person's death by causing multi-system dysfunction. Sexual contact is the most common way that HIV is transmitted from one person to another or from a mother to her unborn child during childbirth. Traditional birth attendants must receive education and training in safe health practices, raising awareness, and preventing and controlling diseases. For this research, our target population consists of 1st-semester B.Sc. Nursing students. It is well known that nursing students frequently come into contact with HIV/AIDS patients while on clinical rotations and that despite this, they remain highly susceptible to the illness because of their ignorance about it. Therefore, there is a necessity for implementing a program that can enhance their understanding and protect them from HIV/AIDS.

TransGINmer: Identifying viral sequences from metagenomes with self-attention and Graph Isomorphism Network

Viruses, abundant across diverse environments, play pivotal roles in microbial ecosystems and impact human health. Traditional virus studies are limited by their reliance on culture cultivation, which has been mitigated by metagenomics. It obtains nucleotide sequences of all microorganisms from the environment samples through the next-generation sequencing technology. This advancement prompts the need for efficient viral identification methods. To identify viruses accurately and quickly, We propose TransGINmer, a novel deep learning model to identify viral sequences directly from metagenomes. It encodes sequences by a k-mer frequency embedding model, constructs graphs from significant codon token correlations, and classifies them using graph isomorphism neural networks. In comparative tests against some SOTA methods DeepVirFinder, VirSorter2 and PhaMer on the testing dataset, the Amazon River dataset, the Sharon dataset and the CAMI Strain dataset, TransGINmer demonstrates superior accuracy, sensitivity, specificity, and AUC values, showcasing its potential as a robust tool for viral identification from metagenomes. TransGINmer is freely available at Github (https://github.com/xizhilangcc/TransGINmer).

Detection of Disease in Calves using Artificial Intelligence

Background: Livestock farming is experiencing a digital transformation and is becoming more information-driven. However, this type of data is often kept in separate storage towers, making it incapable of practicing its potential to boost animal welfare. Lumpy Skin Disease (LSD) is a serious threat to the health of cattle worldwide and has caused financial problems for many cattle farming enterprises. It has been shown that combining machine learning (ML) and artificial intelligence (AI) with biosensor data and conventional visual inspections can improve the identification and diagnosis of this serious condition. Methods: This study presents an extremely precise livestock farming framework that combines data streams from a wide array of disciplines of dairy cattle to see if ever wider/vast data sources enhance the overall projections for diseases and if using the more complicated prediction models can reimburse for less diverse data to some extent. Using images from the farming landscape, this study highlights the utility of convolutional neural networks (CNNs) in the identification of the Lumpy Skin Disease Virus (LSDV) in animals. Result: An analysis of the relative weight given to individual factors in predicting accuracy shows that disease in dairy cattle results from the intricate interactions between many life domains/parameters, such as housing, nutrition and climate; that prediction performance is enhanced by incorporating a wider range of data sources; and that current information can be repurposed to produce useful information for vaccine development. The study highlights the potential of data-driven dairy interventions, focusing on artificial intelligence for disease prediction in cattle, to improve animal welfare and reduce the risk of disease. A Convolutional Neural Network (CNN) based model effectively classified skin conditions with an overall accuracy of 73.89% after 27 training epochs. This study demonstrates CNN’s useful applications in the field of veterinary medicine by highlighting its potential for early detection of Lumpy Skin Disease (LSD).

Viruses in Wastewater-A Concern for Public Health and the Environment.

Wastewater monitoring provides essential information about water quality and the degree of contamination. Monitoring these waters helps identify and manage risks to public health, prevent the spread of disease, and protect the environment. Standardizing the appropriate and most accurate methods for the isolation and identification of viruses in wastewater is necessary. This review aims to present the major classes of viruses in wastewater, as well as the methods of concentration, isolation, and identification of viruses in wastewater to assess public health risks and implement corrective measures to prevent and control viral infections. Last but not least, we propose to evaluate the current strategies in wastewater treatment as well as new alternative methods of water disinfection.

