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Journal articles on the topic 'Growth Trajectories'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

McKeague, I. W., S. López-Pintado, M. Hallin, and M. Šiman. "Analyzing growth trajectories." Journal of Developmental Origins of Health and Disease 2, no. 06 (October 12, 2011): 322–29. http://dx.doi.org/10.1017/s2040174411000572.

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2

Bartels, Helena C., Clare O'Connor, Olivia Mason, Ricardo Segurado, John Mehegan, and Finnoula McAuliffe. "125: Fetal growth trajectories." American Journal of Obstetrics and Gynecology 218, no. 1 (January 2018): S89—S90. http://dx.doi.org/10.1016/j.ajog.2017.10.102.

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3

Beal, Judy. "Growth Trajectories of Preterm Infants." MCN, The American Journal of Maternal/Child Nursing 33, no. 5 (September 2008): 326. http://dx.doi.org/10.1097/01.nmc.0000334909.93506.86.

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4

Regnault, Nolwenn, and Matthew W. Gillman. "Importance of Characterizing Growth Trajectories." Annals of Nutrition and Metabolism 65, no. 2-3 (2014): 110–13. http://dx.doi.org/10.1159/000365893.

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5

Rzehak, Peter, Nolwenn Regnault, and Berthold Koletzko. "Analysis of Child Growth Trajectories." Annals of Nutrition and Metabolism 65, no. 2-3 (2014): 99–100. http://dx.doi.org/10.1159/000366542.

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6

Huang, R. C., T. A. Mori, J. P. Newnham, G. Kendall, D. Doherty, F. J. Stanley, L. I. Landau, W. H. Oddy, L. J. Palmer, and L. J. Beilin. "5D-5 Antenatal growth trajectories are associated with postnatal growth trajectories and cardiovascular outcomes." Early Human Development 83 (September 2007): S74. http://dx.doi.org/10.1016/s0378-3782(07)70145-7.

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7

Karkach, Arseniy. "Trajectories and models of individual growth." Demographic Research 15 (November 7, 2006): 347–400. http://dx.doi.org/10.4054/demres.2006.15.12.

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8

Lee, Who-Seung, Neil B. Metcalfe, Denis Réale, and Pedro R. Peres-Neto. "Early growth trajectories affect sexual responsiveness." Proceedings of the Royal Society B: Biological Sciences 281, no. 1777 (February 22, 2014): 20132899. http://dx.doi.org/10.1098/rspb.2013.2899.

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The trajectory of an animal's growth in early development has been shown to have long-term effects on a range of life-history traits. Although it is known that individual differences in behaviour may also be related to certain life-history traits, the linkage between early growth or development and individual variation in behaviour has received little attention. We used brief temperature manipulations, independent of food availability, to stimulate compensatory growth in juvenile three-spined sticklebacks Gasterosteus aculeatus . Here, we examine how these manipulated growth trajectories affected the sexual responsiveness of the male fish at the time of sexual maturation, explore associations between reproductive behaviour and investment and lifespan and test whether the perceived time stress (until the onset of the breeding season) influenced such trade-offs. We found a negative impact of growth rate on sexual responsiveness: fish induced (by temperature manipulation) to grow slowest prior to the breeding season were consistently quickest to respond to the presence of a gravid female. This speed of sexual responsiveness was also positively correlated with the rate of development of sexual ornaments and time taken to build a nest. However, after controlling for effects of growth rate, those males that had the greatest sexual responsiveness to females had the shortest lifespan. Moreover, the time available to compensate in size before the onset of the breeding season (time stress) affected the magnitude of these effects. Our results demonstrate that developmental perturbations in early life can influence mating behaviour, with long-term effects on longevity.

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9

Gough, Ethan K., Erica EM Moodie, Andrew J. Prendergast, Robert Ntozini, Lawrence H. Moulton, Jean H. Humphrey, and Amee R. Manges. "Linear growth trajectories in Zimbabwean infants." American Journal of Clinical Nutrition 104, no. 6 (November 2, 2016): 1616–27. http://dx.doi.org/10.3945/ajcn.116.133538.

