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Near Human takes us into the borders of human and animal life. In the animal facility, fragile piglets substitute for humans who cannot be experimented on. In the neonatal intensive care unit, extremely premature infants prompt questions about whether they are too fragile to save or, if they survive, whether they will face a life of severe disability. Drawing on ethnographic fieldwork carried out on farms, in animal-based experimental science labs, and in hospitals, Mette N. Svendsen shows that practices of substitution redirect the question of \"what it means\" to be human to \"what it takes\" to be human. The near humanness of preterm infants and research piglets becomes an avenue to unravel how neonatal life is imagined, how societal belonging is evaluated, and how the Danish welfare state is forged. This courageous multi-sited and multi-species approach cracks open the complex ethical field of valuating life and making different kinds of pigs and different kinds of humans belong in Denmark.

[This corrects the article DOI: 10.1371/journal.pone.0260234.].

Seneca Valley virus (SVV) is known to cause vesicular disease in swine, presenting new challenges to the pig industry. Recent studies have investigated the relationship between disrupted copper ion homeostasis and viral replication, suggesting that copper dysregulation has a significant impact on the replication of various viruses. Research has also shown that mitochondria-associated endoplasmic reticulum membrane (MAM) and NF-κB are involved in the innate immune response triggered by viral infections. However, the exact mechanisms by which copper (Cu), MAM, and NF-κB affect SVV replication remain unclear. In this study, it was found that SVV induces an imbalance in copper homeostasis, leading to dynamic changes in MAM while inhibiting the NF-κB pathway. This inhibition results in decreased levels of IL-6, IL-1[beta], TNF-[alpha], IFN-[alpha], and IFN[lambda]3. Furthermore, the disruption of copper homeostasis in SVV-infected PK-15 cells regulates the NF-κB pathway through MAM, promoting SVV replication. This research provides valuable insights into the regulation of copper metabolism during SVV infection and establishes a theoretical framework for understanding the pathogenesis and immune activation mechanisms associated with SVV.

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most harmful pathogens in the swine industry. Our previous studies demonstrated that the small extracellular domain (ECL2) of CLDN4 effectively blocks PRRSV infection. In this study, we explored the in vivo administration of swine ECL2 (sECL2) and found that it blocked HP-PRRSV infection and alleviated histopathological changes in organs. Notably, sECL2 stimulated cytokine production in the lungs. We observed that CLDN4 upregulated the expression of IFN-[beta] at low doses of GP3. While high doses of GP3 inhibited the activity of the IFN-[beta] promotor, regardless of whether CLDN4 was present. GP3 also downregulated IFN-[beta] by decreasing the phosphorylation of TBK1 and IRF3. These findings highlight functional differences in GP3 under quantity control, which account for the variations in IFN-[beta] induction during the early and late stages of infection. Our results indicate that sECL2 is a promising candidate drug for developing treatments to control PRRS.

Pork accounts for more than one-third of meat produced worldwide and is an important component of global food security, agricultural economies, and trade. Infectious diseases are among the primary constraints to swine production, and the globalization of the swine industry has contributed to the emergence and spread of pathogens. Despite the importance of infectious diseases to animal health and the stability and productivity of the global swine industry, pathogens of swine have never been reviewed at a global scale. Here, we build a holistic global picture of research on swine pathogens to enhance preparedness and understand patterns of emergence and spread. By conducting a scoping review of more than 57,000 publications across 50 years, we identify priority pathogens globally and regionally, and characterize geographic and temporal trends in research priorities. Of the 40 identified pathogens, publication rates for eight pathogens increased faster than overall trends, suggesting that these pathogens may be emerging or constitute an increasing threat. We also compared regional patterns of pathogen prioritization in the context of policy differences, history of outbreaks, and differing swine health challenges faced in regions where swine production has become more industrialized. We documented a general increasing trend in importance of zoonotic pathogens and show that structural changes in the industry related to intensive swine production shift pathogen prioritization. Multinational collaboration networks were strongly shaped by region, colonial ties, and pig trade networks. This review represents the most comprehensive overview of research on swine infectious diseases to date.

