Effects of a Complex Feed Additive on Productivity and Blood Parameters of Laying Hens Using Stochastic Fractal-based Neural Network Model

Document Type : Original Paper

Authors

1 Federal State-Funded Educational Institution of Higher Education “St. Petersburg State University of Veterinary Medicine”, St. Petersburg, 196084, Russia

2 Research and Education Center “Industrial Biotechnologies”, Immanuel Kant Baltic Federal University, Kaliningrad, 236016, Russia

3 All-Russian Research Institute for Agricultural Microbiology, Pushkin, St. Petersburg , 196608, Russia 4School of Natural Sciences, University of Kent, Canterbury, CT2 7NJ, UK

4 School of Natural Sciences, University of Kent, Canterbury, CT2 7NJ, UK

Abstract

Neural networks (NNs) benefit biomedicine and agriculture, especially when relying on the specificity and implementation of stochastic fractal-supported models. In the poultry industry, a particular challenge is the search for an ideal sorbent-based complex additive to minimize the loss of valuable feed components that can be tailored to groups of gastrointestinal microorganisms. The aim of this study was thus to develop and apply a mathematical model and Gaussian NN to analyze productivity and blood parameters of laying hens when administering a complex feed additive from the mineral shungite sorbent, plus a nutritive supplement of brown seaweed meal. We developed and built a computational NN that modelled the stochastic ManyToOne relationship of an array of hens’ main blood parameters and performance traits. The results presented herein were that the artificial computational stochastic fractal-based NN (EuclidNN) first effectively analyzed the profiles of operational taxonomic units (OTUs) of the physiological/biochemical blood parameters. Also, correlation coefficients were highly positive in relation to certain zootechnical indicators, suggesting that feed additive intake may have led to changes in these performance traits. Calculations suggested that when implementing the feed additive, the values of the Cognitive Salience Index (CSI) vector vCSI2 declined. Hereby, this vector correlates with, and affects the egg production trait. Moreover, there was a certain relationship between the feed additive intake and feed and water consumption. Further, EuclidNN computed the respective bioconsolidation indices of hens and, simultaneously, processed several profiles of OTUs for all experimental variants. It also contributed to the calculation of bioconsolidation index values for each variant, i.e., a quantitative assessment of the physiological/biochemical blood descriptors, depending on diet. Collectively, the poultry productivity prediction based on the developed model and NN is pivotal as an initial step for future improvements of economically important traits in chickens when using novel and efficient complex feed additives.

