Search Results (57)

The rapid expansion of the meat duck industry in China has intensified environmental challenges, particularly those related to managing high-moisture duck manure. Fermentation bed systems, utilizing rice husks as a primary substrate, offer a sustainable solution by promoting waste decomposition and improving animal welfare. This study investigated microbial diversity in rice husk-based fermentation bed materials across different usage durations to assess their ecological feasibility. Samples were collected from a duck farm in Linyi, China, after one, three, five and seven batches of duck rearing (21 days per batch). Microbial communities were analyzed using polymerase chain reaction–denaturing gradient gel electrophoresis (PCR-DGGE), followed by cluster analysis, principal component analysis (PCA) and sequencing of recovered DGGE bands. The results revealed significant shifts in microbial composition, with low similarity (18% overall) and distinct abundance patterns among groups. Bacteroidetes abundance increased with prolonged usage, while Staphylococcus aureus was only detected in the first batch. A total of 32 sequenced bands identified dominant phyla, including Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes. Group 4 (seven batches) exhibited the highest microbial diversity and richness (Shannon index: 2.68; mean abundance: 16.33 bands), which was attributed to organic matter accumulation and nutrient release during fermentation. These findings demonstrate that rice husk-based fermentation beds maintain robust microbial diversity over time, effectively supporting waste degradation and duck health. We conclude that rice husks are a viable, eco-friendly substrate for waterfowl fermentation bed systems, with periodic microbial supplementation recommended to enhance long-term efficacy. This work provides critical insights for optimizing sustainable livestock farming practices. Full article

Novel waterfowl reoviruses (nWRVs) have been reported in several countries, but their circulation and genetic characteristics in Vietnam remain poorly understood. In this study, we investigated nWRVs in northern Vietnam through molecular detection, virus isolation, experimental infection in ducklings, and molecular analysis of the sigma C-encoding (sC) gene. We also applied immunoinformatic tools to explore the antigenic and structural features of the sC protein. nWRVs were detected in 15.6% of tested samples across ten provinces. Three isolates were successfully recovered, all showing a characteristic cytopathic effect—syncytium formation—in Vero cells. When tested in ducklings (n = 72), the isolates caused disease of varying severity, but all induced characteristic gross and microscopic lesions, particularly ecchymotic hemorrhages and large necrotic foci in the liver and spleen. Phylogenetic analysis based on sC sequences placed the Vietnamese isolates (n = 14) within the nWRV clade, with evidence of two genetically distinct groups. Our immunoinformatic analysis identified four predicted B-cell epitopes located in the head and body domains of the sC protein, with little variation. Full article

Anseriformes (waterfowl) and Charadriiformes (shorebirds) are well-recognized natural reservoirs of low pathogenic (LP) influenza A viruses (IAVs). Historically, LP IAVs circulate among healthy individuals during seasonal, and often transcontinental, migrations. However, following the introduction of clade 2.3.4.4b highly pathogenic (HP) A/Goose/Guangdong/1/1996 lineage H5 IAV to North America in 2021, countless wild birds succumbed to fatal infections across the Western Hemisphere. Due to their small size and cryptic plumage patterns, opportunities for carcass recovery and postmortem evaluation in sanderlings (Calidris alba) and other shorebirds are rare. A multispecies mortality event in coastal Virginia, USA, in March–April 2024 included sanderlings among other wild bird species. Nine sanderlings underwent postmortem evaluation and clade 2.3.4.4b H5 IAV RNA was detected in pooled oropharyngeal-cloacal swabs from 11/11 individuals by real-time reverse transcription polymerase chain reaction. Histopathology was similar to that in waterfowl and included necrosis in the pancreas and brain and less commonly in the gonad, adrenal gland, spleen, liver, and intestine. Immunohistochemistry revealed IAV antigen labeling in necrotic neurons of the brain (neurotropism) and epithelial cells of the pancreas, gonad, and adrenal gland (epitheliotropism). Describing HP IAV-attributed pathology in shorebirds is key to understanding ecoepidemiology and population health threats in order to further document and compare pathogenesis among avian species. Full article

