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Journal articles on the topic 'Growth pattern'

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Published: 4 June 2021
Last updated: 18 February 2022

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1

Han, Jiawei, and Jian Pei. "Mining frequent patterns by pattern-growth." ACM SIGKDD Explorations Newsletter 2, no. 2 (December 2000): 14–20. http://dx.doi.org/10.1145/380995.381002.

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2

Buchanan, Mark. "Pattern of growth." Nature Physics 8, no. 11 (November 2012): 775. http://dx.doi.org/10.1038/nphys2471.

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3

Kenmogne, Edith Belise. "The Impact of the Pattern-Growth Ordering on the Performances of Pattern Growth-Based Sequential Pattern Mining Algorithms." Computer and Information Science 10, no. 1 (December 25, 2016): 23. http://dx.doi.org/10.5539/cis.v10n1p23.

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Sequential Pattern Mining is an efficient technique for discovering recurring structures or patterns from very large datasetwidely addressed by the data mining community, with a very large field of applications, such as cross-marketing, DNA analysis, web log analysis,user behavior, sensor data, etc. The sequence pattern mining aims at extractinga set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan and PrefixSpan.In this paper, we are interested in the study of the impact of the pattern-growthordering on the performances of pattern growth-based sequential pattern mining algorithms.To this end, we introduce a class of pattern-growth orderings, called linear orderings, for which patterns are grown by making grow either the currentpattern prefix or the current pattern suffix from the same position at eachgrowth-step.We study the problem of pruning and partitioning the search space followinglinear orderings. Experimentations show that the order in which patternsgrow has a significant influence on the performances.
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4

Edith Belise, Kenmogne, Nkambou Roger, Tadmon Calvin, and Engelbert Mephu Nguifo. "A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach." Journal of Advanced Computer Science & Technology 6, no. 2 (June 4, 2017): 20. http://dx.doi.org/10.14419/jacst.v6i2.7011.

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Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.
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Tsukahara, Tetsuo, Yoshie Shimoyama, Tomoki Ebata, Yukihiro Yokoyama, Tsuyoshi Igami, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Shigeo Nakamura, and Masato Nagino. "Cholangiocarcinoma with intraductal tubular growth pattern versus intraductal papillary growth pattern." Modern Pathology 29, no. 3 (January 15, 2016): 293–301. http://dx.doi.org/10.1038/modpathol.2015.152.

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6

Lander, Arthur D. "Pattern, Growth, and Control." Cell 144, no. 6 (March 2011): 955–69. http://dx.doi.org/10.1016/j.cell.2011.03.009.

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7

Jian Pei, Jiawei Han, B. Mortazavi-Asl, Jianyong Wang, H. Pinto, Qiming Chen, U. Dayal, and Mei-Chun Hsu. "Mining sequential patterns by pattern-growth: the PrefixSpan approach." IEEE Transactions on Knowledge and Data Engineering 16, no. 11 (November 2004): 1424–40. http://dx.doi.org/10.1109/tkde.2004.77.

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8

Isakkson, OGP, J.-O. Jansson, RG Clark, and I. Robinson. "Significance of the Secretory Pattern of Growth Hormone." Physiology 1, no. 2 (April 1, 1986): 44–47. http://dx.doi.org/10.1152/physiologyonline.1986.1.2.44.

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The plasma concentration of growth hormone fluctuates widely with pronounced peaks at intervals of a few hours and troughs of nearly vanishingly low concentrations in between. The pattern of secretion varies, and different patterns affect growth differently. Tall children usually have frequent growth hormone peaks of a high amplitude, whereas short, healthy children usually have fewer peaks of a lower amplitude. Male and female rats have different patterns, and a "masculine" pattern promotes growth more than a "feminine" pattern. If the same amount of growth hormone is administered in several pulses rather than continuously, the effect on growth is much greater.
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9

Eefsen, Rikke Løvendahl, Gert G. Van den Eynden, Gunilla Høyer-Hansen, Pnina Brodt, Ole Didrik Laerum, Peter B. Vermeulen, Ib Jarle Christensen, et al. "Histopathological Growth Pattern, Proteolysis and Angiogenesis in Chemonaive Patients Resected for Multiple Colorectal Liver Metastases." Journal of Oncology 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/907971.