Keywords

References

Anastasiadis AD. 2005. Neural networks training and applications using biological data. PhD Thesis. School of Computer Science and Information Systems, University of London, London, UK.
Batushansky A, Toubiana D & Fait A. 2016. Correlation-based network generation, visualization, and analysis as a powerful tool in biological studies: A case study in cancer cell metabolism. BioMed Research International, 2016: 8313272. DOI: 10.1155/2016/8313272
Buryakov NP, Zaikina AS, Trukhachev VI, Buryakova MA, Kosolapova VG, Nikonov IN, Medvedev IK, Fathala MM & Aleshin DE. 2023. Influence of dietary addition of mineral shungite and Fucus vesiculosus on production performance, egg quality, nutrients digestibility, and immunity status of laying hens. Animals, 13: 3176. DOI: 10.3390/ani13203176
Casals J, Jerez M & Sotoca S. 2005. Modeling and Forecasting Time Series Sampled at Different Frequencies. Office for Official Publications of the European Communities. Luxembourg. ISBN 92-79-01327-0.
Chakraverty S, Jena RM & Jena SK. 2023.Time-fractional Order Biological Systems with Uncertain Parameters. Synthesis Lectures on Mathematics and Statistics. Springer Nature Switzerland AG. Cham, Switzerland. ISBN 978-3-031-01295-2. DOI: 10.1007/978-3-031-02423-8
Chernov TI, Tkhakakhova AK & Kutovaya OV. 2015. Assessment of diversity indices for the characterization of the soil prokaryotic community by metagenomic analysis. Eurasian Soil Science, 48: 410–415. DOI: 10.1134/S1064229315040031
Colbrook M, Antun J, Vegard H & Anders C. 2022. The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem. Proceedings of the National Academy of Sciences of the United States of America, 119: e2107151119. DOI: 10.1073/pnas.2107151119
D’Addona DM. 2014. Neural network. In: Laperrière L & Reinhart G (Eds.). CIRP Encyclopedia of Production Engineering. Springer. Berlin, Heidelberg, Germany. DOI: 10.1007/978-3-642-20617-7_6563
Dall'Alba G, Casa PL, Abreu FPD, Notari DL & de Avila e Silva S. 2022. A survey of biological data in a big data perspective. Big Data, 10: 279–297. DOI: 10.1089/big.2020.0383
de Almeida LGB, de Oliveira ÉB, Furian TQ, Borges KA, Tonini da Rocha D, Salle CTP & Moraes HL de S. 2020. Artificial neural networks on eggs production data management. Acta Scientiae Veterinariae, 48. DOI: 10.22456/1679-9216.101462
Dvoryatkina S, Smirnov E & Lopukhin A. 2017. New opportunities of computer assessment of knowledge based on fractal modeling. US-China Foreign Language, 15: 452–459. DOI: 10.17265/1539-8080/2017.07.005
Everitt BS, Landau S, Leese M & Stahl D. 2011. Cluster Analysis. 5th ed. John Wiley & Sons. Chichester, UK.
Filntisi A, Papangelopoulos N, Bencurova E, Kasampalidis I, Matsopoulos G, Vlachakis D & Kossida S. 2013. State-of-the-art neural networks applications in biology. International Journal of Biological Sciences, 2: 63–85. DOI: 10.4018/ijsbbt.2013100105
Gafarov FM & Galimyanov AF. 2018. [Artificial Neural Networks and Applications]. Kazan University Publishing House. Kazan, Russia.
Gao X, Cassidy A, Schwarzschild MA, Rimm EB & Ascherio A. 2012.Habitual intake of dietary flavonoids and risk of Parkinson disease. Neurology, 78: 1138–1145. DOI: 10.1212/WNL.0b013e31824f7fc4
Garrapa R. 2018. Numerical solution of fractional differential equations: A survey and a software tutorial. Mathematics, 6: 16. DOI: 10.3390/math6020016
Ge SS & Wang C. 2002. Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks, 13: 214–221. DOI: 10.1109/72.977306
Ghahri H, Habibian R & Fam A. 2010. Evaluation of the efficacy of esterified glucomannan, sodium bentonite, and humic acid to ameliorate the toxic effects of aflatoxin in broilers. Turkish Journal of Veterinary & Animal Sciences, 34: 385–391. DOI: 10.3906/vet-0903-19
Gharajeh MS. 2018. Chapter eight – Biological big data analytics. Advanced Computing, 109: 321–355. DOI: 10.1016/bs.adcom.2017.08.002
Goodfellow I, Bengio Y & Courville A. 2016. Deep Learning. MIT Press. Cambridge, MA, USA.
Gorodnichev RM, Pestryakova LA, Ushnitskaya LA, Levina SN & Davydova PV. 2019. [Methods of Environmental Research. Fundamentals of Statistical Data Processing: Educational and Methodological Manual]. NEFU Publishing House. Yakutsk, Russia. ISBN 978-5-7513-2737-8.
Grishanov GV & Grishanova YuN. 2010. [Methods for Studying and Assessing Biological Diversity]. I. Kant Russian University. Kaliningrad, Russia.