As a crucial characteristic waterfowl breed, the egg-laying performance of Lion-Headed Geese serves as a core indicator for precision breeding. Under large-scale flat rearing and selection practices, high phenotypic similarity among individuals within the same pedigree coupled with traditional manual observation and existing automation systems relying on fixed nesting boxes or RFID tags has posed challenges in achieving accurate goose–egg matching in dynamic environments, leading to inefficient individual selection. To address this, this study proposes YOLO-Goose, an improved YOLOv8s-based method, which designs five high-contrast neck rings (DoubleBar, Circle, Dot, Fence, Cylindrical) as individual identifiers. The method constructs a lightweight model with a small-object detection layer, integrates the GhostNet backbone to reduce parameter count by 67.2%, and employs the GIoU loss function to optimize neck ring localization accuracy. Experimental results show that the model achieves an F1 score of 93.8% and mAP50 of 96.4% on the self-built dataset, representing increases of 10.1% and 5% compared to the original YOLOv8s, with a 27.1% reduction in computational load. The dynamic matching algorithm, incorporating spatiotemporal trajectories and egg positional data, achieves a 95% matching rate, a 94.7% matching accuracy, and a 5.3% mismatching rate. Through lightweight deployment using TensorRT, the inference speed is enhanced by 1.4 times compared to PyTorch-1.12.1, with detection results uploaded to a cloud database in real time. This solution overcomes the technical bottleneck of individual selection in flat rearing environments, providing an innovative computer-vision-based approach for precision breeding of pedigree Lion-Headed Geese and offering significant engineering value for advancing intelligent waterfowl breeding. Full article

Monitoring waterfowl populations is essential for informing habitat management, conservation strategies, and sustainable harvest regulations. Many target species such as mallards and northern pintails are keystone components of wetland ecosystems, serving as ecological indicators due to their sensitivity to environmental changes. The integration of drone technology and artificial intelligence (AI) is significantly transforming the field of wildlife conservation and habitat monitoring. Existing methods for waterfowl monitoring face critical challenges such as low accuracy in identifying overlapping image regions and limited segmentation accuracy in complex habitats. To address these issues, this paper presents an end-to-end system and several new methods for efficiently and accurately identifying waterfowl populations in their natural habitats using AI and drone imagery. We applied advanced deep learning models to drone imagery for detecting and counting waterfowl. To handle overlapping regions in consecutive images, we developed a bird-location-based method that quickly and accurately identifies overlaps. For habitat segmentation, we proposed an effective approach combining Meta’s Segment Anything Model (SAM) with a ResNet50 classifier. Additionally, we used ChatGPT to generate clear, easy-to-read reports summarizing detection results. Experimental results show that our bird detection model (Faster R-CNN) achieved 86.57% mAP, our habitat segmentation method reached 85.1% accuracy (average F1 score: 81.8%), and our overlap detection method maintained an error rate below 5% with faster performance compared to traditional techniques. These outcomes highlight the practical effectiveness of our integrated pipeline for wildlife conservation and habitat monitoring. Full article

Duck enteritis virus (DEV), goose parvovirus (GPV), and muscovy duck parvovirus (MDPV) all have similar symptoms after infection, such as severe diarrhea, which seriously affects the healthy development of the waterfowl industry. Hence, it is important to devise a rapid and precise assay for the detection of these three viruses. In this study, a TaqMan probe-based multi-quantitative polymerase chain reaction (qPCR) assay was developed and optimized. Three specific primers and probes were designed according to the conserved regions of UL6 of DEV, REP of GPV, and VP1 of MDPV, respectively. DEV demonstrated a detection limit of 11.6 copies, GPV detected a limit of 95 copies, and MDPV showcased a detection limit of 14.8 copies. The correlation coefficient is greater than 0.99, and the amplification efficiency is 89% to 93%. These results indicate that the multiplex qPCR assay has high sensitivity, specificity, and stability. Of the 215 clinical samples used in this study, 33 tested DEV positive, 25 tested GPV positive, and 24 tested MDPV positive. Overall, the assay established in the current study presents a rapid, efficient, specific, and sensitive tool for of detecting DEV, GPV, and MDPV. Full article