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The purpose of this study was to characterise growth patterns, proteolysis, and angiogenesis in colorectal liver metastases from chemonaive patients with multiple liver metastases. Twenty-four patients were included in the study, resected for a median of 2.6 metastases. The growth pattern distribution was 25.8% desmoplastic, 33.9% pushing, and 21% replacement. In 20 patients, identical growth patterns were detected in all metastases, but in 8 of these patients, a second growth pattern was also present in one or two of the metastases. In the remaining 4 patients, no general growth pattern was observed, although none of the liver metastases included more than two growth patterns. Overall, a mixed growth pattern was demonstrated in 19.3% of the liver metastases. Compared to metastases with pushing, those with desmoplastic growth pattern had a significantly up-regulated expression of urokinase-type plasminogen activator receptor (P=0.0008). Angiogenesis was most pronounced in metastases with a pushing growth pattern in comparison to those with desmoplastic (P=0.0007) and replacement growth pattern (P=0.021). Although a minor fraction of the patients harboured metastases with different growth patterns, we observed a tendency toward growth pattern uniformity in the liver metastases arising in the same patient. The result suggests that the growth pattern of liver metastases is not a random phenomenon.
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10

Subasinghe, Shyamantha. "Urban Growth: From pixel to reality." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-353-2019.

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<p><strong>Abstract.</strong> Urban growth is a complex process created through the interaction of human and environmental conditions. The spatial configuration and dynamic process of urban growth is an important topic in contemporary geographical studies (Thapa and Murayama, 2010). However, urban growth pattern recognition is a challengeable task and it has become one of the major fields in Cartography. Since classical era of cartography, several methods have been employed in modelling and urban growth pattern recognition. It shows that there is no agreement among cartographer or any other spatial scientists on how to map the diverse patterns of urban growth.</p><p>Typical urban theories such as von Thünen’s (1826) bid-rent theory, Burgess’s (1925) concentric zone model, Christaller’s (1933) central place theory, and Hoyt’s (1939) sector model explain the urban structure in different manner. Most of them do not contribute to visualize the urban growth pattern spatiotemporally. Recently, by addressing this limitations, several sophisticated methods are used in urban growth visualization. Among them, morphological spatial pattern analysis (MSPA) is one of emerging raster data analysis methods which allows us to integrate neighbourhood interaction rules in urban growth pattern recognition and visualization. Angel et al. (2010) developed urban land classification (urban, suburban, rural, fringe open space, exterior open space, and rural open space) based on built and non-built land categories and detected three major types of urban growth (infill, extension, and leapfrog). However, developing urban land classifications using binary land use type and recognising only three types of urban growth pattern may be insufficient due to the existence of a higher complexity of urban growth. In such context, the present study introduce a geovisualization approach to map spatial patterns of urban growth using multiple land categories and develops three sub-levels of urban growth pattern for each major urban growth pattern.</p><p>The entire process of urban growth pattern recognition developed in this study can be summarized into three steps (Figure 1): (1) urban land mapping &amp;ndash; Landsat imageries representing two time points (2001 and 2017) were classified into two land categories (built and non-built) and developed into multiple classes using ancillary data, (2) recognizing three major patterns of urban growth (infill, extension, and leapfrog) &amp;ndash; the raster overlay method based on neighbourhood interaction rules, (3) development of sublevels of urban growth &amp;ndash; major three patterns were further developed and visualized nine urban growth patterns, namely low infill (LI), moderate infill (MI), high infill (HI), low extension (LE), moderate extension (ME), high extension (HE), low leapfrog (LL), moderate leapfrog (ML), and high leapfrog (HL). The developed procedure of this study in urban growth pattern recognition was tested using a case study of Colombo metropolitan area, Sri Lanka.</p>
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11

Han, Jia-Wei, Jian Pei, and Xi-Feng Yan. "From sequential pattern mining to structured pattern mining: A pattern-growth approach." Journal of Computer Science and Technology 19, no. 3 (May 2004): 257–79. http://dx.doi.org/10.1007/bf02944897.

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12

Pei, Jian, Jiawei Han, and Wei Wang. "Constraint-based sequential pattern mining: the pattern-growth methods." Journal of Intelligent Information Systems 28, no. 2 (January 20, 2007): 133–60. http://dx.doi.org/10.1007/s10844-006-0006-z.

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13

KARLBERG, J., and K. ALBERTSSON-WIKLAND. "Infancy Growth Pattern Related to Growth Hormone Deficiency." Acta Paediatrica 77, no. 3 (May 1988): 385–91. http://dx.doi.org/10.1111/j.1651-2227.1988.tb10665.x.

