16S RNA; Bayesian network; Black soldier fly; Chicken; Insect in feed; Microbiota; Structural equation model; Biochemistry, Genetics and Molecular Biology (all); Immunology and Microbiology (all); Agricultural and Biological Sciences (all); General Agricultural and Biological Sciences; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology
Abstract :
[en] Feeding chicken with black soldier fly larvae (BSF) may influence their rates of growth via effects on the composition of their gut microbiota. To verify this hypothesis, we aim to evaluate a probabilistic structural equation model because it can unravel the complex web of relationships that exist between the bacteria involved in digestion and evaluate whether these influence bird growth. We followed 90 chickens fed diets supplemented with 0%, 5% or 10% BSF and measured the strength of the relationship between their weight and the relative abundance of bacteria (OTU) present in their cecum or cloaca at 16, 28, 39, 67 or 73 days of age, while adjusting for potential confounding effects of their age and sex. Results showed that OTUs (62 genera) could be combined into ten latent constructs with distinctive metabolic attributes. Links were discovered between these constructs that suggest nutritional relationships. Age directly influenced weights and microbiotal composition, and three constructs indirectly influenced weights via their dependencies on age. The proposed methodology was able to simplify dependencies among OTUs into knowledgeable constructs and to highlight links potentially important to understand the role of insect feed and of microbiota in chicken growth.
Research center :
FARAH. Productions animales durables - ULiège
Disciplines :
Animal production & animal husbandry
Author, co-author :
Detilleux, Johann ; Université de Liège - ULiège > Département de gestion vétérinaire des Ressources Animales (DRA)
Moula, Nassim ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques > Méthodes expérimentales des animaux de laboratoire et éthique en expérimentation animale
Dawans, Edwin ; Université de Liège - ULiège > Département de gestion vétérinaire des Ressources Animales (DRA)
Taminiau, Bernard ; Université de Liège - ULiège > Fundamental and Applied Research for Animals and Health (FARAH) > FARAH: Santé publique vétérinaire
Daube, Georges ; Université de Liège - ULiège > Fundamental and Applied Research for Animals and Health (FARAH) > FARAH: Santé publique vétérinaire
Leroy, Pascal ; Université de Liège - ULiège > Département de gestion vétérinaire des Ressources Animales (DRA)
Language :
English
Title :
A Probabilistic Structural Equation Model to Evaluate Links between Gut Microbiota and Body Weights of Chicken Fed or Not Fed Insect Larvae
This project received funding from the ?Academy for Research and Higher Education? Development Cooperation Committee (ARES-CCD)?, Brussels, Belgium, and ?Fonds Sp?ciaux de la Recherche (Universit? de Li?ge)?, Li?ge, Belgium.Funding: This project received funding from the “Academy for Research and Higher Education— Development Cooperation Committee (ARES-CCD)”, Brussels, Belgium, and “Fonds Spéciaux de la Recherche (Université de Liège)”, Liège, Belgium.
van Huis, A.; Oonincx, D.G.A.B. The environmental sustainability of insects as food and feed. A review. Agron. Sustain. Dev. 2017, 37, 43. [CrossRef]
Moula, N.; Detilleux, J. A meta-analysis of the effects of insects in feed on poultry growth performances. Animals 2019, 9, 201. [CrossRef] [PubMed]