Microsporidiosis is a zoonotic disease that derives from disparate sources. Most of the microsporidial agents are host-specific but some are capable of interspecies transmission, causing disease in various animals including humans. Human microsporidiosis may be caused by 17 species, with Encephalitozoon cuniculi, E. intestinalis and E. hellem mostly being responsible for human infections worldwide. Wildlife and migratory waterfowl can serve as reservoirs of these human-infectious agents and play a significant role in disseminating these pathogens into the environment. The aim of the study was to detect E. cuniculi, E. intestinalis and E. hellem in wild, migratory greater white-fronted geese (Anser albifrons) and other Anatidae members in feacal samples obtained in north-western Poland, using a molecular method. We collected 189 fecal droppings from Anatidae species (75 samples from greater white-fronted geese and 114 from other Anser spp.) during autumn migration. New species specific primers for PCR amplification were used to amplify a fragment of the small subunit ribosomal (SSU) rRNA of E. cuniculi, E. intestinalis and E. hellem. All fecal droppings were negative for E. intestinalis and E. hellem whereas E cuniculi was detected in 6 of 189 fecal samples (3.2%; 95% CI: 1.3–6.3%). In total, 1 of 75 tested fecal samples of greater white-fronted geese was positive (1.3%; 95% CI: 0.08–5.7%) while 5 of 114 (4.4%; 95% CI: 1.6–9.1%) tested fecal samples without exact species affiliation (only Anser sp.) were also positive. The phylogenetic analysis placed the sequences obtained from the birds’ droppings in the clade E. cuniculi from various rodents, wild carnivores and humans. Our results provide the first description of the occurrence and genotyping of the microsporidian E. cuniculi in greater white-fronted geese and in other members of the Anserinae Subfamily. Our findings support the results of other authors that E. cuniculi may originate from diverse sources, including common waterfowl. Our results are important in a One Health context, as wild migrating waterfowl may disseminate this zoonotic agent in remote regions through their migratory behaviour. These species should be considered significant sources of zoonotic pathogens, potentially hazardous to domestic and farmed animals as well as humans. Full article

Low pathogenic H9N2 avian influenza viruses have become widespread in wild birds and poultry worldwide, raising concerns about their potential to spark pandemics or their role in enhancing the virulence and infectivity of H5Nx viruses through genetic reassortment. Therefore, influenza monitoring studies, including those of H9N2 viruses, are crucial for understanding, evaluating, and mitigating the risks associated with avian infections, and have broader implications for global public health. Although H9N2 viruses are not considered enzootic in Kazakhstan, they have been repeatedly detected in wild waterfowls and domestic poultry. In this study, all eight gene segments of influenza A/H9N2 viruses isolated in various regions of Kazakhstan between 2014 and 2020 were sequenced and analyzed. Molecular characterization revealed the presence of genetic markers associated with mammalian infectivity and disease potential. Furthermore, their predicted receptor binding site sequences indicate their potential capacity to attach to human-type receptors. These findings highlight the importance of continued surveillance and molecular investigation to better understand the evolution and zoonotic potential of H9N2 viruses in Kazakhstan. Full article

Goose parvovirus (GPV), duck enteritis virus (DEV), Muscovy duck parvovirus (MDPV), duck hepatitis A virus type 1 (DHAV-1), duck hepatitis A virus type 3 (DHAV-3), duck Tembusu virus (DTMUV), and novel duck reovirus (NDRV) are significant pathogens that spread extensively among waterfowl populations, causing economic losses for the waterfowl industry. In order to detect seven pathogens simultaneously, a visual gene chip for the detection of multiple waterfowl disease pathogens was developed in this study. The gene chip was capable of specifically amplifying GPV, DEV, MDPV, DHAV-1, –DHAV-3, DTMUV, and NDRV. The sensitivity results showed that the lowest detection limit of the gene chip was 1 copy/μL for single and mixed samples. The reproducibility and stability tests demonstrated that the gene chip developed in this experiment exhibited not only excellent reproducibility but also remarkable stability, remaining functional for a minimum of 180 days. Compared to qPCR methods, the results showed that the sensitivity of the gene chip was slightly better than that of the qPCR method in detecting both single and mixed pathogens of the seven viruses. In this study, a total of 210 clinical samples were detected by the gene chip and qPCR, respectively, and the results of the two methods had a concordance rate of 98.1~100%, with a kappa value of 0.952, indicating that the consistency of the two detection methods was good. Full article