Grosu GF, Hopp AV, Moca VV, Bârzan H, Ciuparu A, Ercsey-Ravasz M, Winkel M, Linde H & Mureșan RC. 2023. The fractal brain: Scale-invariance in structure and dynamics. Cerebral Cortex, 33: 4574–4605. DOI: 10.1093/cercor/bhac363
Hsieh WW. 2000. Nonlinear canonical correlation analysis by neural networks. Neural Networks, 13: 1095–1105. DOI: 10.1016/S0893-6080(00)00067-8
IAKI.RF. 2024. [Vitamin B2 (Riboflavin)]. LLC "Institute of Allergology and Clinical Immunology", IAKI.RF. https://xn--80apbh.xn--p1ai/analyzes/vitaminy/vitamin-v2-riboflavin/.
Ignatov I & Mosin O. 2014. [Composition and structural properties of fullerene analogous mineral shungite. Mathematical model of interaction of shungite with water molecules]. Naukovedenie, 2(21): 12TVN214.
Jahanmiri F & Parker DC. 2022. An overview of fractal geometry applied to urban planning. Land, 11: 475. DOI: 10.3390/land11040475
Jolliffe IT. 2002. Principal Component Analysis. 2nd ed. Springer. New York, NY, USA. ISBN 978-0-387-95442-4. DOI: 10.1007/b98835
Khlivnenko LV. 2015. [Practice of Neural Network Modeling]. Voronezh State Technical University. Voronezh, Russia. ISBN 978-5-7731-0429-2.
Kirikovich SA, Kirikovich YuK & Kurepin AA. 2012. [The influence of exogenous factors on the productivity, safety and natural resistance of animals]. [Collection of Scientific Papers of the Stavropol Research Institute of Animal Husbandry and Forage Production], 2: 264–272.
Kochish II, Smolensky VI, Laptev GY, Romanov MN, Nikonov IN, Panin AN, Surai PF, Ilina LA, Myasnikova OV, Selina MV, Korenyuga MV. 2019. Practical Recommendations for the Use of Feed Additives to Improve the Productivity and Stress Resistance of Egg Poultry. Sel'skokhozyaistvennye tekhnologii. Moscow, Russia.
Kochish II, Romanov MN, Nikonov IN. 2020a. Ways to improve the productivity of chickens (using a feed additive based on shungite as an example). In: [Materials of the XIX International Scientific and Practical Conference “Modern Trends in Agricultural Production in the World Economy”]. FSBEI HE “Kuzbass State Agricultural Academy”. Kemerovo, Russia. Pages 37–44.
Kochish II, Pozyabin SV, Vorobyov NI & Nikonov IN. 2020b. Fractal bioconsolidation of microorganisms in the intestines of laying hens due to the use of a feed additive from the mineral shungite. In: Materials of the 2nd International Scientific and Practical Conference on Molecular Genetic Technologies for Analysis of Gene Expression Related to Animal Productivity and Disease Resistance. Moscow, Russia, 25 December 2020. Sel'skokhozyaistvennye tekhnologii. Moscow, Russia. Pages 59–75. DOI: 10.18720/SPBPU/2/k20-5 (In Russian with English summary)
Kochish II, Romanov MN, Myasnikova OV, Nikonov IN, Selina MV, Korenyuga MV, Zimin EE, Sharafetdinov GR & Martynov VV. 2021. Methodical Recommendations on the Use of Antimicrobial Feed Additive for the Prevention of Stress in Industrial Crosses of Laying Hens. Sel'skokhozyaistvennye tekhnologii. Moscow, Russia. ISBN 978-5-6047654-0-1. DOI: 10.18720/SPBPU/2/z21-33 (In Russian)
Kochish I, Vorobyov N, Nikonov I & Selina M. 2022. Neural network fractal model to evaluate the effectiveness of antimicrobial feed additives in egg poultry farming. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 13: 13A12S. DOI: 10.14456/ITJEMAST.2022.250
Kochish II, Brazhnik EA, Vorobyov NI, Nikonov IN, Korenyuga MV, Myasnikova OV, Griffin DK, Surai PF & Romanov MN. 2023. Features of fractal conformity and bioconsolidation in the early myogenesis gene expression and their relationship to the genetic diversity of chicken breeds. Animals, 13: 521. DOI: 10.3390/ani13030521
Laptev GYu, Ilyina LA, Nikonov IN, Kochish II, Romanov MN, Smolensky VI, Panin AN, Yildirim EA, Novikova NI, Filippova VA & Dubrovin AV. 2017. [Determination of intestinal microbiocenoses of chickens of the Hisex breed by the T-RFLP method in ontogenesis]. Acta Naturae, 9(SI 1): 33. (In Russian)
Levchenko PV, Zhuchok AYu, Gugushvili NN & Inyukina TA. 2020. The change in hematological indices of blood of laying hens when using photomodulation in the early post-embryonic period. Collection of Scientific Papers of KRCAHVM, 9: 365–369. DOI: 10.34617/17qr-r266 (In Russian with English summary)
Mascarenhas WF. 2018. Fast and accurate normalization of vectors and quaternions. Computational and Applied Mathematics, 37: 4649–4660. DOI: 10.1007/s40314-018-0594-6