Sacbrood viruses and select Lake Sinai virus variants dominated Apis mellifera colonies symptomatic for European foulbrood.

European foulbrood (EFB) is a prevalent disease in the European honey bee (Apis mellifera) in the United States, which can lead to colony decline and collapse. The bacterial components of EFB are well-studied, but the diversity of viral infections within infected colonies has not been explored. In this study, we use meta-transcriptomics sequencing of 12 honey bee hives, symptomatic (+, n = 6) and asymptomatic (-, n = 6) for EFB, to investigate viral infection associated with the disease. We assembled 41 viral genomes, belonging to three families (Iflaviridae, Dicistroviridae, and Sinhaliviridae), all previously reported in honey bees, including Lake Sinai virus, deformed wing virus, sacbrood virus, Black queen cell virus, and Israeli acute paralysis virus. In colonies with severe EFB, we observed a higher occurrence of viral genomes (34 genomes) in contrast to fewer recovered from healthy colonies (seven genomes) and a complete absence of Dicistroviridae genomes.We observed specific Lake Sinai virus clades associated exclusively with EFB + or EFB - colonies, in addition to EFB-afflicted colonies that exhibited an increase in relative abundance of sacbrood viruses. Multivariate analyses highlighted that a combination of site and EFB disease status influenced RNA virome composition, while EFB status alone did not significantly impact it, presenting a challenge for comparisons between colonies kept in different yards. These findings contribute to the understanding of viral dynamics in honey bee colonies compromised by EFB and underscore the need for future investigations to consider viral composition when investigating EFB.IMPORTANCEThis study on the viromes of honey bee colonies affected by European foulbrood (EFB) sheds light on the dynamics of viral populations in bee colonies in the context of a prevalent bacterial brood disease. The identification of distinct Lake Sinai virus and sacbrood virus clades associated with colonies affected by severe EFB suggests a potential connection between viral composition and disease status, emphasizing the need for further investigation into the role of viruses during EFB infection. The observed increase in sacbrood viruses during EFB infection suggests a potential viral dysbiosis, with potential implications for honey bee brood health. These findings contribute valuable insights related to beekeeping practices, offering a foundation for future research aimed at understanding and mitigating the impact of bacterial and viral infection in commercial honey bee operations and the management of EFB.

COECG-resnet-GWO-SVM: an optimized COVID-19 electrocardiography classification model based on resnet50, grey wolf optimization and support vector machine

AbstractCoronavirus disease 2019 (COVID-19) has swiftly spread throughout the globe, causing widespread infection in various countries and regions, and was declared a pandemic by World Health Organization (WHO) in 2020. Computer algorithms and models can help in the identification and classification of the COVID-19 virus in the medical domain, especially in CT, and X-rays and Electrocardiography tests with rapid and accurate results. In this paper, a COVID-19 electrocardiography classification model based on grey wolf optimization and support vector machine will be presented. A public online electrocardiography dataset was investigated in this paper with two classes (COVID-19, and Normal. The proposed model consists of three phases. The first phase is the feature extraction based on Resnet50. The second phase is the feature selection based on grey wolf optimization. The third phase is the classification based on the support vector machine. The experimental trials show that the proposed model achieves the highest accuracy possible when it is compared with other models that use different feature extraction and selection models, such as Alexnet and whale optimization algorithms. Also, the proposed model achieves the highest testing accuracy possible with 99.1% while related work that used hexaxial feature mapping and deep learning achieved 96.20% with an improvement of 2.9%. The achieved testing accuracy and its performance metrics such as Precision, Recall, and F1 Score support the research findings that the proposed model, while achieving the highest accuracy possible, it also consumes less time in the training by selecting a minimum number of features if it is compared with other related works which use the same dataset.