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10

Li, Ning, Kiranmoy Das, and Rongling Wu. "Functional mapping of human growth trajectories." Journal of Theoretical Biology 261, no. 1 (November 2009): 33–42. http://dx.doi.org/10.1016/j.jtbi.2009.07.020.

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11

Coffey, M. P., J. Hickey, and S. Brotherstone. "Growth trajectories of Holstein dairy cows." Proceedings of the British Society of Animal Science 2005 (2005): 13. http://dx.doi.org/10.1017/s1752756200009248.

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All genetic selection in dairy cattle is applied to traits that are measured during the animal’s productive life, mostly those recorded during early productive life because genetic evaluations are best calculated from unbiased early data. Since body lipid is normally accumulated partly as a function of body protein accretion and in part as a function of degree of maturity (which is also related to protein content), it follows that selection for yield in relatively mature life, and concomitant alteration of body lipid profiles, may have altered early life growth profiles as well. If this is the case, parameters of early life growth may be used to predict later life events such as production and functional traits associated with the degree of maturity when these events occur. The objectives of this study were: 1) to model the growth of dairy cows of average and high genetic merit from birth to the end of third lactation and identify any differences in their growth curves.

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12

Wang, Shuang, Wei Xiong, Weiping Ma, Stephen Chanock, Wieslaw Jedrychowski, Rongling Wu, and Frederica P. Perera. "Gene-Environment Interactions on Growth Trajectories." Genetic Epidemiology 36, no. 3 (February 6, 2012): 206–13. http://dx.doi.org/10.1002/gepi.21613.

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13

Dmitriew, Caitlin M. "The evolution of growth trajectories: what limits growth rate?" Biological Reviews 86, no. 1 (April 11, 2010): 97–116. http://dx.doi.org/10.1111/j.1469-185x.2010.00136.x.

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14

Woyuan, Niu, Ryosuke Saga, Hiroshi Tsuji, and Yukie Majima. "Proposed Optimal Growth Pathfinding Method Based on Growth Trajectories." International Journal of Knowledge and Systems Science 6, no. 4 (October 2015): 70–89. http://dx.doi.org/10.4018/ijkss.2015100105.

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In this study, the authors propose an optimal growth pathfinding method to support learners in effectively mastering a set of capabilities. Under the assumption of prerequisite relationships among learning objectives, the main processes of the method are as follows: (1) extracting the capability structure from growth trajectories, (2) remodeling the problem as a traveling salesman problem with restrictions among learning objectives, and (3) generating the cost matrix and obtaining the optimal growth path. In addition, a flexible approach to data standardization as a step of capability structure extraction is discussed. The proposed method is also applied to a software engineer growth dataset with 30 responders.

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15

Barker, Edward D., Fionnuala M. McAuliffe, Fiona Alderdice, Julia Unterscheider, Sean Daly, Michael P. Geary, Mairead M. Kennelly, et al. "The Role of Growth Trajectories in Classifying Fetal Growth Restriction." Obstetrics & Gynecology 122, no. 2, PART 1 (August 2013): 248–54. http://dx.doi.org/10.1097/aog.0b013e31829ca9a7.

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16

Cole, T. J. "Commentary: Methods for calculating growth trajectories and constructing growth centiles." Statistics in Medicine 38, no. 19 (July 12, 2019): 3571–79. http://dx.doi.org/10.1002/sim.8129.

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17

Bjorklund, Mats. "Phenotypic Variation of Growth Trajectories in Finches." Evolution 47, no. 5 (October 1993): 1506. http://dx.doi.org/10.2307/2410163.

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18

Rollins, Nancy K. "Trajectories of Fetal Brain Growth at MRI." Radiology 303, no. 1 (April 2022): 171–72. http://dx.doi.org/10.1148/radiol.212908.

