Utilizing the R Language in Exploring Neuroscientific Findings in Mexico:
Environmental Factors and Neuroeducation
DOI:
https://doi.org/10.56162/transdigital289Keywords:
Data mining, Language R, Neurosciences, Neuroscience, MethodologyAbstract
The current investigation delves into neuroscience findings concerning neuroeducation and educational research conducted in Mexico or by Mexican scholars. The study aims to perform a comprehensive review of the scientific output pertaining to neuroscientific discoveries associated with environmental factors and their interplay with neuroeducation in the Mexican context, employing data mining techniques. Specifically, the analysis is carried out using the R Language and involves exploration of the PubMed database. Among the initial pool of 76,844 records, 13 articles were selected for further analysis based on alignment with the research objectives. The utilization of data mining as a methodological approach not only streamlines document analysis but also facilitates exploration through additional tools such as sentiment analysis or opinion mining. These methods introduce novel avenues for conducting research by leveraging the extensive body of web-based literature.
References
Alwateer, M., Almars, A., Areed, K., Elhosseini, M., Haikal, A., & Badawy, M. (2021). Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier. Sensors, 21(13), 4579. https://doi.org/10.3390/s21134579
Biessels, G. J., Strachan, M. W., Visseren, F. L., Kappelle, L. J., & Whitmer, R. A. (2014). Dementia and cognitive decline in type 2 diabetes and prediabetic stages: towards targeted interventions. The Lancet. Diabetes & endocrinology, 2(3), 246–255. https://doi.org/10.1016/S2213-8587(13)70088-3
Buendia-Lozada, E. (2022), github. environment_neuroeducation: https://github.com/buendiaenr1/environment_neuroeducation
Briz, R. A. & Serrano, A. A. (2018). Aprendizaje de las matemáticas a través de lenguaje de programación R en Educación Secundaria. Educación matemática, 30(1), 133-162. https://doi.org/10.24844/EM3001.05
Calderón-Garcidueñas, L., & Ayala, A. (2022). Air Pollution, Ultrafine Particles, and Your Brain: Are Combustion Nanoparticle Emissions and Engineered Nanoparticles Causing Preventable Fatal Neurodegenerative Diseases and Common Neuropsychiatric Outcomes? Environmental Science & Technology, 56(11), 6847–6856. https://doi.org/10.1021/acs.est.1c04706
Calderón-Garcidueñas, L., Stommel, E.W., Rajkumar, R.V., Mukherjee, P.S. & Ayala, A. (2021). Particulate Air Pollution and Risk of Neuropsychiatric Outcomes. What We Breathe, Swallow, and Put on Our Skin Matters. Int J Environ Res Public Health, 18(21). https://doi.org/10.3390/ijerph182111568
Carracedo, P., Puertas, R., & Marti, L. (2021). Research lines on the impact of the COVID-19 pandemic on business. A text mining analysis. J Bus Res, 132, 586-593. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562908/
Coumans, J. M. J., Danner, U. N., Intemann, T., De Decker, A., Hadjigeorgiou, C., Hunsberger, M., Moreno, L. A., Russo, P., Stomfai, S., Veidebaum, T., Adan, R. A. H., Hebestreit, A., & Family Consortium (2018). Emotion-driven impulsiveness and snack food consumption of European adolescents: Results from the I. Family study. Appetite, 123, 152–159. https://doi.org/10.1016/j.appet.2017.12.018
Chaddock-Heyman, L., Erickson, K. I., Kienzler, C., Drollette, E. S., Raine, L. B., Kao, S. C., Bensken, J., Weisshappel, R., Castelli, D. M., Hillman, C. H., & Kramer, A. F. (2018). Physical Activity Increases White Matter Microstructure in Children. Frontiers in neuroscience, 12, 950. https://doi.org/10.3389/fnins.2018.00950
Chan, D., & Galli, M. G. (2020) Aplicación de técnicas estadísticas mutivariadas con el lenguaje de programación en R en investigaciones educativas del nivel superior. RAES, 12(20), 123-136. http://www.revistaraes.net/revistas/raes20_art8.pdf
Cheng, X., Cao Q., & Liao, S. S. (2020). An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation. Journal of Information Science, 48(2), 304-320. https://doi.org/10.1177/0165551520954674
Cryer, J. D. & Chan, K. S. (2008). Times series analysis with applications in R. Springer.
