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Zhou X, Guan R, Cai H, Wang P, Yang Y, Wang X, Li X, Song H. Machine learning based personalized promotion strategy of piglets weaned per sow per year in large-scale pig farms. Porcine Health Manag 2022; 8:37. [PMID: 35948988 PMCID: PMC9364547 DOI: 10.1186/s40813-022-00280-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/01/2022] [Indexed: 11/10/2022] Open
Abstract
Background The purpose of this study was to analyze the relationship between different productive factors and piglets weaned per sow per year (PSY) in 291 large-scale pig farms and analyze the impact of the changes in different factors on PSY. We chose nine different algorithm models based on machine learning to calculate the influence of each variable on every farm according to its current situation, leading to personalize the improvement of the impact in the specific circumstances of each farm, proposing a production guidance plan of PSY improvement for every farm. According to the comparison of mean absolute error (MAE), 95% confidence interval (CI) and R2, the optimal solution was conducted to calculate the influence of 17 production factors of each pig farm on PSY improvement, finding out the bottleneck corresponding to each pig farm. The level of PSY was further analyzed when the bottleneck factor of each pig farm changed by 0.5 standard deviation (SD). Results 17 production factors were non-linearly related to PSY. The top five production factors with the highest correlation with PSY were the number of weaned piglets per litter (WPL) (0.6694), mating rate within 7 days after weaning (MR7DW) (0.6606), number of piglets born alive per litter (PBAL) (0.6517), the total number of piglets per litter (TPL) (0.5706) and non-productive days (NPD) (− 0.5308). Among nine algorithm models, the gradient boosting regressor model had the highest R2, smallest MAE and 95% CI, applied for personalized analysis. When one of 17 production factors of 291 large-scale pig farms changed by 0.5 SD, 101 pig farms (34.7%) can increase 1.41 PSY (compared to its original value) on average by adding the production days, and 60 pig farms (20.6%) can increase 1.14 PSY on average by improving WPL, 45 pig farms (15.5%) can increase 1.63 PSY by lifting MR7DW. Conclusions The main productive factors related to PSY included WPL, MR7DW, PBAL, TPL and NPD. The gradient boosting regressor model was the optimal method to individually analyze productive factors that are non-linearly related to PSY. Supplementary Information The online version contains supplementary material available at 10.1186/s40813-022-00280-z.
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Affiliation(s)
- Xingdong Zhou
- Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology and College of Veterinary Medicine of Zhejiang Agriculture and Forestry University, 666 Wusu Street, Lin'an District, Hangzhou, 311300, Zhejiang, People's Republic of China
| | - Ran Guan
- Shandong New Hope Liuhe Agriculture and Animal Husbandry Technology Co., Ltd (NHLH Academy of Swine Research), No. 6596 Dongfanghong East Road Yuanqiao Town, Dezhou, 253000, Shandong, People's Republic of China
| | - Hongbo Cai
- Intelligent Engine Department, The Ant Financial (Hang Zhou, Network Technology Co., Ltd, A Space, No. 569 Xixi Road, Xihu District, Hangzhou, 310023, Zhejiang, People's Republic of China
| | - Pei Wang
- Beijing Center for Animal Disease Control and Prevention, No. 19 Xiangrui Street, Biological Medicine Base, Daxing District, Beijing, 102629, People's Republic of China
| | - Yongchun Yang
- Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology and College of Veterinary Medicine of Zhejiang Agriculture and Forestry University, 666 Wusu Street, Lin'an District, Hangzhou, 311300, Zhejiang, People's Republic of China
| | - Xiaodu Wang
- Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology and College of Veterinary Medicine of Zhejiang Agriculture and Forestry University, 666 Wusu Street, Lin'an District, Hangzhou, 311300, Zhejiang, People's Republic of China.
| | - Xiaowen Li
- Shandong New Hope Liuhe Agriculture and Animal Husbandry Technology Co., Ltd (NHLH Academy of Swine Research), No. 6596 Dongfanghong East Road Yuanqiao Town, Dezhou, 253000, Shandong, People's Republic of China.
| | - Houhui Song
- Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology and College of Veterinary Medicine of Zhejiang Agriculture and Forestry University, 666 Wusu Street, Lin'an District, Hangzhou, 311300, Zhejiang, People's Republic of China.