Effectiveness of Frequent Viral Load Testing Plus Additional Interventions to Prevent HIV Transmission in Heterosexual, Serodiscordant Couples - Yunnan Province, China, 2019-2021.

HIV transmission among serodiscordant couples remains a persistent issue in China. However, the practice of combining counseling with antiretroviral therapies (ART) to enhance ART adherence is not widely implemented or recommended in Chinese health guidelines. This randomized controlled trial suggests that increased follow-up, counseling, and awareness of HIV risk can enhance ART compliance, thereby maximizing treatment efficacy. Early testing and counseling of serodiscordant couples, following the identification of a human immunodeficiency virus (HIV) positive spouse, is crucial for initiating ART and reducing the risk of seroconversion in the uninfected partner. Implementing a combination of ART and adjunct counseling in China is advisable.

Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family

PurposeIn this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is achieved through a deep neural network model that relies on convolutional neural networks (CNNs). Since viruses within the same family share similar genetic and structural characteristics, the classification process becomes more challenging, necessitating more robust models. With the rapid evolution of viral genomes and the increasing need for timely classification, we aimed to provide a robust and efficient tool that could increase the accuracy of viral identification and classification processes. Contribute to advancing research in viral genomics and assist in surveilling emerging viral strains.MethodsBased on a one-dimensional deep CNN, the proposed tool is capable of training and testing on the Coronaviridae family, including SARS-CoV-2. Our model’s performance was assessed using various metrics, including F1-score and AUROC. Additionally, artificial mutation tests were conducted to evaluate the model’s generalization ability across sequence variations. We also used the BLAST algorithm and conducted comprehensive processing time analyses for comparison.ResultsDeepVirusClassifier demonstrated exceptional performance across several evaluation metrics in the training and testing phases. Indicating its robust learning capacity. Notably, during testing on more than 10,000 viral sequences, the model exhibited a more than 99% sensitivity for sequences with fewer than 2000 mutations. The tool achieves superior accuracy and significantly reduced processing times compared to the Basic Local Alignment Search Tool algorithm. Furthermore, the results appear more reliable than the work discussed in the text, indicating that the tool has great potential to revolutionize viral genomic research.ConclusionDeepVirusClassifier is a powerful tool for accurately classifying viral sequences, specifically focusing on SARS-CoV-2 and other subtypes within the Coronaviridae family. The superiority of our model becomes evident through rigorous evaluation and comparison with existing methods. Introducing artificial mutations into the sequences demonstrates the tool’s ability to identify variations and significantly contributes to viral classification and genomic research. As viral surveillance becomes increasingly critical, our model holds promise in aiding rapid and accurate identification of emerging viral strains.

Identification of respiratory virus in indoor air of hospitals: A comparison of adult and children's hospital

Air sampling was done inside adult and children's hospitals that were selected to treat severe cases of COVID-19. Influence of peripheral factors such as particle concentration, air velocity, sampling point dimensions, distance from the patient bed, sampling time, the flow rate of sampling pump, and factors related to COVID-19 patients (disease period, mask use, number, and age) were analyzed using multivariate analysis (RT-PCR). The results showed that 5.8% (N = 8) of indoor air samples were positive for the presence of the coronavirus. The presence of viruses in the indoor air of hospitals has a strong positive relationship with particles and the age of patients while it has a reverse relationship with the air cleaner, ventilation system, and distance from the patients. Therefore, the higher particle concentration, the age of hospitalized patients, and the remarkable number of patients increase the probability of the presence and identification of the coronavirus in the indoor air of hospital wards. Also, the presence of an air cleaner, a suitable ventilation system especially a mechanical one, and increasing the distance from the patients reduces the possibility of virus existence in the indoor air and its identification. In general, the results showed that the adult hospital has more polluted indoor air than the children's hospital in terms of the presence of SARS-COV-2. Sanitation and engineering measures like upgrading the ventilation system, particularly in vulnerable wards of hospitals are recommended.