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19

Khatir, Mohammed Rachid, Yahia Lebbah, and Rachid Nourine. "A pattern-growth approach for mining trajectories." Multiagent and Grid Systems 16, no. 2 (July 24, 2020): 117–33. http://dx.doi.org/10.3233/mgs-200324.

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20

Magwene, Paul M. "Comparing Ontogenetic Trajectories Using Growth Process Data." Systematic Biology 50, no. 5 (September 1, 2001): 640–56. http://dx.doi.org/10.1080/106351501753328785.

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21

Wei, Xin, Keith B. Lenz, and Jose Blackorby. "Math Growth Trajectories of Students With Disabilities." Remedial and Special Education 34, no. 3 (July 16, 2012): 154–65. http://dx.doi.org/10.1177/0741932512448253.

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22

Ng, S. K., and G. J. McLachlan. "Mixture models for clustering multilevel growth trajectories." Computational Statistics & Data Analysis 71 (March 2014): 43–51. http://dx.doi.org/10.1016/j.csda.2012.12.007.

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23

Kejak, Michal, Stephan Seiter, and David Vávra. "Accession trajectories and convergence: endogenous growth perspective." Structural Change and Economic Dynamics 15, no. 1 (March 2004): 13–46. http://dx.doi.org/10.1016/j.strueco.2003.07.003.

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24

Castellacci, Fulvio. "Technology clubs, technology gaps and growth trajectories." Structural Change and Economic Dynamics 19, no. 4 (December 2008): 301–14. http://dx.doi.org/10.1016/j.strueco.2008.07.002.

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25

Björklund, Mats. "PHENOTYPIC VARIATION OF GROWTH TRAJECTORIES IN FINCHES." Evolution 47, no. 5 (October 1993): 1506–14. http://dx.doi.org/10.1111/j.1558-5646.1993.tb02171.x.

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26

Knight, Charles A., and Kevin R. Knupp. "Precipitation Growth Trajectories in a CCOPE Storm." Journal of the Atmospheric Sciences 43, no. 11 (June 1986): 1057–73. http://dx.doi.org/10.1175/1520-0469(1986)043<1057:pgtiac>2.0.co;2.

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27

Landau-Crangle, Erin, Niels Rochow, Tanis R. Fenton, Kai Liu, Anaam Ali, Hon Yiu So, Gerhard Fusch, Michael L. Marrin, and Christoph Fusch. "Individualized Postnatal Growth Trajectories for Preterm Infants." Journal of Parenteral and Enteral Nutrition 42, no. 6 (February 8, 2018): 1084–92. http://dx.doi.org/10.1002/jpen.1138.

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28

Lu, Amy, Thore J. Bergman, Colleen McCann, Ashley Stinespring-Harris, and Jacinta C. Beehner. "Growth trajectories in wild geladas (Theropithecus gelada)." American Journal of Primatology 78, no. 7 (March 7, 2016): 707–19. http://dx.doi.org/10.1002/ajp.22535.

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29

Kirkpatrick, Mark, and David Lofsvold. "The evolution of growth trajectories and other complex quantitative characters." Genome 31, no. 2 (January 15, 1989): 778–83. http://dx.doi.org/10.1139/g89-137.

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Growth trajectories differ from many other quantitative, characters in that they are characterized by a continuous function rather than by a finite number of discrete measurements. We review here recently developed methods for predicting the evolution of growth trajectories under the influence of natural or artificial selection. Using our method, analysis of genetic data from mice shows that the patterns of genetic variation arising from developmental processes impose constraints on evolution of growth trajectories. These constraints can be quantified to reveal the families of growth trajectories that can be produced by selection and those families that cannot. The data suggest there may be relatively few evolutionary degrees of freedom for growth trajectories despite the presence of abundant additive genetic variation to alter size and (or) growth rate at every age. The description of these constraints may be useful to both biologists who would like to determine the evolutionary options available to natural populations and to breeders who would like to alter growth trajectories to economically improve domesticated species. Our methods and conclusions can be generalized to other kinds of "infinite-dimensional" or complex characters, including morphological shapes and norms of reaction.Key words: growth trajectories, quantitative genetics, constraints, infinite-dimensional characters, body size.

