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When Grandmother Pig comes for a visit, Oliver and Amanda learn just how much fun it is to have a grandmother in the house.

Background The eye of the domestic pig ( Sus scrofa domestica ) is an ex vivo animal model often used in vision sciences research (retina studies, glaucoma, cataracts, etc.). However, only a few papers have compiled pig eye anatomical descriptions. The purpose of this paper is to describe pig and human eye anatomical parameters to help investigators in their choice of animal model depending on their study objective. Methods A wide search of current medical literature was performed (English language) using PubMed. Anteroposterior axial length and corneal radius, astigmatism, vertical and horizontal diameter, and pachymetry (slit-scan and ultrasound) were measured in five enucleated pig eyes of animals 6 to 8 months old. Results Horizontal corneal diameter was 14.31 ± 0.25 mm (CI 95% 14.03 mm–14.59 mm), vertical diameter was 12.00 ± 0 mm, anteroposterior length was 23.9 ± 0.08 mm (CI 95% 23.01 mm–29.99 mm), central corneal ultrasound pachymetry was 877.6 ± 13.58 μm (CI 95% 865.70 μm–889.50 μm) and slit-scan pachymetry was 906.2 ± 15.30 μm (CI 95% 892.78 μm–919.61 μm). Automatic keratometry (main meridians) was 41.19 ± 1.76D and 38.83 ± 2.89D (CI 95% 40.53D–41.81D and 37.76D–39.89D respectively) with an astigmatism of 2.36 ± 1.70D (CI 95% 1.72D–3.00D), and manual keratometry was 41.05 ± 0.54D and 39.30 ± 1.15D (CI 95% 40.57D–41.52D and 38.29D–40.31D respectively) with an astigmatism of 1.75 ± 1.31D (CI 95% 0.60D–2.90D). Conclusion This paper describes the anatomy of the pig eyeball for easy use and interpretation by researchers who are considering their choice of animal model in vision sciences research.

A review of published literature was conducted to identify pasture pig production system features that pose risks to animal welfare, and to develop recommendations aimed at improving the wellbeing of the animals managed in those systems. Pasture pig production systems present specific challenges to animal welfare that are inherent to the nature of these systems where producers have little room to make improvements. However, these systems present other challenges that could be reduced with a carefully designed system, by adopting appropriate management strategies and by avoiding management practices that are likely to negatively affect animal wellbeing. In pasture pig production systems, exposure to extreme temperatures, potential contact with wildlife and pathogens (especially parasites), vulnerability to predators, risk of malnutrition, pre-weaning piglet mortality, complexity of processes for monitoring and treating sick animals, and for cleaning and disinfection of facilities and equipment are among the main threats to animal welfare.

Asia is not only the primary region for global pig production but also the largest consumer of pork worldwide. Although the pig production in Asia has made great progress in the past, it still is confronted with numerous challenges. These challenges include: inadequate land and feed resources, a substantial number of small-scale pig farms, escalating pressure to ensure environmental conservation, control of devastating infectious diseases, as well as coping with high temperatures and high humidity. To solve these problems, important investments of human and financial capital are required to promote large-scale production systems, exploit alternative feed resources, implement precision feeding, and focus on preventive medicine and vaccines as alternatives to antibiotics, improve pig breeding, and increase manure recycling. Implementation of these techniques and management practices will facilitate development of more environmentally-friendly and economically sustainable pig production systems in Asia, ultimately providing consumers with healthy pork products around the world.

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods.

This scientific opinion focuses on the welfare of pigs on farm, and is based on literature and expert opinion. All pig categories were assessed: gilts and dry sows, farrowing and lactating sows, suckling piglets, weaners, rearing pigs and boars. The most relevant husbandry systems used in Europe are described. For each system, highly relevant welfare consequences were identified, as well as related animal‐based measures (ABMs), and hazards leading to the welfare consequences. Moreover, measures to prevent or correct the hazards and/or mitigate the welfare consequences are recommended. Recommendations are also provided on quantitative or qualitative criteria to answer specific questions on the welfare of pigs related to tail biting and related to the European Citizen's Initiative ‘End the Cage Age’. For example, the AHAW Panel recommends how to mitigate group stress when dry sows and gilts are grouped immediately after weaning or in early pregnancy. Results of a comparative qualitative assessment suggested that long‐stemmed or long‐cut straw, hay or haylage is the most suitable material for nest‐building. A period of time will be needed for staff and animals to adapt to housing lactating sows and their piglets in farrowing pens (as opposed to crates) before achieving stable welfare outcomes. The panel recommends a minimum available space to the lactating sow to ensure piglet welfare (measured by live‐born piglet mortality). Among the main risk factors for tail biting are space allowance, types of flooring, air quality, health status and diet composition, while weaning age was not associated directly with tail biting in later life. The relationship between the availability of space and growth rate, lying behaviour and tail biting in rearing pigs is quantified and presented. Finally, the panel suggests a set of ABMs to use at slaughter for monitoring on‐farm welfare of cull sows and rearing pigs.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs’ health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs’ health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs’ health and welfare.