Epidemiological exploration of fleas and molecular identification of flea-borne viruses in Egyptian small ruminants

The study aimed to investigate molecularly the presence of flea-borne viruses in infested small ruminants with fleas. It was carried out in Egypt’s Northern West Coast (NWC) and South Sinai Governorate (SSG). Three specific primers were used targeting genes, ORF103 (for Capripoxvirus and Lumpy skin disease virus), NS3 (for Bluetongue virus), and Rdrp (for Coronavirus), followed by gene sequencing and phylogenetic analyses. The results revealed that 78.94% of sheep and 65.63% of goats were infested in the NWC area, whereas 49.76% of sheep and 77.8% of goats were infested in the SSG region. Sheep were preferable hosts for flea infestations (58.9%) to goats (41.1%) in the two studied areas. Sex and age of the animals had no effects on the infestation rate (p > 0.05). The season and site of infestation on animals were significantly different between the two areas (p < 0.05). Ctenocephalides felis predominated in NWC and Ctenocephalides canis in SSG, and males of both flea species were more prevalent than females. Molecular analysis of flea DNA revealed the presence of Capripoxvirus in all tested samples, while other viral infections were absent. Gene sequencing identified three isolates as sheeppox viruses, and one as goatpox virus. The findings suggest that Capripoxvirus is adapted to fleas and may be transmitted to animals through infestation. This underscores the need for ongoing surveillance of other pathogens in different regions of Egypt.

Identification and molecular characterization of Wanghe virus: A novel member of the Solinviviridae family from mosquitoes, China

Arboviruses have always been a significant public health concern. Metagenomic surveillance has expanded the number of novel, often unclassified arboviruses, especially mosquito-borne and mosquito-specific viruses. This report presents the first description of a novel single-stranded RNA virus, Wanghe virus, identified from mosquitoes that were collected in Shandong Province in 2022. In this study, a total of 4,795 mosquitoes were collected and then divided into 105 pools according to location and species. QRT-PCR and nested PCR were performed to confirm the presence of Wanghe virus, and its genomic features and phylogenetic relationships were further analyzed. Our results revealed that Wanghe virus was detected in 9 out of the 105 mosquito pools, resulting in a minimum infection rate (MIR) of 0.19 % (9/4,795). One complete genome sequence and three viral partial sequences were obtained from the Wanghe virus-positive pools. Pairwise distance analysis indicated that these amplified sequences shared high nucleotide identity. Phylogenetic analysis demonstrated that Wanghe virus is most closely related to Guiyang Solinvi-like virus 3, which belongs to Solinviviridae. Further analyses indicated that Wanghe virus is a new, unclassified member of Solinviviridae.

Molecular identification of avian influenza virus A subtypes H5 and H7 in domestic geese and ducks in Basrah, South of Iraq

Molecular identification of avian influenza virus A subtypes H5 and H7 in domestic geese and ducks in Basrah, South of Iraq

Dengue dynamics: Prognostic and disease monitoring through molecular and serological profiling of clinical isolates.

Dengue fever is a mosquito-bome illness that affects millions of people worldwide every year. With no vaccination available, early detection and treatment are critical. It is found in 112 countries and poses a risk to travellers, particularly in metropolitan areas. Laboratory diagnoses vary according to objectives, resources, and schedule, with sensitivity and specificity must be balanced for effective testing. The current study is a cross-sectional diagnostic study and samples from suspected patients of dengue was collected from May to November 2023 and transported to laboratory. RT-PCR and Dengue Duo Rapid test diagnosis techniques were used and total 48 clinical samples were included in this study. A total of 48 clinical samples were collected. Blood was collected from the suspected cases of dengue and further subjected to different molecular and serological parameters. Serum was separated from all blood samples. RNA was isolated by silica column extraction method which is further utilized as a template for amplification and detection of dengue serotyping. Master Mix was prepared for the amplification and detection of dengue virus by Rotor-Gene Q Real-Time PCR Machine and further serological profiling of positive dengue cases was studied by conventional PCR. Our laboratory effectively standardized an RT-PCR-based approach for molecular identification of dengue virus in clinical specimens. This adaptive technique, which uses numerous primer sets, displayed good specificity and sensitivity in serotype detection. The technology provides for quick and reliable identification of dengue virus infections, allowing for targeted treatment and preventative actions for successful disease management in highly populated regions.

