Background: Porcine parvovirus (PPV) is a key etiological agent of reproductive failure in swine, resulting in considerable economic losses for the pig farming industry. Methods: The present study was conducted to investigate the molecular prevalence, genetic characteristics, seroprevalence and associated pathological alterations in pigs from Assam, India. Result: Out of the 82 tissue samples screened by PCR targeting 226 bp specific to VP2 gene encoding capsid protein, 9 (10.97%) were found positive for PPV. These samples consisted mainly of pooled aborted fetal materials. Subsequently, next-generation sequencing of a representative clinical sample was carried out. Phylogenetic analysis of VP1 and NS1 genes revealed a high degree of nucleotide identity (99.77-100%) with contemporary PPV strains from China and South Korea. Phylogenetic clustering suggested that the VP1 sequence belongs to the 27a/27a-like lineage. Gross pathological alterations in aborted fetuses included hydrothorax and visceral congestion, whereas histopathology revealed necrosis in multiple organs and lymphoid depletion, which is suggestive of systemic viral effects. Serological screening of 224 pigs using a commercial ELISA kit divulged a seroprevalence of 23.21%, indicating widespread exposure to PPV among both healthy and diseased animals. These combined findings confirm the circulation of potentially virulent PPV strains in Assam and highlight the need of frequent surveillance, which will help in devising appropriate control strategies to mitigate reproductive losses in the swine population.

Object detection has made significant strides in recent years, but it remains a challenging task to accurately and quickly identify and detect objects. While humans can easily recognize objects in images or videos regardless of their appearance, computers face difficulties in this task. Object detection plays a crucial role in computer vision and finds applications in various domains such as healthcare, security, agriculture, home automation and more. To address the challenges of object detection, several techniques have been developed including RCNN, Faster RCNN, YOLO and Single Shot Detector (SSD). In this paper, we propose a modified YOLOv5s architecture that aims to improve detection performance. Our modified architecture incorporates the C3Ghost module along with the SPP and SPPF modules in the YOLOv5s backbone network. We also utilize the Adam and Stochastic Gradient Descent (SGD) optimizers. The paper also provides an overview of three major versions of the YOLO object detection model: YOLOv3, YOLOv4 and YOLOv5. We discussed their respective performance analyses. For our evaluation, we collected a database of pig images from the ICAR-National Research Centre on Pig farm. We assessed the performance using four metrics such as Precision (P), Recall (R), F1-score and mAP @ 0.50. The computational results demonstrate that our method YOLOv5s architecture achieves a 0.0414 higher mAP while utilizing less memory space compared to the original YOLOv5s architecture. This research contributes to the advancement of object detection techniques and showcases the potential of our modified YOLOv5s architecture for improved performance in real world applications.

Viral infection disrupts the normal regulation of the host gene’s expression. In order to normalise the expression of dysregulated host genes upon virus infection, analysis of stable reference housekeeping genes using quantitative real-time-PCR (qRT-PCR) is necessary. In the present study, healthy and African swine fever virus (ASFV) infected porcine tissues were assessed for the expression stability of five widely used housekeeping genes (HPRT1, B2M, 18 S rRNA, PGK1 and H3F3A) as reference genes using standard algorithm. Total RNA from each tissue sample (lymph node, spleen, kidney, heart and liver) from healthy and ASFV-infected pigs was extracted and subsequently cDNA was synthesized, and subjected to qRT-PCR. Stability analysis of reference genes expression was performed using the Comparative delta CT, geNorm, BestKeeper and NormFinder algorithm available at RefFinder for the different groups. Direct Cycle threshold (CT) values of samples were used as an input for the web-based tool RefFinder. HPRT1 in spleen, 18 S rRNA in liver and kidney and H3F3A in heart and lymph nodes were found to be stable in the individual healthy tissue group (group A). The majority of the ASFV-infected organs (liver, kidney, heart, lymph node) exhibited H3F3A as stable reference gene with the exception of the ASFV-infected spleen, where HPRT1 was found to be the stable gene (group B). HPRT1 was found to be stable in all combinations of all CT values of both healthy and ASFV-infected porcine tissues (group C). Of five different reference genes investigated for their stability in qPCR analysis, the present study revealed that the 18 S rRNA, H3F3A and HPRT1 genes were optimal reference genes in healthy and ASFV-infected different porcine tissue samples. The study revealed the stable reference genes found in healthy as well as ASF-infected pigs and these reference genes identified through this study will form the baseline data which will be very useful in future investigations on gene expression in ASFV-infected pigs.

