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Artigos de revistas sobre o tema "Intelligence – genetics"

Autor: Grafiati

Publicado: 25 de julho de 2024

Última modificação: 28 de julho de 2024

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1

Farzan, Raed. "Artificial intelligence in Immuno-genetics." Bioinformation 20, no. 1 (2024): 29–35. http://dx.doi.org/10.6026/973206300200029.

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Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatmen

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2

BARNETT, S. A. "GENETICS OF INTELLIGENCE." Developmental Medicine & Child Neurology 5, no. 5 (2008): 522. http://dx.doi.org/10.1111/j.1469-8749.1963.tb10712.x.

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3

Deary, Ian J., Frank M. Spinath, and Timothy C. Bates. "Genetics of intelligence." European Journal of Human Genetics 14, no. 6 (2006): 690–700. http://dx.doi.org/10.1038/sj.ejhg.5201588.

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4

Plomin, Robert, and Jenae Neiderhiser. "Quantitative Genetics, Molecular Genetics, and Intelligence." Intelligence 15, no. 4 (1991): 369–87. http://dx.doi.org/10.1016/0160-2896(91)90001-t.

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5

Sternberg, Robert J., Elena L. Grigorenko, and Kenneth K. Kidd. "Intelligence, race, and genetics." American Psychologist 60, no. 1 (2005): 46–59. http://dx.doi.org/10.1037/0003-066x.60.1.46.

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6

VASYLKIVSKYI, Mikola, Ganna VARGATYUK, and Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.

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The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key com

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Krittanawong, Chayakrit, Kipp W. Johnson, Edward Choi, et al. "Artificial Intelligence and Cardiovascular Genetics." Life 12, no. 2 (2022): 279. http://dx.doi.org/10.3390/life12020279.

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Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynami

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8

Plomin, Robert, and Frank M. Spinath. "Intelligence: Genetics, Genes, and Genomics." Journal of Personality and Social Psychology 86, no. 1 (2004): 112–29. http://dx.doi.org/10.1037/0022-3514.86.1.112.

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9

Plomin, Robert, and Sophie von Stumm. "The new genetics of intelligence." Nature Reviews Genetics 19, no. 3 (2018): 148–59. http://dx.doi.org/10.1038/nrg.2017.104.

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10

Plomin, Robert, and Stephen A. Petrill. "Genetics and intelligence: What's new?" Intelligence 24, no. 1 (1997): 53–77. http://dx.doi.org/10.1016/s0160-2896(97)90013-1.

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11

Velden, M. "Smarten up on intelligence genetics." Nature 503, no. 7476 (2013): 342. http://dx.doi.org/10.1038/503342c.

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12

Johnson, Wendy. "Understanding the Genetics of Intelligence." Current Directions in Psychological Science 19, no. 3 (2010): 177–82. http://dx.doi.org/10.1177/0963721410370136.

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13

Lombroso, Paul J., ROBERT J. STERNBERG, and ELENA L. GRIGORENKO. "Genetics of Childhood Disorders: I. Genetics and Intelligence." Journal of the American Academy of Child & Adolescent Psychiatry 38, no. 4 (1999): 486–88. http://dx.doi.org/10.1097/00004583-199904000-00024.

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KAUFMAN, ALAN S. "Genetics of Childhood Disorders: II. Genetics and Intelligence." Journal of the American Academy of Child & Adolescent Psychiatry 38, no. 5 (1999): 626–28. http://dx.doi.org/10.1097/00004583-199905000-00029.

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Lombroso, Paul J., and ROBERT PLOMIN. "Genetics of Childhood Disorders: III. Genetics and Intelligence." Journal of the American Academy of Child & Adolescent Psychiatry 38, no. 6 (1999): 786–88. http://dx.doi.org/10.1097/00004583-199906000-00030.

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16

Solomon, Benjamin D. "Can artificial intelligence save medical genetics?" American Journal of Medical Genetics Part A 188, no. 2 (2021): 397–99. http://dx.doi.org/10.1002/ajmg.a.62538.

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17

Toga, Arthur W., and Paul M. Thompson. "GENETICS OF BRAIN STRUCTURE AND INTELLIGENCE." Annual Review of Neuroscience 28, no. 1 (2005): 1–23. http://dx.doi.org/10.1146/annurev.neuro.28.061604.135655.

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18

Kristensen, Vessela, and Dag Undlien. "Artificial intelligence (AI) tools in genetics." Open Access Government 42, no. 1 (2024): 134–35. http://dx.doi.org/10.56367/oag-042-11133.