Ettcheto, M., Busquets, O., Cano, A., Sánchez-Lopez, E., Manzine, P. R., Espinosa-Jimenez, T., Verdaguer, E., Sureda, F. X., Olloquequi, J., Castro-Torres, R. D., Auladell, C., Folch, J., Casadesús, G., & Camins, A. (2021). Pharmacological Strategies to Improve Dendritic Spines in Alzheimer's Disease. Journal of Alzheimer's disease: JAD, 82(s1), S91–S107. https://doi.org/10.3233/JAD-201106
Fantini, D. (2019). easyPubMed: Search and Retrieve Scientific Publication Records from PubMed. R package version 2.13. https://CRAN.R-project.org/package=easyPubMed
Fuller, R., Landrigan, P., Balakrishnan, K., Bathan, G., Bose-O’Reilly, S., Brauer, M., . . . Yan, C. (2022, Mayo 17). Pollution and health: a progress update. Lancet Planet Health. https://doi.org/10.1016/S2542-5196(22)00090-0
Franco-Juárez, B., Gómez-Manzo, S., Hernández-Ochoa, B., Cárdenas-Rodríguez, N., Arreguin-Espinosa, R., Pérez de la Cruz, V., & Ortega-Cuellar, D. (2021). Effects of High Dietary Carbohydrate and Lipid Intake on the Lifespan of C. elegans. Cells, 10(9), https://doi.org/10.3390/cells10092359
Gabriel-Ortiz, G., Mireles-Ramírez, M. A., Pacheco-Moisés, F. P., Ramírez-Jirano, L. J., Bitzer-Quintero, O. K., Delgado-Lara, D. L., Flores-Alvarado, L.J., Mora-Navarro, M.A., Huerta, M. & Torres-Mendoza, B. (2021). Are electrophysiological and oligodendrocyte alterations an element in the development of multiple sclerosis at the same time as or before the immune response? International Journal of Neuroscience, 131(12), 1221-1230. https://doi.org/10.1080/00207454.2020.1786087
Horowitz, A. M., Fan, X., Bieri, G., Smith, L. K., Sanchez-Diaz, C. I., Schroer, A. B., Gontier, G., Casaletto, K. B., Kramer, J. H., Williams, K. E., & Villeda, S. A. (2020). Blood factors transfer beneficial effects of exercise on neurogenesis and cognition to the aged brain. Science, 369(6500), 167–173. https://doi.org/10.1126/science.aaw2622
Hoyeol, K. (2022). Sentiment Analysis: Limits and Progress of the Syuzhet Package and its Lexicons. Digital Humanities Quaterly, 16(2), 1. http://www.digitalhumanities.org/dhq/vol/16/2/000612/000612.html
Ihaka, R., & Gentleman, R. (1996). R: A Language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5, 299-314. https://doi.org/10.1080/10618600.1996.10474713
Jarero-Basulto, J., Gasca-Martínez, Y., Rivera-Cervantes, M., Ureña-Guerrero, M., Feria-Velasco, A., & Beas-Zarate, C. (2018). Interactions Between Epilepsy and Plasticity. Pharmaceuticals, 11(17). https://doi.org/10.3390/ph11010017
Jiang, W., & Chen, Y. (2022). Air Pollution, Foreign Direct Investment, and Mental Health: Evidence From China. Frontiers in public health, 10, 858672. https://doi.org/10.3389/fpubh.2022.858672
Kwartler, T. (2017). Text Mining in Practice with R. John Wiley & Sons Ltd.