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14

Smith, R. I., F. J. Harvey, and M. G. R. Cannell. "Pattern of Tea Shoot Growth." Experimental Agriculture 26, no. 2 (April 1990): 197–208. http://dx.doi.org/10.1017/s0014479700018196.

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SUMMARYTea shoot development was measured by sequentially sampling shoots from seven clones growing at Nsuwadzi Research Station, Mulanje, Malawi. A Gompertz growth curve best described the increase in shoot length with time, an exponential curve described the relation between dry weight and shoot length, and a quadratic curve related dry weight to fresh weight. The parameters of these curves differed among clones and provide a basis for exploring genetic influences on tea yield. The most rapid elongation of shoots of all clones occurred about 42 days after plucking, considered the optimum age for harvest at Mulanje. Clones selected at Mulanje produced longer shoots with greater dry weight but slightly lower dry matter content per unit stem length than clones selected at Kericho, Kenya. The dry:fresh weight ratio of 0.22 used at Mulanje to calculate dry matter yields was too high for most clones.
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15

Shamsuddin, L., and A. K. M. Shamsuddin. "Growth pattern of Bangladeshi fetus." International Journal of Gynecology & Obstetrics 70 (2000): B30. http://dx.doi.org/10.1016/s0020-7292(00)86183-4.

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16

ZLOTOGORA, J., R. REGEV, S. HADAR, and T. C. IANCU. "Growth Pattern in Krabbe's Disease." Acta Paediatrica 75, no. 2 (March 1986): 251–54. http://dx.doi.org/10.1111/j.1651-2227.1986.tb10194.x.

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17

Baral, J. K., and Sujata Mohanty. "The Growth Pattern of Nam." India Quarterly: A Journal of International Affairs 47, no. 3 (July 1991): 21–32. http://dx.doi.org/10.1177/097492849104700302.

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18

Takenouchi, Toshiki, Tomoko Uehara, Kenjiro Kosaki, and Seiji Mizuno. "Growth pattern of Rahman syndrome." American Journal of Medical Genetics Part A 176, no. 3 (January 31, 2018): 712–14. http://dx.doi.org/10.1002/ajmg.a.38616.

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19

BAHEL, ATUL, JUN PAN, and MUSHTI V. RAMAKRISHNA. "GROWTH PATTERN OF SILICON CLUSTERS." Modern Physics Letters B 09, no. 13 (June 10, 1995): 811–16. http://dx.doi.org/10.1142/s0217984995000759.

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Tight-binding molecular dynamics simulated annealing technique is employed to search for the ground state geometries of silicon clusters containing 11–17 atoms. These studies revealed that layer formation is the dominant growth pattern in all these clusters. Fullerene-like precursor structures consisting of fused pentagon rings are also observed. The atoms in all these clusters exhibit pronounced preference for residing on the surface.
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20

Lu, Ding. "Transition of China’s growth pattern." Frontiers of Economics in China 6, no. 4 (November 16, 2011): 535–55. http://dx.doi.org/10.1007/s11459-011-0146-0.

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21

Aker, Scott, and William Healy. "TEMPORAL PATTERN OF GROWTH IN ALSTROEMERIA." HortScience 25, no. 9 (September 1990): 1079G—1079. http://dx.doi.org/10.21273/hortsci.25.9.1079.

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Alstroemeria `Regina' and A. `Orchid' Linn. plants were grown in rhizotrons to facilitate non-destructive observation of shoot, rhizome, and storage root growth. In plants grown at 21/11 C or 21/21 C day/night temperatures under either 8 hr night interruption or an 8 hour short days, storage root growth was favored by cool (11 C) night temperatures and long days. The seasonal patterns of storage root and rhizome growth were inversely related to the seasonal pattern of shoot growth. Growth of shoots and rhizomes followed a cyclic pattern. The cycles of shoot and rhizome growth were in phase with each other until the plants resumed vegetative growth due to high soil temperature. At this point, the cycles of shoot and rhizome growth were shifted out of phase with each other. Thinning shoots by 60% resulted in delay and damping out of the peak of storage root growth; the cyclic growth of storage roots was disrupted when plants were thinned by 60% such that the cycles of active storage root growth were delayed by 1 week.
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22

Aker, Scott, and William Healy. "TEMPORAL PATTERN OF GROWTH IN ALSTROEMERIA." HortScience 25, no. 9 (September 1990): 1079g—1079. http://dx.doi.org/10.21273/hortsci.25.9.1079g.

