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Academic Journal of Environmental Biology, 2020, 1(1); doi: 10.38007/AJEB.2020.010102.

Molecular Biology Technology in Environmental Biology under the Background of Artificial Intelligence


Cioara Tudor

Corresponding Author:
Cioara Tudor

Charles Sturt University, Australia


The accelerating development of artificial intelligence not only promotes a qualitative leap in my country's science and technology, but also further stimulates social and economic development. The application of molecular biology technology(MBT) in environmental biology can improve the current ecological environment, so as to better meet the needs of modern people's life. In this paper, taking activated sludge denitrification as an example, the sludge DNA was first extracted, and then the sludge DNA was amplified by PCR using fluorescence quantitative PCR technology, and then the IFAS system was used to denitrify the sewage, BOD5 inlet and outlet water quality changes, in order to evaluate the water quality standards. The experimental results show that after denitrification, the water quality can basically meet the discharge standard of Class B, and the removal rate of organic matter in sewage can also reach about 90%, which verifies that MBT can effectively degrade and remediate complex pollutants in the environment .


Artificial Intelligence, Molecular Biology Technology, Environmental Biology, Activated Sludge Denitrification

Cite This Paper

Cioara Tudor. Molecular Biology Technology in Environmental Biology under the Background of Artificial Intelligence. Academic Journal of Environmental Biology (2020), Vol. 1, Issue 1: 10-17. https://doi.org/10.38007/AJEB.2020.010102.


[1] Colombo N, Sessa C, Bois A D, et al. ESMO–ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease. Annals of Oncology, 2019, 30( 5):672-705.

[2] Mccord R P, Taipale M,  Teichmann S, et al. Voices on technology: The molecular biologists' ever-expanding toy box. Molecular Cell, 2020, 82(2):221-226.

[3] Abdallah B B, Andreu I, Chatti A, et al. Size Fractionation of Titania Nanoparticles in Wild Dittrichia viscosa Grown in a Native Environment. Environmental Science and Technology, 2020, 54(14):8649-8657. https://doi.org/10.1021/acs.est.9b07267

[4] P Spégel, Mulder H . Metabolomics Analysis of Nutrient Metabolism in β-Cells. Journal of Molecular Biology, 2020, 432(5):1429-1445. https://doi.org/10.1016/j.jmb.2019.07.020

[5] Santos, R. O, Rehage, et al. Combining data sources to elucidate spatial patterns in recreational catch and effort: fisheries-dependent data and local ecological knowledge applied to the South Florida bonefish fishery. Environmental Biology of Fishes, 2019, 102(2):299-317.

[6] Rahman M J, Amin S, Nahiduzzaman M, et al. Influence of seasons, habitat sanctuaries, gears and environmental variables on the catches of hilsa shad (Tenualosa ilisha) in Bangladesh waters. Journal of environmental biology, 2018, 39(5SPEC.):767-776. https://doi.org/10.22438/jeb/39/5(SI)/21

[7] Tabti D, Laouar M, Rajendran K, et al. Identification of desirable mutants in quantitative traits of lentil at early (M_2) generation. Journal of environmental biology, 2018, 39(2):137-142. https://doi.org/10.22438/jeb/39/2/MRN-476

[8] Thrall J H, Li X, Li Q, et al. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. Journal of the American College of Radiology, 2018, 15( 3):504-508. https://doi.org/10.1016/j.jacr.2017.12.026

[9] Bin Y U, Kumbier K . Artificial intelligence and statistics. Frontiers of Information Technology & Electronic Engineering, 2018, 19(01):6-9. https://doi.org/10.1631/FITEE.1700813

[10] Koch, Marta. Artificial Intelligence Is Becoming Natural.. Cell, 2018, 173(3):531-533. https://doi.org/10.1016/j.cell.2018.04.007

[11] Syam N, Sharma A . Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 2018, 69(FEB.):135-146.

[12] Selvarajoo K, Piras V, Chiow A . Long Range Order and Short Range Disorder in Saccharomyces cerevisiae Biofilm. Engineering Biology, 2019, 3(1):12-19. https://doi.org/10.1049/enb.2018.5008

[13] Roger M, Brown F, Gabrielli W, et al. Efficient Hydrogen-Dependent Carbon Dioxide Reduction by Escherichia coli. Current Biology: CB, 2018, 28(1):140-145.e2. https://doi.org/10.1016/j.cub.2017.11.050

[14] Pullaguri N, Nema S, Bhargava Y, et al. Triclosan alters adult zebrafish behavior and targets acetylcholinesterase activity and expression. Environmental toxicology and pharmacology, 2020, 75(Apr.):103311.1-103311.7. https://doi.org/10.1016/j.etap.2019.103311

[15] Fasciglione G, Goi M G, Yommi A, et al. Revaluation of waste from fishing industry through generation of chitosan coatings to improve quality and extend shelf-life of minimally processed lettuce. Postharvest Biology and Technology, 2020, 170(12):1-9.

[16] Reyna-Figueroa J, MF Rodríguez-Sánchez,  Matsumoto P, et al. Decrease in the Hospital Stay of Neonates with Suspected Nosocomial Sepsis with the Use of a Molecular Biology Technique. Journal of Biosciences and Medicines, 2019, 07(3):44-51. https://doi.org/10.4236/jbm.2019.73005

[17] Bucci E M . Xylella fastidiosa, a new plant pathogen that threatens global farming: Ecology, molecular biology, search for remedies. Biochem Biophys Res Commun, 2018, 502(2):173-182. https://doi.org/10.1016/j.bbrc.2018.05.073

[18] Kaul D . An Overview of Coronaviruses including the SARS-2 Coronavirus – Molecular Biology, Epidemiology and Clinical Implications. Current Medicine Research and Practice, 2020, 10( 2):54-64. https://doi.org/10.1016/j.cmrp.2020.04.001

[19] Ratner L . Molecular biology of human T cell leukemia virus. Seminars in Diagnostic Pathology, 2020, 37( 2):104-109. https://doi.org/10.1053/j.semdp.2019.04.003

[20] Precious S V, Rosser A E,  Dunnett S B . [Methods in Molecular Biology] Huntington's Disease Volume 1780 || Translating Antisense Technology into a Treatment for Huntington's Disease. 2018, 10.1007/978-1-4939-7825-0(Chapter 23):497-523. https://doi.org/10.1007/978-1-4939-7825-0