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Artificial intelligence (AI) tools in genetics Vessela Kristensen and Dag Undlien uncover AI tools in genetics, from variant recognition to clinical implementation. Most people are curious about how their bodies work (and the ways they occasionally do not). This curiosity extends towards how our bodies are built, their functions, and what maintains life and health. Most people think that science is remote from the lives they lead, and the decisions that they make day by day, but this is far from the truth. Our understanding of genetics may affect our choices at our doctor’s office about our he

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19

Antonelli-Ponti, Mayra, and Madeline Crosswaite. "Teachers' Perceptions about the Etiology of Intelligence and Learning Difficulties." International Journal of Educational Psychology 8, no. 2 (2019): 162. http://dx.doi.org/10.17583/ijep.2019.3777.

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The etiology of intelligence and learning difficulties are interpreted and perceived in different ways within society. The present study aims to explore the perceptions of a sample of n=501 Brazilian teachers regarding genetic and environmental influences on intelligence and learning difficulties. Using numerical scales, it was observed that importance was ascribed by teachers to genetic and environmental influences across both the intelligence and learning difficulties domains. A multiple choice items test revealed differences on the perceptions of teachers according to gender, age, schooling

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20

Penke, Lars, Jaap J. A. Denissen, and Geoffrey F. Miller. "The evolutionary genetics of personality." European Journal of Personality 21, no. 5 (2007): 549–87. http://dx.doi.org/10.1002/per.629.

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Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation‐selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation‐selection balance seems best at explaining

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21

Greenspan, Neil S. "Genes, Heritability, ‘Race’, and Intelligence: Misapprehensions and Implications." Genes 13, no. 2 (2022): 346. http://dx.doi.org/10.3390/genes13020346.

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The role of genetics in determining measured differences in mean IQ between putative racial groups has been a focus of intense discussion and disagreement for more than 50 years. While the last several decades of research have definitively demonstrated that genetic variation can influence measures of cognitive function, the inferences drawn by some participants in the controversy regarding the implications of these findings for racial differences in cognitive ability are highly dubious. Of equal importance, there is no compelling scientific rationale for focusing on and devoting substantial ef

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22

Piffer, Davide. "Sex Differences in Intelligence: A Genetics Perspective." Mankind Quarterly 58, no. 1 (2017): 112–16. http://dx.doi.org/10.46469/mq.2017.58.1.10.

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23

Arslan, Ruben, and Lars Penke. "Zeroing in on the Genetics of Intelligence." Journal of Intelligence 3, no. 2 (2015): 41–45. http://dx.doi.org/10.3390/jintelligence3020041.

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24

Plomin, R., and I. J. Deary. "Genetics and intelligence differences: five special findings." Molecular Psychiatry 20, no. 1 (2014): 98–108. http://dx.doi.org/10.1038/mp.2014.105.

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25

Narita, Akira, Masao Ueki, and Gen Tamiya. "Artificial intelligence powered statistical genetics in biobanks." Journal of Human Genetics 66, no. 1 (2020): 61–65. http://dx.doi.org/10.1038/s10038-020-0822-y.

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26

Paul, Diane B. "Textbook Treatments of the Genetics of Intelligence." Quarterly Review of Biology 60, no. 3 (1985): 317–26. http://dx.doi.org/10.1086/414428.

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27

Shakeshaft, Nicholas G., Maciej Trzaskowski, Andrew McMillan, et al. "Thinking positively: The genetics of high intelligence." Intelligence 48 (January 2015): 123–32. http://dx.doi.org/10.1016/j.intell.2014.11.005.

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28

Martschenko, Daphne. "DNA Dreams': Teacher Perspectives on the Role and Relevance of Genetics for Education." Research in Education 107, no. 1 (2019): 33–54. http://dx.doi.org/10.1177/0034523719869956.

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Behavioural genetics regards intelligence and educational attainment as highly heritable (genetically influenced) and polygenic (influenced by many genes) traits. Researchers in the field have moved beyond identifying whether and how much genes influence a given outcome to trying to pinpoint the genetic markers that help predict them. In more recent years, behavioural genetics research has attempted to cross-over into the field of education, looking to play a role in education research and the construction of education policy. In response to these developments, this paper explores PreK-12 Amer

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29

Elliott, Maxwell L., Daniel W. Belsky, Kevin Anderson, et al. "A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains." Cerebral Cortex 29, no. 8 (2018): 3496–504. http://dx.doi.org/10.1093/cercor/bhy219.

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Abstract People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, tot

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30

Lett, Tristram A., Bob O. Vogel, Stephan Ripke, et al. "Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence." Cerebral Cortex 30, no. 4 (2019): 2708–19. http://dx.doi.org/10.1093/cercor/bhz270.