Diaz Carrasco, J.M.; Casanova, N.A.; Fernández Miyakawa, M.E. Microbiota, gut health and chicken productivity: What is the connection? Microorganisms 2019, 7, 374. [CrossRef] [PubMed]
Richards, P.; Fothergill, J.; Bernardeau, M.; Wigley, P. Development of the caecal microbiota in three broiler breeds. Front. Vet. Sci. 2019, 6, 201. [CrossRef]
Jozefiak, D.; Rutkowski, A.; Martin, S.A. Carbohydrate fermentation in the avian ceca: A review. Anim. Feed Sci. Technol. 2004, 133, 1–15. [CrossRef]
Yeoman, C.J.; Chia, N.; Jeraldo, P.; Sipos, M.; Goldenfeld, N.D.; White, B.A. The microbiome of the chicken gastrointestinal tract. Anim. Health Res. Rev. 2012, 13, 89–99. [CrossRef]
Ocejo, M.; Oporto, B.; Hurtado, A. 16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan. Sci. Rep. 2019, 9, 2506. [CrossRef]
Kers, J.G.; Velkers, F.C.; Fischer, E.A.J.; Hermes, G.D.A.; Stegeman, J.A.; Smidt, H. Host and environmental factors affecting the intestinal microbiota in chickens. Front. Microbiol. 2018, 9, 235. [CrossRef]
Zhang, Y.; Liu, Y.; Li, J.; Xing, T.; Jiang, Y.; Zhang, L.; Gao, F. Dietary resistant starch modifies the composition and function of caecal microbiota of broilers. J. Sci. Food Agric. 2020, 100, 1274–1284. [CrossRef]
Biasato, I.; Ferrocino, I.; Dabbou, S.; Evangelista, R.; Gai, F.; Gasco, L.; Cocolin, L.; Capucchio, M.T.; Schiavone, A. Black soldier fly and gut health in broiler chickens: Insights into the relationship between cecal microbiota and intestinal mucin composition. J. Anim. Sci. Biotechnol. 2020, 11, 11. [CrossRef]
Borrelli, L.; Coretti, L.; Dipineto, L.; Bovera, F.; Menna, F.; Chiariotti, L.; Nizza, A.; Lembo, F.; Fioretti, A. Insect-based diet, a promising nutritional source, modulates gut microbiota composition and SCFAs production in laying hens. Sci. Rep. 2017, 7, 16269. [CrossRef] [PubMed]
Biasato, I.; Ferrocino, I.; Grego, E.; Dabbou, S.; Gai, F.; Gasco, L.; Cocolin, L.; Capucchio, M.T.; Schiavone, A. Yellow mealworm inclusion in diets for heavy-size broiler chickens: Implications for intestinal microbiota and mucin dynamics. Animals 2020, 10, 1909. [CrossRef] [PubMed]
Moula, N.; Hornick, J.L.; Cabaraux, J.F.; Korsak, N.; Dawans, E.; Antoine, N.; Taminiau, B.; Detilleux, J. Effects of dietary black soldier fly larvae on performance of broilers mediated or not through changes in microbiota. J. Insects Food Feed. 2017, 4, 31–42. [CrossRef]
Mahmood, T.; Guo, Y. Dietary fiber and chicken microbiome interaction: Where will it lead to? Anim. Nutr. 2020, 202, 1–8. [CrossRef]
Tran, G.; Gnaedinger, C.; Mélin, C. Feedipedia, an Online Encyclopedia of Animla Feeds. Available online: https://www. feedipedia.org/node/16388 (accessed on 25 January 2022).
Dörper, A.; Veldkamp, T.; Dicke, M. Use of black soldier fly and house fly in feed to promote sustainable poultry production. JIFF 2020, 7, 761–780. [CrossRef]
Allali, I.; Arnold, J.W.; Roach, J.; Cadenas, M.B.; Butz, N.; Hassan, H.M.; Koci, M.; Ballou, A.; Mendoza, M.; Ali, R.; et al. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome. BMC Microbiol. 2017, 17, 194. [CrossRef]
Xia, Y.; Sun, J. Hypothesis testing and statistical analysis of microbiome. Genes Dis. 2017, 4, 138–148. [CrossRef]
Gloor, G.B.; Macklaim, J.M.; Pawlowsky-Glahn, V.; Egozcue, J.J. Microbiome datasets are compositional: And this is not optional. Front Microbiol. 2017, 8, 2224. [CrossRef]
Holmes, I.; Harris, K.; Quince, C. Dirichlet multinomial mixtures: Generative models for microbial metagenomics. PLoS ONE 2012, 7, e30126.
Egozcue, J.J.; Graffelman, J.; Ortego, M.I.; Pawlowsky-Glahn, V. Some thoughts on counts in sequencing studies. NAR Genom. Bioinform. 2020, 2, lqaa094.