Helicobacter species (spp.) is a gram-negative spiral-shaped motile bacterium that causes gastritis in pigs and also colonizes in the human stomach. The present study assessed the prevalence of Helicobacter spp. in pig gastric mucosa and the stool of pig farmers in Assam, India. A total of 403 stomach samples from pig slaughter points, 74 necropsy samples of pigs from pig farms, and 97 stool samples from pig farmers were collected. Among the pig stomach samples, 43 (20.09%) of those with gastritis showed the presence of Gram-negative, spiral-shaped organisms, while only 3.04% of stomach samples without lesions had these organisms. Scanning Electron Microscopy (SEM) of urease-positive stomach samples revealed tightly coiled Helicobacter bacteria in the mucus lining. Histopathological examination showed chronic gastritis with hemorrhagic necrosis, leucocytic infiltration, and lymphoid aggregates. PCR confirmed the presence of Helicobacter suis in 19.63% of pig stomach samples and 2.08% of pig farmer stool samples. Additionally, 3.12% of the stool samples from pig farmers were positive for Helicobacter pylori. Phylogenetic analysis revealed distinct clusters of Helicobacter suis with other Helicobacter spp. These findings highlight the prevalence of Helicobacter in both pig gastric mucosa and pig farmer stool. The findings highlight the need for improved sanitation and hygiene practices among pig farmers to minimize the risk of Helicobacter infection in humans.

Pig posture is closely linked with livestock health and welfare. There has been significant interest among researchers in using deep learning techniques for pig posture detection. However, this task is challenging due to variations in image angles and times, as well as the presence of multiple pigs in a single image. In this study, we explore an object detection and segmentation algorithm based on instance segmentation scoring to detect different pig postures (sternal lying, lateral lying, walking, and sitting) and segment pig areas in group images, thereby enabling the identification of individual pig postures within a group. The algorithm combines a residual network with 50 layers and a feature pyramid network to extract feature maps from input images. These feature maps are then used to generate regions of interest (RoI) using a region candidate network. For each RoI, the algorithm performs regression to determine the location, classification, and segmentation of each pig posture. To address challenges such as missing targets and error detections among overlapping pigs in group housing, non-maximum suppression (NMS) is used with a threshold of 0.7. Through extensive hyperparameter analysis, a learning rate of 0.01, a batch size of 512, and 4 images per batch offer superior performance, with accuracy surpassing 96%. Similarly, the mean average precision (mAP) exceeds 83% for object detection and instance segmentation under these settings. Additionally, we compare the method with the faster R-CNN object detection model. Further, execution times on different processing units considering various hyperparameters and iterations have been analyzed.

Porcine reproductive and respiratory syndrome (PRRS) and African swine fever (ASF) are economically important diseases of pigs throughout the world. During an outbreak, all age groups of animals except piglets < 1 month of age were affected with symptoms of high fever, cutaneous hemorrhages, vomition with blood, diarrhea, poor appetite, ataxia, and death. The outbreak was confirmed by the detection of the N gene of the porcine reproductive and respiratory syndrome virus (PRRSV) and the VP72 gene of the African swine fever virus (ASFV) by PCR in representative blood samples from affected pigs followed by Sanger sequencing. Mixed infection was also confirmed by simultaneous detection of both the viruses using multiplex PCR. Phylogenetic analysis of both the viruses revealed that the outbreak was related to ASFV and PRRSV strains from China which were also closely related to the PRRSV and ASFV strains from the recent outbreak from India. The study confirmed the involvement of genotype II of ASFV and genotype 2 of PRRSV in the present outbreak. Interestingly, PRRSV associated with the present outbreak was characterized as a highly pathogenic PRRSV. Therefore, the present study indicates the possibility of future waves or further outbreaks of these diseases (PRRS and ASF) in this region. This is the first report of ASFV and PRRSV co-infection in pigs from India.

African swine fever (ASF) was first reported in 1921, and since then has posed a major threat to the world pig industry and still remains a major challenge as there is no vaccine or therapy available. In May 2020, the first incidence of ASF was recorded in India, followed by a number of outbreaks in the north eastern part of India. In this study, we report the first whole genome of an Indian isolate of ASF virus (ASF/IND/20/CAD/543) using next generation sequencing and compared with the other ASFV complete genome. On phylogenetic analysis, the virus was assigned to genotype II on the basis of p72 genotyping. However, the whole genome based phylogeny distinguished it from other genotype II isolates of clade 1.1.1. This study adds to our understanding of ASFV’s genetic diversity and molecular evolution.