Duck Tembusu virus (DTMUV), duck hepatitis virus (DHV), Muscovy duck reovirus (MDRV), and Muscovy duck parvovirus (MDPV) represent four emergent infectious diseases impacting waterfowl, which can be challenging to differentiate due to overlapping clinical signs. In response to this, we have developed a one-step multiplex real-time fluorescence quantitative reverse transcription PCR (qRT-PCR) assay, capable of simultaneously detecting DTMUV, DHV, MDRV, and MDPV. This method exhibits high specificity, avoiding cross-reactivity with other viruses such as Fowl adenoviruses (FADV), infectious bursal disease virus (IBDV), infectious bronchitis virus (IBV), infectious laryngotracheitis virus (ILTV), Haemophilus paragallinarum (Hpg), duck circovirus (DUCV), goose astrovirus (GoAstV), and mycoplasma gallisepticum (MG). The limit of detection (LOD) established for DTMUV, DHV, MDRV, and MDPV was determined to be 27 copies/μL. In the repeatability test, the intra-assay and inter-assay coefficients of variation (CVs) of the recombinant plasmid standard were less than 2%. Utilizing this method, we analyzed 326 clinical specimens sourced from Guangxi over the period spanning October 2021 through December 2023, yielding promising and precise outcomes. The qRT-PCR method established herein exhibits commendable specificity, sensitivity, and repeatability. Furthermore, it boasts a high clinical detection rate, making it a highly effective tool for diagnosing these pathogenic agents in waterfowl. Full article

Erysipelas is a significant problem in the waterfowl farming in Poland, and information on the characteristics of the Erysipelothrix rhusiopathiae strains causing this disease is limited. In this study, we determined the serotypes, antimicrobial susceptibility, and potential mechanisms of resistance gene transfer in E. rhusiopathiae isolates (n = 60) from domestic geese and ducks. We also developed a multiplex PCR for the detection of resistance genes. The antimicrobial susceptibility of the isolates was assessed using the broth microdilution method. Resistance genes, integrative conjugative element (ICE)-specific genes, phage-specific genes, and serotype determinants were detected by PCR. Multilocus sequence typing (MLST) was performed for selected resistant strains. The comparative analyses included 260 E. rhusiopathiae strains whose whole genome sequences (WGSs) are publicly available. E. rhusiopathiae isolates represented 7 serotypes, among which serotypes 5 (38.3%) and 1b (28.3%) were the most common. All strains were susceptible to β-lactams, and the vast majority of them were resistant to tetracycline (85%) and enrofloxacin (80%). The percentages of isolates resistant to other antimicrobials used ranged from 3.3% to 16.7%. Ten isolates (16.7%) were found to be multidrug resistant (MDR). The genotypic resistance profiles of the E. rhusiopathiae strains corresponded to their phenotypic resistance, and the amplification patterns obtained using the 10-plex PCR developed in this study were fully consistent with the results of single PCRs. The most prevalent resistance gene was tetM. In enrofloxacin-resistant strains, nonsynonymous mutations in the gyrA and parC genes were identified. The presence of ICE-specific genes was confirmed in resistant strains, and in MDR isolates of serotype 8 that represented sequence type (ST) 113, prophage DNA (Javan630-like) linked to the lsaE gene was additionally detected. The results indicate that β-lactam antibiotics should be the first choice for the treatment of waterfowl erysipelas in Poland. ICEs, including a transposon from the Tn916/Tn1545 family, and bacteriophages are most likely responsible for the transfer of resistance genes in E. rhusiopathiae. Full article