African swine fever (ASF) is a devastating disease in domestic and wild pigs. Since the first outbreak of ASF in August 2018 in China, the disease has spread throughout the country with an unprecedented speed, causing heavy losses to the pig and related industries. As a result, strategies for managing the disease are urgently needed. This paper summarizes the important aspects of three key elements about African swine fever virus (ASFV) transmission, including the sources of infection, transmission routes, and susceptible animals. It overviews the relevant prevention and control strategies, focusing on the research progress of ASFV vaccines, anti-ASFV drugs, ASFV-resistant pigs, efficient disinfection, and pig farm biosecurity. We then reviewed the key technical points concerning pig farm repopulation, which is critical to the pork industry. We hope to not only provide a theoretical basis but also practical strategies for effective dealing with the ASF epidemic and restoration of pig production.

Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.

Improving sow productivity and welfare have been long-withstanding goals for the swine industry. Identifying novel traits and phenotypes to accomplish these objectives is needed. The objective of the current study was to determine if activity-based phenotypes collected by the NUtrack livestock monitoring system, NUtrack, could serve as early-life indicator traits for mothering ability in swine. The phenotypes collected included distance traveled, average velocity, angle rotated, time allocated to eating, lying lateral, lying sternal, standing, and sitting. The response variables selected in first parity females to model mothering ability were gestation length, number born alive, and number weaned. Simple linear regression models were generated to analyze the relationship between activity traits and reproductive measures. The results of this study indicate that select activity traits may be used to explain a portion of the variability in gilt reproductive performance. This information is foundational to informing future selection decisions pertaining to the use of activity traits in breeding programs. Early indicator traits for swine reproduction and longevity support economical selection decision-making. Activity is a key variable impacting a sow’s herd life and productivity. Early-life activities could contribute to farrowing traits including gestation length (GL), number born alive (NBA), and number weaned (NW). Beginning at 20 weeks of age, 480 gilts were video recorded for 7 consecutive days and processed using the NUtrack system. Activity traits included angle rotated (radians), average speed (m/s), distance traveled (m), time spent eating (s), lying lateral (s), lying sternal (s), standing (s), and sitting (s). Final daily activity values were averaged across the period under cameras. Parity one data were collected for all gilts considered. Data were analyzed using linear regression models (R version 4.0.2). GL was significantly impacted by angle rotated (p = 0.03), average speed (p = 0.07), distance traveled (p = 0.05), time spent lying lateral (p = 0.003), and lying sternal (0.02). NBA was significantly impacted by time spent lying lateral (p = 0.01), lying sternal (p = 0.07), and time spent sitting (p = 0.08). NW was significantly impacted by time spent eating (p = 0.09), time spent lying lateral (p = 0.04), and time spent sitting (p = 0.007). This analysis suggests early-life gilt activities are associated with sow productivity traits of importance. Further examination of the link between behaviors compiled utilizing NUtrack and reproductive traits is necessitated to further isolate behavioral differences for potential use in selection decisions.

In recent years, the price of pork in China continues to fluctuate at a high level. The forecast of pork price becomes more important. Single prediction models are often used for this work, but they are not accurate enough. This paper proposes a new method based on Census X12-GM(1,1) combination model. Monthly pork price data from January 2014 to December 2020 were obtained from the State Statistics Bureau(Mainland China). Census X12 model was adopted to get the long-term trend factor, business cycle change factor and seasonal factor of pork price data before September 2020. GM (1,1) model was used to fit and predict the long-term trend factor and business cycle change factor. The fitting and forecasting values of GM(1,1) were multiplied by the seasonal factor and empirical seasonal factor individually to obtain the fitting and forecasting values of the original monthly pork price series. The expression of GM(1,1) model for fitting and forecasting long-term trend factor and and business cycle change factor was X.sup.(1) (k) = -1704.80e.sup.-0.022(k-1) + 1742.36. Empirical seasonal factor of predicted values was 1.002 Using Census X12-GM(1,1) method, the final forecast values of pork price from July 2020 to December 2020 were 34.75, 33.98, 33.23, 32.50, 31.78 and 31.08 respectively. Compared with ARIMA, GM(1,1) and Holt-Winters models, Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) of Census X12-GM(1,1) method was the lowest on forecasting part. Compared with other single model, Census X12-GM(1,1) method has better prediction accuracy for monthly pork price series. The monthly pork price predicted by Census X12-GM(1,1) method can be used as an important reference for stakeholders.