Matveev OA & Torshkov AA. 2020. Morphological indicators of blood of chicken-broilers in post-public ontogenesis. Učenye zapiski Kazanskoj gosudarstvennoj akademii veterinarnoj mediciny im. N.È. Baumana Scientific Notes, Kazan Bauman State Academy of Veterinary Medicine, 241: 138–142. DOI: 10.31588/2413-4201-1883-241-1-138-142 (In Russian with English summary)
Meakin P. 1999. A historical introduction to computer models for fractal aggregates. Journal of Sol-Gel Science and Technology, 15:97–117. DOI: 10.1023/A:1008731904082
Meireles MRG, Almeida PEM & Simoes MG. 2003. A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Industrial Electronics, 50: 585–601. DOI: 10.1109/TIE.2003.812470
Menshikov VV. 1997. [Clinical Diagnosis – Laboratory Basics]. Labinform Publishing House. Moscow, Russia. ISBN 5-89429-002-3. (In Russian)
Milevski I. 2024. Effect of Erythropoietin on Erythrogenesis. Vitamin B12 and Folic Acid in Erythropoiesis. MedUniver. (In Russian)
Minsky M & Papert S. 1969/1988. Perceptrons: An Introduction to Computational Geometry. MIT Press. Cambridge, MA, USA. ISBN 978-0-262-53477-2.
Moroz LI & Maslovskaya AG. 2020. Hybrid stochastic fractal-based approach to modeling the switching kinetics of ferroelectrics in the injection mode. Mathematical Models and Computer Simulations, 12: 348–356. DOI: 10.1134/S207004822003014X
Nematinia E & Abdanan Mehdizadeh S. 2018. Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network. Journal of Food Measurement and Characterization, 12: 1449–1459. DOI: 10.1007/s11694-018-9760-1
Nikolenko S, Kadurin A & Arkhangelskaya E. 2018. Deep Learning. Dive into the World of Neural Networks. Peter. St. Petersburg, Russia. (In Russian)
Nikolić D, Muresan RC, Feng W & Singer W. 2012. Scaled correlation analysis: A better way to compute a cross-correlogram. European Journal of Neuroscience, 35: 1–21. DOI: 10.1111/j.1460-9568.2011.07987.x
Ojo RO, Ajayi AO, Owolabi HA, Oyedele LO & Akanbi LA. 2022. Internet of things and machine learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200: 107266. DOI: 10.1016/j.compag.2022.107266
Okolelova TM & Mansurov RF. 2013. Efficiency of adsorbents in compound feeds contaminated with mycotoxins. Ptitsevodstvo, Poultry Farming, 11: 17–18. (In Russian)
Pitelinskiy KV & Shimanskiy SA. 2013. Application of neural networks in socioeconomic system studies. Vestnik Moskovskogo instituta lingvistiki [Vestnik of Moscow State Linguistic University], 1: 88–91. (In Russian with English summary)
Pitelinsky KV & Tyurkin AA. 2007. Applying of neuronic networks at an estimation of the real estate. In NTI-2007: Information Society, Intelligent Information Processing, Information Technologies, Proceedings of the International Conference Dedicated to the 55th Anniversary of VINITI. Moscow, Russia, 24–26 October 2007. VINITI of RAS. Moscow, Russia. Pages 251–253. (In Russian with English summary)
Popov VA. 2013. Probability Theory. Part 2. Random Variables. Kazan University. Kazan, Russia. (In Russian)
Przybyła-Kasperek M & Marfo KF. 2024. A multi-layer perceptron neural network for varied conditional attributes in tabular dispersed data. PloS One, 19: e03110. DOI: 10.1371/journal.pone.0311041
Pukhalskiy YaV, Vorobyev NI, Loskutov SI & Laktionov YuV. 2023. Genotypic screening for the resistance of leguminous crops to the effects of heavy metals, based on neuron profiling of their amino acid exudation. Agrarnyy vestnik Urala [Agrarian Bulletin of the Urals], 05(234): 83‒96. DOI: 10.32417/1997-4868-2023-234-05-83-96. (In Russian with English summary)
Pyrhönen L, Willems T, Mikkola A & Naets F. 2024. Inertial parameter identification for closed-loop mechanisms: adaptation of linear regression for coordinate partitioning. Journal of Computational and Nonlinear Dynamics, 19: 051001. DOI: 10.1115/1.4064794
Rosenblatt F. 1962. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Cornell Aeronautical Laboratory, Inc., Cornell University. Buffalo, NY, USA.
Schmidhuber J. 2015. Deep learning in neural networks: An overview. Neural Networks, 61: 85–117. DOI: 10.1016/j.neunet.2014.09.003
Sergeev AP & Tarasov DA. 2017. [Introduction to Neural Network Modeling]. Ural University Publishing House. Ekaterinburg, Russia. (In Russian)