Virus ecology and 7-year temporal dynamics across a permafrost thaw gradient.

Soil microorganisms are pivotal in the global carbon cycle, but the viruses that affect them and their impact on ecosystems are less understood. In this study, we explored the diversity, dynamics, and ecology of soil viruses through 379 metagenomes collected annually from 2010 to 2017. These samples spanned the seasonally thawed active layer of a permafrost thaw gradient, which included palsa, bog, and fen habitats. We identified 5051 virus operational taxonomic units (vOTUs), doubling the known viruses for this site. These vOTUs were largely ephemeral within habitats, suggesting a turnover at the vOTU level from year to year. While the diversity varied by thaw stage and depth-related patterns were specific to each habitat, the virus communities did not significantly change over time. The abundance ratios of virus to host at the phylum level did not show consistent trends across the thaw gradient, depth, or time. To assess potential ecosystem impacts, we predicted hosts in silico and found viruses linked to microbial lineages involved in the carbon cycle, such as methanotrophy and methanogenesis. This included the identification of viruses of Candidatus Methanoflorens, a significant global methane contributor. We also detected a variety of potential auxiliary metabolic genes, including 24 carbon-degrading glycoside hydrolases, six of which are uniquely terrestrial. In conclusion, these long-term observations enhance our understanding of soil viruses in the context of climate-relevant processes and provide opportunities to explore their role in terrestrial carbon cycling.

Enhancing Polymerase Chain Reaction Efficiency for Foot-and-mouth disease virus identification with Unfunctionalized Nanoparticles

Enhancing Polymerase Chain Reaction Efficiency for Foot-and-mouth disease virus identification with Unfunctionalized Nanoparticles

Prevalencia e infección mixta de virus en cultivos de maracuyá amarillo (Passiflora edulis f. flavicarpa) en el Valle del Cauca, Colombia

Yellow passion fruit crop is affected by multiple phytosanitary problems. Among the main ones are viruses of the genera Potyvirus (soybean mosaic virus, SMV), Cucumovirus (cucumber mosaic virus, CMV), Tymovirus (tymovirus passiflorae, PYMV) and Begomovirus (passionfruit leaf distortion virus, PLDV). Information about the prevalence and interaction between these viruses is scarce. The objective of this study was to verify the prevalence and identify the types of mixed viral infections of potyvirus, cucumovirus, tymovirus, and begomovirus in passion fruit crops in Valle del Cauca, Colombia. Passion fruit leaf samples with symptoms were collected and their nucleic acids were purified. Virus identification was performed by PCR using universal primers by viral genus, and specific primers for the SMV, CMV, PFYMV and PLDV viruses. The incidence of each virus by municipality was analyzed and the severity of the symptoms was compared with the diagnosis made. In total, 66 samples were collected in the municipalities of Toro, Dagua, Roldanillo, Bolívar, La Unión and El Cerrito. It was established that the most prevalent viruses are PLDV followed by SMV and CMV. The presence of PYMV was not detected in the samples analyzed. The presence of double and triple infections among potyvirus, cucumovirus and begomovirus were identified for the first time, finding greater severity of symptoms with a greater number of viruses identified per sample. This research provides key results for the design of control strategies for viral diseases in passion fruit.

Epidemiological investigation on diseases of Larimichthys crocea in Ningbo culture area.