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30

Keuschnigg, Marc. "Scaling trajectories of cities." Proceedings of the National Academy of Sciences 116, no. 28 (June 24, 2019): 13759–61. http://dx.doi.org/10.1073/pnas.1906258116.

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Urban scaling research finds that agglomeration effects—the higher-than-expected outputs of larger cities—follow robust “superlinear” scaling relations in cross-sectional data. But the paradigm has predictive ambitions involving the dynamic scaling of individual cities over many time points and expects parallel superlinear growth trajectories as cities’ populations grow. This prediction has not yet been rigorously tested. I use geocoded microdata to approximate the city-size effect on per capita wage in 73 Swedish labor market areas for 1990–2012. The data support a superlinear scaling regime for all Swedish agglomerations. Echoing the rich-get-richer process on the system level, however, trajectories of superlinear growth are highly robust only for cities assuming dominant positions in the urban hierarchy.

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31

Ramírez-Luzuriaga, María J., John Hoddinott, Reynaldo Martorell, Shivani A. Patel, Manuel Ramírez-Zea, Rachel Waford, and Aryeh D. Stein. "Linear Growth Trajectories in Early Childhood and Adult Cognitive and Socioemotional Functioning in a Guatemalan Cohort." Journal of Nutrition 151, no. 1 (November 26, 2020): 206–13. http://dx.doi.org/10.1093/jn/nxaa337.

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ABSTRACT Background Growth faltering in early childhood is associated with poor human capital attainment, but associations of linear growth in childhood with executive and socioemotional functioning in adulthood are understudied. Objectives In a Guatemalan cohort, we identified distinct trajectories of linear growth in early childhood, assessed their predictors, and examined associations between growth trajectories and neurodevelopmental outcomes in adulthood. We also assessed the mediating role of schooling on the association of growth trajectories with adult cognitive outcomes. Methods In 2017–2019, we prospectively followed 1499 Guatemalan adults who participated in a food supplementation trial in early childhood (1969–1977). We derived height-for-age sex-specific growth trajectories from birth to 84 mo using latent class growth analysis. Results We identified 3 growth trajectories (low, intermediate, high) with parallel slopes and intercepts already differentiated at birth in both sexes. Children of taller mothers were more likely to belong to the high and intermediate trajectories [relative risk ratio (RRR): 1.21; 95% CI: 1.15, 1.26, and RRR: 1.11; 95% CI: 1.07, 1.15, per 1-cm increase in height, respectively] compared with the low trajectory. Children in the wealthiest compared with the poorest socioeconomic tertile were more likely to belong to the high trajectory compared with the low trajectory (RRR: 2.24; 95% CI: 1.29, 3.88). In males, membership in the high compared with low trajectory was positively associated with nonverbal fluid intelligence, working memory, inhibitory control, and cognitive flexibility at ages 40–57 y. Sex-adjusted results showed that membership in the high compared with low trajectory was positively associated with meaning and purpose scores at ages 40–57 y. Associations of intermediate compared with low growth trajectories with study outcomes were also positive but of lesser magnitude. Schooling partially mediated the associations between high and intermediate growth trajectories and measures of cognitive ability in adulthood. Conclusions Modifiable and nonmodifiable risk factors predicted growth throughout childhood. Membership in the high and intermediate growth trajectories was positively associated with adult cognitive and socioemotional functioning.

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32

van Uitert, Evelyne M., Niek Exalto, Graham J. Burton, Sten P. Willemsen, Anton H. J. Koning, Paul H. C. Eilers, Joop S. E. Laven, Eric A. P. Steegers, and Régine P. M. Steegers-Theunissen. "Human embryonic growth trajectories and associations with fetal growth and birthweight." Human Reproduction 28, no. 7 (April 7, 2013): 1753–61. http://dx.doi.org/10.1093/humrep/det115.