Liang, Y. Y., Zhang, L. D., Luo, X., Wu, L. L., Chen, Z. W., Wei, G. H., Zhang, K. Q., Du, Z. A., Li, R. Z., So, K. F., & Li, A. (2022). All roads lead to Rome - a review of the potential mechanisms by which exerkines exhibit neuroprotective effects in Alzheimer's disease. Neural regeneration research, 17(6), 1210–1227. https://doi.org/10.4103/1673-5374.325012
Liu, S., Mo, C., Lei, L., Lv, F., Li, J., Xu, X., Lu, P., Wei, G., Huang, X., Zeng, X., & Qiu, X. (2023). Association of ultraprocessed foods consumption and cognitive function among children aged 4-7 years: a cross-sectional data analysis. Frontiers in nutrition, 10, 1272126. https://doi.org/10.3389/fnut.2023.1272126
Liu, C., Xie, B., Chou, C. P., Koprowski, C., Zhou, D., Palmer, P., Sun, P., Guo, Q., Duan, L., Sun, X., & Anderson Johnson, C. (2007). Perceived stress, depression and food consumption frequency in the college students of China Seven Cities. Physiology & behavior, 92(4), 748–754. https://doi.org/10.1016/j.physbeh.2007.05.068
Lu, W., Hackman, D. A., & Schwartz, J. (2021). Ambient air pollution associated with lower academic achievement among US children: A nationwide panel study of school districts. Environmental epidemiology, 5(6), e174. https://doi.org/10.1097/EE9.0000000000000174
Martínez-Leo, E., & Segura Campos, M. (2020). Effect of ultra-processed diet on gut microbiota and thus its role in neurodegenerative diseases. Nutrition, 71, 110609. https://doi.org/10.1016/j.nut.2019.110609
Medhat, W., Hassan, A. & Korashy (2014). Sentiment analysis algorithms an application: A survey. Ain Shams Engineering Journal, 4(5), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011
Miola, A., De Filippis, E., Veldic, M., Ho, A. M., Winham, S. J., Mendoza, M., Romo-Nava, F., Nunez, N. A., Gardea Resendez, M., Prieto, M. L., McElroy, S. L., Biernacka, J. M., Frye, M. A., & Cuellar-Barboza, A. B. (2022). The genetics of bipolar disorder with obesity and type 2 diabetes. Journal of affective disorders, 313, 222–231. https://doi.org/10.1016/j.jad.2022.06.084
Mohai, P., Kweon, B.S., Lee, S. & Ard, K. (2011). Air pollution around schools is linked to poorer student health and academic performance. Health Affairs, 30(5), 852-862. https://doi.org/10.1377/hlthaff.2011.0077
Mhatre, S. (2020). RedgateHub. Text Mining and Sentiment Analysis with R. https://www.red-gate.com/simple-talk/databases/sql-server/bi-sql-server/text-mining-and-sentiment-analysis-with-r/
Najafi, N., Movehed, K., Barzegar, Z., & Samani, S. (2018). Environmental Factors Affecting Students’ Stress in the Educational Environment: A Case Study of Shiraz Schools. International Journal of School Health, 5(2), 1-7. https://doi.org/10.5812/intjsh.67153
Nicholson, S., Baccarelli, A., & Prada, D. (2022). Role of brain extracellular vesicles in air pollution-related cognitive impairment and neurodegeneration. Environ Res, 204(Pt C), 112316. https://doi.org/10.1016/j.envres.2021.112316
Northstone, K., Joinson, C., Emmett, P., Ness, A., & Paus, T. (2012). Are dietary patterns in childhood associated with IQ at 8 years of age? A population-based cohort study. Journal of epidemiology and community health, 66(7), 624–628. https://doi.org/10.1136/jech.2010.111955
Noll, M., Noll P. R. e S., Mendonça, C. R. Dos Santos, R. A. P. & Aparecida, S. E. (2021). Effects of ultra-processed food on cognition and learning of adolescents: a rapid systematic review [version 1; peer review: peer review discontinued]. F1000Research, 10, 866. https://doi.org/10.12688/f1000research.55336.1
Øverby, N. C., Lüdemann, E., & Høigaard, R. (2013). Self-reported learning difficulties and dietary intake in Norwegian adolescents. Scandinavian Journal of Public Health, 41(7), 754–760. https://doi.org/10.1177/1403494813487449