Plumage color is a key trait for identifying waterfowl breeds with significant economic importance. A white-feathered group has recently emerged within the native Matahu duck population, presenting an opportunity for breeding new lines. However, the genetic basis for this plumage variation is still unknown, necessitating further research. This study aims to identify the genetic mechanisms underlying the emergence of white-feathered individuals in the Matahu duck population through combined genome and transcriptome analysis, providing insights for selective breeding and the development of new white-feathered lines. In this study, a total of 1344 selected genes and 1406 significantly differentially expressed genes were identified through selection signal analysis and transcriptomic analysis, respectively. The functional enrichment of these genes revealed several key signaling pathways, including those related to cGMP-PKG, cAMP, PI3K-Akt, and MAPK. Furthermore, important candidate genes involved in melanin biosynthesis, such as MITF, MC1R, TYR, TYRP1, and ABCB6, were identified. Notably, 107 genes were detected by both methods, and, among these, DGKI, GPRC5B, HMX1, STS, ADGRA1, PRKAR2B, and HOXB9 are suggested to play a role in melanin formation and potentially influence plumage traits. Through the integrative approach combining genomic selection signals and transcriptomic analyses, we identified several candidate genes directly associated with plumage color, including MITF, TYR, TYRP1, and MC1R, along with multiple signaling pathways linked to melanin formation. We hypothesize that the expression of DGKI, GPRC5B, HMX1, STS, ADGRA1, PRKAR2B, and HOXB9, detected by both methods, may be closely related to the regulation of plumage color traits. These findings provide a foundational basis for further research aimed at elucidating the genetic mechanisms governing plumage color variation in ducks. Full article

Influenza A viruses (IAVs), which belong to the Orthomyxoviridae family, are RNA viruses characterized by a segmented genome that allows them to evolve and adapt rapidly. These viruses are mainly transmitted by wild waterfowl. In this study, we investigated the evolutionary processes of H7Nx (H7N1, H7N2, H7N3, H7N4, H7N5, H7N6, H7N7, H7N8, H7N9) viruses, which pose a significant pandemic risk due to the known cases of human infection and their potential for rapid genetic evolution and reassortment. The complete genome sequences of H7Nx influenza viruses (n = 3239) were compared between each other to investigate their phylogenetic relationships and reassortment patterns. For the selected viruses, phylogenetic trees were constructed for eight genome segments (PB2, PB1, PA, HA, NP, NA, M, NS) to assess the genetic diversity and geographic distribution of these viruses. Distinct phylogenetic clades with remarkable geographic patterns were found for the different segments. While the viruses were consistently grouped by subtype based on the NA segment sequences, the phylogeny of the other segment sequences, with the exception of the NS segment, showed distinct grouping patterns based on geographic origin rather than formal subtype assignment. Reassortment events leading to complex phylogenetic relationships were frequently observed. In addition, multiple cases of previously undescribed reassortments between subtypes were detected, emphasizing the fluidity of H7Nx virus populations. These results indicate a high degree of genetic diversity and reassortment within H7Nx influenza viruses. In other words, H7Nx viruses exist as constantly changing combinations of gene pools rather than stable genetic lineages. Full article

Enteric parasites pose significant threats to both human and veterinary health, ranking among the top causes of mortality worldwide. Wild migratory waterfowl, such as ducks, may serve as hosts and vectors for these parasites, facilitating their transmission across ecosystems. This study conducted a molecular screening of enteric parasites in three species of wild ducks of the genus Anas (A. acuta, A. platyrhynchos and A. crecca) from Portugal, targeting Blastocystis sp., Balantioides coli, Cryptosporidium spp., Encephalitozoon spp., and Enterocytozoon bieneusi. Fecal samples from 71 ducks were analyzed using PCR and sequencing techniques. The results revealed a 2.82% occurrence of Blastocystis sp. subtype 7 and Cryptosporidium baileyi, marking the first molecular detection of these pathogens in wild ducks in Portugal. While previous studies have documented these parasites in Anas spp. in other regions, this study contributes novel data specific to the Portuguese context. No evidence of Balantioides coli, Encephalitozoon spp. or Enterocytozoon bieneusi was found. These findings highlight the potential role of migratory ducks as vectors for zoonotic protozoa, emphasizing the need for enhanced surveillance of avian populations to mitigate cross-species transmission risks. Further research is warranted to understand the global public health implications associated with migratory waterfowl. Full article

The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 (mAP@0.5) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds. Full article