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Abstract Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surfa

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31

Reis, Cecília, and J. A. Tenreiro Machado. "Computational Intelligence in Circuit Synthesis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (2007): 1122–27. http://dx.doi.org/10.20965/jaciii.2007.p1122.

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This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a

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32

Sachin Kumar Tripathi, Khyati Rao, Rajiv Ratan Singh, and Pradeep kumar Yadav. "Artificial Intelligence and its Role in Forensic Karyotyping: A Systematic Review." Indian Journal of Forensic Medicine & Toxicology 18, no. 1 (2024): 114–18. http://dx.doi.org/10.37506/900g7477.

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Introduction: One of the most important aspects of forensic investigations and genetic research is forensickaryotyping, which involves analyzing a person’s chromosomes to find genetic anomalies and establishidentification. The development of artificial intelligence (AI) technology offers a chance to improve and automate the forensic karyotyping procedure. This study examines the possible advantages and difficulties of artificial intelligence (AI) in forensic karyotyping. In forensic science, forensic karyotyping is essential for providing an accurate interpretation of genetic data for use in l

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33

Denkena, Berend, Helge Henning, and Leif-Erik Lorenzen. "Genetics and intelligence: new approaches in production engineering." Production Engineering 4, no. 1 (2009): 65–73. http://dx.doi.org/10.1007/s11740-009-0191-z.

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34

Wilkins, Adam S. "Charles Darwin: Genius or Plodder?" Genetics 183, no. 3 (2009): 773–77. http://dx.doi.org/10.1534/genetics.109.110452.

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There is no doubt about the magnitude of Charles Darwin's contributions to science. There has, however, been a long-running debate about how brilliant he was. His kind of intelligence was clearly different from that of the great physicists who are deemed geniuses. Here, the nature of Darwin's intelligence is examined in the light of Darwin's actual style of working. Surprisingly, the world of literature and the field of neurobiology might supply more clues to resolving the puzzle than conventional scientific history. Those clues suggest that the apparent discrepancy between Darwin's achievemen

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35

Matuchansky, Claude. "Intelligence clinique et intelligence artificielle." médecine/sciences 35, no. 10 (2019): 797–803. http://dx.doi.org/10.1051/medsci/2019158.

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L’intelligence artificielle (IA) actuelle en médecine peut se prévaloir de grandes performances, particulièrement en analyse d’images à visées diagnostique et pronostique, mais, en pratique clinique quotidienne, les résultats de l’IA fondés sur des données probantes restent peu nombreux. Dans cet article, sont analysés les caractéristiques de l’intelligence clinique en pratique médicale, puis les succès et promesses de l’IA, de même que les limites, réserves et critiques apportées à l’introduction de l’IA en clinique de première ligne. Est soulignée l’importance de certains aspects éthiques et

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36

Nelissen, Jo M. C. "Parent - Child Resemblance: Genetics, Education and Chance." Education and Society 39, no. 2 (2021): 29–50. http://dx.doi.org/10.7459/es/39.2.03.

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In this article, it is argued that it makes sense to define and distinguish three levels of human intelligence: intelligence as genotypical potential, intelligence as actualised in environmental interaction, and intelligence as measured by tests (IQ). This raises the questions of what is meant by the term “intelligence as potential”, and how and in what sense does a child’s cognitive potential express the parents’ potential and genetics? The larger the number of genes involved in a certain trait, the more possibilities emerge for the formation of new combinations for that trait. The degree of

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37

Li, Mengjie, Wenting Zhang, and Xiaoyi Zhou. "Identification of genes involved in the evolution of human intelligence through combination of inter-species and intra-species genetic variations." PeerJ 8 (April 16, 2020): e8912. http://dx.doi.org/10.7717/peerj.8912.

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Understanding the evolution of human intelligence is an important undertaking in the science of human genetics. A great deal of biological research has been conducted to search for genes which are related to the significant increase in human brain volume and cerebral cortex complexity during hominid evolution. However, genetic changes affecting intelligence in hominid evolution have remained elusive. We supposed that a subset of intelligence-related genes, which harbored intra-species variations in human populations, may also be evolution-related genes which harbored inter-species variations b

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38

Hwang, Liang-Dar. "Do People with Lower IQ Have Weaker Taste Perception? A Hidden Supplementary Table in ‘Is the Association Between Sweet and Bitter Perception Due to Genetic Variation?’." Twin Research and Human Genetics 23, no. 2 (2020): 123–24. http://dx.doi.org/10.1017/thg.2020.19.