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Alstroemeria `Regina' and A. `Orchid' Linn. plants were grown in rhizotrons to facilitate non-destructive observation of shoot, rhizome, and storage root growth. In plants grown at 21/11 C or 21/21 C day/night temperatures under either 8 hr night interruption or an 8 hour short days, storage root growth was favored by cool (11 C) night temperatures and long days. The seasonal patterns of storage root and rhizome growth were inversely related to the seasonal pattern of shoot growth. Growth of shoots and rhizomes followed a cyclic pattern. The cycles of shoot and rhizome growth were in phase with each other until the plants resumed vegetative growth due to high soil temperature. At this point, the cycles of shoot and rhizome growth were shifted out of phase with each other. Thinning shoots by 60% resulted in delay and damping out of the peak of storage root growth; the cyclic growth of storage roots was disrupted when plants were thinned by 60% such that the cycles of active storage root growth were delayed by 1 week.
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23

Yang, Fujun, Zhengwei Dong, Yingran Shen, Jinghan Shi, Yulin Wu, Zhipeng Zhao, Gening Jiang, and Xiao Song. "Cribriform growth pattern in lung adenocarcinoma: More aggressive and poorer prognosis than acinar growth pattern." Lung Cancer 147 (September 2020): 187–92. http://dx.doi.org/10.1016/j.lungcan.2020.07.021.

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24

Belise, Kenmogne Edith, Tadmon Calvin, and Nkambou Roger. "A pattern growth-based sequential pattern mining algorithm called prefixSuffixSpan." ICST Transactions on Scalable Information Systems 4, no. 12 (January 18, 2017): 152103. http://dx.doi.org/10.4108/eai.18-1-2017.152103.

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25

Jaffe, Craig A., D. Kim Turgeon, Kenneth Lown, Roberta Demott-Friberg, and Paul B. Watkins. "Growth hormone secretion pattern is an independent regulator of growth hormone actions in humans." American Journal of Physiology-Endocrinology and Metabolism 283, no. 5 (November 1, 2002): E1008—E1015. http://dx.doi.org/10.1152/ajpendo.00513.2001.

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The importance of gender-specific growth hormone (GH) secretion pattern in the regulation of growth and metabolism has been demonstrated clearly in rodents. We recently showed that GH secretion in humans is also sexually dimorphic. Whether GH secretion pattern regulates the metabolic effects of GH in humans is largely unknown. To address this question, we administered the same daily intravenous dose of GH (0.5 mg · m−2 · day−1) for 8 days in different patterns to nine GH-deficient adults. Each subject was studied on four occasions: protocol 1 (no treatment), protocol 2 (80% daily dose at 0100 and 10% daily dose at 0900 and 1700), protocol 3 (8 equal boluses every 3 h), and protocol 4 (continuous GH infusion). The effects of GH pattern on serum IGF-I, IGF-binding protein (IGFBP)-3, osteocalcin, and urine deoxypyridinoline were measured. Hepatic CYP1A2 and CYP3A4 activities were assessed by the caffeine and erythromycin breath tests, respectively. Protocols 3 and 4 were the most effective in increasing serum IGF-I and IGFBP-3, whereas protocols administering pulsatile GH had the greatest effects on markers of bone formation and resorption. All GH treatments decreased CYP1A2 activity, and the effect was greatest for pulsatile GH. Pulsatile GH decreased, whereas continuous GH infusion increased, CYP3A4 activity. These data demonstrate that GH pulse pattern is an independent parameter of GH action in humans. Gender differences in drug metabolism and, potentially, gender differences in growth rate may be explained by sex-specific GH secretion patterns.
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26

White, Howard, and Edward Anderson. "Growth versus Distribution: Does the Pattern of Growth Matter?" Development Policy Review 19, no. 3 (September 2001): 267–89. http://dx.doi.org/10.1111/1467-7679.00134.

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27

Asha, P., and T. Jebarajan. "Efficient Mining Of High Utility Patterns Using Frequent Pattern Growth Algorithm." i-manager's Journal on Software Engineering 8, no. 2 (December 15, 2013): 32–36. http://dx.doi.org/10.26634/jse.8.2.2537.

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28

Doo, Songhwa, and JaeYoon Chang. "Exploring the change patterns in retirees’ psychological health using growth mixture modeling." Korean Journal of Industrial and Organizational Psychology 28, no. 2 (May 31, 2015): 99–126. http://dx.doi.org/10.24230/kjiop.v28i2.99-126.