Pánico, P., Velasco, M., Salazar, A., Picones, A., Ortiz-Huidobro, R., Guerrero-Palomo, G., Salgado-Bernabé, M. E., Ostrosky-Wegman, P., & Hiriart, M. (2022). Is Arsenic Exposure a Risk Factor for Metabolic Syndrome? A Review of the Potential Mechanisms. Frontiers in Endocrinology, 13. https://doi.org/10.3389/fendo.2022.878280
PYPL. (2024). Página oficial de The Popularity of Programming Language Index. https://pypl.github.io/PYPL.html
Riggs, L., Piscione, J., Laughlin, S., Cunningham, T., Timmons, B. W., Courneya, K. S., Bartels, U., Skocic, J., de Medeiros, C., Liu, F., Persadie, N., Scheinemann, K., Scantlebury, N., Szulc, K. U., Bouffet, E., & Mabbott, D. J. (2017). Exercise training for neural recovery in a restricted sample of pediatric brain tumor survivors: a controlled clinical trial with crossover of training versus no training. Neuro-oncology, 19(3), 440–450. https://doi.org/10.1093/neuonc/now177
Ramírez-Moreno, M., Duarte-Jurado, A., Gopar-Cuevas, Y., Gonzalez-Alcocer, A., Rodriguez-Rocha, H., & Garcia-Garcia, A. (2019, Mayo 17). Autophagy Stimulation Decreases Dopaminergic Neuronal Death Mediated by Oxidative Stress. Molecular Neurobiology, 8136–8156. https://doi.org/10.1007/s12035-019-01654-1
Reinoso Lorente, A., Vázquez Rodríguez, R., & Pérez Risquet, C. (2020). Uso del lenguaje R para el procesamiento de datos y la generación de mapas sobre COVID19. Revista Cubana de Transformación Digital, 1(3), 37-50. https://rctd.uic.cu/rctd/article/view/84/26
R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
Silge, J., & Robinson, D. (2022). Relationships between words: n-grams and correlations. Text Mining with R: A Tidy Approach. https://www.tidytextmining.com/ngrams.html
Toral-Rios, D., Pichardo-Rojas, P. S., Alonso-Vanegas, M., & Campos-Peña, V. (2020). GSK3? and Tau Protein in Alzheimer’s Disease and Epilepsy. Frontiers in Cellular Neuroscience, 14(19). https://doi.org/10.3389/fncel.2020.00019
Velásquez, H. J. D., Olaya, O. Y., & Franco, C. C. J. (2010). Análisis y predicción de series de tiempo en mercados de energía usando el lenguaje R. Dyna, 78(165), 287-296. http://www.scielo.org.co/pdf/dyna/v78n165/a30v78n165.pdf
World Health Organization. (2021) Obesity and overweight: key facts. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
Xiong, X., Zhu, L. N., Dong, X. X., Wang, W., Yan, J., & Chen, A. G. (2018). Aerobic Exercise Intervention Alters Executive Function and White Matter Integrity in Deaf Children: A Randomized Controlled Study. Neural plasticity, 3735208. https://doi.org/10.1155/2018/3735208
Xu, H., Sun, Y., Wan, Y., Zhang, S., Xu, H., Yang, R., Wang, W., Zeng, H., Xu, S., Hao, J. & Tao, F. (2019). Eating pattern and psychological, symptoms: A coss-sectional study based on a national large sample of Chinese adolescents. Journal of Affective Disorders, 224(1), 155-163. https://doi.org/10.1016/j.jad.2018.10.090
Zahedi, H., Kelishadi, R., Heshmat, R., Motlagh, M. E., Ranjbar, S. H., Ardalan, G., Payab, M., Chinian, M., Asayesh, H., Larijani, B., & Qorbani, M. (2014). Association between junk food consumption and mental health in a national sample of Iranian children and adolescents: the CASPIAN-IV study. Nutrition, 30(11-12), 1391–1397. https://doi.org/10.1016/j.nut.2014.04.014
![](http://revista-transdigital.org/public/journals/1/submission_289_295_coverImage_en_US.png)
Downloads
Autor de correspondencia
El autor de correspodencia se identifica con el siguiente símbolo: *Published
How to Cite
License
Copyright (c) 2024 Xitlali Torres-Aguilar, Enrique R. P. Buendia-Lozada, Dulce María C. Flores-Olvera
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles in Transdigital are licensed under a Creative Commons Attribution 4.0 International License. Authors hold the copyright and retain publishing rights without restrictions.