Quinn, T.P.; Erb, I.; Gloor, G.; Notredame, C.; Richardson, M.F.; Crowley, T.M. A field guide for the compositional analysis of any-omics data. Gigascience 2019, 8, giz107. [CrossRef] [PubMed]
Weiss, S.; Xu, Z.Z.; Peddada, S.; Amir, A.; Bittinger, K.; Gonzalez, A.; Lozupone, C.; Zaneveld, J.R.; Vázquez-Baeza, Y.; Birmingham, A.; et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 2017, 5, 27. [CrossRef] [PubMed]
Berg, G.; Rybakova, D.; Fischer, D.; Cernava, T.; Vergès, M.C.; Charles, T.; Chen, X.; Cocolin, L.; Eversole, K.; Corral, G.H.; et al. Microbiome definition re-visited: Old concepts and new challenges. Microbiome 2020, 8, 103. [CrossRef] [PubMed]
Vatcheva, K.P.; Lee, M.; McCormick, J.B.; Rahbar, M.H. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology 2016, 6, 227. [CrossRef] [PubMed]
Layeghifard, M.; Hwang, D.M.; Guttman, D.S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 2017, 25, 217–228. [CrossRef]
Dohlman, A.B.; Shen, X. Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference. Exp. Biol Med. 2019, 244, 445–458. [CrossRef]
Pearl, J. Causality: Models, Reasoning, and Inference, 1st ed.; Cambridge University Press: Cambridge, UK, 2000; pp. 1–384.
Scanagatta, M.; Salmerón, A.; Stella, F. A survey on Bayesian network structure learning from data. Prog. Artif. Intell. 2019, 8, 425–439. [CrossRef]
Grunwald, P. Minimum description length tutorial. In Advances in Minimum Description Length. Theory and Applications, 1st ed.; Grunwald, P.D., Myung, J.I., Pitt, M.A., Eds.; MIT Press: Cambridge, MA, USA, 2005; pp. 23–81.
Ji, Z.; Xia, Q.; Meng, G. A review of parameter learning methods in Bayesian network. In Advanced Intelligent Computing Theories and Applications, 1st ed.; Huang, D.S., Han, K., Eds.; Springer: London, UK, 2015; Volume 3, pp. 3–12.
Saborío-Montero, A.; Gutiérrez-Rivas, M.; García-Rodríguez, A.; Atxaerandio, R.; Goiri, I.; López de Maturana, E.; Jiménez Montero, J.A.; Alenda, R.; González-Recio, O. Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study. J. Anim Breed. Genet. 2020, 137, 36–48. [CrossRef]
Tiezzi, F.; Fix, J.; Schwab, C.; Shull, C.; Maltecca, C. Gut microbiome mediates host genomic effects on phenotypes: A case study with fat deposition in pigs. Comput. Struct. Biotechnol. J. 2020, 19, 530–544. [CrossRef]
Haenlein, M.; Kaplan, A.M. A beginner’s guide to partial least squares analysis. Underst. Statistics 2004, 3, 283–297. [CrossRef]
Detilleux, J.; Theron, L.; Duprez, J.N.; Reding, E.; Humblet, M.F.; Planchon, V.; Delfosse, C.; Bertozzi, C.; Mainil, J.; Hanzen, C. Structural equation models to estimate risk of infection and tolerance to bovine mastitis. Genet. Sel. Evol. 2013, 45, 6. [CrossRef] [PubMed]
de Waal, A.; Yoo, K. Latent variable Bayesian networks constructed using structural equation modelling. In Proceedings of the 21st International Conference on Information Fusion, Cambridge, UK, 10–13 July 2018; pp. 688–695.
Detilleux, J. Tolerance to bovine clinical mastitis: Total, direct, and indirect milk losses. J. Dairy Sci. 2018, 10, 3334–3343. [CrossRef] [PubMed]
Gunzler, D.; Chen, T.; Wu, P.; Zhang, H. Introduction to mediation analysis with structural equation modeling. Shanghai Arch. Psychiatry 2013, 25, 390–394. [PubMed]
Hoc, B.; Noël, G.; Carpentier, J.; Francis, F.; Caparros, M.; Megido, R. Optimization of black soldier fly (Hermetia illucens) artificial reproduction. PLoS ONE 2019, 30, e0216160. [CrossRef]
Stanley, D.; Hughes, R.J.; Geier, M.S.; Moore, R.J. Bacteria within the gastrointestinal tract microbiota correlated with improved growth and feed conversion: Challenges presented for the identification of performance enhancing probiotic bacteria. Front. Microbiol. 2016, 7, 187. [CrossRef] [PubMed]
Conrady, S.; Jouffe, L. Bayesian Networks & BayesiaLab: A Practical Introduction for Researchers, 1st ed.; Bayesia: Franklin, TN, USA, 2015; pp. 1–371.