The aim of this study was to identify effective genetic markers for the Antigen Processing Associated Transporter 1 (TAP1), α (1,2) Fucosyltransferase 1 (FUT1), Natural Resistance Associated Macrophage Protein 1 (NRAMP1), Mucin 4 (MUC4) and Mucin 13 (MUC13) diarrhea-resistance genes in the local pig breeds, namely Shanghai white pigs, Fengjing pigs, Shawutou pigs, Meishan pigs and Pudong white pigs, to provide a reference for the characterization of local pig breed resources in Shanghai. Polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLR) and sequence sequencing were applied to analyze the polymorphisms of the above genes and to explore the effects on the immunity of Shanghai local pig breeds in conjunction with some immunity factors. The results showed that both TAP1 and MUC4 genes had antidiarrheal genotype GG in the five pig breeds, AG and GG genotypes of the FUT1 gene were detected in Pudong white pigs, AA antidiarrheal genes of the NRAMP1 gene were detected in Meishan pigs, the AB type of the NRAMP1 gene was detected in Pudong white pigs, and antidiarrheal genotype GG of the MUC13 gene was only detected in Shanghai white pigs. The MUC13 antidiarrhea genotype GG was only detected in Shanghai white pigs. The TAP1 gene was moderately polymorphic in Shanghai white pigs, Fengjing pigs, Shawutou pigs, Meishan pigs and Pudong white pigs, among which TAP1 in Shanghai white pigs and Shawutou pigs did not satisfy the Hardy–Weinberg equilibrium. The FUT1 gene of Pudong white pigs was in a state of low polymorphism. NRAMP1 of Meishan pigs and Pudong white pigs was in a state of moderate polymorphism, which did not satisfy the Hardy–Weinberg equilibrium. The MUC4 genes of Shanghai white pigs and Pudong white pigs were in a state of low polymorphism, and the MUC4 genes of Fengjing pigs and Shawutou pigs were in a state of moderate polymorphism, and the MUC4 genes of Fengjing pigs and Pudong white pigs did not satisfy the Hardy–Weinberg equilibrium. The MUC13 gene of Shanghai white pigs and Pudong white pigs was in a state of moderate polymorphism. Meishan pigs had higher levels of IL-2, IL-10, IgG and TNF-α, and Pudong white pigs had higher levels of IL-12 than the other pigs. The level of interleukin 12 (IL-12) was significantly higher in the AA genotype of the MUC13 gene of Shanghai white pigs than in the AG genotype. The indicator of tumor necrosis factor alpha (TNF-α) in the AA genotype of the TAP1 gene of Fengjing pigs was significantly higher than that of the GG and AG genotypes. The indicator of IL-12 in the AG genotype of the Shawutou pig TAP1 gene was significantly higher than that of the GG genotype. The level of TNF-α in the AA genotype of the NRAMP1 gene of Meishan pigs was markedly higher than that of the AB genotype. The IL-2 level of the AG type of the FUT1 gene was obviously higher than that of the GG type of Pudong white pigs, the IL-2 level of the AA type of the MUC4 gene was dramatically higher than that of the AG type, and the IgG level of the GG type of the MUC13 gene was apparently higher than that of the AG type. The results of this study are of great significance in guiding the antidiarrhea breeding and molecular selection of Shanghai white pigs, Fengjing pigs, Shawutou pigs, Meishan pigs and Pudong white pigs and laying the foundation for future antidiarrhea breeding of various local pig breeds in Shanghai.

The locations of pigs in the group housing enable activity monitoring and improve animal welfare. Vision-based methods for tracking individual pigs are noninvasive but have low tracking accuracy owing to long-term pig occlusion. In this study, we developed a vision-based method that accurately tracked individual pigs in group housing. We prepared and labeled datasets taken from an actual pig farm, trained a faster region-based convolutional neural network to recognize pigs’ bodies and heads, and tracked individual pigs across video frames. To quantify the tracking performance, we compared the proposed method with the global optimization (GO) method with the cost function and the simple online and real-time tracking (SORT) method on four additional test datasets that we prepared, labeled, and made publicly available. The predictive model detects pigs’ bodies accurately, with F1-scores of 0.75 to 1.00, on the four test datasets. The proposed method achieves the largest multi-object tracking accuracy (MOTA) values at 0.75, 0.98, and 1.00 for three test datasets. In the remaining dataset, the proposed method has the second-highest MOTA of 0.73. The proposed tracking method is robust to long-term occlusion, outperforms the competitive baselines in most datasets, and has practical utility in helping to track individual pigs accurately.