Due to the high-density farming of Larimichthys crocea over the years, diseases caused by pathogens such as bacteria, viruses, and parasites frequently occur in Ningbo, posing a huge threat and challenge to the sustainable and healthy development of the L. crocea's bay farming industry. In order to understand the diseases occurrence in L. crocea farming in Ningbo area, an epidemiological investigation of L. crocea diseases was carried out through regular sampling in 2023. From April to October 2023, routine sampling of L. crocea was conducted monthly in various farming areas in Ningbo. Each time, live or dying L. crocea with obvious clinical symptoms were sampled, with a total number of 55 L. crocea collected. The samples were preserved in ice bags and transported to the laboratory for pathogen detection(including bacterial isolation and identification,virus identification, and parasites detection). A total of fifty-five fish dying L. crocea with obvious clinical symptoms were collected in this study, of which 78.18% (43/55) were detected with symptoms caused by pathogenic infection, while 21.82% (12/55) did not have identified pathogens, which were presumed to be breeding abrasions, nutritional metabolic disorders, unconventional pathogens infection or other reasons. A total of twenty-five pathogenic bacteria strains were isolated, which mainly were Pseudomonas plecoglossicida and Vibrio harveyi, accounting for 52% (13/25) and 32% (8/25) of the pathogenic bacteria strains, respectively. Among them, both V. harveyi and Streptococcus. iniae co-infected one fish. Additionally, three other bacterial strains including Nocardia seriolae, Staphylococcus Saprophyticus, and Photobacterium damselae subsp.damselae were isolated. Microscopic examination mainly observed two parasites, Cryptocaryon irritans and Neobenedenia girellae. In virus detection, the red sea bream iridovirus (RSIV) was mainly detected in L. crocea. Statistical analysis showed that among the fish with detected pathogens, 55.81% (24/43) had bacterial infections, 37.21% (16/43) had parasitic infections, and 37.21% (16/43) had RSIV infections. Among them, five fish had mixed infections of bacteria and parasites, three had mixed infections of bacteria and viruses, three had mixed infections of parasites and viruses, and one L. crocea had mixed infections of viruses, bacteria, and parasites. These findings indicate that these three major types of diseases are very common in the L. crocea farming area in Ningbo, implying the complexity of mixed infections of multiple diseases.

Lack of abundant core virome in Culex mosquitoes from a temperate climate region despite a mosquito species-specific virome.

In arthropod-associated microbial communities, insect-specific viruses (ISVs) are prevalent yet understudied due to limited infectivity outside their natural hosts. However, ISVs might play a crucial role in regulating mosquito populations and influencing arthropod-borne virus transmission. Some studies have indicated a core virome in mosquitoes consisting of mostly ISVs. Employing single mosquito metagenomics, we comprehensively profiled the virome of native and invasive mosquito species in Belgium. This approach allowed for accurate host species determination, prevalence assessment of viruses and Wolbachia, and the identification of novel viruses. Contrary to our expectations, no abundant core virome was observed in Culex mosquitoes from Belgium. In that regard, we caution against rigidly defining mosquito core viromes and encourage nuanced interpretations of other studies. Nonetheless, our study identified 45 viruses of which 28 were novel, enriching our understanding of the mosquito virome and ISVs. We showed that the mosquito virome in this study is species-specific and less dependent on the location where mosquitoes from the same species reside. In addition, because Wolbachia has previously been observed to influence arbovirus transmission, we report the prevalence of Wolbachia in Belgian mosquitoes and the detection of several Wolbachia mobile genetic elements. The observed prevalence ranged from 83% to 92% in members from the Culex pipiens complex.IMPORTANCECulex pipiens mosquitoes are important vectors for arboviruses like West Nile virus and Usutu virus. Virome studies on individual Culex pipiens, and on individual mosquitoes in general, have been lacking. To mitigate this, we sequenced the virome of 190 individual Culex and 8 individual Aedes japonicus mosquitoes. We report the lack of a core virome in these mosquitoes from Belgium and caution the interpretation of other studies in this light. The discovery of new viruses in this study will aid our comprehension of insect-specific viruses and the mosquito virome in general in relation to mosquito physiology and mosquito population dynamics.