Ryynänen, O.P.; Leppänen, T.; Kekolahti, P.; Mervaala, E.; Töyräs, J. Bayesian network model to evaluate the effectiveness of continuous positive airway pressure treatment of sleep apnea. Healthcare Inform. Res. 2018, 24, 346–358. [CrossRef]
Kullback, S.; Leibler, R.A. On information and sufficiency. Ann. Math. Stat. 1951, 22, 79–86. [CrossRef]
Chernick, M.R. The jackknife: A resampling method with connections to the bootstrap. Wiley Interdiscip. Rev. Comput. Stat. 2012, 4, 224–226. [CrossRef]
Rychlik, I. Composition and function of chicken gut microbiota. Animals 2020, 10, 103. [CrossRef] [PubMed]
Chica Cardenas, L.A.; Clavijo, V.; Vives, M.; Reyes, A. Bacterial meta-analysis of chicken cecal microbiota. PeerJ 2021, 9, e10571. [CrossRef] [PubMed]
Stanley, D.; Geier, M.S.; Chen, H.; Hughes, R.J.; Moore, R.J. Comparison of fecal and cecal microbiotas reveals qualitative similarities but quantitative differences. BMC Microbiol. 2015, 15, 51. [CrossRef] [PubMed]
Videvall, E.; Strandh, M.; Engelbrecht, A.; Cloete, S.; Cornwallis, C.K. Measuring the gut microbiome in birds: Comparison of faecal and cloacal sampling. Mol. Ecol. Resour. 2018, 18, 424–434. [CrossRef]
Lee, S.J.; Cho, S.; La, T.M.; Lee, H.J.; Lee, J.B.; Park, S.Y.; Song, C.S.; Choi, I.S.; Lee, S.W. Comparison of microbiota in the cloaca, colon, and magnum of layer chicken. PLoS ONE 2020, 15, e0237108. [CrossRef]
Johnson, J.S.; Spakowicz, D.J.; Hong, B.Y.; Petersen, L.M.; Demkowicz, P.; Chen, L.; Leopold, S.R.; Hanson, B.M.; Agresta, H.O.; Gerstein, M.; et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 2019, 10, 5029. [CrossRef]
Larivière, J.M.; Michaux, C.; Farnir, F.; Detilleux, J.; Verleyen, V.; Leroy, P. Reproductive performance of the Ardennaise chicken breed under traditional and modern breeding management systems. Int. J. Poultry Sci. 2009, 8, 446–451. [CrossRef]
Moula, N.; Antoine-Moussiaux, N.; Farnir, F.; Philippart de Foy, M.; Leroy, P. Performances zootechniques de la poule Ardennaise, une race ancienne pour le futur? Ann. Méd. Vét. 2009, 153, 66–75.
Kaakoush, N.O.; Sodhi, N.; Chenu, J.W.; Cox, J.M.; Riordan, S.M.; Mitchell, H.M. The interplay between Campylobacter and Helicobacter species and other gastrointestinal microbiota of commercial broiler chickens. Gut Pathog. 2014, 6, 18. [CrossRef]
Xiao, S.S.; Mi, J.D.; Mei, L.; Liang, J.; Feng, K.X.; Wu, Y.B.; Liao, X.D.; Wang, Y. Microbial diversity and community variation in the intestines of layer chickens. Animals 2021, 11, 840. [CrossRef] [PubMed]
Zou, A.; Sharif, S.; Parkinson, J. Lactobacillus elicits a ‘Marmite effect’ on the chicken cecal microbiome. Jpn. Biofilms. Microbiomes. 2018, 4, 27. [CrossRef]
Zelezniak, A.; Andrejev, S.; Ponomarova, O.; Mende, D.R.; Bork, P.; Patil, K.R. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl. Acad. Sci. USA 2015, 112, 6449–6454. [CrossRef] [PubMed]
Sergeant, M.J.; Constantinidou, C.; Cogan, T.A.; Bedford, M.R.; Penn, C.W.; Pallen, M.J. Extensive microbial and functional diversity within the chicken cecal microbiome. PLoS ONE 2014, 9, e91941. [CrossRef] [PubMed]
Svihus, B.; Choct, M.; Classen, H.L. Function and nutritional roles of the avian caeca: A review. World Poultry Sci. J. 2013, 69, 249–263. [CrossRef]
Karasawa, Y. Significant role of the nitrogen recycling system through the ceca occurs in protein-depleted chickens. J. Exp. Zool. 1999, 283, 418–425. [CrossRef]