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Lopera-Madrid J, Medina-Magües LG, Gladue DP, Borca MV, Osorio JE. Optimization in the expression of ASFV proteins for the development of subunit vaccines using poxviruses as delivery vectors. Sci Rep 2021; 11:23476. [PMID: 34873256 PMCID: PMC8648923 DOI: 10.1038/s41598-021-02949-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/23/2021] [Indexed: 11/09/2022] Open
Abstract
African swine fever virus (ASFV) causes a highly contagious hemorrhagic disease that affects domestic pig and Eurasian wild boar populations. To date, no safe and efficacious treatment or vaccine against ASF is available. Nevertheless, there are several reports of protection elicited by experimental vaccines based on live attenuated ASFV and some levels of protection and reduced viremia in other approaches such as DNA, adenovirus, baculovirus, and vaccinia-based vaccines. Current ASF subunit vaccine research focuses mainly on delivering protective antigens and antigen discovery within the ASFV genome. However, due to the complex nature of ASFV, expression vectors need to be optimized to improve their immunogenicity. Therefore, in the present study, we constructed several recombinant MVA vectors to evaluate the efficiency of different promoters and secretory signal sequences in the expression and immunogenicity of the p30 protein from ASFV. Overall, the natural poxvirus PrMVA13.5L promoter induced high levels of both p30 mRNA and specific anti-p30 antibodies in mice. In contrast, the synthetic PrS5E promoter and the S E/L promoter linked to a secretory signal showed lower mRNA levels and antibodies. These findings indicate that promoter selection may be as crucial as the antigen used to develop ASFV subunit vaccines using MVA as the delivery vector.
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Affiliation(s)
- Jaime Lopera-Madrid
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Lex G Medina-Magües
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Douglas P Gladue
- Plum Island Animal Disease Center, Agricultural Research Service (ARS), Greenport, NY, 11944, USA
| | - Manuel V Borca
- Plum Island Animal Disease Center, Agricultural Research Service (ARS), Greenport, NY, 11944, USA
| | - Jorge E Osorio
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, 53706, USA
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Recent Advances in Understanding the Influence of Zinc, Copper, and Manganese on the Gastrointestinal Environment of Pigs and Poultry. Animals (Basel) 2021; 11:ani11051276. [PMID: 33946674 PMCID: PMC8145729 DOI: 10.3390/ani11051276] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/24/2021] [Accepted: 04/26/2021] [Indexed: 12/31/2022] Open
Abstract
Simple Summary Pigs and poultry, similar to humans, need regular consumption of zinc, copper, and manganese for normal functioning. To ensure adequate dietary intake, and prevent deficiency, their diets are supplemented with sufficient, often excessive, levels of these minerals or even at higher levels, which have been associated with improvements in their health and/or growth. However, if provided in excess, mineral quantities beyond those required are simply excreted from the animal, which is associated with negative consequences for the environment and even the development of antimicrobial resistance. Therefore, it is of great interest to better understand the dynamics of zinc, copper, and manganese in the intestine of pigs and poultry following consumption of supplemented diets, and how the requirements and benefits related to these minerals can be optimized and negative impacts minimized. The intestine of pigs and poultry contains vast numbers of microorganisms, notably bacteria, that continually interact with, and influence, their host. This review explores the influence of zinc, copper, and manganese on these interactions and how novel forms of these minerals have the potential to maximize their delivery and benefits, while limiting any negative consequences. Abstract Zinc, copper, and manganese are prominent essential trace (or micro) minerals, being required in small, but adequate, amounts by pigs and poultry for normal biological functioning. Feed is a source of trace minerals for pigs and poultry but variable bioavailability in typical feed ingredients means that supplementation with low-cost oxides and sulphates has become common practice. Such trace mineral supplementation often provides significant ‘safety margins’, while copper and zinc have been supplemented at supra-nutritional (or pharmacological) levels to improve health and/or growth performance. Regulatory mechanisms ensure that much of this oversupply is excreted by the host into the environment, which can be toxic to plants and microorganisms or promote antimicrobial resistance in microbes, and thus supplying trace minerals more precisely to pigs and poultry is necessary. The gastrointestinal tract is thus central to the maintenance of trace mineral homeostasis and the provision of supra-nutritional or pharmacological levels is associated with modification of the gut environment, such as the microbiome. This review, therefore, considers recent advances in understanding the influence of zinc, copper, and manganese on the gastrointestinal environment of pigs and poultry, including more novel, alternative sources seeking to maintain supra-nutritional benefits with minimal environmental impact.
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