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33

Hejnowicz, Zygmunt. "Trajectories of principal directions of growth, natural coordinate system in growing plant organ." Acta Societatis Botanicorum Poloniae 53, no. 1 (2014): 29–42. http://dx.doi.org/10.5586/asbp.1984.004.

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In symplasticly growing organs the principal directions of growth, which are indicated by the eigenvectors of the symmetric part of the growth tensor, can be associated with each positional point and joined up to form a network of orthogonal trajectories, unless the growth is isotropic. The trajectories represent a natural coordinate system suitable for description of growing organs. These trajectories often can be recognized in patterns of nonrandom alignments in the cell wall network: these alignments are normal to anticlinal and periclinal walls. Coordinate systems that fit the trajectories in different types of growing organ are listed.

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34

Van de Schoot, Rens. "Latent Growth Mixture Models to estimate PTSD trajectories." European Journal of Psychotraumatology 6, no. 1 (March 2, 2015): 27503. http://dx.doi.org/10.3402/ejpt.v6.27503.

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35

Veylit, Lara, Bernt‐Erik Sæther, Jean‐Michel Gaillard, Eric Baubet, and Marlène Gamelon. "Many lifetime growth trajectories for a single mammal." Ecology and Evolution 11, no. 21 (October 2021): 14789–804. http://dx.doi.org/10.1002/ece3.8164.

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36

Fergusson, Leopoldo, Tatiana Hiller, and Ana María Ibáñez. "Growth and inclusion trajectories of Colombian functional territories." Sobre México Temas de Economía 1, no. 1 (October 20, 2020): 79–125. http://dx.doi.org/10.48102/rsm.v1i1.75.

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We describe the patterns of economic growth and social progress in Colombian “functional territories”. Unlike political/administrative divisions that emerge at least partly for historical reasons unrelated to economic interactions, functional territories reflect the patterns of spatial agglomeration and economic interactions in a territory. Using a novel definition of functional territories, our analysis reveals significant fragmentation of economic interactions: close to 66% of municipalities (holding about 20% of the country’s population) have no significant links to neighboring areas. A set of comparatively more (but still only partially) integrated and more populous municipalities have stronger links between them. This “rural-urban” space holds just around 31% of total population. The rest of Colombians are in “urban” or “Metropolitan” highly-populated and more integrated clusters. We describe these territories along two dimensions: economic growth or “dynamism” and progress in social indicators or “inclusion”. To do so we propose a simple conceptual framework that organizes the diverse inputs that might help boost these outcomes. Larger and more urbanized agglomerations exhibit visible advantages in these inputs. Moreover, long-run institutional determinants best help differentiate territories. Consistent with this, larger and more urbanized agglomerations have better outcomes, especially when measuring economic activity. Also, more dynamic places tend to be the more inclusive ones, even though recent improvements in dynamism do not correlate with improvements in inclusion.

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37

Bonser, R. H. C., and J. F. V. Vincent. "Technology trajectories, innovation, and the growth of biomimetics." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221, no. 10 (September 30, 2007): 1177–80. http://dx.doi.org/10.1243/09544062jmes522.

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Publication rate of patents can be a useful measure of innovation and productivity in science and technology. Patenting activity in new technological fields follows a sigmoid (S-shaped) path. Qualitative and quantitative models in management and economics literature explain why such patterns of productivity may occur. TRIZ analysis suggests that patents are generated in bursts during the evolution of a product and that they are at different levels of inventiveness. The tendency is for the inventiveness to reduce as the product is more mature. This makes it possible to guess at the lifetime stage of a product and gauge its maturity and profitability. An analysis of patenting activity and other measures of inventiveness in the emerging field of biomimetics was presented, and future trends in biologically-inspired innovation was discussed.

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38

Mason, Shawn T., Nida Corry, Neda F. Gould, Nicole Amoyal, Vincent Gabriel, Shelly Wiechman-Askay, Radha Holavanahalli, Sean Banks, Lisa Arceneaux, and James A. Fauerbach. "Growth Curve Trajectories of Distress in Burn Patients." Journal of Burn Care & Research 31, no. 1 (January 2010): 64–72. http://dx.doi.org/10.1097/bcr.0b013e3181cb8ee6.