Using PCR and RCA Techniques to Investigate the Variants of Cassava Mosaic Virus and Their Distribution in Ghana

Cassava mosaic disease (CMD) caused by cassava begomoviruses is the major constraint to cassava production in Ghana. The disease is known to cause reduction in root yield. To ascertain the distribution of viruses causing CMD, 95 diseased cassava samples were collected in two agroecological zones of Ghana-Deciduous Forest zone and the Transitional zone. On a scale of 1-5, CMD severity was scored. Mean CMD severity score was 2.9, however there was no significant difference (p &amp;gt; 0.05) between the zones. Averagely, CMD score of &amp;gt; 2.8 in 71% of farms visited was recorded. Polymerase chain reaction (PCR) and rolling circle amplification (RCA) were employed for virus identification. PCR revealed that African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV) and mixed infections were prevalent in both zones. EACMV-Cameroon strain was also identified to be common within these zones. The transitional zone had the highest percentage of CMD infection. Unamplified samples from PCR were amplified using rolling circle amplification (RCA) technique. Amplification and characterisation of complete genome sequences of two isolates were carried out. The complete genome of 2780 nucleotides from samples showed a high similarity to African cassava mosaic virus-Ghana (ACMV-GH). Sequences clustered with ACMV-Ivory Coast, ACMV Nigeria-Ogo, ACMV-BF, ACMV-UG having &amp;gt; 96% identity. This shows the close relation that exists amongst the ACMV strains in Africa. These findings highlight the need for a continuous survey of CMD to help manage the disease in the country.

Molecular characterization of Sweet potato feathery mottle virus and sweet potato yield loss due to its infection

Abstract. Wirya GNAS, Selangga DGW, Listihani L, Temaja IGRM, Sudiarta IP. 2024. Molecular characterization of Sweet potato feathery mottle virus and sweet potato yield loss due to its infection. Biodiversitas 25: 2355-2362. Sweet potatoes are one of the main sources of carbohydrates for Balinese. "Nasi selo," one of Bali's specialties, uses sweet potato as one of its main ingredients. For the past few years, cultivated sweet potatoes in Bali, Indonesia, manifested symptoms of diseases, particularly purple ring spots, occurring on its young and old leaves. The causative agent of the purple ring symptom is still unknown. Therefore, this study aims to analyze the molecular characteristics of SPFMV Bali isolate and the yield loss estimation it causes. Research methods that were used in this study are survey, sampling, virus identification with RT-PCR using universal Potyvirus as its primer, and field observation to assess the yield loss estimation. Field observation was conducted in five districts in Bali province: Badung, Gianyar, Bangli, Karangasem, and Klungkung. The result indicates that SPFMV infection has been found in these 5 districts, with the highest incidence and severity occurring in Bangli at 86% and 53%. The nucleotide homology of the SPFMV Bali isolates gene has the closest genetic relationship with isolates found in South Korea (MH388494, MH388494) and East Timor (MF572056), at &gt;99%. Based on the latest phylogroup classification, SPFMV is categorized into phylogroups A and B. Phylogroup A consists of SPFMV with O-I strain, O-II strain, and EA strain; phylogroup B only consists of SPFMV with RC strain. Isolate SPFMV found in Bali, has an O strain that belongs to the same group as the O-I strain, also seen in South Korea, East Timor, and China. Actual yield loss that was found by comparing sweet potatoes suffering from purple rings symptom with the one without purple rings symptom reveals a yield loss of 50.9%. Harvested sweet potatoes with purple ring symptoms showed quality deterioration in malformation, discoloration, hollowing, and changes in color and shape. Yield loss of up to 29.53% in fields with a disease severity of 53% has caused a loss of IDR 2,105,166.