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Using growth mixture modeling which tracks unobserved heterogeneity in population, the current study explored the change patterns of retirees’ psychological health and investigated factors influencing each developmental trajectory. Specifically, based on role theory, continuity theory, and resources theory, it was hypothesized that retirees would display three different types of psychological patterns: the maintaining, enhancing, and declining pattern. In order to test the above expectation, I adapted panel data of the Korean Retirement and Income Study including a total of 436 retirees who participated in survey every other year since 2005 to 2011. Results revealed that there are two distinct change patterns of retirees’ psychological health: the enhancing pattern and the declining pattern. Following was logistic regression analysis which investigated factors influencing each pattern. As a result, it was found that those with higher education and pre-retire satisfaction on health and economic status were more likely to display the enhancing pattern. On the other hand, those who retired late and retired due to health problems were more likely to display the declining pattern.
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29

SHIRASUNA, Kanemitsu, Manami KAWAMOTO, Munehiro SAKA, Kazuya WATATANI, Hideyuki FURUSAWA, Masahiro URADE, and Tokuzou MATSUYA. "Growth pattern of adenoid cystic carcinomas." Japanese Journal of Oral & Maxillofacial Surgery 36, no. 11 (1990): 2538–43. http://dx.doi.org/10.5794/jjoms.36.2538.

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30

Haukka, J., J. Suvisaari, L. Häkkinen, and J. Lönnqvist. "Growth pattern and risk of schizophrenia." Psychological Medicine 38, no. 1 (May 17, 2007): 63–70. http://dx.doi.org/10.1017/s0033291707000839.

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BackgroundFoetal nutrition and growth seem to affect the risk of developing schizophrenia. Exposure to famine during foetal development and low birthweight increase the risk. However, few studies have investigated the association between schizophrenia and adult height and weight or patterns of growth.MethodThe study population consisted of two subpopulations: families with at least one member with schizophrenia, and families of offspring of mothers with psychotic disorder, and controls. Using a seven-parameter model of height growth curves, we compared the parameters of persons who later developed schizophrenia and their unaffected siblings from the same families. We also studied how growth curve parameters differed in children with genetic risk for schizophrenia and controls, and whether weight, height and body mass index (BMI) at different ages predicted later development of schizophrenia.ResultsThe predicted growth curves based on a parametric model were nearly identical for persons with schizophrenia and their unaffected siblings. Adult height of daughters of mothers with psychoses was borderline significantly (p=0.0536) lower compared to controls, while no difference was detected among sons (p=0.3283).ConclusionsNo association between growth characteristics and schizophrenia in families with at least one member with schizophrenia was found. Family-related factors should be taken into account as possible confounders in future studies on growth and schizophrenia.
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31

Kashimura, Hiroshi, Kuniaki Ogasawara, Tomohiko Mase, and Akira Kurose. "Unusual growth pattern of a meningioma." Surgical Neurology International 3, no. 1 (2012): 63. http://dx.doi.org/10.4103/2152-7806.97007.

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32

Li, B., F. Li, and J. H.-C. Wang. "Mechanical stress dominates cell growth pattern." Journal of Biomechanics 39 (January 2006): S399. http://dx.doi.org/10.1016/s0021-9290(06)84618-x.

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33

Yu, Seongjae. "The Growth Pattern of Samsung Electronics." International Studies of Management & Organization 28, no. 4 (December 1998): 57–72. http://dx.doi.org/10.1080/00208825.1998.11656750.

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34

Jönsson, Henrik, and Pawel Krupinski. "Modeling plant growth and pattern formation." Current Opinion in Plant Biology 13, no. 1 (February 2010): 5–11. http://dx.doi.org/10.1016/j.pbi.2009.10.002.

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35

Babic, M., B. Glisic, and I. Scepan. "Mandibular growth pattern in Turner's syndrome." European Journal of Orthodontics 19, no. 2 (April 1, 1997): 161–64. http://dx.doi.org/10.1093/ejo/19.2.161.

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36

Miguel, M. San, A. Amengual, and E. Hernández-García. "Transient pattern dynamics and domain growth." Phase Transitions 48, no. 1-3 (March 1994): 65–83. http://dx.doi.org/10.1080/01411599408200354.

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37

Kessler, David A., Joel Koplik, and Herbert Levine. "Pattern selection in fingered growth phenomena." Advances in Physics 37, no. 3 (June 1988): 255–339. http://dx.doi.org/10.1080/00018738800101379.