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39

Bonser, R. H. C., and J. F. V. Vincent. "Technology trajectories, innovation and the growth of biomimetics." Journal of Biomechanics 39 (January 2006): S348. http://dx.doi.org/10.1016/s0021-9290(06)84383-6.

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40

Huang, R. C., T. A. Mori, J. P. Newnham, G. Kendall, D. Doherty, F. J. Stanley, L. I. Landau, W. H. Oddy, L. J. Palmer, and L. J. Beilin. "P1-27 The definition of postnatal growth trajectories." Early Human Development 83 (September 2007): S90. http://dx.doi.org/10.1016/s0378-3782(07)70197-4.

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41

Huang, R. C., T. A. Mori, J. P. Newnham, G. Kendall, D. Doherty, F. J. Stanley, L. I. Landau, W. H. Oddy, L. J. Palmer, and L. J. Beilin. "P1-29 The definition of antenatal growth trajectories." Early Human Development 83 (September 2007): S90—S91. http://dx.doi.org/10.1016/s0378-3782(07)70199-8.

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42

WU, RONGLING, CHANG-XING MA, MARK C. K. YANG, MYRON CHANG, RAMON C. LITTELL, UPASANA SANTRA, SAMUEL S. WU, et al. "Quantitative trait loci for growth trajectories in Populus." Genetical Research 81, no. 1 (February 2003): 51–64. http://dx.doi.org/10.1017/s0016672302005980.

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43

Protasov, Vladimir Yu. "Linear switching systems with slow growth of trajectories." Systems & Control Letters 90 (April 2016): 54–60. http://dx.doi.org/10.1016/j.sysconle.2016.01.006.

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44

Grantz, Katherine L., Jagteshwar Grewal, Paul S. Albert, Ronald Wapner, Mary E. D’Alton, Anthony Sciscione, William A. Grobman, et al. "Dichorionic twin trajectories: the NICHD Fetal Growth Studies." American Journal of Obstetrics and Gynecology 215, no. 2 (August 2016): 221.e1–221.e16. http://dx.doi.org/10.1016/j.ajog.2016.04.044.

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45

Besharati, Melody, Frauke von Versen-Höynck, Kris Kapphahn, and Valerie Lynn Baker. "Examination of fetal growth trajectories following infertility treatment." Journal of Assisted Reproduction and Genetics 37, no. 6 (May 18, 2020): 1399–407. http://dx.doi.org/10.1007/s10815-020-01785-8.

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46

Yeo, Gillian B., Tamma Sorbello, Annette Koy, and Luke D. Smillie. "Goal orientation profiles and task performance growth trajectories." Motivation and Emotion 32, no. 4 (August 5, 2008): 296–309. http://dx.doi.org/10.1007/s11031-008-9099-8.

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47

Morgan, Paul L., George Farkas, and Qiong Wu. "Kindergarten Children’s Growth Trajectories in Reading and Mathematics." Journal of Learning Disabilities 44, no. 5 (August 19, 2011): 472–88. http://dx.doi.org/10.1177/0022219411414010.

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48

Kwok, Man Ki, Guy Freeman, Shi Lin Lin, Tai Hing Lam, and Catherine M. Schooling. "Simulated growth trajectories and blood pressure in adolescence." Journal of Hypertension 31, no. 9 (September 2013): 1785–97. http://dx.doi.org/10.1097/hjh.0b013e3283622ea0.

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49

ZYSMAN, JOHN. "How Institutions Create Historically Rooted Trajectories of Growth." Industrial and Corporate Change 3, no. 1 (1994): 243–83. http://dx.doi.org/10.1093/icc/3.1.243.

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50

Lee, Jaekyung. "Tripartite Growth Trajectories of Reading and Math Achievement." American Educational Research Journal 47, no. 4 (December 2010): 800–832. http://dx.doi.org/10.3102/0002831210365009.

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