Yak (Bos grunniens) is a unique bovine species and considered as lifeline of highlanders. The male subfertility in yak is a matter of concern that causes huge economic loses. The spermatogenesis and male reproduction machinery are critically governed by Y-linked genes which tend to acquire necessary information in the course of evolution. The Y-linked fertility genes are present in multiple copies with testis-limited expression. To understand this novel complexity, 12 male-specific region of Y chromosome (MSY) genes have been studied in the yak. Targeted genes are amplified in male and female genomic DNA and confirmed the male derived specificity. Moreover, testis and sperm-specific expressions of MSY genes are distinct among different tissues. The quantitative polymerase chain reaction results validate the expression pattern of these genes in various tissues with predominant expression intestis and sperm. The sequencing of resultant yak MSY genes gives significant result and shows similarity with cattle (Bos indicus), but few nucleotide mismatches define the proposition of infertile male in the F1 hybrid of cattle and yak. The identified MSY genes can be used to establish male-specific characteristics and to differentiate male and female yak genotypically. Further, these genes may act as valuable resources to understand the capacity of spermatogenesis, embryogenesis, cellular growth, azoospermia and malesubfertility in the yak.

Yak is an iconic symbol of Tibet and high altitudes of Northeast India. It is highly cherished for milk, meat, and skin. However, yaks suffer drastic change in milk production, weight loss, etc, when infested by parasites. Among them, infestation by leeches is a serious problem in the Himalayan belt of Northeast India. The parasite feeds on blood externally or from body orifices, like nasopharynx, oral, rectum, etc. But there has been limited data about the leech species infesting the yak in that region because of the difficulties in morphological identification due to plasticity of the body, changes in shape, and surface structure and thus, warrants for the molecular characterization of leech. In anticipation, this study would be influential in proper identification of leech species infesting yak track and also helpful in inventorying of leech species in Northeast India. Here, we investigated, through combined approach of molecular markers and morphological parameters for the identification of leech species infesting yak. The DNA sequences of COI barcode fragment, 18S and 28S rDNA, were analyzed for species identification. The generated sequences were subjected to similarity match in global database and analyzed further through Neighbour-Joining, K2P distance based as well as ML approach. Among the three markers, only COI was successful in delineating species whereas the 18S and 28S failed to delineate the species. Our study confirmed the presence of the species from genus Hirudinaria, Haemadipsa, Whitmania, and one species Myxobdella annandalae, which has not been previously reported from this region.

Bovine respiratory disease (BRD) is a condition of severe pneumonia caused due to environmental stress followed by a multiple viral and bacterial pathogens.The present study aimed to detect the BRD agents such as Mannhaeimia haemolytica, Pasteurella multocida and Histophilus somni and IBR in yaks suffering from respiratory infections. For the study, 157 deep nasal and blood samples were collected from yak of Arunachal Pradesh and processed for isolation and PCR detection of respiratory bacterial pathogens such as M. haemolytica, P. multocida and H. somni and seroscreening of IBR. From 157 samples, one M. haemolytica and two H. somni isolates were recovered. This is the first report of H. somni and M. haemolytica isolation from yak nasal samples from India. In mPCR, 23 yak nasal samples (14.6%) amplified for M. haemolytica, two (1.2%) for P. multocida and six samples for H. somni (4.6%) by simplex PCR. Concurrence of H. somni and M. haemolytica in three samples, and M. haemolytica and P. multocida type B in two samples was also observed.Very high seroprevalence for IBR (37%) by Indirect ABELISA was recorded.The study concluded that highest number of samples were positive for M. haemolytica (14.6%) and emphasizes its role in respiratory infections along with high seroprevalence of IBR. It also reflected the diagnostic importance of mPCR/PCR in regions inaccessible for fresh sample collection and transportation as an alternate to isolation procedures.

There is dearth of availability of surveillance reports elucidating the role of mithun, yak and their hybrid populations inhabiting north-eastern regions of India in the epidemiology of foot-and-mouth disease (FMD). This study conducted during 2015–2016, therefore highlights the serological status of the disease in these animals of Arunachal Pradesh. A total of 857 sera from 804 yaks, 44 mithuns and 9 hybrids of mithun and yak were collected and an indirect 3AB nonstructural protein (NSP) ELISA was performed to detect NSP-antibodies (NSP-Abs). A prevalence of FMD virus (FMDV) NSP reactive sera in 25.12% (202/804) yak and 2.27% (1/44) mithun was estimated, while 0% seroreactivity was observed in their hybrids. The findings inferred that FMDV circulates in the said animals populating Arunachal Pradesh. Hence, necessary control measures including prophylactic vaccination for FMD should be put into practice to safeguard these rare threatened species considered to be the pride of the country.