Sharapova VV. 2011. [The use of additives from fucus algae and shungite in feeding laying hens]. Author's Abstract. Cand. Agric. Sci. Dissertation. GNU VNITIP of the Russian Agricultural Academy. Sergiev Posad, Russia. (In Russian)
Siriani ALR, Miranda IBdC, Mehdizadeh SA & Pereira DF. 2023. Chicken tracking and individual bird activity monitoring using the BoT-SORT algorithm. AgriEngineering, 5: 1677–1693. DOI: 10.3390/agriengineering5040104
Sutrop U. 2001. List task and a cognitive salience index. Field Methods, 13: 263–276. DOI: 10.1177/1525822X0101300303
Taye MM. 2023. Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions. Computation, 11: 52. DOI: 10.3390/computation11030052
Tietz NW. 1997. Encyclopedia of Clinical Laboratory Tests. Menshikov VV, Ed. Labinform Publishing House. Moscow, Russia. (In Russian)
Tukhbatov IA. 2013. Meat productivity formation of the broiler chickens due to the sorbent feed additive. Kormoproizvodstvo [Fodder Journal], 8: 40–42. (In Russian with English summary)
Tyurina LE. 2022. Scientific and practical justification for the use of mineral substances from the sources of the Krasnoyarsk territory in feeding farm animals and poultry, Dissertation. Krasnoyarsk State Agrarian University. Krasnoyarsk, Russia. (In Russian)
Vats T, Dutt S, Kumar R & Siril PF. 2016. Facile synthesis of pristine graphene-palladium nanocomposites with extraordinary catalytic activities using swollen liquid crystals. Scientific Reports, 6: 33053. DOI: 10.1038/srep33053
Vorobyov NI, Bogolyubova NV, Platonov AV, Nikonov IN, Selina MV, Guselnikova AA & Sidnev NY. 2023a. Effect of feed supplements on blood biochemical parameters and intensity of metabolic processes in cows: the neural network modeling method. International Journal of Chemical and Biochemical Sciences, 24: 165–170.
Vorobyov NI, Selina MV, Guselnikova AA, Nikonov IN & Sidnev NYu. 2023b. Program for neural network analysis of biochemical parameters of animal blood. Certificate of state registration of a computer program: RU 2023662779. Russia.
Wentzel ES. 1999. [Probability Theory]. 6th ed. Vysshaya Shkola. Moscow, Russia. ISBN 5-06-003650-2. (In Russian)
Widrow B, Greenblatt A, Kim Y & Park D. 2013. The No-Prop algorithm: A new learning algorithm for multilayer neural networks. Neural Networks, 37: 182–188. DOI: 10.1016/j.neunet.2012.09.020
Yang X, Bist RB, Subedi S & Chai L. 2023. A computer vision-based automatic system for egg grading and defect detection. Animals, 13: 2354. DOI: 10.3390/ani13142354
Yarovan NI. 2005. Use of Khotynetsk natural zeolites of the Oryol Region to normalize free radical oxidation in pigs. In: Materials of the 3rd International Symposium on Modern Problems of Veterinary Dietology and Nutritional Science. St. Petersburg, Russia. Pages 170–171. (In Russian)
Yeung KY & Ruzzo WL. 2001. Principal component analysis for clustering gene expression data. Bioinformatics, 17: 763–774. DOI: 10.1093/bioinformatics/17.9.763
Young G, Nippgen F, Titterbrandt S & Cooney MJ. 2010. Lipid extraction from biomass using co-solvent mixtures of ionic liquids and polar covalent molecules. Separation and Purification Technology, 72: 118–121. DOI: 10.1016/j.seppur.2010.01.009
Yurkovych N, Mar'yan M, Opachko M & Seben V. 2023. Methods of the fractal approach in science education: innovative technology and concepts of computer modeling. Physical and Mathematical Education, 38: 73–78. DOI: 10.31110/2413-1571-2023-038-3-010
Zaikina AS, Buryakov NP, Vorobyov NI & Nikonov IN. 2022. Neural network analysis of the compliance of the microbial-organismic biosystem of poultry intestines with a fractal-stochastic model. Permskij agrarnyj vestnik, Perm Agrarian Journal, 4(40): 98–106. DOI: 10.47737/2307-2873_2022_40_98. (In Russian with English summary)
Zasorin AV, Zubkov DG, Selmenskij GE, Nikonov IN & Alimpiev SV. 2019. Method for Reducing the Negative Impact of Mycotoxins in Poultry (Options). National Center for Biotechnology Information. PubChem Patent Summary for RU-2680009-C1.
Zhu B, Shin U & Shoaran M. 2021. Closed-loop neural prostheses with on-chip intelligence: A review and a low-latency machine learning model for brain state detection. IEEE Transactions on Biomedical Circuits and Systems, 15: 877–897. DOI: 10.1109/TBCAS.2021.3112756
Poultry Science Journal
Volume 13, Issue 2
June 2025
Pages 243-252

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