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38

Chinsamy, Anusuya, and Andrzej Elzanowski. "Evolution of growth pattern in birds." Nature 412, no. 6845 (July 2001): 402–3. http://dx.doi.org/10.1038/35086650.

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39

French, M. B., P. Vaitkus, E. Cukerman, A. Sirek, and O. V. Sirek. "Secretory pattern of canine growth hormone." American Journal of Physiology-Endocrinology and Metabolism 252, no. 2 (February 1, 1987): E268—E272. http://dx.doi.org/10.1152/ajpendo.1987.252.2.e268.

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Our aim was to define the secretory pattern of growth hormone (GH) under basal conditions in fasted, conscious, male dogs accustomed to handling. Blood samples were withdrawn from a cephalic vein at 15-min intervals. In this way, any ultradian rhythms, if present, could be detected within the frequency range of 0.042-2 cycles/h. In addition, samples were drawn at either 1- or 2.5-min intervals for 2.5 or 5 h to determine whether frequency components greater than 2 cycles/h were present. GH was measured by radioimmunoassay and the raw data were submitted to time series analysis employing power spectral estimation by means of fast Fourier transformation techniques. Peak plasma levels were up to 12 times higher than the baseline concentration of approximately 1 ng/ml. Spectral analysis revealed an endogenous frequency of 0.22 cycles/h, i.e., a periodicity of 4.5 h/cycle. The results indicate that under basal conditions the secretory bursts of canine GH are limited to one peak every 4.5 h.
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40

Nagai, T., S. Ohta, and Kenji Kawasaki. "Computer Simulation of Cellular Pattern Growth." Materials Science Forum 94-96 (January 1992): 313–18. http://dx.doi.org/10.4028/www.scientific.net/msf.94-96.313.

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41

Cohen, M. Michael, and Sven Kreiborg. "Growth pattern in the Apert syndrome." American Journal of Medical Genetics 47, no. 5 (October 1, 1993): 617–23. http://dx.doi.org/10.1002/ajmg.1320470508.

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42

Braga, G., G. Cattaneo, P. Flocchini, and C. Quaranta Vogliotti. "Pattern growth in elementary cellular automata." Theoretical Computer Science 145, no. 1-2 (July 1995): 1–26. http://dx.doi.org/10.1016/0304-3975(94)00155-c.

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43

Mortola, Jacopo P., and anna Noworaj. "Breathing pattern and growth: comparative aspects." Journal of Comparative Physiology B 155, no. 2 (1985): 171–76. http://dx.doi.org/10.1007/bf00685210.

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44

Guyuron, Bahman, and Carlos I. Lasa. "Unpredictable Growth Pattern of Costochondral Graft." Plastic and Reconstructive Surgery 90, no. 5 (November 1992): 880–86. http://dx.doi.org/10.1097/00006534-199211000-00024.

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45

Guyuron, Bahtnan, Carlos I. Lasa, Leonard B. Kaban, and David H. Perrott. "Unpredictable Growth Pattern of Costochondral Graft." Plastic and Reconstructive Surgery 90, no. 5 (November 1992): 887–89. http://dx.doi.org/10.1097/00006534-199211000-00025.

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46

Militzer, Klaus. "Hair Growth Pattern in Nude Mice." Cells Tissues Organs 168, no. 4 (2001): 285–94. http://dx.doi.org/10.1159/000047845.

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KUKKONEN, ILKKA, and JOHANNES ENROTH. "Growth pattern of Carex arenaria (Cyperaceae)." Botanical Journal of the Linnean Society 146, no. 2 (October 2004): 145–51. http://dx.doi.org/10.1111/j.1095-8339.2004.00319.x.

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Mathai, M., S. Thomas, A. Peedicayil, A. Regi, P. Jasper, and R. Joseph. "Growth pattern of the Indian fetus." International Journal of Gynecology & Obstetrics 48, no. 1 (January 1995): 21–24. http://dx.doi.org/10.1016/0020-7292(94)02237-2.

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Doutriaux, Jérôme. "Growth pattern of academic entrepreneurial firms." Journal of Business Venturing 2, no. 4 (September 1987): 285–97. http://dx.doi.org/10.1016/0883-9026(87)90022-x.

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Marshall, Guillermo, and Eduardo Arguijo. "Growth pattern formation in Bénard flows." Physica A: Statistical Mechanics and its Applications 185, no. 1-4 (June 1992): 146–50. http://dx.doi.org/10.1016/0378-4371(92)90448-y.

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