Eeckhaut, V.; Van Immerseel, F.; Croubels, S.; De Baere, S.; Haesebrouck, F.; Ducatelle, R.; Louis, P.; Vandamme, P. Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum. Microb. Biotechnol. 2011, 4, 503–512. [CrossRef]
Polansky, O.; Sekelova, Z.; Faldynova, M.; Sebkova, A.; Sisak, F.; Rychlik, I. Important metabolic pathways and biological processes expressed by chicken cecal microbiota. Appl. Environ. Microbiol. 2015, 82, 1569–1576. [CrossRef]
Biddle, A.; Stewart, L.; Blanchard, J.; Leschine, S. Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities. Diversity 2013, 5, 627–640. [CrossRef]
De Maesschalck, C.; Eeckhaut, V.; Maertens, L.; De Lange, L.; Marchal, L.; Daube, G.; Dewulf, J.; Haesebrouck, F.; Ducatelle, R.; Taminau, B.; et al. Amorphous cellulose feed supplement alters the broiler caecal microbiome. PoultSci 2019, 98, 3811–3817. [CrossRef]
Louis, P.; Flint, H.J. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 2017, 19, 29–41. [CrossRef] [PubMed]
Li, H.Y.; Zhang, H.L.; Zhao, F.J.; Wang, S.Q.; Wang, Z.X.; Wei, Z.Y. Modulation of gut microbiota, short-chain fatty acid production, and inflammatory cytokine expression in the cecum of porcine Deltacoronavirus-infected chicks. Front. Microbiol. 2020, 11, 897. [CrossRef] [PubMed]
Rivière, A.; Selak, M.; Lantin, D.; Leroy, F.; De Vuyst, L. Bifidobacteria and butyrate-producing colon bacteria: Importance and strategies for their stimulation in the human gut. Front. Microbiol. 2016, 7, 979. [CrossRef] [PubMed]
Lee, K.C.; Kil, D.Y.; Sul, W.J. Cecal microbiome divergence of broiler chickens by sex and body weight. J. Microbiol. 2017, 55, 939–945. [CrossRef] [PubMed]
Amaretti, A.; Gozzoli, C.; Simone, M.; Raimondi, S.; Righini, L.; Pérez-Brocal, V.; García-López, R.; Moya, A.; Rossi, M. Profiling of protein degraders in cultures of human gut microbiota. Front. Microbiol. 2019, 10, 2614. [CrossRef]
Slobodkin, A. The Family Peptostreptococcaceae. In The Prokaryotes, 1st ed.; Rosenberg, E., DeLong, E.F., Lory, S., Stackebrand, E., Thompson, F., Eds.; Springer: Berlin, Germany, 2014; pp. 291–302.
Zhou, C.; Chen, L.L.; Lu, R.Q.; Ma, W.W.; Xiao, R. Alteration of intestinal microbiota composition in oral sensitized C3H/HeJ mice is associated with changes in dendritic cells and T cells in mesenteric lymph nodes. Front. Immunol. 2021, 12, 631494. [CrossRef]
Flint, H.J.; Scott, K.P.; Duncan, S.H.; Louis, P.; Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes. 2012, 3, 289–306. [CrossRef]
Awad, W.A.; Mann, E.; Dzieciol, M.; Hess, C.; Schmitz-Esser, S.; Wagner, M.; Hess, M. Age-related differences in the luminal and mucosa-associated gut microbiome of broiler chickens and shifts associated with Campylobacter jejuni infection. Front. Cell Infect. Microbiol. 2016, 6, 154. [CrossRef]
Stamilla, A.; Ruiz-Ruiz, S.; Artacho, A.; Pons, J.; Messina, A.; Lucia Randazzo, C.; Caggia, C.; Lanza, M.; Moya, A. Analysis of the microbial intestinal tract in broiler chickens during the rearing period. Biology 2021, 10, 942. [CrossRef]
Donaldson, E.E.; Stanley, D.; Hughes, R.J.; Moore, R.J. The time-course of broiler intestinal microbiota development after administration of cecal contents to incubating eggs. PeerJ 2017, 5, e3587. [CrossRef] [PubMed]
De Vilela Souza, J.V.; Kheravii, S.K.; Bajagai, Y.S.; Kolakshyapati, M.; Wu, S.; Ruhnke, I. Inclusion, of black soldier fly larvae in a meat chicken diet has minor effect on caeca microbiota. In Proceedings of the 32nd Annual Australian Poultry Science Symposium; University of Sydney: Sydney, Australia, 2021; Volume 32, p. 146.