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AbstractThis paper is about Nick’s contribution to the field of taste genetics, how I became involved and how a study on the genetic association between the perception of sweetness and bitterness ended up examining the influence of intelligence on taste perception.

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39

Holm, Liisa. "Artificial Intelligence and Molecular Biology." Trends in Genetics 10, no. 6 (1994): 216. http://dx.doi.org/10.1016/0168-9525(94)90261-5.

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Beaujean, A. Alexander. "Intelligence, Race, and Genetics: Conversations with Arthur R. Jensen." Intelligence 31, no. 1 (2003): 93–94. http://dx.doi.org/10.1016/s0160-2896(02)00160-5.

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41

Weiss, Volkmar. "The advent of a molecular genetics of general intelligence." Intelligence 20, no. 2 (1995): 115–24. http://dx.doi.org/10.1016/0160-2896(95)90028-4.

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42

Tabery, James. "Why Is Studying the Genetics of Intelligence So Controversial?" Hastings Center Report 45, S1 (2015): S9—S14. http://dx.doi.org/10.1002/hast.492.

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Callier, Shawneequa L., and Vence L. Bonham. "Taking a Stand:The Genetics Community's Responsibility for Intelligence Research." Hastings Center Report 45, S1 (2015): S54—S58. http://dx.doi.org/10.1002/hast.500.

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44

Pu, Wen Yu, Yan Nian Rui, Lian Sheng Zhao, and Chun Yan Zhang. "Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks." Advanced Materials Research 102-104 (March 2010): 846–50. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.846.

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Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence,

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45

Deary, Ian J. "Intelligence." Current Biology 23, no. 16 (2013): R673—R676. http://dx.doi.org/10.1016/j.cub.2013.07.021.

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46

Mahto, Rajendra Kumar. "Artificial Intelligence for Meiosis and Mitosis Analysis." Journal of Electrical Systems 20, no. 2 (2024): 2267–71. http://dx.doi.org/10.52783/jes.1993.

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In cellular biology, meiosis and mitosis are essential processes that control cell division and replication as well as the transfer of genetic material. A thorough understanding of these intricate processes is essential for many fields, such as cancer research, genetics, and developmental biology. In this study, we suggest building a Mitosis and Meiosis Analysis System (MMAS) that uses artificial intelligence (AI) methods to make automated analysis and meiotic event characterization easier. The MMAS uses machine learning models, deep learning frameworks, and sophisticated image processing algo

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Bouchard, Thomas J. "Genes, Evolution and Intelligence." Behavior Genetics 44, no. 6 (2014): 549–77. http://dx.doi.org/10.1007/s10519-014-9646-x.

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Nisa, Khoirun, and Deny Setiawan. "PENGEMBANGAN MODUL INTERAKTIF BERBASIS MULTIPLE INTELLIGENCE DENGAN AUTOPLAY PADA KONSEP PERUBAHAN MATERI GENETIK DI IKIP BUDI UTOMO MALANG." Edubiotik : Jurnal Pendidikan, Biologi dan Terapan 3, no. 02 (2018): 30–36. http://dx.doi.org/10.33503/ebio.v3i02.168.

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Technology-based learning needs to be done immediately in order to carry out more interested in learning and fostering student motivation. Furthermore, learning should be done byeach student characteristics.One of which is multiple intelligences based learning. Through the application of multiple intelligence assisted technology based learning, it is expected that the learning objectives will be more easily achieved by students. Interactive module based on multiple intelligences that has been validated was the product of this research and developmental experiment. This research was limited to

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Pagliarini, Luigi. "Polymorphic intelligence." Artificial Life and Robotics 12, no. 1-2 (2008): 24–28. http://dx.doi.org/10.1007/s10015-007-0498-9.

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50

Baughman, Holly M., Julie Aitken Schermer, Livia Veselka, Juliette Harris, and Philip A. Vernon. "A Behavior Genetic Analysis of Trait Emotional Intelligence and Alexithymia: A Replication." Twin Research and Human Genetics 16, no. 2 (2013): 554–59. http://dx.doi.org/10.1017/thg.2012.151.

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This replication study examines relations between alexithymia and trait emotional intelligence (trait EI) at the phenotypic, genetic, and environmental levels. A sample of 1,444 same-sex twin pairs (850 MZ pairs and 594 DZ pairs) completed the Toronto Alexithymia Scale-20. A subset of 494 same-sex twin pairs (287 MZ pairs and 207 DZ pairs) had earlier completed the Trait Emotional Intelligence Questionnaire. Individual differences in alexithymia were attributable to genetic, non-shared environmental, and shared environmental factors. All but one of the facets of alexithymia were negatively and

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