Dabbou, S.; Lauwaerts, A.; Ferrocino, I.; Biasato, I.; Sirri, F.; Zampiga, M.; Bergagna, S.; Pagliasso, G.; Gariglio, M.; Colombino, E.; et al. Modified black soldier fly larva fat in broiler diet: Effects on performance, carcass traits, blood parameters, histomorphological features and gut microbiota. Animals 2021, 11, 1837. [CrossRef] [PubMed]
Wu, Y.; Zhang, H.; Zhang, R.; Cao, G.; Li, Q.; Zhang, B.; Wang, Y.; Yang, C. Serum metabolome and gut microbiome alterations in broiler chickens supplemented with lauric acid. Poult. Sci. 2021, 100, 101315. [CrossRef] [PubMed]
Abd El-Hack, M.E.; Shafi, M.E.; Alghamdi, W.Y.; Abdelnour, S.A.; Shehata, A.M.; Noreldin, A.E.; Ashour, E.A.; Swelum, A.A.; Al-Sagan, A.A.; Alkhateeb, M.; et al. Black soldier fly (Hermetia illucens) meal as a promising feed ingredient for poultry: A comprehensive review. Agriculture 2020, 10, 339. [CrossRef]
Weththasinghe, P.; Hansen, J.Ø.; Mydland, L.T.; Øverland, M. A systematic meta-analysis based review on black soldier fly (Hermetia illucens) as a novel protein source for salmonids. Rev. Aquac. 2021. [CrossRef]
Shumo, M.; Osuga, I.M.; Khamis, F.M.; Tanga, C.M.; Fiaboe, K.K.M.; Subramanian, S.; Ekesi, S.; van Huis, A.; Borgemeister, C. The nutritive value of black soldier fly larvae reared on common organic waste streams in Kenya. Sci. Rep. 2019, 9, 10110. [CrossRef]
DiGiacomo, K.; Leury, B. Review: Insect meal: A future source of protein feed for pigs? Animal 2019, 13, 3022–3030. [CrossRef]
Dabbou, S.; Gai, F.; Biasato, I.; Capucchio, M.T.; Biasibetti, E.; Dezzutto, D.; Meneguz, M.; Plachà, I.; Gasco, L.; Schiavone, A. Black soldier fly defatted meal as a dietary protein source for broiler chickens: Effects on growth performance, blood traits, gut morphology and histological features. J. Anim. Sci. Biotechnol. 2018, 9, 49. [CrossRef]
Marmion, M.; Ferone, M.T.; Whyte, P.; Scannell, A.G.M. The changing microbiome of poultry meat; from farm to fridge. Food Microbiol. 2021, 99, 103823. [CrossRef]
Moula, N.; Scippo, M.L.; Douny, C.; Degand, G.; Dawans, E.; Cabaraux, J.F.; Hornick, J.L.; Medigo, R.C.; Leroy, P.; Francis, F.; et al. Performances of local poultry breed fed black soldier fly larvae reared on horse manure. Anim Nutr. 2018, 4, 73–78. [CrossRef]
Grünwald, P.D. Introducing the minimum description length principle. In Advances in Minimum Description Length. Theory and Applications, 1st ed.; Grünwald, P.D., Myung, I.J., Pitt, M.A., Eds.; MIT Press: London, UK, 2004; pp. 3–23.
Lehmann, D.R.; Hulbert, J. Are three-point scales always good enough? J. Marketing Res. 1972, 9, 444–446. [CrossRef]
Kim, S.K.; Frisby, C.L. Gaining from discretization of continuous data: The correspondence analysis biplot approach. Behav. Res. 2019, 51, 589–601. [CrossRef] [PubMed]
Safari, S.; Baratloo, A.; Elfil, M.; Negida, A. Evidence based emergency medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emergency 2016, 4, 111–113. [PubMed]
